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Spiking neural networks (SNNs) can do inference with low power consumption due to their spike sparsity. Although SNNs can be combined with neuromorphic hardware to achieve efficient inference, they are often difficult to train directly due to discrete non-differentiable spikes. As an alternative, ANN-SNN conversion is an efficient way to achieve deep SNNs by converting well-trained artificial neural networks (ANNs). However, the existing methods commonly use constant threshold for conversion. A high constant threshold value prevents neurons from rapidly delivering spikes to deeper layers and causes high time delay. In addition, the same response for different inputs may result in information loss during the information transmission. Inspired by the biological adaptive threshold mechanism, we propose a multistage adaptive threshold (MSAT) method to alleviate this problem. Instead of using a single, constant value, the threshold is adjusted in multistages, adapting to each neuron's firing history and input properties. Specifically, for each neuron, the dynamic threshold is positively correlated with the average membrane potential and negatively correlated with the rate of depolarization. The adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spikes faster and transmit more information. Moreover, we analyze the spikes of inactivated neurons error, which is pervasive in early time steps. We also propose spike confidence accordingly to measure confidence about the neurons that correctly deliver spikes. Such spike confidence in early time steps is used to determine whether to elicit the spike to alleviate the spikes of inactivated neurons error. Combined with the proposed methods, we examine the performance on CIFAR-10, CIFAR-100, and ImageNet datasets. We also conduct sentiment classification and speech recognition experiments on the IDBM and Google speech commands datasets, respectively. Experiments show that our methods can achieve near-lossless and lower latency ANN-SNN conversion. In summary, we build a biologically inspired multistage adaptive threshold for converted SNN, with comparable performance to state-of-the-art methods while improving energy efficiency. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes. [ABSTRACT FROM AUTHOR]

The development of synthetic active matter requires the ability to design materials capable of harnessing energy from a source to carry out work. Nature achieves this using chemical reaction cycles in which energy released from an exergonic chemical reaction is used to drive biochemical processes. Although many chemically fuelled synthetic reaction cycles that control transient responses, such as self‐assembly, have been reported, the generally high complexity of the reported systems hampers a full understanding of how the available chemical energy is actually exploited by these systems. This lack of understanding is a limiting factor in the design of chemically fuelled active matter. Here, we report a minimalistic synthetic responsive reaction cycle in which adenosine diphosphate (ADP) triggers the formation of a catalyst for its own hydrolysis. This establishes an interdependence between the concentrations of the network components resulting in the transient formation of the catalyst. The network is sufficiently simple that all kinetic and thermodynamic parameters governing its behaviour can be characterised, allowing kinetic models to be built that simulate the progress of reactions within the network. While the current network does not enable the ADP‐hydrolysis reaction to populate a non‐equilibrium composition, these models provide insight into the way the network dissipates energy. Furthermore, essential design principles are revealed for constructing driven systems, in which the network composition is driven away from equilibrium through the consumption of chemical energy. [ABSTRACT FROM AUTHOR]

Summary: Source location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay. [ABSTRACT FROM AUTHOR]

Platooning is an effective way to reduce air resistance and fuel consumption in vehicular transportation. However, stochastically generated disturbances can significantly impact the string stability of platoons. This article proposes a novel control law, based on sliding mode control and leader‐predecessor communications, designed to improve the exponential mean‐square string stability of platoons with Ito‐type stochastic disturbances that consider friction force. Specifically, our approach employs a third‐order nonlinear dynamic model to capture the relevant spatiotemporal correlations present in vehicle platoons. The proposed longitudinal control strategy effectively stabilizes the platoons with time delay and was verified through simulation experimentation. In this study, the stability of sliding mode motion is thoroughly investigated by means of sub‐reachable and practical stability analysis with sliding mode control. The findings of this study provide a theoretical foundation for further research on vehicle platoons with increasingly complicated interference and time delays. Overall, our results demonstrate that our approach can achieve significant improvements in both fuel economy and exhaust emissions while maintaining high levels of string stability, further contributing to the development of efficient and sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Mitochondria reside at the crossroads of catabolic and anabolic metabolism—the essence of life. How their structure and function are dynamically tuned in response to tissue-specific needs for energy, growth repair, and renewal is being increasingly understood. Mitochondria respond to intrinsic and extrinsic stresses and can alter cell and organismal function by inducing metabolic signaling within cells and to distal cells and tissues. Here, we review how the centrality of mitochondrial functions manifests in health and a broad spectrum of diseases and aging. Mitochondria, central to metabolic processes, adapt their structure and function to meet cellular energy and repair demands. Understanding of their role in responding to stresses and signaling for cellular and systemic health, disease, and aging is expanding. [ABSTRACT FROM AUTHOR]

