Tag: nevin manimala

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-59129-y. Online ahead of print.

ABSTRACT

The importance of data clustering is importance in assisting the decision-making process in any given complex system especially in an environment where data is large-scale and high-dimensional and heterogeneous. This paper introduces an evolutionary adaptation of the Chimp Optimization Algorithm (EVCHOA) to enhance the quality and strength of clustering when applied to the real world. The suggested solution implements an adaptive evolutionary update mechanism into the chimp-inspired search process to increase the exploration-exploitation ratio and avoid the early convergence. The effectiveness of EVCHOA in practice can be illustrated with references to intelligent learning systems, where the use of clustering allows analyzing the information about student performance and assisting in the making of individual decisions. Tests are done on real-life datasets implemented in a distributed computing framework and the outcomes are resolved against known methods of clustering, such as K-means, DBSCAN, hierarchical clustering, mean shift, and Gaussian mixture models. The evaluation of performance is based on conventional validity indices including DaviesBouldin Index, Silhouette Score, Adjusted Rand Index (ARI) and CalinskiHarabasz Index. The findings indicate that EVCHOA always achieves better clustering performance resulting in more coherent group structures and better interpretability by decision-makers. Within the framework of intelligent learning environments, the offered approach allows identifying student profiles more accurately, interfering with the target intervention, distributing resources in a more adaptive manner, and may support data-informed instructional planning. It is evident in these results that evolutionary metaheuristics is a useful tool in the operational research field and provides data-driven and scalable solutions to decision support in a variety of application fields.

PMID:42342790 | DOI:10.1038/s41598-026-59129-y

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58601-z. Online ahead of print.

ABSTRACT

Cloud computing is a key enabler of modern computing services, offering scalability and flexibility. However, efficient management of cloud resources remains challenging due to limited capacity and the increasing number of tasks requiring timely execution. An effective task scheduling strategy is therefore essential to improve resource allocation and utilization, reduce operational costs and energy consumption, and support high availability-especially for long-term jobs. In this paper, we propose a new scheduling approach that combines a Social-Based Algorithm (SBA) with Deep Reinforcement Learning (DRL), referred to as SBA-DRL. This method allocates tasks to resources by learning from workload patterns and adapting to workload characteristics in a batch scheduling context. We evaluate SBA-DRL using both a synthetic dataset and the real-world Google Cloud Jobs (GoCJ) under workloads ranging from 200 to 1,000 tasks. On the synthetic dataset, our method reduces cost by 20.21% and energy consumption by 25.31%, while improving resource utilization by 9.36%. On the GoCJ dataset, it achieves up to 28.94% lower cost, 8.16% less energy use, and a 14.04% increase in resource utilization. In both cases, SBA-DRL also demonstrates better performance in resource allocation and high-availability management compared to existing heuristics, meta-heuristics, hybrid, and machine learning-based schedulers. These results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.

PMID:42342783 | DOI:10.1038/s41598-026-58601-z

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58701-w. Online ahead of print.

ABSTRACT

Type D personality, defined by the co-occurrence of negative affectivity and social inhibition, has been associated with poorer health outcomes in chronic diseases, including asthma. However, the psychological mechanisms underlying the relationship between Type D personality and asthma control remain unclear. This cross-sectional study included 150 adult patients with asthma recruited from a tertiary outpatient clinic. Participants completed the Asthma Control Test (ACT), Type D Personality Scale (DS-14), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Type D personality traits were identified in 39.3% of the sample. Compared with non-Type D individuals, patients with Type D personality had higher depressive and anxiety symptom scores and poorer sleep quality (all p < 0.05). Mediation analyses using bootstrapping (5,000 resamples) revealed significant indirect effects of Type D personality on asthma control through depression (β = -0.088, 95% CI [-0.132, -0.047]), anxiety (β = -0.115, 95% CI [-0.158, -0.078]), and sleep quality (β = -0.057, 95% CI [-0.091, -0.029]). These indirect effects remained statistically significant after adjustment for age, sex, smoking status, and FEV1 (% predicted). These findings suggest that Type D personality is indirectly associated with poorer asthma control through increased psychological symptom burden and impaired sleep quality.

PMID:42342775 | DOI:10.1038/s41598-026-58701-w

Sci Rep. 2026 Jun 24. doi: 10.1038/s41598-026-58020-0. Online ahead of print.

ABSTRACT

Lung disease classification using chest X-ray (CXR) images has become essential for early diagnosis and improved clinical decision-making. However, challenges such as low image quality, feature similarity among diseases, and classification instability reduce diagnostic reliability. To address these issues, this study proposes a novel ELSTM-AZOA framework for multiclass lung disease classification using the NIH CXR dataset. Initially, the collected CXR images are preprocessed using the balance contrast enhancement technique to improve image quality. U-Net + + is then employed for accurate lung region segmentation, followed by feature extraction using statistical and gray level co-occurrence matrix features. The extracted features are classified using an enhanced long short-term memory (ELSTM) network, while the American zebra optimization algorithm (AZOA) optimizes the model parameters to maximize classification accuracy. The proposed framework classifies six categories: healthy lung, tuberculosis, pneumonia, lung cancer, COPD, and COVID-19. Experimental results demonstrate that the proposed ELSTM-AZOA model achieves superior performance compared with existing methods, obtaining 6.36% higher accuracy and 6.43% higher precision. The findings confirm that the proposed framework provides robust, reliable, and promising computer-aided lung disease classification.

PMID:42342773 | DOI:10.1038/s41598-026-58020-0

Cancer Treat Rev. 2026 Jun 17;148:103170. doi: 10.1016/j.ctrv.2026.103170. Online ahead of print.

ABSTRACT

Background Early onset pancreatic cancer (EOPC), defined for the primary analysis as pancreatic ductal adenocarcinoma (PDAC) diagnosed before the age of 50 years, is an increasingly recognised clinical and epidemiological entity. Because published studies use heterogeneous age thresholds, the present review prespecified <50 years as the main definition and interpreted studies using other cutoffs in sensitivity or narrative analyses. Methods We systematically searched PubMed, EMBASE, and the Cochrane Library for studies published between January 1990 and December 2024 that reported outcomes specific to EOPC. Search terms were reformatted with standard quotation marks and included “pancreatic cancer”, “pancreatic adenocarcinoma”, “pancreatic ductal adenocarcinoma”, “early onset”, “young onset”, “young adult”, “age < 50”, “age less than 50”, and “premature”. Meta-analytic procedures and epidemiological interpretation were re-reviewed with statistical/epidemiological input. Results 40 studies encompassing more than 285,000 patients were included. Global incident EOPC cases increased from 24,480 (1990) to 42,254 (2021), representing a 72·6% rise. Age-standardised prevalence rate increased by 17·0% (1·65 per 100,000 in 2021). EOPC patients have a 3·08% per year increase in the youngest age group (20-29 years) over 2010-2021. Germline pathogenic variants were identified in 17·3% of EOPC patients (vs 6·4% in older cohorts; OR 2·41, 95% CI 1·87-3·11). EOPC patients were more likely to receive treatment (OR 2·95, 95% CI 2·54-3·43; I² = 13%) and showed modestly improved overall survival (pooled HR 0·89, 95% CI 0·80-0·99; I² = 16%) compared with average or late onset disease. Conclusions EOPC is an increasingly recognised and clinically challenging subset of PDAC. The substantial hereditary and potentially actionable molecular burden supports universal germline testing and comprehensive tumour genomic profiling, particularly in younger patients and in KRAS wild-type disease. PARP inhibitors should be described as improving progression-free survival or disease-control outcomes in selected BRCA-mutated metastatic PDAC rather than as having established a statistically significant overall survival benefit.

PMID:42341370 | DOI:10.1016/j.ctrv.2026.103170

Clin Psychol Rev. 2026 Jun 16;128:102780. doi: 10.1016/j.cpr.2026.102780. Online ahead of print.

ABSTRACT

Mindfulness, mindful eating and intuitive eating practices are associated with healthier eating and lower body weight. However, experimental research in this area has shown mixed effects on food intake and theoretical accounts are underdeveloped. This systematic review and meta-analysis aimed to examine the effect of mindfulness, mindful eating and intuitive eating interventions on food intake and appetite (hunger and fullness) in adults and children and compare effects across different subgroups to investigate potential mechanisms of action. Five electronic databases (PsycINFO, MEDLINE, EMBASE, Web of Science and Scopus) were searched for studies that experimentally manipulated mindfulness and/or mindful eating and/or intuitive eating, included a non-mindfulness control group and measured food intake (kcal or grams or percentage consumed or number of pieces consumed) and/or appetite (using visual analogue scales). Forty-one articles assessing mindfulness and mindful eating interventions were included (no relevant intuitive eating interventions were identified). Random-effects meta-analyses showed that mindfulness/mindful eating reduced food intake (n = 46 studies, SMD = -0.24, 95% CI [-0.35, -0.12], p < 0.001) but had no statistically significant effect on appetite (n = 11 studies). There were no significant subgroup differences observed between studies with different settings, interventions or food intake measures. However, effect sizes were substantially larger in laboratory-based studies. Overall, findings indicate that mindfulness and mindful eating reliably reduce food intake in controlled settings, but currently there is no evidence they influence appetite. The review underscores the need for higher quality and more ecologically valid studies using sensitive, real-world measures of appetite and food intake, and further work to clarify the mechanisms of action underpinning the effects of mindfulness and mindful eating.

PMID:42341365 | DOI:10.1016/j.cpr.2026.102780

Econ Hum Biol. 2026 Jun 20;62:101623. doi: 10.1016/j.ehb.2026.101623. Online ahead of print.

ABSTRACT

A gap in mortality rates between rural and urban areas emerged in the late 1990s as rural residents began dying at a higher rate than their urban counterparts. This mortality gap has been widening ever since. The growing mortality gap is pronounced among prime working-age adults (ages 25-54), which has important implications for rural health, productivity, and economic development. Despite the heightened mortality rates in rural areas in recent decades, there is still a limited understanding of the health-related mechanisms fueling this worsening rural-urban mortality disparity. Using nationally representative data from the 1999-2000-2017-March 2020 cycles of the National Health and Nutrition Examination Survey, we combine individual-level data, including biomarkers, and place-based characteristics to create a comprehensive dataset spanning 20 years. In a descriptive analysis, we use linear regression models to estimate the magnitude of rural-urban gaps in health behaviors and outcomes-or mortality risk indicators-for the overall adult and prime working-age populations. We document a suite of rural health disadvantages, which may be informative for understanding the growing rural longevity gap. These disadvantages are often attenuated and no longer statistically significant after accounting for county-level characteristics. A complementary decomposition analysis indicates that county characteristics account for a larger share of the variation in health measures than rural-urban status. Our results can inform decision-makers aiming to improve rural health and economic outcomes and may spur further research using biomarker data alongside place-based characteristics.

PMID:42341364 | DOI:10.1016/j.ehb.2026.101623

J Fr Ophtalmol. 2026 Jun 24;49(7):104935. doi: 10.1016/j.jfo.2026.104935. Online ahead of print.

ABSTRACT

PURPOSE: Macular edema is a significant cause of vision loss in uveitis. Effective control of underlying inflammation is essential to treating macular edema in non-infectious uveitis, typically with corticosteroids. In this study, we examine the use of sequential dexamethasone and fluocinolone acetonide steroid intravitreal implants to promote rapid improvement and sustained control in non-infectious uveitic macular edema.

METHODS: Sixteen eyes from eleven patients with recurrent uveitic macular edema who received sequential intravitreal dexamethasone and fluocinolone acetonide implants were studied via retrospective chart review in 3-month intervals over a 24-month observation window. All patients had received at least one prior dexamethasone implant, which was used both therapeutically and to assess the intraocular-pressure response to an intravitreal corticosteroid before proceeding to the fluocinolone acetonide implant. The primary outcome was the recurrence rate of clinically significant macular edema after treatment. Chart review was performed to identify recurrences and need for additional anti-inflammatory treatment. We also evaluated change in visual acuity, intraocular pressure, and central subfield thickness throughout the study duration.

RESULTS: Twelve eyes (75%) did not experience a recurrence in clinically significant macular edema after treatment with both intravitreal injections. The rate of retreatment (25%) was reduced compared to patients who only received dexamethasone. There was a rapid and sustained improvement in macular edema, though there was no statistically significant improvement in visual acuity. Intraocular pressure initially increased but stabilized after the first 3-month interval.

CONCLUSIONS: Sequential administration of dexamethasone and fluocinolone acetonide intravitreal implants reduces the rate of significant macular edema recurrences in non-infectious uveitis. There is a significant quantitative improvement in macular edema, and further studies may demonstrate a noticeable benefit in visual acuity and quality of life with fewer retreatments.

