Browse Articles

Mechanistic evaluation of niosomal Rhoifolin in antileishmanial therapy and its additive potential with meglumine antimoniate

Predicting drilling fluid lost circulation volume and radius using petrophysical well logs and machine learning approaches

High prevalence of vitamin D deficiency among school-age children in Tunisia: a first national cross-sectional study

An interpretable surrogate modeling framework for rice husk ash concrete using copula-based virtual sampling

Assessment of phyto-remediation efficiency and crop productivity of castor-based rice mill effluent (RME) wetland system

Glazed and unglazed transpired solar collectors for sustainable buildings: a comparative assessment

Abstract Transpired solar collectors (TSCs) preheat ventilation air and reduce conductive heat loss through the building envelope, thereby acting as dynamic insulation. Direct outdoor comparisons of glazed and unglazed TSC modules operating under identical conditions remain limited. A systematic experimental study was conducted on two façade-mounted modules with the same perforated galvanised-steel absorber and 110 mm plenum: an unglazed TSC (UTSC) and a glazed TSC (GTSC) fitted with 4 mm low-iron tempered glass. The system was tested outdoors in Amman, Jordan, under clear-sky conditions (January-March 2025) at specific airflow rates of 18–144 m 3 /(h·m 2 ) and solar irradiance of 200–1000 W/m 2 . Passive natural-convection operation of the GTSC was also evaluated at several cavity heights. Performance was quantified using physically bounded metrics covering thermal performance, heat exchange, exergy, wall heat-loss recapture, ventilation load reduction, and economic return. Across the tested range, the GTSC achieved thermal efficiency of 48–75% and exergy efficiency up to 12%, outperforming the UTSC (42–65%) by 6–9% points. The UTSC wall heat-loss recapture index decreased from 93% at low irradiance to 63% at peak irradiance, consistent with dynamic-insulation behaviour, while GTSC ventilation load reduction increased monotonically from 60% to 90%. Under passive operation, the GTSC reached 55% efficiency at 3.0 m cavity height without fan energy. Economic assessment using Jordanian energy prices indicated payback periods of 4.4 years (UTSC) and 5.2 years (GTSC). The results provide experimentally grounded guidance on when glazing is justified for sustainable ventilation preheating in buildings.

A comparative study of large language models (ChatGPT/DeepSeek) in generating structured thyroid ultrasound reports

An exploratory assessment of icodextrin for ultrafiltration failure subtypes in peritoneal dialysis

Abstract Although icodextrin was initially developed for rapid transporters (type 1 UFF), emerging evidence suggests its effects might be independent of peritoneal membrane transport characteristics. This study aimed to evaluate the suitability of 7.5% icodextrin for all PD patients with UFF. We enrolled 19 patients with UFF and 24 non-UFF PD controls. All participants underwent standard, modified, Mini-, and Double-Mini peritoneal equilibrium tests (PET). Key parameters assessed included sodium sieving (∆DNa60, ∆D/PNa60), osmotic conductance to glucose, small-pore ultrafiltration (UFSP), free water transport (FWT), and net ultrafiltration. Correlation analyses were conducted between icodextrin-induced UF and these metrics. Descriptive analysis revealed variations in icodextrin UF, ∆D/PNa60, and FWT were identified across the four UFF types. Numerically, icodextrin UF was highest in type 1 and type 2 UFF, while FWT appeared lower in type 2. Net icodextrin UF correlated positively with ∆D/PNa60, ∆DNa60, and UFSP. Our findings indicate that 7.5% icodextrin may be suitable for PD patients with type 1 and 2 UFF, but appears perhaps less effective in those with type 3 and 4 UFF.Moreover, icodextrin ultrafiltration may be predictable through UFSP measurement, offering a potential clinical tool for patient management.

