Search Results (3,152)

Hearing impairments affect approximately 1.5 billion individuals worldwide, highlighting the critical need for effective communication tools between deaf and hearing populations. Traditional sign language translation (SLT) models predominantly rely on gloss-based methods, which convert visual sign language inputs into intermediate gloss sequences before generating textual translations. However, these methods are constrained by their reliance on extensive annotated data, susceptibility to error propagation, and inadequate handling of low-frequency or unseen sign language vocabulary, thus limiting their scalability and practical application. Drawing upon multimodal translation theory, this study proposes the open-vocabulary sign language translation (OVSLT) method, designed to overcome these challenges by integrating open-vocabulary principles. OVSLT introduces two pivotal modules: Enhanced Caption Generation and Description (CGD), and Grid Feature Grouping with Advanced Alignment Techniques. The Enhanced CGD module employs a GPT model enhanced with a Negative Retriever and Semantic Retrieval-Augmented Features (SRAF) to produce semantically rich textual descriptions of sign gestures. In parallel, the Grid Feature Grouping module applies Grid Feature Grouping, contrastive learning, feature-discriminative contrastive loss, and balanced region loss scaling to refine visual feature representations, ensuring robust alignment with textual descriptions. We evaluated OVSLT on the PHOENIX-14T and CSLDaily datasets. The results demonstrated a ROUGE score of 29.6% on the PHOENIX-14T dataset and 30.72% on the CSLDaily dataset, significantly outperforming existing models. These findings underscore the versatility and effectiveness of OVSLT, showcasing the potential of open-vocabulary approaches to surpass the limitations of traditional SLT systems and contribute to the evolving field of multimodal translation. Full article

The European Union aims to achieve climate neutrality by 2050, prioritizing energy efficiency particularly in the building sector. Despite significant policies, such as the EU Green Deal and Renovation Wave initiative, the rate of deep energy renovations remains insufficient, with only 0.2% annually versus the 3% required. Multiple barriers hinder the progress of deep energy renovations (DERs), including fragmentation among stakeholders, the limited coordination of RDI (Research, Development, and Innovation) efforts, and a lack of systemic approaches. The objective of this paper is to illustrate a holistic methodological approach for enhancing the DER market uptake based on transition pathways theory (TPT) and is designed to drive structural evolution in DER markets aimed at overcoming their main current constraints. To this end, five key transition pathways are outlined—namely institutionalization, clusterization, capitalization, digitalization, and exploitation—and are conceived for fostering coordination, integration, promotion, and efficient scaling of innovations along the whole DER value chain. This approach was tested in seven EU building market ecosystems under the H2020 re-MODULEES project, aimed at developing a market activation platform conceived as a digital enabler for next-generation One-Stop Shops (OSSs). This project yielded practical evidence on the potentiality of the TPT frame to strengthen and empower local ecosystems through stakeholders’ engagement and cooperation. The findings suggest that the TPT-based approach tested in re-MODULEES can effectively address structural challenges in diverse DER renovation markets, and for this reason, it may be also tested and extended in other ecosystems across Europe in order to be validated as a strategic approach at the EU level for facilitating the transition to low-carbon buildings. Full article

The formal study of computer malware was initiated in the seminal work of Fred Cohen in the mid-80s, who applied elements of Computation Theory in the investigation of the theoretical limits of using the Turing Machine formal model of computation in detecting viruses. Cohen gave a simple but realistic formal definition of the characteristic actions of a computer virus as a Turing Machine that replicates itself and proved that detecting this behaviour, in general, is an undecidable problem. In this paper, we complement Cohen’s approach by providing a simple generalization of his definition of a computer virus so as to model any type of malware behaviour and showing that the malware/non-malware classification problem is, again, undecidable. Most importantly, beyond Cohen’s work, our work provides a generic theoretical framework for studying anti-malware applications and identifying, at an early stage, before their deployment, several of their inherent vulnerabilities which may lead to the construction of zero-day exploits and malware strains with stealth properties. To this end, we show that for any given formal system, which can be seen as an anti-malware formal model, there are infinitely many, effectively constructible programs for which no proof can be produced by the formal system that they are either malware or non-malware programs. Moreover, infinitely many of these programs are, indeed, malware programs which evade the detection powers of the given formal system. Full article

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications. Full article

Considering the power demand in high-altitude special environmental areas and the peak-regulation issues in the power system caused by the uncertainties associated with wind and photovoltaic power as well as load, a gas–hydro coordinated peak-shaving method that considers source-load uncertainty is proposed. Firstly, based on the regulation-related characteristics of hydropower and gas power, a gas−hydro coordinated operation mode is proposed. Secondly, the system operational risk caused by source-load uncertainty is quantified based on the Conditional Value-at-Risk theory. Then, the cost of deep peak shaving in connection with gas-fired power generation is estimated, and a gas−hydro coordinated peak-shaving model considering risk constraints and deep peak shaving is established. Finally, a specific example verifies that the proposed gas−hydro coordinated peak-regulation model can effectively improve the economy of the system. The total system profit increased by 36.03%, indicating that this method enhances the total system profit and achieves better peak-shaving effects. Full article

Photovoltaic (PV) energy is gaining traction due to its direct conversion of sunlight to electricity without harming the environment. It is simple to install, adaptable in size, and has low operational costs. The power output of PV modules varies with solar radiation and cell temperature. To optimize system efficiency, it is crucial to track the PV array’s maximum power point. This paper presents a novel fixed-point FPGA design of a nonlinear maximum power point tracking (MPPT) controller based on synergetic control theory for driving autonomously standalone photovoltaic systems. The proposed solution addresses the chattering issue associated with the sliding mode controller by introducing a new strategy that generates a continuous control law rather than a switching term. Because it requires a lower sample rate when switching to the invariant manifold, its controlled switching frequency makes it better suited for digital applications. The suggested algorithm is first emulated to evaluate its performance, robustness, and efficacy under a standard benchmarked MPPT efficiency (${\eta }_{MPPT}$) calculation regime. FPGA has been used for its capability to handle high-speed control tasks more efficiently than traditional micro-controller-based systems. The high-speed response is critical for applications where rapid adaptation to changing conditions, such as fluctuating solar irradiance and temperature levels, is necessary. To validate the effectiveness of the implemented synergetic controller, the system responses under variant meteorological conditions have been analyzed. The results reveal that the synergetic control algorithm provides smooth and precise MPPT. Full article

Improving well-being at work is crucial for increasing employment among construction workers and ensuring the healthy and sustainable development of China’s construction industry. Construction workers generally experience insufficient sleep, heavy workloads, work–family conflict, etc. At present, the new generation of construction workers (NGCWs) born after 1980 is gradually becoming the main force at construction sites in China. The value concepts, life attitudes, and personality traits of this group are significantly different from those of the older generations. Given the generational differences among construction workers, this study focuses on the formation mechanism and explores the evolutionary laws of well-being at work among NGCWs. In-depth interviews with 23 new-generation construction workers were conducted, and data analysis followed a three-step coding process based on grounded theory. Then, a three-stage formation mechanism model was constructed through continuous analysis. Finally, the casual and stock–flow diagrams were drawn and simulated on the basis of the system dynamics. The results indicated that well-being at work was directly influenced by internal work motivation. Both individual characteristics and the external environment played a role in shaping work motivation; however, the key difference lay in the fact that the external environment impacted internal work motivation through the mediation of individual-environment matching. Moreover, enhanced well-being at work led to a higher level of workers’ internal needs, which, in turn, further increased the complexity of individual-environment matching. Meanwhile, individual characteristics affected the process by which motivation was transformed into well-being at work. The level of well-being showed an upward tendency under the synergistic influence of different factors; the increasing rate was high and subsequently low. Furthermore, salary, job competence, and belonging needs can significantly affect well-being at work. These findings provide theoretical support and practical references to China’s construction companies and government departments for the purpose of improving NGCWs’ well-being at work. Full article