PMID:42341355 | DOI:10.1016/j.jfo.2026.104935

Psychol Rep. 2026 Jun 24:332941261457360. doi: 10.1177/00332941261457360. Online ahead of print.

ABSTRACT

The Information Technology (IT) field is growing in the direction of the need to not only possess good technical skills but also good interpersonal skills to be able to cooperate and achieve success in a project. Emotional intelligence (EI) is a concept that has been identified as significant to the effectiveness of communications among IT professionals. This is a quantitative research on how EI and communication skills (CS), and other related psychological variables, including self-efficacy (SE), communication apprehension (CA), empathy, and social influence (SI), relate to each other. The participants were 535 IT professionals, comprising software developers, system analysts, project managers, and IT consultants. The study employed a purposive sampling method to administer the structured questionnaire. Data were analyzed using Statistical Package for the Social Sciences (SPSS) for preliminary analyses and SmartPLS 4 for confirmatory factor analysis (CFA) and structural equation modelling (SEM). The results indicate that EI, SE, and empathy are positively associated with CS, CA shows a negative association, whereas SI is a significant predictor of CS in a group setting (all paths p < 0.05). The novelty of this study is its combined analysis of the EI and various communication and related psychological constructs in the specific context of IT professionals, which has not been studied extensively in the literature. The results also present practical recommendations for the IT organizations since they highlight the necessity of developing emotional competencies that can improve communication and teamwork, and consequently, improve individual and organizational performance.

PMID:42341347 | DOI:10.1177/00332941261457360

JMIR Mhealth Uhealth. 2026 Jun 24;14:e68394. doi: 10.2196/68394.

ABSTRACT

BACKGROUND: Unguided internet-based cognitive behavioral therapy (ICBT) is a low-cost and scalable treatment for major depressive disorder (MDD), but its long-term effects in Chinese populations remain unclear.

OBJECTIVE: This study aimed (1) to explore the short- and long-term effectiveness of unguided ICBT in treating adults with MDD; (2) to investigate the short- and long-term effects on disease-related symptoms, individual and social functioning, and quality of life; and (3) to assess the acceptability and satisfaction with the ICBT.

METHODS: An 8-week randomized controlled trial (ChiCTR2100046425) was conducted between August 2021 and June 2023 in Shenzhen, China, with 159 participants in the immediate ICBT group (7-module ICBT course plus usual care) and 158 in the waitlist control (WLC) group (usual care). The WLC group later completed the same ICBT course and follow-up assessments. Outcome measures (depressive and anxiety symptoms, psychological distress, social functioning, self-efficacy, quality of life, and stigma) were assessed before and after treatment and at 3-, 6-, and 12-month follow-ups for ICBT participants. Remission and response, adherence, and satisfaction were evaluated by predefined standards.

RESULTS: Among 300 participants analyzed (mean age 28.49, SD 7.0 years; female: n=225, 75%), dropout rates were 22.4% (34/152) in the immediate ICBT group versus 6.3% (10/158) in the WLC group. At posttreatment, the immediate ICBT group showed greater reduction in depressive symptoms versus WLC (mean difference -3.65, SE 0.60; P<.001; d=0.50), with higher remission (80/121, 66.1% vs 58/148, 39.2%; P<.001) and response rates (50/121, 41.3% vs 27/148, 18.2%; P<.001). At 12-month follow-up, the depressive symptoms were improved compared with that at pretreatment (mean difference -3.90, SE 0.32; P<.001; d=0.70), and no significant change was observed in comparison with the outcomes at posttreatment (mean difference -0.81, SE 0.33; P=.33; d=-0.15). ICBT treatment also exhibited similar short- and long-term effects on secondary outcomes, with significant improvement of disease-related symptoms, individual and social functioning, and quality of life. Moreover, the majority of the participants treated with ICBT reported high acceptability of and satisfaction with the ICBT course.

CONCLUSIONS: Unguided ICBT effectively reduces depressive symptoms and enhances functioning in Chinese patients with MDD, with sustained benefits over 12 months. Its scalability and low-cost nature make it a promising option for resource-limited settings.

PMID:42341343 | DOI:10.2196/68394