Selective electrosynthesis of urea from carbon source (CO2) and nitrogen (N2, NO3−, NO2−) source provides a sustainable strategy for production of essential nitrogen‐based fertilizers. The traditional technology of urea synthesis employs two consecutive processes, synthesis of NH3 from N2+H2 and urea from NH3+CO2, which always involves high energy consumption and environmental concerns. By contrast, urea electrosynthesis avoids the above harsh steps and meanwhile has higher atomic economy. Herein, we present a comprehensive review on the progress and prospect of urea electrosynthesis from abundant carbon and nitrogen sources via coupling of C−N under ambient conditions, and also discuss the mechanism researches, strategies of substrates activation, current challenges and future opportunities of urea electrosynthesis. The urea electrosynthesis via C−N coupling realizes abundant and low‐cost inorganic carbon and nitrogen sources for efficient resource utilization and provides guidance and reference for C−C, C−S or other coupling reactions. [ABSTRACT FROM AUTHOR]

High-pressure proton exchange membrane (PEM) water electrolysis for hydrogen production is a crucial method to achieve low energy consumption, high efficiency, minimal pollution, and seamless integration with storage systems. Despite its potential, the current application of high-pressure PEM water electrolysis faces several challenges. This paper provides a concise analysis of the research advancements in high-pressure PEM water electrolysis for hydrogen production, focusing on technical bottlenecks within high-pressure devices. It explores key issues such as gas cross-permeability, membrane degradation, membrane shedding, and hydrogen embrittlement encountered during high-pressure PEM water electrolysis for hydrogen production. Furthermore, this paper summarizes the latest research directions and proposed solutions based on existing findings, aiming to offer effective references for enhancing the safety, scalability, and industrial application of high-pressure PEM water electrolysis technology. [Display omitted] • State-of-art technology of high-pressure PEMWE for hydrogen production was discussed. • Gas cross-permeability, membrane degradation and hydrogen embrittlement of PEMWE were analyzed. • The latest research directions and proposed solutions for the PEMWE were presented. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

This study considers the option to build a hydrogen city in Al Khobar, Kingdom of Saudi Arabia, and analyzes the hydrogen storage characteristic to develop a net zero energy system only powered by wind and solar generators. The size of the hydrogen energy storage is estimated based on seasonal variability. Larger estimations may follow the interannual variability, which unfortunately is more difficult to predict. A 1.3 GW installed capacity of wind and solar could permit delivery of dispatchable electricity to the grid at an average electricity power of 0.2 GW, plus production of fuel to cover other non-electric energy uses of 0.07 GW average fuel power. While 0.145 TWh of hydrogen energy storage would be required to deal with seasonal variability, up to 5 times this amount may be necessary to manage interannual and decadal variability, coupled with 0.645 GW of electrolyzer capacity and 0.5 GW of hydrogen energy generators. [ABSTRACT FROM AUTHOR]

Food waste has become a major problem in this era and is considered a waste of resources. While food waste is increasing, the energy demand is increasing as well. Producing energy from food waste can be a suitable solution for the two problems. Food waste is rich in nutrients and organic compounds. This composition makes food waste suitable to produce hydrogen. Hydrogen has attracted the attention of researchers these days because of its high ability to produce energy with no side products other than water. Food waste can be converted to hydrogen through a thermochemical process called gasification. Gasification converts biomass into a mixture of combustible gases via partial oxidation under high temperatures. In this review, different types of food waste gasification are explored. The results from recent studies are summarized in tables and compared based on the hydrogen yield and process variables. Incrementing temperature and residence time favored hydrogen production along with decrementing feed concentration. Moreover, the usage of a suitable catalyst has significantly enhanced the hydrogen production. The results have shown that food waste gasification has a promising ability to produce hydrogen, however more studies are required to investigate more on the economic feasibility of the process before commercializing it. • The potential of hydrogen production from Food Waste gasification is studied. • Studies of different food waste gasification types are investigated to demonstrate the influence of process variables. • Influence of temperature, residence time, feedstock concentration and catalyst type on hydrogen yield has been investigated. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has the potential for improving the sustainability of metal processing through decreased energy and materials usage compared to casting and forging. Laser powder bed fusion (LPBF) of high-temperature alloys such as nickel alloy 718 is one of the key modalities supporting this effort. One of the major drawbacks to LPBF is its slow build speed on the order of 5–10 cubic centimeters per hour print speed. This experimental study investigates how to increase the productivity of the LPBF process by switching from a traditional Gaussian laser shape to a ring laser shape using a nLight multi-modal laser. The objective is to increase productivity, reducing energy consumption and time, without sacrificing mechanical properties by switching to the ring laser thereby improving the sustainability of LPBF. Results include measuring the energy consumption of an Open Additive LPBF system during 718 printing and comparing the microstructure and mechanical properties of the two different lasers. [ABSTRACT FROM AUTHOR]

The escalation of cloud services, driven by their accessibility, improved performance, and cost-effectiveness, has led cloud service providers to consistently seek methods to expedite job completion, thereby boosting profits and reducing energy consumption expenses. Despite developing numerous scheduling algorithms, many of these techniques address only a specific objective within the scheduling process. To efficiently achieve better optimization results for the cloud task scheduling problem, a novel approach, the Enhanced Whale Optimization Algorithm (EWOA), is proposed. EWOA integrates the WOA with the Lévy flight. The incorporation of Lévy flight is tailored to broaden the search space of WOA, expediting convergence with adaptive crossover. The EWOA model is simulated using the Cloudsim tool and evaluated under diverse test conditions. The effectiveness of EWOA is assessed by employing various parameters and comparing them with existing algorithms. The results demonstrate that EWOA outperforms other algorithms in resource utilization, energy consumption, and execution cost, establishing its superiority in addressing the complexities of multi-objective cloud task scheduling. [ABSTRACT FROM AUTHOR]

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings. [ABSTRACT FROM AUTHOR]

Reproduction includes two energy investmentsÑthe energy in the offspring and the energy expended to make them. The former is well understood, whereas the latter is unquantified but often assumed to be small. Without understanding both investments, the true energy costs of reproduction are unknown. We present a framework for estimating the total energy costs of reproduction by combining data on the energy content of offspring (direct costs) and the metabolic load of bearing them (indirect costs). We find that direct costs typically represent the smaller fraction of the energy expended on reproduction. Mammals pay the highest reproductive costs (excluding lactation), ~90% of which are indirect. Ectotherms expend less on reproduction overall, and live-bearing ectotherms pay higher indirect costs compared with egg-layers. We show that the energy demands of reproduction exceed standard assumptions. [ABSTRACT FROM AUTHOR]

This article explores the potential and limitations of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in comparison to the human brain. It acknowledges that while AI can mimic the neural networks of the human brain, it falls short in terms of learning efficiency, intuition, and creativity. The article also raises concerns about the significant energy consumption of AI. In contrast, it highlights the energy efficiency of the human brain and the role of neurotransmitters and neuromodulators in its functioning. The author recognizes the contributions of Miklos Palkovits, a researcher in chemical neuroanatomy, and emphasizes the ongoing efforts to understand the complexities of the human brain. The accompanying document provides a comprehensive list of references and citations from scientific articles on neuroscience, AI, and energy consumption, offering diverse perspectives and research findings for library patrons conducting related research. [Extracted from the article]

Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data. [ABSTRACT FROM AUTHOR]

Soy-based biodiesel can reduce well-to-wheels greenhouse gas (GHG) emissions per unit energy (i.e., gCO2e/MJ) by 66%-72% as compared to the petroleum-based diesel fuel with currently adopted agricultural and industrial practices. Biodiesel can reduce particulate matter and carbon monoxide emissions with a manageable degree of increase in NOx emissions. From the perspective of GHG emissions reduction per unit travelling distance (i.e., gCO2e/mile), the application of B20 in compression ignition engines without the adjustment in engine control unit (ECU) settings will not extract the best carbon emissions reduction that B20 could achieve. Optimizing the engine control settings permits re-calibration to achieve the maximum brake fuel conversion efficiency (BFE) based on comprehensive understanding on the impact of both "fuel" and "ECU calibration" on BFE and other criteria pollutant emissions. The maximum GHG emissions reduction with B20 application is experimentally measured with the optimized ECU calibration, thus providing the understanding of the combined impact of biodiesel fuel and calibrations on engine performance and emissions. Six steady operating modes were considered, that can be combined to estimate the US federal test procedure BFE and emissions over the Federal Test Protocol (FTP) 75 cycle. Combined with the weight factors to simulate the EPA FTP 75 cycle from these 6 "mini-map" test points, 0.53% improvement in the energy requirement per unit traveling distance (i.e., MJ/mile) is achieved for B20 with the final ECU calibration, in addition to the degree of GHG emissions reduction on a "gCO2e/MJ" basis from the use of B20 blend of soy biodiesel of ~12.5% reduction in gCO2e/MJ, for a total GHG emissions reduction of 13%. [ABSTRACT FROM AUTHOR]

During the urine storage phase, tonically contracting urethral musculature would have a higher energy consumption than bladder muscle that develops phasic contractions. However, ischaemic dysfunction is less prevalent in the urethra than in the bladder, suggesting that urethral vasculature has intrinsic properties ensuring an adequate blood supply. Diameter changes in rat or mouse urethral arterioles were measured using a video‐tracking system. Intercellular Ca2+ dynamics in arteriolar smooth muscle (SMCs) and endothelial cells were visualised using NG2‐ and parvalbumin‐GCaMP6 mice, respectively. Fluorescence immunohistochemistry was used to visualise the perivascular innervation. In rat urethral arterioles, sympathetic vasoconstrictions were predominantly suppressed by α,β‐methylene ATP (10 μM) but not prazosin (1 μM). Tadalafil (100 nM), a PDE5 inhibitor, diminished the vasoconstrictions in a manner reversed by N‐ω‐propyl‐l‐arginine hydrochloride (l‐NPA, 1 μM), a neuronal NO synthesis (nNOS) inhibitor. Vesicular acetylcholine transporter immunoreactive perivascular nerve fibres co‐expressing nNOS were intertwined with tyrosine hydroxylase immunoreactive sympathetic nerve fibres. In phenylephrine (1 μM) pre‐constricted rat or mouse urethral arterioles, nerve‐evoked vasodilatations or transient SMC Ca2+ reductions were largely diminished by l‐nitroarginine (l‐NA, 10 μM), a broad‐spectrum NOS inhibitor, but not by l‐NPA. The CGRP receptor antagonist BIBN‐4096 (1 μM) shortened the vasodilatory responses, while atropine (1 μM) abolished the l‐NA‐resistant transient vasodilatory responses. Nerve‐evoked endothelial Ca2+ transients were abolished by atropine plus guanethidine (10 μM), indicating its neurotransmitter origin and absence of non‐adrenergic non‐cholinergic endothelial NO release. In urethral arterioles, NO released from parasympathetic nerves counteracts sympathetic vasoconstrictions pre‐ and post‐synaptically to restrict arteriolar contractility. Key points: Despite a higher energy consumption of the urethral musculature than the bladder detrusor muscle, ischaemic dysfunction of the urethra is less prevalent than that of the bladder.In the urethral arterioles, sympathetic vasoconstrictions are predominately mediated by ATP, not noradrenaline.NO released from parasympathetic nerves counteracts sympathetic vasoconstrictions by its pre‐synaptic inhibition of sympathetic transmission as well as post‐synaptic arteriolar smooth muscle relaxation.Acetylcholine released from parasympathetic nerves contributes to endothelium‐dependent, transient vasodilatations, while CGRP released from sensory nerves prolongs NO‐mediated vasodilatations.PDE5 inhibitors could be beneficial to maintain and/or improve urethral blood supply and in turn the volume and contractility of urethral musculature. [ABSTRACT FROM AUTHOR]

In this paper the photobiomodulation on isolated mitochondria of bovine liver is studied as a thermodynamic process of conversion of energy. This analysis is conducted by considering a particular set-up for the photobiomodulation experiments of interest. It allows, in particular, the computation of the electromagnetic field and the related energetic quantities in the stimulated organelles. The measurements of the excess of biochemical power density produced by the illuminated mitochondria are performed at regular time intervals after the experiments. The calculations and the measurements finally allow us to obtain the first results on the efficiency of the process of conversion of electromagnetic energy into excess of biochemical energy released by the isolated organelles. [ABSTRACT FROM AUTHOR]

