Search Results (10,467)

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

Effective weed control is essential for sustainable and safe rice production, particularly under the long-term and widespread use of chemical herbicides. Oilseed rape (Brassica napus L.) is one of the most important oil crops worldwide, and the oilseed rape–rice rotation system is widely practiced in China. It has been reported to exhibit strong allelopathy on various plants, but the feasibility of using its straw incorporation for weed control in transplanted rice fields remains unclear. In this study, a natural weed management strategy based on shallow tillage of oilseed rape straw (ORS) was evaluated through laboratory bioassays, greenhouse experiments, and field trials. The results indicated that soil decomposition liquids (SDLs) of ORS exhibited strong dose- and decomposition time-dependent allelopathic effects on seven paddy weed species, while rice showed markedly lower sensitivity. ORS incorporated at 700–1100 g/m² generally exhibited high integrated allelopathic inhibition (in lab) and population control effects (in greenhouse) on paddy weeds, especially Leptochloa chinensis (L.) Nees, Cyperus iria L., and Cyperus difformis L. Among the growth parameters of ORS allelopathic stress, root growth was the most sensitive indicator, followed by shoot growth and seed germination. Greenhouse experiments displayed variety-dependent impact on the transplanted rice seedlings, with Xiushui134 and Yongyou1540 showing relatively high tolerance. Field trials revealed that ORS incorporation at 1100 g/m² for 10 d achieved a satisfactory control of population (77.7–84.9%) and fresh weight (80.7–95.6%) across Gramineae, Cyperaceae and Broadleaf weeds, without adverse impact on the growth of transplanted rice seedlings (Yongyou1540). This treatment also significantly promoted theoretical grain yield by 13.4–19.4%. Overall, shallow tillage of oilseed rape straw provides a feasible and environmentally friendly weed control strategy for transplanted rice systems. Full article

Although numerous studies have focused on the potential application of heterotrophic nitrification–aerobic denitrification (HNAD) bacteria in wastewater treatment, research exploring their potential in aquaculture biofloc systems remains limited. In this study, a promising HNAD strain, identified as Stutzerimonas stutzeri MJ20, was isolated from mature biofloc. This strain efficiently utilized low-cost carbon sources (e.g., glucose) and small-molecule carbon sources (e.g., sodium acetate and sodium succinate). Under conditions with glucose as the carbon source, a carbon-to-nitrogen (C/N) ratio of 15, pH 6–9, temperature 25–35 °C, salinity 0–35‰, and shaker speed of 0–150 rpm, it achieved removal rates of 95–100% for NH₄⁺-N, NO₂⁻-N, and NO₃⁻-N at initial concentrations of 100 mg/L each. Even at higher concentrations (up to 200 mg/L NH₄⁺-N and 500 mg/L for both NO₂⁻-N and NO₃⁻-N), removal rates exceeded 99%. Under mixed nitrogen sources, strain MJ20 demonstrated efficient nitrogen removal, preferentially utilizing NH₄⁺-N, with only minimal and transient accumulation of nitrite and nitrate. Genomic analysis revealed that MJ20 carries key denitrification genes, including napA, nirS, norB and nosZ, and possesses complete pathways for nitrate reduction to nitrogen gas and ammonia assimilation, although typical autotrophic nitrification genes were not detected. Combined genomic data and autotrophic culture experiments indicated that, in addition to utilizing various organic carbon sources, the strain also exhibited certain autotrophic growth capabilities. Furthermore, MJ20 showed strong flocculation ability (flocculation rate > 96% within 16 h), sensitivity to multiple common antibiotics, and no toxicity to zebrafish, demonstrating favorable biosafety. In simulated seawater aquaculture wastewater with a C/N ratio of 5, it achieved a total nitrogen removal rate exceeding 94% within 72 h. These results indicate that strain MJ20 possesses comprehensive advantages, including efficient nitrogen removal, broad carbon source adaptability, strong environmental resilience, minimal accumulation of intermediate nitrogen products, excellent flocculation ability, and high biosafety. These traits highlight its potential for application in biofloc systems and in treating aquaculture tail water with a low C/N ratio. This study provides theoretical insights and practical guidance for screening HNAD bacteria suitable for biofloc systems. Full article

Groundwater is a key strategic resource underpinning water security, and its effective management requires reliable, high-frequency monitoring data. In mountainous regions such as the flysch Carpathians in southern Poland, natural springs are particularly sensitive indicators of aquifer system dynamics. This study analyzes the role of springs in the national groundwater observation and research network and identifies barriers to the implementation of automated monitoring of spring discharge. The research covered 28 springs operating within the regional monitoring network of the Polish Geological Institute—National Research Institute in the Carpathian region. Classical hydrogeological spring classifications were applied and complemented with proprietary criteria addressing formal-legal, technical, and environmental conditions affecting the feasibility of automation. The results show that all of the analysed springs exhibited a Meinzer’s variability index (V) exceeding 100%, and numerous objects showed a coefficient of variation (CV) above 150%, providing quantitative evidence that standard weekly manual measurements statistically fail to capture rapid flow dynamics and peak discharge events. To bridge the gap between hydrodynamic observations and monitoring logistics, this study introduces a novel methodological contribution: the F-T-S-N screening framework. This proprietary, multi-criteria classification quantifies Formal-legal, Technical, Structural, and Nature-environmental barriers to telemetry implementation. The application of this framework demonstrates that the main obstacles to modernization are non-technological. The proposed classification serves as a practical, transferable tool that supports the rational planning of monitoring network automation in other mountainous regions with similar hydrogeological conditions. Full article

Tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, with children representing a particularly vulnerable population in whom diagnosis is often challenging. Pediatric TB is typically paucibacillary and presents with non-specific clinical manifestations, limiting the sensitivity of microbiological confirmation and increasing reliance on immunological tests. The Tuberculin Skin Test (TST) and Interferon-Gamma Release Assays (IGRAs) are the most widely used tools for detecting Mycobacterium tuberculosis infection, yet both have important limitations, especially in young children and in Bacillus Calmette–Guérin (BCG)-vaccinated populations. TST lacks specificity due to cross-reactivity with BCG and environmental mycobacteria, while IGRAs, although more specific, require laboratory infrastructure and may have reduced sensitivity in early childhood. The Tuberculosis Skin Test (TBST), based on M. tuberculosis-specific antigens such as ESAT-6 and CFP-10, has emerged as a promising alternative that combines the operational simplicity of TST with the antigenic specificity of IGRA. This paper reviews the immunological principles, diagnostic performance, and practical considerations of TBST in pediatric populations, with direct comparison to TST and IGRA. Evidence from recent studies suggests that TBST may offer improved specificity over TST, with broadly comparable diagnostic accuracy to IGRA in some settings, although findings are not fully consistent across studies. Particular attention is given to its performance in BCG-vaccinated children and, based on emerging evidence, in those under five years of age. The potential role of TBST in clinical algorithms and public health strategies is discussed, along with current evidence gaps and future research priorities. Full article

Cropland protection enforcement is central to food security and sustainable land management, yet small-scale encroachments within Permanent Basic Farmland (PBF) boundaries frequently evade conventional field surveys and reactive inspection regimes. Existing remote sensing approaches rely mainly on comprehensive land-cover classification or bi-temporal change detection, which often generate alerts beyond the regulatory scope and require annotation efforts that limit county-scale deployment. To address this gap, this study reframes PBF monitoring as a boundary-constrained anomaly screening task, defined as the detection of surface conditions that deviate from expected cultivation norms within legally defined parcels. To operationalise this task, we adapt a DeepLabv3+-based segmentation pipeline by incorporating an auxiliary edge branch and a composite loss to improve sensitivity to minority-class anomalies and preserve fragmented parcel boundaries. The model is trained on the LoveDA dataset and evaluated in Mancheng District, Hebei Province, China, without site-specific fine-tuning. Multi-temporal imagery from 2021 to 2023 is further used as a post hoc consistency check to distinguish persistent anomalies from transient surface conditions, rather than to model temporal dynamics explicitly. Cross-regional zero-shot evaluation further examines model robustness under heterogeneous environmental conditions. Benchmarked against five comparison architectures, the adapted pipeline achieves a Recall of 61.25%, representing a 10.24 percentage-point improvement over DeepLabv3+ and expanding the set of candidate encroachments for field verification. This result should be interpreted in terms of screening sensitivity rather than overall segmentation optimisation. The outputs are intended as preliminary screening leads that support, rather than replace, expert review. The principal contribution of this study therefore lies in reframing PBF monitoring as an operational anomaly-screening task aligned with enforcement needs, rather than in proposing a fundamentally new segmentation architecture. Full article

Photovoltaic technologies represent an increasingly relevant alternative for developing renewable energy sources, particularly those based on light-harvesting materials such as perovskite solar cells (PSCs) and dye-sensitized solar cells (DSSCs), which have achieved efficiencies of 27.3% and 13.0%, respectively. In this context, phenothiazine (PTZ) has attracted considerable interest as a structural block due to its outstanding structural and photophysical properties, which also represent low production costs and reduced environmental impact. This review presents recent advances in the design and development of phenothiazine-based organic materials for photovoltaic applications, analyzing the main synthetic routes for obtaining this nucleus, as well as the fundamental aspects related to the operation of solar cells, including relevant device parameters. Furthermore, several studies focused on the synthesis, characterization, and performance of new phenothiazine-derived molecules used in photovoltaic devices are also examined. Finally, the most relevant conclusions are discussed, and future perspectives for the use of these materials in solar technologies are proposed. Full article

This paper evaluates the efficiency of modernization of the regional railway line Prievidza–Jelšovce in Slovakia using cost–benefit analysis (CBA), reflecting increased investment costs and the potential electrification of the line. The assessment is based on a detailed analysis of transport demand and infrastructure conditions, where daily railway passenger volumes range between 2300 and 3700 passengers, while individual car transport exceeds 10,000 passengers per day in most sections. Two alternative modernization variants were evaluated. The results show that the project generates socio-economic benefits, particularly through travel time savings amounting to approximately €42.3 million and reductions in operating costs and externalities. Significant environmental benefits were identified, especially in the case of the more advanced variant, with reductions in air pollution reaching €56.3 million and greenhouse gas emissions reaching €42.2 million. Despite these benefits, the economic evaluation indicates negative net economic outcomes for both variants. The total economic investment costs (excluding VAT and adjusted for economic appraisal) reach €543.4 million for the EIA variant and €511.9 million for the proposed variant, resulting in net economic values of −€186.2 million and −€70.8 million, respectively. The results therefore suggest that neither variant achieves full economic efficiency under the given assumptions, although the proposed variant performs significantly better. The findings highlight the strong sensitivity of project efficiency to investment costs and the scope of modernization. The study confirms the necessity of regularly updating CBA analyses in transport projects, as changes in input parameters can substantially influence investment decision-making. Full article

As cleaner production gains prominence in wooden product manufacturing, green evaluation of process schemes during early design is crucial. However, dust concentration, a key environmental indicator in wood product manufacturing, is often evaluated in a subjective and fragmented manner, which greatly hinders the selection of green process schemes in early design. To address this gap, an adaptability evaluation model for green process schemes was proposed based on process knowledge graphs (PKG) and fuzzy Bayesian network (FBN), with the objective of minimizing dust concentration. First, a PKG for wooden products was constructed based on the requirement-function-structure-characteristic-process-equipment (RFSCPE) ontology using patents and process manuals. Second, candidate process schemes were generated via the PKG, and dust-related causal relationships encoded in the PKG were mapped onto a Bayesian network structure. Third, conditional probabilities were obtained by combining probabilistic hesitant fuzzy sets and experimental dust data. The FBN was then updated to perform probabilistic reasoning on dust concentration. Finally, a case study on a wooden toy car validated the proposed approach, and sensitivity analysis identified the key dust-influencing factors, thereby providing quantitative support for greener process decisions. Full article

The widespread residual ciprofloxacin (CIP) poses severe environmental and health risks, demanding efficient detection methods. Herein, a Zr–Tb bimetallic MOF (ZTM) was green-synthesized via a room-temperature aqueous route with disodium terephthalate as ligand, and developed as a ratiometric fluorescent probe for CIP detection. Structural characterization confirmed Tb³⁺ was successfully incorporated into the Zr-MOF framework, endowing ZTM with high stability and excellent luminescence. The absorption edge of ZTM (320–330 nm) overlapped with CIP’s 330 nm absorption peak, so 327 nm was selected as the excitation wavelength. Under this excitation, ZTM showed a strong Tb³⁺ emission at 657 nm; upon CIP addition, the 657 nm peak was quenched, while the 491 nm emission was enhanced, realizing a distinct ratiometric response. The ratio I₄₉₁/I₆₅₇ was linear with CIP concentration (0.5–25 μM, R² = 0.992), with a limit of detection far below the statutory 30 μM limit (0.16 μM). ZTM also exhibited excellent selectivity, good pH tolerance (5.0–8.0) and rapid response (1 min). Mechanism analysis revealed that the response was mainly due to the inner filter effect (IFE) between ZTM and CIP. This work provides a green-synthesized MOF probe for sensitive and selective CIP detection in environmental samples. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Background: Gestation is a period of significant biological plasticity where the intrauterine environment influences fetal development via “fetal programming”. This study systematically reviews and meta-analyzes the association between genetic determinants—specifically the NR3C1, FKBP5, and CRHR1 genes, chosen for their pivotal role in the functional regulation and feedback sensitivity of the hypothalamic–pituitary–adrenal (HPA) axis—and stress reactivity during pregnancy. Methods: Following PRISMA guidelines, a systematic search was conducted across PubMed, Scopus, and Web of Science, yielding an initial total of 1430 records. After removing duplicates and screening 669 studies, a total of 34 primary observational studies were included in the systematic review and qualitative synthesis. For the quantitative synthesis, 27 articles provided sufficient data, resulting in k = 39 independent effect sizes analyzed via a mixed-effects model to account for tissue-specific and cohort-specific outcomes. Results: Systematic analysis reveals that maternal psychosocial stress significantly correlates with NR3C1 hypermethylation, acting as a biological mediator for neonatal cortisol dysregulation and hippocampal volume reduction. The FKBP5 rs1360780 polymorphism emerged as a key moderator of structural vulnerability, showing a “double-hit” effect when combined with epigenetic alterations. Furthermore, the study identifies sex-specific susceptibility, with divergent placental trajectories for male and female fetuses. Meta-analytic estimates confirmed the robustness of these associations (Rosenthal Fail-Safe N = 431,000), despite a general trend toward statistical significance (p = 0.079) in heterogeneous cohorts. Conclusions: The findings underscore a stable link between genetic determinants and prenatal stress reactivity. The interaction between molecular predisposition and environmental factors defines the health of the mother–infant dyad. These results advocate for a transition toward Precision Prenatal Medicine, integrating polygenic risk scores and epigenetic monitoring to implement early, targeted preventive interventions. Full article

The aim of this study is to conduct a comprehensive assessment of the water quality of the Sidi Mohammed Ben Taiba (SMBT), one of the largest drinking water reservoirs in northwestern Algeria, by integrating chemical and biological indicators. The assessment combines the Drinking Water Quality Index (DWQI), the Irrigation Water Quality Index (IWQI), the Organic Pollution Index (OPI) and zooplankton-based biological indicators (Zoo-IQ). A total of 23 physicochemical parameters were analyzed and interpreted using multivariate statistical approaches. This study fills an important knowledge gap by evaluating long-term temporal variability (January 2018–May 2025) and recent spatial heterogeneity (June 2023–May 2025), aiming to support sustainable water management. The results indicate that the reservoir water quality is generally suitable for drinking purposes (22.3 < DWQI < 54.0), is deemed excellent for agricultural irrigation (65 < IWQI < 69) and that the reservoir surface waters are slightly polluted to unpolluted (0.3 < OPI < 1.1). However, a deterioration in water quality has been detected in recent years, linked to increasing nutrient concentrations, as confirmed by the TSI–SD index. Despite the early signs of nutrient enrichment, the Zoo-IQ index remained within the moderate to good range, suggesting a certain degree of resilience in the zooplankton community. However, pronounced seasonal fluctuations observed in the Zoo-IQ and species diversity (H′) during periods of environmental stress serve as an early warning signal of emerging problems that may negatively affect water quality indices (WQI, IWQI, OPI). Station S4, located at the confluence of Wadi Belhassen and Wadi Farhat, descending from the Dahra mountain range in Algeria, has been identified as the most sensitive area and a potential hotspot for future pollution. The study provides robust data on the quality of reservoir water, offering a valuable decision-making tool for artificial reservoir managers and contributing to sustainable water management by identifying risk areas and supporting the implementation of preventive measures. Full article

Background: Acute cellular rejection (ACR) after heart transplantation remains incompletely explained despite standardized immunosuppression. Environmental exposures may contribute to residual immune activation; however, prior studies have focused primarily on air pollution rather than residential land-use composition. Objectives: To determine whether buffer-specific residential environmental composition is associated with rejection risk and whether these associations are scale-dependent and domain-specific. Methods: In this retrospective single-center cohort study, 30 heart transplant recipients contributed 267 biopsy-linked observations. Residential land-use composition was quantified within 300 m, 500 m, 700 m, and 1000 m buffers and aggregated into five domains: trees, other green surroundings, roads, water, and industrial land. Associations with ACR were evaluated using clustered logistic regression models adjusted for time since transplantation. Results: The strongest and only statistically robust associations after FDR correction were observed within the 300 m buffer. Tree-dominant (OR 1.42, 95% CI 1.22–1.65, q = 0.010) and industrial land exposure (OR 1.50, 95% CI 1.28–1.76, q = 0.010) were independently associated with increased odds of ACR. At 500 m, the association with trees persisted nominally (OR 1.39, 95% CI 1.03–1.88, p = 0.034), but did not remain significant after FDR correction, whereas water exposure showed a non-significant trend (OR 1.28, p = 0.057), which did not reach statistical significance. No associations were observed beyond 700 m across all models. Conclusions: Residential environmental composition may be associated with acute cellular rejection after heart transplantation in a scale-dependent manner, with signals confined to the immediate residential environment. Tree-dominant exposure within 300 m showed an association in clustered models; however, this finding was attenuated in mixed-effects sensitivity analyses. These results should be considered exploratory and hypothesis-generating study. Full article

Indoor Environmental Quality (IEQ) strongly influences health, comfort, and learning performance in academic buildings, yet assessment practices remain fragmented and rarely aligned with sustainability goals. This study conducted a PRISMA 2020-guided systematic literature review to identify, screen, and map IEQ indicators for educational facilities and to develop a sustainability-aligned framework for classroom evaluation. Searches of Google Scholar, Scopus, and Web of Science (2010–2025) yielded 365 records; after de-duplication and eligibility screening, 142 peer-reviewed studies were included. From these, 118 unique IEQ indicators were extracted and classified into six domains: thermal comfort, indoor air quality, acoustic quality, visual comfort, environmental quality, and spatial quality. Using sustainability-oriented screening criteria (measurability, relevance, reliability, data accessibility, understandability, and long-term applicability), 50 indicators (42%) were retained as methodologically robust, while 68 (58%) were excluded due to weak standardization or limited practical applicability. The retained indicators were systematically mapped to the environmental, social, and economic pillars and aligned with key SDGs (3, 4, 7, 11, and 13). The resulting Sustainability-Aligned IEQ Indicator Framework integrates quality-screened indicators with pillar/SDG alignment and a mixed-method pathway that combines objective monitoring and occupant perception, supporting context-sensitive evaluation, particularly for naturally ventilated and tropical learning environments. Full article