Search Results (10,380)

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

Empirical parameter settings in steam-based soil disinfestation for Panax notoginseng (a valuable medicinal plant) often hinder the simultaneous optimization of pathogen control and energy efficiency. To address this limitation, this study aims to develop a parameter regulation framework that integrates multi-output regression with scenario-oriented intelligent decision-making. Initially, a comprehensive dataset comprising critical parameters—steam pressure (P_steam), soil compaction (C_soil), and heating time (t_heat)—was established. A random search (RS) hyperparameter optimization scheme was employed to comparatively evaluate the multi-output predictive performance of Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) for the joint estimation of soil temperature (T_soil) and root-rot pathogen kill rate (Kill_rate). Subsequently, by integrating total energy consumption (E_total) and operating electricity cost models, a constrained search algorithm was implemented to develop three objective-oriented decision modes: “maximize Kill_rate”, “minimize C_electricity”, and “maximize Efficiency”. Results demonstrate that the RS-optimized SVR yielded superior multi-output performance, achieving R² of 0.968 for T_soil (MAE = 2.44 °C) and 0.808 for Kill_rate (MAE = 7.85%). Compared to conventional empirical configurations, the proposed decision modes exhibited significant advantages across diverse scenarios. In the “maximize Kill_rate” mode, dynamic extensions of t_heat facilitated theoretical complete inactivation even under challenging heating conditions, effectively eliminating disinfection “blind spots” inherent in fixed-duration strategies. Under the “minimize C_electricity” mode, precise regulation of P_steam reduced operational electricity costs by 18.2% while satisfying the constraint of Kill_rate ≥ 95%. Furthermore, the “maximize Efficiency” mode identified an optimal operating point at C_soil = 64 kPa (P_steam = 0.4 MPa, t_heat = 13 min), thereby mitigating performance degradation associated with excessive tillage or high media rigidity and achieving an optimized cost–benefit ratio. By synthesizing high-fidelity multi-output regression with a flexible multi-mode decision-making framework, this study provides an intelligent solution for soil disinfestation in protected agriculture, facilitating the coordinated optimization of phytosanitary efficacy, energy expenditure, and economic viability. Full article

Autonomous driving in underground mines faces significant challenges due to Global Navigation Satellite System (GNSS) denial and harsh environmental conditions. Mainstream multi-sensor fusion and Simultaneous Localization and Mapping (SLAM) schemes have achieved substantial progress in underground navigation, but their deployment in feature-sparse tunnels may still face challenges related to computational burden and perception robustness. This study explores an infrastructure-assisted navigation architecture that transforms the roadway into a structured luminous guidance channel by deploying programmable Light Emitting Diode (LED) strips along the tunnel roof. The proposed system simplifies complex three-dimensional pose estimation into a two-dimensional visual servoing task targeting optical signals. Central to this approach is a robust data fusion strategy that utilizes a topology matching algorithm to map noisy Ultra-Wide-band (UWB) coordinates onto a discrete LED index space, thereby providing a reliable global positioning reference. Furthermore, a hierarchical fault-tolerant controller based on a Finite State Machine (FSM) is designed to facilitate seamless degradation to a UWB-assisted ultrasonic wall-following mode in the event of visual degradation, supporting fault-tolerant operation under controlled laboratory conditions. Experimental results in a laboratory simulation environment demonstrate that the system achieves millimeter-level static initialization accuracy, a dynamic tracking Root Mean Square Error of approximately 4 cm, and a 100% autonomous recovery rate from visual failures in straight tunnels. These results demonstrate the feasibility of the proposed infrastructure-assisted route under controlled laboratory conditions and suggest its potential as an engineering reference for structured underground transport scenarios with acceptable infrastructure modification. Full article

Underwater images often suffer from mixed degradations, including motion blur, which reduce structural clarity and adversely affect downstream vision tasks. To address this problem, we propose a physically guided Transformer framework for underwater motion deblurring. The proposed method combines two-stage cepstrum-based blur estimation with a point spread function (PSF)-guided self-attention mechanism. Specifically, blur parameters are first robustly estimated through cepstrum analysis, ellipse fitting, and negative-peak refinement, and the resulting PSF is then embedded into the Transformer attention module to guide feature aggregation. On the real underwater benchmark datasets UIEB Challenge-60 and EUVP330, the proposed method achieves UIQM/UCIQE scores of 4.09/0.56 and 3.40/0.58, respectively, significantly outperforming UFPNet and Phaseformer, thereby demonstrating superior perceptual restoration in terms of sharpness, contrast, and color consistency. On the synthetic test set, the proposed method attains 24.23 dB PSNR and 0.918 SSIM, outperforming both recent deep models and classical non-blind deconvolution methods, which confirms its strong restoration fidelity and structural consistency. In the controlled water-tank experiments, the proposed method consistently achieves the best performance under different camera motion speeds, demonstrating excellent robustness and practical applicability. Overall, the proposed framework provides an effective and physically interpretable solution for underwater motion deblurring. Full article

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article

Tool-using AI agents couple a language model with controller logic, memory, and external tools such as browsers, email, calendars, file systems, and transaction APIs. This architecture expands capability, but it also enlarges the security boundary: agents routinely ingest untrusted content while holding privileges that can reveal private data and trigger external side effects. The resulting failures are not limited to poor text generation; they include prompt injection, indirect injection through tool outputs, confused-deputy behavior, unauthorized actions, and misleading claims about the tool state. Because large-scale testing on deployed products is difficult, vendor-specific, and ethically sensitive, we present a transparent, theoretical simulation-based framework for evaluating user-facing risk in tool-using agents. The methodological contribution is a formal threat model that separates compromise, harm, and severity, and a Monte Carlo evaluation pipeline that maps architectural choices (permissions, retrieval, memory exposure, and approvals) and defensive controls to comparable outcome metrics. We instantiate the framework for six representative threat scenarios and nine defense configurations, reporting attack success rate (ASR), benign task success, latency overhead, and severity-weighted harm. Across scenarios, the least-privilege tool design is the strongest single broad control, human-in-the-loop approvals sharply reduce high-impact actions and exports but degrade under user error and habituation, retrieval allowlisting nearly eliminates indirect injection while leaving other channels largely unaffected, and rate limiting reduces tail severity more than ASR. These results position agent safety as an architectural and operational problem and because they arise from an assumption-explicit simulator rather than field measurements, should be read as comparative design guidance rather than incident-rate estimates for any deployed product. Full article

Background/Objectives: Malaria remains a major global health burden, particularly in sub-Saharan Africa, where the recent invasion and urban expansion of Anopheles stephensi are increasing transmission risk in densely populated areas. Conventional vector control strategies, including widespread insecticide application, are progressively losing efficacy due to the rapid spread of resistance. These limitations have accelerated the development of genetic control approaches aimed at either suppressing vector populations or replacing them with genetically modified mosquitoes incapable of transmitting pathogens, with the shared objective of reducing disease transmission. For population suppression strategies, an essential component is a conditional regulatory system that enables precise control of toxic or otherwise deleterious effector proteins. The most widely used platform, the tetracycline-dependent (Tet) system, modulates gene expression in response to tetracycline. However, this system can exhibit leaky expression and variable regulation, which may compromise its reliability and limit its application in certain contexts. The dihydrofolate reductase (DHFR) destabilization domain (DD) system, developed in Drosophila, offers an alternative strategy for post-translational control of protein stability. In this system, proteins fused to a destabilization domain are rapidly degraded unless stabilized by the small molecule trimethoprim (TMP), enabling tight and reversible control. In Drosophila and prior reports, this system has been associated with relatively low fitness costs, although such effects have not been systematically evaluated in mosquitoes. Before adapting this system for mosquito genetic control, it is therefore essential to assess the impact of TMP exposure on key life-history traits. Methods: Here, we assessed the effects of varying TMP concentrations on mosquito development, survival, and reproductive output. Results: Our results demonstrate that low concentrations of TMP exposure had no detectable effects on immature development, adult survival, or reproductive output under the conditions tested, supporting the implementation of the DHFR-DD system in mosquitoes. Importantly, these effects were dose-dependent, with moderate to high TMP concentrations producing measurable impacts on mosquito fitness. Conclusions: These findings provide a foundational step toward the development of more precise and reliable conditional expression systems for genetic vector control, advancing innovative strategies to mitigate malaria transmission in high-risk regions. Full article

Curcumin, a plant-derived polyphenolic compound, exhibits diverse pharmacological activities such as antioxidant, anti-inflammatory, anticancer, neuroprotective, and cardiovascular protective effects, and is widely used in food, medicine, and other fields. However, its poor water solubility and easy oxidative degradation limit its extensive application in biomedicine. To solve these problems, a series of biomedical polyurethanes (Cur-PU) with similar molecular weights but different PEG contents were successfully synthesized using HO-PCL-OH and HO-PEG-OH as soft segments and curcumin as a chain extender. The results indicated that increasing the PEG content reduced the T¹_m, T¹_c, and H¹_c of Cur-PU, along with a slower crystallization rate and lower crystallinity. More importantly, a higher PEG content decreased the water contact angle but increased water solubility and water uptake, which, combined with reduced crystallinity, enhanced hydrophilicity, swelling ratio, curcumin release rate, and degradation rate in an enzymatic solution and pH 8.0 buffer. Thus, precise regulation of Cur-PU’s degradation and curcumin release was achieved by controlling the PEG content. Biocompatibility tests confirmed that Cur-PU exhibited excellent antioxidant and antibacterial activities, making it a highly promising biomedical material. Full article

Proteomic research in the genus Vitis has progressed from early biochemical studies of soluble proteins to high-resolution, quantitative analyses encompassing all major organs and derived products. This review provides a comprehensive synthesis of advances in grapevine and wine proteomics. In leaves, studies have revealed extensive remodeling of photosynthetic, antioxidant, and defense pathways under biotic (e.g., Plasmopara viticola, Erysiphe necator, Xylella fastidiosa, Candidatus Phytoplasma vitis) and abiotic stresses (drought, salinity, heat, light). Bud proteomics elucidated hormonal regulation and mechanisms of dormancy release, while root studies identified nitrate-dependent metabolic shifts and adaptive protein networks. Cell culture models enabled controlled investigation of elicitor responses, stilbene biosynthesis, and temperature-induced proteome changes. In berries, proteomics clarified developmental transitions from fruit set to ripening, emphasizing proteins related to secondary metabolism, vacuolar transport, and stress tolerance. Comparative analyses across cultivars and environments identified biomarkers linked to aroma, color, and texture. The wine proteome revealed selective persistence of grape-derived proteins (e.g., thaumatin-like proteins, chitinases) and yeast peptides influencing stability and sensory properties, while Botrytis cinerea infection significantly alters this balance by degrading PR proteins and introducing fungal enzymes. Altogether, the Vitis proteome emerges as a dynamic, multifunctional system crucial for understanding plant adaptation, enological quality, and biomarker discovery. Full article

Background/Objectives: Gastroesophageal reflux disease (GERD) is prevalent in infants and frequently managed with acid-suppressive medications that elevate gastric pH. This pilot study aimed to evaluate how varying gastric pH levels (2.5, 4.0 and 6.0) influence the hydrolysis of milk proteins in human milk (HM), cow’s milk-based infant formula (CMF), and goat milk-based infant formula (GMF). Methods: Samples were subjected to a 30 min in vitro gastric digestion using pediatric human gastric juice obtained from clinical donors. Protein degradation was analyzed via SDS-PAGE densitometry, comparing digested aliquots to undigested controls. Results: At pH 2.5, caseins were highly digested in all samples, especially in HM and GMF. At pH 4.0, GMF displayed an apparent 51% greater casein degradation relative to CMF and HM in this pilot analysis. α-lactalbumin degradation was markedly higher in GMF at all pH levels; notably, at pH 4.0 and 6.0, only GMF exhibited digestion of this protein. Albumin showed almost complete degradation in HM and GMF at pH 2.5, and GMF maintained greater degradation at higher pH levels. β-lactoglobulin (absent in HM) was better digested in GMF at pH 2.5, whereas CMF showed higher hydrolysis observed at pH 4.0 and 6.0. Lactoferrin digestion was most efficient in HM and GMF at pH 2.5, with no differences observed at higher pH levels. Conclusions: These preliminary findings suggest that GMF may offer digestive advantages for infants with GERD under pharmacological acid suppression, particularly regarding casein and α-lactalbumin breakdown at higher pH. The distinct digestion kinetics of CMF and GMF at different pH levels provide a physiological basis for targeted infant feeding strategies. Further large-scale studies are required to validate these exploratory observations. Full article

Background/Objective: Mental fatigue (MF) significantly impairs psychomotor performance in dynamic sports; however, its specific impact on closed-skill precision-demanding tasks remains underexplored. This study investigated the acute effects of experimentally induced MF exposure on psychophysiological responses, mood states, and archery shooting performance. Methods: Fifteen well-trained male compound-bow archers participated in a randomized crossover study. Participants completed an MF condition (30 min modified Stroop task) and a control condition (CON; passive viewing of a neutral documentary), separated by a 72 h washout period. Continuous heart rate (HR), archery shooting accuracy, ratings of perceived exertion (RPE), rating scale of mental effort (RSME), state anxiety (VAS-A), mood states, and exercise enjoyment scale (EES) were assessed. Results: The Stroop task successfully induced subjective MF. Consequently, shooting accuracy significantly deteriorated in the MF condition compared to that in the CON condition (p < 0.001; g = 0.731). While HR and VAS-A remained consistent across conditions, the MF condition elicited a significant increase in RPE (p = 0.007; g = 0.836) and RSME (p = 0.010; g = 0.794). Furthermore, MF significantly increased feelings of anger and fatigue while drastically reducing PACES (p < 0.001; g = 1.530). Conclusions: Acute MF significantly degrades fine motor accuracy in precision sports. The pronounced dissociation between elevated RPE and stable peripheral physiological strain suggests that performance decline is driven by top-down cognitive burden rather than physiological limitations. Therefore, systematic monitoring of cognitive load is crucial for optimizing performance in precision sports. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

Polyester resin biocomposites containing biochar have attracted attention for improving mechanical strength and thermal stability while promoting sustainability. The pyrolysis temperature of biochar and its proportion in the polymer matrix are key factors affecting biocomposite performance. This study examined how biochar pyrolysis temperatures (400, 600, 800 °C) and incorporation levels (10, 20, 30 wt.%) influence the physical, chemical, mechanical, flammability, and morphological properties of polyester-based biocomposites. The samples were analyzed for density, water absorption, FTIR, XRD, flexural and tensile strength, ignition time, structural degradation, volumetric loss, and SEM microstructure. Biocomposites with 30 wt.% biochar produced at 800 °C showed the best mechanical properties, with a flexural strength of 95.3 MPa and an elastic modulus of 4417.4 MPa, representing increases of 14.5% and 45.7%, respectively, over the control. FTIR and XRD results revealed decreased aliphatic groups and increased aromaticity at higher pyrolysis temperatures, improving interactions between the matrix and biochar. These biocomposites also demonstrated enhanced thermal stability, with an ignition time of approximately 963 s, delayed structural degradation, and reduced volumetric loss (~19.3%). Overall, pyrolysis temperature and biochar content significantly influence the structural, mechanical, and thermal properties of polyester biocomposites, showing that biochar serves as a sustainable, performance-enhancing component in thermoset polymer matrices. Full article

Petroleum contamination of soils remains a significant environmental problem in boreal regions, where low temperatures constrain natural attenuation processes and complicate bioremediation. Nitrogen biostimulation is widely used to enhance petroleum hydrocarbon degradation; however, the combined effects of temperature regime, nitrogen form, contamination level, and nitrogen dosage remain insufficiently resolved for sandy podzolic soils of northern regions. This study investigated nitrogen-assisted biostimulation of petroleum-contaminated sandy podzolic soil collected in the Khanty–Mansi Autonomous Okrug (Western Siberia, Russia) using a factorial experimental design. Soil samples were artificially contaminated with crude oil at concentrations of 25, 50, and 100 g kg⁻¹ and incubated under warm and cold temperature regimes. Two nitrogen sources, urea and ammonium nitrate, were applied at several dosages. Changes in residual petroleum hydrocarbon content were monitored together with the abundance of culturable microorganisms under the applied cultivation conditions at the intermediate contamination level on day 60. Nitrogen supplementation enhanced petroleum hydrocarbon removal relative to the untreated control, but the magnitude of the effect depended substantially on temperature, nitrogen form, and contamination level. Under the tested conditions, ammonium nitrate was generally associated with stronger hydrocarbon removal than urea, particularly at the intermediate contamination level (50 g kg⁻¹). The results indicate that the response to nitrogen biostimulation in sandy boreal soils is controlled by interacting experimental factors rather than by nitrogen addition alone. These findings improve the positioning of nutrient-assisted remediation in cold-region soils and provide a basis for future mechanistic and field-scale studies. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article