搜索结果：Environmental management

Trait-Species Distribution Models (trait-SDM) help to understand the importance of plant strategies to niches, assess their generality across species and provide a path to predicting species distributions from a shortlist of traits. Yet published trait-environment associations show considerable inconsistency. Region-scale models may leverage more species, traits, trait ranges and climatic gradients, than at local scales, while retaining biogeographic coherence, which is lost in global compilations. Here we fit trait-SDMs with six traits using multilevel models for over 90 eucalypt tree taxa. We model presence-absence in 1 km2 grid cells which contain multiple survey plots, arrayed along environmental gradients which span 120,000 km2, 8°C mean temperature and 900 mm annual precipitation. We found stem sapwood density, bark thickness, seed mass and maximum height were the most influential predictors in a multi-trait model of environmental responses to temperature, water deficit, soil depth and pH. Combined, they explained 9%-19% of variance between species in environmental responses. We found less support for specific leaf area and leaf size. Species occurred unimodally along environmental gradients. Trait-environment terms indicated species with dense stems were more likely in drier climates, thicker bark in warmer climates and that both thinner bark and larger seeds increased occurrence in shallow soils. Taller species were more common and more likely to occur towards sites that were warmer and wetter than average along the gradient. Our work has wider implications: trait-SDMs help to test trait-based theory about realised niches. Single trait models reflect the maximum potential explanation of niche differentiation by a trait, while multi-trait models represent integrated phenotypes responding to multidimensional niches. For planning and management, such trait-SDMs can provide useful predictions of where certain kinds of species occur or could be restored. But they will leave much uncertainty, especially for identifying which particular species occur where.

Industrial solar microgrids experience pronounced peak demand due to aggregated production processes, auxiliary systems, and shift-based operation. Conventional demand response approaches rely on voluntary participation and aggregated energy metrics, which limits their effectiveness in industrial environments. This study presents an IoT-based demand-side energy management framework for industrial microgrids that explicitly distinguishes active power, reactive power, and apparent power during peak operation. The system integrates real-time power factor monitoring, automated reactive power compensation, and prioritized load control using low-latency IoT communication. A high-fidelity simulation model was developed in Proteus 8.15 and validated using a physical IoT prototype calibrated against industrial measurement data. Performance evaluation combined experimental measurements with historical load data from an operating industrial facility. Automatic power factor correction reduced apparent power demand by 41.58% due to reactive power mitigation. Real power decreased by 1.6 and 2.6% for both hardware and simulation respectively. The result shows that power factor correction reduces current and releases inverter capacity. It does not produce large reductions in real energy consumption. Automated shedding of non-critical loads reduced real power demand by 23.46% during peak periods. The non-critical loads include auxiliary lighting and support equipment rated at approximately 0.063 kW. These loads represent 4 percent of the monitored site load. Classification followed production continuity, safety requirements, and operational redundancy. Load factor increased by 23.4%, and both peak demand and peak-to-average ratio decreased by 20% relative to baseline operation. The results demonstrate that automated industrial demand-side energy management improves electrical performance and peak demand characteristics without reliance on voluntary user response. The proposed framework provides a practical foundation for power-quality-aware demand management in industrial solar microgrids.

Airborne microbial contamination in dairy cattle housing is strongly influenced by housing conditions and management practices. This study evaluated the influence of environmental and housing parameters on total bacterial, coliform, and mold levels across four dairy farms. Microclimatic variables, including temperature, relative humidity, wind speed, bedding moisture, air volume per cow, particulate matter (PM1, PM2.5, PM10), and total volatile organic compounds (TVOCs), were measured. Comparative analyses showed that air volume per cow and bedding moisture were consistently associated with variability in total microbial and mold counts, while particulate matter and wind speed were linked to differences in airborne coliforms. Generalized linear mixed models indicated that most environmental variables did not have statistically significant effects, with the exception of farm type for coliforms and temperature for molds. The predominance of non-significant environmental effects, together with more consistent differences observed between farms, suggests that variability in airborne microbial levels is more strongly associated with farm-specific management and housing characteristics than with individual environmental parameters. Overall, the findings highlight the combined influence of housing design, management practices, and environmental conditions, emphasizing the importance of optimized ventilation and bedding management to improve air quality in dairy cattle housing.

The livestock sector is a major source of greenhouse gas emissions. With rising meat demand in Asia, balancing sustainability and nutrition is critical. This study assessed the environmental and nutritional impacts of partially replacing conventional meat with cultivated meat in the Korean diet using a hybrid life cycle assessment integrating greenhouse gas emissions and nutritional indicators. Substitution scenarios of 10-75% were modeled for beef, pork, and chicken, incorporating emission variability to evaluate trade-offs. Beef substitution achieved the largest emission reductions, up to 74.6%, while pork showed moderate benefits and chicken variable outcomes. Nutritionally, cultivated meat improved protein-to-fat ratios across scenarios, particularly in chicken substitution, with modeled protein increases up to 355% and fat reductions up to 325%. Overall, results indicate environmental performance depends on baseline meat type, while nutritional outcomes remain favorable. These findings provide preliminary evidence relevant to sustainable development discussions in future food systems.

Continuous glucose monitoring (CGM) has markedly advanced diabetes care by enabling real-time visualization of glycaemic variability, prevention of hypoglycaemia, and direct integration into therapeutic decision-making. As CGM use expands in routine practice and automated insulin delivery systems, however, accuracy has become a critical determinant of treatment safety. This mini-review summarizes recent advances in CGM accuracy management across three major driving forces: (1) accelerating technological innovation, including multi-analyte sensors, non-invasive devices, and artificial intelligence (AI)-based signal processing; (2) systematization and international harmonization of regulatory accuracy frameworks, exemplified by U.S. Food and Drug Administration (FDA) integrated CGM (iCGM) and the proposed CGM in Europe (eCGM) concept; and (3) growing societal demands for transparency, including public disclosure of performance data and strengthened lot-to-lot evaluation. We outline the four key dimensions of CGM accuracy-analytical accuracy, clinical accuracy, trend accuracy, and precision. We then review the evolution of regional accuracy standards, focusing on highly influential frameworks in the United States and Europe. Key considerations in accuracy-study design are discussed, along with clinical risks associated with reduced accuracy optimization, and progress toward global standardization. Finally, we examine future directions in the era of next-generation technologies, such as multi-analyte and non-invasive sensors, AI-driven accuracy optimization, and progress toward international standardization. This review provides an overview of the current landscape and future directions of CGM accuracy management in an era where fluctuations in accuracy directly affect treatment safety. We aim to clarify the perspectives required in both clinical practice and research to ensure safe and effective use of CGM.

Glioblastoma (GBM) remains the most aggressive adult brain tumor, with median survival largely unchanged over two decades. Antibody-based therapies have shown promise in hematologic and systemic cancers, but translation to GBM has been hindered by the tumor's hostile microenvironment and immune evasion mechanisms. A structured literature search was conducted in PubMed, Scopus, Web of Science, and ClinicalTrials.gov for studies published between 2010 and 2025. Eligible publications included preclinical investigations, clinical trials, and reviews addressing antibody-based therapies, tumor microenvironmental barriers, and computational innovations. Data were synthesized into thematic categories: mechanisms of resistance, antibody-based platforms, nanotechnology-assisted delivery, and artificial intelligence (AI)-driven strategies. Antibody therapeutics including monoclonal antibodies, antibody-drug conjugates, bispecific antibodies, and photoimmunotherapy show potential to enhance tumor targeting and immune activation. Key barriers such as the blood-brain barrier, immunosuppressive cell infiltration, and tumor heterogeneity significantly restrict efficacy. Novel approaches, including AI-enabled antibody design, digital twin modeling, and biomarker-driven patient stratification, offer opportunities to improve precision and overcome resistance. Combination strategies with radiotherapy, vaccines, or adoptive cell therapies further expand therapeutic potential. By reframing antibody therapy through the lens of barrier disarmament and technological integration, this review positions antibody-based approaches as realistic pillars of future GBM management. Strategic innovations in delivery, engineering, and computational modeling may transform antibodies from experimental tools into cornerstone therapies for this lethal malignancy.

