Search Results (2,576)

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

The servo drive system serves as the core power unit in high-end equipment such as industrial robots and computerized numerical control (CNC) machine tools, where mechanical resonance and shaft torque ripple induced by elastic deformation and backlash severely degrade motion accuracy and system stability. Conventional resonance suppression approaches, predominantly based on PI control and notch-filter-augmented PI control, suffer from critical limitations: high sensitivity to resonant frequency variations, inability to systematically enforce physical shaft torque constraints, poor robustness against parameter uncertainties and external disturbances, and significant degradation of dynamic performance when resonance is aggressively suppressed. This paper establishes a two-inertia elastic system model to investigate the effects of elastic deformation and backlash nonlinearities, revealing the mechanisms of mechanical resonance and torque ripple, and proposes control strategies for resonance suppression and shaft torque ripple limitation. A novel hierarchical control architecture is designed, consisting of a Luenberger-observer-based model predictive control (MPC) speed controller, and a super-twisting sliding mode controller (ST-SMC) for the current loop. Luenberger observer-based MPC with ST-SMC strategy is to simultaneously obtain: (a) enhanced robustness via state estimation, (b) superior dynamic performance via SMC, and (c) guaranteed shaft torque constraint satisfaction via MPC. Compared with conventional PI control and notch-filter-based PI control, simulation results demonstrate that Luenberger observer-based MPC with ST-SMC strategy effectively suppresses resonance, limits shaft torque ripple, and enhances the system’s disturbance rejection capability. Full article

With the development of avionics systems towards high integration and high power density, the thermal management of electronic equipment in ATR chassis is facing severe challenges, and the extreme aviation environment further exacerbates the difficulty of heat dissipation. Traditional fixed control strategies suffer from problems such as energy consumption, redundancy, and local overheating, whereas single-model predictive control (MPC) is prone to local optimization. This paper proposes a thermal management optimization scheme based on the ACO-MPC hybrid framework: Firstly, a compact thermal model integrating aviation environmental parameters, such as high-altitude, low-pressure conditions and vibration impacts, is constructed. The balanced truncation method is adopted for model order reduction in this study. By retaining the key thermodynamic characteristics of the system, the original three-dimensional thermal model containing more than 800 nodes is simplified to 25 core nodes, which ensures simulation accuracy while improving computational efficiency; Secondly, the ACO-MPC hybrid framework is designed, which uses Ant Colony Optimization (ACO) for global optimization to provide optimized initial values for Model Predictive Control (MPC), breaking through the local optimization limitation of MPC and realizing the collaboration of “global optimization—dynamic control”; Finally, the effectiveness of the framework is verified in three typical aviation scenarios. The results show that compared with traditional methods, this framework has significantly improved heat dissipation efficiency, energy consumption control, and temperature stability, and has strong adaptability to environmental disturbances, which can be migrated to the ATR chassis of different specifications. Full article

The characteristics of the dynamic reactive power demand of a hybrid multi-infeed HVDC transmission system during the post-fault recovery period are analyzed and a coordinated control strategy to improve the fault recovery characteristics of the hybrid multi-infeed HVDC transmission system is proposed in this paper. During the process of fault recovery, the LCC-HVDC adopts a progressive staggering recovery strategy. At the same time, according to the reactive power shortage of LCC-HVDC, the dynamic power limiter is used to adjust the upper and lower limit values of the outer loop power controller of VSC-HVDC, and the reactive power generated by the VSC-HVDC can be rapidly adjusted. Therefore, the problem of excessive reactive power demand during the recovery process can be solved and the reactive power demand can be satisfied with the proposed strategy. Moreover, the ability of VSC-HVDC to provide reactive power support can be fully utilized. Finally, a simulation model of a hybrid tri-infeed HVDC system is built using PSCAD/EMTDC (Version 4.6.2) software to verify the effectiveness of the proposed control strategy. Full article

