Search Results (5,035)

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

Background: Catechol-O-methyltransferase (COMT) catalyzes catecholamine O-methylation and contributes to dopamine turnover, potentially influencing levodopa requirements in Parkinson’s disease (PD). We evaluated whether the Gothelf COMT haplotype—and its constituent variants rs2075507, rs4680 (Val158Met), and rs165599—differ in frequency between PD cases and controls. We then tested associations between these variants and clinical phenotypes, with a prespecified focus on levodopa equivalent daily dose (LEDD). Finally, we examined whether haplotype structure and allele-specific context (e.g., background-dependent effects) help explain observed genotype–phenotype relationships in the PD cohort. Aim: Analysis of the rs2075507, rs4680 and rs165599 at individual and haplotype level between control and diseased groups. Furthermore, analysis of association of individual SNPs or haplotype level with clinical outcomes. Subjects and methods: Fifty-five individuals with Parkinson’s disease (PD) and fifty-three neurologically healthy controls were enrolled at a single center. Genomic DNA was isolated from peripheral blood, and three COMT variants—rs2075507 (promoter), rs4680/Val158Met (coding), and rs165599 (3′UTR)—were genotyped by Sanger sequencing. Allele, genotype, and tri-marker haplotype frequencies were estimated, and case–control differences were evaluated. Within the PD cohort, associations with clinical outcomes—primarily levodopa equivalent daily dose (LEDD)—were analyzed using multivariable linear models. Statistical tests were two-sided, with multiplicity control as specified in the corresponding tables. Results: The rs2075507 polymorphism showed a robust additive association with LEDD; each A allele predicted higher dose (LEDD ≈ +1331 mg/day, p = 0.001) after adjusting for age and sex. The tri-haplotype test did not show significant association with LEDD. Nevertheless, rs2075507 SNP strongly marked downstream backgrounds: in AA carriers, rs4680–rs165599 haplotypes were enriched for Val (G) and rs165599-G; in GG carriers, for rs165599-A with mixed Val/Met; and GA was A-loaded at both loci. Exact tests confirmed that AA and GG differed in rs4680–rs165599 composition, whereas GA vs. GG was not significant. Conclusions: The promoter variation at rs2075507 may represent the genetic contributor to levodopa dose requirements when modeled with SNP–SNP interactions, with its effect is modified mostly by rs165599 polymorphism. Tri-haplotypes do not independently predict LEDD. The rs4680 (coding) and rs165599 (3′UTR) context appears to fine-tune rather than determine dosing needs, mainly via interaction with rs2075507 SNP. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

Computer-aided modeling and mathematical optimization of energy systems are essential for improving operational efficiency and achieving emission reductions, particularly for steam turbine systems with part-load-dependent efficiency characteristics. Mixed-Integer Linear Programming (MILP) is the state of the art, due to its short computational times and reliable convergence. However, its simplifications often reduce model accuracy. Mixed-Integer Nonlinear Programming (MINLP) offers high accuracy but faces long computational times and potential convergence issues. Recent advancements in Mixed-Integer Quadratically Constrained Programming (MIQCP) offer a promising approach for more accurate energy system modeling by enabling quadratic and bilinear representations while avoiding the full complexity of nonlinear programs. This study compares the optimization methods MILP, MINLP and MIQCP for the operational optimization of a steam turbine system. The parameterization of the models is based on hourly measurement data of two real-world steam turbines. Key evaluation criteria include accuracy, computational time, implementation complexity and the deviation in the calculated optimum. The results show that MIQCP improves accuracy compared with MILP while requiring lower computational time than MINLP. Overall, the results demonstrate that MIQCP provides a suitable compromise between model accuracy and computational efficiency for the operational optimization of steam turbine systems. Full article