Factors influencing Somalia household’s willingness to pay renewable energy: employing structural equation modeling

Hunting by humans affects the navigation of two endangered mammals in Zimbabwe and Brazil

Assessment of healing after primer endodontic treatment and retreatment using fractal analysis and PAI score over a 24-month follow-up

End-to-end DOA estimation via self-supervised cascaded DNNs with array errors mitigation

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks

An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia

Abstract The aim of this study is to develop and validate a machine learning-based predictive model to assess the risk of acquired bloodstream infection (BSI) in ICU pneumonia patients. Data were obtained from the MIMIC-IV database and Dazhou Central Hospital. The MIMIC-IV cohort was randomly divided into a training set and an internal test set, and the Dazhou cohort served as an external validation set. Candidate predictors included demographic variables, comorbidities, severity scores, microbiological results, and ICU-course variables. Boruta feature selection followed by univariable and multivariable logistic regression was used to identify predictors for model development. To address class imbalance, SMOTE was applied during model training, and model discrimination was evaluated by the area under the receiver operating characteristic curve (AUC). SHAP was used to interpret the final model. Because several leading predictors in the primary model were only available after ICU admission, we additionally performed an admission-only sensitivity analysis after excluding post-admission variables such as ICU length of stay and sputum culture results. The final post-admission model incorporated six predictors: ICU length of stay, sputum culture for Gram-negative bacteria, sputum culture for Gram-positive cocci, APSIII score, SOFA score, and liver disease. Among the evaluated algorithms, the SMOTE-GBM model showed the best discrimination, with an AUC of 0.753 (95% CI 0.719–0.788) in the internal test set and 0.703 (95% CI 0.576–0.830) in the external validation set. SHAP analysis showed that ICU length of stay and sputum culture results were the major contributors to the post-admission model predictions. In the admission-only sensitivity analysis, model discrimination decreased, with the best AUC reaching 0.751 (95% CI 0.716–0.785) in the internal test set and 0.632 (95% CI 0.493–0.770) in the external validation set, indicating that early prediction using admission-available variables alone remained challenging. An interpretable post-admission machine-learning model showed moderate discrimination for reassessing BSI risk in ICU patients with pneumonia; however, limited external precision and the weaker performance of the admission-only sensitivity analysis indicate that further refinement is required before clinical implementation, especially for early decision-making.

Dynamic multimodal expressions support inference of both presence and relative salience of blended emotions

Abstract Few studies explore nonverbal communication of blended emotions, despite common reports of experiencing multiple emotions simultaneously. In two pre-registered experiments, we investigated if participants can accurately judge how prominently different emotions are expressed in dynamic multimodal portrayals of blended emotions. Actors portrayed pairwise combinations of anger, disgust, fear, happiness, and sadness in varying proportions, using facial gestures, body movement and vocal sounds. In Study 1, participants were instructed to choose two scales (out of 5 available scales: anger, disgust, fear, happiness, and sadness) that best described their impression of the emotional content of the portrayals, and rate how clearly both of the two chosen emotions were perceived. In Study 2, participants were instead free to choose any number of scales (out of the 5 available ones) in the emotion rating task. Results were consistent across both studies and showed that all blended emotions were accurately perceived with significantly higher ratings on scales corresponding to intended vs. non-intended emotions. Participants could also accurately judge the relative salience of each emotion, i.e., which was the dominant and the less dominant emotion in the blend. To summarize, results revealed nuanced perception of both presence and relative salience of blended emotions, which may help us navigate in a complex social environment.

Conversion of waste cooking oil to environmentally acceptable surfactants for enhanced oil recovery

Abstract Enhanced oil recovery (EOR) is a tertiary method used to extract crude oil remaining in reservoirs. With growing concerns about waste generation and its environmental impacts, waste cooking oil (WCO) has emerged as both a pollutant and a valuable renewable feedstock. WCO can be efficiently converted into sustainable surfactants with favorable surface and interfacial properties suitable for reservoir conditions. In this study, anionic and nonionic surfactants were synthesized from WCO: ethoxylated dodecylbenzene sulfonate (EABS14, aromatic) and ethoxylated hydrolyzed waste oil (EHWO14, aliphatic). Isopropanol was incorporated as a co-surfactant to enhance microemulsion performance. Phase behavior was evaluated at salinities of 50 × 10³, 100 × 10³, and 200 × 10³ ppm and at temperatures of 50 and 70 °C. Solubilization parameters and microemulsion phase volumes were used to determine optimal conditions. At optimum salinity, the dynamic interfacial tension (IFT) decreased significantly, reaching 1 × 10⁻³, 1 × 10⁻², and 8 × 10⁻² mN/m for EHWO14 + EABS14+CS, EHWO14, and EHWO14 + EABS14, respectively. Contact angles were reduced from 155° to 27°, 35°, and 22°, indicating effective wettability alteration. Flooding experiments confirmed the efficiency of WCO-derived surfactants, with maximum oil recovery at 100 × 10³ ppm and 50 °C. Recovery factors were 71.00% for EHWO14, 73.33% for EHWONa, 72.91% for EHWONa+EHWO14, and highest at 79.00% for the EHWO14 + EABS14+CS blend. These results demonstrate that WCO valorization into surfactants provides an eco-friendly and effective EOR alternative.

