Search Results (551)

Biofilm formation is a major contributor to antibiotic resistance in Klebsiella pneumoniae, posing a serious challenge to current therapeutic strategies. Thus, this study aims to evaluate the antibiofilm activity of three essential oils Thymus hirtus Willd. Ssp. algeriensis Boiss, Syzygiuma romaticum, and Eucalyptus globulus against four clinical isolates of ESBL-producing K. pneumoniae, along with the reference strain K. pneumoniae ATCC 700603. The antibiofilm activity of essential oils was assessed with crystal violet assay using MICs ranging from 3.38 ± 0.2 to 27.1 ± 0.56 mg/mL, 2 ± 0.19 to 32 ± 0.55 mg/mL, and 13.78 ± 0.62 to 110.25 ± 3.37 mg/mL, for TEO, SEO and EEO, respectively. In vitro tests showed that S. aromaticum EO and T. algeriensis EO exhibited the best anti-adhesive activity with a percentage of up to 75.39%, while no difference was observed between the EO in their eradication activity. Microscopic observations confirmed the disorganization of the biofilm after treatment with T. algeriensis. The molecular docking analysis of the three EOs main compounds with MrkH, SdiA and MrkD revealed that SdiA was the most favorable target, with p-cymene (−7.7 kcal/mol), α-pinene (−7.5 kcal/mol), and eucalyptol (−7.1 kcal/mol) showing the strongest binding affinities. Thymol and p-cymene showed also a favorable affinity with MrkD. Overall, p-cymene and α-pinene demonstrated the most favorable binding profiles, whereas linalool exhibited the weakest predicted interactions. These results highlight the promising potential of these EOs, as multi-target antibiofilm agents against MDR- K. pneumoniae biofilms. Full article

Accurate prediction of pavement performance is essential for sustainable pavement management, especially in flood-prone regions where environmental stressors accelerate deterioration. This study develops a machine learning-based comparative framework to evaluate the contributions of baseline pavement condition, maintenance and rehabilitation (M&R) activities, and environmental exposure to predicting changes in Pavement Condition Index (ΔPCI) across 11,214 matched pavement segments in Southeast Texas from 2019 to 2023. Three nested modeling scenarios were evaluated using Linear Regression, Random Forest, and XGBoost, with performance evaluated using R², MAE, and RMSE. Baseline variables alone showed limited predictive capability, whereas adding M&R history produced the largest improvement. Environmental and flood-related variables provided further gains, particularly for nonlinear ensemble models. XGBoost achieved the highest predictive performance in the fully integrated scenario (R² = 0.65, MAE = 10.63, RMSE = 14.02). SHAP analysis identified SDI2019 and PCI2019 as the strongest predictors, while selected M&R and environmental variables also contributed meaningfully. The findings demonstrate that integrating treatment history and environmental exposure substantially improves pavement performance prediction and supports more sustainable, climate-resilient pavement management and helps agencies prioritize maintenance and allocate resources more effectively. Full article

Air entrainment in self-compacting concrete (SCC) is governed by coupled interactions between chemical admixtures, empirical workability behaviour, aggregate-skeleton geometry and early air-bubble stability. In highly flowable mixtures, the hardened air-void system cannot be assessed reliably from total air content alone because bubble escape, redistribution and coalescence in the fresh state may change the final pore structure. This study evaluates the link between early fresh-air retention and hardened air-void characteristics in 25 SCC mixtures arranged according to a five-level Graeco-Latin square design. The analysed factors were air-entraining admixture (AEA) dosage (0.00–0.20% by mass of cement), binder type, water-to-binder ratio (0.29–0.41) and the volumetric paste-to-aggregate filling parameter φ (1.1–1.5). The aggregate skeleton was kept constant to separate paste-composition and volumetric-filling effects from aggregate grading. Fresh concrete was characterised by slump-flow diameter, T₅₀ flow time, density and air content after 5 and 15 min; these quantities were treated as empirical workability and early-retention indicators, not as direct rheological parameters. Hardened concrete was examined after 28 days according to EN 480-11 using total hardened air content A, spacing factor L, micropore content A₃₀₀ and specific surface α. The slump-flow diameter ranged from 50 to 79 cm, fresh air content after 5 min from 1.6% to 8.6%, air loss between 5 and 15 min from 0.41 to 1.12 percentage points, hardened air content from 1.20% to 8.59%, and spacing factor from 0.13 to 0.44 mm. Strong correlations were obtained between fresh and hardened air contents (A₅ vs. A: r = 0.920, R² = 0.846, p < 0.001, 95% CI for r: 0.824–0.964; A₁₅ vs. A: r = 0.922, R² = 0.849, p < 0.001, 95% CI for r: 0.828–0.965), while hardened air content was strongly and inversely related to spacing factor (A vs. L: r = −0.907, R² = 0.822, p < 0.001, 95% CI for r: −0.958 to −0.797). The recalculated ANOVA showed that statistical significance was response-dependent: w/b was significant for early air loss ΔA (F = 4.190, p = 0.040, partial η² = 0.677) and micropore content A₃₀₀ (F = 4.058, p = 0.044, partial η² = 0.670), whereas binder type showed near-threshold tendencies for fresh and hardened air contents. No single factor was statistically significant for all air-void descriptors. The SDI-based approach is therefore presented as a bounded explanatory framework, not as an externally validated prediction model. Direct durability claims, including freeze–thaw resistance, require separate experimental verification. Full article

