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Abnormal network traffic detection is a key cybersecurity technology that assists in identifying and preventing malicious network attacks. Existing methods for detecting abnormal network traffic typically rely on complex machine learning models and a large amount of labeled data. Consequently, these methods are challenging to apply to different scenarios without retraining the model and cannot effectively handle large-scale, ongoing network attacks in real-time. To address these issues, this paper proposes a classification method based on a network flow energy model. It utilizes a reverse statistical physics model to learn target traffic features in the network, allowing it to be based on macroscopic real observations or real data without the need for manual labeling. Subsequently, the paper combines the concept of the energy model to construct a network traffic recognition model. This model judges whether a sample conforms to the main statistical distribution. Specifically, the method describes individual behavior characteristics and interaction features between traffic packets through the local field and coupling field in the energy model. By combining these two features, the method calculates the sample' s energy. If the energy is below a threshold, the sample aligns with the main distribution, indicating normal data; otherwise, it is considered abnormal data. As this method does not rely on manual labeling, it can adapt to various network environments without the need for repetitive training. This addresses current issues in traffic abnormality detection methods, which struggle to adapt to different scenarios and require extensive labeling. To evaluate the effectiveness of this method, the paper validates it using the Kitsune-2018 and CTU-13 datasets. Experimental results demonstrate that the proposed method achieves good classification performance and overall effectiveness in network traffic classification tasks. This further indicates its accuracy in performing network flow classification tasks and its adaptability to changing scenarios. [ABSTRACT FROM AUTHOR]

Due to the high deployment flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have gained a significant attention in civilian and military applications. One of the main essential aspects of UAV is coverage path planning (CPP), which autonomously obtains sufficient paths to cover the entire region of interest (RoI). Several advantages have been offered by UAVs' CPP such as cost and time efficiency, reduced human intervention, resource optimization, data collection, scalability, and adaptability. However, the flight time of UAVs is constrained by battery capacity, necessitating energy-efficient solutions to prolong flight duration. This paper introduces a novel approach for energy-efficient multi-UAV multi-region CPP to generate appropriate paths that cover multiple disjoint regions, aiming to minimize overall energy consumption. First, we employ a back-and-forth strategy to generate intra-region path patterns with minimum turns and propose a smoothing turns approach (STA) based on Bezier curves to effectively reduce an energy consumption due to taking turns. Then, the inter-region path planning is formulated as a multi-constraint optimization problem and solved utilizing the CPLEX solver for small-scale problems and heuristic approaches for large-scale ones. A region allocation approach is proposed to assign RoIs to appropriate UAVs. Simulations are conducted to evaluate the performance of the proposed approach in terms of energy consumption. Comparative results against EECPPA and Nearest Neighbor (NN) approaches demonstrate advantages in energy consumption reduction. Besides, heuristic methods yield superior solutions for large-scale problems within shorter execution times compared to the CPLEX solver. These comparisons highlight the superiority of the proposed approach over existing methods in generating higher-quality solutions. [ABSTRACT FROM AUTHOR]

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock. [ABSTRACT FROM AUTHOR]

Fretting wear is one of the main forms of failure of mechanically connected structures. Most of the current studies are based on constant normal contact loading conditions. However, in actual engineering, there are widely existing multiaxial loading conditions with variable normal load and axial load, which leads to the fact that the results of the current research on fretting wear under constant normal load cannot explain the fretting wear behavior under multiaxial alternating loading conditions. Therefore, taking the fretting wear of titanium alloy as an example, simulation analysis and experimental test study of fretting wear under variable biaxial loading was carried out. Firstly, a fretting wear model subjected to cyclic alternating biaxial loading was established. Then, the fretting wear behaviors were simulated under different normal load amplitudes, displacement load amplitudes, and phase differences of biaxial loading. Based on the Q-P curve analysis method, the fretting wear mechanisms under these complex micromotion cases were discussed. Finally, the biaxial fretting wear test was carried out on a biaxial fretting wear tester, which verified the rationality of the wear morphology of the finite element model. The research results show that the wear scar of the variable normal load is different from that of the constant normal load, the mean value of the normal load under the variable load remains the same as the value of the constant normal load, the wear depth and width under the variable normal load are larger than those under the constant normal load. For a certain displacement amplitude and phase difference, the wear width and depth increase with the increase of normal load amplitude, and the wear scar changes from " W" to " W + V" . For a certain normal load amplitude and phase difference, the wear width and depth increase with the increase of displacement amplitude. For a certain normal load amplitude and displacement amplitude, when the biaxial loading phase difference changes, the maximum wear depth of 0 ° phase difference occurs in the contact trailing edge, the maximum wear depth of 180 ° phase difference occurs in the contact leading edge, and the wear depth of the leading and trailing edges of the 90 ° and 270 ° phase difference are close to each other in the extreme value. [ABSTRACT FROM AUTHOR]

