Search Results (181)

To establish the best approach for conserving a species, it is necessary to understand the biology of that species. To better understand the behavior of American black bears (Ursus americanus), we observed 246 black bears for 7950 h in nature over a 24-year period to quantify how the bears communicated. Black bears communicated using several different behaviors. These included thirteen types of vocalizations, eight olfactory behaviors, eight marking behaviors, sixteen different body postures and gestures constituting their body language, and various emotional expressions. Some behaviors appeared to be automatic, including facial expression, ear movements, some forms of body language, the intensity of various vocalizations, and various moans. Other behaviors appeared to be intentional, including mechanically generated sounds and actions that could be used to bluff or deceive, such as the chomping of teeth, huffing, swatting, false charging, and various vocalizations. The conservation of black bears can be improved by establishing management strategies that take into account the vocal and non-vocal communication of the bears. Conflicts and negative encounters between humans and bears can be reduced through behavioral modifications by humans based on our new understanding of the communication system of bears. Knowledge of the communication system of the black bear provides a basis for improved conservation through the non-lethal management of bears involved in bear–human conflicts. Full article

The development of neuroanatomy and neurophysiology has revealed many new details about neurons’ operation over the past few decades, requiring modifications to their theoretical models. The development of computing technology enables us to consider the fine details the new model requires, but it necessitates a different approach. To achieve that goal, the disciplinarity of science must be revisited for living matter, the theoretical model must be updated, and a series of processes instead of states must be considered; furthermore, new mathematics, algorithms, and computing technologies for the new view are also needed. We provide an algorithm implementing the mathematics of the updated theoretical model that considers the neuronal current to consist of charged ions (and so considers thermodynamic effects) and opens the way for explaining the mechanical, optical, etc., consequence phenomena of the electrical operation. We use a new technology in this effort: a tool designed to achieve extreme accuracy in simulating high-speed electronic circuits. The algorithm applies the cross-disciplinary unified electrical/thermodynamic model, along with an unusual programming method, to provide new insights into neuronal operations, describe the processes that take place in living matter, and determine their computing implementation. As has long been suspected, the faithful simulation of biological processes requires accurately mapping biological time to technical computing time. Therefore, the paper focuses on time handling in biology-targeting computations, especially in large-scale tasks. We also touch on the question of simulating the operation of their network, which is contrasted with that of Spiking Neural Networks. The way technical computing works inhibits efforts to achieve the required accuracy in reproducing the temporal behavior of biological operations using conventional computer programs. Full article

Sustainable performance in green production hinges on collective employee engagement. While prior research has largely focused on the influence of sustainable leadership in fostering employee role behaviors and team relationship-oriented behaviors, this study delves into the critical role of taking charge behavior, which is related to the organization’s additional performance growth and long-term development. This study, grounded in the Conservation of Resources theory, explores how sustainable leadership encourages taking charge behavior through employee resilience as a mediator and colleague support as a moderator. Using the longitudinal method, data from 386 paired responses were collected from corporate employees across two time periods. Hypotheses were tested using hierarchical regression analysis, supplemented by path analysis to explore mediating and moderating effects. The findings indicate that sustainable leadership can enhance taking charge behavior by strengthening employee resilience, and in environments with robust colleague support, the impact of sustainable leadership on improving employee resilience is magnified, resulting in a more effective promotion of taking charge. This study contributes both theoretically and practically to the field of sustainable leadership. Full article

This paper presents a novel batch Forward Osmosis (FO) process for hydropower generation. It focuses on analyzing the parameters needed to make the proposed osmotic power plant implementable with currently available technology. Starting from the solution–diffusion model and using flow and mass balance equations, the equations that describe the behavior of the system over time are obtained. Membrane orientation, concentration polarization, reverse solute flux, and membrane fouling are not considered. The equations for calculating the operation time for the charging and discharging stages are obtained. Also, an equation for calculating the required membrane area to make the duration of the two stages the same is obtained. The results indicate that a volume of approximately $30.4 {\mathrm{m}}^3$ discharging through a $0.84 \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{h}$ diameter outflow jet towards a turbine could generate an energy of $25 \mathrm{k}\mathrm{w}·\mathrm{h}.$ The discharging stage would take $12 \mathrm{h}$, and with a membrane with a water permeability constant $A_m=1.763·{10}^{-12} \mathrm{m}/(\mathrm{s}·\mathrm{P}\mathrm{a})$, the charging stage would require a membrane superficial area ${Ar}_m=1·{10}^4 {\mathrm{m}}^2$ to have the same duration. The proposed osmotic power plant, whose working principle is based on volume change over time, contrary to pressure retarded osmosis, whose working principle requires expending energy to extract energy from the salinity gradient, could deliver greater net produced energy with comparatively lower operational costs as it does not require high-pressure pumps or energy recovery devices as are required in pressure-retarded osmosis. The use of several tanks that charge and discharge alternatively can make the system generate energy as if it were a continuous process. Full article

Solid oxide electrolysis cells (SOECs) have emerged as a promising technology for efficient energy storage and CO₂ utilization via H₂O–CO₂ co-electrolysis. While most previous studies focused on planar or tubular configurations, this work investigated a novel flat, tubular SOEC design using a comprehensive 3D multi-physics model developed in COMSOL Multiphysics 5.6. This model integrates charge transfer, gas flow, heat transfer, chemical/electrochemical reactions, and structural mechanics to analyze operational behavior and thermo-mechanical stress under different voltages and pressures. Simulation results indicate that increasing operating voltage leads to significant temperature and current density inhomogeneity. Furthermore, elevated pressure improves electrochemical performance, possibly due to increased reactant concentrations and reduced mass transfer limitations; however, it also increases temperature gradients and the maximum first principal stress. These findings underscore that the design and optimization of flat tubular SOECs in H₂O–CO₂ co-electrolysis should take the trade-off between performance and durability into consideration. Full article

With the rapid advancement of the electric vehicle (EV) industry, the ownership of EVs and their charging power have increased significantly, gradually exerting a greater impact on the power grid. To meet the diverse charging needs of different EV users, the coordinated planning of fast- and slow-charging stations can reduce the influence of charging loads on the power grid while fulfilling user demands and increasing the number of EVs that can be served. This paper establishes a collaborative planning model for multi-type charging stations (CSs), considering the comprehensive satisfaction of EV users. Firstly, a comprehensive satisfaction model of multi-type EV users considering their behavioral characteristics is established to characterize the impact of fast- and slow-charging CSs on the satisfaction of different types of users. Secondly, a two-layer cooperative planning model of multi-type CSs considering comprehensive satisfaction of EV users is established to determine the location of CSs and the number of fast- and slow-charging configurations to satisfy the users’ demand for different types of charging piles. Thirdly, a solution algorithm for the two-layer planning model based on the greedy theory algorithm is proposed, which transforms the upper layer charging pile planning model into a charging pile multi-round expansion problem to speed up the model solving. Finally, the validity of the proposed models is verified through case studies, and the results show that the planning scheme obtained can take into account the user’s charging satisfaction while guaranteeing the economy, and at the same time, the scheme has a positive significance in the promotion of new energy consumption, reduction in network loss, and alleviation of traffic congestion. Full article

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network coupling framework is established based on a road network model with multi-source information fusion. Second, considering the multiple-intersection features of urban road networks, a time-flow model is developed. A time-optimal path selection method is designed based on the topological structure of the road network. Then, an EV driving energy consumption model is developed, accounting for both the mileage energy consumption and air conditioning energy consumption. Next, the user travel characteristics are finely modeled under two scenarios: working days and rest days. A user charging decision model is established using a fuzzy logic inference system, taking into account the state of charge (SOC), average electricity price, and parking duration. Finally, the Monte Carlo method is applied to simulate user travel and charging behavior. A simulation of the spatiotemporal distribution of the EV charging load was conducted in a specific area of Jiangning District, Nanjing. The simulation results show that there is a significant difference in the time distribution of EV charging loads between working days and rest days, with peak-to-valley differences of 3100.8 kW and 3233.5 kW, respectively. Full article

Dynamic on-resistance (R_ON) of commercial GaN on Si normally off high-electron-mobility transistor (HEMT) devices is a very important parameter because it is responsible for conduction losses that limit the power conversion efficiency of high-power switching converters. Due to charge trapping effects, dynamic R_ON is always higher than in DC, a behavior known as current collapse. To study how short-time dynamics of charge trapping and release affects R_ON we use rectangular 0–5 V gate voltage pulses with durations in the 1 μs to 100 μs range. Measurements are first carried out for single pulses of increasing duration, and it is found that R_ON depends on both pulse duration and drain current I_D, being higher at shorter pulse durations and lower I_D. For a train of five pulses, R_ON decreases with pulse number, reaching a steady state after a time interval of 100 μs. The response to a five pulses train is compared to that of a square-wave signal to study the time evolution of R_ON toward a dynamic steady state. The DC R_ON is also measured, and it is a factor of ten smaller than dynamic R_ON at the same I_D. This confirms that a reduction in trapped charges takes place in DC as compared to the square-wave switching operation. Additional off-state stress tests at V_DS = 55 V reveal the presence of residual surface traps in the drain access region, leading to four times increase in R_ON in comparison to pristine devices. Finally, the dynamic R_ON is also measured by the double-pulse test (DPT) technique with inductive load, giving a good agreement with results from single-pulse measurements. Full article

Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency. Full article

The dielectric relaxation behavior of polymeric materials is critical to their performance in electronic, insulating, and energy storage applications. This study presents an electrical fractional model (EFM) based on fractional calculus and the complex electric modulus ($M^{*}\mathit{=}{M}^{\prime }\mathit{+}i{M}^{″}$) formalism to simultaneously describe two key relaxation phenomena: $\alpha$-relaxation and interfacial polarization (Maxwell–Wagner–Sillars effect). The model incorporates fractional elements (cap-resistors) into a modified Debye equivalent circuit to capture polymer dynamics and energy dissipation. Fractional differential equations are derived, with fractional orders taking values between 0 and 1; the frequency and temperature responses are analyzed using Fourier transform. Two temperature-dependent behaviors are considered: the Matsuoka model, applied to $\alpha$-relaxation near the glass transition, and an Arrhenius-type equation, used to describe interfacial polarization associated with thermally activated charge transport. The proposed model is validated using literature data for amorphous polymers, polyetherimide (PEI), polyvinyl chloride (PVC), and polyvinyl butyral (PVB), successfully fitting dielectric spectra and extracting meaningful physical parameters. The results demonstrate that the EFM is a robust and versatile tool for modeling complex dielectric relaxation in polymeric systems, offering improved interpretability over classical integer-order models. This approach enhances understanding of coupled relaxation mechanisms and may support the design of advanced polymer-based materials with tailored dielectric properties. Full article

As a critical storage technology, the material selection and structural design of flash memory devices are pivotal to their storage density and operational characteristics. Although van der Waals materials can potentially take over the scaling roadmap of silicon-based technologies, the scaling mechanisms and optimization principles at low-dimensional scales remain to be systematically unveiled. In this study, we experimentally demonstrated that the floating-gate length can significantly affect the memory window characteristics of memory devices. Experiments involving various floating-gate and tunneling-layer configurations, combined with TCAD simulations, were conducted to reveal the electrostatic coupling behaviors between floating gate and source/drain electrodes during shaping of the charge storage capabilities. Fundamental performance characteristics of the designed memory devices, including a large memory ratio (82.25%), good retention (>50,000 s, 8 states), and considerable endurance characteristics (>2000 cycles), further validate the role of floating-gate topological structures in manipulating low-dimensional memory devices, offering valuable insights to drive the development of next-generation memory technologies. Full article

Hotels expect front-line staff to demonstrate greater flexibility and proactively take on more responsibility beyond their job duties, which helps to provide better customer service in an environment of uncertainty and change. Accordingly, employees’ taking charge behaviors have received widespread attention in academia and practice. Through a three-wave online survey of 352 front-line employees and their supervisors from 13 high-star hotels in the Greater Bay Area of China, this study examined the influence mechanisms of role clarity and job competence on the employees’ taking charge behavior and the moderating effect of supervisor developmental feedback. The findings indicate that frontline employees’ role clarity and job competence can enhance taking charge behavior by increasing their organization-based self-esteem. It empirically validates Proactive Motivation Theory and clarifies that employees’ proactive engagement in extra-role responsibilities depends not only on their willingness but also on sufficient competence and a clear understanding of their job roles. Supervisor developmental feedback is more acceptable to employees as a form of informational support and can enhance the impact of role clarity and job competence on frontline employees’ taking charge behaviors. Full article

Digital–AI transformation is revolutionizing the modern workplace, yet its complexity has left many aspects of employee responses underexplored. While previous research has examined some employee reactions to technological change, the nuanced impact of ambivalence toward digital–AI transformation on employees’ proactive behavior remains a largely uncharted area. This is especially significant as proactive behavior is crucial for the successful implementation of digital–AI transformation. While presenting unprecedented opportunities, digital–AI transformation has also triggered intricate psychological responses among employees, with ambivalence toward it being particularly prominent. Building on threat–rigidity theory, this study aims to fill a research gap by exploring the impact of ambivalence on employees’ proactive behavior during digital–AI transformation. Using survey data collected from 343 employees undergoing digital–AI transformation, we tested a structural model linking ambivalence, job engagement, and future work self-salience to taking-charge behavior. The results reveal that ambivalence toward digital–AI transformation negatively affects taking-charge behavior. Furthermore, both future work self-salience and job engagement fully mediate this relationship. Additionally, job engagement and future work self-salience jointly play a chained mediating role in the negative effect of ambivalence toward digital–AI transformation on taking-charge behavior. Our findings provide actionable insights for organizations seeking to mitigate ambivalence and foster proactive employee engagement in digital transformation initiatives. Full article

Improving user-level energy efficiency is critical for reducing the load on the power grid and addressing the challenges created by tight power balance when operating domestic air conditioning equipment under time-of-use (ToU) pricing. This paper presents a data-driven control method for HVAC (heating, ventilation, and air conditioning) systems that is based on model predictive control (MPC) and takes ToU electricity pricing into account. To describe building thermal dynamics, a multi-layer neural network is constructed using time-delayed embedding, with the rectified linear unit (ReLU) serving as the activation function for hidden layers. Using this piecewise affine approximation, an optimization model is developed within a receding horizon control framework, integrating the data-driven model and transforming it into a mixed-integer linear programming issue for efficient problem solving. Furthermore, this research suggests a hybrid optimization model for integrating air conditioning systems and battery energy storage systems. By employing a rolling time-domain control method, the proposed model minimizes the frequency of switching between charging and discharging states of the battery energy storage system, improving system reliability and efficiency. An Internet of Things (IoT)-based home energy management system is developed and validated in a real laboratory environment, complemented by a distributed integration solution for the energy management monitoring platform and other essential components. The simulation results and field measurements demonstrate the system’s effectiveness, revealing discernible pre-cooling and pre-charging behaviors prior to peak electricity pricing periods. This cooperative economic operation reduces electricity expenses by 13% compared to standalone operation. Full article

To understand the charging behavior of electric vehicle (EV) users and the sustainable use of the flexibility resources of EV, EV charging behavior analysis and load prediction via order data of charging stations was proposed. The user probability distribution model is established from the characteristic dimensions of EV charging initial time, initial state of charge, power level, and charging time. Under the conditions of specific districts, seasons, multiple EV types, and specific weather, the Monte Carlo simulation method is used to predict the EV load distribution at the physical level. The correlation between users’ willingness to charge and the electricity price is analyzed, and the logistic function is used to establish the charging load prediction model on the economic level. Taking a city in Henan Province, China, as an example, the calculation results show that the EV charging load distribution varies with the district, season, weather, and EV type, and the 24 h time-of-use (TOU) electricity price and EV quantity distribution are analyzed. The proposed method can better reflect EV charging behavior and accurately predict EV charging load. Full article