Bovine ocular squamous cell carcinoma (BOSCC) is the most common ocular tumour in cattle, with a multifactorial aetiology involving ultraviolet (UV) radiation, genetic factors, pigmentation, and management practices. A detailed epidemiological characterisation of BOSCC in the Azores, Portugal, is provided, with particular emphasis on its spatial distribution and potential risk determinants. Data were obtained through an epidemiological questionnaire completed by field veterinarians between August 2023 and March 2025. A total of 85 BOSCC cases were recorded across 62 farms-45 on Terceira Island and 17 on São Miguel Island. All affected animals were adult Holstein Friesian dairy cows, managed under extensive pasture-based systems. The nictitating membrane was the most frequently affected structure (69.5%), and multiple lesions occurred in 20% of the cases. Farms located at 200-400 m of altitude presented the highest number of cases. Continuous exposure to UV under pasture-based management represents the main environmental risk factor. Although periocular pigmentation may provide partial protection, other environmental and genetic factors can also contribute to tumour development. Artificial insemination is considered a promising preventive tool, enabling genetic selection for protective traits such as periocular pigmentation. This research provides the first regional epidemiological characterization of BOSCC in the Azores, highlighting the interplay among environmental, genetic, and management-related factors in disease occurrence.

Understanding how different ecological and anthropogenic drivers shape community structure is a central goal in ecology, particularly in spatially heterogeneous and rapidly changing systems. Fishes contribute to many key ecosystem functions and services on coral reefs, and a variety of physical, biological, and anthropogenic factors influence their distributions, habitat use, and ecological roles. Although habitat complexity is consistently shown to be important for reef fish ecology, it is rarely fully represented in large-scale analyses. When included, it is often measured using coarse or one-dimensional metrics, and seldom evaluated alongside other known ecological drivers. Here, we use three-dimensional habitat and overlapping fish census data collected at 89 sites throughout the main Hawaiian Archipelago to determine the role habitat structure plays in fish community structure compared to traditionally hypothesized environmental and anthropogenic drivers. We examined the impact of habitat structure (rugosity, fractal dimension, and coral cover), environmental conditions (depth, temperature, chlorophyll a, and wave exposure), and anthropogenic pressures (sedimentation, effluent pollution, coastal development, tourism, and fishing pressure) on four community descriptors: biomass, species richness, abundance, and community composition; the latter based on fish body size, diet, grouping behavior, and position in water column. Rugosity was the dominant driver of all community descriptors but was closely followed by environmental variables. The composition of behavioral traits in fish communities were strongly shaped by habitat structure, reflecting patterns in habitat use and predator-prey dynamics. Where structural complexity was not the primary effect, environmental conditions, such as temperature, were more strongly associated, particularly with body size distributions. Our results show that degraded reef conditions (i.e., reduced rugosity and coral cover) support communities with lower biomass and limited traits, which likely translates to a narrower range of ecosystem functions and services. These findings illustrate how different dimensions of habitat structure, in combination with environmental gradients, filter community traits and influence ecological organization. We offer a framework for predicting how management actions that alter habitat structure may cascade through fish communities to affect ecosystem functions and services. Maintaining structural features of reef habitats may therefore be essential to supporting the functional diversity and resilience of coral reef communities.

Forest composition and structural diversity play a key role in shaping habitat availability and biodiversity, particularly influencing the occurrence of saproxylic species associated with tree-related microhabitats (TreMs). TreMs and deadwood are essential features of forest ecosystems, offering critical habitats for saproxylic arthropods that contribute to decomposition, nutrient cycling, and overall forest resilience. Understanding the complex interactions between forest structures, TreMs, and arthropod communities is crucial for biodiversity conservation and sustainable forest management. This study examines how forest composition, deadwood characteristics, and TreMs influence saproxylic arthropods' diversity in the Black Forest, Germany. Using environmental DNA (eDNA) metabarcoding, we assessed arthropod communities across 135 forest plots with varying degrees of retention. Our findings reveal that specific TreMs, such as cavities, mosses, and insect galleries, were associated with arthropod richness patterns, particularly for Collembola, Hemiptera, and Arachnida. Canopy closure emerged as the most consistent predictor of arthropod richness, while tree-related microhabitats, lying deadwood volume, snag volume, and forest management intensity showed additional taxon-specific associations. These findings reveal that species richness is associated with multiple ecological factors, highlighting the complexity of forest ecosystems. They underscore the need for forest management strategies that preserve deadwood and TreMs, enhance structural heterogeneity, and consider broader indicators of forest naturalness to effectively support arthropod biodiversity.

Plastic overconsumption and improper disposal pose growing threats to human health and environmental sustainability. Children are particularly vulnerable to plastic-related exposures, while mothers, strongly influence household consumption and waste management behaviors. Although nurse-led education effectively enhances health literacy, its potential to promote family-based sustainability practices remains underexplored. To evaluate the effectiveness of a nursing-led, family-centered educational program in improving mothers' and children's knowledge, attitudes, and practices regarding safe plastic use and recycling. A quasi-experimental pretest-posttest study was conducted at X University Children's Hospital, Egypt, from March to August 2024. A total of 200 participants (100 mothers and 100 school-aged children aged 6-12 years) were recruited using simple random sampling. The intervention included two structured educational sessions delivered through lectures, group discussions, visual materials, and booklets. Five validated tools assessed outcomes: mothers' demographic and knowledge questionnaire, mothers' attitude scale, mothers' practices checklist, children's recycling knowledge questionnaire, and children's recycling attitudes scale. Data were analyzed using IBM SPSS version 26. Statistical tests included McNemar, chi-square, and correlation analyses, with significance set at p&#x2009;<&#x2009;.05. Significant improvements were observed post-intervention. Mothers' knowledge increased from 43% to 82%, attitudes from 58% to 91%, and practices from 54% to 88% (p&#x2009;=&#x2009;.001). Children's recycling knowledge improved from 34% to 89%, and attitudes from 33% to 82% (p&#x2009;=&#x2009;.001). Mothers' education, residence, and occupation were positively associated with knowledge and practices (p&#x2009;<&#x2009;.05). Children's age and educational level correlated positively with knowledge (p&#x2009;=&#x2009;.010). Post-intervention findings indicated strong interrelationships among knowledge, attitudes, and practices. Nurse-led, family-centered educational programs effectively foster sustainable household behaviors and reduce environmental health risks. Integrating sustainability education into school health services and community-based nursing initiatives may strengthen long-term behavioral change and contribute to broader public health and environmental sustainability goals.

Sheep welfare outcomes vary depending on production systems, breeds, and environmental conditions. This study examined the effects of extensive, semi-extensive, and semi-intensive sheep production systems on animal welfare in Serbia, using the AWIN Welfare Protocol to evaluate 30 farms. Welfare indicators were categorised into resource-based, management-based, and animal-based metrics. The results indicated that there was no significant difference in space allowance among the production systems (p > 0.05). This suggests that the space provided was adequate for semi-intensive farms and suitable for both semi-extensive and extensive farms. However, management practices showed significant variations (p < 0.05), indicating diverse impacts on sheep welfare. No ocular discharge or stereotypic behaviours were observed, while respiratory issues, social withdrawal, and excessive itching were found to have a very low prevalence across all farms. The primary welfare concern identified in the extensive farms was the use of painful mutilations. Semi-extensive and semi-intensive farms had significantly higher rates of tail docking (p < 0.05) and poorer fleece cleanliness. These findings highlight the necessity of addressing the root causes of poor welfare to improve sheep welfare standards. Therefore, achieving sustainable welfare outcomes requires an integrated approach that combines genetic suitability, adequate housing, and effective management practices.

