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Drought trends in the Arabian Peninsula using SPEI and SPEDI indices and their implications for climate adaptation
Predicting medication non-adherence using machine learning: Incorporating Complementary and Alternative Medicine (CAM) beliefs in Malaysian chronic disease patients
Medication non-adherence among chronic disease patients remains a major contributor to poor health outcomes and medication wastage, particularly in multi-ethnic populations such as Malaysia where cultural and religious beliefs strongly influence health behaviours. This study aimed to develop and evaluate machine learning models that integrate demographic, clinical, and complementary and alternative medicine (CAM) belief factors to predict medication non-adherence among patients with type 2 diabetes mellitus, hypertension, and dyslipidaemia. A cross-sectional survey was conducted using a structured questionnaire comprising demographic and clinical data, history of CAM use, the 17-item Complementary and Alternative Medicine Beliefs Inventory (CAMBI), and the Malaysian Medication Adherence Scale (MALMAS). Twelve conventional machine learning algorithms and three stacked ensemble models were utilised using both balanced and unbalanced datasets with all variables as well as feature-selected variables. The best-performing model was a stacked ensemble using logistic regression-selected variables with the unbalanced dataset, achieving the highest AUC of 0.816. Feature selection identified significant variables including CAM beliefs (natural and holistic), race, number of daily doses, number of medications prescribed, religion, educational level, treatment duration, and hypertension status which were later interpreted using SHapley Additive exPlanations (SHAP) analysis. Model performance was further evaluated using the Youden Index and Decision Curve Analysis (DCA) to stratify patients into lower- and higher-risk groups, with a suitable cutoff identified at 0.4. These findings show that incorporating cultural and belief-related factors into machine learning models provides a novel, population-specific approach to predict better non-adherence and guide targeted interventions to reduce medication wastage in chronic disease management.
Encoding Force‐Responsive Speed Bumps Into Slide‐Ring Networks for Programmable Mechanical Properties
ABSTRACT Slide‐ring (SR) materials exploit mobile crosslinkers to achieve exceptional toughness and fatigue resistance via the “pulley effect.” However, macrocycle sliding in conventional SR networks remains passive and unregulated, lacking the ability to temporally or spatially program energy dissipation. Inspired by station‐regulated shuttling in molecular machines, we introduce mechanically gated “speed bumps” into SR architectures. Here, we report a mechanically interlocked polyurethane network PU‐DP‐TED‐a , in which pillar[5]arene‐based macrocycles ( DP ) function as sliding crosslinkers and strategically positioned Diels–Alder adducts ( TED ) act as force‐labile speed bumps along the polymer axle. Under tension, macrocycles are pushed against TED units, triggering a retro‐Diels–Alder reaction that transiently arrests ring sliding and dissipates mechanical energy. This force‐triggered, sequential “arrest‐and‐release” mechanism provides spatiotemporal control over energy dissipation. Relative to a non‑interlocked control containing TED but no DP , the resulting material exhibits a rare combination of enhancements: Young's modulus increases by 9.9‑fold (from 3.46 to 34.2 MPa), tensile strength by 6.0‑fold (from 3.28 to 19.8 MPa), elongation at break by 2.9‑fold (from 155% to 449%), and toughness by 16.6‑fold (from 3.74 to 62.0 MJ m − 3 ). This work establishes a paradigm for active, force‑programmable SR materials with multi‑stage energy dissipation and adaptive mechanical behavior.
Temporal-sequential modeling for remaining useful life estimation of turbofan engine
Abstract Accurate prediction of Remaining Useful Life (RUL) is essential for predictive maintenance of turbofan engines, as it helps reduce unexpected failures and maintenance costs while improving system reliability. The objective of this study is to develop an accurate and robust deep learning framework for RUL prediction using multivariate time-series sensor data and to investigate the influence of different optimization algorithms on prediction performance. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model is proposed, where convolutional layers extract local degradation features and LSTM layers learn the long-term temporal relationships present in engine degradation data. A sliding-window strategy together with early RUL capping is employed to improve training stability and model generalization. The proposed model is evaluated on all four subsets (FD001–FD004) of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and achieves RMSE values of 12.79, 21.94, 15.09, and 33.71, respectively. Among the evaluated subsets, the model delivers the best performance on FD001 and outperforms several existing deep learning approaches reported in the literature. In addition, five optimization algorithms are systematically compared under the same experimental settings. The results show that Stochastic Gradient Descent (SGD) provides the best convergence behaviour and prediction accuracy for the proposed architecture. Overall, the study demonstrates that combining CNN–LSTM with an appropriate optimization strategy improves the reliability and accuracy of RUL prediction for turbofan engines.
