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Binary mixture of ionic liquid and span 80 for oil spill remediation: Synthesis and performance evaluation

Nazar M, Ahmad A, Hussain SMS, Moniruzzaman M

Mar Pollut Bull, 2024 May;202:116311.
PMID: 38574502 DOI: 10.1016/j.marpolbul.2024.116311

The synthesis of new surfactants helps to mitigate the environmental and financial effects of oil spills by providing efficient cleanup options. Herein, this study provides the development of a binary mixture of Span 80 and Choline myristate [Cho][Mys], a surface-active ionic liquid (SAIL) as green dispersant for oil spill remediation. The synergistic interaction at a 60:40 (w/w) ratio significantly lowered the critical micelle concentration (cmc) to 0.029 mM. Dispersion efficiency tests with Arab crude oil showed optimal performance at a 60:40 ratio of Span 80 and [Cho][Mys] (1:25 dispersant to oil ratio, v/v), achieving 81.16 % dispersion effectiveness in the baffled flask test. The binary mixture demonstrated superior emulsion stability (6 h) and the lowest interfacial tension (1.12 mN/m). Acute toxicity experiments revealed the dispersant's practical non-toxicity with an LC50 value of 600 mg/L. Overall, this environmentally benign surfactant combination shows promise as a safe and effective oil spill dispersant.
Fulltext Formulation and Optimization of Effective Oil Spill Dispersants Composed of Surface-Active Ionic Liquids and Nonionic Surfactants

Nazar M, Ahmad A, Hussain SMS, Moniruzzaman M

ACS Omega, 2024 Jul 16;9(28):30636-30644.
PMID: 39035979 DOI: 10.1021/acsomega.4c02742

The use of chemical dispersants to remove oil spills in aquatic environments raises serious concerns, including heightened toxicity and limited biodegradability, which diminish their effectiveness. This study aimed to develop an environmentally friendly formulation by combining two nonionic surfactants (Tween 80, Span 80) with two surface-active ionic liquids (SAILs): 1-butyl-3-methylimidazolium lauroyl sarcosinate [Bmim][Lausar] and choline myristate [Cho][Mys], to remediate crude oil spill. The performance of the formulation was evaluated by its emulsion stability, surface tension, interfacial tension (IFT), and effectiveness. The toxicity and biodegradability of the formulation were also assessed to ensure their safe application in aquatic environments. The formulation (F9) exhibited the most stable emulsion, maintaining stability even after 5 h with a critical micelle concentration (CMC) of 3.52 mM. The efficiency of the formulation in dispersing various crude oils (Arab, Ratawi, and Doba) ranged from 70.12 to 93.72%. Acute toxicity tests conducted on zebrafish demonstrated that the formulation, with an LC50 value of 450 mg L-1, exhibited practically nontoxicity after 96 h. The formulation showed rapid biodegradability, exceeding 60% within a 28-day testing period. This research presents a promising approach for synthesizing the green formulation which can contribute to mitigating the environmental impacts of oil spills and enhancing the efficiency of cleanup operations.
Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches

Javed MF, Fawad M, Lodhi R, Najeh T, Gamil Y

Sci Rep, 2024 Apr 10;14(1):8381.
PMID: 38600161 DOI: 10.1038/s41598-024-57896-0

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.
Fulltext The effectiveness of the community reinforcement approach (CRA) in the context of quality of life and happiness among people using drugs

Khalid MT, Khalily MT, Saleem T, Saeed F, Shoib S

Front Public Health, 2024;12:1229262.
PMID: 38504677 DOI: 10.3389/fpubh.2024.1229262