The growing significance of mobile robots in a full spectrum of areas of life creates new challenges and opportunities in robotics. One critical aspect to consider is energy utilization, as accurate prediction plays a vital role in a robot's reliability and safety. Furthermore, precise prediction offers economic advantages, particularly for robotic fleets, where energy management systems can optimize maintenance costs and operational efficiency. The following review describes the state of the art of energy usage prediction for different types of mobile robots, highlights current trends, and analyses algorithms' complexity (in implementation and execution), accuracy, and universality. [ABSTRACT FROM AUTHOR]

This paper presents a validation study of the subchannel analysis code SLTHEN used for the core thermal-hydraulic design of the Prototype Gen-IV sodium-cooled fast reactor (PGSFR). To assess the performance of the ENERGY model of SLTHEN, four liquid metal heating experiments conducted by ORNL, WARD, and KIT with hexagonal assemblies of wire-wrapped rod bundles were analyzed. These experiments were performed with 19- and 61-pin bundles and varying power distributions of axial and radial peaking factors up to 1.4 and 3.0, respectively. The coolant subchannel temperatures measured at different axial locations were compared with the SLTHEN predictions with the Novendstern, Chiu-Rohsenow-Todreas (CRT), and Cheng-Todreas (CT) correlations for flow split and mixing in wire-wrapped pin bundles. The results showed that the SLTHEN predicts the measured subchannel temperatures reasonably well with root-mean-square errors of ∼10 % and maximum errors of ∼20 %. It was also observed that the CRT and CT correlations consistently outperform the Novendstern correlation.

Exploring substantial solar irradiation and recuperating excess heat generated during photovoltaic energy conversion is a critical issue. The efficiency of photovoltaic systems (PV) is significantly depend on the increased operating temperatures encountered by solar radiation. One conceivable option for improving the conversion of solar energy is to integrate a photovoltaic (PV) panel with a thermal-electric generator (TEG) material module to create a hybrid system. This study proposed a parallel PV-TEG hybrid module that effectively harvests the maximum solar energy spectrum while maximizing the use of heat generated by the thermoelectric material to improve the overall system efficiency. The proposed module consists of a photovoltaic unit, thermoelectric material module and passive cooling of fluid channels. The aim of this work was to develop a PV-TEG hybrid system and create an energy simulation model in a MATLAB environment to analyze the model's performance under various operational conditions by applying both theoretical and experimental approaches. Findings showed considerable concurrence. At 13:00, when the PV surface temperature was 54 °C, the PV efficiency reached to its lowest value of 12.0 %. Nevertheless, the highest TEG efficiency recorded was 4.7 % at 12:00 h. The efficiency of the TEG module was significantly affected by weather conditions, inlet cooling water temperature, and fluid flow rate in comparison to both the PV efficiency and the thermal efficiency.

This study presents an enhancement to the Switch optimization model for hydrogen energy planning by integrating the capability to consider the construction and operation of hydrogen electrolysis plants and the operation of water distribution systems. This integration was achieved through the addition of two new modules, and their effectiveness is demonstrated through their application in a case study for Durham region. The study highlights the significance of incorporating water distribution systems into energy planning, demonstrating how optimal locations for hydrogen plants can significantly influence water and power demand, as well as alter the total operating costs. The enhanced Switch model showcases its improved capability to assist policymakers and stakeholders in transitioning towards a sustainable energy future. [Display omitted] • Two new modules developed and integrated with the Switch model are presented. • The enhanced Switch model considers water-hydrogen-power interconnections. • Case study on hydrogen transition in Durham Region is used to test the model. • The zone Whitby is identified by the model as optimal for an electrolysis plant. [ABSTRACT FROM AUTHOR]

