Effect of sequential pretreatment combinations on the composition and enzymatic hydrolysis of hazelnut shells

Hazelnut shells, a high lignin containing biomass, were subjected to individual and sequential liquid hot water (LHW), alkaline (AP) and dilute acid pretreatments (DAP). Among the single pretreatments, LHW demonstrated the highest cellulose recovery of 98.1%, DAP resulted in the highest hemicellulose solubilization of 56.0%, and AP of the highest lignin removal of 49.6%. Employing two-step pretreatment on hazelnut shells, in general, demonstrated an enhanced action of the second pretreatment; therefore, the sequence of the pretreatment methods had a significant impact on both substrate characteristics and enzymatic hydrolysis efficiency of biomass. In terms of delignification, AP-LHW achieved 60.7% lignin removal, while LHW-DAP showed the highest hemicellulose removal of 93.8% and DAP-LHW resulted in the highest cellulose recovery of 94.0%. Structural properties of raw and pretreated hazelnut shells were observed by FTIR. The maximum glucose recovery of 54.9% was observed in DAP-LHW pretreated samples. For this pretreatment combination, almost 1.8 MJ total energy was required to recover 10.2 g glucose. The findings indicated that complete removal of the physical barrier of lignin and hemicellulose might not be essential; partial relocation of lignin and alteration of cellulose structure may also be efficient in increasing the sugar recovery from the lignocellulosic biomass.

Rayel O, Moritz GL, Rebelatto JL. Towards Improving TSCH Energy Efficiency: An Analytical Approach to a Practical Implementation

The IEEE 802.15.4-2015 standard defines a number of Medium Access Control (MAC) layer protocols for low power wireless communications, which are desirable for energy-constrained Internet of Things (IoT) devices. Originally defined in the IEEE 802.15.4e amendment, the Time Slotted Channel Hopping (TSCH) has recently been attracting attention from the research community due to its reduced contention (time scheduling) and robustness against fading (channel hopping). However, it requires a certain level of synchronization between the nodes, which can increase the energy consumption. In this work, we implement the Guard Beacon (GB) strategy, aiming at reducing the guard time usually implemented to compensate for imperfect synchronization. Moreover, besides presenting a realistic energy consumption model for a Contiki Operating System-based TSCH network, we show through analytical and practical results that, without the proposed scheme, the power consumption can be more than 13% higher.

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination. The selection of the cluster head (CH) plays a crucial role in gathering and aggregating the data from the cluster members and forwarding the data to the sink node. The inefficient CH selection causes packet failures during the data transfer and early battery depletion nearer to the sink. This paper proposes a cluster tree-based routing protocol (CT-RPL) to increase the life span of the network and avoid the data traffic among the network nodes. The CT-RPL involves three processes, namely cluster formation, cluster head selection, and route establishment. The cluster is formed based on the Euclidean distance. The CH selection is accomplished using a game theoretic approach. Finally, the route is established using the metrics residual energy ratio (RER), queue utilization (QU), and expected transmission count (ETX). The simulation is carried out by using a COOJA simulator. The efficiency of a CT-RPL is compared with the Routing Protocol for Low Power and Lossy Networks (RPL) and energy-efficient heterogeneous ring clustering routing (E2HRC-RPL), which reduces the traffic load and decreases the packet loss ratio. Thus, the CT-RPL enhances the lifetime of the network by 30–40% and the packet delivery ratio by 5–10%.

Learning automata based energy-efficient AI hardware design for IoT applications

Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low-energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit for pervasive applications. Fundamental to this circuit is systematic encoding of binarized input data fed into maximally parallel logic blocks. The allocation of these blocks is optimized through a design exploration and automation flow using field programmable gate array-based fast prototypes and software simulations. The design flow allows for an expedited hyperparameter search for meeting the conflicting requirements of energy frugality and high accuracy. Extensive validations on the hardware implementation of the new architecture using single- and multi-class machine learning datasets show potential for significantly lower energy than the existing AI hardware architectures. In addition, we demonstrate test accuracy and robustness matching the software implementation, outperforming other state-of-the-art machine learning algorithms. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.