INTRODUCTION: The Community Reinforcement Approach is an evidence-based treatment modality for alcohol and drug addiction treatment with proven efficacy and cost-effectiveness. The present study investigated the effectiveness of the Community Reinforcement Approach (CRA) in the context of quality of life among drug addicts.
MATERIALS AND METHODS: A total of 60 inpatient substance abusers post detoxification in Fountain House, Lahore, Pakistan, participated in this study. Fountain House was selected as the Minnesota model is primarily used there. Therefore, a new treatment approach was introduced to investigate its effectiveness for individuals with substance abuse. A randomized 12-week trial was conducted as a substance use disorders (SUDs) treatment program. Persons with SUD (i.e., identified patients) enrolled in a residential treatment program were randomized into the integrated model of the Community Reinforcement Approach (CRA) and traditional Minnesota model treatment (n = 30), and traditional Minnesota model treatment only (TMM; n = 30). All the participants in the experimental group attended the group therapy sessions and other activities in the facility in addition to the treatment conditions. The participants attended the individual therapeutic sessions, which were conducted according to the CRA guidelines used in the experimental group. In this study, each individual in the CRA treatment group received 12 one-to-one sessions ranging from 45 min to 1 h. The WHOQOL-BREF scale and Happiness Scale (1) were used for data collection.
RESULT: The results showed a significant increase in the quality of life of participants in the treatment group with CRA compared with the control group with TMM. The findings also indicated that the individuals in the treatment group with CRA had improved levels of happiness compared with individuals with TMM.
DISCUSSION: The CRA is an effective and adaptable treatment approach that works well in combination with other treatment approaches. The proven efficacy, compatibility, and cost-effectiveness distinguish it from other treatment methods.
IMPLICATIONS: The CRA should be adapted, assessed, and evaluated further, especially in Pakistan, where there is a pressing need to adopt an effective treatment strategy for addiction problems.
Synergistic adsorption of methylene blue using ternary composite of phosphoric acid geopolymer, calcium alginate, and sodium lauryl sulfate

Khan MI, Sufian S, Shamsuddin R, Farooq M, Saafie N

Environ Sci Pollut Res Int, 2024 Jul 02.
PMID: 38955975 DOI: 10.1007/s11356-024-33573-7

The removal of dyes from the aquatic ecosystem is necessary being a major threat to life. For enhanced remediation of methylene blue (MB) dye, a new ternary biopolymer-geopolymer-surfactant composite adsorbent is synthesized by combining phosphoric acid geopolymer (PAGP), calcium alginate (Alg), and sodium lauryl sulfate (SLS). During the synthesis of the composites, PAGP and SLS were mixed with the alginate matrix, producing porous hybrid beads. The PAGP-SLS-alginate (PSA) beads prepared were characterized using different analytical tools, i.e., scanning electron microscopy (SEM), Fourier transform infrared spectrophotometry (FTIR), X-ray diffractometry (XRD), surface area and porosimetery (SAP), and thermogravimetric analysis (TGA). To ascertain the ideal conditions for the adsorption process, a batch reactor procedure was used to investigate the effects of several parameters on MB adsorption, including pH (2, 4, 6, 8, 10), PSA adsorbent dosage (0.06-0.12 g), MB concentration (50-500 mg/L), contact time (15 to 300 min), and temperature (25, 35, and 45 °C). The SEM investigation indicated that ~ 1860 μm-sized PSA beads with 6-8 μm voids are generated. Based on XRD, FTIR, and SAP examinations, the material is amorphous, having numerous functional groups and an average pore size of 6.42 nm. Variation of pH has a little effect on the adsorption process, and the pH of 7.44 was found to be the pHpzc of the PSA beads. According to the findings of the batch study, equilibrium adsorption was obtained in 270-300 min, showing that the adsorption process was moderately slow-moving and effective. The dye adsorption linearly increased with initial dye concentration over concentration range of 50-500 mg/L and reciprocally decreased with rise in temperature. 0.06 g adsorbent dose, 25 °C, pH10, and 270 min were found to be the better conditions for adsorption experiments. Langmuir isotherm fitted well compared to Freundlich, Temkin, and Dubinin-Radushkevich (DR) isotherm models on the experimental data, and the maximum adsorption capacity(qmax) calculated was 1666.6 mg. g-1. Pseudo-second-order (PSO) kinetics model and multi steps (two) intra particle diffusion (IPD) model fitted well on the adsorption kinetics data. The system's entropy, Gibbs free energy, and change in enthalpy were measured and found to be -109.171 J. mol-1. K-1, - 8.198 to - 6.014 kJ. mol-1, and - 40.747 kJ. mol-1. Thermodynamics study revealed that adsorption process is exothermic, energetically favorable and resulting in the decrease in randomness. Chemisorption is found to be the dominant mechanism as confirmed by pH effect, Langmuir isotherm, PSO kinetics, IPD model, and thermodynamics parameters. PSA beads were successfully regenerated using ethanol in a course of 120 min and re-used for five times. To sum up, the PSA adsorbent's impressive adsorption capability of 1666.66 mg/g highlights its potential as a successful solution for methylene blue removal. The results of this study add to the expanding corpus of information on sophisticated adsorption materials and demonstrate PSA's potential for real-world uses in wastewater treatment and environmental clean-up.
Green solution for oil spills: A review on the role of surface-active ionic liquids

Nazar M, Ahmad A, Hussain SMS, Sulaimon AA, Moniruzzaman M

Adv Colloid Interface Sci, 2025 Feb;336:103362.
PMID: 39626337 DOI: 10.1016/j.cis.2024.103362

Oil spills have long-lasting and harmful impacts on the environment, particularly on aquatic ecosystems. This review provides a comprehensive overview of conventional methods for oil spill removal, highlighting both their advantages and limitations. Traditional methods for addressing oil spills, including physical, thermal, biological and chemical techniques, often prove insufficient, with chemical dispersants being the most popular approach. However, the concern about the toxicity and low biodegradability of these dispersants have led researchers to explore more effective and ecologically benign alternatives. Recently, surface-active ionic liquids (SAILs) have gained interest due to their amphiphilic nature, green and biodegradable characteristics, and excellent performance under various temperature and salinity conditions. In this review, the molecular composition of SAILs, with a specific emphasis on the roles of their head groups, alkyl chains, and counter anions, has been discussed. Additionally, the aggregation behavior of SAILs, their ability to reduce interfacial tension (IFT), and their potential to form stable emulsions, which are important for effective oil dispersion, has been also discussed in the paper. This review also examines key environmental factors such as temperature and salinity that influence the efficacy of oil dispersion using SAILs. The study investigates the possibilities of SAILs as an environmentally friendly substitute for conventional dispersants, while also discussing the challenges and possible future paths for the industry. However, the long-term environmental effects of SAILs and their degradation products are still uncertain, underscoring the necessity of future research. Insights into the optimization of SAIL formulations, their environmental impact, and the feasibility of large-scale application are also discussed, offering a forward-looking perspective on the development of next-generation oil spill remediation technologies.
Monster of the Night: Identifying Pakistani Gender-Based, Religious, and Cultural Influences on Sleep Paralysis Among University Students

Khan FF, Haq MSU, Ashfaq A, Saud M, Ibrahim A

Cult Med Psychiatry, 2025 Jan 25.
PMID: 39856384 DOI: 10.1007/s11013-024-09892-2

The present study has explored the folk knowledge about the phenomenon of sleep paralysis in Pakistani society. The research aimed to gain a nuanced glimpse focusing on three major factors, culture, religion, and gender, that influence the lived experiences of those who face sleep paralysis. In this qualitative research, to have a holistic perception of indigenous knowledge about it, we selected both male and female participants who have experienced sleep paralysis. The findings indicate that there is an influence of Pakistani culture and religion regarding the experiences of the people with sleep paralysis, and gender is linked with the folklore on creatures that were linked to fairy tales and Islamic teachings. The study also revealed that Pakistani ethnic diversity has created a pool of versatility for identifying different experiences regarding sleep paralysis. These experiences were not just a part of the medical situation but portrayed the multicultural facets that are embedded in the individuals throughout their lives. Lastly, the study suggests that there is a complexity within the interactions between culture, religion, and gender on sleep paralysis. This needs to be further investigated to create culturally appropriate therapies that may have a favorable effect on both physical and mental health outcomes.
Fulltext Study protocol for a randomized control trial investigating the effectiveness of a multifaceted mHealth approach on adherence to antihypertensive treatment among patients in Pakistan

