Search Results (193,494)

Background: Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Few approaches combine population-level surveillance with individual-level monitoring within a single analytical framework. Methods: This work developed a dual machine learning framework. Study 1 modeled the daily count of respiratory admissions (chapter X of the ICD-10) in São Paulo, Brazil, from 2017 to 2022 as a nowcasting task, combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated by walk-forward over 30 bimonthly windows. Study 2 applied an XGBoost and Random Forest pipeline to 913 diaphragmatic-breathing sessions from 17 patients aged 9 to 16 years in the Respire Bem system, with Asthma Control Test and salivary cortisol represented using evidence-based synthetic simulation. Results: Study 1 achieved a mean MAE of 18.22, RMSE of 23.99, and R² of 0.675, exceeding the seasonal baseline by 41.5%, with a significant advantage over all three baselines (Wilcoxon and Diebold–Mariano, p ≤ 0.038). Ablation showed each single-component configuration to be significantly worse than the full hybrid, but removing the environmental block cost only 0.27 admissions per day, an effect indistinguishable from zero. SHAP rankings were stable across windows (Kendall W = 0.640), led by NO₂, PM2.5, and temperature. In Study 2 the pipeline ran end-to-end on real behavioral data, but because the outcomes were simulated, no predictive-accuracy metric is reported. Conclusions: Study 1 delivers a validated population-level nowcasting model whose accuracy rests mainly on the temporal structure of the series. Study 2 contributes a real behavioral dataset and a reproducible pipeline; the clinical validity of the digital biomarkers remains open and requires prospective work with directly measured outcomes. Full article

The authoritarianism of the Modern/colonial (Western) one-world ontology includes an insistence on the nature of time, and this dogmatism persists in the face of other temporalities. The normative status of coloniality is evident in the ways trans and takatāpui communities are compelled to operate on straight, Settler time, from research ethics and sexualities education to medical wait times and parliamentary submissions. To begin, we outline the research on which this claim is based, which employed wānanga as a mode of co-designing decolonising, gender-affirming (sexualities) education, and how deviations from the intended approach allowed the study to centre what was important to the participants. Next, we unpack the predicaments of resistance as we sought to embrace temporal multiplicity and the need for an alternate strategy for holding spacetime. This strategy emerged through Waitī’s whaikōrero and poem, which construe the participants’ contributions as seeds to be cultivated in their own time. Across the three wānanga, three seeds emerged. Whakataukī and pūrākau are employed to unpack these seeds from the wānanga, illustrating the importance of valuing all contributions, both absent and present, and how whanaungatanga offers a strategy for meeting participants on their own terms and in their own time, kanohi ki te kanohi. Full article

A key challenge in port–road logistics is the need to align operational priorities with efficient and sustainable freight transportation decisions. This study develops an Analytic Hierarchy Process (AHP)-based decision-support system for the Multi-Objective Electric Vehicle Routing Problem (MOEVRP) in port–road logistics. Initially, the AHP method is used to determine the relative importance of cost-efficient route planning, vehicle and port management, environmental impact management, energy efficiency, and operational efficiency and well-being based on expert judgment. Next, the resulting priority weights are then used to inform the decision-making framework for the MOEVRP, which determines routing decisions by minimizing total cost, carbon emissions, and maximum vehicle working time while accounting for electric vehicle constraints and charging behavior. This issue is critical in rapidly developing industrial corridors such as Thailand’s Eastern Economic Corridor, where growing freight demand, energy constraints, and environmental pressures must be managed simultaneously. By linking port–road operational priorities with the routing model, the proposed framework provides a structured approach for evaluating trade-offs between economic, environmental, and operational considerations during the transition toward low-carbon freight transportation. A case study in Chonburi–Rayong provinces demonstrates the applicability of the integrated system in a real-world maritime–land logistics corridor. The findings contribute to the design of more sustainable supply chain systems that support renewable and decarbonized logistics. Full article

Laboratory-grade force platforms are the reference standard for quantifying ground reaction forces but remain costly and confined to well-resourced laboratories, limiting field-based use. This study evaluated the accuracy and between-session reliability of a portable force platform (Impera, Spinitalia) under static loading, and its concurrent validity during vertical jumping, against a criterion-standard piezoelectric plate with calibration traceable to national standards (4Jump, Kistler). Accuracy and between-session reliability were assessed against five tared loads (21.6–103.2 kg) applied at five plate locations across two sessions 24 h apart. Concurrent validity was assessed during squat and countermovement jumps performed by ten physically active men, using intraclass correlation coefficients, Bland–Altman analysis and the linear fit method applied to the entire force–time waveform. Both platforms underestimated the lightest load by about 4% and approached zero error at higher loads; the between-device difference was 0.19 kg and did not translate into a difference in proportional accuracy (p = 0.151), with no device × position interaction. Between-session coefficients of variation were below 1% for both devices. Concurrent agreement was good to excellent for all jump variables (ICC 0.867–0.996), and waveform agreement was close to unity (R² ≥ 0.997), with about 2% amplitude underestimation, greater in participants generating the highest impact forces. The Impera platform provides accurate and reliable measurements under static loading, and force–time data that agree closely with the 4Jump during vertical jump testing. Full article

This exploratory mixed-methods research study examined 27 Western YouTubers who are pro-China with over 644 million views and 4.7 million subscribers in 2024. We analyzed their top 10 videos N = 270) with human coding using both qualitative and quantitative content analysis and most-liked comments on these videos using human–machine collaboration with generative AI and audience metrics, showing how influencer marketing techniques can be used to engage audiences and the potential public diplomacy benefits for China on global social media. This study identified the influencer appeals and content attributes in the videos that YouTubers used to promote China’s image and refute the criticisms of China to their Western peers. We found that most videos use Chinese in addition to English, and almost all videos related to China feature at least one China accomplishment icon. Their audiences are mostly a mix of Chinese and Western audiences. These YouTubers’ most common appeals are authenticity and homophily. They present a very friendly character and share their daily experiences in China. Content comparing China with the West or refutation of Western criticism was not found more effective in engaging audiences than videos not using these approaches. By speaking up for China, these YouTubers and their followers form a pro-China community on YouTube, offering perspectives different from what they received from Western mainstream media to global audiences. Full article

Background/Objectives: Prothrombin time (PT), international normalized ratio (INR), and activated partial thromboplastin time (aPTT) are routinely used coagulation assays, and analyzer–reagent differences may affect result comparability. This study evaluated analytical comparability between the Sysmex CS-2500 System using Siemens reagents and the Tokra Medical NOVAE II for PT, INR, and aPTT. Methods: Residual routine citrated plasma specimens were measured on both systems. The CS-2500 was designated as the comparator system. Passing–Bablok regression and Bland–Altman analysis were used as the primary method-comparison approaches; Pearson and Spearman correlations and exploratory categorical agreement were secondary analyses. Results: Fifty paired measurements were analyzed for PT and INR and 54 for aPTT. For all three parameters, the 95% confidence interval (CI) for the Passing–Bablok intercept included 0 and the 95% CI for the slope included 1, providing no statistically supported evidence of constant or proportional bias by regression. Mean paired differences (NOVAE II minus CS-2500) were +1.27 s for PT, +0.023 for INR, and +2.32 s for aPTT. The 95% limits of agreement were −0.15 to +2.69 s for PT, −0.112 to +0.159 for INR, and −1.70 to +6.33 s for aPTT. Categorical agreement was influenced by analyzer-specific reference intervals and by the low prevalence of abnormal results. Conclusions: The two analyzer–reagent systems showed positive analytical associations, but the magnitude and dispersion of paired differences varied by assay. Because no clinical equivalence margins were prespecified and markedly pathological or therapeutic-range samples were sparsely represented, these findings support analytical comparison and local verification but do not establish clinical interchangeability. Full article

Recent advances in artificial intelligence have significantly improved credit card fraud detection, with deep learning emerging as an effective approach for learning complex transaction patterns. However, as financial transaction datasets continue to grow, training deep learning models on a single computing node becomes increasingly computationally expensive, motivating the adoption of cloud-based distributed learning. Because distributed deep learning partitions training data across multiple worker nodes, this study evaluates how different data partitioning strategies influence predictive performance, computational efficiency, and scalability. The proposed framework was evaluated under both independently and identically distributed (IID) and non-independent and identically distributed (non-IID) data partitioning strategies, including random, stratified, label skew, quantity skew, temporal skew, and amount skew, using single-node, three-worker, and seven-worker configurations. Experimental results demonstrate a maximum training speedup of 4.181× and a 76.084% reduction in average epoch training time while maintaining consistently high recall across all partitioning strategies; however, the ${\mathrm{F}}_1$-score decreased from 0.696 to 0.587 (approximately 16%), due to increased false-positive predictions. The evaluated partitioning strategies exhibited different trade-offs between predictive performance and computational efficiency. These findings demonstrate that the proposed framework provides a scalable solution for cloud-based credit card fraud detection while offering practical insights into the influence of data partitioning strategies on distributed deep learning performance. Full article

Increasing soil salinisation challenges food production, since most crops are highly sensitive to salinity. The identification of wild halophytes adapted to saline environments with nutritional value represents a promising strategy for food production on salt-affected farmlands. Assessing the nutritional potential of such species requires evaluating their proximate composition, mineral profile, and antioxidant properties. In parallel, citizen-science approaches complement and enhance scientific research by actively engaging potential final consumers in the research process, thereby improving the societal relevance, dissemination, and potential impact of scientific findings. In this study, we characterised the nutritional profile of the facultative halophyte Cakile maritima and found it to be comparable, and in some respects superior, to that of other conventional leafy vegetables, particularly regarding its mineral composition and bioactive compounds. Moreover, similar though not identical nutritional characteristics were observed in two C. maritima leaf morphotypes analysed, since differences in dry matter, ashes, total proteins and carbohydrates were identified. Interestingly, vitamin C accumulation was organ- and morphotype-dependent. Finally, the biochemical characterisation was complemented with an online survey that showed a clear predisposition of consumers towards the incorporation of wild edible plants into their diet, together with a sensory evaluation in which 32 participants assessed the acceptability of up to 11 dishes prepared using C. maritima. Sensory evaluation revealed a prominent bitter flavour amongst dishes containing C. maritima, with weighted scores for negative perceptions slightly exceeding those for positive ones, suggesting that its sensory profile may limit its acceptance by the general public while offering potential for specific consumer segments. Overall, our findings highlight the nutritional potential of C. maritima and support its valorisation as a sustainable species for saline agriculture. Full article

Improving agricultural labour productivity is essential for enhancing agricultural competitiveness, rural development, and long-term economic sustainability, particularly in transition economies where agriculture continues to play a strategic role. Although previous studies have examined the roles of agricultural finance, government support, and macroeconomic conditions separately, limited evidence exists on their joint long-run and short-run relationships with agricultural labour productivity in Kazakhstan. This study addresses this gap by examining the relationships between government expenditure, agricultural credit, inflation, and agricultural labour productivity using annual data for the period 2004–2025. The autoregressive distributed lag (ARDL) bounds testing approach is employed to distinguish between long-run equilibrium relationships and short-run adjustment dynamics. Prior to estimation, stationarity is examined using augmented Dickey–Fuller and Phillips–Perron unit root tests, while the Bai–Perron multiple structural breakpoint test is used to account for structural change. The robustness of the long-run estimates is further evaluated using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegrating regression (CCR). The findings indicate the existence of a stable long-run relationship among the variables. Government expenditure is positively associated with agricultural labour productivity in both the long run and the short run, whereas agricultural credit exhibits a negative long-run association and no statistically significant short-run relationship. Inflation is not found to be significantly associated with agricultural labour productivity within the estimated model. The robustness estimators produce results that are broadly consistent with the ARDL findings, while diagnostic and stability tests confirm the adequacy of the estimated model. By jointly examining fiscal, financial, and macroeconomic factors, explicitly accounting for structural change, and validating the long-run estimates using alternative cointegration estimators, this study provides updated country-specific evidence on agricultural labour productivity in Kazakhstan and contributes to the broader literature on agricultural productivity in transition economies. Full article

Hospital pathology laboratories represent challenging healthcare environments for Indoor Air Quality (IAQ) management due to the coexistence of hazardous chemical emissions, transient pollutant peaks, and stringent ventilation requirements. This study proposes an integrated data-driven framework for IAQ assessment and predictive ventilation management based on a high-resolution monitoring campaign conducted over 17 calendar days in a pathology grossing room (V = 113.90 m³) and an adjacent chemical storage room (V = 56.03 m³). The monitoring system generated 4356 synchronized 1-min observations, of which 4232 complete multivariate records were retained after data-quality screening. The proposed methodology combines three complementary analytical layers: (i) predictive modelling of pollution episodes using supervised machine learning architectures; (ii) multivariate anomaly detection to identify atypical environmental states; and (iii) temporal dependency analysis based on Granger causality and Bayesian networks to investigate predictive relationships between occupancy-related indicators, ventilation behaviour, and pollutant evolution. This integrated framework enables the transition from descriptive IAQ assessment toward predictive environmental management in healthcare facilities. Baseline statistical diagnostics demonstrated the limited capability of conventional linear approaches, with an Ordinary Least Squares (OLS) model explaining only 7.4% of TVOC variability (R² = 0.074). Ventilation assessment identified an approximately 38% deficit relative to the selected ASHRAE 170 ventilation requirement in the monitored grossing room. Among the evaluated predictive models, Random Forest achieved the highest test-set performance (R² = 0.78; MAE = 10.5 ppb), enabling short-term forecasting of TVOC evolution. Isolation Forest identified 212 atypical environmental states, corresponding to 5.01% of the valid analytical observations, with substantially higher TVOC concentrations than under normal operating conditions. The proposed framework establishes a transferable methodology for predictive IAQ assessment and ventilation management in chemically intensive healthcare facilities, providing decision-support information for risk-informed HVAC operation within existing regulatory and ventilation requirements. Full article

The generation of biomass residues and biowaste is both a challenge to the environment and to resource management worldwide but is also a significant renewable resource for energy and material recovery. Biomass residues are utilised along multiple pathways such as direct use for energy production, anaerobic digestion and biogas production, biofuel generation, composting, soil amendment, and conversion into value-added products at a global scale. On the other hand, many lignocellulosic and other biomass residues are characterized by low bulk density, heterogeneous characteristics, and seasonal availability, thus complicating collection, handling, storage, and transportation. Densification into solid fuel pellets is a viable solution for transforming these low-density residues into a compact, handleable, and transportable form with increased energy density. Although biomass residues have global potential, past pelletisation studies in Punjab, Pakistan, have generally considered only a few of two to three seasonal feedstocks that do not cover the uncertainty of potential feedstock availability in several seasons of the year, alongside the judgment of suitable biomass input. This study was, therefore, designed to explore nine agro-residues with different seasons in Punjab, e.g., tree residues, grass clippings, animal waste, crop residues, and herb biomass, for robust conversion into solid fuel pellets. Gate-to-gate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods were used to evaluate the production system of the pellets from biomass residues. A functional unit of 1 tonne of biomass solid fuel pellets was defined, and the resulting environmental impacts were modelled using GaBi software and the ReCiPe 2016 methodology. Process performance and production hotspots were assessed by attributing the environmental impacts that occurred at individual pellet-production stages to the overall impacts, while LCC was used to evaluate economic costs, both internal and external, from pellet production. The results of hotspot analysis indicated that flash/pneumatic drying represented the most important contributor for all investigated impact categories, followed by the pellet-milling and packaging stages. The total estimated cost of the pellets was USD 72.6 t⁻¹, with an internal cost of USD 51.7 t⁻¹ and an environmental cost of USD 20.9 t⁻¹. Two other energy scenarios provided evidence through which to understand the potential for reductions in GHG emissions linked to the production of pellets. These findings suggest that differing and seasonally accessible biomass residues in Punjab can be converted into solid biofuels to concurrently aid sustainable waste management, energy security, climate-change amelioration, and rural economic growth. This study is novel because it compares nine heterogeneous biomass residues in a seasonal common-pellet production framework, using an integrated environmental and economic approach to provide stakeholders with practical evidence they can use to select the biomass feedstock that is most environmentally and economically preferable. These results help achieve the Sustainable Development Goals 7, 11, 12, and 13. Full article

Background/Objectives: Methotrexate (MTX) is an effective antineoplastic and immunosuppressive agent whose clinical use can be limited by nephrotoxicity. Increasing evidence suggests that dysregulated angiotensin II-mediated signaling contributes to MTX-induced renal injury. Olmesartan (OLM), an angiotensin II type 1 receptor (AT1R) blocker, possesses renoprotective properties; however, its therapeutic efficacy may be limited by suboptimal pharmacokinetics and tissue deli\very. This study aimed to develop OLM-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles (OLM-PLGA) and investigate their nephroprotective efficacy and underlying molecular mechanisms in MTX-induced nephrotoxicity. Methods: OLM-PLGA nanoparticles were prepared and evaluated for their physicochemical characteristics. The in vivo nephroprotective effects of OLM-PLGA were investigated in male Sprague–Dawley rats, which were allocated into four groups: OLM (10 mg/kg), OLM-PLGA (10 mg/kg), MTX-treated, and normal control. Renal function, oxidative stress, inflammation, apoptosis, and fibrosis were assessed, together with renal gene expression of AT1R and extracellular signal-regulated kinase 1/2 (ERK1/2). Results: Compared with free OLM, OLM-PLGA provided superior protection against MTX-induced renal dysfunction and oxidative stress, as evidenced by improved renal function and enhanced antioxidant defense. OLM-PLGA also exerted greater anti-inflammatory and anti-apoptotic effects and attenuated renal fibrotic changes, accompanied by reduced renal expression of α-smooth muscle actin and collagen. These protective effects were associated with decreased AT1R gene expression and suppression of downstream ERK1/2 signaling. Conclusions: PLGA-based nanoencapsulation enhanced the nephroprotective efficacy of OLM against MTX-induced nephrotoxicity and was associated with the modulation of the AT1R/ERK1/2 signaling axis. OLM-PLGA may therefore represent a promising nanotherapeutic approach for improving OLM delivery and renal protection during MTX treatment. Full article

Myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders driven mainly by somatic mutations in JAK2, CALR or MPL, but their clinical phenotype is also shaped by chronic inflammation, immune dysregulation, vascular complications and microenvironmental remodeling. Emerging evidence suggests that the gut microbiota may contribute to this inflammatory and immunometabolic landscape; however, the current literature remains heterogeneous and its translational relevance is still insufficiently defined. This critical narrative review maps the available evidence linking the gut microbiota, microbial metabolites and systemic microbial signatures to MPN biology. We distinguish direct human MPN data from indirect mechanistic evidence derived from studies of intestinal barrier dysfunction, thrombo-inflammation, hematopoietic regulation, allogeneic hematopoietic cell transplantation and infection risk. Across human MPN cohorts, the most consistent findings are not uniform changes in global microbial diversity, but rather alterations in specific immunoregulatory taxa, particularly reduced Firmicutes/Faecalibacterium-related communities and dysbiotic signatures associated with JAK2V617F status. Mechanistically, dysbiosis and impaired intestinal barrier integrity may facilitate low-grade endotoxemia, TLR4/NF-κB activation, cytokine amplification, endothelial activation and platelet priming. In parallel, microbial metabolites may influence hematopoietic stem cell programs, the bone marrow niche, megakaryopoiesis and thrombopoiesis. Treatment exposure and diet are relevant modifiers of the microbiota–inflammation axis, although available interventional data remain preliminary. Mendelian randomization and multi-omics studies provide hypothesis-generating evidence for microbiota–metabolome–MPN interactions, but require longitudinal validation, functional studies and contamination-aware analytical pipelines, especially for low-biomass blood and bone marrow samples. Microbiota-targeted strategies, including nutritional interventions and fecal or washed microbiota transplantation, represent promising but still investigational approaches, particularly in immunocompromised or post-transplant settings. Future studies should integrate microbiome, metabolome, genome, proteome, inflammatory biomarkers and clinical outcomes while controlling for diet, antibiotics, treatment exposure and driver mutation status. Such an approach may clarify whether the microbiota is a biomarker, mediator or therapeutic target in MPNs. Full article

Reliable estimation of the air change rate from CO₂ decay measurements is essential for ventilation assessment, but results may strongly depend on the assumed background concentration and the selected analysis window. This study compares the ISO 12569 two-point and multi-point methods with a study-specific nonlinear least-squares (NLSQ) exponential-fitting approach developed for systematic window-sweep analysis. A synthetic benchmark with known decay parameters was used to quantify the sensitivity of the estimated air change rate N to background-concentration error and time-window selection. The framework was then applied to CO₂ decay measurements from four rooms and to an OpenFOAM-generated CFD decay case using dense N(t_beg,t_end) stability maps and automatic plateau selection. The benchmark showed that admissible windows shrink as background uncertainty increases, while late, tail-dominated intervals are particularly prone to bias. The two ISO methods produced similar results, whereas the NLSQ approach generally preserved stable solutions over longer windows when fitting started sufficiently early. For three measured rooms, stable regions yielded effective N values of approximately 0.14, 0.75, and 1.24 1/h, while one room remained non-stationary. To assess energy implications, N was propagated into the ventilation heat-loss coefficient H_vent = 0.34 NV. Illustrative late-tail scenarios showed that background-sensitive window selection may translate into substantial over- or underestimation of ventilation-related heat-loss indicators. Full article

Predictive analytics has emerged as an important component of hospital automation by enabling proactive resource management and data-driven decision-making. Efficient allocation of patient monitors in operating rooms represents a practical application where forecasting can support perioperative safety and resource management. This study compared five forecasting models for predicting patient monitor availability using integrated clinical, operational, and equipment management data to identify appropriate forecasting approaches for operating room resource planning. We conducted a retrospective longitudinal study using monthly surgical operational data from the anesthesia information system and equipment-related data from the medical equipment management system of a tertiary referral hospital. The outcome was the monthly number of available patient monitors recorded in the equipment management system, which served as the reference value for evaluating five prediction models, including naïve persistence, autoregressive integrated moving average (ARIMA), multivariable linear regression, LSTM, and hybrid LSTM–regression. Model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Across a 36-month study period, the mean monthly surgical volume was 1960 (SD 182) procedures, with a mean operative duration of 105.0 (SD 3.5) minutes. Weighted multivariable regression showed that service age (standardized β = 0.72, 95% CI 0.66–0.78), maintenance frequency (standardized β = 0.09, 95% CI 0.03–0.15), and surgical volume (standardized β = 0.15, 95% CI 0.09–0.21) were positively associated with available patient monitor, whereas mean operative duration was inversely associated (standardized β = −0.19, 95% CI −0.26 to −0.12). The naïve persistence (RMSE 0.17, MAE 0.03, MAPE 0.16%) and ARIMA (RMSE 0.17, MAE 0.04, MAPE 0.21%) showed higher predictive performance, while the multivariable linear regression, LSTM-only, and LSTM–regression models showed relatively higher prediction errors. Beyond predictive accuracy, the model provides an operational framework for transforming routinely collected clinical and equipment data into actionable information for automated resource planning. This study showed that predictive analytics can support hospital automation by enabling proactive patient monitoring and resource planning. Classical statistical models remain robust alternatives for hospital resource forecasting in small-sample settings, while regression-based approaches provide interpretability for operational decision-making. Full article