This study proposes a robust control strategy to improve the stability and reliability of grid-following inverters with LCL filters, particularly under varying disturbances and instability conditions. A detailed survey of existing control strategies is presented to identify their limitations and highlight the advantages of Internal Model-Based Control (IMC). The analytical representation of IMC has been examined by demonstrating its inherent robustness against system uncertainties and external disturbances. The research focuses on a control method for grid-following inverters operating under challenging conditions such as grid disturbances, nonlinearities, and parameter variations. The effect of these factors on inverter performance is analyzed, and corresponding mitigation strategies such as advanced filtering and adaptive control mechanisms are discussed. A simulation framework is developed to assess the effectiveness of the proposed IMC-based control approach under various grid conditions. The results confirm that IMC enhances system stability, reduces harmonic distortion, and improves dynamic response. Moreover, the outcomes highlight the potential of IMC as a robust and adaptive control solution by providing valuable evaluations for advancing inverter technologies in weak grid environments and optimizing filter designs to achieve improved power quality. Full article

The current retired recycling system suffers from “systemic coordination failure”, primarily due to ambiguous responsibility boundaries hindering interenterprise collaboration, unequal profit distribution discouraging technological innovation investment, and low participation from both consumers and recycling enterprises undermining the efficiency of recycling channels. However, the simplified tripartite game models commonly adopted in existing research exhibit significant limitations in explaining and addressing the above practical challenges, as they fail to incorporate consumers and third-party recyclers as strategic decision-makers into the analytical framework. To address these issues, this study develops, for the first time, a five-party evolutionary game model involving governments, vehicle manufacturers, battery producers, third-party recyclers, and consumers within a reverse supply chain framework. We further employ system dynamics to simulate the dynamic evolution of stakeholder strategies. The results show that: (1) When tri-party synergistic benefits exceed 15, the system transitions from resource dissipation to circular regeneration. (2) Government subsidies reaching the threshold of 2 effectively promote low-carbon transformation across the industrial chain. (3) Bilateral synergistic benefits of 12 can stimulate green technological innovation and industrial upgrading. (4) Establishing a multi-stakeholder governance framework is key to enhancing resource circulation efficiency. This research provides quantitative evidence and policy implications for constructing an efficient and sustainable power battery recycling system. Full article

This paper introduces an enhanced bandgap reference (BGR) design, addressing the shortcomings of traditional circuits, such as significant temperature drift, limited power-supply rejection, and inadequate load-driving capacity. The proposed design incorporates a symmetric folded common-emitter–common-base BJT amplifier with MOS-assisted biasing, employed in the proposed BGR, enforcing branch voltage symmetry to effectively suppress intrinsic offset caused by structural mismatch. By reducing the amplifier input offset, the circuit achieves improved reference voltage stability, a lower temperature coefficient (TC), and an enhanced power-supply rejection ratio (PSRR). Additionally, a negative-feedback adaptive current-adjustment driver is implemented to dynamically adjust the output current in response to real-time load changes. This method bolsters the load-driving capability and maintains a stable reference output across varying load conditions. The circuit was simulated using a 0.18 μm BCD process, revealing that with a 3.3 V supply voltage, the BGR produces a stable output voltage of 2.5 V, with a TC of $2.372\times {10}^{-6}$ °C⁻¹. The simulated PSRR is −114.2 dB at DC and −62.07 dB at 1 kHz. Moreover, under a 3.3 V supply, sweeping the load capacitance from 0.1 μF to 100 μF demonstrates that the reference voltage remains consistently regulated at 2.5 V, confirming its excellent load tolerance and output stability. Full article

Electron beam powder bed fusion (EB-PBF) Ti6Al4V often exhibits high vertical surface roughness, limiting its use in high-end applications. In this study, an infrared continuous-wave laser was applied to precisely polish the vertical surface. An orthogonal design identified the optimal condition as 10,400 kW/cm² power density, 800 mm/s scanning speed, and one pass, achieving a minimum Sa of 0.24 μm and a 98.03% reduction compared with the as-built surface. To address the adhered semi-molten particle characteristics of EB-PBF sidewalls, a molten-pool-dynamics-based polishing model was developed and validated, yielding an error as low as 1.24%. Simulations indicate that power density governs the final morphology by controlling molten pool coverage, scanning speed affects polishing efficiency via thermocapillary force, and polishing time influences surface quality by triggering or avoiding melt splashing. Full article

Biomolecule–photosensitizer conjugates have rapidly evolved into one of the most powerful strategies for improving the selectivity, efficacy, and translational potential of photodynamic therapy (PDT). By integrating photosensitizers (PSs) with carbohydrates, amino acids, peptides, aptamers, proteins, cofactors, vitamins or antibodies, these constructs overcome long-standing limitations of classical PDT, including poor solubility, insufficient tumour accumulation, and strong dependence on oxygen availability. Beyond enhancing receptor-mediated uptake and enabling precise interactions with the tumour microenvironment (TME), bioconjugation also modulates aggregation, photochemical properties, intracellular accumulation, and immune system activation. A particularly transformative trend is the emergence of supramolecular architectures in which photosensitizers form defined nanostructured aggregates with peptides or proteins. Once considered an undesirable phenomenon, aggregation is now recognized as a tenable feature that governs photochemical behaviour. Engineered aggregates can undergo environment-triggered disassembly to monomeric, photoactive states, or operate as semiconductor-like nanodomains capable of Type I reaction through symmetry-breaking charge separation. This shift toward oxygen-independent radical pathways offers a promising solution to the challenge of hypoxia, a hallmark of the TME that severely compromises conventional Type II PDT. Parallel advances in 3D experimental platforms such as tumour organoids and organ-on-chip systems provide physiologically relevant validation of these conjugates, enabling the assessment of penetration, subcellular localization, immunogenic cell death, and therapeutic synergy within realistic TME conditions. Collectively, the integration of biomolecular targeting with controlled supramolecular design is redefining the landscape of PDT. Future progress will depend on designing conjugates that retain high activity under hypoxia, engineering dynamic aggregate states, and systematically validating these systems in advanced TME-mimetic models. Together, these developments position biomolecule–photosensitizer conjugates as a versatile and increasingly less oxygen-dependent class of next-generation phototherapeutic agents. Full article

The dry area in northwest China (ARNC), with its tough climate, serious soil erosion, and poor soil quality, is one of China’s most fragile ecosystems. Studying changes in plant growth cycles here is very important for improving environmental monitoring and making plans to adapt to climate change. While vegetation growing season parameters (Start/End of Season: SOS/EOS) serve as vital indicators of ecosystem dynamics, comprehensive understanding has been constrained by limited long-term phenological datasets and insufficient exploration of multi-factor interactions. This study used PLS-SEM to analyze 27-year (1990–2016) vegetation index data, systematically quantifying spatiotemporal variations in growing season phenology and disentangling climate–terrain driving mechanisms. The results revealed the following key findings. (1) Spatial heterogeneity in phenological patterns, with the annual average Start of Season (SOS) and End of Season (EOS) being 114.7 Day and 301.7 Day, respectively, exhibiting a northwest–high to southeast–low gradient. The findings indicate a prolongation of the vegetation growing season, with significant spatial variability. (2) Interannual fluctuations showed the SOS and EOS coefficient of variation (CV) values of 0.230 and 0.234, respectively, with southeastern regions displaying higher instability than northwestern counterparts. (3) The spatial variation in SOS/EOS is primarily influenced by meteorological and geographical factors, with an explanatory power exceeding 30%. This research advances mechanistic understandings of arid ecosystem responses to environmental stressors, providing a scientific foundation for targeted ecological restoration, desertification mitigation, and sustainable land management in climate-sensitive drylands. Full article

Introduction: The informal food sector in Kisumu City, largely run by women informal food vendors, plays a crucial role in the urban food system. However, these female-led businesses faced disproportionate risks stemming from COVID-19-related policies, exacerbating gendered vulnerabilities. This paper explores the gender gaps of post-pandemic recovery strategies and their implications for resilience, recovery, and sustainability of women-led informal food businesses. Methods: This cross-sectional study was guided by the Intersectionality-Based Policy Analysis (IBPA) framework. In collaboration with the Pamoja Community-Based Organization, we employed qualitative methods grounded in community-based participatory approaches. Data were collected through key informant interviews (n = 20), depth interviews (n = 20), focus group discussions (n = 40), and a review of policy documents (n = 2). Data was analyzed guided by the eight principles of the IBPA framework alongside Braun and Clarke’s six-phased thematic analysis approach. Results: Findings indicated that power dynamics in the formulation of post-pandemic policies and top-down implementation approaches excluded women informal food vendors from meaningfully participating in policy processes. For example, female vendors were excluded from the recovery priorities as the strategies adopted had limited to no targeted gender-responsive interventions. As such, women informal food vendors faced several challenges during recovery, including limited government support, barriers to accessing credit facilities, heightened household and unpaid care work, gender-based violence, sexual exploitation, and insecurity. The female vendors employed both individual agency and collective action to facilitate recovery. Discussion: Gender-responsive COVID-19 policies were critical to addressing the disproportionate impact of the pandemic on women-led informal food businesses. Moving forward, a comprehensive understanding of existing sociocultural inequalities is crucial for designing post-pandemic strategies that are gender-inclusive and promote equitable recovery. Such an approach would enhance women informal food vendors’ resilience to emergencies and their contribution to urban household food security and livelihood. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO₂ emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO₂ emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks. Full article

The sailfish optimizer is a recent swarm-intelligence-based optimization algorithm which mimics the hunting behavior of sailfish in the ocean. It consists of two types of populations, namely, sailfish and sardine, where sailfish represent the candidate solutions and sardines freely maneuver in the search space. Existing research studies have shown that the sailfish optimizer suffers from limited global exploration capability, with local optimum stagnation and slow convergence speed. To address these limitations, an improved sailfish optimizer, namely, the Multi-Strategy Sailfish Optimizer, is proposed in this study. This improved version incorporates a modified search strategy for both sailfish and sardines, a non-linear attack power parameter, an improved hunting procedure, and a dynamic sardine population. The impact of all suggested improvements is analyzed experimentally. Several experiments with single-objective problems presented at the Congress on Evolutionary Computation in 2022 are performed to prove the effectiveness and efficiency of the proposed algorithm. This improved algorithm is compared with well-known optimization algorithms, such as the whale optimization algorithm, the sine–cosine algorithm, etc., and improved variants of those algorithms. A convergence behavior analysis is also performed using convergence graphs. Furthermore, the significance of the work is statistically validated. The analysis indicates that the Multi-Strategy Sailfish Optimizer performs significantly better than other optimization algorithms. It is also applied to solve the tension/compression spring design problem and the mobile robot path planning problem. Full article

Bioclimatic monitoring at vineyard scale is essential for irrigation management and disease-risk assessment, yet many systems rely on expensive commercial stations or generic IoT nodes with limited validation and little focus on small and medium-sized winegrowers. This application-driven engineering work investigates whether decision-support-grade bioclimatic data for precision viticulture can be obtained from a low-cost station, by proposing a solar-powered proximal node that integrates soil, plant, and atmospheric sensors on a dedicated PCB that communicates via LoRaWAN. The node operates in a 15-min cycle, with sensing parameters selected to provide the minimum information required for key Precision Viticulture applications. It was deployed in a commercial vineyard side by side with a commercial station, quantifying sensor agreement, communication reliability, and energy consumption. The results show low error rates and consistent agronomic interpretation of environmental conditions, disease risk, precipitation events, and soil and water dynamics. The LoRaWAN link reached a 97% packet-delivery ratio with an average consumption of about 2.5 Wh per day. Material cost is approximately 260 €, one order of magnitude lower than a comparable station. These results indicate that, under real vineyard conditions and compared with a commercial reference, the proposed low-cost system provides agronomically useful, reliable bioclimatic monitoring. Full article