Complex optimization problems are widely encountered in engineering design, intelligent manufacturing, communication systems, and wireless sensor network deployment. However, the original Enterprise Development Optimization Algorithm (EDOA) still suffers from insufficient population diversity, weak search guidance, and limited adaptability in balancing exploration and exploitation when solving high-dimensional and multimodal optimization problems. To address these issues, this paper proposes a Multi-Strategy Improved Enterprise Development Optimization Algorithm (MIEDOA). First, a Strategic Diversification Initialization (SDI) strategy is developed by integrating Sobol sequence sampling, random initialization, and Gaussian perturbation to improve the diversity and distribution quality of the initial population. Second, an Organizational Synergy Learning (OSL) mechanism is introduced to enhance search guidance through the collaborative utilization of elite information, population mean information, and peer interaction. Third, an Adaptive Governance with Feedback Regulation (AGFR) strategy is designed to dynamically regulate the exploration–exploitation behavior according to the current population fitness state. The proposed MIEDOA is evaluated on the CEC2017 and CEC2020 benchmark suites and compared with representative EDOA variants, CEC winner algorithms, and other advanced optimization methods. The experimental results indicate that MIEDOA generally achieves competitive performance in terms of solution quality, convergence behavior, and robustness across different benchmark scenarios. In addition, strategy effectiveness analysis, parameter sensitivity analysis, and statistical tests further provide evidence supporting the effectiveness of the proposed strategies. Finally, MIEDOA is applied to a three-dimensional wireless sensor network deployment problem. The results suggest that the proposed algorithm can obtain competitive deployment solutions and satisfactory coverage performance under different node scales, demonstrating its potential applicability to practical engineering optimization problems. Full article

Background: Malaria and dengue continue to pose significant public health challenges in Asia, with differing temporal trends and regional distributions. However, comparative and long-term assessments of their disease burden and future trajectories remain limited. Methods: Using Global Burden of Disease Study 2021 data, we estimated age-standardized incidence rates (ASIR), disability-adjusted life years (DALYs-ASR), and estimated annual percentage changes (EAPCs) for global, Asian, and Chinese populations by age, sex, and socio-demographic index (SDI). Correlations with SDI and population density were analyzed, and an importation risk index for China was developed. Future trends to 2030 were projected using Bayesian age-period-cohort modeling. Findings: From 1990 to 2021, dengue ASIR increased globally and in China, particularly in middle-SDI regions, whereas malaria ASIR and DALYs-ASR declined substantially, with the most pronounced reductions observed in China. Dengue DALYs-ASR were highest among children under five, while incidence peaked in adolescents; malaria burden was concentrated in young children and young adults. Sex-specific differences were observed, with higher dengue incidence in females but greater DALY rates in males. Geographically, Southeast Asian countries contributed most to the estimated importation risk for both diseases. Projections indicate continued increases in dengue burden through 2030, alongside further declines in malaria. Conclusions: Malaria and dengue exhibit divergent epidemiological patterns across Asia, with declining malaria burden but rising dengue incidence. These findings highlight the need for differentiated control strategies, strengthened regional collaboration, and enhanced surveillance of cross-border transmission. Full article

