Browse Articles

Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers

Abstract The integration of generative artificial intelligence tools in education has emerged as a transformative approach to enhancing learning outcomes, particularly in the context of sustainable development goals (SDG4). Therefore, the present study investigates the connection between generative artificial intelligence tool usage (GenAITU) and academic achievement (AA) in the context of SDG4. We assessed the mediating role of shared metacognition (SMC) and cognitive offloading (COL) in this relationship among preservice teachers (PSTs). The indicators, including performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and use behavior (UB), are derived from adapting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) for GenAITU. The authors surveyed 465 students from five universities in Wuhan, China, using a 7-point Likert scale through a time-lag design. Statistical analysis was performed through partial least squares structural equation modeling (PLS-SEM), to determine the relationship between variables. Findings indicated that two components of GenAITU, namely PE and UB, showed significant positive associations with AA, while the other two, EE and FC, did not show significant and positive relationships with AA. Results also showed that three dimensions of GenAITU, namely EE, FC, and UB have a positive and significant association with SMC while PE has a positive and significant connection with SMC. All four components of GenAITU like PE, EE, FC, and UB have positive and significant links with COL. SMC and COL have a positive and significant relationship with AA. Results also indicated that SMC mediated the connections between GenAITU (EE, FC, and UB) and AA. Outcomes also indicated that COL mediated the connections between GenAITU (PE, EE, FC, and UB) and AA. The current study shows that SMC and COL were strong mediators of the association between GenAITU and AA. The results of our study provide guidance to teachers, curriculum planners, and university management to successfully integrate GenAITU into the education for PSTs.

Efficacy of BCG vaccination against COVID-19 in health care workers and non-health care workers: A meta-analysis of randomized controlled trials

Background The Bacillus Calmette-Guérin (BCG) vaccine has shown potential non-specific protection against infectious diseases through “trained immunity”, which may offer cross-protection against viral infections. However, there is no consensus on whether BCG vaccination could prevent COVID-19 or reduce its symptoms. Methods PubMed, Cochrane Library, Embase and Web of Science were searched for randomized controlled trials on BCG vaccination and COVID-19 prevention, covering studies from the inception of each database to 2 May 2024. We included studies where participants, not infected with COVID-19, were vaccinated with BCG or placebo. We excluded non-randomized trials, studies without full texts, unrelated interventions, and those not reporting relevant outcomes. Clinical data on COVID-19 infection, severity, hospitalization, mortality, and other adverse events, were extracted and analyzed. The DerSimonian–Laird random-effects model and the Cochrane Collaboration’s risk of Bias Tool were used for analysis and risk of bias assessment. Results A total of 12 RCTs involving 18,086 patients were finally included. For the prophylactic effect of BCG on COVID-19, pooled results showed no statistically significant difference between BCG and placebo (pooled RR 1.02; 95%CI: 0.91–1.14). There was no statistically significant difference between non-health care workers (pooled RR 0.91; 95%CI: 0.67–1.24) and health care workers (pooled RR 1.03; 95%CI: 0.93–1.15). Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. Subgroup analysis also showed no significant difference between BCG and placebo in non-health care workers or health care workers. Conclusions Vaccination of BCG could not effectively prevent COVID-19 infection or decrease COVID-19 symptoms both in non-health care workers and health care workers.

Cross-lagged panel relationship between physical activity atmosphere, psychological resilience and mobile phone addiction on college students

Registered Report: Neural correlates of thematic role assignment for passives in Standard Indonesian

Previous studies conducted across multiple languages have found processing differences between patient-first and agent-first word orders. However, the results of these studies have been inconsistent as they do not identify a specific event-related potential (ERP) component as a unique correlate of thematic role processing. Furthermore, these studies generally confound word order with frequency, as patient-first structures tend to be infrequent in the languages that have been investigated. There is evidence that frequency of syntactic structure plays an important role in language processing. To address this potential confounding variable, we test Standard Indonesian, a language where passive structures occur with high frequency and are comparable in frequency to active structures. In Standard Indonesian, there is evidence from acquisition, corpus, and clinical data indicating that the use of passive is frequent. In the present study, 60 native speakers of Indonesian read 100 sentences (50 active and 50 passive) while EEG was recorded. Our findings reveal neural correlates of thematic role processing in the passive sentence condition – specifically, a positive shift corresponding to a P600 on the verb, and a more sustained positivity on the second noun phrase. These findings support existing evidence that sentences with a ‘non-default’ word order impose increased cognitive load, as reflected by ERPs, even when they occur with higher frequency in the language.