Elucidating response effects of anammox-based nitrogen removal processes for municipal wastewater using big data analysis and automated machine learning

DFT investigation of hydroxyl radical scavenging mechanisms in bioactive terpenoids from Syzygium nervosum

Abstract Reactive oxygen species, particularly the hydroxyl radical ( $${\hbox {OH}^{\bullet }}$$ ), drive oxidative stress and cellular damage. This study employs density functional theory (M06-2X/def2-TZVP//M06-2X/ma-def2-SVP with SMD solvation) to identify the most promising antioxidant compounds from thirteen terpenoids isolated from Syzygium nervosum . Radical adduct formation (RAF) emerges as the dominant scavenging mechanism with low kinetic barriers (14–49 kJ/mol), indicating room-temperature reactivity. Dehydroisolongifolene exhibits the lowest RAF barrier (14 kJ/mol) and exceptional multi-mechanism performance, making it the top candidate for experimental validation. (+)-Carotol shows balanced activity across multiple mechanisms, ideal for broad-spectrum applications, while (-)-myrtenol demonstrates the best RAF performance among monoterpenoids. Sesquiterpenoids outperform monoterpenoids in electron transfer due to enhanced $$\pi$$ -conjugation, whereas monoterpenoids excel in radical addition kinetics. Oxygen functionalization, conjugation extent, and site accessibility are key determinants of antioxidant capacity. These computational predictions provide a rational basis for prioritizing dehydroisolongifolene, (+)-carotol, and (-)-myrtenol in bioassay-guided fractionation, accelerating the discovery of novel antioxidants for burn wound healing, neuroprotection, and oxidative stress management.

Deep learning-based prediction of PFAS toxicity in zebrafish

Abstract One such problem that is important in designing reliable circuits is soft due to high-energy particle strikes in combinational circuits. Soft errors affect the configuration bits that determine the circuit routing and logic and produce permanent errors. Though soft errors are temporarily natured, the constant repetitive soft errors of configuration or routing bits can cause permanent functional errors, unless addressed. To overcome this challenge, an extensive SER reduction mechanism is introduced in this study. The approach successfully combines Evolutionary-based Failure Probability analysis, logical implementation via AIG, and multi-level hypergraph-based partitioning. Circuit partitioning is performed by the METIS tool, and logical implementation is provided by the ABC tool. This is a continuous improvement of circuit designs to maximize the SER. These techniques are applied to the ISCAS’85 benchmark circuits, and the results demonstrate a significantly smaller average SER decrease of 23.7% compared to previously reported techniques. The proposed approach highlights its robustness in the design of complex architectures with several flexible examples of circuit designs. This is a practical and scalable method in modern integrated circuits to reduce SER and significantly enhance the reliability of the circuits against a soft error.

YATSIDroid: an android malware detection framework based on artificial immune system

Abstract Malware detection has become a challenging issue in the field of software engineering. There is an exponential growth of smartphones and apps in the market. Android has gained popularity due to the number of free apps available in its official market. The functionality of apps relies on the permission model. Cybercriminals exploit app permissions to create and distribute malware-infected apps, which they publish in deceptive repositories for smartphone users. This study proposes a malware detection model that works on the principle of feature ranking approaches and a two-stage semi-supervised machine learning classifier YATSI (Yet Another Two-Stage Idea). YATSI is a meta-algorithm that permits distinct machine learning algorithms to apply in the first stage. The experimental result reveals that our proposed algorithm, i.e., YATSIDroid (Artificial Immune System’s Paradigm with YATSI), improves the performance in identifying the malware-infected apps by using the limited labeled dataset as an input. The empirical result shows that YATSIDroid can detect 98.5% of malware-infected apps with 60% of the labeled dataset.