NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users' private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare

BACKGROUND

In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain.

NEW METHOD

A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions.

RESULTS

Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare.

An all phosphorene lattice nanometric spin valve

Phosphorene is a unique semiconducting two-dimensional platform for enabling spintronic devices integrated with phosphorene nanoelectronics. Here, we have designed an all phosphorene lattice lateral spin valve device, conceived via patterned magnetic substituted atoms of 3d-block elements at both ends of a phosphorene nanoribbon acting as ferromagnetic electrodes in the spin valve. Through First-principles based calculations, we have extensively studied the spin-dependent transport characteristics of the new spin valve structures. Systematic exploration of the magnetoresistance (MR) of the spin valve for various substitutional atoms and bias voltage resulted in a phase diagram offering a colossal MR for V and Cr-substitutional atoms. Such MR can be directly attributed to their specific electronic structure, which can be further tuned by a gate voltage, for electric field controlled spin valves. The spin-dependent transport characteristics here reveal new features such as negative conductance oscillation and switching of the sign of MR due to change in the majority spin carrier type. Our study creates possibilities for the design of nanometric spin valves, which could enable integration of memory and logic elements for all phosphorene 2D processors.

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network

The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models' performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models' performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%.

Strain-controlled spin transport in a two-dimensional (2D) nanomagnet

Semiconductors with controllable electronic transport coupled with magnetic behaviour, offering programmable spin arrangements present enticing potential for next generation intelligent technologies. Integrating and linking these two properties has been a long standing challenge for material researchers. Recent discoveries in two-dimensional (2D) magnet shows an ability to tune and control the electronic and magnetic phases at ambient temperature. Here, we illustrate controlled spin transport within the magnetic phase of the 2D semiconductor CrOBr and reveal a substantial connection between its magnetic order and charge carriers. First, we systematically analyse the strain-induced electronic behaviour of 2D CrOBr using density functional theory calculations. Our study demonstrates the phase transition from a magnetic semiconductor → half metal → magnetic metal in the material under strain application, creating intriguing spin-resolved conductance with 100% spin polarisation and spin-injection efficiency. Additionally, the spin-polarised current-voltage (I-V) trend displayed conductance variations with high strain-assisted tunability and a peak-to-valley ratio as well as switching efficiency. Our study reveals that CrOBr can exhibit highly anisotropic behaviour with perfect spin filtering, offering new implications for strain engineered magneto-electronic devices.

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

Energy efficient IRS assisted 6G network for Industry 5.0

The real world applications are more prone to difficulties of challenges due to fast growth of technologies and inclusion of artificial intelligence (AI) based logical solutions. The massive internet-of-things (IoT) devices are involved in number of Industry 5.0 applications like smart healthcare, smart manufacturing, smart agriculture, smart transportation. Advanced wireless techniques, customization of services and different technologies are experiencing a major transformation. The desire to increase the communication reliability without adding energy overhead is the major challenge for massive IoT enabled networks. To cope up with the above challenges, Industry 5.0 requirements needs to be monitored at the remote level which again adds on the communication challenge. Use of relays in 6G based wireless networks is denied due to high requirement of energy. Therefore in this paper, Intelligent reflecting surfaces (IRSs) assisted energy constrained 6G wireless networks are studied. To provide seamless connection between the communicating mobile nodes, IRS with an array of reflecting elements are configured in the system set up. A use-case scenario of IRS enabled network in Internet-of-Underwater things (IoUT) for smart ocean transportation is also provided. The IRS assisted wireless network is evaluated for target rates achieved. A power consumption model of the IRS supported system is also proposed to optimise the energy efficiency of the system. Further, the paper evaluates the impact of number of reflecting elements N on the IRS and the phase resolution b of each element on the system performance. The energy efficiency improves by 20% for IRS with [Formula: see text] with [Formula: see text] over IRS with [Formula: see text].