Methicillin-resistant Staphylococcus aureus (MRSA) infection in hospitalized patients is dominated by community-acquired strains: genomic epidemiological evidence
Objective This study aimed to systematically investigate the molecular epidemiological characteristics of methicillin-resistant Staphylococcus aureus (MRSA) in Ningxia hospitals, to elucidate their genetic evolutionary relationships, and to delineate the genomic and phenotypic profiles of the dominant lineages. Methods Clinical isolates of MRSA strains collected between 01/01/2024 and 30/06/2024 were analyzed, employing second-generation gene sequencing technology, combined with MLST and SCC mec typing, along with evaluation of drug resistance and virulence genes. A phylogenetic tree was constructed to analyze strain homology. Results A total of 74 non-duplicate Staphylococcus aureus strains (67 MRSA and 7 MSSA) were collected. The most common clonal strain was ST59-IVa, accounting for 46.27%. This strain exhibited a high prevalence of resistance genes mecA and blaZ , at 91.04%. All five ST22-IVa strains were found to lack mecA and erm genes but showed β-lactam resistance, while possessing both lukS/F-PV ( PVL ) and tsst-1 virulence genes, indicating a significant toxicity risk. Genetic evolution analysis revealed that ST3355, ST4513, and ST59 were closely related, all belonging to SCC mec types IVa; the other ST types exhibited mutations at various loci, with ST5 as the central node, resulting in a wider array of ST and SCC mec typing. Conclusion The ST59-IVa clone is the predominant MRSA strain in Ningxia hospitals, exhibiting multidrug resistance and virulence gene profiles consistent with national trends. However, the emergence of hypervirulent ST22-IVa strains with atypical resistance mechanisms warrants increased vigilance. We recommend enhancing the rational use of antibiotics in hospitals and implementing molecular surveillance for these highly virulent strains.
Optimizing tissue lysis and DNA extraction protocols to enhance bacterial diversity profiling in the Drosophila melanogaster gut microbiome
Abstract The gut microbiota is a dynamic community that influences host metabolism, immunity, and overall health. Accurate characterization of this community requires robust and reproducible DNA extraction methods; however, technical biases introduced during tissue lysis and DNA isolation remain major challenges in microbiome research, particularly in animal model systems. In this study, we compared two lysis methods (manual pestle homogenization and bead-beating) and two commercial DNA extraction kits (Qiagen and Zymo) to evaluate their impact on microbiota profiling in a microbial community standard (MCS) and Drosophila melanogaster gut samples, a tractable model for host–microbe interactions. Full-length 16S rRNA sequencing was performed using Oxford Nanopore Technologies (ONT), followed by taxonomic classification with EPI2ME and downstream diversity analyses using standard pipelines implemented through a custom script. Our data revealed that extraction and lysis methods influence microbial composition, with some protocols resulting in additional species-level assignments in MCS samples, likely reflecting limitations of sequencing technology and taxonomic misidentifications. Pestle homogenization, combined with the Qiagen kit, recovered the highest number of detected taxa and showed a more balanced representation of both Gram-positive and Gram-negative bacteria across the Drosophila gut samples. These findings highlight that extraction methodology influences microbial diversity estimates in Drosophila gut samples, although sequencing approach and taxonomic classification accuracy may also contribute to the observed differences. Our results emphasize that the choice of DNA extraction protocol should be guided by the biological question being addressed and highlight the need for standardized protocols to ensure reproducibility across microbiome studies, particularly those using model systems.
High-accuracy pseudospectral scheme for fractional diffusion-wave problems based on cardinal functions
Fractional diffusion-wave equations (FDWEs) are essential for modeling complex physical phenomena with memory and hereditary properties, such as anomalous diffusion and viscoelasticity, which classical integer-order models fail to capture accurately. In this paper, we introduce an efficient and high-accuracy pseudospectral scheme utilizing Legendre cardinal functions (LCFs) as basis functions to solve both second- and fourth-order FDWEs. By reformulating the governing equations into equivalent integral forms and developing direct matrix representations for the Caputo fractional derivative and fractional integral operators, we systematically transform the original problem into a solvable system of algebraic equations. Detailed convergence analysis and numerical experiments confirm that this method consistently achieves spectral convergence. A key novelty of this technique, and what significantly advances it beyond previous efforts in the literature, is its exploitation of the cardinal properties of LCFs to avoid numerical integral evaluations when computing basis coefficients entirely. Consequently, this approach dramatically reduces computational overhead while delivering superior accuracy and efficiency compared to existing finite difference and collocation methods.
Comparison of American joint committee on cancer staging system 7th and 8th editions for head and neck squamous cell carcinoma
Association between anthropometric measures and abdominal wall thickness in patients with obesity: A cross-sectional CT study
Background Abdominal wall thickness (AWT) is a critical determinant of safe abdominal access in patients with obesity, yet it is not routinely assessed preoperatively. Simple, bedside methods for evaluating factors associated with AWT are lacking. This study aimed to evaluate the association between readily available anthropometric measurements and AWT across multiple surgically relevant abdominal regions. Methods In this prospective cross-sectional study, 105 patients with obesity (body mass index [BMI] ≥30 kg/m 2 ) who underwent computed tomography (CT) imaging were included. AWT was measured at nine predefined anatomical points corresponding to commonly used surgical landmarks. Anthropometric parameters, including BMI, neck circumference, mid-upper arm circumference, and waist circumference, were recorded. Correlation analyses were performed to assess the associations between anthropometric measurements and CT-measured AWT. Results BMI demonstrated a moderate and consistent positive association with AWT across all anatomical regions (rs = 0.34–0.55, all p < 0.001). Waist circumference and mid-upper arm circumference showed moderate associations, whereas neck circumference demonstrated weak and inconsistent associations. AWT varied across anatomical regions, with greater thickness observed in the lower abdomen. Female patients had significantly greater AWT at several sites. These findings highlight regional variability in AWT and its association with anthropometric measurements. Conclusions Anthropometric measurements, particularly BMI, were significantly associated with AWT across multiple abdominal regions in patients with obesity. These findings provide additional insight into regional abdominal wall variability and may support future development of bedside assessment tools for abdominal access planning. Further studies are needed to develop and validate predictive models for clinical application.
Nonlinear dynamics and chaotic behavior of curved self-sustaining BNNTs under combined thermal and magnetic fields
Evaluation of a tele-refraction model to address the lack of human resources in Kenya
Background Uncorrected Refractive Errors (URE) constitute a significant public health concern in Kenya, where limited availability of qualified eye care professionals continues to impede equitable access to refractive services. This shortage is particularly acute in rural and underserved regions, resulting in significant unmet visual health needs. The implementation of telemedicine; specifically, a tele-refraction model utilizing low-cost, portable devices, has emerged as a viable and cost-effective approach to decentralize service delivery, enhance accessibility, and mitigate the burden of URE at the primary healthcare level. Methods This non-inferiority diagnostic accuracy study was conducted in Machakos. County, Kenya, to evaluate the performance of the OneSight EssilorLuxottica Foundation’s ClickCheck™ tele-refraction model in comparison with conventional face-to-face retinoscopy, which served as the reference (gold) standard. A total of 92 patients (184 eyes) were examined. The tele-refraction procedure entailed a trained technician performing objective refraction using the ClickCheck™ device, followed by subjective refinement under the real-time supervision of a remote optometrist via telecommunication. Statistical analyses assessed the agreement between the final subjective prescriptions obtained from both methods, focusing on the clinical conformity rate for spherical equivalent (SE), cylindrical power, and power vector components within a strict clinical tolerance limit of ±0.50 diopters (D). Results The study demonstrated a high level of clinical concordance between the telerefraction model and the conventional gold-standard refraction method. The mean difference in spherical equivalent (SE) for objective refraction was minimal (+0.08 D), indicating negligible bias between the two techniques. Importantly, the final subjective prescriptions exhibited excellent clinical interchangeability, with 93.01% of cases falling within the ± 0.50D tolerance for SE and 94.41% within the same tolerance for cylindrical power magnitude. Furthermore, for clinically significant astigmatism, axis alignment showed complete conformity within the 10 degrees tolerance range across all cases. Conclusion The tele-refraction model using the ClickCheck™ device, combined with remotely guided subjective refraction, demonstrated accuracy, reliability, and non-inferiority when compared with conventional in-person refraction. These findings substantiate the clinical validity of tele-refraction, which holds the potential to mitigate the acute shortage of eye care professionals and to enhance equitable access to refractive error correction services among underserved populations in Kenya.
