Wastewater metagenomics in Africa: Opportunities and challenges. by Stephen Kanyerezi, Fatma Zahra Guerfali, Abbas Abel Anzaku, Oluwasegun Adesina Babaleye, Tracey Calvert-Joshua, Julien Alban Nguinkal, Oluwaseun Paul Amoo, Chiraz Atri, Waqasuddin Khan, Iqra Saleh, M Imran Nisar, Arthur Shem Kasambula, Koketso Morapedi, Gerald Mboowa

“…The advent of metagenomics has dramatically expanded our understanding of microbial communities, particularly through the study of wastewater, which serves as a rich source of microbial data. In Africa, wastewater metagenomics presents unparalleled opportunities for public health monitoring, antimicrobial resistance (AMR) tracking, and the discovery of new microbial species and functions. …”
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KANDiff: Kolmogorov–Arnold Network and Diffusion Model-Based Network for Hyperspectral and Multispectral Image Fusion by Wei Li, Lu Li, Man Peng, Ran Tao

“…This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration.…”
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Provenance-Finding of Dust Storms Using AVHRR Sensor Images of NOAA Satellite (Case Study: Southwest of Iran) by Mohammad Hossein Rezaei Moghaddam, Mojtaba Mahdian Boruojeni

“…Dust pollution of this area drawn to the south of Khuzestan. 4- Conclusion In general, using 24 AVHRR images and the use of detection techniques, a total 62 points were identified as the source of dust production. …”
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Assembly and comparative analysis of the complete mitochondrial of Spodiopogon sagittifolius, an endemic and protective species from Yunnan, China by Chao Xu, Wei Bi, Ren-yi Ma, Pin-rong Li, Feng Liu, Zhen-wen Liu

“…Synteny and dN/dS analyses revealed structural and functional conservation of mitochondrial genomes in closely related species, with most protein-coding genes under purifying selection (dN/dS < 1). …”
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Metric learning unveiling disparities: A novel approach to recognize false trigger images in wildlife monitoring by Rui Zhu, Enting Zhao, Chunhe Hu, Jiangjian Xie, Junguo Zhang, Huijian Hu

“…By integrating K-means clustering for sample selection and a triplet loss function for model optimization, the approach effectively distinguishes subtle feature differences in false-triggered images. …”
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Complete sequencing of the mitochondrial genome of tea plant Camellia sinensis cv. ‘Baihaozao’: multichromosomal structure, phylogenetic relationships, and adaptive evolutionary an... by Zhiyin Chen, Zhiyin Chen, Zhiyin Chen, Zhiyin Chen, Zixu Wang, Zixu Wang, Zixu Wang, Zixu Wang, Wei Zhou, Wei Zhou, Wei Zhou, Wei Zhou, Sijie Liu, Sijie Liu, Sijie Liu, Sijie Liu, Yuxin Xiao, Yuxin Xiao, Yuxin Xiao, Yuxin Xiao, Yihui Gong, Yihui Gong, Yihui Gong, Yihui Gong

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Trust and AI weight: human-AI collaboration in organizational management decision-making by Yanjun Wen, Jiale Wang, Xiaoxi Chen

“…Study 2 surveyed 210 managers using employee performance evaluation tasks.ResultsThe results of Study 1 indicated that trust in AI enhances the decisional weight attributed to AI agents, and willingness to collaborate with AI mediates trust in AI and the weight of AI in personnel selection. The findings of Study 2 revealed that the perceived free will of AI agents negatively moderates the relationship between trust in AI and willing to collaborate with AI, such that the relationship is weaker when individuals perceive a higher degree of free will in AI agents than a lower degree.DiscussionTheoretically, this paper advances the understanding of the function of trust in human-AI interaction by exploring the trust development from attitude to act in human-AI cooperative decision-making. …”
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Utilising machine learning classification models for meteorological drought monitoring and analysis by Iqra Mumtaz, Rizwan Niaz, Zamama Sajid, Abdu Qaid Alameri, Zulfiqar Ali, Khaled A. Gepreel

“…These findings highlight the superior performance of SVM in drought prediction and emphasise the importance of model selection and data preprocessing in enhancing drought monitoring for improved water resource management and climate adaptation strategies.…”
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Physarum-inspired multi-parameter adaptive routing protocol for coal mine hybrid wireless mesh networks by Guangzhi Han, Haifeng Jiang, Liansheng Lu, Shanshan Ma, Shuo Xiao