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Gender differences in COVID-19 knowledge, risk perception, and public stigma among the general community: Findings from a nationwide cross-sectional study in India

Introduction

Individual and community characteristics predictive of knowledge, perception, and attitude on COVID-19, specifically on gender, have not been adequately explored.

Objective

To examine the gender differences in COVID-19 knowledge, self-risk perception and public stigma among the general community and to understand other socio-demographic factors which were predictive of them.

Method

A nationally representative cross-sectional multi-centric survey was conducted among adult individuals(≥18 yrs) from the community member (N = 1978) from six states and one union territory of India between August 2020 to February 2021. The participants were selected using systematic random sampling. The data were collected telephonically using pilot-tested structured questionnaires and were analyzed using STATA. Gender-segregated multivariable analysis was conducted to identify statistically significant predictors (p < 0.05) of COVID-19-related knowledge, risk perception, and public stigma in the community.

Results

Study identified significant differences between males and females in their self-risk perception (22.0% & 18.2% respectively) and stigmatizing attitude (55.3% & 47.1% respectively). Highly educated males and females had higher odds of having COVID-19 knowledge (aOR: 16.83: p < 0.05) than illiterates. Highly educated women had higher odds of having self-risk perception (aOR: 2.6; p < 0.05) but lower public stigma [aOR: 0.57; p < 0.05]. Male rural residents had lower odds of having self-risk perception and knowledge [aOR: 0.55; p < 0.05 & aOR: 0.72; p < 0.05] and female rural residents had higher odds of having public stigma [aOR: 1.36; p < 0.05].

Conclusion

Our study findings suggest the importance of considering thegender differentials and their background, education status and residential status in designing effective interventions to improve knowledge and reduce risk perception and stigma in the community about COVID-19.

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Most data nowadays are stored in the cloud; therefore, cloud computing and its extension-fog computing-are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.

A misbehavior detection framework for cooperative intelligent transport systems

With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message's plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.

Factors associated with COVID-19 stigma during the onset of the global pandemic in India: A cross-sectional study

Objective

To assess factors associated with COVID-19 stigmatizing attitudes in the community and stigma experiences of COVID-19 recovered individuals during first wave of COVID-19 pandemic in India.

Methods

A cross-sectional study was conducted in 18 districts located in 7 States in India during September 2020 to January 2021 among adults > 18 years of age selected through systematic random sampling. Data on socio demographic and COVID-19 knowledge were collected from 303 COVID-19 recovered and 1,976 non-COVID-19 infected individuals from community using a survey questionnaire. Stigma was assessed using COVID-19 Stigma Scale and Community COVID-19 Stigma Scale developed for the study. Informed consent was sought from the participants. Univariate and multivariate binary logistic regression analysis were conducted.

Results

Half of the participants (51.3%) from the community reported prevalence of severe stigmatizing attitudes toward COVID-19 infected while 38.6% of COVID-19 recovered participants reported experiencing severe stigma. Participants from the community were more likely to report stigmatizing attitudes toward COVID-19 infected if they were residents of high prevalent COVID-19 zone (AOR: 1.5; CI: 1.2-1.9), staying in rural areas (AOR: 1.5; CI:1.1-1.9), belonged to the age group of 18-30 years (AOR: 1.6; CI 1.2-2.0), were male (AOR: 1.6; CI: 1.3-1.9), illiterate (AOR: 2.7; CI: 1.8-4.2), or living in Maharashtra (AOR: 7.4; CI: 4.8-11.3). COVID-19 recovered participants had higher odds of experiencing stigma if they had poor knowledge about COVID-19 transmission (AOR: 2.8; CI: 1.3-6.3), were staying for 6-15 years (AOR: 3.24; CI: 1.1-9.4) in the current place of residence or belonged to Delhi (AOR: 5.3; CI: 1.04-26.7).