Silver-copper hybrid nanocomposite thermal interface materials for power electronic device packaging
Abstract Thermal interface materials (TIMs) play a crucial role in the thermal management of power electronic devices. In this study, a new class of silver-copper micro-nano hybrid composite pastes (AgCuMNHs) was developed as die-attach TIMs by exploiting the size and shape characteristics of copper microparticles (CuMPs), silver nanoflakes (AgNFs), and silver nanoparticles (AgNPs). The AgCuMNHs form Ag-Cu hybrid nanocomposites through low-temperature sintering achieved a high thermal conductivity of up to 330.0 W/(m·K), with a corresponding electrical conductivity of 5.6 × 10 7 S/m, under the optimized processing conditions of 220 ℃ and 0.7 MPa. It was found that the solvent choices play a significant role in the paste formulation since the polarity of solvent would highly affect the disperse and distribution of the silver nanoparticles and nanoflakes in the pastes, which dictated the degree of sintering in the hybrid nanocomposites and hence showed large differences in material thermal and electrical conductivities. To further validate the AgCuMNH as high performance TIMs for power electronic device packaging, they were used as the die-attach pastes for a model LED chip packaging structure. The effective die-attach thermal resistance of the AgCuMNH-based device was 0.56 K/W. Compared with the best commercially available low-temperature sintered silver paste from Kyocera (CT2700R7S), whose effective die-attach thermal resistance in the same device was measured to be 1.00 K/W in the parallel tests, the AgCuMNH composites demonstrated a more than 40% improvement in performance as the TIMs for packaged devices.
EEG spectral biomarkers of postoperative delirium in spinal surgery: A high-resolution analysis
Background Postoperative delirium is common in older adults following spinal surgery, yet current EEG-based detection methods rely on broad conventional frequency bands that may obscure clinically meaningful oscillatory changes. More granular spectral approaches may reveal candidate frequency-specific features that could improve diagnostic performance. Methods We recorded single-electrode frontal EEG (Fp1) from 47 patients at four perioperative timepoints and compared traditional band-level power with high-resolution 1-Hz spectral analysis (1–45 Hz). Group differences were evaluated using non-parametric statistics with FDR correction, and diagnostic accuracy was assessed using AUROC and threshold-optimized metrics. All diagnostics thresholds were derived and evaluated in the same dataset. Results Seven patients (14.9%) developed delirium. Conventional band analysis detected only reduced theta power during active delirium (|r| = 0.31; AUROC = 0.53). In contrast, 1-Hz analysis revealed a richer pattern: elevated 1-Hz power (|r| = 0.27), reduced 3–7 Hz power (|r| = 0.25–0.27), and increased 14–15 Hz power (|r| = 0.22–0.26). The 23-Hz narrow-band feature achieved the best discrimination (AUROC = 0.74), despite showing no significant effect within the traditional beta band (12–30 Hz). Several frequencies remained altered even after clinical resolution of delirium. Conclusions In this exploratory analysis, high-resolution spectral analysis identified candidate frequency-specific EEG features that distinguished delirium cases more accurately than conventional frequency bands in this cohort. Broadband averaging may not detect frequency-specific oscillatory patterns; adopting finer spectral resolution could enhance the utility of single-channel EEG for routine clinical monitoring.
Multi-domain identification of myocardial infarction incidence using explainable AI: the overlooked role of periodontal health
A comparative study of Korean and Chinese learners’ strategy use in request-refusal continuous dialogue speech acts
In daily communications, the use of speech acts that may threaten others’ faces, such as request and refusal, is inevitable. By methods of DCT with 24 coherent request-refusal scenarios as well as optimized classification of request and refusal strategies, differences in request-refusal continuous dialogue speech acts between Native Korean (NK) and Chinese Korean language learners of intermediate level (CKLI) and advanced level (CKLA) were examined. There were statistically significant differences in frequencies of NK, CKLA, and CKLI using request and refusal strategies in different scenarios. Overall, NK used more categories and numbers of strategies than CKLI. The gap between NK and CKLI might narrow as language proficiency increases. Furthermore, not only the single influence factor that affected strategy choice but also the interactions between these factors, suggesting that people might behave differently when faced with complex scenarios consisting of multiple factors. This study also proposes a new research methodology that inserts factors affecting strategy choice into individual scenarios, designs multiple independent scenarios with the same factors, and analyses the effects of these factors and their interactions on strategy choice. The results of the associated analysis could be used to assist in the development of a detailed instructional plan.
Pre-analytical considerations of the recovery and downstream analysis of circulating nucleic acids
Bioassay-guided isolation of antioxidant, antibacterial, and antidiabetic compounds from Aleuritopteris bicolor of Nepal: In vitro/in silico study
Aleuritopteris bicolor , a fern species known locally as “Raani Sinkaa” in Nepal, has traditionally been used to treat wounds, diarrhea, dysentery, and gastritis. Despite its widespread use, scientific data on its bioactive compounds and bioactivities remain limited. To address this gap, this study aimed to isolate bioactive compounds from A. bicolor and evaluate their antioxidant, antibacterial, and antidiabetic properties using both in vitro and in silico methods. The 70% methanol extract of A. bicolor afforded three compounds: 20-hydroxyecdysone ( 1 ), and a mixture of quercetin 3- O - β -D-glucopyranoside ( 2 ) and quercetin 3- O - β -D-galactopyranoside ( 3 ), isolated via column chromatography. The structures of the isolated compounds were elucidated based on spectroscopic (NMR) and spectrometric (LC-MS) analyses. Compound 1 exhibited weak antioxidant activity, with an IC 50 of 38.38 ± 3.41 µg/mL in the DPPH free radical scavenging assay. In contrast, the mixture of compounds 2 and 3 demonstrated significant antioxidant activity, with an IC 50 of 1.56 ± 0.3 µg/mL, surpassing the standard ascorbic acid (IC 50 : 4.15 ± 0.3 µg/mL). Compounds 2 and 3 also showed moderate antibacterial activity against Staphylococcus aureus and Klebsiella pneumoniae . Furthermore, these compounds exhibited notable inhibition of α -amylase and α -glucosidase enzymes in in vitro assays, with EC 50 values of 99.48 ± 3.2 µg/mL and 68.53 ± 1.7 µg/mL, respectively. In silico molecular docking analyses supported these enzyme inhibitions by revealing favorable interactions between compound 2 and key catalytic residues of the target enzymes, with binding energies of −7.2 kcal/mol for α -amylase and −7.1 kcal/mol for α -glucosidase. Additionally, ADMET analysis suggests further lead optimization of compounds 2 and 3 . This study successfully isolated three bioactive compounds from A. bicolor and recommends further medicinal chemistry and in vivo investigations to establish their therapeutic potential as antidiabetic phytomedicine.
Behavioural adaptation of 158 rescued dogs from abroad during the first six months after adoption in Germany
Abstract In several European countries, adopting rescued dogs from abroad is common, but the dogs’ behaviour has rarely been studied. This first prospective study involved five telephone interviews with 151 owners of 158 dogs from 18 countries. The first interview, collecting each dog’s preliminary data given to the owner by the animal welfare organisation, took place before or few days after adoption to households in Germany, the subsequent ones at one, six and twelve weeks and six months post-adoption. Specific behaviour categories were classified into behaviour scales, and a personality score consisting of 18 behaviour scales was created for each dog and time point. The personality score improved significantly over six months. Age, sex, size, region of origin and type of placement (direct, via shelter/foster home) of the dog and dog experience and residential area of the owner did not significantly influence the personality score. Significantly higher personality scores were determined for dogs housed with conspecific and those without external dog training. Six months after adoption, 79.9% of 149 owners said they would adopt another dog from abroad in future. Our study shows that most dogs adapted well to their new lives, but owners should anticipate behavioural problems, particularly fear-related behaviour.
Psychometric properties of the Arabic Perceived Digital Well-Being Scale (PDWS) in Saudi young adults
Rapid digital transformation in Saudi Arabia has intensified young adults’ day-to-day reliance on smartphones, increasing the need for balanced, culturally appropriate measures of perceived digital well-being. This study aimed to psychometrically validate the Arabic version of the Perceived Digital Well-Being Scale (PDWS) among Saudi young adults aged (18–25). A cross-sectional instrument-validation study was conducted with (N = 450) participants. The full sample was randomly split into two independent analytic subsamples: Sample (1) for exploratory factor analysis (EFA) (n = 225) and Sample (2) for confirmatory factor analysis (CFA) (n = 225); convergent validity was examined in both subsamples. EFA supported a clear three-factor structure (Emotional, Social, Cognitive). CFA confirmed a second-order model with excellent fit: χ²(116)=133.275, p = .130, χ²/df=(1.149), CFI=(.986), TLI=(.983), RMSEA=(.026), and SRMR=(.055). Convergent validity was supported by positive associations with life satisfaction (SWLS) and negative associations with fear of missing out (FoMO), with the same pattern observed across both subsamples (Sample (2): SWLS r=(.416–.550), p < .01; FoMO r=(−.321–-.254), p < .01). Internal consistency was acceptable across subsamples (Cronbach’s α=(.759–.886); McDonald’s ω=(.761–.889)). Overall, the Arabic PDWS demonstrates strong initial evidence of structural validity, theoretically consistent convergent validity, and adequate reliability, supporting its use as a multidimensional measure of perceived smartphone-related digital well-being among Saudi emerging adults.