“…Mesh clients with application service and routing function in hybrid wireless mesh networks can form a highly robust hybrid network with mesh routers. …”
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Analysis of variant interactions in families with autism points to genes involved in the development of the central nervous system. by Jacek Kaczyński, Marta Pasenkiewicz-Gierula

“…Analysis of the statistical interactions also points to genes whose biological functions are not yet known.…”
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Effects of Soil Texture on Effective Mixing Depth and Solute Loss by WANG Jie, ZHENG Hao, LI Zihan, REN Shaocong, WANG Shuoguo, LIAN Qijie, WEI Yujie, CAI Chongfa

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Making Blind Robots See: The Synergy Between Fluorescent Dyes and Imaging Devices in Automated Proteomics by Wayne F. Patton

“…Since rare proteins cannot be amplified by any type of PCR method, sensitive detection is critical to proteome projects. Fluorescence methods deliver streamlined detection protocols, superior detection sensitivity, broad linear dynamic range and excellent compatibility with modern microchemical identification methods such as mass spectrometry. …”
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Effects of Baduanjin exercise on cognitive impairment in older adults: a systematic review and meta-analysis by Xiao-Gang Gong, Le-Peng Wang, Ling-Ling Yang, Feng Liu, Dao-Ning Zhang, A-Yuan Zhang, A-Yuan Zhang

“…Seven RCTs involving 539 community-dwelling older adults met the inclusion criteria.ResultsBaduanjin exercise significantly improved global cognitive function [mean difference (MD) = 2.15; 95% CI, 1.53 to 2.76; p &lt; 0.00001], memory (standardized mean difference = 0.59; 95% CI, 0.38 to 0.80; p &lt; 0.00001), executive function (SMD = 0.26; 95% CI, 0.07 to 0.44; p = 0.007), and physical health (MD = −0.86; 95% CI, −0.26 to −0.46; p &lt; 0.00001). …”
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METABOLITE PROFILING OF MORINGA USING PY-GCMS AND TOLERANCE EVALUATION TO ALUMINUM ON IN VITRO CULTURE by Rudiyanto, Agus Purwito Purwito, Darda Efendi, Andri Martin

“…ABSTRACT Moringa oleifera Linn. has received substantial scientific interest due to its numerous bioactive compounds and its function as a nutritional resource. The absorption of aluminum by plants hinders several metabolic and physiological processes, leading to inhibited plant development and decreased agricultural output. …”
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Comparison of Language Models in Skills Extraction From Vacancies and Resumes by Lyubov Komarova, Vladimir Soloviev, Alexey Kolosov

“…The result of the study is the selection of the best model and the derivation of functions from the summary text, as well as a comparison of the two sets of functions with each other. …”
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Gallium oxide (Ga2O3) as a radiation detector distinguishing neutrons and gammas by D. J. Valdes, L. Rendon, J. Winkelbauer, P. Koehler, S. C. Vogel, K.-X. Sun

“…Gallium oxide (Ga2O3) is a promising ultrawide bandgap semiconductor for radiation detection with the potential of integrating electronic and scintillation functions within a single crystal device. …”
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Bearing Small Sample Fault Diagnosis based on Data Generation and Transfer Learning by Dinghui Wu, Qin Fang, Chuyi Wu

“…First， the source dataset is trained on the training network to obtain the source model， and then a small amount of drive-end data is used as the target dataset to fine-tuning to obtain the target model. …”
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Comparatively profiling the transcriptome of human, Porcine and mouse oocytes undergoing meiotic maturation by Naru Zhou, Xin Wang, Yi Xia, Zongliang Liu, Lei Luo, Rentao Jin, Xianhong Tong, Zhenhu Shi, Zhichao Wang, Heming Sui, Yangyang Ma, Yunsheng Li, Zubing Cao, Yunhai Zhang

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Gene-Environment Interactions in Stress Response Contribute Additively to a Genotype-Environment Interaction. by Takeshi Matsui, Ian M Ehrenreich

“…To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C ('E37'), a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose) and temperature (37°C as opposed to 30°C). …”
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Entity Model Test and Analysis of Local Scour of Three Different Structures of Artificial Reefs by Shishi Zhu, Yongqi Yao, Zheng Li, Shuo Zhang, Shike Gao

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