Conclusion

Findings indicated presence of stigmatizing attitudes in the community as well as experienced stigma among COVID-19 recovered across selected study sites in India during the first wave of COVID-19 pandemic. Study recommends timely dissemination of factual information to populations vulnerable to misinformation and psychosocial interventions for individuals affected by stigma.

Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.

Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review

While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Differentiating acute from chronic insomnia with machine learning from actigraphy time series data

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.

Development & validation of scales to assess stigma related to COVID-19 in India

Background & objectives

COVID-19 pandemic has triggered social stigma towards individuals affected and their families. This study describes the process undertaken for the development and validation of scales to assess stigmatizing attitudes and experiences among COVID-19 and non-COVID-19 participants from the community.

Methods

COVID-19 Stigma Scale and Community COVID-19 Stigma Scale constituting 13 and six items, respectively, were developed based on review of literature and news reports, expert committee evaluation and participants' interviews through telephone for a multicentric study in India. For content validity, 61 (30 COVID-19-recovered and 31 non-COVID-19 participants from the community) were recruited. Test-retest reliability of the scales was assessed among 99 participants (41 COVID-19 recovered and 58 non-COVID-19). Participants were administered the scale at two-time points after a gap of 7-12 days. Cronbach's alpha, overall percentage agreement and kappa statistics were used to assess internal consistency and test-retest reliability.

Results

Items in the scales were relevant and comprehensible. Both the scales had Cronbach's α above 0.6 indicating moderate-to-good internal consistency. Test-retest reliability assessed using kappa statistics indicated that for the COVID-19 Stigma Scale, seven items had a moderate agreement (0.4-0.6). For the Community COVID-19 Stigma Scale, four items had a moderate agreement.

Interpretation & conclusions

Validity and reliability of the two stigma scales indicated that the scales were comprehensible and had moderate internal consistency. These scales could be used to assess COVID-19 stigma and help in the development of appropriate stigma reduction interventions for COVID-19 infected, and mitigation of stigmatizing attitudes in the community.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

An Optimized Framework for WSN Routing in the Context of Industry 4.0

The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique.

Effect of yoga on depression in hypothyroidism: A pilot study

Background

The prevalence of hypothyroidism among Indian women is 15.8%. Depression is frequently reported in hypothyroidism. Yoga is an effective intervention for depression. However, the influence of yoga on depression in patients with hypothyroidism has not been studied.

Aim

The present study investigated the effect of a 3-month integrated yoga intervention (3-IY) on depression, lipid indices, and serum thyroid-stimulating hormone (sTSH) levels among female patients having hypothyroidism, and mild-to-moderate depression.

Method

The present single-arm pre-post design study was conducted in thirty-eight women (average age 34.2 ± 4.7 years). Participants received a 3-IY comprising asanas, pranayama, and relaxation techniques for 60 min daily (5 days a week). Depression, sTSH, lipid profile indices, Body Mass Index (BMI), fatigue, anxiety, and stress were assessed at baseline and after 12 weeks. Thyroid medication was kept constant during the study period. Data were analysed using R Studio software.

Result

A significant (P < 0.05) reduction in depression (58%), sTSH (37%), BMI (6%), fatigue (64%), anxiety (57%), lipid profile indices (HLD increased significanty), and stress (55%) levels was observed after 3 months, compared with the corresponding baseline levels.

Conclusion

The 3-IY is useful for reducing depression, dyslipidemia, and sTSH in women with hypothyroidism and depression. Further studies with a larger sample size and a robust research design using objective variables must be conducted to strengthen the study findings.

Viscosity prediction of Pongamia pinnata (Karanja) oil by molecular dynamics simulation using GAFF and OPLS force field

The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.