In Thailand, diesel buses are notorious for their poor energy efficiency and contribution to air pollution. To combat these issues, battery electric buses (BEBs) have emerged as a promising alternative. However, their high initial costs have posed challenges for fleet management, especially for agencies such as the Bangkok Mass Transit Authority (BMTA). This study aims to revolutionize BEB fleet management by developing an energy model tailored to the BMTA's needs. The methodology consists of two crucial steps: analyzing BMTA bus routes and designing fleet management and charging systems. Through this process, the study seeks to determine the maximum number of BEBs that can be operated on each route with the fewest chargers possible. The results reveal exciting possibilities. Within the city bus landscape, two out of five BMTA bus routes show potential for transitioning to BEBs, provided they meet a maximum energy requirement of 200 kWh every two rounds. This analysis identifies routes ripe for BEB adoption while considering the limitations of battery size. In the next step, the study unveils a game-changing strategy: a maximum of 13 BEBs can operate on two routes with just four chargers requiring 150 kW each. This means fewer chargers and more efficient operations. Plus, the charging profile peaks at 600 kW from 4:00 to 8:00 p.m., showing when and where the fleet needs power the most. However, the real eye-opener? Significant energy savings of THB 10.44 million per year compared to diesel buses, with an initial investment cost savings of over 37%. These findings underscore the potential for BEB fleet management to revolutionize public transportation and save money in the long run. However, there is more work to be done. The study highlights the need for real-time passenger considerations, the development of post-service charging strategies, and a deeper dive into total lifetime costs. These areas of improvement promise even greater strides in the future of sustainable urban transportation. [ABSTRACT FROM AUTHOR]

For reliable use of wireless sensor networks, energy, and delay optimization are equally crucial. Packets must therefore be routed via the path with the least amount of delay and energy consumption possible. For both clustering and non-clustering WSN scenarios, this problem remains an exploratory challenge. The optimization problem is presented here as a multi-objective problem in the clustering and non-clustering WSN contexts. A new energy model is presented for Wireless Sensor Networks (WSN) that has two more components: switching between transmission and reception modes and using the CSMA/CA protocol for packet transfer. This optimization problem is solved in two working environments: clustering and non-clustering, using a stochastic optimization technique particle swarm optimization (PSO) that uses particles to explore the search space. The proposed PSO-based approach increases WSN lifetime by 45% over ACO and twice as much as GA when compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The result additionally demonstrates the WSN's delay-tolerant routing in two operational scenarios. The proposed routing framework offers the potential for prolonging the lifetime of WSNs in many real-time applications, including area monitoring, healthcare monitoring, habitat monitoring, and industrial monitoring. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technologies have transformed manufacturing, by providing greater control over material deposition and consumption. Thanks to greater customization and their high strength-to-mass ratio, the AM of composite materials has significantly grown over the past few years. The main focus in this research area is improving printing precision and higher production rates. However, there is a lack of thorough analysis on the energy consumption of fused filament fabrication (FFF) machines for composite manufacturing, especially when associated with mechanical and economic aspects. We designed an experimental method, based on flow analysis for measuring the impact of temperature parameters on total cost, energy consumption, and tensile resistance of composite parts made by FFF. The user should be able to improve FFF efficiency regarding economic, energy, and technical aspects and obtain recommendations for setting up and using the machine. This study confirms that combining traditional economic and technical indicators (total cost and tensile resistance) with emerging energy indicators (specific energy consumption) can be successfully applied to additive manufacturing to provide an overview of printing parameters impact. Results are yielding information to support optimization investigations depending on the need and goal. For example, two tested parameter combinations that offer similar tensile properties (4% reduction in tensile resistance compared to the best combination) show a 20% difference with lower energy consumption. [ABSTRACT FROM AUTHOR]

Nowadays, with the increasing growth of the Internet of Things (IoT), where reliable sensors are needed to operate for extended periods, the issue of energy consumption efficiency has become crucial. To address this, it is suggested to utilize low-power network (LPN) technology for IoT sensor networks. Additionally, a detailed analysis of sensor node performance and a comprehensive understanding of energy consumption sources in the sensor are necessary to tackle the energy management challenge. Therefore, it is highly valuable to have a model that can analyze the performance of the sensor node in various operation modes. In this article, we analyze the impact of various parameters on sensor node performance and present a comprehensive model for the sensor node energy consumption in the network based on long-range/long-range wide-area network (LoRa/LoRaWAN) technologies. This model enables the analysis of network performance and the estimation of energy consumption in different modes of the sensor node. The model can be practically utilized in the optimal design of sensor nodes in IoT networks based on LoRa/LoRaWAN technology, with a focus on increasing sensor lifetime. [ABSTRACT FROM AUTHOR]

In general, for livestock building, a simple system for controlling the HVAC system that is based only on the indoor air temperature is applied. This study evaluated the applicability of temperature humidity index (THI)-based ventilation systems installed in livestock buildings using a building energy simulation model. Based on the monitoring data of an actual pig house, the integrated predictive performance of the indoor temperature and humidity of the building energy simulation model was calibrated. Additionally, the indoor environment of the existing temperature-based control and THI-controlled ventilation systems was compared through simulations. The average THI and the section exceeding the THI threshold were both lower compared to the existing system. This was because the part load ratio of the fans of the THI-controlled ventilation system was higher than that of the existing system, indicating better ventilation. These results suggest that THI-based ventilation systems can overcome the limitations of existing temperature-based systems and effectively improve the thermal stress management of livestock buildings. This study portrays that the indoor environment can be improved by simply changing control variables, while presenting the field applicability and research value of THI-based control systems in the management of livestock environments.

The decanter-centrifuge is widely used for dewatering and thickening of civil and industrial sludge. The latest generation decanters, both the bowl motors (main) and the screw (back-drive) are often driven by variable-frequency drives (VFD), with the back-drive able to recover the energy during braking. We created a decanter centrifuge energy model equipped with a braking recovery system during the sludge dewatering process, with the aim of identifying its optimal operating conditions for both energy consumption and product quality. Specific models at 15–20–25 m3 h−1 and various differentials speed (Δn) were used to derive a general model, then validated with experiments at 18–20 m3 h−1. Specific models used to identify the best operating conditions in terms of specific energy (e) and energy recovery (ERec) show that at 15–20–25 m3 h−1 the lowest energy consumptions were 1.88–1.76–1.57 kWh m−3, respectively, instead, ERec was 5.88–0.31–12.10 kW respectively, highlighting that a high recovery is not necessarily linked to an increased energy saving. The accuracy of these models was confirmed by high values of correlation coefficients R2 and very low Root Mean Square Errors (RMSE) in each case. The general model, extrapolated from the specific models, makes it possible to predict specific consumptions at different flow rates within the operating range of the decanter. This was validated with an experimental test at 18–20 m3 h−1 with R2 above 97 % and RMSE 2,59E-02 kWh m3. The dry matter content in the cake decreases when the Δn or the feed rate increases.In conclusion, the decanter centrifuge model could represent a useful tool for optimizing the sludge dehydration process.

Purpose – This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor. Design/methodology/approach – In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner. Findings – In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical. Originality/value – The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock.

The growing significance of mobile robots in a full spectrum of areas of life creates new challenges and opportunities in robotics. One critical aspect to consider is energy utilization, as accurate prediction plays a vital role in a robot’s reliability and safety. Furthermore, precise prediction offers economic advantages, particularly for robotic fleets, where energy management systems can optimize maintenance costs and operational efficiency. The following review describes the state of the art of energy usage prediction for different types of mobile robots, highlights current trends, and analyses algorithms’ complexity (in implementation and execution), accuracy, and universality.

Biorefinery systems that are embedded in their local setting provide an attractive framework for the valorisation of locally available food- and other bio-waste streams. They can aid in the provision of local bio-waste processing facilities as well as the targeted revalorisation of local bio-waste feedstocks by converting them in locally desired biorefinery products. Since food- and other bio-waste feedstocks are often diffuse feedstocks, small-scale biorefineries that are tailored for their local setting are the most suitable biorefining system for their processing. Whereas small-scale biorefineries cannot rely on the economy-of-scale to be an economic sustainable endeavour, they need to be meticulously optimised according to multiple sustainability objectives. These objectives can be of economic, societal, or environmental nature. A commonly used optimisation criterion in these problems is the energy requirements of the entire biorefinery system. For many commonly used biorefinery processes mass balance models are available (which are often mechanistic models), however, energy balances are difficult to obtain. Chemical process simulators, like Aspen Plus, provide an extensive toolkit to easily model the mass- and energy balances of a multitude of chemical processes. However, especially in the context of multi-objective optimisation, the obtained white-box models are too complex to simulate the considered processes efficiently consecutively. Therefore, in this contribution, a critical analysis is presented of the use of white-box versus the black-box models in the context of the multi-objective optimisation of a small-scale biorefinery. An in-house developed biorefinery network is re-modelled in Aspen Plus and used as a digital twin for the development of a surrogate model. Eventually, the modelled biorefinery network is optimised using both models and a comprehensive evaluation is drafted.

Karst ecological civilization is required to construct for energy production and consumption in the Karst rocky desertification (KRD) area in southern China. It is of great practical significance for rural areas to determine the distribution of animal and poultry manure resources, in order to realize the highly efficient energy utilization. In this study, the research area was selected as Guizhou Province with the serious KRD in southern China. The nine cities/prefectures were collected from the statistical yearbook as the data source. Four types of livestock manure resources were selected to estimate the potential of per capita livestock utilization. The regional characteristics of livestock and poultry resources were defined to quantitatively using mathematical statistics. There was some difference in livestock and poultry manure resources between Karst counties and nonkarst counties, as well as a correlation with the level of KRD. A biogas mode was proposed for the energy utilization of livestock and poultry manure resources. The economic feasibility of the model was measured as well. The following three conclusions were drawn: 1) The theoretical resources of four types of livestock and poultry manure were 52.874 million tons in 2017, and the recoverable resources were 36.358 6 million tons, which was dominated by cow manure and pig manure. Spatially, the growth trend was from the southeast to northwest, with significant differences among counties. 2) The amount of manure fertilizer resources was 18.15 million tons, the amount of energy conversion resources was 18.208 6 million tons, the amount of standard coal was reduced, and the annual energy consumption of firewood was replaced by 15.254 4 million tons, indicating the comparative advantages of energy utilization in livestock manure. Cites of Zunyi and Bijie in the northwest shared the promising potential for large-scale utilization. 3) rural energy was dominated by electrification and gasification in recent years, considering the rural energy development trend, regional resource endowment, population age structure, and population shrinkage. Some recommendations were given to construct the rural energy production and consumption revolution demonstration zone, due to the population shrinkage and population age structure. The large-scale biogas project/bio-gas model was proposed to select livestock manure as the raw material in the plateau mountainous area with potential-mild KRD. The optimal structure of energy consumption with ecologically friendly, social, and economic feasibility can be expected to alleviate the regional energy poverty and economic pressure, and then reduce the incidence of diseases for a better indoor environment. The finding can provide a strong reference to consolidate the rock desertification control for the health and wellbeing of local community households. [ABSTRACT FROM AUTHOR]

An experimental visualization is undertaken to investigate the impact dynamic behaviors of water, absolute ethanol, and low surface energy droplets with different viscosities impacting on hydrophobic surfaces. Droplets' impacting behaviors, including spreading, rebounding, and oscillation retraction, are observed and quantitatively characterized by transient spreading factor and maximum spreading diameter. Effects of droplet impact velocity, surface wettability, and droplet viscosity on the impact dynamics are explored and analyzed. As the droplet impact velocity increases, the droplet kinetic energy increases, resulting in an increase in the spreading factor and spreading velocity simultaneously. Hydrophobic surfaces are not easy to be wetted by water droplets due to their low surface energy, leading to the partial rebound of water droplets when impacting on the hydrophobic surfaces. However, this phenomenon does not occur when low surface energy droplets, such as absolute ethanol and simethicone, impact on hydrophobic surfaces at the same velocity. The increasing droplet viscosity enhances the viscous dissipation, slowing down the impact process and inhibiting the droplet spreading, oscillation, and retraction behaviors. Based on the energy conservation method, a universal model for the maximum spreading factor of low surface energy droplets with different viscosities impacting on hydrophobic surface was established. According to the experimental results, a new spreading time model tm = 2D0/U0 was proposed to enhance applicability of the model for low surface energy droplets with high viscosity, reducing the calculation error to less than 10%. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most challenging issues that need to be optimized. In this paper, we propose an energy-efficient coverage path planning algorithm to solve the CPP problem. The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region. To do so, the flight path is optimized and the number of turns is reduced to minimize the energy consumption. The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint. Then, the coverage path planning problem is formulated, where the exact solution is determined using the CPLEX solver. For small-scale problems, the CPLEX shows a better solution in a reasonable time. However, the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems. Thus, to solve the model for large-scale problems, simulated annealing forCPP is developed. The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver. Finally, we compare the simulated annealing against the greedy algorithm. The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality. [ABSTRACT FROM AUTHOR]

A big problem of our time is the constant increase in the energy used. An efficient energy household is one possible solution. Increasing efficiency requires examining the energy balance. System disturbance is one of the factors influencing the energy balance of systems. Social Engineering is a form of system disruption that manifests itself in covert and conscious system control. The present study examines the harmful energy role of Social Engineering through the parallels between the regulatory circuit of control theory and the operating model of Social Engineering. The result of the study highlights the importance of cyber defense.

As a result of rapidly evolving technology, the energy hunger of the world is also increasing. Depletion of fossil resources is also a problem in addition to growing energy hunger. Climate change also presents us with ongoing challenges that also affect energy supplies. These problems and challenges must be answered and solutions must be found. Mankind needs to switch to the use of climate-neutral resources and to the operation of energy-efficient models. Increasing efficiency also requires the development of an effective control system. The basic element of system control is the cybernetic loop. The present study examines the efficiency of the first phase of the cybernetic loop, the efficiency of sampling.

The removal of fossil fuel subsidies and the introduction of carbon pricing have been discussed for more than a decade, but their potential contribution to emission reduction is still uncertain, especially in relation to the potential indirect impact of revenue recycling. We have created a simulation model, GSI-IF, designed to assess the emission reduction potential resulting from removing fossil fuel subsidies and recycling part of the avoided subsidy and additional revenue from carbon pricing to renewable energy and energy efficiency. Our results show that emissions could decline by 7.1% in 2030 and up to 19.8% in 2050 compared to a baseline scenario. We find that subsidy removal is most effective in reducing emissions in countries with a high incidence of fossil fuel subsidies and it has stronger impact in the short term. The recycling of carbon pricing is most relevant for larger economies and its impact accumulates over time, generating growing GHG reductions year after year. In the current context (year 2022) with high energy prices, heavy stress on fiscal balances, and the renewed ambition of most governments to reduce emissions toward Net Zero in 2050, subsidy removal and carbon pricing hold promise in the toolbox of decarbonization options while improving fiscal sustainability.

Due to the recent climate change, organizations all over the globe are developing plans for reducing carbon emissions by developing clean energy technologies and energy efficient devices. However, the path for transition to green energy system is still unclear and in general, the representation of green energy supply for transition pathways is limited. Therefore, this study outlines a plan for getting Swedish energy sector completely carbon neutral by 2050. The approach can also be applicable to the majority of nations worldwide. Computer based simulations are performed on Energy PLAN software for making clean, green and sustainable energy system that can balance every component of entire energy system during the study period 2022 to 2050. This study takes into account the sustainable use of renewable sources for all economic sectors as well as the interchange of energy with nearby nations under the two scenarios. Additionally, the energy system works in tandem with other industries to create a fully carbon-free environment. The results revealed that, 50% de-carbonization is possible till 2035 and 100% de-carbonization is possible till 2050. This enables a discussion of how ambitious 10-year goals might serve as a first step toward the mid-century elimination of fossil fuels from the energy sector.

In Thailand, diesel buses are notorious for their poor energy efficiency and contribution to air pollution. To combat these issues, battery electric buses (BEBs) have emerged as a promising alternative. However, their high initial costs have posed challenges for fleet management, especially for agencies such as the Bangkok Mass Transit Authority (BMTA). This study aims to revolutionize BEB fleet management by developing an energy model tailored to the BMTA’s needs. The methodology consists of two crucial steps: analyzing BMTA bus routes and designing fleet management and charging systems. Through this process, the study seeks to determine the maximum number of BEBs that can be operated on each route with the fewest chargers possible. The results reveal exciting possibilities. Within the city bus landscape, two out of five BMTA bus routes show potential for transitioning to BEBs, provided they meet a maximum energy requirement of 200 kWh every two rounds. This analysis identifies routes ripe for BEB adoption while considering the limitations of battery size. In the next step, the study unveils a game-changing strategy: a maximum of 13 BEBs can operate on two routes with just four chargers requiring 150 kW each. This means fewer chargers and more efficient operations. Plus, the charging profile peaks at 600 kW from 4:00 to 8:00 p.m., showing when and where the fleet needs power the most. However, the real eye-opener? Significant energy savings of THB 10.44 million per year compared to diesel buses, with an initial investment cost savings of over 37%. These findings underscore the potential for BEB fleet management to revolutionize public transportation and save money in the long run. However, there is more work to be done. The study highlights the need for real-time passenger considerations, the development of post-service charging strategies, and a deeper dive into total lifetime costs. These areas of improvement promise even greater strides in the future of sustainable urban transportation.

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The removal of fossil fuel subsidies and the introduction of carbon pricing have been discussed for more than a decade, but their potential contribution to emission reduction is still uncertain, especially in relation to the potential indirect impact of revenue recycling. We have created a simulation model, GSI-IF, designed to assess the emission reduction potential resulting from removing fossil fuel subsidies and recycling part of the avoided subsidy and additional revenue from carbon pricing to renewable energy and energy efficiency. Our results show that emissions could decline by 7.1% in 2030 and up to 19.8% in 2050 compared to a baseline scenario. We find that subsidy removal is most effective in reducing emissions in countries with a high incidence of fossil fuel subsidies and it has stronger impact in the short term. The recycling of carbon pricing is most relevant for larger economies and its impact accumulates over time, generating growing GHG reductions year after year. In the current context (year 2022) with high energy prices, heavy stress on fiscal balances, and the renewed ambition of most governments to reduce emissions toward Net Zero in 2050, subsidy removal and carbon pricing hold promise in the toolbox of decarbonization options while improving fiscal sustainability. [ABSTRACT FROM AUTHOR]

The deformation of reinforced concrete elements is a main consideration during the design process due to its importance for the service life of structures. Deformation (deflections and cracking) control has an effect at both the design and construction stages. The determination of crack width based on simple code provisions, leads to conservative results, due to many affecting parameters being unknown. The exact determination of the crack width is a very complicated process with significant computational load. In the current paper, a simplified procedure is proposed for the prediction of crack width at different loading stages. The procedure is based on a previously published energy model that uses the integration of the moment-curvature relationship to take into consideration the load stage and most affecting design variables. The obtained results using the simplified model are then tested against previously published experimental data and a good agreement is shown. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), balancing authentication and energy is a major concern while deploying for wireless applications. Due to the presence of attackers, node consumes excessive energy for packet replication or transmission. In existing work, it is observed that attention was not done on balancing energy and data authentication. Location aided routing will also support for achieving high network lifetime. Fuzzy decision approach was widely used in sensor network for ensuring quality of routing and transmission. In the proposed work, Fuzzy enhanced query based secure energy efficient multicast routing is implemented. Query based location based cluster formation is done for quick packet arrival. Optimal multicast routes are found to forward the packets with reliability. The reliable routes are identified using reliable index. Fuzzy decision model is integrated to provide secure and energy based network model for packet transmission. Network Simulator (NS2.35) is used for simulation for analyzing the performance of proposed protocol in terms of various network parameters.

The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.

This article focuses on developing an energy simulation model through Building Information Modeling (BIM) and Building Energy Modeling (BEM)to optimize energy in building design in Vietnam. Reducing the energy consumption in buildings will help reduce operating costs, impact the environment, and increase the efficiency of buildings. However, there is limited research on buildings with complex structures and configurations, detailed surface design, and envelope construction, especially in simulating details through BIM. The author proposes converting from BIM to BEM to simulate energy in buildings and optimize the factors related to building construction in the envelope design of the building. These factors include wall cladding materials, mirror materials, the window ratio on the walls, and other details. This study has effectively created an energy model for a public building, allowing for the calculation of the Energy Intensity Index (EUI) and annual energy costs for various scenarios. By altering factors associated with the design and construction process, the system has the potential to decrease both energy intensity and usage costs for the building. The study results will help designers and building managers improve and enhance energy efficiency in building projects. [ABSTRACT FROM AUTHOR]

This study suggested a plan for making low carbon society (LCS) in Pakistan by 2050. Low Emissions Analysis Platform is used for simulation the entire energy system and forecasted the future CO2 emissions in relation to the use of renewable and fossil fuel resources under the two scenarios namely high carbon society (HCS) and LCS. Additionally, marginal abatement cost of future energy system is forecasted for the study period 2022–2050. The results revealed that, 100% de‐carbonization is possible under the LCS scenario. Further, this scenario suggested that 1123.15 TWh units of green energy are generated to meet the total energy demand of 966.05 TWh till 2050. Biomass has greater share of 380.16 TWh followed by wind 370.67 TWh, solar 358.74 TWh, and hydro 13.58 TWh respectively. CO2 emissions are reduced from 151.63 million metric tons in HCS scenario to 8.90 million metric tons in LCS scenario till 2050 but cost is increased from 46.36 billion US dollar in HCS scenario to 546.49 billion US dollar in LCS scenario for the year 2050. The technique can also be applicable to the developing, under‐developed and developed nations around the globe. [ABSTRACT FROM AUTHOR]

