Security risk models against attacks in smart grid using big data and artificial intelligence

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Emotion detection from handwriting and drawing samples using an attention-based transformer model

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

ChestCovidNet: An Effective DL-based Approach for COVID-19, Lung Opacity, and Pneumonia Detection Using Chest Radiographs Images

Currently used lung disease screening tools are expensive in terms of money and time. Therefore, chest radiograph images (CRIs) are employed for prompt and accurate COVID-19 identification. Recently, many researchers have applied Deep learning (DL) based models to detect COVID-19 automatically. However, their model could have been more computationally expensive and less robust, i.e., its performance degrades when evaluated on other datasets. This study proposes a trustworthy, robust, and lightweight network (ChestCovidNet) that can detect COVID-19 by examining various CRIs datasets. The ChestCovidNet model has only 11 learned layers, eight convolutional (Conv) layers, and three fully connected (FC) layers. The framework employs both the Conv and group Conv layers, Leaky Relu activation function, shufflenet unit, Conv kernels of 3×3 and 1×1 to extract features at different scales, and two normalization procedures that are cross-channel normalization and batch normalization. We used 9013 CRIs for training whereas 3863 CRIs for testing the proposed ChestCovidNet approach. Furthermore, we compared the classification results of the proposed framework with hybrid methods in which we employed DL frameworks for feature extraction and support vector machines (SVM) for classification. The study's findings demonstrated that the embedded low-power ChestCovidNet model worked well and achieved a classification accuracy of 98.12% and recall, F1-score, and precision of 95.75%.

Classification of clinically actionable genetic mutations in cancer patients

Personalized medicine in cancer treatment aims to treat each individual's cancer tumor uniquely based on the genetic sequence of the cancer patient and is a much more effective approach compared to traditional methods which involve treating each type of cancer in the same, generic manner. However, personalized treatment requires the classification of cancer-related genes once profiled, which is a highly labor-intensive and time-consuming task for pathologists making the adoption of personalized medicine a slow progress worldwide. In this paper, we propose an intelligent multi-class classifier system that uses a combination of Natural Language Processing (NLP) techniques and Machine Learning algorithms to automatically classify clinically actionable genetic mutations using evidence from text-based medical literature. The training data set for the classifier was obtained from the Memorial Sloan Kettering Cancer Center and the Random Forest algorithm was applied with TF-IDF for feature extraction and truncated SVD for dimensionality reduction. The results show that the proposed model outperforms the previous research in terms of accuracy and precision scores, giving an accuracy score of approximately 82%. The system has the potential to revolutionize cancer treatment and lead to significant improvements in cancer therapy.

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.

AD-CAM: Enhancing Interpretability of Convolutional Neural Networks with a Lightweight Framework - From Black Box to Glass Box

In the realm of machine vision, the convolutional neural network (CNN) is a frequently used and significant deep learning method. It is challenging to comprehend how predictions are formed since the inner workings of CNNs are sometimes seen as a black box. As a result, there has been an increase in interest among AI experts in creating AI systems that are easier to understand. Many strategies have shown promise in improving the interpretability of CNNs, including Class Activation Map (CAM), Grad-CAM, LIME, and other CAM-based approaches. These methods do, however, have certain drawbacks, such as architectural constraints or the requirement for gradient computations. We provide a simple framework termed Adaptive Learning based CAM (Adaptive-CAM) to take advantage of the connection between activation maps and network predictions. This framework includes temporarily masking particular feature maps. According to the Average Drop-Coherence-Complexity (ADCC) metrics, our method outperformed Score-CAM and another CAM-based activation map strategy in Residual Network-based models. With the exception of the VGG16 model, which witnessed a 1.94% decline in performance, the performance improvement spans from 3.78% to 7.72%. Additionally, Adaptive-CAM generates saliency maps that are on par with CAM-based methods and around 153 times superior to other CAM-based methods.

Internet of Things-based sustainable environment management for large indoor facilities

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm's environmental parameters can help to curtail the negative impacts of the environment on chickens' health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O2), carbon dioxide (CO2), carbon monoxide (CO), and ammonia (NH3) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O2 concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO2 levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO2 levels within the control sheds. The NH3 gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection

Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods.

NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs' fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R²). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R² of 0.929.

Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s.