Spin-selective response tunability in two-dimensional nanomagnet

Recent reports on the two-dimensional (2D) material CrOCl revealed magnetic ordering and spin polarisation with Curie TemperatureTc∼ 160 K, values higher than most diluted magnetic semiconductors. Here, we investigate the uniaxial and biaxial strain-dependent electronic and transport properties of CrOCl monolayer using first-principles based calculations. The calculated Young's modulus indicates high mechanical flexibility for the application of high strain. Our study shows that strain can induce phase changes from a bipolar magnetic semiconductor → half metal → magnetic metal in the material, leading to interesting spin-resolved conductance with 100% spin filtering. Furthermore, the current-voltage (I-V) response showed conductance fluctuations, characterised by peak to valley ratio and switching efficiency offering high strain assisted tunability. Overall, CrOCl shows a highly anisotropic behaviour with the material displaying 100% spin polarisation in the tensile strain region. The electronic, transport and mechanical properties indicate that CrOCl is a versatile 2D material with multi-phase capabilities having promising applications for future nanospintronic devices.

Bi-stimuli assisted engineering and control of magnetic phase in monolayer CrOCl

Magnetic phase control and room temperature magnetic stability in two-dimensional (2D) materials are indispensable for realising advanced spintronic and magneto-electronic functions. Our current work employs first-principles calculations to comprehensively study the magnetic behaviour of 2D CrOCl, uncovering the impact of strain and electric field on the material. Our studies have revealed that uniaxial strain leads to the feasibility of room temperature ferromagnetism in the layer and also detected the occurrence of a ferromagnetic → antiferromagnetic phase transition in the system, which is anisotropic along the armchair and zigzag directions. Beyond such a strain effect, the coupling of strain and electric field leads to a remarkable enhancement of the Curie temperature (T_c) ∼ 450 K in CrOCl. These predictions based on our detailed simulations show the prospect of multi-stimuli magnetic phase control, which could have great significance for realizing magneto-mechanical sensors.

1048 Prevalence Of Positional Obstructive Sleep Apnea (OSA) In Patients With OSA-COPD Overlap Syndrome

Introduction

Positional OSA (non-supine apnea-hypopnea index [AHI] &lt; 5 events/hr) is present in 30% of patients with OSA. We demonstrated that in patients with OSA- COPD overlap syndrome the AHI inversely correlated with the degree of gas trapping, suggesting a stabilizing effect on the upper airway. We hypothesized that sleep position would be less important, resulting in a lower prevalence of positional OSA.

Methods

Patients underwent a polysomnogram that demonstrated OSA (AHI &gt; 5 events/hr). To confirm COPD, patients had spirometry performed and a chest computed tomography for measurements of percent gas trapping.

Results

Sixteen patients [6 (38%) males, 55±7 years/old, FEV1 1.2±0.5 L, FEV1 % Predicted 45±19%, FVC 2.3±0.8 L, FVC % Predicted 69±20%, FEV1/FVC 51±12%, BMI 33±9 kg/m2)] were diagnosed with OSA (AHI 15±12 events/hour). Four patients (25%) had positional OSA (AHI 13±6 events/hr, non-supine AHI 1±1 event/hr) compared to 12 patients who were non-positional [AHI 16±13 events/hr (p=0.95)]. There was no difference in age [52±8 and 56±7 yrs (p=0.3)] or severity of obstruction in those with and without positional OSA [FEV1 1.4±4 L and 1.1±0.5 L, (p=0.3), FEV1 % predicted 50±17% and 44±20%, (p=0.7), FVC 2.9±0.8 L and 2.1±0.8 L (p=0.1), FVC % predicted 78±21% and 66±20%, (p=0.3), and FEV1/FVC 50±11% and 51±12%, (p=0.8), respectively]. However, patients with positional OSA were less heavy than those with non-positional OSA [BMI 23±3 and 37±8 kg/m2, respectively (p=0.005)]. Finally, there was no difference in the CT-Derived % Gas Trapping in those with and without positional OSA [48±37% and 36±25%, (p=0.6), respectively].

Conclusion

The prevalence of positional OSA in patients with OSA-COPD overlap is similar to OSA patients without COPD. Despite the presence of obstructive disease and gas trapping that may affect upper airway stability, other factors including body position and BMI remain important determinants for developing OSA in patients with COPD.