Background/Objectives: Genitourinary cancers represent a major and growing source of cancer burden worldwide; however, important disparities persist across the Americas. This study aimed to evaluate the incidence, mortality, and disability burden of prostate, testicular, bladder, and kidney cancers across 38 countries and territories using Global Burden of Disease (GBD) 2023 estimates, with emphasis on temporal trends and sociodemographic inequalities. Methods: A descriptive ecological study was conducted using Global Burden of Disease (GBD) 2023 estimates. Age-standardized incidence, mortality, and disability-adjusted life year (DALY) rates per 100,000 population were analyzed for prostate, bladder, kidney, and testicular cancers. Burden estimates were obtained from GBD 2023 data, and temporal trend analyses were conducted using age-standardized rates from 2000–2023. Temporal trends were assessed using weighted log-linear regression to estimate annual percentage changes (APCs) based on age-standardized rates from 2000–2023. Results: In 2023, prostate cancer accounted for the greatest genitourinary cancer burden across the Americas, with high incidence concentrated in high-income North America, whereas mortality and DALY rates were disproportionately elevated in Latin America and the Caribbean. Across all cancer types, high-SDI regions consistently exhibited higher incidence but more favorable mortality and disability profiles. Testicular cancer incidence increased across all SDI quintiles, although mortality reductions were mainly observed in high-SDI settings. Bladder and kidney cancers demonstrated similar epidemiological patterns, with declining mortality trends in high-income regions but persistent or increasing burden in lower-SDI countries. Mortality-to-incidence disparities remained substantial across Latin America and the Caribbean, which may reflect differences in healthcare resources, early detection, treatment availability, or other contextual factors not directly captured in the GBD database. National extremes included Bermuda prostate ASIR 170.63 and Dominica DALYs 1423.30 per 100,000. Conclusions: The burden of genitourinary cancers across the Americas remains strongly associated with socioeconomic inequalities. Although higher-resource settings have achieved important reductions in mortality and disability, these gains have not been equitably distributed across the region. Strengthening health system capacity, improving early diagnosis, and ensuring equitable access to evidence-based cancer care are essential to reduce avoidable mortality and improve long-term outcomes throughout the Americas. Full article

The mean stand diameter at breast height (DBH) is a key indicator of stand structure and productivity and is widely used in forest resource inventory and management planning. When using regional inventory data, nonlinear interactions between plot-level conditions and predictor variables can undermine the stability of traditional empirical equations across varying site qualities and stand densities. To improve the accuracy and robustness of inventory-scale predictions of mean stand DBH, this study utilized data from 854 forest plots and employed stand age, site class index (SCI), and stand density index (SDI) as independent variables. The predictive performance of traditional growth equations, machine learning models (Random Forest, XGBoost, LightGBM, and support vector machine), and deep learning models (MLP and CNN, ResNet, RNN) was systematically compared, and ensemble learning strategies were further applied to optimize model performance. The results indicated that the Weibull model based solely on stand age achieved the best fit (R² = 0.669). Incorporating SCI and SDI greatly improved model explanatory capability with R² rising to 0.838. XGBoost and CNN further improved predictive performance (R² = 0.852 and 0.861, respectively), while the ensemble model exhibited the highest goodness-of-fit (R² = 0.893), outperforming all individual models. Compared with linear regression, machine learning models demonstrated superior predictive capability. A feature importance analysis indicated that stand age, site quality and stand density together drive mean stand DBH prediction, among which stand age and stand structural characteristics are the dominant influencing factors, whereas SCI and SDI have comparatively weaker effects. Overall, the ensemble model substantially enhanced the prediction accuracy of mean DBH in Pinus yunnanensis stands, thereby providing for precision forest management and ecological function assessment. Full article

