Search Results (6,627)

In the context of China’s “Dual Carbon” goals, the composite policy mechanism combining carbon trading and carbon taxation is widely considered a key pathway to achieve emission reductions. Although households are a major source of carbon emissions, their consumption behaviour has long remained outside the mainstream carbon reduction system, as existing policies focus primarily on enterprises and lack sufficient household-level participation and incentive mechanisms. Because China has not yet implemented an actual carbon tax, this study uses household high-carbon consumption dependency (HCD) as a proxy variable to capture the hypothetical administrative pressure that a carbon tax would impose on high-carbon consumption. Based on the concept of “Carbon Inclusion”, we construct an analytical framework for a composite mechanism that combines the carbon trading pilot policy (ETS) with this carbon-tax proxy. Using data from the China Family Panel Studies (CFPS) and a two-way fixed-effects panel model, we empirically test the impact of this composite mechanism on household carbon emissions (total volume) and carbon intensity. The findings show that, while the composite mechanism does not lead to a statistically significant reduction in total household carbon emissions, it effectively lowers household carbon intensity by restraining high-carbon consumption and optimizing the consumption structure. This decoupling of intensity from total volume occurs because the mechanism reduces the share of high-carbon consumption (a compositional effect) but does not suppress total consumption growth (a scale effect). This result remains robust across multiple tests, confirming the policy effectiveness of the composite mechanism at the micro-individual level. By reducing carbon intensity without suppressing total consumption, this mechanism contributes directly to sustainable development, aligning with UN Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action). The main contributions of this paper are threefold: (1) it moves beyond traditional single-policy or single-agent studies by linking a carbon-trading-and-proxy-carbon-tax composite mechanism with household carbon consumption; (2) it explores a Carbon Inclusion pathway that connects households, enterprises and the nation; and (3) it provides empirical support and a theoretical reference for improving household-level emission reduction policies and promoting public participation in achieving the “Dual Carbon” goals. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

N-nitrosamine compounds, a disinfection byproduct of chlorination and chloramination in water and wastewater treatment processes, are classified as a probable human carcinogen. The current review focuses on analysing the feasibility of membrane technology while examining the challenges and opportunities in the elimination of N-nitrosamine compounds, particularly NDMA, from wastewater. To systematically attain this goal, this paper uses a systematic literature review that screens and critically assesses peer-reviewed experimental and numerical published papers on N-nitrosamine removal, occasioning in 37 high-quality papers for synthesis. In this regard, a detailed analysis of experimental and numerical studies elaborates that conventional RO membranes often introduce a specific low removal of NDMA from wastewater due to their low molecular weight and neutral charge, which addresses a critical issue. The critical analysis of the experimental and numerical studies depicts that the membrane type, structural properties, and chemical interaction have a key role in the removal of NDMA. To systematically improve the NDMA removal, a wide set of investigations have explored innovative treatment methods, including Nano pore plugging and hydrophilic coatings. This demonstrates potential for improving NDMA removal, albeit at the penalty of reduced water permeability. Additionally, the heat treatment of membranes has attained a notable improvement, ensuing in NDMA rejection of up to 92%. A multi-stage RO configuration model has depicted a maximum NDMA rejection of 93.1%. The future research should focus on investigating possible improvement of NDMA removal from wastewater such as Nano pore plugging and hydrophilic coatings, besides optimising RO configurations and membrane designs with a deeper understanding of membrane fouling. Full article

The rapid emergence of multi-drug resistant (MDR) bacteria has become a major health concern, driving the need to identify new antimicrobial resources. Recently, endophytes, inhabiting in internal tissues of medicinal plants, have drew important interest from the scientific community, as reservoirs of bioactive metabolites. Numerous studies highlight the symbiotic relationship between plants and their endophytes, in which these microorganisms produce antimicrobial compounds, helping the host plant’s defense against pathogens. Plantago major (commonly known as plantain) is widely recognized for its therapeutic properties, especially for its antimicrobial properties. In this study, endophytic fungi were isolated from Plantago major, morphologically characterized and identified using ITS sequencing. Their antibacterial activity was assessed using the agar diffusion assay. In total, 21 endophytic fungal isolates were obtained from different plant tissues, including leaves, stems, roots, and flowers. Antibacterial assays against methicillin-resistant Staphylococcus aureus (MRSA) were investigated on PDA, SDA, and CDA media. Amongst the isolates, nine strains (MD-H1, MD-L1, MD-L2, MD-L3, MD-L4, MD-L5, MD-R1, MD-T1, MD-T2, and MD-T10) showed medium to strong antibacterial effects, with inhibition zones exceeding 15 mm. The result suggests that endophytic fungi associated with Plantago is a valuable source of anti-MRSA compounds. Further work will focus on identifying the secondary metabolites responsible for this activity and elucidating their chemical structures, providing a basis for the development of new potent antibiotic agents. Full article

This study addresses the growing necessity to understand how artificial intelligence (AI) competency and soft skills jointly influence organizational innovation and performance in the era of digital transformation. Despite the rapid adoption of AI technologies across industries, organizations continue to face significant challenges in effectively integrating technical AI capabilities with essential human-centric soft skills such as communication, adaptability, and leadership. This gap often limits the realization of AI-driven value and sustainable competitive advantage. The primary challenge in this research area is the lack of comprehensive models that simultaneously examine AI competency and soft skills within a unified framework, particularly in emerging economies where digital maturity varies widely. Existing studies tend to focus either on technical competencies or behavioral factors in isolation, leading to fragmented insights. To address these challenges, this study proposes a novel integrated research model that examines the combined effects of AI competency and soft skills on innovation outcomes and organizational performance. The model is empirically validated using structural equation modeling (SEM), providing robust evidence of the interrelationships among key constructs. The findings reveal that both AI competency and soft skills significantly contribute to innovation capability, which in turn enhances organizational performance. The study offers important theoretical and practical implications by bridging the gap between technical and human dimensions of AI adoption, thereby providing a more holistic understanding of digital transformation success. Full article

The rapid expansion of wireless communication in urban environments requires antenna systems that balance high electromagnetic performance with stringent aesthetic and security constraints. This review examines recent advances in concealed antenna technologies integrated into building structures, with a focus on performance variation, material-induced attenuation, and emerging concealment strategies. Techniques such as transparent conductors on glass, structural embedding within walls, and camouflage-based designs are shown to significantly influence resonance behavior, radiation efficiency, and pattern characteristics compared to free-space operation. Despite these challenges, optimized solutions including transparent conductive oxide arrays, wideband embedded antenna geometries, and metasurface-enhanced window structures can partially recover performance while maintaining optical transparency above 70%. Material loading effects are found to induce resonant frequency shifts of approximately 10–44%, depending on dielectric properties and environmental conditions. Transparent antenna arrays achieve gains ranging from 0.34 to 13.2 dBi, while signal-transmissive wall systems demonstrate transmission improvements of up to 22 dB relative to untreated building materials. These technologies enable a wide range of applications, including 5G and beyond-5G cellular networks across sub-6 GHz and millimeter-wave bands, as well as Internet of Things systems and smart city infrastructure. However, key challenges remain, including the need for comprehensive characterization of building material electromagnetic properties, optimization of multilayer structural environments, and the development of standardized design and evaluation methodologies. This review provides a unified framework for understanding the tradeoffs associated with antenna concealment and identifies critical research directions for the development of building-integrated wireless systems in next-generation communication networks. Full article

Infrared small-target segmentation (IRSTS) is crucial for a wide range of applications, including maritime search-and-rescue operations and intelligent traffic surveillance. However, current deep learning methods struggle with dynamic scale variations in infrared small targets, resulting in false detections and missed detections, alongside inadequate core localization accuracy. To address these challenges, we propose an infrared small-target segmentation framework founded on morphological attention and an energy core loss function, IRSTS_Unet. Specifically, we design a Dynamic Shape-adaptive Deformable Attention Module (DSDAM), which achieves parameterized feature extraction via “initial localization–offset deformation–precise sampling”. This approach enables the network to differentially focus on target cores and background cues to suppress clutter. To improve the efficiency of multi-scale feature aggregation, we embed the DSDAM within both the feature extraction and cross-layer fusion stages. Furthermore, we formulate a Core Energy-aware Core-Priority loss (CECP-Loss) function that incorporates the energy prior distribution of small targets, effectively counteracting the “core dilution” phenomenon endemic to conventional loss functions. Through extensive experiments on multiple public datasets, we demonstrate that IRSTS_U-Net outperforms state-of-the-art approaches in terms of both detection accuracy and robustness. Full article

Epidemiological studies have demonstrated that kidney aging is a risk factor for acute kidney injury (AKI) and chronic kidney disease (CKD). Therefore, understanding the mechanisms of kidney aging is key to designing novel anti-kidney aging strategies. In this regard, animal models of kidney aging are essential tools. In this review article, we focus on D-galactose (D-gal)-induced accelerated aging in rodents. This animal aging model is a popular and widely used experimental method in the field of aging and aging-related degenerative disorders. It has been shown that the major characteristics of the D-gal-induced aging process are increased oxidative stress, decreased antioxidant enzymes, elevated cell death, increased tissue fibrosis, and accumulation of inflammatory mediators. This review focuses on D-gal-induced kidney aging in mice and rats, with discussions on both kidney aging mechanisms and anti-kidney aging regimens using this model. It is our belief that D-gal induction of accelerated kidney aging will continue to be used as a convenient platform for elucidating kidney aging mechanisms and exploring novel anti-kidney aging targets that may slow down kidney aging and retard the development of aging-related renal disorders. Full article

Bioaerosol emissions from biological treatment processes like composting, livestock operations, and wastewater plants pose notable occupational and environmental health risks. Biofiltration is a common mitigation measure for gaseous pollutants, but its effectiveness in controlling bioaerosols is less studied. This review synthesizes current evidence on biofiltration for the removal of bioaerosols. Findings indicate that biofiltration can significantly reduce emissions from waste-related biological processes, although results vary widely and depend heavily on design and operational factors. In composting, agricultural, and wastewater treatment contexts, fungal bioaerosols are consistently removed with high efficiency, often over 90%. Conversely, bacterial removal shows greater variability, from negligible to above 90%, influenced primarily by airflow rate, bed depth, and media stability. Systems with residence times of tens of seconds and bed depths of at least 1 m tend to reliably reduce bacterial counts, whereas undersized, high-flow systems experience marked efficiency losses. The choice of packing material is also crucial; mature, stable media maintain performance, whereas nutrient-rich or unstable substrates can lead to fungal emissions, turning the biofilter into a secondary source. Data on endotoxin removal are limited and remain insufficient for firm design recommendations. Overall, biofiltration’s effectiveness depends on complex interactions among physical retention, biological stability, and design. These insights emphasize the need for future research to focus on standardized, performance-based design criteria supported by consistent reporting and full-scale validation. Full article

This study examines workplace bullying within the middle-management tier of a large Gauteng-based retail organisation in South Africa, with a focus on structural organisational pressures and perceptual differences among managers. While traditional research often emphasises individual personality traits or victim demographics, this study explores how organisational conditions—particularly the “middle management squeeze” and performance-driven Key Performance Indicators (KPIs)—are reflected in workplace behaviours. Grounded in a positivist paradigm, a quantitative cross-sectional survey was conducted among a probability-based sample of 253 retail managers. Data were collected using the Negative Acts Questionnaire (NAQ-22) and analysed using Exploratory Factor Analysis (EFA) and nonparametric inferential tests. The findings indicate that task-related negative acts, such as micromanagement (M = 2.00) and persistent monitoring (M = 1.87), are frequently experienced. EFA identified two dimensions—General Harassment and Managerial Control—accounting for 62% of the total variance. Inferential results show that perceptions of General Harassment differ significantly across educational groups (p = 0.0268), whereas perceptions of Managerial Control remain consistent (p = 0.3378). These findings indicate that social forms of incivility are interpreted differently across educational cohorts, while task-related managerial practices are widely normalised. The study highlights the importance of understanding workplace bullying as both a structural and perceptual phenomenon and underscores the need for organisational interventions that address systemic pressures rather than relying solely on individual-level approaches. Full article