Support

R01-HL089856, R01-HL089897

0567 Hypoxic Burden and Apnea-Hypopnea Duration in Patients with Positional Obstructive Sleep Apnea

Introduction

Recently, the measurement of the hypoxic burden and apnea-hypopnea duration has been shown to correlate with mortality in patients with obstructive sleep apnea (OSA). We hypothesized that in patients with mild positional OSA (apnea-hypopnea index [AHI] &lt; 5 events/hr in the non-supine position) the hypoxic burden would be increased and apnea-hypopnea duration shortened and similar to patients with non-positional OSA.

Methods

Fourteen patients with positional OSA and 24 patients non-positional OSA with similar severity of OSA based on the respiratory event index (REI) were included. All patients had a home sleep apnea test for suspected OSA. The hypoxic burden was calculated by the multiplication of REI and the mean area under the desaturation curves.

Results

Thirty-eight patients [12 (35%) males, 50±12 yrs, BMI 35±7 kg/m2, Epworth Sleepiness Scale (ESS) 11±8, REI 10±3 events/hr, apnea-hypopnea duration 19±4 sec, mean SaO2 94±2%, lowest SaO2 79±8%, % total sleep time (TST) SaO2 &lt; 90% 11±16%, hypoxic burden 30±17 %min/hr] completed the study. Fourteen patients [9 (64%) males, 46±14 yrs, BMI 31±6 kg/m2, ESS 7±5, REI 9±3 events/hr, mean SaO2 94±2%, lowest SaO2 81±6%, %TST SaO2 &lt; 90% 4±6%] had positional OSA (supine REI 16±7 events/hr, non-supine REI 3±1 events/hr) and 24 patients had non-positional OSA [3 (13%) males, 52±10 yrs, BMI 38±7 kg/m2, ESS 12±9, REI 10±3 events/hr, mean SaO2 94±2%, lowest SaO2 77±9%, %TST SaO2 &lt; 90% 14±19%]. The hypoxic burden was elevated in both the positional and non-positional OSA patients with no difference between the groups (26±19 %min/hr and 32±15 %min/hr, respectively, p=0.13). The apnea-hypopnea duration was similar in positional and non-positional OSA patients (20±3 sec and 18±4 sec, respectively, p=0.08 sec).

Conclusion

In patients with mild positional OSA the hypoxic burden, which has been associated with cardiovascular mortality, is elevated and similar to patients with non-positional OSA.

Support

None

0610 Prevalence of Positional Obstructive Sleep Apnea Based on 3% Vs 4% Oxygen Desaturation Using Home Sleep Apnea Testing

Introduction

Approximately 30% of patient with obstructive sleep apnea (OSA) have positional OSA [non-supine apnea-hypopnea index (AHI) &lt; 5 events/hr]. However, the prevalence is based on variable definitions for hypopneas related to the degree of oxygen desaturation. In addition, use of a home sleep apnea test (HSAT) to identify positional OSA is limited. We hypothesized that in patients evaluated with an HSAT, using a definition for hypopneas based on 4% compared to 3% oxygen desaturation will significantly decrease the percentage diagnosed with positional OSA.

Methods

Fourteen patients with positional OSA based on a non-supine respiratory event index (REI) &lt; 5 events/hr were included. The initial diagnosis was determined based on a hypopnea definition of ≥ 3% oxygen desaturation. The studies were reanalyzed using a hypopnea definition of ≥ 4% oxygen desaturation.

