Browse Articles

Beyond survival: a cross-sectional analysis of guideline recommendations for cognitive, sexual, and psychological problems in adult cancer survivors

Bird flu H5N1 in Australia: protect Australian species from bird flu virus

Stakeholders’ discourse on generative AI in higher education: Insights from topic modeling and sentiment analysis

Owing to their remarkable proficiency in academic tasks, generative AI (GenAI) systems have been widely embraced in the education sector, stimulating debate among academics about their opportunities and challenges. However, extant studies tend to be narrowly focused, typically drawing on micro-level observations from small samples of students and educators, which limits the scope of reported findings. The present study contributes to the literature by capturing stakeholders’ discussions on the role of GenAI in higher education at scale. It does so by employing over 230K tweets shared on the subject of GenAI and education and training a topic modeling algorithm on this corpus. The model identified seven major themes that encapsulate the use cases and implications of GenAI adoption in higher education as articulated by stakeholders: learning assistant, research tool, productivity tool, assessment and examination, educational technology, adoption policy, and resources . Additionally, the study performed sentiment analysis to reveal the specific themes on which stakeholders expressed disproportionately positive and disproportionately negative attitudes toward GenAI adoption. By combining stakeholder theory and a big data approach, the current findings highlight both the practical and institutional concerns of GenAI beyond immediate classroom implications, such as student learning and assessment.

Global model for riverine suspended sediment concentration from Landsat

Abstract Long-term suspended sediment concentration data are essential for understanding the response of rivers in a changing world. Here, we trained and evaluated a model that uses surface reflectance from multiple Landsat satellites (TM, ETM+, and OLI) to estimate suspended sediment concentration (SSC) in rivers over multiple decades on a global scale. First, we built the largest global matchup database recorded, consisting of remote sensing surface reflectance data and in-situ SSC ( N  = 240,224). Second, we derived empirical models for harmonizing water surface reflectance across Landsat TM, ETM+, and OLI using ~ 88 million riverine surface reflectance observations from 1984 to 2021. After applying the harmonization to water surface reflectance in the matchup database, we developed the SSC prediction model using an extreme gradient boosting algorithm (XGBoost). The model was trained and evaluated with in-situ SSC data ranging from low to extremely turbid water (min = 0.1 mg/L and max = 5,760 mg/L). Our model achieved accuracy in predicting SSC comparable to or better than existing models (RMSE = 5.22 mg/L, RMSLE = 0.24, MAPLE = 22.53%, and relative error = 0.75). Our model shows consistent spatial and temporal predictions despite being a single, global SSC algorithm, suggesting successful spatial-temporal cross-validation during model development. The model will support the development of long-term, global riverine SSC records across Landsat missions.

Bangla validation of the smartphone addiction scale and the academic underachievement scale: The associative role of classroom mindful attention in the relationship between smartphone addiction and underachievement among adolescents and young adults

This study aimed to validate the Smartphone Addiction Scale–Short Version (SAS-SV) and the Perceived Academic Underachievement Scale (PAUS) for Bangladeshi adolescents and young adults. In addition, the study investigated whether classroom mindful attention has an indirect association with the relationship between smartphone addiction and academic underachievement in the target population. A cross-sectional survey using convenience sampling was conducted with 712 participants recruited from different educational institutions in Bangladesh. Bangla versions of SAS-SV, Classroom Mindful Attention Regulation Scale, and PAUS were administered in collecting data. Factor structure, reliability, and validity of the measures were assessed, and statistical mediation to determine the indirect association was conducted to achieve the study aims. Both SAS-SV and PAUS showed excellent internal consistency (Cronbach’s α  = 0.95 and 0.91, respectively) and confirmed the predefined factor structures with good model fits (CFI = 0.99, TLI = 0.99, RMSEA = 0.05, SRMR = 0.03; PAUS: CFI = 0.99, TLI = 0.99, RMSEA = 0.04, SRMR = 0.03). While determining the convergent validity of the measures, both smartphone addiction and perceived academic underachievement were associated with lower GPAs and reduced classroom mindful attention. Moreover, the SAS-SV and PAUS were found to be invariably applicable across age, gender, and data collection mode. Mediation analyses indicate that the self-awareness component of classroom mindful attention had an indirect association with the relationship between smartphone addiction and perceived academic underachievement in young adults ( B  = 0.014, 95% CI [0.001, 0.028], p  = .033) and the combined sample ( B  = 0.020, 95% CI [0.005, 0.036], p  = .009), whereas no significant indirect association was observed among adolescents. These findings highlight the associative role of classroom self-awareness in the relationship between smartphone addiction and perceived academic underachievement and underscore the potential role of mindfulness-based interventions in educational settings.

Multispectral and anatomical assessment of chromium and nickel accumulation in urban weeds

Editorial Note: In Vitro and In Vivo Antitumor Activity of [Pt(O,O′-acac)(γ-acac)(DMS)] in Malignant Pleural Mesothelioma

Association of oral frailty, social support, and technophobia with depressive symptoms in Chinese older adults with chronic diseases: a cross-sectional study

Could this mysterious disappearing organ hold the key to longevity?

Expression of Concern: Multiobjective intuitionistic fuzzy programming under pessimistic and optimistic applications in multivariate stratified sample allocation problems

Channel-resolved excitation trends in Si, GaAs, and NiO for sub-GeV dark matter detection

Abstract The direct detection of sub-GeV dark matter requires target systems capable of converting very small deposited energies into observable low-energy excitations. In this work, we develop a phenomenological comparative framework for examining electronic, phononic, and magnonic response trends in three representative condensed-matter targets: silicon (Si), gallium arsenide (GaAs), and nickel oxide (NiO). The purpose of the framework is not to provide an ab initio prediction of absolute event rates, but to compare how material-specific excitation scales and effective channel weights influence the relative response under a common set of dark matter assumptions. The total response is decomposed into channel-resolved contributions and expressed in terms of normalized response rates and fractional channel measures. To address the model dependence of the effective channel weights, we supplement the baseline calculation with a weight-sensitivity analysis in which the leading channel is penalized while competing channels are enhanced. Within the adopted literature-guided phenomenological parameterization, Si remains electron-dominated, GaAs remains phonon-dominated under moderate adverse perturbations, and NiO remains magnon-dominated. However, the unit-weight test shows that the GaAs classification is conditional on including material-guided channel weighting, emphasizing that the present results should be interpreted as normalized comparative trends rather than absolute detector-rate predictions. The analysis therefore supports a cautious material-specialization picture: semiconducting, polar, and magnetic targets can preferentially emphasize different low-energy excitation signatures, but the quantitative hierarchy depends on the adopted response functions, channel weights, thresholds, and interaction assumptions. The study provides a transparent baseline for organizing multi-target detection strategies and identifies the main limitations that must be addressed by future microscopic or ab initio calculations.

αKG-mediated carnitine synthesis drives DNA repair via histone acetylation

Unraveling dynamic factors in Dialectic Behavioural Treatment for Adolescents (DBT-A): A study protocol

Dialectical Behavioural Therapy for Adolescents (DBT-A) is an effective treatment for adolescents exhibiting features of borderline personality disorder (BPD). However, some do not benefit, potentially leading to major negative outcomes later in life. Previous quantitative studies have highlighted the importance of dynamic (changeable) factors in determining treatment success, including the therapeutic alliance, emotional dynamics between therapist and adolescent, and the motivation of those involved. Nevertheless, the specific contribution of these factors to the therapeutic process and perceived treatment success remains unclear. This study protocol outlines a multicenter, qualitative longitudinal study designed to explore how dynamic factors in DBT-A are experienced by adolescents, parents, and therapists at various stages of therapy. The study will track treatment journeys (trajectories) of fifteen youth during DBT-A therapy across four child and adolescent psychiatry institutions in the Netherlands. The triad of adolescent, parent(s), and therapist will be interviewed separately at four time points during treatment: at the start and after three, six, and nine months (resulting in 180 interviews in total). The semi-structured interviews, based on a theoretical framework, will be analyzed using reflexive thematic analysis, employing a combination of deductive framework analysis and inductive open coding methods. By bringing together the triad of perspectives, and monitoring how these perspectives evolve over time, this study will yield valuable, practice-based insights into how dynamic factors unfold during DBT-A. Insights from this study will offer therapists specific, actionable guidance for modifying dynamic factors to better address the unique needs of adolescents with BPD features.

Five-miRNA signature for breast cancer detection in a northern Mexico border cohort

How the calendar came to be

Temporal evolution of carbon footprint and emission reduction pathways of major crops in Inner mongolia under China’s dual-carbon goals

As an important grain-producing region in northern China, the Inner Mongolia Autonomous Region is characterized by arid and semi-arid agricultural ecosystems, limited water resources, and relatively high dependence on agricultural inputs such as irrigation, chemical fertilizers, diesel fuel, and plastic film mulching. Clarifying the temporal evolution of input-related carbon footprints in crop production is important for promoting agricultural low-carbon transformation while maintaining regional food security. Based on an input-based carbon footprint accounting framework with reference to life cycle assessment principles, this study quantified the carbon footprint of major crop production in Inner Mongolia from 2006 to 2023. Six major emission sources were considered: chemical fertilizers, pesticides, agricultural plastic film, diesel fuel, irrigation, and soil tillage. The results showed that agricultural input use experienced a transition from rapid expansion to partial stabilization. Fertilizer, pesticide, and diesel consumption increased during the early and middle stages of the study period and then stabilized or declined after the mid-2010s, whereas plastic film use continued to increase.The reported effective irrigated area increased sharply in 2022. This increase should be interpreted primarily as a statistical breakpoint caused by the official application of the Third National Land Survey results and the updated land-use classification system, rather than as a sudden one-year physical expansion of irrigation. The total carbon footprint increased from 448.88 × 10⁴ t CO₂-eq in 2006 to 720.07 × 10⁴ t CO₂-eq in 2023, representing an increase of 60.4%. Cropland tillage and chemical fertilizer application were the dominant emission sources, accounting for 38.2%–45.9% and 25.1%–31.4% of total emissions, respectively. Although the total carbon footprint and carbon footprint per unit planted area increased, the carbon footprint per unit agricultural output value decreased by 66.8%, indicating improved carbon-economic efficiency. However, this decline may also be influenced by market price changes and crop value structure. The findings suggest that future mitigation should focus on conservation tillage, precise fertilizer management, water-saving and energy-efficient irrigation, plastic film recycling or substitution, and improved agricultural carbon monitoring. This study provides a regional-scale reference for developing low-carbon agricultural pathways in arid and semi-arid farming systems.

The bidirectional association between highly processed food consumption and major depressive disorder among adults in Spain

Academia should recognize community partnerships as having societal impact

Helikite aerial sensing system: An innovative approach for monitoring construction sites

This study proposes an innovative Helikite-based aerial sensing system as a reliable aerial monitoring solution for construction sites. Construction site monitoring requires real-time, unobstructed, and long-duration coverage to ensure worker safety and project efficiency, but this remains challenging due to the limitations of existing methods. Key factors contributing to this challenge include: 1) fixed cameras are prone to occlusion and have a restricted range in dynamic construction environments; 2) UAVs are limited by short battery life, limited wind tolerance, and high maintenance costs; and 3) there is a lack of bird’s-eye-view datasets. This study hypothesizes that the Helikite aerial sensing system can operate stably across the wind speed range typical of construction site operations. The methodology involves three core steps: 1) designing a wind-resistant Helikite-based system capable of long-duration operation; 2) constructing a specialized dataset—comprising 1,000 original bird’s-eye-view images of construction sites expanded to 3,000 through data augmentation—to address the data gap; and 3) validating the system using the YOLO-v5 model for worker recognition on an independent test set of 100 images. Experimental results show the YOLO-v5 model achieved 0.9827 accuracy, 0.9850 precision, 0.9920 recall, and 0.9820 F1-score on the validation set, confirming the high quality of Helikite-captured data and the system’s effectiveness.

Determination of muscle activation in upper limb during gait among stroke patients with homolateral limb synkinesis: a cross-sectional analytical pilot study

Abstract Post-stroke neural damage can impair sequential muscle activation, which may lead to associated reactions (ARs) during effortful tasks. During gait this may manifest as homolateral limb synkinesis (HLS), where voluntary movement of paretic limb evokes involuntary movement in the corresponding ipsilateral limb. Upper limb muscle activation underlying HLS during ambulation remains poorly understood, hence this study aimed to investigate muscle activation using surface electromyography (sEMG) in post-stroke HLS patients. This study included 22 participants, 11 in each group. sEMG recordings were obtained from four agonist–antagonist muscle pairs of affected upper limb in stroke participants and dominant limb in controls during a 10-m gait trial at self-selected pace. sEMG signals were normalised to a reference contraction. Group comparisons were performed using Mann–Whitney U test, followed by false discovery rate (FDR) correction for multiple comparisons. Initial analyses demonstrated significantly greater normalised activation in pectoralis major, middle deltoid, biceps brachii, extensor carpi radialis brevis, and flexor carpi ulnaris among HLS participants, which after FDR persisted only in biceps brachii, extensor carpi radialis brevis, and flexor carpi ulnaris. HLS during gait involves abnormal activation of predominantly the elbow and wrist musculature, with non-rhythmic aberrant muscle activity in proximal and distal muscles compared to controls.