Malaria prevalence in Pakistan: A systematic review and meta-analysis (2006-2021)

Malaria is one of the major public health issues globally. Malaria infection spreads through mosquito bites from infected female Anopheles mosquitoes. This study aims to conduct a systematic review and meta-analysis on malaria prevalence in Pakistan from 2006 to 2021. We searched PubMed, Science Direct, EMBASE, EMCare, and Google Scholar to acquire data on the prevalence of malaria infections. We performed a meta-analysis with a random-effects model to obtain the pooled prevalence of malaria, Plasmodium vivax, and Plasmodium falciparum. Meta-analysis was computed using R 4.1.2 Version statistical software. I² and time series analysis were performed to identify a possible source of heterogeneity across studies. A funnel plot and the Freeman-Tukey Double Arcsine Transformed Proportion were used to evaluate the presence of publication bias. Out of the 315 studies collected, only 45 full-text articles were screened and included in the final measurable meta-analysis. Pooled malaria prevalence in Pakistan was 23.3%, with Plasmodium vivax, Plasmodium falciparum, and mixed infection rates of 79.13%, 16.29%, and 3.98%, respectively. Similarly, the analysis revealed that the maximum malaria prevalence was 99.79% in Karachi and the minimum was 1.68% in the Larkana district. Amazingly, this systematic review and meta-analysis detected a wide variation in malaria prevalence in Pakistan. Pakistan's public health department and other competent authorities should pay close attention to the large decrease in mosquito populations to curb the infection rate.

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Morphological, Magnetic and Optical Properties of α-Fe₂O₃ Nanoflowers

We report the morphological, structural and magnetic properties of the flower like iron oxide α-Fe2O3 samples prepared by the polyol method. The α-Fe2O3 samples were prepared by using different amount of the iron chloride in the starting materials and the impact of the different iron chloride amount on the morphology of the precursor and after heat treatment of the samples was investigated. The X-ray diffraction (XRD) analysis confirmed the formation of the α-Fe2O3 phase without detecting any impurity phase. The transmission electron microscopy (TEM) and the field emission scanning electron microscopy (FESEM) results showed that the flower like structures are composed of nanopetals with an average thickness and width of 60 nm and 735 nm respectively. A strong impact on the formation of the flower like iron oxide and the morphologies of these samples was observed with the variation of iron chloride concentration during synthesis process. The magnetic hysteresis measurements demonstrated that as prepared samples displayed ferromagnetic behavior and magnetic properties were found to be depending on the morphologies of as-prepared samples. The band gap energy was measured by using Tauc's method, and values for all the samples were found to be in the range 1.94-2.27 eV. The results obtained in the present work show that the α-Fe2O3 can be used as potential candidate material for use in gas sensors, photocatalysis and energy storage devices.

Effect of Zinc Nitrate Concentration on the Optical and Morphological Properties of ZnO Nanorods for Photovoltaic Applications

We report the effect of zinc nitrate (ZN) concentration on the growth of zinc oxide (ZnO) nanorods and their optical and morphological properties. As prepared ZnO nanorods on glass substrate were characterized using field emission scanning electron microscopy (FE-SEM), ultra violet-visible (UV-Vis), Raman and Photo-luminescence (PL) spectroscopy. FE-SEM results show that the nanorods were obtained for the 0.033 and 0.053 M concentration of ZN. As the ZN concentration increased from 0.033 M to 0.053 M, the diameter of the nanorods was increased. It indicated that the diameter of the nanorods was affected by the ZN concentration. The Raman spectra of nanorods show only one peak at 438 cm(-1) corresponding to E2(high) high mode, which means that ZnO nanorods grown perpendicularly on the glass substrate, i.e., the ZnO nanorod arrays are highly c-axis oriented. Room-temperature PL spectrum of the as-grown ZnO nanorods reveals a near-band-edge (NBE) emission peak and defect induced green light emission. The green light emission band at -579 nm might be attributed to surface oxygen vacancies or defects. The UV-visible measurements reflect that the total transmittance for the as grown ZnO nanorods is over 80%. The simple technique presented in this study to grow ZnO nanorods on a glass substrate can be helpful for making the cost effective photovoltaic devices.

