Browse Articles

Real time estimation of battery SOC and autonomous charging strategy for dynamic energy storage charging robot with extended Kalman filter

Evaluating the spatiotemporal skill of bias-corrected NMME forecasts against climatological forecasts for seasonal precipitation in China

Carbon reduction potential and selection strategies of emerging construction-phase technologies

Abstract Carbon reduction has emerged as a critical global challenge. The building sector, as the primary contributor to carbon emissions, stands to benefit substantially from the adoption of emerging technologies during construction. This study quantitatively evaluates the carbon reduction potential of 25 emerging construction technologies, investigates their reduction pathways, and provides a comparative analysis and classification of these techniques. The results demonstrate that renewable resources and waste reduction technologies offer substantial reductions in carbon emissions from energy consumption and waste transportation, presenting a key avenue for achieving carbon neutrality in the construction phase. Prefabrication technologies, which relocate traditional on-site curing processes to factories while retaining only assembly tasks on-site, reduce emissions by over 90%. Technologies streamlining construction processes exhibit varying reduction rates, influenced by their impact on workflows. High-strength and high-performance materials, which optimize concrete and steel use on-site, show the least reduction (< 30%). A phase-specific technology selection strategy is proposed and validated through a case study, which demonstrates a 28.49% reduction in on-site emissions during construction through the integrated application of multiple technologies. In conclusion, this study quantifies the carbon reduction potential and pathways of emerging construction technologies, offering data-driven insights for technology selection, industry adoption, and policy development.

Workplace ostracism influences hotel employees pro-environmental behaviour through green work engagement

Abstract This study examines how workplace ostracism influences hotel employees’ pro-environmental behavior, highlighting the psychological and organizational conditions that shape sustainable actions in the hospitality sector. Guided by social cognitive theory, the model tests green work engagement as a mediating mechanism and green management initiatives and environmental passion as boundary conditions. Data were collected through a structured questionnaire administered to 528 employees from four- and five-star hotels in major Chinese cities. Using Hayes’ PROCESS macro, the study assessed both mediation and moderated effects. Results show that workplace ostracism significantly reduces employees’ pro-environmental behavior, and this relationship is partially mediated by green work engagement. Moreover, green management initiatives strengthen the negative association between workplace ostracism and green work engagement, while environmental passion strengthens the effect of green work engagement on pro-environmental behavior. These findings contribute to sustainability and hospitality research by demonstrating how interpersonal exclusion undermines environmentally responsible behavior and by identifying organizational practices and individual motivations that can intensify or enhance these effects.

Isolation and characterization of lytic bacteriophages with therapeutic potential against multidrug resistant Klebsiella pneumoniae from Ethiopia

Platelet-rich plasma promotes cellular recovery from nicotine-induced toxicity via autophagy modulation

Abstract Chronic exposure to nicotine significantly exacerbates periodontitis, a prevalent inflammatory disease, by inducing cellular processes such as autophagy and inflammation in gingival fibroblasts. Current therapies often fail to fully address these cellular alterations in smokers, highlighting a need for innovative therapeutic and regenerative approaches. This study explores the therapeutic potential of Platelet-Rich Plasma (PRP), a blood-derived product, to modulate nicotine-induced biological activities in primary gingival fibroblasts, particularly in the case of periodontitis in smokers. Gingival fibroblasts were treated with increasing concentrations of nicotine, which led to senescence and autophagy. Nicotine at high concentrations triggered cellular vacuolization, and a decrease in metabolism, viability and proliferation. Concomitant cell treatment with 10% PRP reversed nicotine effects and significantly increased cell migration potential. In Caenorhabditis elegans, PRP reduced the nicotine-induced autophagic activity. A screening of the gingival fibroblast secretome revealed a modulation of autophagy-related cytokines in response to nicotine and/or PRP. The findings demonstrate that PRP could effectively inhibit nicotine-induced autophagy in gingival fibroblasts, offering insights into its possible use as a therapeutic tool for managing periodontitis in smokers. The study underscores the potential of PRP in altering disease progression by modulating key cellular processes affected by smoking.