Results

Fourteen patients [9 (64%) males, 46±14 yrs, BMI 31±6 kg/m2, ESS 7±5, REI 9±3 events/hr, mean SaO2 94±2%, lowest SaO2 81±6%, %TST SaO2 &lt; 90% 4±6%] were identified with positional OSA (supine REI 16±7 events/hr, non-supine REI 3±1 events/hr) using a hypopneas definition of ≥ 3% oxygen desaturation. When reanalyzed using a hypopnea ≥ 4% oxygen desaturation there was a significant decrease in the REI to 7±2 events/hr (p&lt;0.001). Three patients (21%) no longer were considered to have OSA. These patients were younger (32±14 vs. 50±11yrs, p=0.03) and had less severe OSA (REI 6±1 vs. 9±3 events/hr (p=0.04), but there was no difference in BMI (32±11 vs. 31±5 kg/m2, p=0.9) or mean and lowest SaO2 (96±0.4 vs. 94±2%, p=0.13, and 82±8 vs. 81±6%, p=0.9, respectively).

Conclusion

In patients with mild positional OSA, using a hypopnea definition of at least 4% vs. 3% oxygen desaturation on a HSAT will have a significant effect on the overall REI and often exclude patients who would otherwise be treated for OSA.

Support

None.

High efficiency spin filtering in magnetic phosphorene

We present high efficiency spin filtering behaviour in magnetically rendered phosphorene, doped with various 3d block elements. A phase diagram was obtained depicting the presence of various electronic and magnetic states.

Two-dimensional C3N based sub-10 nanometer biosensor

The current observation demonstrates the usefulness of the two-dimensional C₃N system as a next generation bio-sensor for the sequencing of various nucleobases, offering new leads for future developments in bioelectronics, superior sensing architectures and sustainable designs.

Quantification of picric acid on nanosphere polypyrrole modified electrode by stripping voltammetric method

The electrochemical studies of picric acid were carried out in acidic, neutral and basic buffer media at bare glassy carbon (GC) and polypyrrole modified GC electrode. Cyclic Voltammogram (CV) of picric acid exhibited three reduction peaks at -0.4, -0.8 and -1.5V (vs. Ag/AgCl) and two oxidation peaks at 0.8 and 1.4V (vs. Ag/AgCl). Among the various pH studied, highly sensitive response was observed at pH 1.0. The effect of scan rate was studied between 25 and 500 mVs-1 at the optimal pH.CV results revealed the adsorption-controlled reaction at the electrode surface. The GC electrode was modified with polypyrrole conducting polymer film to enhance the electrocatalytic activity of the reductive species. Atomic force microscopy (AFM) images showed the nanosphere morphology of the polypyrrole film, which was coated uniformly on the electrode surface. Under optimum experimental conditions, the influence of concentration on the stripping signal was studied. The linear range of detection was found between 50 ppb and 250 ppb with the lower limit of detection of 10±3 ppb.

CHANGES IN IRISIN RELEASE IN RESPONSE TO PERIPHERAL KISSPEPTIN-10 ADMINISTRATION IN HEALTHY AND OBESE ADULT MEN

Context

Kisspeptin role in metabolism has been implicated recently. However, the nature of the signals that may connect body fat/muscle tissues with the central nervous system governing energy homeostasis remains to be elucidated.

Objective

The present study was designed to investigate the effects of peripheral kisspeptin-10 administration on irisin release in human males.

Subjects and methods

Kisspeptin-10 was administered to normal weight (n=8) and obese (n=8) men. Sequential blood sampling was performed for 30 minutes pre and 210 minutes post kisspeptin injection at 30 minutes interval. ELISA kit was used to detect plasma irisin levels.

Results

There is a significant (P<0.0001) effect of Kisspeptin-10 administration on irisin release in both normal weight and obese participants. Mean irisin levels (96.24 ± 1.351 ng/mL) at 210 minutes were significantly (P<0.0001) enhanced as compared to pre-kisspeptin (59.18 ± 4.815 ng/mL) in normal weight subjects. In obese subjects mean irisin levels (75.76 ± 4.06 ng/mL) were significantly (P<0.0001) elevated at 180 minutes post-kisspeptin when compared with pre-kisspeptin irisin levels (41.28 ± 2.89 ng/mL).