Arshed M, Mahmud AB, Umer MF, Mashhadi F, Kawish AB

Pak J Med Sci, 2025 Jan;41(1):22-28.
PMID: 39867791 DOI: 10.12669/pjms.41.1.9272

BACKGROUND & OBJECTIVES: Poor medication adherence is an essential contributor to Pakistan's high prevalence of uncontrolled hypertension. This study will be aimed to assess the efficacy of a one-of-a-kind developed intervention in improving medication adherence and treatment outcomes in hypertension patients.
METHODS: Twleve months duration long randomized controlled trial from January to December 2021 will be carried out at Shaikh Zayed Medical Complex (SZMC), Lahore. A total of 440 patients aged 18 years and older diagnosed with hypertension in the last month with non- adherence to antihypertensive therapy <80% of pills used in the last 30 days and have access to a smartphone will be randomized into either the intervention group (n=220) or the control group (n=220). For the intervention arm, a comprehensive intervention, the "Multi-Aid-Package," consisting of seven items: written, voice, and graphics messages, animated video, educational material, and a 24/7 help service, has been designed. Standard care will be provided to the control group. The primary outcome will be improved adherence to antihypertensive medication, while the secondary outcome will be an alteration in systolic blood pressure (SBP). The analysis will be intention to treat.
CONCLUSION: According to this study, if the multifunctional Multi-Aid-Package proves to be a useful mobile health tool for improving hypertension patients' medication adherence, it will also significantly affect systolic blood pressure. In Pakistan and other comparable low- and middle-income countries LMICs, the Multi-Aid-Package ought to be taken into consideration as a means of enhancing adherence to medications among hypertension patients.
Fulltext Phosphoric acid based geopolymer foam-activated carbon composite for methylene blue adsorption: isotherm, kinetics, thermodynamics, and machine learning studies

Khan MI, Sufian S, Hassan F, Shamsuddin R, Farooq M

RSC Adv, 2025 Jan 16;15(3):1989-2010.
PMID: 39845118 DOI: 10.1039/d4ra05782a

In this study, a binary composite adsorbent based on activated carbon and phosphoric acid geopolymer foam (ACP) was prepared by combining phosphoric acid geopolymer (PAGP) with activated carbon (AC) and applied for the removal of methylene blue (MB). Activated carbon was thoroughly mixed with a mixture of fly ash and metakaolin in varying ratios, followed by phosphoric acid activation and thermal curing. The ACP adsorbent was characterized using scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectrophotometer, X-ray diffractometer (XRD), surface area analyser (SAP), and thermogravimetric analyser (TGA). Batch analysis was performed to examine the effects of various adsorption parameters including pH (2, 4, 6, 7, 8, and 10), adsorbent dosage (0.06-0.2 g), MB concentration (50-250 mg L-1), contact duration (up to 240 minutes), and temperature (25-55 °C). The ACP with 70% PAGP and 30% AC was found to be the most suitable adsorbent as it maintained its structure and exhibited better MB adsorption. The ACP had a surface area of 47.36 m2 g-1 and a pore size of 5.6 nm and was found to be amorphous in nature. The adsorption equilibrium reached in 240 minutes at pH 7, indicating an efficient adsorption process. The adsorption increased with the initial dye concentration and decreased with the increase in temperature. The ideal parameters for adsorption of MB using ACP include 0.2 g of adsorbent, 25 °C, pH 10, and 240 minutes. The adsorption data fitted well with the Langmuir isotherm, pseudo-second-order (PSO) kinetics model, and three-step intraparticle diffusion (IPD) model. The adsorption capacity calculated using the Langmuir isotherm was 204.8 mg g-1 with an R 2 = 0.989. Thermodynamics parameters showed that the adsorption process was exothermic, energetically favourable, and associated with a decrease in entropy. According to the FTIR findings, pH effect, Langmuir isotherm, PSO kinetics, IPD model, and thermodynamics factors, chemisorption is identified as the predominant process. Different machine learning models, i.e., gaussian process regression (GPR), support vector regression (SVR and SVR-rbf), random forest regression (RFR), decision tree regression (DTR) and artificial neural network (ANN), were trained and tested using adsorption capacity and % removal data. The ANN model (random search) demonstrated better performance compared to other models, achieving an R 2 value of 0.873 for adsorption capacity and 0.799 for % removal on test data.
Fulltext Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis

Safari MAM, Masseran N, Majid MHA, Tajuddin RRM

Sci Rep, 2025 Apr 03;15(1):11516.
PMID: 40181061 DOI: 10.1038/s41598-025-96043-1

Accurate estimation techniques are crucial in statistical modeling and reliability analysis, which have significant applications across various industries. The three-parameter Weibull distribution is a widely used tool in this context, but traditional estimation methods often struggle with outliers, resulting in unreliable parameter estimates. To address this issue, our study introduces a robust estimation technique for the three-parameter Weibull distribution, leveraging the probability integral transform and specifically employing the Weibull survival function for the transformation, with a focus on complete data. This method is designed to enhance robustness while maintaining computational simplicity, making it easy to implement. Through extensive simulation studies, we demonstrate the effectiveness and resilience of our proposed estimator in the presence of outliers. The findings indicate that this new technique significantly improves the accuracy of Weibull parameter estimates, thereby expanding the toolkit available to researchers and practitioners in reliability data analysis. Furthermore, we apply the proposed method to real-world reliability datasets, confirming its practical utility and effectiveness in overcoming the limitations of existing estimation methodologies in the presence of outliers.
Comparative analysis of text-based plagiarism detection techniques

Sajid M, Sanaullah M, Fuzail M, Malik TS, Shuhidan SM

PLoS One, 2025;20(4):e0319551.
PMID: 40198663 DOI: 10.1371/journal.pone.0319551

In text analysis, identifying plagiarism is a crucial area of study that looks for copied information in a document and determines whether or not the same author writes portions of the text. With the emergence of publicly available tools for content generation based on large language models, the problem of inherent plagiarism has grown in importance across various industries. Students are increasingly committing plagiarism as a result of the availability and use of computers in the classroom and the generally extensive accessibility of electronic information found on the internet. As a result, there is a rising need for reliable and precise detection techniques to deal with this changing environment. This paper compares several plagiarism detection techniques and looks into how well different detection systems can distinguish between content created by humans and content created by Artificial Intelligence (AI). This article systematically evaluates 189 research papers published between 2019 and 2024 to provide an overview of the research on computational approaches for plagiarism detection (PD). We suggest a new technically focused structure for efforts to prevent and identify plagiarism, types of plagiarism, and computational techniques for detecting plagiarism to organize the way the research contributions are presented. We demonstrated that the field of plagiarism detection is rife with ongoing research. Significant progress has been made in the field throughout the time we reviewed in terms of automatically identifying plagiarism that is highly obscured and hence difficult to recognize. The exploration of nontextual contents, the use of machine learning, and improved semantic text analysis techniques are the key sources of these advancements. Based on our analysis, we concluded that the combination of several analytical methodologies for textual and nontextual content features is the most promising subject for future research contributions to further improve the detection of plagiarism.
Fulltext High performance white blood cell classification using ensemble CNNs with Monte Carlo dropout and conformal prediction for clinical uncertainty quantification

Mehmood S, Alharbi AAK, Alkhattabi M, Khan MA, Khan MA

Front Med (Lausanne), 2026;13:1959061.
PMID: 42935807 DOI: 10.3389/fmed.2026.1959061

Automated classification of white blood cell (WBC) images may support cell-morphology workflows, but high accuracy alone does not establish clinical readiness. We developed an Uncertainty-Guided Deep Ensemble (UGDE) combining MobileNetV3-Large, EfficientNet-B0, and ShuffleNetV2 with Monte Carlo dropout, temperature scaling, split conformal prediction, and Grad-CAM. The primary dataset contained 16,027 single-cell images from 78 patients (33 with leukemia and 45 without leukemia). All stated development partitions were grouped by patient, and augmentation was restricted to the proper training subset. On the held-out internal test set (3,205 images from 16 patients), UGDE achieved approximately 99% image-level accuracy and macro-F1 of 0.99. Expected calibration error was 2.3% before and 0.8% after temperature scaling. Internal empirical conformal coverage was 94.2%, 89.7%, and 79.7% at nominal coverage levels of 95%, 90%, and 80%, respectively; these slight shortfalls are reported without claiming that the targets were met. External evaluation used 2000 Raabin-WBC images after mapping the nine model outputs to five shared leukocyte categories and yielded 94% descriptive accuracy. Grad-CAM examples were used as post-hoc visualizations and were not interpreted as causal explanations. These retrospective results support further evaluation of uncertainty-aware WBC image classification, but they do not demonstrate performance on whole slides, automated differential counts, prospective patients, or clinical workflows.
How do financial fragility and ICT penetration affect renewable energy consumption and green growth in top-polluting economies?