Stag beetles, recognized as common saproxylic insects, are valued for their vibrant coloration and distinctive morphology. These beetles play a crucial ecological role in decomposition and nutrient cycling, serving as a vital functional component in ecosystem functioning. Although previous studies have confirmed that stag beetles are predominantly fungivores, the fluctuations in their intestinal fungal communities at different developmental stages remain poorly understood. In the current study, high-throughput sequencing was employed to investigate the dynamic changes within intestinal fungal communities at various developmental stages in the stag beetle Dorcus hopei. Results showed that microbial diversity was higher during the larval stage than during the pupal and adult stages. Furthermore, significant differences were identified in the composition of the intestinal fungal communities across the larval, pupal, and adult stages, suggesting that developmental transitions may be crucial factors contributing to variations in fungal community composition and diversity. Dominant genera included Candida, Scheffersomyces, Phaeoacremonium, and Trichosporon. Functional predictions indicated a greater diversity and relative abundance of endosymbiotic fungi in the larval gut, suggesting a potential dependency of larvae on beneficial gut fungi for nutrient acquisition. Additionally, the application of abundance-based β-null deviation and niche width analyses revealed that the adult gut exerted a stronger selection pressure on its fungal community, favoring certain taxa. This selection process culminates in a more robust co-occurrence network of fungal communities within the adult gut, thereby enhancing their adaptability to environmental fluctuations. This study advances our understanding of the intestinal fungal community structure in stag beetles, providing a crucial theoretical foundation for the development of saproxylic beetle resources, biomass energy utilization, plastic degradation strategies, and beetle conservation efforts. [ABSTRACT FROM AUTHOR]

The demand for materials with multifunctional qualities has skyrocketed with the quickening pace of design and engineering of smart and effective electronic devices like piezoelectric sensors. Herein, a high-temperature piezoelectric bismuth layer-structured ferroelectric material has been incorporated with MnO2 to form Bi3TiTaO9:xwt%MnO2 with x = 0–0.3, (BTTO:xMn) to investigate the influence of MnO2 on the ferroelectric properties, energy storage efficiency and piezoelectric properties. Despite the intercalation of Mn-ions at the lattice location, the structure of all ceramics remains preserved. At x = 0.2, the addition of MnO2 to BTTO lattices has enhanced the material's multifunctional properties. At room temperature, it has shown a high remnant polarization (Pr) of 11.04 μC cm−2, a recoverable energy density (Wrec) of ∼ 0.98 J cm−3, an energy conversion efficiency (η) of ∼63%, a high piezoelectric co-efficient (d33) of 20 pC/N and a dielectric constant (εr) of 133 with very low dielectric loss, which are much improved compared to those of pure BTTO ceramics. Furthermore, even after annealing at 600 °C, the BTTO:0.2 Mn ceramic has shown excellent piezoelectric thermal stability, retaining 80% (16 pC/N) of its initial value. The achieved results clearly indicate that the BTTO:0.2 Mn ceramic is a promising candidate for future wide-temperature pulse power applications and high-temperature piezoelectric devices. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency. [ABSTRACT FROM AUTHOR]

Green manufacturing is one of the most important development directions in mechanical processing field. Electrical discharge machining (EDM), one of the non-traditional machining, is increasingly used. However, there were hardly any studies on the evaluation of energy consumption and carbon emissions in EDM. In this study, a quantitative assessment model of carbon emission in EDM was built based on the emission factor method. The tool electrode wear, harmless treatment of residual tool electrodes and working fluid, and electrical energy consumed by the equipment were considered in this assessment model. EDM drilling experiments were conducted to verify the effectiveness of the proposed model. The effects of pulse width, pulse interval, and peak current on machining time, surface roughness, energy consumption, and carbon emissions were analyzed. The CNC system, cooling system, and power supply consumed about 95% of the total energy. In small hole EDM drilling, the total carbon emissions from the preparation and waste residue treatment of workpiece and tool electrode were almost negligible due to the small material removal volume. The carbon emissions generated by electrical energy consumption account for about 50% of the total carbon emissions. Carbon emissions can be minimized to 72 g and energy consumption can be reduced to a minimum of 37.48 Wh when processing a small hole with the diameter of 1 mm and the depth of 6 mm by EDM drilling. [ABSTRACT FROM AUTHOR]

The demand for 3D-printed high-performance polymers (HPPs) is on the rise across sectors such as the defense, aerospace, and automotive industries. Polyethyleneimine (PEI) exhibits exceptional mechanical performance, thermal stability, and wear resistance. Herein, six generic and device-independent control parameters, that is, the infill percentage, deposition angle, layer height, travel speed, nozzle temperature, and bed temperature, were quantitatively evaluated for their impact on multiple response metrics related to energy consumption and mechanical strength. The balance between energy consumption and mechanical strength was investigated for the first time, contributing to the sustainability of the PEI material in 3D printing. This is critical considering that HPPs require high temperatures to be built using the 3D printing method. PEI filaments were fabricated and utilized in material extrusion 3D printing of 125 specimens for 25 different experimental runs (five replicates per run). The divergent impacts of the control parameters on the response metrics throughout the experimental course have been reported. The real weight of the samples varies from 1.06 to 1.82 g (71%), the real printing time from 214 to 2841 s (~ 1300%), the ultimate tensile strength from 15.17 up to 80.73 MPa (530%), and the consumed energy from 0.094 to 1.44 MJ (1500%). The regression and reduced quadratic equations were validated through confirmation runs (10 additional specimens). These outcomes have excessive engineering and industrial merit in determining the optimum control parameters, ensuring the sustainability of the process, and the desired functionality of the products. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive comparison between two prominent Maximum Power Point Tracking (MPPT) techniques employed in solar energy systems: the Perturb and Observe (P&O) method and the Analytical Solution Fractional Open Circuit Voltage (ASFOCV) method. To assess the effectiveness of these MPPT approaches, a simulation study was conducted using four SHARP NDQ295 model photovoltaic panels, configured as two panels in series and two in parallel. Both the P&O and ASFOCV MPPT methods were evaluated under various scenarios of radiation levels and temperature changes. The results unequivocally demonstrate the superior performance of the ASFOCV MPPT method over the P&O MPPT method. The ASFOCV method notably enhanced converter output power by up to 5% when compared to the P&O method, leading to more efficient energy production. Furthermore, the ASFOCV method exhibited rapid stabilization of output voltage during abrupt weather changes, outperforming the P&O method in this regard. This study underscores the potential of the ASFOCV MPPT method to enhance the efficiency of solar energy systems and its adaptability to fluctuating environmental conditions. Future research endeavors could focus on mitigating the ASFOCV method's sensitivity to temperature variations and conducting real-world applications to further investigate its performance under practical circumstances. [ABSTRACT FROM AUTHOR]

[Display omitted] The mounting global energy demand urges surplus electricity generation. Due to dwindling fossil resources and environmental concerns, shifting from carbon-based fuels to renewables is vital. Though renewables are affordable, their intermittent nature poses supply challenges. In these contexts, aqueous flow batteries (AFBs), are a viable energy storage solution. This study tackles AFBs' energy density and efficiency challenges. Conventional strategies focus on altering molecule's solubility but overlook interface's transport kinetics. We show that triggering electrostatic forces at the interface can significantly enhance the mass transport kinetics of redox active molecules by introducing a powerful electrostatic flux over the diffusional flux, thereby exerting a precise directionality on the molecular transport. This approach of controlling the directionality of molecular flux in an all iron redox flow battery amplifies the current and power rating with approximately 140 % enhancement in the energy density. [ABSTRACT FROM AUTHOR]

