Browse Articles

Nitrogen-doped ZnO and TiO2 supported on activated carbon for dual pollutant degradation under UV/H2O2 process

Abstract Nitrogen-doped ZnO and TiO 2 nanoparticles supported on GAC were simulated and characterized. Various techniques including SEM, EDX, XPS, Raman, and DRS were employed to confirm both the successful nitrogen doping and the effective immobilization of ZnO and TiO₂ particles onto the GAC substrate. The resulting N-ZnO/AC and N-TiO 2 /AC catalysts were applied in the photo-degradation of ammonia and phenol within a semi-continuous flow photocatalytic reactor. Photocatalytic activity assessments were performed on both catalysts with flow rate and pH variations. These investigations indicated that the optimal degradation of both contaminants occurred at 8 L/min flowrate and a moderate pH level. To comprehensively evaluate the impact of various independent parameters on degradation efficiency, Response Surface Methodology (RSM) was applied. Optimization of the UV/Catalyst/H 2 O 2 process for the N-ZnO/AC catalyst was conducted using a Box-Behnken design. The predicted photo-degradation efficiency for both ammonia and phenol were found in excellent agreement. Optimization process revealed that the maximum photo-degradation efficiency was achieved under specific conditions: 120 min of irradiation time, 0.86 g L − 1 catalyst dose, an H 2 O 2 concentration of 45 mM, an initial ammonia concentration of 96.55 ppm, and an initial phenol concentration of 10 ppm.

Genetic diversity analysis of North Dakota public soybean breeding program cultivars

Linking resource efficiency to environmental sustainability in developing countries

US science after a year of Trump: what has been lost and what remains

Optimization of academic performance and mental health in college students through an AI-driven personalized physical exercise and mindfulness intervention system

Abstract This research examines an artificial intelligence-driven personalized intervention system that integrates physical exercise and mindfulness practices to support academic performance and psychological wellbeing among university students in eastern China. A 16-week controlled intervention study enrolled 328 undergraduate students from three comprehensive universities, comparing three conditions: AI-personalized interventions ( n  = 110), standardized interventions ( n  = 108), and controls ( n  = 110). The AI system employed machine learning algorithms to analyze multidimensional student data and generate tailored recommendations. Results indicated that the AI-personalized group was associated with larger improvements across academic metrics (10.28% GPA increase, 95% CI [8.94, 11.62], d = 0.89, p  < 0.001), psychological parameters (36.7% stress reduction, 95% CI [33.2, 40.1], d = 1.42, p  < 0.001), and physiological indicators (28.4% HRV improvement, 95% CI [24.8, 32.0], d = 1.13, p  < 0.001) compared to standardized interventions and controls. Regression analysis identified intervention adherence, sleep quality improvement, and stress reduction as factors associated with outcomes. The hybrid neural network architecture combining student feature analysis, exercise matching, and mindfulness adaptation offers a framework for personalized health interventions in academic settings. These findings, while promising, are specific to Chinese university contexts with particular cultural and technological characteristics, and cross-cultural validation remains necessary before broader generalization.

Molecular characterisation of the invasive terrestrial nemertean Geonemertes pelaensis: long and complex mitogenome and presence of NUMTs

Study decision-making to understand how technology will affect behaviour

Dietary patterns and sperm DNA fragmentation in idiopathic infertile men: A case-control study

Mistaken identity and the psychology of human recognition

Factor of safety prediction for high road embankments using mixed effects random forest and bee colony optimization

To gain public trust, make art central to science communication

Microbial degradation of Diospyros melanoxylon biomass by Trichoderma atroviride for plant growth promotion of finger millet

Fossil-fuel phase out is not enough: countries must remove atmospheric carbon

Genomic risk prediction of type 2 diabetes in people living with and without HIV

Abstract Type 2 diabetes (T2D) risk prediction remains a challenge, particularly in underrepresented populations, including people living with HIV (PWH) and those of non-European ancestry. We evaluated the performance of two metaPRS (polygenic risk score) models, integrating genetic markers related to inflammation and lipid metabolism, in predicting T2D risk across ancestry groups (African and European), with and without HIV. The metaPRS were generated in a subset from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study (6,034 Black; 11,972 White) and validated in 7,580 (4,120 Black; 3,460 White) PWH from the Centers for AIDS Research of Integrated Clinical Systems (CNICS), as well as an additional 4,152 (2,586 Black; 1,566 White) seronegative participants from REGARDS. Incorporating the metaPRS into models provided non-significant improvements in T2D risk prediction compared to single-trait T2D PRS and clinical risk factors. Performance was similar in PWH and in people without HIV, suggesting that these general population-derived genetic scores are transferable to PWH. Future studies should focus on refining PRS models in diverse populations and exploring genetic factors specific to PWH regarding T2D risk.

‘Shattered’: US scientists speak out about how Trump policies disrupted their careers

Response of free-headed segmental piles with mechanical joints to lateral loading

Abstract Segmental piles with mechanical joints(hereinafter, mechanically-jointed piles), as an improved pile type, have been widely adopted in construction projects. Due to their structural differences from conventional single piles, their mechanical responses diverge significantly, particularly under lateral loading. Gaps form at the mechanical joint between two single pile segments in mechanically-jointed piles, amplifying distinctions in mechanical response compared to conventional piles. To investigate the mechanical behavior of mechanically-jointed piles under lateral loading, this study develops a calculation theory for mechanical response based on the *m*-method—a standard approach for conventional single piles. The theory’s feasibility is validated via numerical simulations. Results indicate that numerical simulation align closely with *m*-method calculations: pile head displacement error is 4.8%, rotation error 6.2%, maximum bending moment error 24.9%, and maximum shear force error 8.2%. Comparative analysis of conventional single piles and mechanically-jointed piles with free ends reveals that under lateral loading, mechanically-jointed piles exhibit approximately 30% larger pile head displacement and approximately 55% greater rotation than conventional piles, indicating reduced deformation resistance. However, the results indicate that the mechanically-jointed pile can effectively reduce the maximum bending moment in the pile shaft. This reduction suggests a potential for optimizing the pile design and enhancing its lateral resistance performance under certain conditions.

US funding cuts harm aspiring young scientists, too

Postural stability during a longitudinal expedition in an isolated and confined Antarctic environment

Social context shapes facial dynamics: human and machine decoding of conversation topics

Abstract Conversation is fundamental to the human species, with facial expressions playing a crucial role in establishing shared understanding within specific conversational contexts. We hypothesized that variations in conversation topics, differing in levels of tension and personal disclosure, would elicit distinct facial behavior dynamics. We assessed facial activity during two types of natural conversations with varying tension levels in triads of unacquainted individuals: "get-to-know-each-other" and “moral dilemma” discussions. Human observers classified the conversation type with 82.11% accuracy based on facial dynamics alone. Strikingly, a machine learning model using three facial action units (AUs 4, 6, and 12) during speech-free moments achieved comparable accuracy (82.14%). Further analyses revealed that the model relied on the temporal dynamics of these AUs to distinguish conversational contexts. These findings show that machine-based facial coding, coupled with deep learning, can infer conversational context from facial expressions, offering a scalable tool for analyzing natural social interaction.

Training and external validation of machine learning supervised prognostic models of upper tract urothelial cancer (UTUC) after nephroureterectomy

Abstract The European association of Urology (EAU) suggests a prognostic stratification of Upper Tract Urothelial Cancer (UTUC) based on high and low risk patients, with Radical nephroureterectomy (RNU) and bladder cuff resection being the gold standard for the treatment of non-metastatic High risk UTUC. However, no consensus on post-operative patient management or tools that predict who would benefit the most from a close follow-up rather than adjuvant chemotherapy regimen exist. in Machine Learning (ML) is gaining interest in Urology providing models for prognostic prediction purpose; It’s role in UTUC has not yet been investigated. We aim to develop and validate multiple supervised ML models based on patient- and tumor- related features to predict prognosis in patients with preoperative Histological or Imaging proved UTUC treated with RNU within a multiethnic large cohort. Data from an international multicenter large cohort of histologically proven UTUC patients from Asia and Europe treated with RNU were retrospectively collected. Twenty different ML-supervised predictive models were first trained and then external validate with two separate set. Nomograms were constructed based on 8 independent prognostic factors (age, gender, grading, pT, pN, presence of Carcinoma in Situ (CIS), multifocality and Lymphovascular invasion(LVI)) to predict 6 Outcomes (Overall Survival (OS), Cancer Specific Survival (CSS) and Disease Free Survival (DFS) at 3 and 5 year). Performances were compared using Area-under-curve (AUC) of Receiver-Operating Characteristics (ROC). A total of 3129 patients were enrolled: 637 Asian Patients (training cohort) and 2492 European patients (validation cohort). Upon training assessment, LR models achieved the best results, being the best model for prediction of 4/6 outcomes, with the best result in CSS both at 3 and 5 years (AUC: 0.85, 0.84, 0.81 for CSS-3y, CSS-5y and DFS-3y respectively). Upon external validation, LR(CSL) models achieve the best results, being the number 1 model for prediction of 3/6 outcomes (AUC: 0.84, 0.79, 0.77 for CSS-3y, OS-3y and OS-5y respectively). ML is a promising technology in the field of UTUC. Our model achieve favorable results in terms of prediction of prognosis after RNU, especially in terms of CSS at 3 and 5 years, moreover is the first model of prognosis taking into account the differences in epidemiology existing between European and Asian patients. Further clinical validation and verification of its reliability for the case selection of adjuvant therapy are needed to assess its use in clinical practice linked to clinical decision making. ML is an advancing technology in the field of medicine and urology, which can also be applied to the definition of the prognosis of patients with UTUC undergoing RNU. Our study represents the first experience investigating this potential.