Conclusion

Our findings suggest that kisspeptin may have a novel therapeutic potential to induce irisin release in humans which may have anti-obesity effects.

A Novel Framework and Enhanced QoS Big Data Protocol for Smart City Applications

Various heterogeneous devices or objects will be integrated for transparent and seamless communication under the umbrella of Internet of things (IoT). This would facilitate the open access of data for the growth of various digital services. Building a general framework of IoT is a complex task because of the heterogeneity in devices, technologies, platforms and services operating in the same system. In this paper, we mainly focus on the framework for Big Data analytics in Smart City applications, which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication to improve the quality of life of citizens. A novel approach is proposed in this paper to enhance energy conservation and reduce the delay in Big Data gathering at tiny sensor nodes used in IoT framework. To implement the Smart City scenario in terms of Big Data in IoT, an efficient (optimized in quality of service) wireless sensor network (WSN) is required where communication of nodes is energy efficient. Thus, a new protocol, QoS-IoT(quality of service enabled IoT), is proposed on the top layer of the proposed architecture (the five-layer architecture consists of technology, data source, data management, application and utility programs) which is validated over the traditional protocols.

Effectiveness of TB sensitization initiatives in improving the involvement of self help group members in rural TB control in south India

Background

The 'End TB strategy' has highlighted the importance of inter-sectoral collaboration and community mobilization for achieving zero TB deaths by 2020.

Objective

The aim of the study was to develop and test a model TB sensitization programme involving self help groups (SHGs).

Methodology

This experimental study was conducted in two blocks (intervention and control), in Tiruvallur district. The intervention content included short-lecture, musical story telling activity, role play, short film on TB. The impact was compared at baseline, third and sixth months in terms of SHGs' awareness, promotion of awareness, identification and referral of presumptive TB cases and provision of TB treatment.

Results

A total of 764 vs 796 SHGs were enrolled in control and intervention groups, respectively. The knowledge attitude, and practice score (lower score indicated a better attitude and practice), from baseline to 6 months was significantly reduced (29 to 24) in the intervention group. Similarly, a significant difference was observed in identification and referral of chest symptomatics in the intervention group at 3 and 6 months. During the 3 month follow-up a significantly higher proportion of SHG members were involved in TB awareness activities in the intervention (623/748 [83.3%]) vs control group (471/728 [64.7%]; p<0.001).

Conclusions

Findings from this study highlight the feasibility of involving SHGs through a model TB sensitization program for strengthening TB prevention and control activities.

National service for adolescents and adults with severe obsessive–compulsive and body dysmorphic disorders

Aims and Method

National guidelines for the assessment and treatment of obsessive–compulsive disorder (OCD) and body dysmorphic disorder were published in 2005 by the National Institute for Health and Clinical Excellence (NICE). Local services are unable to treat a small but significant number of the most severely ill patients successfully, and the guidelines recommend that such patients should have access to highly specialised care. From 1 April 2007, the Department of Health decided to centrally fund treatment services for severe, chronic, refractory OCD and BDD. We describe a new National Service for Refractory OCD; its rationale, treatments offered, referral criteria and expected clinical outcomes.

Results

Initial results from one centre show an average 42% reduction in OCD symptoms at the end of treatment.

Clinical Implications

The operational challenges and potential generalisability of this model of healthcare delivery are discussed. We present a summary of the progress made so far in establishing a new, coherent National Service for Refractory OCD, 18 months after the NICE guideline was published. the aim of the paper is to educate clinicians about the service and describe its rationale, treatments offered, referral criteria and expected clinical outcomes.