Schematic diagram of the whole process for detecting sulfur-containing components in lithium-sulfur batteries, including graded leaching, efficient oxidation and precise quantitative detection. [Display omitted] • A highly effective approach for the graded leaching and precise quantitative detection techniques of sulfur-containing components within Li-S batteries has been successfully developed. The total detection rate of this method for sulfur-containing components in Li-S batteries exceeds 99 %. • Clarify that the presence of lithium polysulfide in the electrolyte after discharge is the main factor for restricting the full utilization of Li-S batteries' capacity, and propose targeted improvement measures. • This study offers a considerably refined and comprehensive methodology for the capacity fading and failure mechanisms assessment of high-energy-density Li-S batteries. Lithium-sulfur (Li-S) batteries are a strong contender for the new-generation battery system to meet the growing energy demand due to their significantly high energy density (2600 Wh/kg) and cost-effectiveness. However, the practical operating conditions yield an initial capacity of less than 80 % of the theoretical capacity, resulting in a limited lifespan and hindering broader application. What's worse, current mechanism, especially the evolution process of sulfides for the initial capacity degradation is not clear due to the practical difficulties of effective separation and detection of sulfur-containing components. Herein, we have developed an instrumental analysis method enabling graded leaching and quantitative determination of sulfur-containing components. This technology achieves a detection precision surpassing 99.11 %, addressing the inherent deficiency in calculating sulfur-containing components using the decrement method. Applying this method reveals that the presence of lithium polysulfides in the electrolyte (26.34 wt%) after discharging is the primary factor causing insufficient capacity utilization in Li-S batteries. This work not only demonstrates the unique behavior of Li-S batteries at high sulfur loading but also provides a systematic evaluation method to guide further research on high-energy-density batteries, and provides theoretical and technical support to promote the development of high-energy, long-life Li-S batteries. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

An attractive technique for removing CO2 from the environment is sequestration within stable carbonate solids (e. g., calcite). However, continuous addition of alkalinity is required to achieve favorable conditions for carbonate precipitation (pH>8) from aqueous streams containing dissolved CO2 (pH<4.5) and Ca2+ ions. In this study, a pH‐swing process using ion exchange was demonstrated to process 300 L of produced water brine per day for CO2 mineralization. Proton titration capacities were quantified for aqueous streams in equilibrium with gas streams at various concentrations of CO2 (pCO2=0.03–0.20 atm) and at various flow rates (0.5–2.0 L min−1). Energy intensities for the process were determined to be between 30 and 65 kWh per tonne of CO2 sequestered depending on the composition of the brine stream. A life cycle assessment was performed to analyze the net carbon emissions of the technology which indicated a net CO2 reduction for pCO2≥0.12 atm (−0.06–−0.39 kg CO2e per kg precipitated CaCO3) utilizing calcium‐rich brines. The results from this study indicate the ion exchange process can be used as a scalable method to provide alkalinity necessary for the capture and storage of CO2 in Ca‐rich waste streams. [ABSTRACT FROM AUTHOR]

The study was conducted to uncover the emission, combustion, and performance features of the blend of Water Hyacinth biodiesel and Mesua Ferrea seed oil biodiesel with Hydrogen addition on a diesel engine in dual fuel. Pilot fuel is a blend of 50% Water Hyacinth biodiesel and 50% Mesua Ferrea seed oil biodiesel. A single-cylinder compression ignition engine was modified to operate on dual fuel mode with hydrogen. Variations of engine operating parameters such as injection timing, and engine load were performed. The study was conducted with three pilot fuel injection timings (23, 26, and 29°bTDC) and variable engine loadings (20%–100% with an increment of 20%) at an injection pressure of 200 bar and compression ratio of 18. The results indicated that the maximum brake thermal efficiency of 28.11% and a replacement of liquid fuel by 85% was obtained for the WHMF blend powered dual fuel diesel engine at pilot fuel injection timings of 26°bTDC at 100% load. HC, CO, and smoke emissions are reduced with hydrogen due to faster combustion. On the other hand, there was a slight increase in NOx emissions noticed with hydrogen enrichment. [Display omitted] • Pilot fuel is a blend of 50% Water Hyacinth Biodiesel and 50% Mesua Ferrea Biodiesel. • Hydrogen is used with pilot fuel under dual fuel mode to improve combustion. • Hydrogen enrichment improves the brake thermal efficiency of the pilot fuel. • Emissions of CO and HC were reduced with hydrogen addition in the pilot fuel. • Liquid fuel replacement of 85% is achieved at an IT of 26⁰bTDC of the pilot fuel. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Energy demand is continuously increasing around the globe, and the building sector contributes 40% of the total energy consumption, as per the studies. Fossil fuels are the primary cause of harmful gas emissions, thus causing environmental pollution. There is a dire need to introduce innovative techniques to fulfill energy demands while reducing environmental pollution. Phase change materials (PCMs) are the latent thermal storage materials that store thermal energy during phase change from solid to liquid state and vice versa. Thus, using PCMs in structural engineering offers one of the best options for rapidly developing energy-saving materials. To do so, ascaled model, concrete walls room, encapsulating locally available PCMs, was constructed in this study. Three locally available PCMs (glycerin, vegetable ghee, and ferric chloride hexahydrate) have been tested in a controlled environment. The model response is then evaluated for the energy-storing capacity of each PCM while considering the human comfort zone. From the test results, it is concluded that PCMs have a significant effect on improving the thermal energy efficiency of the model without any notable adverse effects. Over the completion of the test, after 12 hr, all the incorporated PCM showed positive results, and a maximum temperature loss of 2.25 K was observed. Among different PCMs, the optimal performance was observed for vegetable ghee, which showed a drop in temperature for all the points at the inner side of the wall, i.e., T3, T4, and T5. [ABSTRACT FROM AUTHOR]

This article introduces a method of enhancing the energy conversion efficiency of a traveling wave rotary ultrasonic motor with a flexible rotor by increasing the friction coefficient at the stator–rotor interface. The increased friction coefficient leads to improvements in energy conversion efficiency by increasing the output power and reducing friction loss power. Experimental verification showed that the friction coefficient in the interface increased from 0.21 to 0.32, the maximum energy conversion efficiency increased from 36.8% to 46.3%, and the interface friction loss was reduced. [ABSTRACT FROM AUTHOR]

Clean alternative energy and a greater focus on climate change aim to increase the integration of Renewable Energy Sources (RES) into power system networks. As a relatively inexpensive renewable energy, wind energy is integrated into the electrical network to reduce its operating costs. A long-term optimal scheduling model for hydro-wind-thermal in a hybrid generation system is established to find the minimum cost trajectory of energy generation at each period under various constraints. Based on the proposed model and different types of power plants, the original complex problem decomposed into hydro-wind-thermal subproblems. The stochastic Dynamic programming technique (SDP) is employed to solve the complete optimization. In this research, the SDP technique is preferred. This technique handles multistage decision processes by splitting problems down into sequential stages. Because it can incorporate nonlinear and stochastic features into a dynamic programming problem, it has been successful in this hybrid system. A penalty factor was added to the model to reduce outflow variations. As can be seen from the results, outflows are very high during peak demand periods and very low during high inflows. Furthermore, the cost decreases as demand increases, from 40,082.26 $/ $GWh$ GWh in May when demand is 10,275 $Gwh$ Gwh to 16,536.32 $/ $GWh$ GWh in January when demand is 17,503 $Gwh$ Gwh. [ABSTRACT FROM AUTHOR]

Energy demand is surging with the rise in population, economic development, and ever-increasing living standards. Due to sustainability and environmental issues, renewable energy sources have emerged as a credible option to meet this increased energy demand. However, it is plagued with the issue of variability and intermittency. Hybrid energy systems are proposed as a possible solution to this problem. The optimal sizing of hybrid energy systems ensures a reliable, efficient, and cost-effective power supply. Therefore, this paper discusses different hybrid energy systems in both on-grid and off-grid configurations, followed by the review of various sizing methodologies. The article also discusses various multi-criteria design indicators acting as decision variables, sensitivity variables, and constraints in different capacities while preparing the mathematical model of hybrid energy systems. As renewable resources and their based systems are inherently uncertain, it becomes imperative to characterize and model the uncertainty associated with such systems. Sincere efforts were made to understand various sources of uncertainty and how to characterize and model these uncertainties using different methodologies. The existing uncertainty modeling approaches were studied, compared, and analyzed. Further, the need for conducting sensitivity analysis and its usage in hybrid energy system design considering different sensitive parameters were also studied. [ABSTRACT FROM AUTHOR]

The usage of C40 steel bars is common in ships, mechanical, metallurgical, automobile, military hardware, and electrical construction fields. During manufacturing of these bars, roll force (RF), roll torque (RT), cropping loss (CL) and drive energy (DE) are important production variables that must be monitored for high-quality output, scrap reduction, and mill safety with minimum consumption of energy. Roll RPM, temperature of bloom, roll gap, bloom cross-section area, and diameter of roll are process characteristics that influence these response parameters. The impact of process variables on response variables during C40 steel rolling is probed in this research. To replicate the rolling procedure, FORGE® NxT 1.1 was employed. Following statistical testing, the simulated findings were validated utilizing data from experiments acquired in a bar rolling plant. The results of an analysis of variance were used to find significant model terms. There have been constructed regression models demonstrating the link between the response parameters and the process parameters. [ABSTRACT FROM AUTHOR]