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Battery management in IoT hybrid grid system using deep learning algorithms based on crowd sensing and micro climatic data
Abstract Hybrid Grid System (HGS) installation in small and large residential area has major challenges due to domestic loads. Domestic loads are in different duty cycle such as (i) continuous duty i.e., vehicle charging, (ii) short time duty, (iii) periodic duty and (iv) intermittent duty. In this paper, proposed HGS comprises of Internet of Thing (IOT), Photovoltaic (PV) system and wind system (PWS) with Lithium-Phosphate battery paralleled with Super-capacitor, Deep learning controller with PWS is termed as IOT enabled PWS (IPWS). IPWS has zero export converters, reduces electricity demand on grid. Zero-export inverter avoids excess energy to grid and excess energy stored in super-capacitor. IPWS has crowd sensing for microclimatic conditions data acquisition system. Microclimatic Data is used for tuning zero export converters and Battery Management System (BMS) through IPWS. IPWS controller perform with different hybrid Deep learning algorithm such as (i) SCO-LSTM controller and JO-LSTM based BMS (ii) JO-LSTM controller and HBO-LSTM based BMS (iii) HBO-LSTM controller and SCO-LSTM based BMS. IPWS reduces time and space complexity in controller. Among the proposed methods, IPWS with JO-LSTM/ HBO-LSTM based BMS eliminates output power fluctuations and increases transient stability (TS) and damping ratio (DR). Comparative analysis for DC—link and super-capacitor in IPWS is presented. IPWS with JO-LSTM controller, super-capacitor suits for residence loads and provides 29% improved power factor, reduces harmonics 14%, DR of 6%, and low TS.
A YOLOv8 algorithm for safety helmet wearing detection in complex environment
Highly selective solid phase extraction of clonazepam from water using a urea modified MOF prior to HPLC analysis
Self-Medication with NSAIDs in Gondar city: prevalence, predictors, and public health implications
AI driven automation for enhancing sustainability efforts in CDP report analysis
Abstract The need for sustainable practices in supply chains is becoming increasingly critical, as businesses face pressure to reduce their carbon footprint while maintaining operational efficiency. This paper proposes a novel hybrid approach that combines Genetic Algorithms (GA) with Long Short-Term Memory (LSTM) networks to optimize supply chain sustainability. The proposed system leverages publicly available Carbon Disclosure Project (CDP)-reported data to predict emissions and optimize resource allocation. The primary objective of this research is to develop a cost-effective, scalable solution that reduces emissions, improves operational efficiency, and ensures regulatory compliance within supply chains. The hybrid model consists of two main components: LSTM networks for predictive modeling of emission trends and GA for optimization of supply chain processes. LSTM is used to forecast future emissions based on historical data, while GA optimizes resource management, including transportation choices and energy consumption, to minimize emissions and operational costs. The system employs a multi-objective optimization approach, addressing the simultaneous goals of emission reduction, operational efficiency, and compliance with environmental regulations. The experimental results demonstrate the effectiveness of the proposed approach. A 23.67% reduction in total emissions was achieved, with the most significant improvements in indirect emissions. The system also improved operational efficiency by 10.98%, while ensuring 100% compliance with environmental regulations, eliminating any penalties. The hybrid GA-LSTM framework offers valuable insights for businesses seeking to meet sustainability targets and provides a practical, data-driven method for improving supply chain performance. The proposed system is not only applicable to large corporations but can also be scaled for use in small and medium-sized enterprises, offering a pathway for widespread adoption of sustainable practices across industries.
Intranasal booster induces durable mucosal immunity against SARS-CoV-2 in mice
Abstract Vaccines against coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are highly effective in preventing severe disease but are less consistent in protecting against infection and transmission. Developing vaccines with enhanced immunogenicity that can provide protection in both the upper and lower respiratory tract (URT and LRT) is crucial. Mucosal immunization induces immunity at the site of initial infection, the respiratory tract, thereby preventing or mitigating infection. Here, we compared immune responses elicited by intramuscular mRNA vaccination alone with those elicited by intramuscular mRNA vaccination followed by intranasal administration of ChAdOx1 nCoV-19 vaccine in mice. Although both vaccination strategies induced strong systemic immunity, robust humoral and cellular mucosal immune responses, including spike-specific IgA and tissue-resident T cells, were only detected upon mucosal vaccination. Compared to unvaccinated animals, mucosal vaccination resulted in migration of T cells and macrophages into the nasal turbinates, as well as migration and proliferation of B and T cells in the nasal-associated lymphoid tissue. While both vaccination regimens provided protection across the entire respiratory tract at 2 weeks post-vaccination, at 12 weeks post-vaccination, only the mice that received a mucosal vaccination remained protected in the URT. Gene-expression profiling of the respiratory tract at 2 days post-infection revealed distinct clustering between groups. Enrichment of immune signaling pathways, including B and T cells receptor pathways, was significantly higher in intranasally vaccinated animals. Together, our study demonstrates that mucosal vaccination provides durable protection against SARS-CoV-2 than intramuscular vaccination alone.
Optimizing sustainability performance through digital dynamic capabilities, green knowledge management, and green technology innovation
Correction: Optimizing positron emission tomography for accurate plant imaging using Monte Carlo simulations to correct positron range effects
Multi-omics study reveals gut microbiota dysbiosis and tryptophan metabolism alterations in GH-PitNET progression
Abstract Growth hormone-secreting pituitary neuroendocrine tumors (GH-PitNETs) arise from the anterior pituitary gland and constitute 20–30% of all PitNETs, representing a significant subset of functional pituitary tumors. Despite their prevalence, the precise mechanisms underlying the development of these tumors remain elusive due to the complex pathophysiology of pituitary neoplasia. To investigate the potential role of the gut microbiome in GH-PitNETs, we conducted a comprehensive study involving 16S rRNA gene sequencing and metabolomics analysis of fecal and serum samples from 20 GH-PitNET patients and 30 healthy controls at Peking Union Medical College Hospital. Our findings revealed a distinct gut microbiota profile in GH-PitNET patients compared to healthy individuals, characterized by dysbiosis with increased abundance of Bacteroides and decreased abundance of Blautia and Bifidobacterium. Notably, alterations in specific bacterial taxa, including Intestinibacter bartlettii, Fusicatenibacter faecihominis, and Massilioclostridium, were observed in GH-PitNET patients. Concomitantly, serum metabolomics analysis identified 154 differentially abundant metabolites in GH-PitNET patients, with significant enrichment in pathways related to tryptophan metabolism. Among these metabolites, 3-indoleacetic acid (IAA) exhibited a obvious change, suggesting its potential research value for disease processing of GH-PitNETs. To further elucidate the mechanistic link between the gut microbiome and GH-PitNETs, we conducted in vitro and in vivo experiments, our results demonstrated that IAA could promote the proliferation of GH3 cells and significantly enhance growth hormone secretion by activating the cAMP pathway. These findings collectively suggest that gut microbiota dysbiosis may contribute to in the development and progression of GH-PitNETs by contributing to metabolic disturbances.
Retraction Note: Blocking C-Raf alleviated high-dose small-volume radiation-induced epithelial mesenchymal transition in mice lung
Ergodic secrecy rate and outage probability in NOMA IRS massive MIMO networks with jamming
17β estradiol activates autophagy and attenuates homocysteine mediated inflammation in endothelial cells through PI3K AKT MTOR signaling
City-scale GPS data reveals impact of spatial configuration and dedicated infrastructure on e-scooter route choice
Abstract Shared e-scooter use has rapidly expanded in major cities worldwide, offering promising solutions for sustainable transport and new data sources to advance the science of cities. This study leverages a city-scale GPS dataset of 14,029 e-scooter trips recorded over a three-month period in 2021 within the Mannheim/Ludwigshafen metropolitan area in Germany. For the first time, our analysis integrates the discrete choice modelling framework with space syntax theory using such large-scale revealed preference data, uncovering new insights into the impact of spatial configuration on routing behaviour. The results highlight the significant role of spatial configuration in e-scooter routing, with space syntax metrics consistently improving model performance and suggesting that riders avoid both places that are not well-integrated on a regional and highly accessible on a local level. Results also reveal that dedicated bicycle infrastructure, including bike lanes and tracks, reduces perceived travel distance by over 51% for e-scooter riders. Additionally, riders exhibit context-dependent behaviour, favouring pedestrian spaces during busy weekdays while avoiding them at other times. These insights can guide policymakers in designing micro-mobility-friendly urban environments.
Enhancing salinity tolerance in wheat: the role of synthetic Strigolactone (GR24) in modulating antioxidant enzyme activities, ion channels, and related gene expression in stress responses
Abstract Wheat (Triticum aestivum L.) is a vital global crop; however, its productivity is facing increasing threats from soil salinity, which affects a significant portion of arable land worldwide. This study investigates the potential of synthetic Strigolactone (GR24) to enhance salinity tolerance in wheat by examining its effects on antioxidant enzyme activity, ion homeostasis, and gene expression. Three wheat cultivars with varying salinity resistance (Sistan, Pishtaz, and Tajen) were treated with 10 µM GR24 under two salinity levels (5 and 15 dS/m). Salinity stress was applied from the 3–4 leaf stage to tillering. GR24 significantly enhanced the activities of antioxidant enzymes such as ascorbate peroxidase, catalase, and polyphenol oxidase while reducing guaiacol peroxidase activity. Proline content, potassium levels, and concentrations of chlorophyll and carotenoids were markedly increased, while sodium ion accumulation and indicators of oxidative damage (malondialdehyde, hydrogen peroxide, and electrolyte leakage) were reduced. These effects improved leaf water retention and overall stress resilience. Furthermore, polyphenol oxidase activity highlighted a potential novel pathway of Strigolactone action involving interactions with other phytohormones. Gene expression analysis via real-time PCR revealed that GR24 modulates the transcription of stress-responsive genes, including antiporter genes crucial for maintaining Na+/K + homeostasis and reducing ion toxicity. Among the cultivars, Sistan and Tajen exhibited the most robust responses at a salinity level of 15 dS/m. These findings underscore the potential of GR24 as a promising tool for enhancing wheat performance in saline environments.
A qualitative study of family caregivers’ experiences in caring for breast cancer patients
Infrared spectral analysis of gastrointestinal neuroendocrine tumors reveals diagnostic biomarkers
Abstract Gastrointestinal neuroendocrine tumors (GI NETs) remain diagnostically challenging due to limitations of current methods. This study pioneers the application of Fourier-transform infrared (FTIR) spectroscopy for GI NET detection through plasma lipid profiling. Analyzing 22 patients and 8 controls, we identified specific biomarker ratios (I3015/I2929 and I3015/I1650) reflecting tumor-associated oxidative stress and membrane alterations. Multivariate analysis revealed excellent diagnostic accuracy (94–96.1% sensitivity, 100% specificity) superior to conventional biomarkers, with PCA showing clear group separation (96.5% variance). The FTIR approach demonstrated significant advantages: rapid analysis (< 5 min), minimal sample requirements (4 μL), and low cost, addressing critical clinical needs. Spectral changes correlated with known lipid metabolism dysregulation in NETs, particularly increased unsaturated fatty acids (3015 cm−1) and altered acyl chain packing (2929 cm−1). These findings establish FTIR as a practical, label-free alternative to invasive diagnostics, with potential for both early detection and treatment monitoring. The identified lipid signatures not only provide robust diagnostic markers but also suggest new therapeutic targets for NET management. This cost-effective technology could transform clinical practice by enabling routine screening and personalized treatment strategies. Future studies should validate these results in larger cohorts and explore correlations with tumor grade and treatment response.
Validity of the test for attentional performance in neurological post-COVID condition
Abstract Neurological post-COVID condition (PCC) often involves attentional deficits that impact daily functioning. Traditional paper-based tests, like the Trail-Making Test (TMT), may inadequately capture these impairments due to their short duration and dependence on numerical and alphabetic sequencing. This study evaluates the validity of three subtests of the computerized Test for Attentional Performance (TAP) as alternatives for detecting attentional impairments in PCC. In the ongoing NEURO LC-19 DE study, 108 subjects aged 18 to 79 years, with PCC-related cognitive complaints (n = 67, 73% f) and healthy controls (n = 41, 56% f) underwent neuropsychological testing. The prevalence of impairment and classification ability of the TAP subtests were evaluated alongside standard paper-based tests, including the TMT and Montreal Cognitive Assessment, using receiver operating characteristic (ROC) analysis and regression. The TAP subtests identified significant impairments in sustained attention and processing speed in one-third of PCC patients, surpassing traditional tests in sensitivity, and classifying PCC with an AUC of 78%. Omissions in sustained attention significantly differentiated groups (OR = 1.14, p = 0.016, 95% CI [1.02–1.26]). Fatigue correlated with poorer performance on speed and accuracy (r > 0.30, p < 0.05). Cognitive slowing is prevalent in neurological PCC but is scarcely captured by conventional assessments. The TAP’s computerized format with automated norming and independence from alphanumeric stimuli shows promise in improving the discriminatory ability for identifying attentional deficits in PCC patients.
Study on the restorative benefits of four behavioural patterns of urban landscape forests under seasonal change
Abstract Urban landscape forests (ULFs) are important green spaces that promote human well-being by providing health benefits and leisure opportunities. Most studies have concentrated only on health promotion differences in terms of plant community characteristics and have ignored the influence of a user’s own activity type. This study explores the restorative effects of different behavioural modes in deciduous ULFs. We chose 4 common behavioural modes, and a grouping experiment was conducted on a ginkgo scenic forest in Shuangliu Central Park, Chengdu, China. A total of 128 subjects were randomly divided into four gender-balanced groups. Physiological and psychological responses were evaluated using blood pressure, heart rate, electroencephalogram (EEG) measurements, and the Profile of Mood States (POMS) scale. The results revealed that the changes in systolic blood pressure and heart rate in the GL group decreased significantly, and diastolic blood pressure decreased significantly. In the monitoring of EEG changes, the α wave and β wave activity in the GS group and GW group were significantly increased. A comparison of the ANCOVAs among the four groups revealed that the α wave activity of the GS group was significantly greater than that of the other three groups (p < 0.001), the β wave activity of the GS group was significantly lower than that of the GW group, and the T–A mood values of the four behaviour pattern groups were significantly lower according to the POMS. According to the overall statistics of the available indicators, the health benefits of walking in autumn landscape forests are greatest, followed by sitting, lying and talking. The results of this research can encourage urban planners to consider appropriate behavioural guidance when developing nature tours or immersive nature projects on the basis of differences in behaviour patterns to gain more scientific insights into activity types.