The need to reduce energy consumption in greenhouse production has grown. Thermal heating demand alone accounts for 80% of conventional greenhouse energy consumption; this significantly reduces production profit. Since microclimate affects crop metabolic processes and output, it is essential to monitor and control it to achieve both quantity and quality production with minimum energy consumption for maximum profit. The Internet of Things (IoT) is an evolving technology for monitoring and controlling environments that have recently been adopted to boost greenhouse efficiency in many applications by integrating hardware and software solutions; therefore, its adoption is thus critical in enabling greenhouse energy consumption minimisation. The first objective of this study is to improve and validate a greenhouse dynamic air temperature model required to simulate or predict indoor temperature. To achieve the first objective, therefore, an existing model was enhanced and a closed loop test experimental data from the IoT cloud-based control system platform deployed in the prototype greenhouse built in Cranfield University was used to validate the model using an optimisation-based model fitting approach. The second goal is to control the greenhouse air temperature in simulation using relatively simple PI and on-off control strategies to maintain the grower's desired setpoint irrespective of the inevitable disturbances and to verify the potential of the controllers in minimising the total energy input to the greenhouse. For the second objective, the simulation results showed that the two controllers maintained the desired setpoint; however, the on-off strategy retained a sustainable oscillation, and the tuned PI effectively maintained the desired temperature, although the average energy used by the controllers is the same. [ABSTRACT FROM AUTHOR]

Due to the recent climate change, organizations all over the globe are developing plans for reducing carbon emissions by developing clean energy technologies and energy efficient devices. However, the path for transition to green energy system is still unclear and in general, the representation of green energy supply for transition pathways is limited. Therefore, this study outlines a plan for getting Swedish energy sector completely carbon neutral by 2050. The approach can also be applicable to the majority of nations worldwide. Computer based simulations are performed on Energy PLAN software for making clean, green and sustainable energy system that can balance every component of entire energy system during the study period 2022 to 2050. This study takes into account the sustainable use of renewable sources for all economic sectors as well as the interchange of energy with nearby nations under the two scenarios. Additionally, the energy system works in tandem with other industries to create a fully carbon-free environment. The results revealed that, 50% de-carbonization is possible till 2035 and 100% de-carbonization is possible till 2050. This enables a discussion of how ambitious 10-year goals might serve as a first step toward the mid-century elimination of fossil fuels from the energy sector. [ABSTRACT FROM AUTHOR]

A big problem of our time is the constant increase in the energy used. An efficient energy household is one possible solution. Increasing efficiency requires examining the energy balance. System disturbance is one of the factors influencing the energy balance of systems. Social Engineering is a form of system disruption that manifests itself in covert and conscious system control. The present study examines the harmful energy role of Social Engineering through the parallels between the regulatory circuit of control theory and the operating model of Social Engineering. The result of the study highlights the importance of cyber defense. [ABSTRACT FROM AUTHOR]

As a result of rapidly evolving technology, the energy hunger of the world is also increasing. Depletion of fossil resources is also a problem in addition to growing energy hunger. Climate change also presents us with ongoing challenges that also affect energy supplies. These problems and challenges must be answered and solutions must be found. Mankind needs to switch to the use of climate-neutral resources and to the operation of energy-efficient models. Increasing efficiency also requires the development of an effective control system. The basic element of system control is the cybernetic loop. The present study examines the efficiency of the first phase of the cybernetic loop, the efficiency of sampling. [ABSTRACT FROM AUTHOR]

Plain knitted fabric made of cotton, commonly used in the clothing industry, has undergone severe cyclical loading, generated by dynamic fatigue tester, simulating the constraints applied to the structure during its use. An energy model, developed from the viscoelastic parameters of Burger's model, is used to study the knitted relaxation after fatigue test. The rheological parameters of the Burger's model are calculated by fitting the experimental curves of the deformation during relaxation of tested knit, using the Microcal Origin 6 software and the equation of Burger's recovery. As shown in this paper, cyclic loadings present a significant effect on viscoelastic parameters, which manifests as the decrease of the elastic deformation (23.96%) and the increase of the viscoelastic and plastic deformation (83.76% and 148.49%, respectively) when the number of fatigue cycles increases from 6000 to 36,000 cycles. We also noticed that the cyclic deformation of the knitted fabric decreases the energy dissipation rate (86.44%). Indeed, the decrease of energy dissipation rate indicates that the velocity of the recovery during relaxation decreases due to increasing of the friction interfilamentous. The friction opposes to the recovery of the structure until a blocked state. This state corresponds to the permanent deformation. The energy model allows us to give an idea of the ability of the knitted fabric to recover its initial dimensions. [ABSTRACT FROM AUTHOR]