The impact of environmental disturbances and sensor deployment variations on damage identification represents a critical bottleneck that constrains the practical effectiveness of structural health monitoring. Existing methods addressing these challenges often suffer from poor interpretability due to information loss during feature extraction or exhibit insufficient sensitivity in identifying early-stage minor damage. This paper proposes a damage identification method based on the Shapelet Transform and Random Forest classifier, which extracts highly interpretable local shape features from vibration response signals to achieve robust identification of structural state changes. The study utilizes measured random vibration response data from a timber truss bridge. The dataset comprises four reference states collected on different dates and five damage states simulated by additional masses ranging from +23.5 g to +193.7 g, with sensors deployed in both vertical and horizontal directions. The Shapelet Transform selects local subsequences with high information gain from the original time series as features, which are subsequently classified using the Random Forest algorithm. The experimental design systematically investigates the influence of different damage severities, sensor locations, and environmental variations on method performance. The results demonstrate that with a Shapelet extraction time of 10 min, the method achieves 100% identification accuracy across multiple operating conditions comprehensively considering environmental variations, sensor location differences, and varying damage severities. When the extraction time is reduced to 5 min, 3 min, and 1 min, the average accuracies are 93.98%, 89.51%, and 58.48%, respectively. The method effectively identifies the minimum simulated damage (+23.5 g), which represents only 0.07% of the total structural mass, while maintaining stable performance under varying sensor locations and environmental conditions. Compared to traditional methods based on global frequency-domain features or statistical characteristics, the proposed method extracts physically meaningful local Shapelet features, offering significant advantages in interpretability. In contrast to deep learning approaches, this method demonstrates greater robustness under limited sample conditions. This study confirms that the combined framework of the Shapelet Transform and Random Forest can effectively address multiple real-world challenges in structural health monitoring, delivering high accuracy, strong robustness, and excellent interpretability, thereby providing a novel approach for developing practical real-time damage identification systems.

Phosphate (PO4) pollution in irrigated catchments and their return-flow and drainage networks threatens water quality and agricultural sustainability, particularly under conditions of intensive fertilization and shallow groundwater. This study presents a predictive approach to estimate PO4 concentration using a Generalized Additive Model (GAM) based on daily monitoring data from the Akarsu Irrigation District in T&#xfc;rkiye's Lower Seyhan Plain. Here, the modeled variable is PO4 in irrigation return-flow/drainage water, measured at the main drainage outlet (L4), which integrates excess irrigation water that has passed through the agricultural landscape and collected surface runoff and subsurface drainage. Downstream of L4, drainage water is conveyed by the main drainage channel; part is reused for irrigation, and the remainder flows toward lagoon and wetland areas and ultimately the Mediterranean Sea. The dataset comprised 522 daily observations from the 2022-2023 water years and included nitrate (NO3), nitrite (NO2), electrical conductivity (EC), pH (hydrogen ion activity), flow rate (Q), and precipitation (P) as predictors. Despite weak pairwise correlations of PO4 with individual variables (maximum r&#xa0;=&#xa0;0.1293 with NO3), the GAM captured nonlinear multivariate relationships and produced good agreement between predicted and measured PO4 at the L4 outlet (mean squared error (MSE) = 0.019966; root mean squared error (RMSE) = 0.1413 mg L-1; mean error = -0.00457 mg L-1; error SD = 0.14136 mg L-1), indicating minimal bias and stable performance. In benchmark comparisons using identical inputs and the same time-structured validation design (80/20 split; random splits were used only for sensitivity analysis), the GAM substantially outperformed linear regression (LR), artificial neural network (ANN), and support vector machine (SVM), which showed very low predictive skill (R2 &#x2248; 0.03-0.05). Predictive performance was evaluated primarily using error-based metrics; R2 was reported only as a goodness-of-fit measure. The L4 outlet drains an intensively managed agricultural catchment dominated by irrigated cropland. Model fit, expressed as explained variance values (training R2&#xa0;=&#xa0;0.832; testing R2&#xa0;=&#xa0;0.788), indicated consistent performance without evidence of substantial overfitting. Overall, the findings demonstrate that GAM-based estimation can reliably reproduce both peak and moderate PO4 concentrations and serve as a practical screening tool for nutrient monitoring at irrigated drainage/return-flow outlets. By leveraging routinely monitored variables, the model can reduce the frequency of laboratory PO4 assays-often requiring additional reagents, consumables, and handling time-thereby lowering analytical workload and spectrophotometric operating time while enabling near-real-time assessment of PO4 dynamics. These results support the use of data-driven estimation to inform nutrient management and reduce eutrophication risk in irrigated catchments by monitoring drainage exports. Phosphate pollution in irrigation areas, particularly in regions with shallow groundwater and intensive agriculture, poses serious environmental and agricultural risks, including eutrophication and water-quality degradation. Conventional methods for phosphate monitoring are often time-consuming, costly, and spatially limited, making them unsuitable for real-time applications. Furthermore, the complex, nonlinear interactions between phosphate concentrations and environmental variables, including nitrate, nitrite, pH, EC, flow rate, and precipitation, challenge traditional predictive approaches. While various machine learning models have been explored for phosphorus prediction, their computational demands and overfitting risks often limit their field-level applicability. Therefore, this study aimed to develop a robust, efficient, and interpretable method for predicting phosphate concentrations using a GAM and leveraging daily environmental data collected in a Mediterranean irrigation district in T&#xfc;rkiye. Daily water samples were collected at the outlet of the L4 agricultural catchment in the Akarsu Irrigation District (AID) on the Lower Seyhan Plain, T&#xfc;rkiye, during the 2022 and 2023 water years. The area is characterized by intensively managed irrigated cropland and shallow groundwater conditions. A total of 522 daily observations were compiled, including PO4, NO3, NO2, EC, pH, flow rate (Q), and precipitation (P). Laboratory analyses were performed using spectrophotometric methods for nutrients and electrochemical measurements of EC and pH, while discharge data were obtained from an on-site automatic monitoring and sampling system.A GAM was developed to represent nonlinear relationships between PO4 and the predictor variables using penalized smoothing functions. Because the dataset is a daily time series, temporal dependence was addressed by including a smoother for time (date/time index) and by fitting the model with an AR(1) residual correlation structure (GAMM). To ensure realistic model evaluation under temporal dependence, predictive skill was assessed primarily using a time-structured (blocked, contiguous) 80/20 split, with the earlier 80% of observations used for training and the later 20% for testing. To assess robustness to the choice of partition (sensitivity analysis only), we additionally repeated the split-fit-evaluate procedure over 100 independent randomized 80/20 splits. These random-split results are reported as a secondary check and are not interpreted as the main estimate of predictive skill under autocorrelation. Model predictive performance was primarily assessed using error-based metrics (MSE, RMSE, bias/mean error, and error SD), while R2 was reported only as explained variance (goodness-of-fit). Residual diagnostics, including inspection of the residual distribution and autocorrelation (ACF), were used to evaluate model assumptions, stability, and potential overfitting. This study developed a data-driven method for estimating PO4 concentrations at the L4 drainage outlet using a Generalized Additive Model. Although same-day Pearson correlations between PO4 and routinely monitored predictors (EC, pH, Q, P, NO2, NO3) were weak (maximum r = 0.1293 for NO3), the GAM captured nonlinear and conditional multivariate effects. It demonstrated strong agreement between predicted and measured PO4 values. Model performance was evaluated primarily using error-based metrics, yielding MSE = 0.019966; RMSE = 0.1413 mg L-1; mean error (bias) = -0.00457 mg L-1; and error SD = 0.14136 mg L-1. R2 was reported only as explained variance (goodness-of-fit): training R2 = 0.8319; testing R2 = 0.7875. Because the dataset is a daily time series, temporal dependence was addressed by fitting a GAMM with a smooth function of time and an AR(1) residual structure; and generalization was assessed using a time-structured (blocked or contiguous) train-test split to reduce information leakage from autocorrelation. Repeated random 80/20 splits were used only as a sensitivity analysis and showed consistent performance (mean R2 = 0.772, SD = 0.0166 across 100 trials). In benchmark comparisons, the GAM substantially outperformed traditional alternatives (LR, ANN, SVM), which showed very low predictive skill for PO4 (R2 &#x2248; 0.03-0.05), highlighting the need for a flexible nonlinear structure to reproduce the observed phosphate dynamics. The model reproduced the overall temporal pattern of PO4, while some underestimation remained for the highest short-duration peaks-consistent with the sparse nature of extreme events in the dataset. Overall, the results support the use of the proposed GAM/GAMM framework as an outlet-scale screening tool for near-real-time identification of periods with elevated PO4, thereby helping to prioritize laboratory sampling and monitoring efforts when direct PO4 measurements are costly or intermittent.

Banana peel Agro-waste is abundantly generated in Ethiopia but is commonly discarded without adequate disposal, causing notable environmental and aesthetic challenges. As part of a sustainable valorization strategy, this study employed banana peels as a low-cost, renewable substrate for citric acid production. The work focused on harnessing Aspergillus niger ATCC 10535 in solid-state fermentation, with process conditions optimized through Response Surface Methodology (RSM) to maximize yield and process efficiency. Citric acid production trials were conducted in a small-scale solid-state fermenter using banana peel agro-waste as the primary substrate. Process parameters were systematically varied, including substrate loading (18, 36, and 54 g), incubation time (3, 6, and 9 days), and fermentation temperature (25 &#xb0;C, 30 &#xb0;C, and 35 &#xb0;C), to evaluate their individual and interactive effects on citric acid yield. Experimental design and statistical optimization were carried out using RSM, applying a Box-Behnken design to model the relationships between process variables and product yield and to determine the optimal operating conditions for maximum citric acid production. The minimum citric acid yield (28.39 g/L) was obtained at 36 g substrate loading, 3 days of incubation, and 25 &#xb0;C, while the maximum yield (45.55 g/L) was achieved at 36 g, 6 days, and 30 &#xb0;C. Statistical analysis confirmed the model's significance (p < 0.01). Temperature and incubation time exhibited positive linear effects on yield, whereas increased substrate loading beyond the optimal range resulted in decreased production. The optimized process parameters determined by the Box-Behnken model were 35.014 g substrate loading, 5.811 days of incubation, and 31.054 &#xb0;C fermentation temperature, yielding 45.563 g/L citric acid. The findings are demonstrated that banana peel agro-waste can be sustainably valorized into citric acid through RSM-optimized, low-level sucrose-supplemented solid-state fermentation by A. niger ATCC 10535, providing a cost-effective and environmentally friendly approach for waste management and value-added product generation.

Land subsidence poses major threats to human and environmental systems in river deltas worldwide, increasing risks of flooding and damage to civil infrastructure. In deltaic settings, land subsidence can be induced by multiple superimposing processes, including autocompaction, groundwater depletion and infrastructural surface loading. The quantification of each individual process is often uncertain, yet crucial for effective adaptation and mitigation. The Vietnamese Mekong Delta (VMD) is a prominent example of such a subsiding delta, with satellite-derived subsidence rates of up to 30&#xa0;mm a-1 and surface elevations largely below 1&#xa0;m above mean sea level. By presenting a fully coupled flow-deformation model with geomechanical parameterization at high vertical resolution, this study, supported by local geodetic leveling observations, provides an unprecedentedly detailed local-scale assessment of land subsidence dynamics for the VMD. The simulation results indicate subsidence rates of 5-6&#xa0;mm a-1 due to groundwater depletion and local infrastructure loading. Additionally integrating one or multiple well-casing failures as localized subsurface disturbances in the model yields spatially heterogeneous subsidence patterns and increases local subsidence rates by an additional 1-20&#xa0;mm a-1, depending on the number of implemented failures. While well-casing failures are known consequences of land subsidence, the hypothesis-driven exploratory simulations employed here indicate that such damage may in turn accelerate subsidence by facilitating subsurface drainage pathways and local head equilibration between aquitards and tapped aquifers. This suggests that well-casing failures could contribute to heterogeneous and locally extreme subsidence dynamics. The results reveal significant delays in subsidence due to past groundwater depletion at the investigated site, underscoring the need for proactive water management strategies in the VMD, supported by comprehensive land subsidence modelling. The insights derived from this localized high-resolution analysis suggest that effective management will require preventing shallow aquifer depletion to avoid triggering the Holocene's pronounced, yet largely inactivated subsidence potential.