Akram MW, Ahmed D, Trunina A, Hamid K, Hafeez M

Environ Sci Pollut Res Int, 2023 Mar;30(13):38810-38818.
PMID: 36586019 DOI: 10.1007/s11356-022-24978-3

Green growth is an extension of traditional economic growth. Financial fragility and ICT penetration are important pillars of green growth sustainability. However, very limited studies have explored this association and provided conflicting results. Thus, our study intends to fill this vacuum by exploring the impact of financial fragility and ICT penetration on renewable energy consumption and green growth for the top five polluting economies over the period 1996-2020. In this study, financial fragility is measured by bank costs and bank non-performing loans. Panel ARDL technique is used to find out long-run and short-run results estimates. Financial fragility reduces renewable energy consumption and green growth in the long run. However, internet penetration enhances renewable energy consumption and green growth in the long run. Our findings suggest imperative policy implications for the green economy.
Fulltext Hybrid semantic-lexical framework for ambiguity detection in software requirements using TF-IDF, character n-grams, and SBERT

Khalid F, Yaseen M, Sattar MA, Ghoul A, Mustapha A

Front Artif Intell, 2026;9:1901190.
PMID: 42898546 DOI: 10.3389/frai.2026.1901190

Ambiguity in software requirements is a major cause of misinterpretation, design defects, rework, and project delays in software engineering. This study proposes a hybrid semantic-lexical ambiguity detection framework that integrates lexical, structural, and contextual representations for identifying ambiguous software requirement statements. Two benchmark datasets were employed, including a Software Requirements Specification (SRS) dataset and a Functional-Non-Functional Requirements (FR-NFR) dataset. Data pre-processing involved tokenisation, lemmatisation, and noise removal, while Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance within training partitions. Three modeling strategies were evaluated: baseline classifiers using TF-IDF and character n-gram features, semantic models based on Sentence-BERT (SBERT) embeddings, and a hybrid representation combining TF-IDF, character n-grams, and SBERT embeddings. Experimental results indicate that baseline models achieved high precision but comparatively lower recall, limiting their ability to identify ambiguous requirements under class imbalance. The proposed hybrid representation achieved the strongest and most balanced performance across both datasets in practical terms, particularly when combined with Support Vector Machine classification, resulting in improved recall and F1-score, although statistical testing indicated that these gains over the strongest lexical baseline were not significant at the 5% level. By jointly modeling lexical vagueness, structural ambiguity patterns, and contextual semantics, the framework captures both explicit and implicit forms of ambiguity that are frequently overlooked by single-representation approaches. To strengthen experimental validity, five-fold stratified cross-validation, transformer-based baseline comparison using fine-tuned BERT, statistical significance testing, and annotation reliability assessment using Cohen's Kappa coefficient were incorporated. The findings demonstrate that integrating lexical, structural, and semantic representations provides a robust and potentially scalable approach for ambiguity detection in software requirements engineering. The proposed framework contributes to improved requirements quality, reduced risk of misinterpretation, and more reliable automated support for requirements analysis.
Spatiotemporal analysis of acute respiratory infection burden among adults aged 15-79 years: A cross-sectional study

Hassan MM, Abdelwahab MM, Ameeq M, Kargbo A

Heart Lung, 2026 Nov;80:102966.
PMID: 42843111 DOI: 10.1016/j.hrtlng.2026.102966

BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity in low- and middle-income countries, yet evidence among working-age adults is limited. This study investigated the prevalence, spatiotemporal distribution, and clinical determinants of ARIs among adults in South Punjab, Pakistan, to inform strategies for improving respiratory health and patient outcomes.
METHODS: A hospital-based cross-sectional study was conducted between November 2025 and February 2026 among 1150 adults aged 15-79 years attending hospitals in Multan, Bahawalpur, and Muzaffargarh. Participants were selected using multistage systematic sampling. Structured interviews collected demographic, environmental, and clinical data. Spatial analyses were performed using GeoDa, while multivariable binary and multinomial logistic regression identified factors associated with ARI occurrence and symptom severity.
RESULTS: Overall, 38.6% of participants reported ARI symptoms. Spatial analyses demonstrated geographic clustering, with the highest burden observed in Multan. Independent predictors of ARIs included biomass fuel use (AOR 4.56, 95% CI 3.12-6.67), indoor smoking (AOR 3.45, 95% CI 2.48-4.80), visible household mould or dampness (AOR 5.67, 95% CI 3.89-8.26), low educational attainment (AOR 1.95, 95% CI 1.42-2.68), and residence in Muzaffargarh (AOR 1.82, 95% CI 1.32-2.51). Symptom severity increased significantly with biomass fuel exposure, indoor smoking, damp housing, and winter months.
CONCLUSIONS: Household environmental exposures and geographic variation were associated with the burden and severity of ARIs among hospital-attending adults. Targeted interventions to reduce household air pollution, improve housing conditions, strengthen seasonal surveillance, and prioritize high-risk communities may improve respiratory outcomes and support evidence-based cardiopulmonary care in resource-limited settings.
A Recent Digitalization in Recycling Industry Attaining Ecological Sustainability: A Comprehensive Outlook and Future Trend