Lighting quality in interior spaces is not determined solely by horizontal illuminance at the workplace, but to a large extent by the spatial distribution of light, in particular by the luminance of ceilings and walls. Building on classical principles of lighting technology and visual perception, this article examines the influence of the ratio of indirect to direct lighting on the perception of room brightness, the spatial impression, and overall preference. To this end, two complementary studies were conducted: a visual assessment of realistic room simulations and a user study in a real meeting room with variable illuminance levels and systematically varied proportions of indirect and direct lighting. The results consistently show that perceived room brightness and user preference correlate much more strongly with the illumination of ceilings and walls than with the horizontally measured illuminance on the table, which was kept constant. A balanced ratio of indirect to direct light—typically in the range of approximately 35% to 65% indirect lighting—is preferred by users, whereas predominantly direct or nearly purely indirect lighting is associated with lower acceptance. The study clearly demonstrates that existing standards, which primarily focus on horizontal illuminance, neglect essential aspects of lighting quality. The findings highlight the need to systematically integrate light distribution, vertical illuminance, and spatial–psychological effects into lighting design, evaluation, and standardization in order to achieve visually comfortable, widely accepted, and spatially appropriate lighting solutions. Full article

Organic Rankine Cycles (ORCs) are widely recognized as an effective solution for Waste heat recovery (WHR). However, the design and optimization of these systems must address the tradeoff between computational efficiency and the need to capture complex component behavior. This requires moving beyond purely energetic 0D modeling approaches to account for constructional, spatial, and operational constraints. This work presents a novel modeling framework with a specific focus on the expansion device. Radial inflow turbine stages are selected for their capability to achieve high pressure ratios while maintaining compactness and high efficiency. Heat exchangers follow a generic one-dimensional counterflow configuration, with a shell-and-tube geometry adopted for sizing purposes. The turbine stages are modeled by resolving several internal sections in order to capture local thermofluid dynamic conditions. The framework predicts turbine efficiency and incorporates a newly developed formulation for shock-induced losses, improving performance prediction under trans-sonic flow conditions. After validation against experimental data, the model is applied to a WHR system integrated with an internal combustion engine fueled by biofuels. The results highlight the existence of optimal operating conditions arising from competing physical mechanisms. The analysis also shows the transition from single-stage to two-stage turbine configurations at high pressure ratios and emphasizes the role of real gas effects in determining stage performance and optimal expansion distribution. The results of simulations carried out for three different working fluids (ethanol, toluene, and R1234ze(E)) highlight that the available mechanical power ranges from 10 to 22 kW for single-stage turbine configurations and from 24 to 36 kW for two-stage configurations, with total system volumes varying between approximately 600 and 9000 L. Among the working fluids considered here, ethanol provides the best overall performance for the present case study. Overall, the proposed approach provides a reliable and computationally efficient tool for the preliminary design and optimization of ORC-based WHR systems. Full article