Membrane and Electrochemical Processes for Water Desalination: A Short Perspective and the Role of Nanotechnology

In the past few decades, membrane-based processes have become mainstream in water desalination because of their relatively high water flux, salt rejection, and reasonable operating cost over thermal-based desalination processes. The energy consumption of the membrane process has been continuously lowered (from >10 kWh m-3 to ~3 kWh m-3) over the past decades but remains higher than the theoretical minimum value (~0.8 kWh m-3) for seawater desalination. Thus, the high energy consumption of membrane processes has led to the development of alternative processes, such as the electrochemical, that use relatively less energy. Decades of research have revealed that the low energy consumption of the electrochemical process is closely coupled with a relatively low extent of desalination. Recent studies indicate that electrochemical process must overcome efficiency rather than energy consumption hurdles. This short perspective aims to provide platforms to compare the energy efficiency of the representative membrane and electrochemical processes based on the working principle of each process. Future water desalination methods and the potential role of nanotechnology as an efficient tool to overcome current limitations are also discussed.

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (Review of 2018 Literature) DIRECT

A review of the literature published in 2018 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented. PRACTITIONER POINTS: Summary of advances in the field of modeling, instrumentation, automation, and optimization in 2018. This review outlines the major contributions of researchers and practitioners that have been published in peer-reviewed journals and conference proceedings. The article is organized into sections for ease of reference, but several reviewed articles are related to more than one section.

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

A review of the literature published in 2019 on topics relating to water resource recovery facilities (WRRFs) in the areas of modeling, automation, measurement and sensors, and optimization of wastewater treatment (or water resource reclamation) is presented.

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors' knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

A Vector Space Model for Neural Network Functions: Inspirations From Similarities Between the Theory of Connectivity and the Logarithmic Time Course of Word Production

The present report examines the coinciding results of two study groups each presenting a power-of-two function to describe network structures underlying perceptual processes in one case and word production during verbal fluency tasks in the other. The former is theorized as neural cliques organized according to the function N = 2 ⁱ - 1, whereas the latter assumes word conglomerations thinkable as tuples following the function N = 2 ⁱ . Both theories assume the innate optimization of energy efficiency to cause the specific connectivity structure. The vast resemblance between both formulae motivated the development of a common formulation. This was obtained by using a vector space model, in which the configuration of neural cliques or connected words is represented by a N-dimensional state vector. A further analysis of the model showed that the entire time course of word production could be derived using basically one single minimal transformation-matrix. This again seems in line with the principle of maximum energy efficiency.

Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI

As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO.

A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.

PCM Cement-Lime Mortars for Enhanced Energy Efficiency of Multilayered Building Enclosures under Different Climatic Conditions

Phase change materials (PCMs) are promising materials for the energy efficiency improvement of building enclosures, due to their energy storage capacity. The thermal behaviour of a multi-layered building enclosure with five different compositions of PCM cement-lime mortars was evaluated under heating and cooling cycles. The behaviour of cement-lime mortars with 20% of microencapsulated PCM mixed with other additions, such as cellulose fibres and perlite, a lightweight aggregate (LWA), were studied under climate conditions of 15 °C-82% RH (cooling) and 30 °C-33% RH (heating) that were applied with a climatic chamber. Temperature and heat flux on both sides of the multi-layered enclosure were experimentally measured in laboratory tests. Temperature was also measured on both sides of the PCM cement-lime mortar layer. It was observed that the addition of the PCM cement-lime mortar layer delayed the heat flux through the enclosure. During a heating cycle, the incorporation of PCM delayed the arrival of the heat wave front by 30 min (8.1% compared to the reference mortar without PCM). The delay of the arrival of the heat wave front during the cooling cycle after adding PCM, compared to the reference mixture, reached 40.6% (130 min of delay). Furthermore, the incorporation of LWA in PCM cement-lime mortars also improved thermal insulation, further increasing energy efficiency of the building enclosure, and can be used not only for new buildings but also for energy rehabilitation of existing building enclosures.