Novel Biomimatic Synthesis of ZnO Nanorods Using Egg White (Albumen) and Their Antibacterial Studies

Zinc oxide (ZnO) is well-recognized as a biocompatible multifunctional material with outstanding properties as well as low toxicity and biodegradability. In this work, a simple and versatile technique was developed to prepare highly crystalline ZnO nanorods by introducing egg white to a bio-inspired approach. X-ray diffraction (XRD) and selected area electron diffraction (SAED) pattern results indicated that the ZnO nanorods have single phase nature with the wurtzite structure. Field emission scanning electron microscopy (FESEM) and Transmission electron microscopy (TEM) results showed the nanometer dimension of the nanorods. Raman, FTIR, and TGA/DTA analyses revealed the formation of wurtzite ZnO. The antibacterial properties of ZnO nanorods were investigated using both Gram-positive and Gram-negative microorganisms. These studies demonstrate that ZnO nanorods have a wide range of antibacterial activities toward various microorganisms that are commonly found in environmental settings. Survival ratio of bacteria decreased with increasing powder concentration, i.e., increase in antibacterial activity. The antibacterial activity of the ZnO nanorods toward Pseudomonas aeruginosa was stronger than that of Escherichia coli and Staphylococcus aureus. Surprisingly, the antibacterial activity did not require specific UV activation using artificial lamps, rather activation was achieved under ambient lighting conditions. Overall, the experimental results suggest that ZnO nanorods could be developed as antibacterial agents against a wide range of microorganisms to control and prevent the spreading and persistence of bacterial infections. This research introduces a new concept to synthesize ZnO nanorods by using egg white as a biological template for various applications including food science, animal science, biochemistry, microbiology and medicine.

Study of Magnetic and Magnetocaloric Behaviour of (1 - Y)La0.7Ca0.3MnO3/(Y)MnFe2O4, (1 - Y)La0.7Ca0.3MnO3/(Y)Ni0.9Zn0.1Fe2O4 Composites

We report the structural, magnetic and magnetocaloric properties of (1 - Y)La0.7Ca0.3MnO3/ (Y)MnFe2O4 (LCMO/MFO) and (1 - Y)La0.7Ca0.3MnO3/(Y)Ni0.9Zn0.1Fe2O4 (LCMO/NZFO) composites. Polycrystalline LCMO/MFO samples were prepared using the conventional solid-state reaction technique. The results of X-ray diffraction indicates mainly LCMO phase without characteristic lines of the MFO and NZFO phase. The magnetic study has revealed that the Curie temperature was influenced by the concentration of MFO and NZFO phases. A large magnetic entropy change has been observed for La0.7Ca0.3MnO3 compound. The value of the maximum magnetic entropy change was found to decrease in the composites samples with increasing the concentration of the MFO and NZFO phases. This investigation suggests that LCMO/MFO and LCMO/NZFO types of composites can give a new kind of refrigeration candidates, which can easily provide the tunable magnetocaloric effect.

Synthesis and Characterization of Nanocrystalline Doped-ZnO Powder for Advanced Varistor Application

The nanocrystalline doped ZnO powder has been synthesized by solution combustion method using sucrose as fuel and zinc acetate as oxidant. The as-prepared nanopowders were characterized by XRD, showing particle size approximately 39 and 48 nm for fuel to oxidant ratio of 1:1 (stoichiometric) and 2:1 (fuel rich). The powders were compacted and sintered for 9 hours. The sintered samples were characterized by SEM and XRD, showing the presence of spinel (Zn7Sb2Ol2) and pyroclore (Zn2Bi3Sb3Ol4) phases at intergranular spacing. The phase distribution [spinel (Zn7Sb2Ol2), pyroclore (Zn2Bi3Sb3O14), β-Bi2O3, and δ-Bi203] was found to be more homogeneous in case of samples obtained by adding the stoichiometric amount of fuel. The current-voltage (J-E) characterization shows the high non-linearity coefficient (α) ~22 and break-down voltage (VB) of ~0.41 kV/mm for the fuel rich sample.

Relationship Between Structural, Morphological, Optical and Magnetic Properties of Transition Metal (TM)-Doped ZnO Nanostructures Prepared by Microwave-Hydrothermal

In this work, pure and 3% TM (Co, Ni, and Cu)-doped ZnO nanostructures were prepared by microwave-hydrothermal method. The striking similarities between changes in the lattice volume, bandgap energy, morphology and saturation magnetization indicated a strong correlation between these properties. XRD, SAED and HRTEM analyses revealed that all the TM-doped ZnO nano-structures have wurtzite structure and no secondary phase was detected. FESEM and TEM results confirmed a higher aspect ratio and highly crystalline nature of nanostructures. Raman spectra revealed that no defect related mode was observed which indicated that the nanostructures have high quality and negligible defects. The value of bandgap was found to decrease with the increase in atomic number of TM dopants. RTFM was observed in all the TM-doped ZnO nanostructures and the value of Ms and Mr were decreased with TM dopants.

Magnetization and Magnetocaloric Effect in Sol-Gel Derived Nanocrystalline Copper-Zinc Ferrite

We report the sol-gel synthesis and magnetocaloric effect in nanocrystalline copper-zinc ferrite (Cu0.5Zn0.5Fe2O4). The synthesized powder was characterized by using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX) and magnetization measurements. The XRD results confirm the formation of single phase spinel structure. The average particle size was found to be ~58 nm. FE-SEM results suggested that the nanoparticles are agglomerated and spherical in shape. Magnetization measurement reveals that Cu0.5Zn0.5Fe2O4 nanoparticles exhibit transition temperature (Tc) above room temperature. The maximum magnetic entropy change (ΔSM)max shows interesting behaviour and was found to vary with the applied magnetic field. This nanopowder can be considered as potential material for magnetic refrigeration above room temperature.

Dimensionality dependent magnetic and magnetocaloric response of La0.6Ca0.4MnO3 manganite

We report the sol-gel synthesis and impact of reduced dimensionality on the magnetocaloric properties of La0.6Ca0.4MnO3 manganite. The synthesized powders were characterized by using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) and magnetization measurements. The XRD results indicated that the La0.6Ca0.4MnO3 nanoparticles have single phase nature with orthorhombic structure. FE-SEM results suggested that the nanoparticles are agglomerated and crystallite size increases with the annealing temperature. Magnetization measurements show that the La0.6Ca0.4MnO3 nanoparticles exhibit transition temperature (T(c)) below room temperature. The transition temperature was found to increase with the increasing the crystallite size. Maximum in magnetic entropy change, (ΔS(M))(max) shows interesting behaviour and was found to vary with the particle size. At magnetic field of 1 T, the value of (ΔS(M))(max) - 0.13 J/kg K was observed at 213 K for the sample annealed at 600 degrees C. Also, the increment in the value of (ΔS(M))(max) was observed at higher annealing temperature. This study shows that the magnetic entropy of pervoskite manganite can be tuned by tuning the crystallite size of the manganites.

Doping dependent properties of Cr-doped ZnO nanostructures prepared by microwave irradiation

In this work, undoped and Cr-doped single-crystalline ZnO nanorods were prepared by a facile microwave assisted solution method. X-ray diffraction (XRD) and transmission electron microscopy (TEM) results showed that Cr-doped ZnO was comprised of single phase nature with hexagonal wurtzite structure up to 5% Cr doping, however, secondary phase ZnCr2O4 appeared upon further increasing the Cr dopant concentration. Field emission scanning electron microscopy (FESEM) and TEM micrographs suggested that the undoped nanorods with an average length of -~2 μm and a diameter in the range of 150-200 nm, respectively were observed. Interestingly, the size of nanorods decreased with the increase of Cr concentration in ZnO. Optical studies depicted that the energy bandgap was decreased with the increase of Cr concentration. Raman scattering spectra of Cr-doped ZnO revealed the lower frequency shift of E2(high) phonon mode with the increase in concentration of Cr dopant, suggested the successful doping of Cr into Zn site in ZnO. Magnetic studies showed that Cr-doped ZnO exhibited room temperature ferromagnetism (RTFM) and the value of magnetization was continuously decreased with the increase in Cr doping.

High sensitivity troponin T in acute medicine; more questions than answers?

BACKGROUND

Troponin testing in acute medicine is routine. The introduction of a high sensitivity assay (hs Tn T) has created uncertainty regarding the clinical significance of 'abnormal' troponin T levels. The previous assay could not detect troponin levels <30 ng/l.

AIMS AND METHODS

To characterize those with a hs Tn T ≥14 ng/l. Prospective cohort study of consecutive admissions to an acute medical unit.

RESULTS

Troponin was measured in 564 consecutive patients (∼50% of all admissions) over 1 month; was ≥14 ng/l in 224 (40%) of which 220 patients had demographic data for this analysis. Median (inter-quartile range) peak troponin was 47.5 ng/l (24-130) and 36% had a Tn T between 14 and 30 ng/l. Mean [standard deviation (SD)] age was 72 (12) years and 57% were male. Only 44 patients (20%) had an acute myocardial infarction, reflecting the increased sensitivity but reduced specificity of the assay. Prognosis was poor with 31% mortality at 1 year. Over a mean (SD) follow-up of 648 (61) days, there were 87 deaths (40%). Those with a primary non-cardiac diagnosis (n = 126) had poorer survival than those with a primary cardiac diagnosis (n = 94). Troponin elevation related to sepsis conferred a very poor prognosis with 24 deaths (70%) over the follow-up period.

