Search Results (7,513)

The study analyzes the long-term development prospects of the Latvian dairy sector until 2032, using an integrated modeling approach that combines the AGMEMOD partial equilibrium model with the TOPSIS multi-criteria evaluation method. The study addresses the main challenge facing the sector—how to maintain productivity in the context of structural consolidation and increasing environmental requirements. The AGMEMOD model was recalibrated using updated data for Latvia for 2015–2023. Two scenarios were developed: A1 “Targeted and intensive farm modernization” and A2 “Limited farm modernization”. Scenario A1 is characterized by gradual technological adoption, leading to higher productivity while keeping total milk production almost unchanged relative to the Baseline scenario, whereas scenario A2 reflects slower modernization and reduced productivity growth. The TOPSIS evaluation identified scenario A1 as the most attractive alternative, as it combines productivity gains and greater adaptability to policy and environmental requirements. The results confirm that technological modernization and flexible policy mechanisms are essential to maintain the competitiveness and productivity performance of Latvia’s dairy sector. The integrated AGMEMOD–TOPSIS approach provides a methodological tool for evidence-based policy analysis and strategic planning in agricultural market management. Full article

Accurate patent valuation remains a persistent challenge in intellectual property management, particularly in the food industry, where technological homogeneity and rapid innovation cycles introduce substantial noise into observable performance indicators. Traditional valuation approaches, whether based on subjective expert judgment or citation-based metrics, often struggle to effectively reduce information uncertainty in this context. To address this limitation, this study proposes an objective, data-driven patent valuation framework grounded in information theory. We construct a multidimensional evaluation system comprising nine indicators across technological, legal, and economic dimensions and apply it to a large-scale dataset of 100,648 invention patents. To address the heavy-tailed nature of patent indicators without sacrificing the information contained in high-impact outliers, we introduce a square-root transformation strategy that stabilizes dispersion while preserving ordinal relationships. Indicator weights are determined objectively via Shannon entropy, capturing the relative scarcity and discriminatory information content of each signal, after which comprehensive value scores are derived using the TOPSIS method. Empirical results reveal that the entropy-based model assigns dominant weights to so-called “costly signals”, specifically PCT applications (29.53%) and patent transfers (24.36%). Statistical correlation analysis confirms that these selected indicators are significantly associated with patent value ($p<0.001$), while bootstrapping tests demonstrate the robustness of the resulting weight structure. The model’s validity is further evaluated using an external benchmark (“ground truth”) dataset comprising 55 patents recognized by the China Patent Award. The proposed framework demonstrates substantially stronger discriminatory capability than baseline methods, awarded patents achieve an average score 2.64 times higher than that of ordinary patents, and the enrichment factor for award-winning patents within the Top-100 ranking reaches 91.5. Additional robustness analyses, including benchmarking against the Weighted Sum Model (WSM), further confirm the methodological stability of the framework, with sensitivity analysis revealing an exceptional enrichment factor of 183.1 for the Top-50 patents. These findings confirm that the Entropy–TOPSIS framework functions as an effective information-filtering mechanism, amplifying high-value patent signals in noise-intensive environments. Consequently, the proposed model serves as a generalizable and theoretically grounded tool for objective patent valuation, with particular relevance to industries characterized by heavy-tailed data and high information uncertainty. Full article

Aquatic animal products are vital to global food security and nutrition, necessitating accurate, scalable, and non-destructive methods for quality assessment in aquaculture. Conventional techniques such as dissection and biochemical analysis are invasive, labor-intensive, and unsuitable for real-time or high-throughput decision-making. This review synthesizes six major categories of non-destructive technologies—electrical, spectroscopic, natural sensory, acoustic, radiographic, and infrared and microwave—classified by their underlying sensing mechanisms and therefore differing measurement capabilities and deployment feasibilities. To support objective technology selection, an Analytic Hierarchy Process (AHP) framework was developed using general performance criteria (cost, accuracy, speed, usability) and one decision-critical application-specific criterion (non-invasiveness), and was demonstrated for ovarian maturation staging in mud crabs by ranking 19 candidate techniques. Accuracy had the highest weight (0.416), but non-invasiveness (0.224) and usability (0.197) substantially influenced the final ranking, illustrating how operational and welfare constraints could shift preferred solutions despite differences in analytical accuracy. Based on the global priority weights (GA), computer vision (CV) was identified as the most suitable option (GA = 0.076), balancing affordability, throughput, ease of deployment, and animal welfare compatibility, whereas high-end modalities such as nuclear magnetic resonance (NMR; GA = 0.073) and computed tomography (CT; GA = 0.070) were constrained by cost and operational complexity. Overall, this review–AHP–case study pipeline provides a transparent and reproducible decision-support basis for selecting non-destructive technologies across aquaculture species and quality targets. Full article

The global energy transition to renewable energy sources requires a rigorous assessment of the environmental impacts of all system components, including power electronics converters (PECs), which play a critical role in adapting generated energy to grid and load requirements. This paper presents a comprehensive comparative assessment of conventional PECs used in renewable energy systems, with a focus on DC-AC, DC-DC, and AC-DC converters. The study combines life cycle assessment (LCA) with the Circular Energy Sustainability Index (CESI) to evaluate both environmental performance and material circularity. The LCA is conducted using a functional unit defined as a representative converter, within consistent system boundaries that encompass material extraction, manufacturing, and end-of-life stages. This approach enables comparability among converter topologies but introduces limitations related to the exclusion of application-specific design optimizations, such as maximum efficiency, spatial constraints, and thermal management. CESI is subsequently applied as a decision-support tool to rank converter technologies according to sustainability and circularity criteria. The results reveal substantial differences among converter types: the controlled rectifier exhibits the lowest environmental impact and the highest circularity score (95.3%), followed by the uncontrolled rectifier (69.3%), whereas the inverter shows the highest environmental burden and the lowest circularity performance (38.6%), primarily due to its higher structural complexity and the material and manufacturing intensity associated with its switching architecture. Full article

Fungal bioaerosols play a critical ecological and health role in intensive poultry production systems. However, their dynamic characteristics and community succession patterns in confined cage environments during winter remain poorly understood. This study investigated a typical confined broiler house in Hebei Province, China, during winter. A combined approach of Andersen six-stage sampling, colony counting, and Internal Transcribed Spacer (ITS) high-throughput sequencing was employed to comprehensively analyze the concentration, particle size distribution, diversity, and community composition of fungal bioaerosols across three key growth stages: 7 days (brooding phase), 21 days (growing phase), and 35 days (finishing phase). The results revealed a significant increasing trend in fungal aerosol concentration as the rearing cycle progressed, increasing from 1125 ± 125 CFU/m³ at day 7 to 3872 ± 565 CFU/m³ at day 35 (p < 0.001), reaching high-risk exposure levels in the later stages. Small-sized fungal bioaerosols (<4.7 μm) were dominant across all stages (54.35–65.50%), with the highest proportion observed at day 21, indicating their potential for deep respiratory deposition and long-distance airborne transmission. The number of Operational Taxonomic Units (OTUs), along with Chao1 and Shannon indices, increased significantly with bird age (p < 0.001), demonstrating a clear community succession from early-stage yeast-dominated forms (e.g., Diutina, Blumeria) to mid- and late-stage assemblages dominated by filamentous fungi (e.g., Aspergillus, Cladosporium, Alternaria). Notably, several zoonotic pathogenic genera were detected throughout all rearing stages, highlighting the potential risks of airborne fungi to animal health, occupational exposure, and environmental safety under winter ventilation restrictions. This study characterizes a stage-dependent pattern of increasing airborne fungal concentrations accompanied by shifts in particle size distribution and community composition under winter confined conditions. The findings provide a crucial scientific basis for optimizing winter ventilation and environmental management strategies, improving environmental control technologies, establishing airborne biosafety standards, and developing targeted fungal monitoring and prevention technologies. Full article

This research examines the influence of environmental, social, and governance (ESG) strategies on corporate financial performance (CFP) in China’s automotive industry, characterized by intense regulatory pressure and fast-paced technological transformation. Using an unbalanced panel dataset of A-share listed automotive firms from 2009 to 2024, this paper combines ESG scores from the Huazheng ESG index with firm-level financial data from CSMAR. CFP is measured through both accounting-based (ROA) and market-based (Tobin’s Q) indicators. Panel regression models are applied to evaluate the influence of overall ESG performance and the three individual pillars, and to assess heterogeneity across ownership types, firm type, and firm age. The results show that ESG performance is significantly and positively associated with ROA, but is insignificantly associated with Tobin’s Q. It is suggested that ESG engagement improves accounting profitability but is not fully reflected in the capital market. Among the three ESG pillars, governance shows the strongest positive link with ROA, while environmental and social performance are weakly associated with ROA. Furthermore, the heterogeneity study shows that the positive relationship between ESG and CFP is more pronounced for non-state-owned firms, vehicle manufacturers, or mature firms. Overall, this paper presents fresh evidence on whether and how ESG initiatives can facilitate sustainable value in China’s automotive sector, offering insights for policymakers and management that may help this industry achieve sustainable growth. Full article

This study adopts a biochemical approach to sequester CO₂ while producing biomass rich in protein and lipids, using an adapted strain of Chlorella vulgaris (ALE-Cv), which had previously evolved to tolerate a gas mixture containing 10% CO₂ and 90% air. The research studied the operating parameters of the batch photobioreactor for ALE-Cv to evaluate the effects of inoculum size, photoperiod, light intensity, pH of culture, and CO₂ supply rate on biomass productivity and CO₂ bio-fixation rate. The optimal conditions were identified as 16:8 h light–dark cycles, 5000 lux, pH 7, 20 mL of 10 g/L inoculum, and 0.6 VVM; the system achieved a maximum total biomass production of 7.03 ± 0.21 g/L with a specific growth rate of 0.712 day⁻¹, corresponding to a CO₂ bio-fixation of 13.4 ± 0.45 g/L in batch cultivation. While the pre-adapted strain of Chlorella vulgaris under the same operating conditions, except for the gas supply, which was air, achieved a maximum total biomass production of 0.52 ± 0.008 g/L, and the total CO₂ bio-fixation was 1.036 ± 0.021 g/L during 7-day cultivation. A novel semi-continuous harvesting process, with and without nutrient addition, was also investigated to maximise biomass yield and enable water recycling for culture media. The maximum biomass production in semi-continuous harvesting process with and without nutrition added was 5.29 ± 0.09 and 9.91 ± 0.11 g/L, while the total corresponding CO₂ bio-fixation was 9.70 ± 0.13 and 18.16 ± 0.11 g/L, respectively, during 15-day cultivation. The findings provide critical insights into enhancing CO₂ bio-fixation through adaptive evolution of ALE-Cv and offer optimal operational parameters for future large-scale microalgae cultivation. This research also links microalgae-based CO₂ sequestration to green technologies and the bioeconomy, highlighting its potential contribution to climate change mitigation while supporting environmental sustainability, food security, and ecosystem resilience. Full article

Based on Conservation of Resources (COR) theory and a socio-technical systems perspective, this study examines how technology-enacted abusive supervision (TAS) influences employees’ counterproductive work behavior (CWB) in digitalized organizational contexts. Conceptualizing TAS as a system-embedded form of digitally mediated control, we argue that technology-amplified supervisory power constitutes a persistent resource threat that reshapes employees’ behavioral regulation strategies. Using three-wave time-lagged survey data from 428 employees working in digital-intensive enterprises in China, we develop and test a moderated mediation model. The results indicate that TAS is positively associated with CWB, with defensive silence serving as a critical mediating mechanism. Although defensive silence may temporarily reduce interpersonal risk, it disrupts feedback and resource replenishment processes, leading to cumulative resource depletion and a higher likelihood of counterproductive behavior over time. Moreover, power distance significantly moderates this indirect effect, such that the mediating role of defensive silence is stronger among employees with higher-power-distance orientations. By integrating leadership research, COR theory, cultural value orientations, and a socio-technical systems perspective, this study advances our understanding of covert resistance and behavioral risk in technology-driven work systems and offers important implications for digital governance and sustainable organizational performance. Full article