Community model in treating obsessive–compulsive and body dysmorphic disorders

Aims and Method

In November 2005, the National Institute for Health and Clinical Excellence published guidelines for the treatment of obsessive–compulsive disorder (OCD) and body dysmorphic disorder. These guidelines incorporated a stepped care approach with different interventions advised throughout the patient pathway. South West London and St George's Mental Health NHS Trust devised a system of expert clinicians with special expertise in OCD/body dysmorphic disorder to help deliver this model of care. To aid the delivery of service it was decided to operationalise the definitions of severity of OCD/body dysmorphic disorder at each of the stepped-care levels. Examples are given as to how this has been applied in practice. Outcome is presented in terms of clinical hours in the first year of operation.

Results

In total, 108 patients were referred to the service in the first year. Many of these patients were treated by offering advice and support and joint working with the community mental health team and psychotherapy in primary care teams who had referred. Sixty-eight patients were treated by a member of the specialist service alone and 57 of these suffered from severe OCD. Outcome data from these 57 patients is presented using an intention-to-treat paradigm. They showed a clinically and statistically significant reduction in OCD symptoms after 24 weeks of cognitive–behavioural therapy comprising graded exposure and self-imposed response prevention. the mean Yale–Brown Obsessive Compulsive Scale score dropped from 28 (severe OCD) to 19 (considerable OCD). Depressive symptoms on the Beck Depression Inventory also decreased by an average 24% over the same period.

Clinical Implications

The feasibility of extending this model of service organisation to other areas and other diagnoses is discussed.

A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

Seasonal plasticity in the peptide neuronal systems: potential roles of gonadotrophin-releasing hormone, gonadotrophin-inhibiting hormone, neuropeptide Y and vasoactive intestinal peptide in the regulation of the reproductive axis in subtropical Indian weaver birds

Two experiments examined the expression of gonadotrophin-releasing and inhibiting hormones (GnRH-I, GnRH-II and GnIH), neuropeptide Y (NPY) and vasoactive intestinal peptide (VIP) in subtropical Indian weaver birds, which demonstrate relative photorefractoriness. Experiment 1 measured peptide expression levels in the form of immunoreactive (-IR) cells, percentage cell area and cell optical density in the preoptic area (GnRH-I), midbrain (GnRH-II), paraventricular nucleus (GnIH), mediobasal hypothalamus [dorsomedial hypothalamus (DMH), infundibular complex (INc), NPY and VIP] and lateral septal organ (VIP) during the progressive, breeding, regressive and nonbreeding phases of the annual reproductive cycle. GnRH-I was decreased in the nonbreeding and VIP was increased in INc in the breeding and regressive states. GnRH-II and NPY levels did not differ between the testicular phases. Double-labelled immunohistochemistry (IHC) revealed a close association between the GnRH/GnIH, GnRH/NPY, GnRH/VIP and GnIH/NPY peptide systems, implicating them interacting and playing roles in the reproductive regulation in weaver birds. Experiment 2 further measured these peptide levels in the middle of day and night in weaver birds that were maintained under short days (8 : 16 h light /dark cycle; photosensitive), exposed to ten long days (16 : 8 h light /dark cycle; photostimulated) or maintained for approximately 2 years on a 16 : 8 h light /dark cycle (photorefractory). Reproductively immature testes in these groups precluded the possible effect of an enhanced gonadal feedback on the hypothalamic peptide expression. There were group differences in the GnRH-I (not GnRH-II), GnIH, NPY and VIP immunoreactivity, albeit with variations in immunoreactivity measures in the present study. These results, which are consistent with those reported in birds with relative photorefractoriness, show the distribution and possibly a complex interaction of key neuropeptides in the regulation of the annual reproductive cycle in Indian weaver birds.

Iatrogenic Perforation of Stomach--A Case Report

Gastric lavage is a routine procedure done in many cases of poisoning and it has been advocated by many as a lifesaving procedure. There may be some instances, where it might be unnecessary, ineffective or even detrimental to life. A 35 year old man walked into a casualty, 2 hours after having ingested 15 benzodiazepine tablets. Lavage was done by an unqualified person using Ewald's tube, leading to iatrogenic perforation. The unwarranted use of the procedure proved to be fatal.