CONCLUSION

Elevated hs Tn T is very common in acute medicine, but myocardial infarction as an explanation is uncommon. Overall, the prognosis is poor with a tendency to worse outcomes in those with a primary 'non-cardiac' diagnosis.

Anomalous switching in Nb/Ru/Sr₂RuO₄ topological junctions by chiral domain wall motion

A spontaneous symmetry breaking in a system often results in domain wall formation. The motion of such domain walls is utilized to realize novel devices like racetrack-memories, in which moving ferromagnetic domain walls store and carry information. Superconductors breaking time reversal symmetry can also form domains with degenerate chirality of their superconducting order parameter. Sr₂RuO₄ is the leading candidate of a chiral p-wave superconductor, expected to be accompanied by chiral domain structure. Here, we present that Nb/Ru/Sr₂RuO₄ topological superconducting-junctions, with which the phase winding of order parameter can be effectively probed by making use of real-space topology, exhibit unusual switching between higher and lower critical current states. This switching is well explained by chiral-domain-wall dynamics. The switching can be partly controlled by external parameters such as temperature, magnetic field and current. These results open up a possibility to utilize the superconducting chiral domain wall motion for future novel superconducting devices.

Neurological improvement following reinstitution of a low phenylalanine diet after 20 years in established phenylketonuria

A 41-year-old woman had meaningful functional improvement following reinstitution of a low phenylalanine diet. She was diagnosed at birth with phenylketonuria and followed strict dietary adherence till the age of 16. Thereafter the diet was discontinued. She subsequently presented with subacute profound visual loss, cognitive dysfunction and paraparesis such that she was bed bound requiring full nursing care. Following dietary intervention there was meaningful improvement such that she was no longer demented and while her vision remains poor she is now independent for activities of daily living. This case report suggests that consideration of reimplementation of dietary intervention is warranted even after a prolonged period of time.

Morphological studies of SnO2 thin films fabricated by using e-beam method

This paper studies the variations in morphology of SnO2 nanostructures thin films deposited by using e-beam technique with the substrate temperature, oxygen partial pressure and the film thickness. The e-beam conditions were optimized to get crystalline nanosheets of SnO2. The films of 100-700 nm thickness were deposited on quartz substrates at temperatures ranging from room temperature (RT) to 300 degrees C and oxygen partial pressure ranging from 0 to 200 sccm. The nanostructured films have been characterized by means of X-ray diffraction (XRD), field emission scanning electron microscope (FE-SEM) and Energy dispersive spectroscopy (EDS) measurements. XRD results show that the films deposited at RT and 100 degrees C were amorphous, however, for 200 degrees C and 300 degrees C, the films showed crystalline nature with rutile structure. Also, the crystallinity increased with the increase of oxygen partial pressure. FE-SEM images revealed that at RT and 100 degrees C of substrate temperature, the film consist of spherical particles, whereas, the films deposited at 200 degrees C and 300 degrees C consist of sheet like morphology having thickness -40 nm and lateral dimension of 1 microm, respectively. The size of the nanosheets increased with the increase of substrate temperature and oxygen partial pressure due to the enhancement in the crystallinity of the films. A possible growth mechanism of the formation of SnO2 nanosheets is discussed.

Magnetic, optical and structural property studies of Mn-doped ZnO nanosheets

We report the synthesis of pure and Mn doped ZnO in the form of nanosheets using a simple and single step procedure involving a microwave assisted chemical method. As prepared Mn-doped ZnO nanosheets were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), ultra violet-visible (UV-Vis), Raman spectroscopy and magnetization measurements. The structural studies using XRD and TEM revealed the absence of Mn-related secondary phases and showed that Mn-doped ZnO comprise a single phase nature with wurtzite structure. FESEM and TEM micrographs show that the average diameter of Mn-ZnO assembled nanosheets is about approximately 50 nm, and the length of a Mn-doped ZnO nanosheet building block which is made up of thin mutilayered sheets is around approximately 300 nm. Concerning the Raman scattering spectra, the shift in peak position of E2 (high) mode toward low frequencies due to the Mn doping could be explained well by means of the spatial correlation model. Magnetic measurements showed that Mn-doped ZnO nanosheets exhibit ferromagnetic ordering at or above room temperature.

Microwave assisted hydrothermal synthesis and magnetocaloric properties of La0.67Sr0.33MnO3 manganite

We report microwave assisted hydrothermal synthesis and magnetocaloric properties of La0.67Sr0.33MnO3 manganite. The synthesized La0.67Sr0.33MnO3 nanoparticles was characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS) and magnetization measurements. The XRD results indicated that La0.67Sr0.33MnO3 nanoparticles have polycrystalline nature with monoclinic structure. FE-SEM results suggested that La0.67Sr0.33MnO3 nanoparticles are assembled into rod like morphology. Magnetization measurements show that La0.67Sr0.33MnO3 nanoparticles exhibit transition temperature (Tc) above room temperature. The maximum magnetic entropy change (deltaS(M))max was found to be 0.52 J/kg K near Tc approximately 325 K at applied magnetc field of 20 kOe. This compound may considered as potential material for magnetic refrigeration near room temperature.

Structural and magnetic properties of Zn1-xcoxO nanorods prepared by microwave irradiation technique

We have successfully synthesized large-scale aggregative flowerlike Zn1-xCo(x)O (0.0 < or = x < or = 0.07) nanostructures, consisting of many branches of nanorods at different orientations with diameter within 100-150 nm (tip diameter approximately 50 nm) and length of approximately 1 microm. The rods were prepared using Zinc nitrate, cobalt nitrate and KOH in 180 Watt microwave radiation for short time interval. The synthesized nanorods were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), field emission transmission electron microscopy (FETEM) and DC magnetization measurements. XRD and TEM results indicate that the novel flowerlike nanostructures are hexagonal with wurtzite structure and Co ions were successfully incorporated into the lattice position of Zn ions in ZnO matrix. The selected area electron diffraction (SAED) pattern reveals that the nanorods are single crystal in nature and preferentially grow along [0 0 1] direction. Magnetic studies show that Zn1-xCo(x)O nanorods exhibit room temperature ferromagnetism. This novel nanostructure could be a promising candidate for a variety of future spintronic applications.

One step synthesis of rutile TiO2 nanoparticles at low temperature

Sphere-like rutile TiO2 nanocrystals have been synthesized by sol-gel method followed by hydrolysis of titanium tetrachloride in deionized water in the presence of ammonium hydroxide as hydrolysis catalyst. The as-prepared TiO2 nanoparticles have single rutile phase with average diameter approximately 26.4 nm. The results show that the temperature has a great influence on the particle size distribution and also crystalline phase (rutile) of TiO2 nanoparticles is consistent with the temperature. Characterization of the as-prepared nanocrystalline powder was carried out by different techniques such as powder X-ray diffraction (XRD), field emission transmission electron microscopy (FE-TEM) and Raman spectroscopy.

Hypoxia-regulated carbonic anhydrase IX expression is associated with poor survival in patients with invasive breast cancer

Tumour hypoxia is a microenvironmental factor related to poor response to radiation, chemotherapy, genetic instability, selection for resistance to apoptosis, and increased risk of invasion and metastasis. Hypoxia-regulated carbonic anhydrase IX (CA IX) has been studied in various tumour sites and its expression has been correlated with the clinical outcome. The purpose of this study was to investigate the correlation of CA IX expression with outcome in patients with invasive breast cancer. We conducted a retrospective study examining the effects of carbonic anhydrase IX (CA IX) on survival in patients with breast cancer. To facilitate the screening of multiple tissue blocks from each patient, tissue microarrays were prepared containing between two and five representative samples of tumour per patient. Immunohistochemistry was used to examine expression of CA IX in patients with breast cancer. The study includes a cohort of 144 unselected patients with early invasive breast cancer who underwent surgery, and had CA IX expression and follow-up data available for analysis. At the time of analysis, there were 28 deaths and median follow-up of 48 months with 96% of patients having at least 2 years of follow-up. CA IX was negative for 107 patients (17 deaths) and positive for 37 patients (11 deaths). Kaplan–Meier survival curves show that survival was superior in the CA IX-negative group with a 2-year survival of 97% for negatives and 83% for positives (log-rank test P=0.01). Allowing for potential prognostic variables in a Cox regression analysis, CA IX remained a significant independent predictor of survival (P=0.035). This study showed in both univariate and multivariate analysis that survival is significantly inferior in patients with tumour expressing CA IX. Prospective studies are underway to investigate this correlation in clinical trial setting.