Engineered biosynthesis of hyaluronic acid in Corynebacterium glutamicum and green synthesis of HA-silver nanocomposites for advanced antimicrobial wound dressings

Rivers constrain female but not male dispersal and genetic structure in brown bears

Abstract Understanding landscape barriers to connectivity is essential for studying wildlife population dynamics and developing conservation strategies that promote genetic exchange. Rivers can fragment landscapes and thereby influence genetic metapopulation structure by restricting individual movement and gene flow, yet their impact on brown bear ( Ursus arctos ) dispersal remains poorly understood. Using a large dataset (N = 519) of SNP-genotypes (96 loci) from fecal samples, we investigated the effects of rivers on sex-specific movement patterns, primarily dispersal and genetic structure in brown bears in northern Sweden. We found that males dispersed over twice as far as females (mean 56.4 km vs. 22.8 km) and crossed rivers significantly more often (42% of male vs. 11% of female dispersals; χ 2  = 49.33, p  < 0.001). Simulated female dispersals in random directions showed higher river crossings (17.7%, t = 90.47, p  < 0.001), suggesting philopatry alone cannot explain the low observed crossing rate. Rivers thus acted as semipermeable barriers for females, with genetic structure analysis (DAPC, spatial PCA) showing weak structuring effects for females, but none for males. Our study underscores the need to identify specific crossing sites, evaluate additive effects of other barriers (e.g., roads), and expand research across Sweden to guide connectivity-focused conservation.

Experimental and molecular docking analyses of antibacterial activity in moroccan Rosmarinus officinalis essential oil

Effect of moisture stress on different cucurbits: a morpho-physiological and biochemical perspective

Evaluation of deep learning models for segmentation of hippocampus volumes from MRI images in Alzheimer’s disease

Abstract The hippocampus is a crucial brain structure associated with Alzheimer’s disease (AD). Precise segmentation is crucial for studying AD progression using deep learning. This study aimed to evaluate the performance of deep learning models in segmenting the left and right hippocampus in MRI images. We hypothesized that deep learning-based approaches would enable precise and accurate segmentation of the left and right hippocampus. We propose U-Net, You Only Look Once version 8 (YOLO-v8), and DeepLab-v3 models using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This study used 300 subjects, comprising 100 subjects (AD), 100 subjects with mild cognitive impairment (MCI), and 100 subjects with normal control (NC), resulting in a total of 7859 image slices. The results showed that the U-Net model exhibited the best Intersection over Union (IoU), which served as a key performance indicator among the three classes: AD (0.639), MCI (0.801), and NC (0.751). In contrast, YOLO-v8 demonstrated lower IoU performance for AD (0.342), MCI (0.465), and NC (0.550), which are considered inappropriate models to segment the left and right hippocampus. We obtained the left hippocampus volume of AD (1557.5 mm³), MCI (1863.3 mm 3 ), and NC (2089.2 mm 3 ). The right hippocampus volumes of AD (1593.4 mm³), MCI (1918.7 mm 3 ), and NC (2280.2 mm 3 ). The U-Net model exhibited the best performance. We expect deep learning-based methods to assist in clinical decisions by providing accurate hippocampus segmentation.

Objective suturing skill assessment in a hands-on course increases medical students’ aspiration to become surgeons

aFGF rescues high glucose-induced senescent fibroblasts and improves diabetic wound healing by regulating SIRT1/STAT3 pathway

Detection of reduced susceptibility of Anopheles Gambiae s.l.  to pirimiphos-methyl in Benin

Multinomial probability model of radiation induced DSB and non-DSB clusters: tandem and bistranded damage clusters

Phenotypic classification of opium poppy genotypes (Papaver somniferum L.) based on morpho-phenological traits

Acousto-holographic investigation of the changes in morphology and cellular biomechanics under oxidative stress

Abstract Oxidative stress is an important cellular phenomenon that’s necessary for the cell to maintain its metabolic activities but causes adverse complications due to abnormal accumulation. It may cause some changes in the mechanical structure and behavior of the cells by affecting the cell membrane and cytoskeleton structure and triggering apoptosis. Moreover, it’s closely related to many conditions such as cancer, neurodegenerative diseases, and aging, making it important to examine and understand this phenomenon very well at the cellular level. This study reports a label-free and contact-free platform to image the morphological and mechanical behavior of live cells using an acousto-holographic microscope. F-actin fluorescence staining and Annexin V-FITC/PI staining were also performed to support our results. Different stages of apoptosis can be morphologically distinguished by our method while mapping the elasticity modulus distribution of the cells. It’s found that C2C12 myoblast cells, which initially had the highest elasticity modulus, have less resistance to apoptosis and undergo a more significant decrease in elasticity modulus under oxidative stress. HCT 116 cancer cells, which were the softest at the beginning, experience a weaker decrease in elasticity modulus under oxidative stress compared to C2C12 and HUVEC cells. This supports the resistance of HCT 116 cells against apoptosis.

Shear behavior and predictive modeling of loess stabilized with rice husk ash

Building user trust in AI chatbots for customer service through human-like cues and perceived reliability

AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns