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This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 µs and ultra-low power consumption of 15 µW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

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This article describes the ENERGY Pro agent-based model using the Overview, Design Concept, and Details + Human Decision-making (ODD+D protocol). The model is empirically explicit and aims to investigate the adoption decisions of homeowners in Amsterdam on different energy-efficient retrofitting (EER) measures. Following the ODD+D protocol, this study uncovers the conceptual framework used for model construction, the spatial microsimulation process of expanding the data, and the model implementation details. The article also describes sensitivity analysis, validation results, and how to use and adapt the model. With this article, the authors aim to make the model replicable and accessible to other researchers and inspire them using the combination of social simulation and spatial microsimulation in studying the energy transition.•The agent-based model is described using the ODD+D protocol.•The combination of simulation methods is used for constructing an empirical model.•The model on energy transition can be adapted for other cities.

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The evaluation of natural ventilation potential for effective sustainable options and innovative green building design strategies is of great interest to architects, researchers and governments. From a retrospective review, we found that the potential evaluation of natural ventilation (NV) cooling effectiveness in the same category based on similar meteorological uncertainty, research objectives and objects showed significant differences. Uncertainties added and uncertainty propagation (both model form uncertainties and parameter uncertainties) could result in large discrepancies between simulation outcomes and real scenarios, especially in the design performance modeling (DPM) phase. In this conceptual design stage, a few parameters are available and therefore decisive. It is necessary to review and identify the key performance indicators and explore the extent to which deviations are caused by inconsistencies or biases in model information. As a basis for more concrete research, we propose statistical tests based on quantitative evaluations to explore the rule of natural ventilation potential volatility and identify whether there is a significant potential improvement resulting from the critical parameter enhancement with the optimal relationship. The showcase is applied in China, where there has been a significant amount of criticism regarding the current building climate zoning due to the perceived coarseness of the system and where there has been an active exploration into the possibility of redefining building climate zoning with a view toward improving its accuracy and effectiveness.

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BACKGROUND: The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency. OBJECTIVE: This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems. METHODS: The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency. RESULTS: Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings. CONCLUSIONS: The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.

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A 9-10-bit adjustable and energy-efficient switching scheme for SAR ADC with one-LSB common-mode voltage variation is proposed. Based on capacitor-splitting technology and common-mode conversion techniques, the proposed switching scheme reduces the DAC switching energy by 96.41% compared to the conventional scheme. The low complexity and the one-LSB common-mode voltage offset of this scheme benefit from the simultaneous switching of the reference voltages of the capacitors corresponding to the positive array and the negative array throughout the entire reference voltage switching process, and the reference voltage of each capacitor in the scheme does not change more than two voltages. The post-layout result shows that the ADC achieves the 54.96 dB SNDR, the 61.73 dB SFDR, and the 0.67 µw power consumption with the 10-bit mode and the 48.33 dB SNDR, the 54.17 dB SFDR, and the 0.47 µw power consumption with the 9-bit mode in a 180 nm process with a 100 kS/s sampling frequency.

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In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms.

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Laws and policies are important instruments to protect and promote innovation. Intellectual property laws like copyright, patent, and confidential commercial information law facilitate can increase business outputs, economic growth, and green production standards. The objective of the article is to analyze copyright, patent, and confidential commercial information laws in Saudi Arabia to see the availability of these laws in encouraging and protecting energy-efficient innovation while contributing to achieving sustainable development. Content analysis, doctrinal research method, and comparative legal analysis were used to achieve the research objective. The research findings demonstrate that the three selected legislation could be extended to protect energy-efficient innovation in Saudi Arabia though there is a need to amend some of the provisions of the existing laws. However, creating public awareness of the availability of laws and proper implementation of the laws are necessary to protect energy-efficient innovation. The article proposes recommendations to the policymakers about the need for further improvement of the law and its enforcement. The findings of this research could fill the gap in the literature on the assessment of intellectual property law to protect energy-efficient innovation in Saudi Arabia.

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Current electrically heated fabrics provide heat in cold climates, suffer from abundant wasted radiant heat energy to the external environment, and are prone to damage by water. Thus, constructing energy-efficient and superhydrophobic conductive fabrics is in high demand. Therefore, we propose an effective and facile methodology to prepare a superhydrophobic, highly conductive, and trilayered fabric with a connected carbon nanotube (CNT) layer and a titanium dioxide (TiO2) nanoparticle heat-reflecting layer. We construct polyamide/fluorinated polyurethane (PA/FPU) nanofibrous membranes via first electrospinning, then performing blade-coating with the polyurethane (PU) solution with CNTs, and finally fabricating FPU/TiO2 nanoparticles via electrospraying. This strategy causes CNTs to be connected to form a conductive layer and enables TiO2 nanoparticles to be bound together to form a porous, heat-reflecting layer. As a consequence, the as-prepared membranes demonstrate high conductivity with an electrical conductivity of 63 S/m, exhibit rapid electric-heating capacity, and exhibit energy-efficient asymmetrical heating behavior, i.e., the heating temperature of the PA/FPU nanofibrous layer reaches more than 83 °C within 90 s at 24 V, while the heating temperature of the FPU/TiO2 layer only reaches 53 °C, as well as prominent superhydrophobicity with a water contact angle of 156°, indicating promising utility for the next generation of electrical heating textiles.

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The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer.

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This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach's efficacy in precise movement activity classification. This method's significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs' superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.

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The process of drying agricultural products for food preservation is a difficult task that requires a significant amount of energy. The increasing cost and depletion of fossil fuels have led to the development of a food dryer that utilizes renewable energy sources. This research paper proposes the design and performance evaluation of an indirectly forced convection desiccant integrated solar dryer (IFCDISD) at the Solar Energy Research Lab at USPCAS-E, NUST Pakistan. Tomatoes were chosen as the test product due to their importance and widespread consumption. The drying process involves slicing the tomatoes and placing them on the IFCDISD rack, where a desiccant called calcium chloride (CaCl2) is integrated into the dryer. The experiments were conducted during both sunshine (SS) hours and Off-sunshine (OSS) hours. The IFCDISD operates using sunlight during SS hours and utilizes the absorbed heat of CaCl2 in OSS hours via a forced DC brushless fan powered by battery charged thro solar panel. The tomatoes were weighed before and after each drying mode, and the moisture removal was calculated. The results show that the dryer efficiency was 50.14 % on day 1, 66 % on day 2, and an overall efficiency of 58.07 %. The moisture content removal was 42.858 % on day 1, 22.9979 % on day 2, and an overall moisture content removal of 58.07 %. Moreover, the payback period is 5.1396 and the carbon mitigation was recorded as 2.0335, and the earned carbon credit was recorded as 11559.6.

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Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

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Metal-organic frameworks (MOFs) that exhibit dynamic phase-transition behavior under external stimuli could have great potential in adsorptive separations. Here we report on a zinc-based microporous MOF (JNU-80) and its reversible transformation between two crystalline phases: large pore (JNU-80-LP) and narrow pore (JNU-80-NP). Specifically, JNU-80-LP can undergo a dehydration-induced cluster consolidation under heat treatment, resulting in JNU-80-NP with a reduced channel that allows exclusion of di-branched hexane isomers while high adsorption of linear and mono-branched hexane isomers. We further demonstrate the fabrication of MOF-polymer composite (JNU-80-NP-block) and its application in the purification of di-branched isomers from liquid-phase hexane mixtures (98 % di-branched) at room temperature, affording the di-branched hexane isomers with 99.5 % purity and close to 90 % recovery rate over ten cycles. This work illustrates an interesting dehydration-induced cluster consolidation in MOF structure and the ensuing channel shrinkage for sieving di-branched hexane isomers, which may have important implications for the development of MOFs with dynamic behavior and their potential applications in non-thermal driven separation technologies.

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This study aimed to investigate bone char's physicochemical transformations through co-torrefaction and co-pyrolysis processes with biomass. Additionally, it aimed to analyze the carbon sequestration process during co-torrefaction of bone and biomass and optimize the process parameters of co-torrefaction. Finally, the study sought to evaluate the arsenic sorption capacity of both torrefied and co-torrefied bone char. Bone and biomass co-torrefaction was conducted at 175 °C-300 °C. An orthogonal array of Taguchi techniques and artificial neural networks (ANN) were employed to investigate the influence of various torrefaction parameters on carbon dioxide sequestration within torrefied bone char. A co-torrefied bone char, torrefied at a reaction temperature of 300 °C, a heating rate of 15 °C·min-1, and mixed with 5 g m of biomass (wood dust), was selected for the arsenic (III) sorption experiment due to its elevated carbonate content. The results revealed a higher carbonate fraction (21%) in co-torrefied bone char at 300 °C compared to co-pyrolyzed bone char (500-700 °C). Taguchi and artificial neural network (ANN) analyses indicated that the relative impact of process factors on carbonate substitution in bone char followed the order of co-torrefaction temperature (38.8%) > heating rate (31.06%) > addition of wood biomass (30.1%). Co-torrefied bone chars at 300 °C exhibited a sorption capacity of approximately 3 mg g-1, surpassing values observed for pyrolyzed bone chars at 900 °C in the literature. The findings suggest that co-torrefied bone char could serve effectively as a sorbent in filters for wastewater treatment and potentially fulfill roles such as a remediation agent, pH stabilizer, or valuable source of biofertilizer in agricultural applications.

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The cleaning and utilization of industry wastewater are still a big challenge. In this work, we mainly investigate the effect of electron transfer among multi-interfaces on water electrolysis reaction. Typically, the CoS2, Co3S4/CoS2 (designated as CS4-2) and Co3S4/Co9S8/CoS2 (designated as CS4-8-2) samples are prepared on a large scale by one-step molten salt method. It is found that because of the different work functions (designated as WF; WF(Co3S4) = 4.48eV, WF(CoS2) = 4.41eV, WF(Co9S8) = 4.18 eV), the effective heterojunctions at the multi-interfaces of CS4-8-2 sample, which obviously improve interface charge transfer. Thus, the CS4-8-2 sample shows an excellent oxygen evolution reaction (OER) activity (134 mV/10 mA cm-2, 40 mV dec-1). The larger double-layer capacitance (Cdl = 17.1 mF cm-2) of the CS4-8-2 sample indicates more electrochemical active sites, compared to the CoS2 and CS4-2 samples. Density functional theory (DFT) calculation proves that due to interface polarization under electric field, the multi-interfaces effectively promote electron transfer and regulate electron structure, thus promoting the adsorption of OH- and dissociation of H2O. Moreover, an innovative norfloxacin (NFX) electrolytic cell (EC) is developed through introducing NFX into the electrolyte, in which efficient NFX degradation and hydrogen production are synergistically achieved. To reach 50 mA cm-2, the required cell voltage of NFX-EC has decreased by 35.2%, compared to conventional KOH-EC. After 2h running at 1 V, 25.5% NFX was degraded in the NFX EC. This innovative NFX-EC is highly energy-efficient, which is promising for the synergistic cleaning and utilization of industry wastewater.

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Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.

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Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

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Blockchain Technology has garnered significant attention due to its immense potential to transform the way transactions are conducted and information is managed. Blockchain is a decentralized digital ledger that is spread across a network of computers, ensuring the secure, transparent, and unchangeable recording of transactions. However, the energy consumption of certain blockchain networks like Bitcoin, Litecoin, Monero, Zcash, and others has generated apprehensions regarding the sustainability of this technology. Bitcoin alone consumes approximately 100 terawatt-hours annually, contributing significantly to global carbon emissions. The substantial energy requirements not only contribute to carbon emissions but also pose a risk to the long-term viability of the blockchain industry. This study reviews articles from eight reputable databases between 2017 to August 2023, employing the systematic review and preferred reporting items for systematic reviews and meta-analyses approach for screening. Therefore, explore the applications of sustainable blockchain networks aimed at reducing environmental impact while ensuring efficiency and security. This survey also assesses the challenges and limitations posed by diverse blockchain applications regarding sustainability and provides valuable foresight into potential future advancements. Through this survey, the aim is to track and verify sustainable practices, facilitating the transition to a low-carbon economy, and promoting environmental stewardship, with a specific focus on highlighting the potential of sustainable blockchain networks in enabling secure and transparent tracking of these practices. Finally, the paper sheds light on pertinent research challenges and provides a roadmap of future directions, stimulating further research in this promising field.

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A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network's energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

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The on-demand regulation of cell wall microstructures is crucial for developing wood as a functional building material for energy management and conversion. Here, a novel strategy based on reactive deep eutectic solvent is developed to one-step in situ fibrillate wood via disrupting the hydrogen bonding networks in cell walls and simultaneously carboxylating wood components, without significantly altering the native hierarchical structures of wood. Benefiting from its distinctive cell wall structure composed of individualized yet well-organized lignocellulose nanofibrils, in situ fibrillated wood exhibits a prominent mesoporous structure with a specific surface area of 81 m2/g. It represents a robust sponge material (5 MPa at 80% strain) with excellent durability. Due to the enhanced compressibility and charge polarization capacity, the in situ fibrillated wood (10 × 11 × 12 mm3) can generate a piezoelectric output voltage of up to 2 V under 221 kPa stress. The favorable microstructural characteristics render in situ fibrillated wood with highly thermal-insulating properties, high solar reflectivity, and mid-infrared emissivity, favoring outdoor passive cooling effects with a subambient temperature drop of 6 °C. Combining its controllable, durable, and eco-friendly attributes, our developed wood sponge represents a versatile structural material suitable for indoor/outdoor energy-saving applications.