The aim of the study was to investigate the effect of vegetation cover and height on soil and plant nutrients across managed and unmanaged agricultural land in a temperate climate. Fresh soil and vegetation samples were collected during the years 2023 and 2024 from 125 different land parcels in the southwest of the UK. Land was either managed for grazing and/or feed production or not managed for agricultural use, and had a range of grass, crop, legume, herb, and flower species. A linear mixed model was used to assess the effect of vegetation height (in cm) and cover (tonnes of dry matter per hectare) on soil and plant nutrients. The results showed plant dry matter (DM) digestibility, acid detergent fibre (ADF), water-soluble carbohydrate, and oil contents increased with vegetation height, and soil DM and neutral detergent fibre (NDF) decreased with vegetation height. The ratio of soil-to-plant OM reduced and ADF increased with increasing vegetation cover. Interactions between vegetation height and cover (i.e., density) were found for the ratio of soil-to-plant OM, ADF, NDF, DM, DM digestibility, oil, water-soluble carbohydrate, and crude protein nutrients. Measuring the interaction between soil and plant properties showed soil OM stocks increased and soil pH decreased with increased vegetation cover across agricultural land. Full article

Objectives: To evaluate whether preoperative anterior segment optical coherence tomography (AS-OCT) parameters differ according to Big Bubble (BB) formation during deep anterior lamellar keratoplasty (DALK) in patients with TGFBI-related corneal stromal dystrophies (CSD). Methods: This retrospective cohort study included 17 eyes from 12 patients undergoing DALK with an attempted BB technique. Stromal deposit depth was assessed by AS-OCT using both a categorical depth-based classification (anterior, mid-, and posterior stroma) and continuous measurements of stromal involvement (µm). The ratio between stromal involvement and the thinnest corneal point was calculated. Intraoperative data included BB success, BB type, and complications. Inter-eye correlation was accounted for in comparisons of continuous variables using linear mixed-effects models. Results: BB formation was achieved in 11 of 17 eyes (64.7%), with type 1 BB observed in all successful cases. BB success was observed in all eyes with anterior or mid-stromal involvement and in 33.3% of eyes with posterior stromal involvement. Greater stromal deposit depth and a higher stromal-depth-to-thinnest-point ratio were observed in eyes in which BB formation failed (p < 0.01). No intraoperative perforations or conversions to penetrating keratoplasty occurred. Inter-observer agreement for AS-OCT measurements was high. Conclusions: BB failure was more frequent in eyes with greater absolute and relative stromal deposit depth, as assessed by preoperative AS-OCT during DALK in TGFBI-related CSD. These AS-OCT-derived parameters may support surgical planning and improve patient selection for BB DALK in this clinical setting. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

In the tropical Andes, rangeland degradation has become one of the main threats to the sustainability of livestock production in the face of climate change. In this context, optimizing the yield and nutritional quality of raw material for silage is essential to sustain livestock productivity. The aim of this study was to identify local accessions (LM) of Lolium multiflorum Lam. with greater forage potential through evaluations in consecutive cuts made at the anthesis phenological stage, using a randomized complete block design with four replicates and ten local accessions (LM1, LM2, LM3, LM4, LM6, LM7, LM8, LM11, LM12 and LM13). The statistical analysis, based on linear mixed models, showed that cuts at anthesis had a significant effect among accessions, revealing high variability in agronomic and nutritional performance across cuts. In LM4, plant height at the fourth cut was 2.48-fold higher than at the first cut. Likewise, LM4 and LM13 were identified as the latest accessions to reach anthesis in the first cut, with a decreasing trend across cuts and stabilization from the third cut onward. These accessions also showed the greatest basal coverage area, increasing 9.94- and 8.18-fold in the fourth cut relative to the first. Fresh forage yields in LM4 and LM13 increased 13.2- and 10.1-fold, and dry matter yields 13.98- and 9.86-fold, compared with the first cut. They also exhibited the highest average daily dry matter ac-cumulation rate. By contrast, the fresh forage and dry matter yields of the remaining accessions were significantly lower than those of LM4 and LM13. The main difference between these two accessions was observed in dry matter percentage, with higher values and a stable trend in LM4 across all cuts. In terms of nutritional quality, LM4 presented crude protein of 24.2% in the second cut and 24.0% in the fourth cut, while digestibility was 86.2% in the second cut and 85.0% in the fourth cut. In conclusion, although the ensiling process was not evaluated in this study, LM4 showed the most stable and outstanding values in both agronomic and nutritional performance, thus emerging as a promising accession for selection and use as raw material for silage production in the tropical Andes. Full article

Background/Objectives: Under general anesthesia, maintaining patients’ blood pressure (BP) is important to prevent organ ischemia. When bleeding occurs, it is sometimes difficult to increase BP with boluses of fluids or transfusions, and vasoconstrictors must be administered. This study investigated circulatory dynamic changes in patients who bled during surgery and were administered phenylephrine, particularly left ventricular end-diastolic volume (EDV), effective arterial elastance (Ea), and left ventricular end-systolic elastance (Ees), calculating each value from the left ventricular–arterial coupling (Ees/Ea). Methods: We calculated Ees/Ea using electrocardiograms, arterial pressure waveforms, and phonocardiograms using an esophageal stethoscope. We investigated the changes in patients’ EDV, Ea, and Ees during two periods: phenylephrine administration and after BP elevation. Results: The seven participants comprised three men and four women. Between the two periods, linear mixed-model analysis revealed that mean arterial pressure (MAP), Ea, and Ees significantly increased over time (MAP; β = 8.7, p < 0.01, Ea; β = 0.22, p < 0.05, Ees; β = 0.73, p < 0.05), while no significant changes were observed in other parameters such as heart rate and EDV. Conventional parameters demonstrated that stroke volume variation significantly decreased (β = −2.0, p = 0.01), systemic vascular resistance index significantly increased (β = 200, p < 0.01), while no significant change was observed in cardiac index (β = −0.03, p = 0.7). In patients administered phenylephrine due to BP decrease from bleeding, significant changes in afterload and cardiac contractility occurred without changes in preload. Conclusions: Our noninvasive method for calculating EDV, Ea, and Ees can be valuable for monitoring hemodynamics under anesthesia. Full article

Gastrointestinal (GI) parasitic infections pose a formidable global challenge to livestock production and continue to affect livestock health and productivity, particularly in tropical and subtropical regions. This study investigated the prevalence, diversity, and epidemiological determinants of GI parasites in 1406 cattle, goats, and sheep from three districts of North Bengal, India (Cooch Behar, Alipurduar, and Jalpaiguri). Parasitological data were analysed using descriptive statistics and inferential methods. Overall prevalence was 69.4%, with cattle showing the highest infection rate (71.62%), followed by sheep (69.30%) and goats (67.19%). Spatial variation was evident among districts, with Cooch Behar recording the highest prevalence (71.20%). Seasonal effects were assessed using Generalized Linear Mixed Models (GLMs), which indicated significantly higher infection probabilities during the monsoon (75.70%) and summer (72.95%) compared with winter (57.78%). The predominant parasite genera identified were Eimeria spp., Strongyloides spp., and Fasciola spp. Host-parasite associations were further explored using Multiple Correspondence Analysis (MCA), revealing distinct clustering patterns, with cattle associated mainly with Eimeria spp. and Strongyloides spp., goats with Trichuris spp. and Nematodirus spp., and sheep with Fasciola spp. and Paramphistomum spp. A species-specific heatmap was used to visualize parasite distribution across host species and seasons, highlighting higher infection intensities during the summer and monsoon periods. Overall, the results demonstrate that GI parasitic infections in North Bengal are influenced by host species and seasonal climatic factors, supporting the implementation of targeted, species- and season-adapted parasite management strategies. Full article

As the participation of photovoltaic–storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush–Kuhn–Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV–storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, β, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels. Full article

Economic diversification is one state method used to best utilize national resources and contribute to economic and sustainable development. This paper examines the impact of governance on economic diversification in a selected number of countries (114) using both governance and economic diversification indicators from 1996 to 2023. The intended outcome of this paper is to determine whether the improvement in the quality of governance, measured by the aggregated WGI index, is positively and statistically associated with an increase in the Economic Complexity Index (ECI). A general linear mixed model (GLMM) was constructed to address the research question by evaluating fixed and random effects based on the analysis of repeated measures. However, the study has some limitations such as using an aggregate governance index rather than each indicator by itself and differences among country groups in development and institutional quality level. The findings reveal that economic diversification is linked to the quality of a country’s institutions. The result shows that (coefficient β = 0.283) with 95% CI, which means that on average, the ECI increased by 0.283 for every one-unit increase in the WGI. Moreover, the increase in ECI exceeded 0.1 for every one-unit increase in WGI 95% of the time. Countries with advanced administrative, economic, and institutional structures are better positioned to achieve their desired economic diversification goals. Thus, decision-makers and legislators, especially in countries with low-levels of institutional quality, need to balance ensuring good governance practices with supporting the country’s economic development. Full article

Background: Neoprene knee sleeves are commonly used to enhance joint stability and mechanical performance during resistance training. However, the specific influence of sleeve density on the force–velocity–power (F–V–P) profile during multi-joint lower-body exercises such as the back squat remains unclear. This study aimed to compare the theoretical F–V–P parameters derived from back squat performance while wearing low-density (LD) versus high-density (HD) knee sleeves. Methods: Fifteen resistance-trained males completed an incremental back squat test under both LD and HD conditions. A linear position transducer recorded barbell displacement and velocity. Individual force–velocity relationships were modelled to determine maximal theoretical force (F₀), velocity (V₀), power (Pmax), and the F–V slope. Paired-sample t-tests, linear mixed models, and Cohen’s d effect sizes were calculated. Clinical relevance was assessed using a threshold defined as 0.2 × the standard deviation of the HD condition. Bayesian analyses were conducted to estimate the probability and magnitude of the observed effects. Results: No statistically significant differences were observed between sleeve conditions for F₀, V₀, Pmax, or F–V slope (p > 0.05, d ≤ 0.37). Nonetheless, HD sleeves yielded slightly higher mean values for F₀, V₀, and Pmax, exceeding the predefined threshold for practical relevance. Bayesian models showed moderate probabilities (~0.80) that HD sleeves outperformed LD, though with limited chances of crossing the clinical significance threshold. Conclusions: Although HD sleeves do not produce systematic changes in F–V–P parameters, their increased material stiffness may provide small yet practically meaningful mechanical advantages in high-force resistance training contexts. Full article

This study investigated how illuminance and spectrum in office lighting affect psychological fatigue, preference, visual comfort, and cognitive performance. Forty adults participated in a repeated-measures experiment under four conditions with two illuminance levels (500, 1000 lx) and two LED types (full-spectrum, conventional). For each condition, Karolinska Sleepiness Scale scores (fatigue), Office Lighting Survey ratings (preference, visual comfort), and Alphanumeric Verification Task performance (work speed, accuracy) were collected. Linear mixed-effects modeling was applied alongside correlation and regression analyses to examine condition effects and associations between variables. Compared to 500 lx, ΔKSS significantly decreased under 1000 lx, confirming that increased illuminance is associated with reduced psychological fatigue. At the same illuminance level, full-spectrum LEDs showed benefits, including lower fatigue and faster responses. Preference and visual comfort showed minimal direct sensitivity to lighting conditions but were moderately and positively correlated, while fatigue exhibited significant negative correlations with both preference and response speed. An interaction between illuminance and spectrum on accuracy suggested a speed–accuracy trade-off under high-illuminance full-spectrum lighting. Overall, the findings indicate that office lighting, particularly illuminance and spectral quality, acts as a human-centered factor shaping an interconnected response network linking fatigue, affective appraisal, and task performance. Full article