Growing pressures on water resources, exacerbated by climate change, resource depletion, and population growth, underline the need for sustainable and energy-efficient wastewater management. Wastewater treatment plants (WWTPs) are among the most energy-intensive elements of the Integrated Water Service, and their environmental performance depends on infrastructure design, resource availability, and treatment configuration. Improving resource efficiency while reducing energy demand and environmental impacts is therefore a priority for water utilities seeking innovative decision-support tools. Within the national project “WATERGY—Energy Efficiency of the Integrated Water Service”, this study proposes a life-cycle-based framework to assess the sustainability of technological interventions in WWTPs. A comparative gate-to-grave Life Cycle Assessment (LCA) was applied to the municipal WWTP of Potenza (Southern Italy). Three sludge End-of-Life Scenarios were assessed: the current landfill-based configuration, an enhanced oxygenation–nitrification setup, and anaerobic digestion with biogas-based cogeneration. Compared to the current scenario, anaerobic digestion with cogeneration reduces Global Warming Potential by 17% and decreases freshwater ecotoxicity by approximately 30%. Compost production shows the highest reduction in ecotoxicity (−51%) but increases fossil resource depletion and acidification due to higher energy demand. Overall, energy recovery pathways, particularly anaerobic digestion with cogeneration, provide the most balanced environmental benefits, supporting more sustainable WWTP operation within the Integrated Water Service. Full article

Amidst the global drive toward sustainable development, this study responds to China’s pressing imperative for a green and low-carbon transition. The research begins by theoretically examining the viability and intrinsic mechanisms through which the cultivation of new-pattern productive forces can foster environmentally sound, high-quality economic growth. Subsequently, by leveraging panel data from 30 Chinese provinces covering the period 2011–2023, a two-way fixed-effects model is deployed to empirically assess the linkage between new-pattern productive forces and green total factor productivity (GTFP). The empirical results demonstrate the following: (1) New-pattern productive forces exert a statistically significant positive influence on GTFP—a finding that withstands multiple robustness checks; (2) Heterogeneity tests reveal that the GTFP-enhancing effect is pronounced in provinces with relatively low carbon intensity, whereas it remains insignificant in high-carbon-intensity regions; (3) Mechanism analysis identifies green technology innovation as a pivotal mediator in the process through which new-pattern productivity improves GTFP; (4) A non-linear, dual-threshold effect characterizes the relationship, wherein the GTFP-promoting impact of new-pattern productive forces strengthens progressively as the development level of green finance crosses successive thresholds. Collectively, these insights advance the understanding of how new-pattern productive forces enable GTFP gains, furnish novel evidence for steering high-quality economic development, and thereby support the broader global sustainability agenda. Full article

Free-roaming cats may contribute to zoonotic risk via parasites and other eukaryotic taxa, yet surveillance in protected island settings is limited and conventional coprology can miss low-intensity or degraded signals. We conducted a cross-sectional 18S rRNA metabarcoding survey to establish a baseline profile of potentially pathogenic eukaryotes in community cats from La Graciosa (Natura 2000, Canary Islands, Spain) prior to large-scale antiparasitic interventions. We analysed 152 faecal samples, including fresh samples collected during a high-throughput TNR campaign (n = 37) and dry environmental deposits (n = 115). Host amplification was reduced using a feline 18S blocking primer; libraries were sequenced with Oxford Nanopore technology; and taxonomy was assigned using SILVA-based classifiers with downstream filtering for veterinary/zoonotic relevance. After quality control, 72 eukaryotic taxa were retained and DNA from at least 24 potentially pathogenic taxa was detected. Dipylidium caninum was most frequent (74.3%; 113/152), and opportunistic fungi/yeasts were common (e.g., Pichia kudriavzevii 42.4%, Diutina catenulata 31.5%). Zoonotic protozoa showed low-to-moderate detection frequency (Acanthamoeba castellanii 13.3%, Toxoplasma gondii 7.9%, Balamuthia mandrillaris 4.6%). Overall richness did not differ between fresh and dry samples (p > 0.05), but fresh samples contained higher richness of potentially pathogenic taxa (p < 0.01). Full article

The global transition toward a low-carbon economy has intensified the interest in green hydrogen as a key enabler of industrial decarbonization. In particular, the steel sector, one of the most carbon-intensive industries, offers significant opportunities for emissions reduction through H₂-based technologies. This study presents a techno-economic assessment of alternative green hydrogen supply pathways, namely alkaline electrolysis and ammonia cracking, and evaluates their integration into hydrogen-based direct reduction (HyDR) routes. Process simulations are performed using Aspen Plus^® V14 to quantify the energy consumption, hydrogen demand, and associated CO₂ emissions across multiple configurations and case studies. A comprehensive 3E (energy, economics, and environmental) evaluation framework is applied to compare system performance and assess the suitability of each pathway for large-scale deployment. The results indicate that ammonia cracking represents a technically viable and potentially competitive hydrogen supply option for steel decarbonization under the assumed operating conditions, highlighting its relevance as a transitional pathway toward low-carbon steel production. Full article

With the growing development of facial micro-expression recognition technology, its practical application value has attracted increasing attention. In real-world scenarios, facial micro-expression recognition typically involves cross-dataset evaluation, where training and testing samples come from different datasets. Specifically, cross-dataset micro-expression recognition employs multi-dataset composite training and unseen single-dataset testing. This setup introduces two major challenges: inconsistent feature distributions across training sets and data imbalance. To address the distribution discrepancy of the same category across different training datasets, we propose a plug-and-play batch regularization learning module that constrains weight discrepancies across datasets through information-theoretic regularization, facilitating the learning of domain-invariant representations while preventing overfitting to specific source domains. To mitigate the data imbalance issue, we propose an Action Unit (AU)-guided generative adversarial network (GAN) for synthesizing micro-expression samples. This approach uses K-means clustering to obtain cluster centers of AU intensities for each category, which are then used to guide the GAN in generating balanced micro-expression samples. To validate the effectiveness of the proposed methods, extensive experiments are conducted on CNN, ResNet, and PoolFormer architectures. The results demonstrate that our approach achieves superior performance in cross-dataset recognition compared to state-of-the-art methods. Full article

Intensive care units (ICUs) are among the most resource-intensive healthcare settings and represent a strategic priority for environmental sustainability policies. While technological solutions are increasingly promoted, sustainable practice in ICUs also depends on nurses’ knowledge, attitudes, and behaviors, which remain insufficiently studied using validated, context-specific tools and longitudinal designs. This research protocol describes a multi-phase, theory-driven study aimed at developing and validating the Knowledge, Attitudes, and Behaviors Questionnaire on Environmental Sustainability in Intensive Care Units (KABQES-ICU) and at evaluating the longitudinal impact of a structured sustainability training program. Phase 1 focuses on instrument development and psychometric validation, grounded in a conceptual framework integrating individual, psychological, and organizational determinants and informed by qualitative evidence from ICU nurses. Phase 2 consists of a longitudinal intervention study assessing changes in sustainability-related competencies and their effects on nurse, patient, caregiver, organizational, and environmental outcomes. This protocol is designed to generate robust evidence to support the integration of sustainability into ICU quality improvement strategies and health policy frameworks without compromising patient safety. Full article

The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity. Full article