Generation and interrogation of a pure nuclear spin state by parahydrogen-enhanced NMR spectroscopy: a defined initial state for quantum computation

We describe a number of studies used to establish that parahydrogen can be used to prepare a two-spin system in a pure state, which is suitable for implementing NMR quantum computation. States are generated by pulsed and continuous-wave (CW) UV laser initiation of a chemical reaction between Ru(CO)(3)(L(2)) [where L(2) = dppe = 1,2-bis(diphenylphosphino)ethane or L(2) = dpae = 1,2-bis(diphenylarsino)ethane] with pure parahydrogen (generated at 18 K). This process forms Ru(CO)(2)(dppe)(H)(2) and Ru(CO)(2)(dpae)(H)(2) on a sub-microsecond time-scale. With the pulsed laser, the spin state of the hydride nuclei in Ru(CO)(2)(dppe)(H)(2) has a purity of 89.8 +/- 2.6% (from 12 measurements). To achieve comparable results by cooling would require a temperature of 6.6 mK, which is unmanageable in the liquid state, or an impractical magnetic field of 0.44 MT at room temperature. In the case of CW initiation, reduced state purities are observed due to natural signal relaxation even when a spin-lock is used to prevent dephasing. When Ru(CO)(3)(dpae) and pulsed laser excitation are utilized, the corresponding dihydride product spin state purity was determined as 106 +/- 4% of the theoretical maximum. In other words, the state prepared using Ru(CO)(3)(dpae) as the precursor is indistinguishable from a pure state.

Multicentre prospective audit of surgical outcomes and acute complications following short course pre-operative radiotherapy for resectable rectal cancer

BACKGROUND

The addition of short course pre-operative radiotherapy to total mesorectal excision reduces local recurrence in resectable adenocarcinoma of the rectum. In a previous retrospective study potential factors associated with early complications following this combination were identified. The aim of this study was to examine these relationships in a prospective multicentre audit.

METHODS

One hundred and seven patients who received short course pre-operative radiotherapy in four cancer centres between 1 October 2001 and 30 September 2002 were included. Data including patient age, radiotherapy field length, overall treatment time, operation type, surgical outcomes and complications occurring within 3 months of the 1st day of radiotherapy were collected. These were compared and combined with the previously studied cohort of 176 patients treated at one centre between 1st January 1998 and 31st December 1999.

RESULTS

In the prospective cohort only patient age (P=0.001) was significantly associated with acute complications. However, both the overall treatment time (median 9.0 vs 11.0 days P <0.0001) and field length (median 16.6 vs 17.0 cm P=0.03) were significantly shorter in this cohort when compared to the previous retrospective study. In patients from both studies (n=283), increasing age (P=0.002) and field length (independent of operation type) (P=0.02) were independently associated with an increased risk of acute complications.

CONCLUSIONS

This study suggests that meticulous selection of patients for short course pre-operative radiotherapy and smaller planning target volumes may be associated with a lower risk of acute complications. The use of MRI scanning to stage pelvic disease may reduce the number of patients with R1 resections receiving short course pre-operative radiotherapy.

Preparing high purity initial states for nuclear magnetic resonance quantum computing

Here we demonstrate how parahydrogen can be used to prepare a two-spin system in an almost pure state which is suitable for implementing nuclear magnetic resonance quantum computation. A 12 ns laser pulse is used to initiate a chemical reaction involving pure parahydrogen (the nuclear spin singlet of H2). The product, formed on the micros time scale, contains a hydrogen-derived two-spin system with an effective spin-state purity of 0.916. To achieve a comparable result by direct cooling would require an unmanageable (in the liquid state) temperature of 6.4 mK or an impractical magnetic field of 0.45 MT at room temperature. The resulting spin state has an entanglement of formation of 0.822 and cannot be described by local hidden variable models.

Diagnosis of Clostridium difficile antibiotic associated diarrhoea culture versus toxin assay

OBJECTIVE

To compare the results of Clostridium Difficile (CD) on culture with detection of C. difficile toxin by Enzyme Immunoassay (EIA) in the stool specimens of hospitalized patients with antibiotic associated diarrhoea (AAD).

PATIENTS AND METHODS

The study included 80 adult patients with AAD and 20 adult patients with non-AAD. Stool specimens of all these subjects were inoculated on cycloserine cefoxitin fructose agar and incubated anaerobically to isolate C. difficile. At the same time, all the stool specimens were tested for C. difficile toxin by EIA technique using cytoclone A and B kit manufactured by Cambridge Biotech Corporation, Worcester, Massachusette.

RESULTS

Out of 80 adult patients with AAD, thirty were females and fifty males. C. difficile was isolated on culture from stool specimen of 16 patients, while twenty-three stool specimens were positive for C. difficile toxin. From 20 control subjects, C. difficile was isolated from stool specimen of only one subject. No stool specimen from the controls was positive for toxin.

CONCLUSION

Diagnosis of CDAAD by culture is difficult and time consuming because of strict anaerobic nature of organism. Moreover, mere isolation of C. difficile on culture is not sufficient to establish the pathogenic role of these isolates. C. difficile toxin detection by EIA technique is a highly sensitive and specific method for diagnosis of CDAAD. Using this method, results are available in three hours time. Therefore, EIA is recommended for rapid diagnosis of CDAAD.

Bacteriological quality of drinking water in Punjab: evaluation of H2S strip test

OBJECTIVE

To assess bacteriological quality of drinking water in Punjab and to evaluate usefulness of H2S strip test in comparison with multiple tube test.

METHOD

Samples of water were tested using H2S strips and multiple tube test.

RESULTS

Maximum bacterial contamination was observed in water from domestic pumps (95.83%). Followed by tap water in rural areas of Punjab (91.30%) and tap water in Lahore (42.85%). Bacterial contamination was significantly higher (p < 0.001) in rural areas as compared to urban areas. Comparison of results of testing water samples by H2S strip test and multiple tube test revealed that H2S strip is 87.24% sensitive and 100% specific for detection of bacterial contamination with a positive predictive value of 100%. It was also observed that 100% water samples negative for total coliforms were also negative by H2S strip method. Moreover, with increase in number of total coliforms in the water samples, positivity by H2S strip method also increased (samples with more than 10 total coliforms/100 ml were 100% positive by H2S strip method). Therefore, H2S strip test can be used as alternative to multiple tube test for detection of bacterial contamination of water supplies.

CONCLUSION

It is concluded that bacterial contamination of water is a significant problem in Punjab. It can be improved by regular monitoring of water supplies. For this purpose use of H2S strip test is advocated at house hold level.

Evaluation of dipstrips, direct gram stain and pyuria as screening tests for the detection of bacteriuria

Two hundred and fifty cases of clinically suspected urinary tract infection were analysed for the detection of bacteriuria. Parameters studied included direct Gram staining, pyuria on microscopic examination of uncentrifuged urine and dip strip method for the detection of blood, protein, nitrite and leucocyte esterase. Significant bacteriuria (colony count 10(5) per ml) was found in 112 cases with a positivity ranging from 65 to 83% for the presence of blood, protein, nitrite and leucocyte esterase. Highest positive predictive values were obtained with the presence of nitrite and leucocyte esterase (98%), blood, protein and nitrite (94%) as well as with blood, protein, nitrite and leucocyte esterase (98%). Both pyuria and direct Gram staining were positive in 85% cases. The combined presence of both these parameters gave 100% positive predictive value. Gram staining combined with pyuria was more effective and economical as compared to the dipstrips for the detection of bacteriuria.

Clinical features of infantile diarrhea associated with single or multiple enteric pathogens

Clinical features of infantile diarrhea were studied among 603 infants from birth to 12 months of age to determine the predominant clinical feature(s) seen in infantile diarrhea associated with a specific enteric pathogen. Among the major clinical features, fever was most often seen in diarrhea due to Yersinia spp. (61.5%) followed by that in rotavirus (26.1%). Vomiting was mostly associated with Vibrio cholerae infection (90.9%) and shigellosis (64.6%). Dehydration was predominant in Vibrio cholerae (90.9%) and Salmonella (84.9%) infections. Bloody diarrhea was mostly due to Shigella infection (74.3%). As regards diarrhea with multiple pathogens, vomiting and dehydration were most frequent with Campylobacter+Enteropathogenic Escherichia coli (EPEC) (88.9% and 77.8%, respectively), while fever was more common with rotavirus+Shigella+Escherichia coli and rotavirus+Giardia. Infection with invasive organisms lead to vomiting, 4-10 stools per day and dehydration significantly more often as compared to infections with non-invasive organisms. Similarly more stools of patients infected with invasive organisms showed presence of blood and more than 5 leukocytes/HPF as compared to those infected with non-invasive organisms.