Pulsed Electric Fields-Assisted Extraction of Valuable Compounds From Arthrospira Platensis: Effect of Pulse Polarity and Mild Heating

The present study aimed to investigate the effect of the main pulsed electric field (PEF) process parameters on the cell damages of A. platensis microalgae and the extractability of valuable compounds [water-soluble proteins (WSP), C-phycocyanin (C-PC), and carbohydrates (CH)]. Aqueous microalgae suspensions (2%, w/w) were PEF-treated at variable field strength (E = 10, 20, 30 kV/cm), total specific energy (W T = 20, 60, 100 kJ/kgsusp), and inlet temperature (25, 35, 45°C), with either monopolar or bipolar square wave pulses (5 μs of width, delay time between pulses of opposite polarities = 1, 5, 10, 20 μs), prior to extraction with water at room temperature (25°C) for up to 3 h. High-pressure homogenization (HPH) treatment (P = 150 MPa, 3 passes) was used to achieve complete cell disruption to quantify the total extractable content of target intracellular compounds. Scanning electron microscopy (SEM) and optical microscopy analyses clearly showed that PEF merely electroporated the membranes of algae cell, without damaging the cell structure and forming cell debris. The application of PEF treatment (monopolar pulses, 20 kV/cm and 100 kJ/kgsusp) at room temperature significantly enhanced the extraction yield of WSP [17.4% dry weight (DW)], CH (10.1% DW), and C-PC (2.1% DW), in comparison with the untreated samples. Bipolar pulses appeared less effective than monopolar pulses and led to extraction yields dependent on the delay time. Additionally, regardless of pulse polarity, a clear synergistic effect of the combined PEF (20 kV/cm and 100 kJ/kgsusp)-temperature (35°C) treatment was detected, which enabled the extraction of up to 37.4% (w/w) of total WSP, 73.8% of total CH, and 73.7% of total C-PC. Remarkably, the PEF treatment enabled to obtain C-phycocyanin extract with higher purity than that obtained using HPH treatment. The results obtained in this work suggest that the application of PEF combined with mild heating could represent a suitable approach for the efficient recovery of water-soluble compounds microalgal biomass.

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector's data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application

The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity.

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.

Inspection of Biomimicry Approaches as an Alternative to Address Climate-Related Energy Building Challenges: A Framework for Application in Panama

In the Panama context, energy consumption in the building sector is mostly related to the conditioning of indoor spaces for cooling and lighting. Different nature strategies can be mimic to strongly impact these two aspects in the building sector, such as the ones presented here. A comprehensive analysis regarding literature related to biomimicry-based approaches destined to improve buildings designs is presented here. This analysis is driven by the increasing energy regulations demands to meet future local goals and to propose a framework for applications in Panama. Such biomimicry-based approaches have been further analyzed and evaluated to propose the incorporation of organism-based design for three of the most climate types found in Panama. Consequently, a SWOT analysis helped realized the potential that biomimicry-based approaches might have in improving the odds of in meeting the local and global regulations demands. The need for multidisciplinary collaboration to accomplish biomimicry-based-designed buildings, brings an increment in the competitivity regarding more trained human-assets, widening the standard-construction-sector thinking. Finally, the analysis presented here can serve as the foundation for further technical assessment, via numerical and experimental means.

Energy Efficiency in RF Energy Harvesting-Powered Distributed Antenna Systems for the Internet of Things

This paper studies a distributed antenna system (DAS) network with radio frequency (RF) energy harvesting (EH) technology where the distributed antenna ports (DAPs) transmit energy and information to multiple users simultaneously. The time division multiple access (TDMA) protocol is adopted, so for each time slot is allowed to receive information, while the rest of the users harvest energy. In order to maximize the system energy efficiency (EE), subject to the EH requirements and data rate requirements of the users, the transmission time and power assignment are jointly optimized. In order to deal with this non-convex problem, based on Dinkelbach theory and the block-coordinate descent (BCD) scheme, an efficient algorithm is designed to obtain the global optimal solution. Then, simulation results are presented to show that the proposed method achieves much higher system EE compared with benchmark methods. With the increase of the user’s minimum information rate, the system EE decreases rapidly.