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models. Full article

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%. Full article

Due to their broad applicability, gauge theories (GTs) play a crucial role in various areas of physics, from high-energy physics to condensed matter. Their formulations on lattices, lattice gauge theories (LGTs), can be studied, among many other methods, with tools coming from statistical mechanics lattice models, such as mean field methods, which are often used to provide approximate results. Applying these methods to LGTs requires particular attention due to the intrinsic local nature of gauge symmetry, how it is reflected in the variables used to formulate the theory, and the breaking of gauge invariance when approximations are introduced. This issue has been addressed over the decades in the literature, yielding different conclusions depending on the formulation of the theory under consideration. In this article, we focus on the mean field theoretical approach to the analysis of GTs and LGTs, connecting both older and more recent results that, to the best of our knowledge, have not been compared in a pedagogical manner. After a brief overview of mean field theory in statistical mechanics and many-body systems, we examine its application to pure LGTs with a generic compact gauge group. Finally, we review the existing literature on the subject, discussing the results obtained so far and their dependence on the formulation of the theory. Full article

This research introduces the first design concept for a ducted coaxial-rotor amphibious spherical robot (BYQ-A1), utilizing the principle of variable mass control. It investigates whether the BYQ-A1’s variable-mass slider has a certain regularity in its impact on the aerodynamic properties of the BYQ-A1. Utilizing the Blade Element Momentum Theory (BEM) and Wall Jet Theory, an aerodynamic calculation model for the BYQ-A1 is established. An orthogonal experimental method is used to conduct tests on the impact of the variable-mass slider on the aerodynamic properties of the ducted coaxial-rotor system and validate the effectiveness of the aerodynamic calculation model. The results show that the slider generates an internal ground effect and ceiling effect within the BYQ-A1 that enhance the lift of the upper and lower rotors when the robot is equipped with it. The increased total lift compensates for the additional aerodynamic drag caused by the presence of the slider. This novel finding provides guidance for the subsequent optimization design and control method research of the BYQ-A1 and also offers valuable references for configuration schemes that incorporate necessary devices between coaxial dual rotors. Full article

Air switchgear is an important power equipment in the transmission, transformation, and distribution process of the power system. Insulation defects can lead to partial discharge, which is one of the primary causes of air switchgear failure. Current monitoring methods primarily rely on detecting ultra-high frequency or ultrasonic signals generated by partial discharge to identify insulation defects. However, these methods are prone to external signal interference, resulting in substantial detection errors. Based on gas discharge theory and engineering practice, this paper uses three typical defects to represent the main insulation defects of air switchgear, namely metal protrusion defects, insulation layer air gap defects, and metal particle defects. After that, the validity of the numerical model to describe the partial discharge process of air switchgear insulation defects is verified by the volt-ampere characteristic curve. The discharge process of three typical defect models was investigated by using the numerical model, and the variation curves of the volume fractions of CO, NO₂, and O₃ gases at different voltage levels and different discharge durations were obtained. After analysis, the volume fractions of the three characteristic gases are unique under different defect models and partial discharge quantities. Finally, this paper designed a partial discharge inversion method based on characteristic gases, and fitted time-domain regression equations and partial discharge inversion regression equations based on the changes in volume fractions of the three characteristic gases measured. The research results of this paper provide a theoretical basis for online detection of partial discharge in high-voltage air switchgear through characteristic gases. The method proposed in this paper can also be applied to other gas-insulated equipment, such as GIS, metal-enclosed switchgear, and vacuum circuit breakers. Full article

Amid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being. Full article

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities. Full article

This study analyzes the dynamic relationships among economic growth, international tourism, renewable energy use, and carbon emissions in the member countries of the Central American Inter-American Integration System (SICA). Conducted using a panel dataset, it was found that economic growth and international tourism increased carbon emissions. On the other hand, it was found that renewable energy usage significantly reduced emissions. The study evaluates the environmental impacts of economic growth in the context of the environmental Kuznets curve and carbon intensity theory. The study also suggests that low-carbon and renewable energy-based tourism practices strengthen both environmental sustainability and long-term economic resilience within the scope of the Energy Transition Theory. In the study, a panel dataset consisting of 140 observations from SICA countries from 2001 to 2020 was used, and the feasible generalized least squares (FGLS) model was applied. As a result, the long-term relationships between the variables were verified with the help of panel cointegration tests. The findings reveal that holistic and long-term policy strategies based on the interaction of economic growth, renewable energy use, and the tourism sector should be developed in order to achieve sustainable development goals in SICA countries. Full article