Search Results (25,413)

Synthetic Aperture Radar (SAR) provides all-weather, all-time imaging capabilities, enabling reliable maritime ship detection under challenging weather and lighting conditions. However, most high-precision detection models rely on complex architectures and large-scale parameters, limiting their applicability to resource-constrained platforms such as satellite-based systems, where model size, computational load, and power consumption are tightly restricted. Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. Firstly, the backbone network integrates depthwise separable convolutions and a Convolutional Block Attention Module (CBAM) to suppress background clutter and extract effective features. Building upon this, a cross-layer feature interaction mechanism is introduced via the Multi-Scale Coordinated Fusion (MSCF) and Bi-EMA Enhanced Fusion (Bi-EF) modules to strengthen joint spatial-channel perception. To further enhance the detection capability, Efficient Feature Learning (EFL) modules are embedded in the neck to improve feature representation. Experiments on the Synthetic Aperture Radar (SAR) Ship Detection Dataset (SSDD) show that this method, with only 1.6 M parameters, achieves a mean average precision (mAP) of 98.35% in complex scenarios, including inshore and offshore environments. It balances the difficult problem of being unable to simultaneously consider accuracy and hardware resource requirements in traditional methods, providing a new technical path for real-time SAR ship detection on satellite platforms. Full article

This study provides a novel experimental setup and methodology for the quantitative investigation of evaporation-induced foaming behaviors in a polymer/small-molecule solution system (PSMS). In traditional dynamic test methods, it is difficult to precisely describe the evaporation-induced foaming process of a multicomponent solution because the concentration of light components in solution continuously decreases during ebullition, causing undesired changes in foaming behavior. In this study, a precisely controlled condensation reflux module was introduced into the setup to maintain pressure, temperature, and concentration of the PSMS at constant levels during the entire ebullition process, allowing dynamic test methods to quantify the evaporation-induced foamability. With this newly proposed device, experimental data of typical PSMS, polyolefin elastomer (POE)/n-hexane solution system, were obtained and modeled to illustrate the foam growth profile, thereby characterizing the dynamic foaming process based on a logistic growth function. The corresponding dimensionless number Σevap was calculated to evaluate evaporation-induced foam stability by analyzing the foam growth profile under varying pressure, concentration, and energy input levels. Furthermore, given that the PSMS represents a highly non-ideal system, the bubble nucleation rate J was modified in this work by introducing a correction coefficient δ to account for the non-ideal effects of macromolecules present in solutions. Additionally, another correction coefficient λ was incorporated into the Gibbs free energy term to adjust for supersaturation of liquid during nucleation. The experiment’s data align well with the modified bubble nucleation rate mechanism proposed herein. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

Remanufacturing design is a green design model that considers remanufacturability during the design process to improve the reuse of components. However, traditional remanufacturing design scheme decision making focuses on the remanufacturability indicator and does not fully consider the carbon emissions of the remanufacturing process, which will take away the energy-saving and emission reduction benefits of remanufacturing. In addition, remanufacturing design schemes rarely consider the human ergonomics of the product, which leads to uncomfortable handling of the product by the customer. To reduce the remanufacturing carbon emission and improve customer comfort, it is necessary to select a reasonable design scheme to satisfy the carbon emission reduction and ergonomics demand; therefore, this paper proposes an integrated multi-criteria decision-making method for remanufacturing design that considers the carbon emission and human ergonomics. Firstly, an evaluation system of remanufacturing design schemes is constructed to consider the remanufacturability, cost, carbon emission, and human ergonomics of the product, and the evaluation indicators are quantified by the normalization method and the Kansei engineering (KE) method; meanwhile, the hierarchical analysis method (AHP) and entropy weight method (EW) are used for the calculation of the subjective and objective weights. Then, a multi-attribute decision-making method based on the combination of an assignment approximation of ideal solution ranking (TOPSIS) and gray correlation analysis (GRA) is proposed to complete the design scheme selection. Finally, the feasibility of the scheme is verified by taking a household coffee machine as an example. This method has been implemented as an application using Visual Studio 2022 and Microsoft SQL Server 2022. The research results indicate that this decision-making method can quickly and accurately generate reasonable remanufacturing design schemes. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

Accurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal information embedded in timestamps. However, this information represents a valuable yet underutilized aspect of sensor-based data that can significantly enhance forecasting performance. In this paper, we propose a novel timestamp-guided knowledge distillation framework (TKDF), which integrates both historical and timestamp information through mutual learning between heterogeneous prediction branches to improve forecasting robustness. The framework comprises two complementary branches: a Backbone Model that captures local dependencies from historical sequences, and a Timestamp Mapper that learns global temporal patterns encoded in timestamp features. To enhance information transfer and reduce representational redundancy, a self-distillation mechanism is introduced within the Timestamp Mapper. Extensive experiments on multiple real-world sensor datasets—covering electricity consumption, traffic flow, and meteorological measurements—demonstrate that the TKDF consistently improves the performance of mainstream forecasting models. Full article

With the continuous expansion of data center scales worldwide, the problem of energy consumption has become increasingly prominent. To address the multi-parameter control challenge in environmental temperature regulation for large data center computer rooms, achieve precise control of hot-aisle temperatures in data centers, and reduce energy waste, this paper designs a multi-parameter model-free adaptive control (MMFAC) algorithm suitable for computer room environmental temperatures. The algorithm integrates the model-free adaptive control (MFAC) algorithm with a weight matrix to perform scaling transformations. Considering the large parameter space of the MFAC controller and the dynamic complexity of data center temperature control systems, compact-form dynamic linearization (CFDL) technology and optimization mathematical methods are used to simplify the parameter identification of the pseudo-Jacobian matrices and the calculation of control quantities for the regulation devices. Simulation experiments based on measured data from a data center show that the proposed algorithm can calculate control quantities for equipment such as air conditioners according to real-time environmental parameter measurements and drive each device based on these control quantities. Meanwhile, the algorithm can reduce errors in key parameters by adjusting the weight matrix. Comparative tests with other control algorithms show that the algorithm has faster response in temperature control and smaller control errors, verifying the effectiveness and application prospects of the algorithm in data center temperature control. Full article

Introduction: The gut microbiota is vital for human health, and its modulation through dietary and pharmaceutical compounds has gained increasing attention. Among gut microbes, Akkermansia muciniphila has been extensively researched due to its role in maintaining intestinal barrier integrity, regulating energy metabolism, and influencing inflammatory responses. Subject: To analyze and synthesize the available scientific evidence on the influence of various bioactive compounds, including prebiotics, polyphenols, antioxidants, and pharmaceutical agents, on the abundance and activity of A. muciniphila, considering underlying mechanisms, microbial context, and its therapeutic potential for improving metabolic and intestinal health. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. Databases such as PubMed, ScienceDirect, Scopus, Web of Science, SciFinder-n, and Google Scholar were searched for publications from 2004 to 2025. Experimental studies in animal models or humans that evaluated the impact of bioactive compounds on the abundance or activity of A. muciniphila were prioritized. The selection process was managed using the Covidence platform. Results: A total of 78 studies were included in the qualitative synthesis. This review compiles and analyzes experimental evidence on the interaction between A. muciniphila and various bioactive compounds, including prebiotics, antioxidants, flavonoids, and selected pharmaceutical agents. Factors such as the chemical structure of the compounds, microbial environment, underlying mechanisms, production of short-chain fatty acids (SCFAs), and mucin interactions were considered. Compounds such as resistant starch type 2, GOS, 2′-fucosyllactose, quercetin, resveratrol, metformin, and dapagliflozin showed beneficial effects on A. muciniphila through direct or indirect pathways. Discussion: Variability across studies reflects the influence of multiple variables, including compound type, dose, intervention duration, experimental models, and analytical methods. These differences emphasize the need for a contextualized approach when designing microbiota-based interventions. Conclusions: A. muciniphila emerges as a promising therapeutic target for managing metabolic and inflammatory diseases. Further mechanistic and clinical studies are necessary to validate its role and to support the development of personalized microbiota-based treatment interventions. Full article

This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: $\left(i\right)$ grid-connected and islanded operation; $\left(ii\right)$ peak shaving; $\left(iii\right)$ voltage support; $\left(iv\right)$ load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article

Pore defects are one of the most common defects in the aluminum alloy electron beam welding process. In this paper, research on the pore defects and related mechanisms of the electron beam welding of dissimilar aluminum alloys was carried out with 1060 and 2A12 aluminum alloys. Under the test conditions, the pore defects of the aluminum alloy joint were related to the penetration status, the porosity of the critically penetrated joint was low, and the porosity of the beam joint increased when it was slightly penetrated. When the welding speed changed from 300 mm/min to 1200 mm/min, the porosity in the critically penetrated joint first increased and then decreased. When the welding speed was set to 300 mm/min and the beam current was set to 26 mA, the porosity of the joints reached its minimum value at 0.23%. Based on the actual process of electron beam welding, a flow simulation model was established to study the aluminum alloy welding process. The stability of the keyhole was related to the electron beam energy density reaching the inner keyhole, so increasing the electron beam for the fully penetrated joints was advantageous for reducing the pore defects. Full article

The production of thermal energy in buildings using internal greenhouses makes it possible to obtain substantial gains in energy consumption and, at the same time, contribute to improving occupants’ thermal comfort (TC) levels. This article proposes a study on the producing and transporting of renewable thermal energy in a circular auditorium equipped with an enveloping semi-circular greenhouse. The numerical study is based on software that simulates the building geometry and the building thermal response (BTR) numerical model and assesses the TC level and indoor air quality (IAQ) provided to occupants in spaces ventilated by the proposed system. The building considered in this study is a circular auditorium constructed from three semi-circular auditoriums supplied with internal semi-circular greenhouses. Each of the semi-circular auditoriums faces south, northeast, and northwest, respectively. The semi-circular auditoriums are occupied by 80 people each: the one facing south throughout the day, while the one facing northeast is only occupied in the morning, and the one facing northwest is only occupied in the afternoon. The south-facing semi-circular greenhouse is used by itself to heat all three semi-circular auditoriums. The other two semi-circular greenhouses are only used to heat the interior space of the greenhouse. It was considered that the building is located in a Mediterranean-type climate and subject to the typical characteristics of clear winter days. The results allow us to verify that the proposed heating system, in which the heat provided to the occupied spaces is generated only in the semi-circular greenhouse facing south, can guarantee acceptable TC conditions for the occupants throughout the occupancy cycle. Full article

Phosphorylated cellulose is proposed as a bio-resin for the removal of heavy metals, as a substitute for synthetic polymer-based materials. Phosphorylation is carried out using kraft pulp fibers as the cellulose source, with phosphate esters and urea as reactants to prevent significant fiber degradation. Herein, phosphorylated fibers, with three types of counterions (sodium, ammonium, or hydrogen), are used in adsorption trials involving four individual metals: nickel, copper, cadmium, and lead. The Langmuir isotherm model is applied to determine the maximum adsorption capacities at four different temperatures (10, 20, 30, and 50 °C), enabling the calculation of the Gibbs free energy (ΔG), entropy (ΔS), and enthalpy (ΔH) of adsorption. The results show that the adsorption capacity of phosphorylated fibers is equal or even higher than that of commercially available resins (1.7–2.9 vs. 2.4–2.6 mmol/g). However, the nature of the phosphate counterion plays an important role in the adsorption capacity, with the alkaline form showing a superior ion exchange capacity than the hybrid form and acid form (2.7–2.9 vs. 2.3–2.7 vs. 1.7–2.5 mmol/g). The thermodynamic analysis indicates the spontaneous (ΔG = (-)16–(-)30 kJ/mol) and endothermic nature of the adsorption process with positive changes in enthalpy (0.45–15.47 kJ/mol) and entropy (0.07–0.14 kJ/mol·K). These results confirm the high potential of phosphorylated lignocellulosic fibers for ion exchange applications, such as the removal of heavy metals from process or wastewaters. Full article

This study investigates whether institutional quality amplifies the emissions-reducing effect of energy efficiency in hydrocarbon-dependent economies. Addressing a gap in the energy–environment literature, it tests how governance conditions shape the effectiveness of technical mitigation strategies. Using panel data from six Gulf Cooperation Council (GCC) countries between 2000 and 2023, we estimate a fixed-effects model with interaction terms between energy intensity (as a proxy for efficiency) and institutional quality (proxied by Control of Corruption). The results show that energy efficiency is associated with lower CO₂ emissions, and this relationship is significantly moderated by institutional quality. We also analyze the emissions impact of natural gas consumption and identify a structural shift following the 2014 energy reforms: while gas use was positively associated with emissions before 2014, the post-reform period shows a weaker or reversed effect. Robustness checks using alternative governance indicators—Regulatory Quality and Government Effectiveness—confirm the moderating role of institutions. The study offers new empirical evidence on the energy–institution–environment nexus and introduces a novel interaction-based methodology suited to resource-rich economies undergoing institutional transition. Full article

This paper presents a declarative framework for resilient machining line planning, integrating line balancing and disruption handling within a unified, interactive decision-support environment. Building upon earlier constraint-based models, the proposed approach incorporates sustainability-oriented improvements through Pareto-based multi-criteria optimization. The model supports trade-off analysis across cost, energy consumption, tool wear, and schedule continuity, enabling predictive planning and adaptive dispatching under operational uncertainty. By combining proactive balancing with reactive disruption handling in a single declarative formulation, the framework addresses a key gap in the current production engineering methodologies. A case study employing real data and real-world-inspired disruption scenarios demonstrates the effectiveness of the approach. Compared to traditional sequential strategies, the framework yields superior performance in terms of solution diversity, responsiveness, and sustainability alignment, confirming its value for next-generation, resilient manufacturing systems. Full article