The polymerization shrinkage stress (SS), degree of conversion (DC), and Vickers hardness (HV) are properties that can affect the performance of resin-based composites (RBCs). This study tested four bulk-fill RBCs used in self-cured mode: Bulk EZ Plus (Zest Dental Solutions), Cention Forte (Ivoclar), Fill-Up! (Coltene), and Stela (SDI Limited), and two light-cured bulk-fill RBCs: Filtek One (Solventum) and SDR flow+ (Dentsply). The test specimens were 6 mm in diameter and 2 mm thick. Axial SS was measured in real time for 4000 s in the self-cured materials and for 1400 s after 10 s of light curing in the light-cured materials (n = 12 for self-cured RBCs; n = 11 for light-cured RBCs). To confirm that the RBCs were adequately polymerized, the DC was assessed using real-time ATR-FTIR spectroscopy, and the HV was measured on the top and bottom surfaces using a 300-gf load for 8 s (n = 5) after 24 h. The SS, DC, and HV differed significantly among the RBCs (p < 0.001). At 1400 s, Cention Forte developed the lowest stress (1.44 MPa), whereas Bulk EZ Plus and Fill-Up! produced the highest stress (3.77 MPa). The self-cured materials continued to develop measurable stress between 1400 s and 4000 s, while the light-cured RBCs had stabilized at 1400 s. Bulk EZ Plus and Stela produced the highest DC values, and Stela had the highest HV. Full article

Background: Hypertension (HTN) and type 2 diabetes mellitus are major global health challenges, and diabetic nephropathy (DN) is a critical complication of diabetes. Although observational studies link HTN to DN progression, causal evidence remains limited. We investigated the potential causal relationship between HTN and DN and quantified the global burden of HTN-attributable type 2 diabetic nephropathy (HTN-T2DN). Methods: We integrated two-sample Mendelian randomization (MR), Bayesian weighted MR, and sensitivity analyses with Global Burden of Disease (GBD) 2021 analyses. The burden of HTN-T2DN was assessed from 1990 to 2021 and projected to 2045. Results: MR provided genetic evidence supporting a potential causal role of HTN in DN (inverse-variance weighted odds ratio = 4.219, 95% CI: 1.807–9.853; p = 0.001). Globally, HTN-T2DN deaths increased to 50,689 and DALYs to 1,151,216 in 2021. Females had higher age-standardized mortality and DALY rates than males, and low-middle sociodemographic index (SDI) regions had the highest burden. By 2045, deaths and DALYs were projected to reach 162,392 and 4.04 million, respectively. Conclusions: HTN may play a potential causal role in DN development and progression. Strengthened blood pressure control, early screening, and tailored policies are essential, particularly for women, older adults, and populations in lower-SDI settings. Full article

Background: Whether the fatal and non-fatal composition of aggregate pancreatitis burden has changed over time remains unclear. We assessed long-term changes in the fatal-to-non-fatal composition of aggregate pancreatitis burden using Global Burden of Disease (GBD) 2023 estimates. Methods: We conducted a systematic descriptive and trend analysis using publicly available estimates from the GBD 2023 Results Tool for incidence, prevalence, deaths, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) across 204 countries and territories from 1990 to 2023. Because GBD reports pancreatitis as an aggregate cause category, the analysis could not distinguish acute pancreatitis, recurrent acute pancreatitis, chronic pancreatitis, or acute exacerbations of chronic pancreatitis. Primary analyses used age-standardised rates per 100,000 population. Four burden–composition metrics were derived within each location–year stratum: the YLL:YLD ratio, YLD:DALY proportion, deaths-to-incidence ratio, and prevalence-to-incidence ratio. Temporal trends were modelled in R version 4.5, using segmented regression, with up to three joinpoints selected by a Bayesian information criterion. Results: Globally, all six age-standardised native GBD measures declined between 1990 and 2023. The age-standardised incidence rate decreased from 37.62 (95% UI 32.20–43.11) to 32.91 (28.84–37.17) per 100,000, prevalence from 93.78 (69.26–126.25) to 68.92 (52.53–90.32), deaths from 1.76 (1.49–2.16) to 1.40 (1.21–1.66), YLDs from 5.70 (2.75–9.45) to 4.34 (2.18–7.04), YLLs from 55.96 (46.50–69.72) to 43.60 (36.89–53.53), and DALYs from 61.66 (50.62–75.61) to 47.94 (40.57–58.16). However, the fatal-to-non-fatal composition changed little: the global YLL:YLD ratio was 9.82 in 1990 and 10.04 in 2023, while the YLD share of DALYs was 0.092 and 0.091, respectively. Joinpoint modelling showed fluctuation rather than a sustained shift toward disability-dominant burden: the global YLL:YLD ratio was stable until 1998, increased from 1998 to 2002 (annual percent change [APC] 1.38%, 95% CI 0.42 to 2.36), and then declined modestly thereafter (APC −0.13%, −0.20 to −0.06). Burden remained higher in males, whereas females had a greater non-fatal share of total burden (YLD:DALY in 2023: 0.134 vs. 0.073). All sociodemographic index strata remained mortality-dominant in both 1990 and 2023; low-SDI settings had the greatest fatal dominance (YLL:YLD 34.94 in 1990; 24.72 in 2023). Using a descriptive YLD:DALY ≥ 0.50 benchmark, 203 of 204 countries and territories remained below the disability-dominant threshold in both years, no country crossed from below to above this benchmark, and only Georgia moved from above to below the benchmark. Conclusions: Despite declines in global incidence, mortality, and DALY rates, the aggregate GBD pancreatitis burden remained overwhelmingly mortality-dominant from 1990 to 2023. Because GBD pancreatitis combines acute and chronic pancreatitis, this finding should be interpreted as describing the modelled aggregate pancreatitis cause category rather than proving subtype-specific mortality dominance. The intensity of fatal dominance varied by sex, SDI, region, age, and country, but a structural shift toward disability-dominant aggregate burden was not observed. Full article

Background: Systemic lupus erythematosus (SLE) is associated with an increased burden of cardiovascular disease (CVD), driven by dyslipidemia, hypertension, obesity, inflammation, and treatment. These factors can impact patient quality of life (QoL) by limiting physical activity. Objectives: To characterize lipid abnormalities as CVD risk factors in a Saudi SLE cohort and assess associations between lipid profile, SLE features, treatment, and patient-reported outcomes of physical activity. Methods: A cohort of adult SLE patients followed at King Saud University Medical City since 2021 was analyzed. Demographics, lipid profiles, blood pressure, BMI, SLEDAI-2K, SDI, disease duration, and treatment data were collected. Physical function and quality of life were assessed using the LupusQoL and PROMIS Physical Function T scores. Univariate and multivariate logistic regression analyses were conducted to identify associations between lipid abnormalities, SLE-related factors, and QoL physical activity measures. Results: A cohort of 169 patients (88.2% female, mean age 39.3 ± 12.4 years) was evaluated to assess the presence of dyslipidemia (23.7%), obesity (BMI ≥ 25, 66.3%), and hypertension (≥130/80 mmHg, 26.0%). Mean SLE duration was 9.2 ± 7.7 years and mean SLEDAI-2K was 11.0 ± 7.0. Among these patients, 52.7% used steroids, 88.2% used antimalarial drugs, and 53.8% used immunosuppressives. Dyslipidemia was associated with lower LupusQoL physical scores (adjusted OR 0.986; 95% CI 0.972–1.000; p = 0.0446). No significant associations were found between lipid levels and the PROMIS Physical Function T score. Conclusions: In this Saudi SLE cohort, dyslipidemia and other modifiable CVD risks were common. Dyslipidemia correlated with poorer LupusQoL-specific physical scores, which highlights the importance of lifestyle changes in patients with SLE. Full article

Human development is increasingly expected to reflect progress in health, education, living conditions, and sustainability. Yet evidence on how specific Sustainable Development Indicators (SDIs) relate to such progress remains limited, especially in studies that jointly consider cross-income heterogeneity, high-dimensional indicators, and nonlinear relationships. This study examines the SDI–HDI relationship across low-, lower-middle-, upper-middle-, and high-income countries using 408 World Bank SDG indicators and UNDP HDI series for 1990–2020. An interpretable Random Forest framework, combined with SHAP rankings and Partial Dependence Plots, identifies the most influential predictors and marginal associations with HDI. The model shows strong predictive performance across income groups and marked heterogeneity in the predictors associated with HDI. In low-income countries, HDI is mainly associated with early-life health conditions and human capital; in lower-middle-income countries, electrification and service access become more prominent; and in upper-middle- and high-income groups, digital connectivity, higher education, and institutional factors gain importance. Mortality-related indicators are consistently associated with lower predicted HDI, whereas literacy, electricity access, and internet use are associated with higher HDI. These results highlight how AI-based analytical tools can support sustainable economic development by identifying income-specific development priorities and structural constraints. They also suggest that disparities in health, education, infrastructure, and digital connectivity may influence the conditions under which entrepreneurial opportunities emerge or remain constrained across development stages. Overall, the SDI–HDI relationship is nonlinear and income-specific, supporting more differentiated, data-driven development strategies. Full article

The upper Sebou River occupies a strategic territory draining varied mountain reaches in northern Morocco. As such, it is rich in surface water resources and karst springs with important downstream uses. However, the variability of rainfall threatens its water potential, making it highly vulnerable and at risk of desiccation. This study explores rainfall trends and their effects on streamflow and water resource availability. Data from three stations representing the upstream section of the watershed, along with two streamflow series—one for the upper Sebou River (Pont Medz) and the other for the Aïn Timdrine karst spring—cover the period from 1956 to 2018. The methodology employs Mann–Kendall trend tests, Sen’s Slope test, and the Standardized Precipitation Index (SPI) for rainfall series, as well as the Streamflow Drought Index (SDI) for hydrological series. The results demonstrate a decline in rainfall since 1979, significant at the 5% threshold. This trend has an immediate impact on the flow rates of the area’s rivers and karst springs, which have also tended to decline, with a succession of dry years and seasons since 1980. This observation highlights the depletion of water resources of the fragile upper Sebou region in the face of decreasing rainfall and snowfall, compounded by the rampant and unsustainable exploitation of groundwater resources linked to the development of irrigated cash crops in the Middle Atlas Mountains. Full article

Solid-state batteries (SSBs) are emerging as a transformative alternative to conventional lithium-ion batteries (LIBs) for next-generation electric vehicles (EVs) by replacing flammable liquid electrolytes with solid-state materials. Compared with current LIB systems delivering approximately 160–300 Wh/kg at the pack level, SSBs are projected to achieve 400–800 Wh/kg, enabling improvements in driving range of nearly 50–100% while simultaneously reducing battery pack mass by 10–30%. These improvements directly enhance vehicle-level energy efficiency by lowering energy consumption from typical values of 150–180 Wh/km in present EVs to projected levels of 110–140 Wh/km in optimized SSB-based architectures. Furthermore, reduced internal resistance and improved electrochemical stability can increase round-trip efficiency from approximately 85–95% in conventional LIBs to values approaching 95–98% under optimized solid-state configurations. The enhanced thermal stability of solid electrolytes significantly reduces the need for active cooling systems, decreasing parasitic thermal-management energy consumption from 10–30% of total vehicle energy demand to below 5–15% in advanced SSB systems. Fast-charging capability is also substantially improved, with projected charging times decreasing from 20–40 min to approximately 10–15 min for 10–80% state-of-charge operation, while maintaining improved safety and reduced risk of thermal runaway. In addition, SSBs demonstrate projected cycle lifetimes exceeding 3000–5000 cycles, compared with 1000–2000 cycles for conventional LIBs, thereby lowering battery replacement frequency and lifecycle energy losses. This paper examines the electrochemical fundamentals, thermal behavior, charging/discharging efficiency, and vehicle-level implications of SSB technology for EV applications. Comparative analyses demonstrate that replacing LIBs with SSBs can increase EV driving range from approximately 400 km to 700–800+ km under equivalent battery mass conditions, while also improving coulombic efficiency beyond 99.5% and reducing self-discharge rates to below 1–2% per month. Current industrial case studies from Toyota, Factorial Energy, Mercedes-Benz, CATL, BYD, QuantumScape, and Samsung SDI further confirm accelerating commercialization pathways toward 2027–2030. Overall, the study demonstrates that SSBs are not merely incremental battery improvements but represent a system-level efficiency technology capable of simultaneously enhancing energy density, reducing thermal and electrical losses, extending vehicle range, accelerating charging, and improving long-term sustainability. Despite persistent challenges related to manufacturing scalability, interfacial resistance, and cost, SSBs are positioned to become a critical enabler of highly efficient, long-range, and safer electric mobility systems beyond 2030. Full article

Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring. Full article