Distribution of organic chemical composition and structure in different metamorphic deformed coals and its microscopic control effects on gas adsorption

Adverse drug reactions (ADRs) are a major cause of morbidity, hospital admissions, and in-hospital mortality, yet remain incompletely captured by post-marketing pharmacovigilance, which suffers from underreporting. Electronic health records (EHRs) contain clinical narratives that can reveal otherwise unreported ADRs. Natural language processing (NLP) offers a scalable means to extract structured information from clinical narratives, supporting ADR detection and assessment. We conducted a retrospective cross-sectional study within a multisite hospital network in Southern Switzerland to develop and evaluate NLP systems for ADR detection and information extraction from electronic discharge summaries. ADR classification models were trained on 400 discharge summaries and compared across multiple machine learning and vectorization strategies against a regular expression (regex) system. Drug and clinical event extraction were evaluated using 100 manually annotated summaries, benchmarking a dictionary-based approach against a two-step deep learning (DL) pipeline integrating transformer-based named entity recognition (NER) with a pharmacovigilance-oriented contextual relevance classifier. Performance was evaluated using standard metrics and a custom top-k ranking metric aligned with pharmacovigilance experts' daily capacity for reviewing positive cases to confirm the presence of ADRs. Logistic regression with Bag-of-Words achieved the best overall performance, combining high precision and effective case ranking. In a simulated deployment, this model identified nearly twice as many discharge summaries containing confirmed ADRs than as regex system. The two-step DL pipeline outperformed the dictionary-based approach for drug and clinical event recognition and accurately classified them according to pharmacovigilance purposes. These results demonstrate that NLP-based analysis of real-world clinical narratives can enhance pharmacovigilance while maintaining a manageable expert workload.

Background:Cardiovascular disease remains the leading cause of mortality worldwide, with residual cardiovascular (CVS) risk remaining high despite pharmacological interventions. Cholesteryl ester transfer protein (CETP) inhibitors have shown promise in improving lipid profiles, but their comparative effectiveness in reducing CVS outcomes remains unclear. This network meta-analysis and network meta-regression aimed to evaluate the comparative effectiveness of CETP inhibitors (Evacetrapib, Anacetrapib, Dalcetrapib, and Torcetrapib) on CVS outcomes, including CVS mortality, all-cause mortality, major adverse cardiovascular events, myocardial infarction, need for revascularization, and stroke.Methods:A Bayesian network meta-analysis was conducted, incorporating data from 10 randomized controlled trials with 84,134 patients diagnosed with cardiovascular disease or at high risk. We compared various CETP inhibitors for their effectiveness in reducing CVS events. Network meta-regression was used to assess the impact of covariates such as age, sex, and baseline lipid levels.Results:Evacetrapib ranked highest for CVS mortality (relative risk [RR]: 0.92, 95% credible interval [CrI]: 0.39–1.87; Surface Under the Cumulative Ranking Curve [SUCRA]: 71.91%) and Anacetrapib for major adverse cardiovascular events (RR: 0.40, 95% CrI: 0.07–1.06; SUCRA: 84.25%). Dalcetrapib showed favorable effects on all-cause mortality (RR: 0.72, 95% CrI: 0.16–1.42; SUCRA: 67.51%) and stroke (RR: 0.75, 95% CrI: 0.41–1.26; SUCRA: 83.03%). Anacetrapib had the highest rank in reducing myocardial infarction (RR: 0.83, 95% CrI: 0.46–1.15; SUCRA: 86.06%).Conclusions:This analysis suggests that Evacetrapib and Anacetrapib are the most effective CETP inhibitors for improving CVS outcomes, with Dalcetrapib showing beneficial effects in certain outcomes. Further studies are needed to confirm these findings.

Vision-Weighted Diagnostic Gain (VWDG) in the Diagnosis of Oral Malignant and Potentially Malignant Lesions: A Multimodal Comparative Analysis.

Hand, foot and mouth disease (HFMD) constitutes a global public health concern. Internet search data offers advantages including vast data volumes, provision of real-time information, and the potential for earlier infectious disease surveillance. The objective of this study is to utilise Baidu Search Index (BSI) to construct a predictive model for HFMD epidemiological surveillance, thereby enhancing HFMD incidence forecasting and early warning capabilities. HFMD cases reported by the National Health Commission of the People's Republic of China from January 2011 to March 2025 were collected. Keywords highly correlated with HFMD were identified using Spearman's rank correlation and cross-correlation analysis, and a comprehensive search index (CSI) for HFMD was constructed. Subsequently, based on the monthly number of HFMD cases and the CSI, autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with exogenous inputs (ARIMAX) models were established. Finally, the predictive accuracy of the models was evaluated using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and standardised mean absolute percentage Error (SMAPE). From January 2011 to March 2025 (total of 171 months), China reported a total of 24,856,589 HFMD cases, with an average of 145,360.2 cases per month. The highest incidence of HFMD occurred between May and July each year. After correlation analysis, five keywords highly associated with HFMD were ultimately included, with a potential time lag of 0 months. The CSI of HFMD exhibits a high Spearman rank correlation (rs=0.937) with monthly reported HFMD cases. The developed ARIMAX(2,0,1)(1,1,1)(12) + CSI(Lag = 0) performs better in terms of fitting and prediction compared to the ARIMA(2,0,1)(1,1,1)(12). The MAE values for ARIMAX and ARIMA are 42,085.93 and 80,260.93, respectively, the RMSE values are 52,235.39 and 98,444.62, respectively, the MAPE values are 0.96% and 2.10%, respectively, and the SMAPE are 0.86% and 0.82%, respectively. The ARIMAX(2,0,1)(1,1,1)(12) + CSI(Lag = 0) model constructed from extensive internet search data in this study has effectively enhanced the prediction of HFMD incidence. Furthermore, it can serve as an early warning system for HFMD. This research provides valuable support for HFMD surveillance.

A unified multimodal framework for chest X-ray retrieval and disease prediction for clinical decision support.

Technological developments have transformed nearly every aspect of society, including investment methods. The types of investments that frequently attract investors are digital assets in the form of cryptocurrencies, stock indices, and gold. Comparative research on these three types of investments is still limited, particularly regarding Bitcoin. Therefore, this study analyzes the asset values ​​of these three types of investments and their predicted growth as considerations in determining promising investments. This quantitative descriptive study analyzes variables using the CAGR (Compound Annual Growth Rate) and AAGR (Average Annual Growth Rate) parameters for Bitcoin, gold, and stock indices in the United States, China, Germany, and Indonesia for the 2020–2024 period. The data is then used to predict asset growth over the next five years (2025–2029). The results show that the three types of assets studied for the 2020–2024 period exhibited growth in value with distinct profile characteristics. Bitcoin, the most volatile asset, offers the potential for significant long-term returns. Meanwhile, the assets with the best potential for growth in the 2025–2029 period, starting with the highest ranking, are Bitcoin, the S&P 500 (United States), and Gold. The predicted values ​​based on AAGR and CAGR for Bitcoin assets in 2029 are Rp75,664,861,688 and Rp7,575,357,132, respectively. Consideration of macroeconomic factors, along with strengthening government regulations and market education, is necessary for successful economic growth. Future research on digital assets such as Ethereum or stablecoins, and analysis of the impact of crypto adoption by countries as national reserves using other parameters, are needed to strengthen references and considerations for future investment options, taking technological advancements into account.

Characterizing climate projections to capture agroecological heterogeneity and topographic gradients is essential for localized planning in complex terrains. This study evaluates historical trends and projected changes in temperature and precipitation across six agroecological zones (AEZs) in South-Kivu, Democratic Republic of the Congo under SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. We evaluated seven CMIP6 models using the Taylor Skill Score (TSS), selecting the three best-performing models per variable and AEZ to construct zone-specific multi-model ensembles. Bias correction was performed using Quantile Mapping (QM) with a two-fold cross validation design (1983-1998 and 1999-2014). Selected models demonstrated high ranking stability across independent historical windows, achieving a weighted mean stability index of 0.83 for precipitation, 0.94 for maximum temperature (Tmax) and 1.00 for minimum temperature (Tmin). For precipitation, BCC-CSM2-MR excelled in low-altitude zones, while CMCC-CM2-SR5 and MPI-ESM1-2-HR were more effective in high-altitude zones. MRI-ESM2-0 and GFDL-ESM4 performed best for temperature across most AEZs. Climate trajectories were analyzed for near-term (2026-2050), mid-term (2051-2075) and long-term (2075-2100) periods relative to a 1983-2014 baseline. Reported projections represent the ensemble mean, while inter-model spread is detailed in the results. Results indicate continuous warming across all AEZs, with Tmin increasing more rapidly than Tmax. Province wide for the near (long) term, Tmax increases range from 0.78°C (1.64°C), 0.82°C (2.59°C), to 0.87°C (2.85°C) under SSP2-4.5, SSP3-7.0, and SSP5-8.5; Tmin increases range from 1.08°C (2.12°C), 1.13°C (3.12°C), and 1.25°C (3.46°C), respectively. Precipitation projections reveal non-linear, AEZ-differentiated trajectories characterized by near-term reductions followed by a long-term transition toward wetting in several zones. The EMAZ exhibits persistent declines (-9.8%) and the THMAZ shows a late-century wetting (+ 21%) under SSP5-8.5. Furthermore, a shift in seasonal redistribution is projected, with the long rain season intensifying and the short rain season contracting. These findings characterize the range of plausible climate trajectories in South Kivu, providing a necessary evidence base for future risk-targeted adaptation planning in eastern DRC and comparable tropical mountain systems.

Data-driven evaluation of constraints and adaptation priorities in Asian and African rice systems: Outcomes from the PAiCE toolkit

The use of southern pig-tailed macaques (Macaca nemestrina) as animal models requires adherence to animal welfare principles and the 3Rs (replacement, reduction, and refinement). This study aimed to characterize the temperament of individual juvenile pig-tailed macaques and to determine the social hierarchy structure within a captive colony to support the development of appropriate management and training strategies. Temperament assesssment was conducted through behavioral observations using a standardized ethogram durig 20-minute focal samping sessions, four sessions per day over 14 consecutive days. The subjects were four juvenile males aged approximately three years housed in colony cage GM 2/4 at the Research Animal Faciity at Lodaya (RAF-L), Primate Research Center, IPB University. We recorded 2,838 behavioral events across five temperament categories (aggresive, neutral, affiliative, submissive, and anxious). A one-way ANOVA with Tukey HSD post hoc test revealed that affiliative behaviors were significantly more frequent than othercategories (p<0.05). Three individuals exhibites predominantly affiliative temperaments, while one exhibited an aggresive temperament. Social hierarchy positions (high rank, middle rank, and low rank) were established by accumulating the frequency of aggressive-submissive interactions among the four individuals. One individual occupied the high-rank position, one occupied the middle rank, and two were categorized as low rank. notably, one low-rank individual that had been hand-reared displayed elevated frequenciea of anxious behaviors, including steretypic pacing. these findings provide a behavioral baseline for refining colony managemnet and training protocols consistent with the 3Rs framework.

Effects of Local Hemostatic Therapy on Tooth Extraction in Patients Undergoing Antithrombotic Therapy: A Systematic Review and Network Meta‐Analysis

Modification of the properties of caking coals through the methods of their treatment is of practical interest, especially in the context of deterioration of the raw material source for coke production and high requirements for the quality of coke. Considering the hydrophobicity of coals and their relatively high porosity, vapor treatment can be an effective method of influencing coal properties. Research on the properties of coal treated with water vapor and crude benzene vapor was conducted using different caking ability methods. Coal moistened to 10%wt. was also investigated for comparison. Four coal samples with varying degrees of coalification, ranging from medium to high rank (Ro = 0.76–1.50%) and characterized by volatile matter (Vdaf = 20.24–37.42%), were investigated. The mechanisms of interaction between coals and water in liquid and vapor form were determined. The results demonstrate that the treatment with water vapor and crude benzene significantly affects the properties of coal A. Specifically, under the influence of water vapor, there is a decrease in the period before the formation of plasticity and an increase in the caking properties of coal A. Coal B and C have good caking ability, so the effect of treatment is less noticeable. The treatment of the coal D leads to an increase in the viscosity of the plastic mass and a decrease in the caking properties. The approaches used in the study of the impact on coal properties can be effectively implemented in production conditions.

We consider tensor factorizations based on sparse measurements of the components of relatively high rank tensors. The measurements are designed in a way that the underlying graph of interactions is a random graph. The setup will be useful in cases where a substantial amount of data is missing, as in completion of relatively high rank matrices for recommendation systems heavily used in social network services. In order to obtain theoretical insights on the setup, we consider statistical inference of the tensor factorization in a high dimensional limit, which we call as dense limit, where the graphs are large and dense but not fully connected. We build message-passing algorithms and test them in a Bayes optimal teacher-student setting in some specific cases. We also develop a replica theory to examine the performance of statistical inference in the dense limit based on a cumulant expansion. The latter approach allows one to avoid blind usage of Gaussian Ansatz which fails in some fully connected systems.

Global optimization of complex, high-dimensional landscapes remains a fundamental challenge in scientific and engineering domains. To mitigate the inherent limitations of premature convergence and diversity loss, this paper proposes CLGMESC, an enhanced variant of the Escape Algorithm (ESC). The proposed algorithm integrates a dimension-wise comprehensive learning (CL) strategy with a hybrid Cauchy-Gaussian mutation (HCGM) operator. The CL strategy reconfigures the learning paradigm for stagnant individuals, enabling them to construct exemplars from multiple high-quality peers and thereby restore population diversity. Synergistically, the HCGM operator utilizes an adaptive weighting mechanism to dynamically balance heavy-tailed Cauchy mutations for global exploration and thin-tailed Gaussian mutations for local refinement, effectively facilitating escapes from local optima. Comprehensive evaluations on the CEC2017 benchmark suite demonstrate that CLGMESC achieves the top rank among ten advanced metaheuristics (including SBO, BBO, PO, DE, PSO, SMA, CPA, and MGO), with Wilcoxon signed-rank tests confirming its statistical superiority ([Formula: see text]) across the majority of test functions. Furthermore, the practical efficacy of CLGMESC was validated through a reservoir production optimization problem using the three-dimensional Egg Model ([Formula: see text] grid). In determining optimal well controls, CLGMESC achieved the highest Net Present Value ([Formula: see text] USD) with the lowest standard deviation, thus substantiating its reliability and robustness in solving computationally intensive real-world engineering problems. The consistently high rankings across diverse benchmarks and the substantial economic gains in the reservoir simulation underscore the algorithm's pronounced capability to maintain a robust exploration-exploitation balance and dynamically escape local optima in demanding parameter spaces.

Reliable heavy machinery requires accurate health assessments of its hydraulic systems. Existing data-driven models often fail to track long-term degradation trends while concurrently ignoring the physical laws governing wear. This oversight produces predictions that contradict the natural irreversible progression of equipment faults. This study introduces the Physics-Informed Monotonic Conformer to address this specific problem. The proposed model combines convolutional inductive biases with global self-attention to merge multi-scale spatiotemporal features. We also implement a monotonicity loss function to enforce physical degradation constraints. This step grounds the purely data-driven network in actual physical realities. Testing on an electrohydrostatic actuator dataset shows the new method surpasses current baseline models. The regularization mechanism also significantly improves physical consistency, yielding a high Spearman rank correlation. The resulting health indicators offer the numerical precision and physical reliability necessary for safety-critical aerospace deployment.

Quantitative physical characterization of medium–high rank coals in northern China: Implications for pore structure evolution around the second coalification jump

The study focuses on the analysis of pore structure and the determination of CO2 adsorption properties of different rank coals originating from the Upper Silesian Coal Basin. Measurements were performed using a low-pressure volumetric apparatus ASAP 2020, Micromeritics, for sample porosity characterisation, and a gravimetric analyser IGA-001, Hiden Isochema, for gas adsorption measurements in the range of elevated adsorbate pressures. CO2 adsorption isotherms were determined in the CO2 pressure and temperature, 0-100 kPa at 0°C and 0-1200 kPa at 30°C, respectively. Coal investigations were complemented by microscopic, technical and densimetric analyses. CO2 adsorption studies showed the absence of a universal model that adequately describes both low-pressure and elevated-pressure processes. Adoption of the Langmuir-Freundlich model proved appropriate for most coals (5 of 6), while for one sample (CR2), a better fit was obtained with the Freundlich model. The analyses confirmed differences in the pore structure and CO₂ adsorption properties of coals with different rank. With increasing degree of coalification, an increase in specific surface area and micropore volume was observed. All coals exhibited narrow micropores with sizes <0.7 nm (ultramicropores). Analysis of pore size distributions by the DFT method showed that the porous structure of the coals was crucial for assessing CO2 adsorption performance and the transport properties of coal. The diffusion rate was not limited by the high rank of coal but rather by the structural heterogeneity of the pores, which could be detected by analysing the pore size distribution.

Dragon fruit (Hylocereus polyrhizus) represents a rapidly growing segment within the global exotic fruit market, valued for both nutritional properties and commercial potential. However, the fruit’s high moisture content (87%) and delicate structural characteristics present challenges for preservation and shelf-life extension. This study employed a hybrid multi-criteria decision-making (MCDM) framework combining Step-wise Weight Assessment Ratio Analysis (SWARA) with EDAS, Combined Compromise Solution (CoCoSo), and MOORA methods to identify the optimal drying technology for dragon fruit processing. Twelve evaluation criteria encompassing quality retention, economic factors, and technical feasibility were weighted through SWARA methodology based on three distinct decision-making perspectives: cost-efficiency, product quality, and process engineering. Eight drying alternatives were evaluated including traditional methods (sun drying and solar cabinet) and advanced technologies (freeze drying, vacuum drying, heat pump drying, and microwave drying). Results demonstrated that freeze drying achieved the highest aggregated score (100%), followed by vacuum drying (87.1%) and heat pump drying (82.4%). Sensitivity analysis confirmed high ranking stability with no rank changes observed across ±30% criterion weight perturbations. Statistical validation through Spearman rank correlation (0.984) and Kendall tau (0.952) indicated strong consensus among the three MCDM methods. The findings provide dragon fruit processors with evidence-based guidance for technology selection, balancing quality preservation against operational economics.

Tree- or branch-shaking displays have been reported in some nonhuman primate species, but large-sample studies addressing their possible functions are rare. The present study examined 842 and 72 tree-shaking (TS) displays by free-ranging male and female Japanese macaques (Macaca fuscata), respectively, recorded in and around the group's provisioned feeding site. In the mating season, TS displays performed by peripheral and non-group (i.e., non-central) males were almost always accompanied by loud vocalizations, whereas central males were less vocal. Also, non-central males rarely performed female-directed aggressive behaviours, whereas central males frequently did so. Aggression towards females by non-central males poses a potential cost, i.e. punishment by central males, whereas vocalizing to attract females appears to have few costs, if any, yet it might provide substantial reproductive benefits. By contrast, central males performed fewer TS displays with vocalizations as they were at less risk of getting punished for threatening or attacking females. In the non-mating season, almost all TS displays were performed by central males; non-central males were rarely observed in or around the feeding site in the non-mating season. TS displays in the non-mating season may help central males to reinforce their high-ranking positions. Most female TS displays were by the alpha female and her daughters in the non-mating season, their probable function also being to reinforce their high ranks.

To compare the effects of various physical therapy interventions on proprioception in individuals with chronic ankle instability (CAI) and to provide an evidence-based medical basis for choosing the best physical therapy. Six electronic databases were systematically searched. From the time the database was created until March 1, 2025. Randomized controlled trials evaluating the efficacy of physical therapy for joint position sense were included. A total of 24 randomized controlled trials including 949 individuals with CAI were included. Seven physical therapy methods were used. Network meta-analysis revealed that balance training (standardized mean difference [SMD]=0.66, 95% confidence interval [CI]: 0.26-1.05), strength training (SMD=0.97, 95% CI: 0.53-1.41), balance combined with strength training (SMD=0.94, 95% CI: 0.50-1.37), cognitive motor training (SMD=0.77, 95% CI: 0.08-1.45), electroacupuncture (SMD=0.84, 95% CI: 0.03-1.65), and whole-body vibration training (SMD=1.00, 95% CI: 0.45-1.55) significantly reduced ankle joint position error. Although whole-body vibration training had the highest ranking (75.4%), it was not significantly better than other therapies. With the exception of electrophysical agents, all six physical therapy treatments significantly improved ankle proprioception and yielded similar results. However, interventions should be chosen carefully, as the certainty of evidence is very low.