Search Results (4,072)

The widespread use of low-cost, highly maneuverable unmanned aerial vehicles (UAVs), such as racing drones, has raised numerous security concerns. These UAVs commonly employ LoRa (Long Range) communication protocols, which feature long-range transmission and strong anti-interference capabilities. However, traditional countermeasure techniques targeting LoRa-based links often suffer from delayed response, poor adaptability, and high power consumption. To address these challenges, this study first leverages neural networks to achieve efficient detection and reverse extraction of key parameters from LoRa signals in complex electromagnetic environments. Subsequently, a continuous preamble injection jamming method is designed based on the extracted target signal parameters. By protocol-level injection, this method disrupts the synchronization and demodulation processes of UAV communication links, significantly enhancing jamming efficiency while reducing energy consumption. Experimental results demonstrate that, compared with conventional approaches, the proposed continuous preamble injection jamming method achieves improved signal detection accuracy, jamming energy efficiency, and effective range. To the best of our knowledge, this protocol-aware scheme, which integrates neural network-based signal perception and denoising, offers a promising and cost-effective technical pathway for UAV countermeasures. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

This research examined the development and testing of hot-melt adhesives incorporating metallic (Al and Fe powders averaging 800 nm) and ferrite additives, designed for reversible bonding technology of polyolefins through electromagnetic energy. The experimental models with Al displayed smooth particles that were fairly evenly distributed within the polymer matrix. Experimental models with Fe suggested that Fe nanopowders are more difficult to disperse within the polymer matrix, frequently resulting in agglomeration. For ferrite powder, there were fewer agglomerations noticed, and the dispersion was more uniform compared to similar composites containing Fe particles. Regarding water absorption, the extent of swelling was greater in the composites that included Al. Because of toluene’s affinity for the matrices, the swelling measurements stayed elevated even with reduced exposure times, and the composites with ferrite showed the lowest swelling compared to those with metallic particles. A remarkable evolution of the dielectric loss factor peak shifting towards higher frequencies with rising temperatures was observed, which is particularly important when the materials are exposed to thermal activation through electromagnetic energy. The reversible bonding experiments were performed on polyolefin samples which were connected longitudinally by overlapping at the ends; specialized hot-melts were employed, using electromagnetic energy at 2.45 GHz, with power levels between 140 and 850 × 10³ W/kg and an exposure duration of up to 2 min. The feasibility of bonding polyolefins using homologous hot-melts that include metallic/ferrite elements was verified. Composites with both matrices showed that the hot-melts with Al displayed the highest mechanical tensile strength values, but also had a relatively greater elongation. All created hot-melts were suitable for reversible adhesion of similar polyolefins, with the one based on HDPE and Fe considered the most efficient for bonding HDPE, and the one based on PP and Al for PP bonding. When bonding dissimilar polyolefins, it seems that the technique is only effective with hot-melts that include Al. According to the reversible bonding diagrams for specific substrates and hot-melt combinations, and considering the optimization of energy consumption in relation to productivity, the most cost-effective way is to utilize 850 × 10³ W/kg power with a maximum exposure time of 1 min. Full article

A Smart campus increasingly operates on the basis of data-driven operations, but an increasing demand for energy puts their control over costs and sustainability at risk. This study addresses the challenge of anticipating and managing energy consumption peaks in multi-campus environments by proposing a hybrid framework that combines advanced time-series forecasting models with a large language model (LLM)-driven multi-agent system. Based on the UNICON dataset, LSTM, CNN, GRU, and a combination architecture are trained and compared in terms of MAE and RMSE. The hybrid configuration achieves the greatest forecasting results by returning the minimum loss values. For the identification of critical periods, we employed a strategy based on median thresholding, which offers a categorization into low, normal, and extreme category, allowing the targeting of peak mitigation actions. We also introduce a multi-agent system based on the LLM, including the data aggregator, the forecaster, and the policy advisor, which create actionable policies informed by context. We also compare LLMs (Qwen-2.5, Gemma-2, Phi-4, Mistral, Llama-3.3) in terms of context accuracy, response relevance, semantic similarity, and retrieval/recall accuracy and fidelity, with Llama-3.3 achieving the best overall results. This framework has shown great potential, not only for energy consumption forecasting but also for developing precise policies on how to effectively manage energy consumption peaks. Full article

Desalination is a vital solution to global water scarcity, yet its substantial energy demand persists as a major challenge. As the core energy-consuming components, pumps are fundamental to both membrane and thermal desalination processes. This review provides a comprehensive analysis of renewable energy source (RES)-driven pumping systems for desalination, focusing on the integration of solar photovoltaic and wind technologies. It examines the operational principles and efficiency of key pump types, such as high-pressure feed pumps for reverse osmosis, and underscores the critical role of energy recovery devices (ERDs) in minimizing net energy consumption. Furthermore, the paper highlights the importance of advanced control and energy management systems (EMS) in mitigating the intermittency of renewable sources. It details essential control strategies, including maximum power point tracking (MPPT), motor drive control, and supervisory EMS, that optimize the synergy between pumps, ERDs, and variable power inputs. By synthesizing current technologies and control methodologies, this review aims to identify pathways for designing more resilient, energy-efficient, and cost-effective desalination plants, supporting a sustainable water future. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

This research employed “Response Surface Methodology (RSM)” to assess the effectiveness of electrocoagulation (EC) in treating olive mill wastewater (OMW) before applying adsorption with olive stone biochar (OS) as a sustainable adsorbent. Several parameters, including reaction time, current density (CD), inter-electrode distance, and the number of electrodes, were optimized. Analysis using Minitab 22.2 resulted in robust regression models with high coefficients of determination (R²). The optimal parameters were CD of 12.41 mA/cm², a time of 45.61 min, an inter-electrode spacing of 1 cm, and a maximum of 6 electrodes, resulting in an energy consumption (ENC) of 9.85 kWh/m³. Significant pollutant percentage removals were achieved: 72.32% for total Kjeldahl nitrogen (TKN), 80.74% for turbidity, 57.44% for total phenol (TPh), 56.9% for soluble chemical oxygen demand (COD_soluble), and 56.6% for total chemical oxygen demand (COD_total). After the EC, the adsorption of pollutants was conducted using OS biochar that was generated through the pyrolysis of OS at a temperature of 500 °C. FTIR analysis of the biochar revealed key absorption bands that indicated the presence of inorganic compounds, aromatic C=C, and phenolic groups O-H. The integrated EC and adsorption (ECA) process demonstrated markedly higher efficiencies, with TPh removal reaching 61.41%, turbidity reduction at 81.92%, TKN reduction at 77.78%, COD_soluble reduction at 70.31%, COD_total reduction at 65.1%, and project cost of $2.88/m³. The ECA process presents a promising treatment approach for OMW. Full article

Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability. Full article

A treatment process was developed for effluents from direct physical lithium-ion battery (LIB) recycling with a focus on the removal of organic contaminants. The high chemical oxygen demand to biological oxygen demand ratio (COD/BOD₅) of 3.9–4.6 indicates that biological treatment is not feasible. Therefore, three advanced oxidation processes were evaluated: UV/H₂O₂ oxidation, the Fenton process and electrochemical oxidation. Two target scenarios were considered, namely compliance with the limit for discharge into the sewer system (COD = 800 mg/L) and compliance with the stricter limit for direct discharge into surface waters (COD = 200 mg/L). Under the investigated conditions, UV/H₂O₂ oxidation and the Fenton process did not meet the required discharge limits and exhibited high chemical consumption. In contrast, electrochemical oxidation achieved both discharge criteria with a lower energy demand, requiring 32.8 kWh/kgCOD_removed for sewer discharge and 95.3 kWh/kgCOD_removed for direct discharge. An economic assessment further identified electrochemical oxidation as the most cost-effective option, with treatment costs of EUR 6.63/m³, compared to EUR 17.31/m³ for UV/H₂O₂ oxidation and EUR 28.66/m³ for the Fenton process. Overall, electrochemical oxidation proved to be the most efficient and environmentally favorable technology for treating wastewater from LIB recycling, enabling compliance with strict discharge regulations while minimizing the chemical and energy demand. Full article

Thermal comfort and energy efficiency are two main goals of heating, ventilation, and air conditioning (HVAC) systems, which use about 40% of the total energy in buildings. This paper aims to predict optimal room temperature, enhance comfort, and reduce energy consumption while avoiding extra energy use from overheating or overcooling. Six Machine Learning (ML) models were tested to predict the optimal temperature in the classroom based on the occupancy characteristic detected by a Deep Learning (DL) model, You Only Look Once (YOLO). The decision tree achieved the highest accuracy at 97.36%, demonstrating its effectiveness in predicting the preferred temperature. To measure energy savings, the study used RETScreen software version 9.4 to compare intelligent temperature control with traditional operation of HVAC. Genetic algorithm (GA) was further employed to optimize HVAC energy consumption while keeping the thermal comfort level by adjusting set-points based on real-time occupancy. The GA showed how to balance comfort and efficiency, leading to better system performance. The results show that adjusting from default HVAC settings to preferred thermal comfort levels as well controlling the HVAC to work only if the room is occupied can reduce energy consumption and costs by approximately 76%, highlighting the substantial impact of even simple operational adjustments. Further improvements achieved through GA-optimized temperature settings provide additional savings of around 7% relative to preferred comfort levels, demonstrating the value of computational optimization techniques in fine-tuning building performance. These results show that intelligent, data-driven HVAC control can improve comfort, save energy, lower costs, and support sustainability in buildings. Full article

Developing cost-effective yet high-performance hard carbon anodes is critical for advancing the commercialization of sodium-ion batteries (SIBs), as they offer a balance of low cost, high capacity, and compatibility with Na⁺ storage mechanisms. Herein, waste towels, an abundant, low-cost precursor with a high carbon yield (>49%), were utilized to synthesize hard carbons via a two-step process: pre-oxidation at 250 °C to stabilize the fibrous structure, followed by carbonization at 1100 °C (THC-1100), 1300 °C (THC-1300), or 1500 °C (THC-1500). Electrochemical evaluations revealed that THC-1300, carbonized at an intermediate temperature, exhibited superior Na⁺ storage performance compared to its counterparts: it delivered a high reversible specific capacity of ~320 mAh/g at 1.0 C (1 C = 320 mA/g), with 78% capacity retention after 200 cycles, demonstrating excellent long-term cyclic stability. Its rate capability was equally impressive, achieving specific capacities of 341.5, 331.2, 302.0 and 234.8 mAh/g at 0.2, 0.5, 2.0 and 5.0 C, respectively, indicating efficient Na⁺ diffusion even at high current densities. Notably, THC-1300 also showed an improved initial Coulombic efficiency (ICE) of 75.4%, reflecting reduced irreversible Na⁺ consumption during the first cycle. These enhancements are attributed to the synergistic effects of THC-1300’s optimized structural and textural properties: a balanced interlayer spacing (d₍₀₀₂₎ = 0.387 nm) that facilitates rapid Na⁺ intercalation, a low BET surface area (1.62 m²/g) helps to minimize electrolyte side reactions. The combined advantages of high specific capacity, improved ICE, and remarkable cycling stability position this waste-towel-derived hard carbon as a highly viable and sustainable candidate for anode materials in next-generation SIBs, addressing both performance and cost requirements for large-scale energy storage applications. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

In the context of China’s dual-carbon agenda and the Digital China initiative, elucidating the role of digital literacy in shaping consumption-based household carbon emissions (HCE) is essential for advancing low-carbon urban living and supporting a broader green transition. Existing research has rarely examined, at the individual level, how digital capability shapes household consumption decisions and the structure of carbon emissions. Accordingly, this study draws on matched household-individual microdata from the China Family Panel Studies (CFPS). We employ a two-way fixed effects model, kernel density analysis, and qualitative comparative analysis. We test the nonlinear effect of digital literacy on household consumption-related carbon emissions and examine its heterogeneity. We also examined the mediating role of perceived environmental pressure, social trust and income level. The research results show that: (1) The net impact of digital literacy on carbon emissions related to household consumption shows an inverted U-shaped curve, rising first and then falling. When digital literacy is low, it mainly increases emissions by expanding consumption channels, reducing transaction costs and improving convenience. Once digital literacy exceeds a certain threshold, the mechanism will gradually turn to optimize the consumption structure, so as to support the low-carbon transformation of individuals. (2) The impact of digital literacy on HCE is structurally different in different types of consumption. In terms of transportation and communication expenditure, the emission reduction effect is the most significant, and with the improvement in digital literacy, this effect will become more and more obvious. For housing-related consumption, the turning point appeared the earliest. With the improvement in digital literacy, its effect will enter the emission reduction stage faster. (3) Digital literacy can reduce carbon emissions related to household consumption by enhancing residents’ perception of environmental pressure and strengthening social trust. However, it may also increase emissions by increasing residents’ incomes, because it will expand the scale of consumption, which will lead to an increase in carbon emissions related to household consumption. (4) The heterogeneity analysis shows that as digital literacy improves, carbon emissions increase more strongly among rural residents, people with low human capital, low-income households, and women. However, the turning-point threshold for emission reduction is relatively lower for women and rural residents. (5) Low-carbon transitions in household consumption are shaped by dynamic interactions among multiple factors, and multiple pathways can coexist. Digital literacy can work with environmental responsibility to endogenously promote low-carbon consumption behavior. It can also, under well-developed infrastructure, empower households and amplify the emission-reduction effects of technology. Full article

This study presents the techno-economic optimization of a hybrid distillation-membrane process for the complete fractionation of liquefied petroleum gas (LPG), targeting high-purity propane, n-butane, and isobutane recovery. The process employs an initial distillation column to separate propane (99% purity) from a propane-enriched stream, which is subsequently fed to a two-stage membrane system using an MFI zeolite hollow-fiber membrane for n-butane/isobutane separation. Through systematic simulation and sensitivity analysis, different membrane configurations were evaluated. The two-stage process with a partial residue-side reflux configuration demonstrated superior economic performance, achieving a total operating cost of 31.58 USD/h. Key membrane parameters—area, permeance, and separation factor—were optimized to balance separation efficiency with energy consumption and cost. The analysis identified an optimal configuration: a membrane area of 800 m², an n-butane permeance of 0.9 kg·m⁻²·h⁻¹, and a separation factor of 40. This setup ensured high n-alkane recovery while effectively minimizing energy use and capital investment. The study concludes that the optimized distillation-membrane hybrid process offers a highly efficient and economically viable strategy for the full utilization of LPG components. Full article