Background: Wireless capsule endoscopy is widely used for diagnosing gastrointestinal diseases, but manual interpretation of capsule videos is time-consuming and can vary between clinicians. Artificial intelligence has been increasingly studied to support capsule analysis and reduce clinical workload. This systematic literature review and meta-analysis summarizes current evidence on artificial intelligence methods applied to wireless capsule endoscopy, with a focus on diagnostic performance, validation strategies, and clinical readiness. Methods: A systematic search was conducted in PubMed, Scopus, Embase, Web of Science, and Google Scholar. Original journal articles were included based on predefined eligibility criteria. The reviewed studies addressed multiple artificial intelligence tasks, including detection, classification, segmentation, and localization of gastrointestinal abnormalities. Results: A total of 72 studies were included. Meta-analysis using random effects models showed high pooled diagnostic performance across clinical indications and gastrointestinal tract locations, with the strongest results reported for bleeding and vascular lesions and more variable performance for inflammatory bowel disease and mixed abnormality categories. The review also identified important clinical and technical barriers that may limit reliability and slow clinical adoption. These included limited external validation, small patient cohorts, retrospective study designs, and inconsistent reporting and evaluation practices. Conclusions: Artificial intelligence methods show strong potential to support wireless capsule endoscopy interpretation. Based on the findings, we propose practical recommendations to improve study design and validation. If these recommendations are applied, future studies may report more robust and reliable results, supporting better translation into clinical workflows. Full article

Model-Based Diagnosis (MBD) is widely used to identify minimal conflicts and repair actions in constraint-based systems. Recent advances in parallel reasoning have significantly reduced runtime in large-scale models through speculative and multicore execution strategies. However, existing approaches primarily focus on computational efficiency and implicitly assume that minimal diagnoses are inherently suitable explanations for human decision makers. In complex configuration environments, minimality does not necessarily imply interpretability, as diagnoses may involve structurally dispersed or semantically heterogeneous constraints. To address this limitation, this paper introduces a multi-objective explainability-aware framework for parallel MDB. Diagnosis selection is formulated as a Pareto optimization problem balancing total computational cost and a formally defined interpretability penalty. Interpretability is quantified using graph-based structural dispersion, semantic entropy, hierarchical complexity, and ambiguity metrics. The proposed E-ParetoDiag algorithm computes non-dominated diagnoses and identifies balanced knee-point solutions without modifying correctness guarantees of underlying diagnosis algorithms. Experimental evaluation on large-scale benchmark datasets demonstrates a measurable trade-off between runtime and interpretability, particularly in dense constraint systems. Comparative analysis against classical selection strategies shows that the proposed approach reduces structural dispersion by up to 18% while increasing computational cost by only 7%. Statistical validation confirms that these improvements are significant (p < 0.01) in medium- and high-density scenarios. The results indicate that aggressive parallelism may improve computational efficiency while increasing explanation complexity, highlighting the need for multi-objective selection strategies. Overall, the proposed framework extends scalable symbolic reasoning toward a human-centered diagnosis paradigm and establishes a principled foundation for explainability-aware optimization in constraint-based systems. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) poses a substantial public health challenge in China owing to its increasing prevalence and substantial economic burden. In response, the diagnosis–intervention packet (DIP) payment reform was implemented to control healthcare costs and enhance service efficiency. Methods: To evaluate the effect of the DIP reform on medical costs, hospitalization days, and individual out-of-pocket payments for COPD inpatients in M City, a pilot city in central China, we conducted an interrupted time series (ITS) analysis using monthly reimbursement records from January 2020 to December 2023. The study included 84,410 hospitalized patients from a city-wide database of 3,241,233 inpatient records with COPD who met the inclusion criteria. The analysis focused on the total healthcare costs, length of stay, and individual out-of-pocket costs. Results: The DIP reform resulted in a 3.7% reduction (95% CI: 0.9% to 6.5%) in the total hospitalization costs in the first month post-reform, with a sustained monthly decline of 0.8% (95% CI: 0.5% to 1.1%). The length of stay decreased from 9.53 (95% CI: 9.31 to 9.75) to 8.74 days (95% CI: 8.62 to 8.86). Conversely, the proportion of out-of-pocket payments relative to total costs increased. Conclusions: While the DIP reform effectively reduced hospitalization costs and days, it led to an increase in individual out-of-pocket payments. Future research should focus on optimizing payment rules, enhancing the supervision of medical services, and refining health insurance policies to achieve the reform’s objectives better and alleviate the financial burden on patients. Full article