Numerical simulation of the effect of installation height on self-priming performance of a prototype self-priming pump

Correction: LRRK2 kinase plays a critical role in manganese-induced inflammation and apoptosis in microglia

An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade

OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks

OrthoNets use the Gram-Schmidt process to achieve orthogonality among filters but do not impose constraints on the internal orthogonality of individual filters. To reduce the risk of overfitting, especially in scenarios with limited data such as medical image, this study explores an enhanced network that ensures the internal orthogonality within individual filters, named the Orthogonal Channel Shuffle Network ( OSNet). This network is integrated into the Detection Transformer (DETR) framework for brain tumor detection, resulting in the OS-DETR. To further optimize model performance, this study also incorporates deformable attention mechanisms and an Intersection over Union strategy that emphasizes the internal region influence of bounding boxes and the corner distance disparity. Experimental results on the Br35H brain tumor dataset demonstrate the significant advantages of OS-DETR over mainstream object detection frameworks. Specifically, OS-DETR achieves a Precision of 95.0%, Recall of 94.2%, mAP@50 of 95.7%, and mAP@50:95 of 74.2%. The code implementation and experimental results are available at https://github.com/dkx2077/OS-DETR.git.

HEPSO-SMC: a sliding mode controller optimized by hybrid enhanced particle swarm algorithm for manipulators

Enhanced hybrid pre-coding and power allocation algorithms for smart irrigation systems using OFDM-based WSNs

Power allocation combined with pre-coding techniques is still an emerging field, with many challenges yet to be resolved. This paper contributes to filling this gap by proposing and evaluating hybrid algorithms that integrate pre-coding with low-complexity power allocation techniques for Orthogonal Frequency Division Multiplexing (OFDM)-based Wireless Sensor Networks (WSN) in smart irrigation systems. The use of linear pre-coding provides an efficient and simple solution to mitigate channel fading. By exploiting the channel’s frequency selectivity, the power allocation algorithms adjust the modulation type and power distribution for each sub-carrier dynamically. As a result, the proposed hybrid algorithms surpass static schemes, offering notable improvements in system performance. These algorithms adjust both the signal constellation size and power distribution based on the Signal-to-Noise Ratio (SNR) values observed across the sub-carriers. Additionally, practical considerations like Rate Maximization (RM) are incorporated to provide flexibility for various application needs. Extensive simulations validate the effectiveness of the proposed algorithms in minimizing power consumption and boosting performance in OFDM-based WSNs for smart irrigation. Numerically, the proposed algorithms can reduce the required SNR by up to 18 dB for a target throughput of 400 bits/symbol and outperforming conventional algorithms in terms of throughput, energy efficiency, and network lifetime, with the pre-coded Greedy power allocation (pre-GPA) algorithm delivering up to 98.5% throughput and the longest system lifespan.

Magnetic and antibacterial properties of substituted cobalt spinel ferrite nanocomposites synthesized via henna green microwave hydrothermal method

Mechanosensitive ion channel Piezo1 modulates the response of rat hippocampus neural stem cells to rapid stretch injury

Traumatic brain injury (TBI) is one of the primary causes of long-term brain disabilities among military personnel and civilians, regardless of gender. A plethora of secondary events are triggered by a primary brain insult, increasing the complexity of TBI. One of the most affected brain regions is the hippocampus, where neurogenesis occurs throughout life due to the presence of neural stem cells (NSC). Preclinical models have been extensively used to better understand TBI and develop effective treatments. Among these, rapid stretch injury has been used to mimic the effect of mechanical stress produced by a TBI on neurons and glia in vitro. In this study, we aimed to determine the impact of rapid stretch on the viability, proliferation, and differentiation of NSC isolated from rat hippocampus (Hipp-NSC) and to determine the role of the stretch-activated ion channel Piezo-1 in modulating their response to mechanical stress. We found that while rapid stretch (30 and 50 PSI) reduced Hipp-NSC viability (measured as a function of LDH release), it did not change their proliferation and differentiation potentials. Interestingly, rapid stretch in the presence of a selective Piezo-1 inhibitor, GsMTx4, or Piezo1 targeting siRNA, directed Hipp-NSC differentiation toward a neurogenic lineage. Additionally, we found that inhibiting Piezo1 with the addition of a rapid stretch injury increased the expression of miRNAs known to regulate neurogenesis. This work uses a novel approach for studying the effect of mechanical stress on NSC in vitro and points to the critical role the stretch-activated ion channel Piezo-1 has in modulating the impact of TBI on hippocampal neurogenesis.

Estimation of concentration and risk assessment of PAHs in urban water resources due to cigarette butt littering

CCCNet: Criss-cross attention enhanced cross layer refinement network for lane detection in complex scenarios

Lane detection plays a crucial role in autonomous driving systems by enabling vehicles to comprehend road structure and ensure safe navigation. However, the current performance of lane line detection models, such as CCNet, exhibits limitations in handling difficult driving conditions like shadows, nighttime, no lines,and dazzle, which significantly impact the safety of autonomous driving. In addition, due to the lack of attention to both the global and local aspects of road images, this issue becomes even more pronounced. To address these challenges, we propose a novel network architecture named Criss-Cross Attention Enhanced Cross-Layer Refinement Network (CCCNet). By integrating the strengths of criss-cross attention and cross-layer refinement mechanisms, CCCNet effectively captures long-range dependencies and global context information from the input images, leading to more reliable lane detection in complex environments. Extensive evaluations on standard datasets, including CULane and TuSimple, demonstrate that CCCNet outperforms CLRNet and other leading models by achieving higher accuracy and robustness, especially in challenging scenarios. In addition, we publicly release our code and models to encourage further research advancements in lane detection technologies at https://github.com/grass2440/CCCNet.

Rapid eigenpatch utility classifier for image denoising

Abstract Under low-illumination conditions, images inevitably contain both Poisson and Gaussian noise. In electron microscopy, there is the added complication whereby increasing the dose-rate, to improve signal-to-noise, damages the specimen being imaged, making certain materials being impossible to characterise. Conventional data smoothing techniques may dampen usable image contrast, and deep-neural network (DNN) based approaches risk the introduction of artefacts. In this work, the complementary strengths of patch-based and DNN approaches are combined into a lightweight denoising architecture such that experimental data integrity is preserved while effectively removing noise. Our approach, the Rapid Eigenpatch Utility Classifier for Image Denoising (REUCID), leverages the speed and data-integrity of a non-local patch-based SVD step to identify key image components, followed by a convolutional neural network (CNN) acting strictly in a classification capacity on the SVD eigenvectors. This classification-only approach to DNN integration represents a significant advance by mitigating the risk of DNN overreach while maintaining denoising effectiveness. We demonstrate superior performance on high angle annular dark field images, where our hybrid method outperforms conventional techniques in enhancing image contrast while preserving genuine structural features.

Analysis of growing season drought characteristics and driving factors for vegetation in the Santun River Irrigation Area in Xinjiang

Global warming is exacerbating the occurrence of droughts, which have a significant impact on society. Drought is one of the main factors limiting the development of the Santun River Irrigation Area in Xinjiang. Clarifying the driving mechanism and spatial and temporal evolution characteristics of drought in this irrigation area is crucial for ensuring the sustainable development of agriculture. In this paper, the temperature vegetation drought index (TVDI) is used as a drought indicator to analyze the spatial and temporal evolution characteristics of drought in the Santun River Irrigation Area in Xinjiang, as well as to reveal the factors influencing drought using a Geoprobe model. The results show that the mean value of the TVDI in the Xinjiang Santun River Irrigation Area during 19 years was 0. 738, categorizing it as medium drought. During this period, there was an increasing trend of drought in spring and autumn and a decreasing trend of drought in summer. The drought in the irrigation district had strong spatial heterogeneity, and overall, the drought was stronger in the northern part of the region than in the southern part of the region. Over the past 19 years, the light drought areas in the irrigation district shifted to the medium and severe drought classes at a rate of 114.9 km2·10a−1. The combined effect of elevation and temperature had the strongest explanatory power for drought occurrence in the irrigated area, with a q-value of 0.869. The results of this study provide a theoretical basis for drought risk assessment and water resource planning in arid regions, as well as a reference for drought monitoring studies in similar regions.

Metformin and chloroquine enhanced the efficacy of cytarabine in acute lymphoblastic leukemia cell lines: a drug repositioning approach

Production of the light-activated elsinochrome phytotoxin in the soybean pathogen Coniothyrium glycines hints at virulence factor

The Dothideomycete pathogen Coniothyrium glycines causes red leaf blotch of soybean, a major disease in Africa. It is one of two fungal plant pathogens on the USDA PPQ Select Agents and Toxins list of pathogens important to the biosecurity of the United States, reflective of its potential to be highly destructive if introduced. Despite its importance, there are no published reports regarding the molecular basis of host infection. Examination of the C. glycines genome revealed a secondary metabolite gene cluster that is similar in gene content and organization to clusters that synthesize light-activated perylenequinone toxins, such as cercosporin. Perylenequinones are non-host specific toxins that, upon exposure to light, generate reactive oxygen species, which have near-universal toxicity to plant hosts. Coniothyrium glycines isolates from eastern and southern Africa were cultured axenically under light and dark conditions. Light-grown cultures produced red-pink pigmentation typical of perylenequinones. Differential gene expression analysis showed that six of the eight genes in the biosynthetic gene cluster, including the polyketide synthase gene, were significantly upregulated in light. Liquid chromatography-mass spectrometry confirmed production of the perylenequinone elsinochrome A, a known virulence factor in other fungal pathogens. On leaves incubated in the dark, significantly fewer lesions formed and symptoms were delayed, compared to leaves incubated in the light. In addition, we identified orthologous gene clusters in more distantly related Dothideomycete plant pathogens where their presence was previously unknown, indicating a broader importance of these toxins to agriculture and fungal ecology. This work provides the first evidence that elsinochrome A may contribute to the virulence of C. glycines.

Impact of two nano-pesticide formulations in combating the two-spotted spider mite, Tetranychus urticae Koch, and their residues in cucumber fruits, Cucumis sativus L

Abstract Nano-pesticides aim to improve the efficacy and safety of conventional pesticides, but as they are still in the early stages of development, data about their environmental fate is insufficient. Therefore, the aim of this study is to compare between the conventional and nano-formulations of chlorfenapyr (CF) and emamectin benzoate (EB), by using chitosan nanoparticles as carriers and evaluating it against the two-spotted spider mite, Tetranychus urticae Koch. The loading capacities were 52.2 and 41.7%, respectively. The nanoparticles sizes of both chlorfenapyr (CF NPs) and emamectin benzoate (EB NPs) were 99.86 and 78.82 nm, respectively. The LC50, s were 68.8, 10.8, 3.6 and 1.1 ppm for chlorfenapyr, emamectin benzoate, nanochlorfenapyr and nanoemamectin benzoate, respectively. Thus, the nano-formulations are 6- and 3-fold more toxic than the conventional ones. The reduction percentages of T. urticae adults reached to 98.9 and 93.8% for CF NP s and EM NP s, respectively. Dissipation kinetics have been determined and the initial deposit after one hour of application was (0.95 and 0.083) and (0.12 and 0.052) mg kg−1 for conventional and nano-formulations, respectively. The t 0.5 and PHI have been determined, t 0.5 were 0.8, 0.6, 0.9, and 0.4 days while PHI values were 7, 5, 3, and 1 day for conventional and nano-formulations, respectively. In conclusion, the nano-formulations exhibit high efficacy in controlling T. urticae adults and have low residue in cucumber fruits. These results cleared that the nanoformulations reduced the concentrations, the residues and increased the efficiency.

Multiple target detection using photonic radar for autonomous vehicles under atmospheric rain conditions

Photonic radar systems offer a promising solution for high-precision sensing in various applications, particularly in autonomous vehicles, where reliable detection of obstacles in real-time is critical for safety. However, environmental conditions such as atmospheric turbulence and rain attenuation significantly impact radar performance, potentially compromising detection accuracy. This study aims to assess the performance of a photonic radar system under different environmental scenarios, including free-space, Gamma-Gamma atmospheric turbulence, and light and heavy rain conditions, with a focus on detecting three distinct targets positioned at various distances. Our simulations demonstrate that Gamma-Gamma atmospheric turbulence introduces variability in the received signal, with fluctuations becoming more pronounced at greater distances. Additionally, rain attenuation was found to substantially degrade performance, with heavy rain causing up to a 1 dBm reduction in received power at 50 meters and nearly a 1.5 dBm reduction at 100 meters, compared to light rain. For three targets located at 50m, 100m, and 150m, the combined effects of rain and turbulence were particularly noticeable at longer distances, with the received power under heavy rain dropping to −100.4 dBm at 150 meters. These findings indicate the importance of accounting for environmental conditions in the design of photonic radar systems, especially for autonomous vehicle applications. Future improvements could focus on developing adaptive radar techniques to compensate for adverse weather effects, ensuring robust and reliable performance under varying operational conditions. The novelty of this study lies in the integration of photonic radar technology with an advanced modeling framework that accounts for both free-space propagation and adverse weather conditions. Unlike conventional radar studies, our work incorporates Gamma-Gamma turbulence modeling and rain attenuation effects to provide a more comprehensive analysis of radar performance in real-world environments. This study also proposes an optimized detection strategy for multiple targets at varying distances, demonstrating the potential of photonic radar for autonomous vehicle applications.