Membrane-assisted radiant cooling for expanding thermal comfort zones globally without air conditioning

In this paper, we present results from a radiant cooling pavilion, demonstrating a method of cooling people without cooling the air. Instead, surfaces are chilled, and thermal radiation is used to keep people cool. A thermally transparent membrane is used to prevent unwanted air cooling and condensation, a required precursor to deploying radiant cooling panels without humidity control in tropical environments. The results from this thermal-comfort study demonstrate the ability to keep people comfortable with radiation in warm air, a paradigm-shifting approach to thermal comfort that may help curb global cooling-demand projections.

We present results of a radiant cooling system that made the hot and humid tropical climate of Singapore feel cool and comfortable. Thermal radiation exchange between occupants and surfaces in the built environment can augment thermal comfort. The lack of widespread commercial adoption of radiant-cooling technologies is due to two widely held views: 1) The low temperature required for radiant cooling in humid environments will form condensation; and 2) cold surfaces will still cool adjacent air via convection, limiting overall radiant-cooling effectiveness. This work directly challenges these views and provides proof-of-concept solutions examined for a transient thermal-comfort scenario. We constructed a demonstrative outdoor radiant-cooling pavilion in Singapore that used an infrared-transparent, low-density polyethylene membrane to provide radiant cooling at temperatures below the dew point. Test subjects who experienced the pavilion (n = 37) reported a “satisfactory” thermal sensation 79% of the time, despite experiencing 29.6 ± 0.9 °C air at 66.5 ± 5% relative humidity and with low air movement of 0.26 ± 0.18 m⋅s⁻¹. Comfort was achieved with a coincident mean radiant temperature of 23.9 ± 0.8 °C, requiring a chilled water-supply temperature of 17.0 ± 1.8 °C. The pavilion operated successfully without any observed condensation on exposed surfaces, despite an observed dew-point temperature of 23.7 ± 0.7 °C. The coldest conditions observed without condensation used a chilled water-supply temperature 12.7 °C below the dew point, which resulted in a mean radiant temperature 3.6 °C below the dew point.

Interplay between Position-Dependent Codon Usage Bias and Hydrogen Bonding at the 5' End of ORFeomes

Codon usage bias exerts control over a wide variety of molecular processes. The positioning of synonymous codons within coding sequences (CDSs) dictates protein expression by mechanisms such as local translation efficiency, mRNA Gibbs free energy, and protein cotranslational folding. In this work, we explore how codon usage affects the position-dependent content of hydrogen bonding, which in turn influences energy requirements for unwinding double-stranded DNA (dsDNA). We categorized codons according to their hydrogen bond content and found differential effects on hydrogen bonding encoded by codon variants. The specific positional disposition of codon variants within CDSs creates a ramp of hydrogen bonding at the 5' end of the ORFeome in Escherichia coli CDSs occupying the first position of operons are subjected to selective pressure that reduces their hydrogen bonding compared to internal CDSs, and highly transcribed CDSs demand a lower maximum capacity of hydrogen bonds per codon, suggesting that the energetic requirement for unwinding the dsDNA in highly transcribed CDSs has evolved to be minimized in E. coli Subsequent analysis of over 14,000 ORFeomes showed a pervasive ramp of hydrogen bonding at the 5' end in Bacteria and Archaea that positively correlates with the probability of mRNA secondary structure formation. Both the ramp and the correlation were not found in Fungi The position-dependent hydrogen bonding might be part of the mechanism that contributes to the coordination between transcription and translation in Bacteria and Archaea A Web-based application to analyze the position-dependent hydrogen bonding of ORFeomes has been developed and is publicly available (https://juanvillada.shinyapps.io/hbonds/).IMPORTANCE Redundancy of the genetic code creates a vast space of alternatives to encode a protein. Synonymous codons exert control over a variety of molecular and physiological processes of cells mainly through influencing protein biosynthesis. Recent findings have shown that synonymous codon choice affects transcription by controlling mRNA abundance, mRNA stability, transcription termination, and transcript biosynthesis cost. In this work, by analyzing thousands of Bacteria, Archaea, and Fungi genomes, we extend recent findings by showing that synonymous codon choice, corresponding to the number of hydrogen bonds in a codon, can also have an effect on the energetic requirements for unwinding double-stranded DNA in a position-dependent fashion. This report offers new perspectives on the mechanism behind the transcription-translation coordination and complements previous hypotheses on the resource allocation strategies used by Bacteria and Archaea to manage energy efficiency in gene expression.

A Cost-Effective System for Aerial 3D Thermography of Buildings

Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy efficiency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV—also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal.

A Preliminary Evaluation of Energy Efficiency for Children with Cerebral Palsy for Driving A Manual Wheelchair and Walking: Use of the Total Heart Beat Index

Aims: This study aims to compare the total heart beat index (THBI) in evaluating energy efficiency between using a manual wheelchair and walking for children with cerebral palsy (CP). Methods: The energy efficiency was measured in 21 participants with CP (mean age, 13.6 ± 3.4 years) who walk or drive a manual wheelchair using a square course. THBI was calculated as total number of heart beats during the exercise period/total distance traveled. Results: Significant differences in the THBI were observed between Gross Motor Function Classification System (GMFCS) levels III and IV (p = .049, effect size = 1.60). No significant differences in THBI were observed between GMFCS levels II and III or between GMFCS levels II and IV (p > .05). Conclusions: The energy efficiency of children with CP who use a manual wheelchair in this study was equal to or better than that for walking.

Actively Operable Thermoresponsive Smart Windows for Reducing Energy Consumption

Efficient usage of finite energy resources is a core approach for preventing major blackouts caused by a severe lack of energy. Smart windows, which modulate thermal energy transferred from the incident sunlight, have attracted tremendous interest as an alternative technology for resolving the fast-approaching energy crisis by suppressing unnecessary energy usage such as air conditioning or heating inside buildings. Here, we demonstrate a set of materials and design concepts for doubly responsive smart windows, which efficiently reduce the consumption of our limited energy reserves. The proposed smart windows are based on the concept of combining the lower critical solution temperature of thermoresponsive polymer hydrogels and the electrical actuation of graphene-based flexible heaters; this combination serves to actively control the passive-type moving thermoresponsive smart window. The proposed smart windows exhibit a highly tunable transparency of above 90%, which corresponds to an almost instantaneous change from high transmission of the incident light to the complete blockage of its penetration under thermal or electrical stimulation. In particular, when the windows of a mockup house are replaced with the developed flexible smart windows, the increment rate of the indoor temperature under white light irradiation reduces drastically. This type of active light control system is expected to create a new opportunity for achieving cost savings on heating, cooling, and lighting through management of light energy transmitted into the interior of a house.

Enhanced Energy Storage Performance of Sodium Niobate-Based Relaxor Dielectrics by a Ramp-to-Spike Sintering Profile

Sodium niobate (NaNbO₃)-based lead-free ceramics have been actively studied for energy storage applications because of their antiferroelectric and/or relaxor features achieved in modified systems. The P-E loops of NaNbO₃-based ceramics are usually hysteretic because of the existence of a metastable ferroelectric phase at room temperature. In this study, by introducing aliovalent cations and A-site vacancies, the relaxor characteristics are greatly enhanced in (Na_1-2xBi_x)(Nb_1-xZr_x)O₃ ceramics, leading to a high energy storage efficiency of above 90%. In addition, sintering aid CuO and a special ramp-to-spike sintering profile were employed to decrease the sintering temperature and reduce the grain size. The modified ceramic exhibits improved insulating properties and hence a higher breakdown strength, leading to a high recoverable energy density of 4.9 J/cm³ and a high energy efficiency of 88% at 430 kV/cm. The ceramic also exhibits satisfactory temperature stability over a wide temperature range from 25 to 125 °C and charge-discharge performance, making it a promising candidate for high-power dielectric energy storage applications.