Khan SAR, Umar M, Yu Z, Nawaz MT

Environ Sci Pollut Res Int, 2023 Oct;30(47):103760-103775.
PMID: 37695483 DOI: 10.1007/s11356-023-29537-y

The management of waste through dual way of recycling (i-e offline and online) is assumed to have a key role in attaining ecological sustainability and enabling circular practices. The research on online recycling is gaining evolution in recent age. Prior literature on the current research theme has failed to provide a comprehensive outlook and future trend. Therefore, the current research intends to elaborate the current research scenario linked with online recycling by critically scrutinizing the prior research over the last 41 years. A comprehensive analysis was conducted using the Scopus database, retrieving a total of 866 articles. These articles were selected to provide a conceptual overview and understanding of the fundamental research conducted in the field. By employing bibliometric analysis this research provides comprehensive detail about evolution, mapping of publications and prominent trends from the year 1981 to 2022 to understand the practices and future trends of online recycling research. The outcomes elucidated that there is exponential increase in research publications relating to online recycling over the last five years. The most influential producer of online recycling research are China, United Kingdom and United States. Chinese Universities has the highest number of publications among all the countries across globe. Moreover, the current research trend is focused on technology based circular economy, industrial ecology, bio-based waste management, dual channel recycling, municipal waste, waste from electrical and electronic equipment (WEEE), environmental impact and lifecycle assessment. Hence, the prominent research perspective and highlighted features could offer recommendation for upcoming studies to contribute in literature and help practitioners, policymakers and professionals move towards circular practices.
Re: The Malaysian Society of Gastroenterology and Hepatology Consensus Statements on Prevention and Early Detection of Hepatocellular Carcinoma in Malaysia

Baloch MSA, Rehman QSU, Saleem MZ

J Gastroenterol Hepatol, 2026 Nov;41(11):3652-3653.
PMID: 42768441 DOI: 10.1111/jgh.70771
Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Awais M, Badruddin N, Drieberg M

Sensors (Basel), 2017 Aug 31;17(9).
PMID: 28858220 DOI: 10.3390/s17091991

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
Chemical constituents and medical benefits of Plantago major

Adom MB, Taher M, Mutalabisin MF, Amri MS, Abdul Kudos MB, Wan Sulaiman MWA, et al.

Biomed Pharmacother, 2017 Dec;96:348-360.
PMID: 29028587 DOI: 10.1016/j.biopha.2017.09.152

The medicinal benefits of Plantago major have been acknowledged around the world for hundreds of years. This plant contains a number of effective chemical constituents including flavonoids, alkaloids, terpenoids, phenolic acid derivatives, iridoid glycosides, fatty acids, polysaccharides and vitamins which contribute to its exerting specific therapeutic effects. Correspondingly, studies have found that Plantago major is effective as a wound healer, as well as an antiulcerative, antidiabetic, antidiarrhoeal, anti-inflammatory, antinociceptive, antibacterial, and antiviral agent. It also combats fatigue and cancer, is an antioxidant and a free radical scavenger. This paper provides a review of the medicinal benefits and chemical constituents of Plantago major published in journals from year 1937 to 2015 which are available from PubMed, ScienceDirect and Google Scholar.
Recent advances in assessment on clear water, soil and air

Ashraf MA, Hanfiah MM

Environ Sci Pollut Res Int, 2017 10;24(29):22753-22754.
PMID: 29149440 DOI: 10.1007/s11356-017-9267-z

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