This paper addresses a critical gap in circular economy (CE) research by introducing a novel methodological framework that integrates Network Data Envelopment Analysis (NDEA) with a chance-constrained programming. The proposed approach captures the interrelated dynamics of economic production and waste treatment subsystems, while accounting for stochastic variables and data uncertainties to provide robust CE efficiency estimates. Using data from 26 European (EU) countries from 2013 to 2020, our results reveal that achieving CE efficiency requires a balanced focus on economic production and waste management. Although strong economic output can support circularity, waste treatment efficiency often plays a decisive role in determining overall CE performance. Moreover, we find that economic size does not necessarily translate into circular efficiency, whilst large economies may face challenges with effective waste management and resource recovery despite their economic status. The proposed approach offers policymakers and practitioners a robust empirical framework to guide CE improvements, particularly in regions where environmental practices lag behind economic achievements. Stronger incentives and regulatory measures are recommended to enhance circular activities within the EU and foster greater circular efficiency across countries.

In recent years, droplet charging technology has become an interdisciplinary research hotspot due to its great potential for application in the frontier fields of microfluidics, materials science, and biomedicine. This technology can regulate droplet charge through physical (e.g., electric field, mechanical vibration, ultrasonic wave) or chemical (e.g., addition of surfactant, electrolyte) methods, which can achieve high-efficiency energy conversion and self-powering of microdevices in the field of energy, promote targeted drug delivery and precise cell manipulation in biomedicine, and enhance the efficiency of pollutant purification in the field of environmental management. However, relevant studies indicate that the charge polarity and magnitude of droplets are influenced by material properties, environmental conditions, and motion states. In both the design and application research of liquid-solid triboelectric nanogenerators (L-S TENG) employing fluoropolymers as solid friction layer materials, and in studies of droplet-atmosphere friction, a general tendency for droplets to become positively charged has been observed. In contrast, research on the generation mechanisms of negatively charged droplets and their efficient control strategies remains relatively scarce. This limitation constrains the comprehensive application and further advancement of droplet charging technologies. In this paper, we systematically review a series of methods to prepare negatively charged droplets and look forward to their future development, aiming to help scholars in the related fields to have a more comprehensive understanding of the current research status and trends in this field.

Canine atopic dermatitis (CAD) is a chronic, pruritic inflammatory disease that affects up to 15% of the global canine population. Its etiopathogenesis is multifactorial, involving genetic, immunological, environmental, and dietary factors. It is characterized by pruritus, erythema, alopecia, and secondary lesions, predominantly affecting the abdomen, extremities, and ears. This retrospective cross-sectional descriptive study analyzed 735 medical records of dogs diagnosed with CAD treated at the Veterinary Specialty Center (CEVET) in Quito, Ecuador, between January 2018 and July 2025. Demographic, clinical, housing, diet, and cohabitation data were collected and statistically analyzed using &#x3c7;2 for qualitative variables and the Kruskal-Wallis test for quantitative variables, with post hoc analysis as appropriate. Additionally, pruritus severity was assessed using the Pruritus Visual Analog Scale (pVAS). A composite Clinical Severity and Distribution Score (CSDS) was also developed to classify disease severity. A multivariate logistic regression model was performed to identify factors associated with severe CAD. The results showed a predominance of CAD in adult dogs (84.2%) and purebred dogs (74.97%), with a slight majority being males (52.38%). Pruritus was the most frequent initial symptom (80.27%), with most cases presenting moderate-to-severe pruritus (pVAS 7-10). The most affected areas were the abdomen (24.49%) and forelimbs (17.68%). The geographical distribution showed a predominance of urban areas (88.84%) and cold climates (86.39%). Based on the CSDS, 53.2% of cases were classified as severe, 44.4% as moderate, and 2.4% as mild. Multivariate analysis revealed that grass exposure was significantly associated with severe CAD (OR = 1.78; 95% CI: 1.22-2.60; p = 0.003), while urban environment showed a non-significant trend toward increased severity (OR = 1.41; p = 0.071). Significant associations were identified involving sex and body weight, age and affected area, and temporal variations in the severity of pruritus, age group, and distribution of lesions. Among breeds, French Bulldogs, Standard Schnauzers, and Shih Tzus had the highest prevalence of CAD. These findings provide the first systematic epidemiological and clinical characterization of CAD in Ecuador, highlighting the role of environmental factors in disease severity and supporting the use of composite clinical scoring approaches in retrospective studies, thereby contributing to understanding of the disease and serving as a reference for early diagnosis, clinical management, and the development of preventive strategies.

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining "lab-to-field gap". These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems.