Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China by Bingwei Wang, Qigen Lin, Tong Jiang, Huaxiang Yin, Jian Zhou, Jinhao Sun, Dongfang Wang, Ran Dai

“…Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. …”
Get full text

Comparative efficacy of incretin drugs on glycemic control, body weight, and blood pressure in adults with overweight or obesity and with/without type 2 diabetes: a systematic revi... by Song Liu, Jiaqiang Hu, Chen Zhao, Hang Liu, Chunyang He

“…Risk of bias was assessed using the version 2 of the Cochrane risk-of-bias tool (ROB2), and a random-effects network meta-analysis was performed using the frequentist approach. …”
Get full text

Interventions for the management of post-COVID-19 condition (long COVID): protocol for a living systematic review and network meta-analysis by Paul Garner, Lawrence Mbuagbaw, Jason W Busse, Dena Zeraatkar, Thomas Agoritsas, Ariel Izcovich, Tari Turner, Lyn Turkstra, Roger S McIntyre, Rachel J Couban, Tyler Pitre, Sarah Kirsh, Tanvir Jassal, Michael Ling, Samantha Chakraborty, Signe A Flottorp

“…Our outcomes of interest will include patient-reported fatigue, pain, postexertional malaise, changes in education or employment status, cognitive function, mental health, dyspnoea, quality of life, physical function, recovery and serious adverse events.For each outcome, when possible, we will perform a frequentist random-effects network meta-analysis. When there are compelling reasons to suspect that certain interventions are only applicable or effective for a subtype of long COVID, we will perform separate network meta-analyses. …”
Get full text

Clinical Observation of Auricular Acupressure Combined with Transcranial Direct Current Stimulation for Nicotine Dependence by KUANG Hongjun, FU Shanshan, HUANG Qian, YANG Layuan, YUAN Yangyang, YUAN Nan, LIU Minquan, TANG Jian, FANG Chuang, ZHONG Feng

“…ObjectiveTo observe the clinical effect of auricular acupressure combined with transcranial direct current stimulation (tDCS) for nicotine dependence.MethodsA total of 90 patients with nicotine dependence were divided into three groups (combination group, tDCS group and auricular acupressure group), using the method of random number table, with 30 cases in each group. …”
Get full text

Advanced Fractional Mathematics, Fractional Calculus, Algorithms and Artificial Intelligence with Applications in Complex Chaotic Systems by Dumitru Baleanu, Yeliz Karaca

“…As a long-term aperiodic and random-like behavior manifested by many nonlinear complex dynamic systems, chaos induces that the system itself is inherently unstable and disordered, which requires the revealing of representative and accessible paths towards affluence of complexity and experimental processes so that novelty, diversity and robustness can be generated. …”
Get full text

Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
Get full text

The effect of gut microbiome-targeted therapies in nonalcoholic fatty liver disease: a systematic review and network meta-analysis by Yijia Song, Yijia Song, Yijia Song, Sutong Liu, Lihui Zhang, Lihui Zhang, Wenxia Zhao, Yuanmei Qin, Yuanmei Qin, Minghao Liu

“…Inconsistency test and publication-bias were assessed by Stata 14.0. Random effect model was used to assemble direct and indirect evidences. …”
Get full text

System-Technology Co-Optimization for Dense Edge Architectures Using 3-D Integration and Nonvolatile Memory by Leandro M. Giacomini Rocha, Mohamed Naeim, Guilherme Paim, Moritz Brunion, Priya Venugopal, Dragomir Milojevic, James Myers, Mustafa Badaroglu, Marian Verhelst, Julien Ryckaert, Dwaipayan Biswas

“…We observe that the 3-D system integration of static random-access memory (SRAM)-based design leads to 9% power savings with 53% footprint reduction at iso-frequency with respect to 2-D implementation for the same memory capacity. …”
Get full text

Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). …”
Get full text

Effect of Tonifying deficiency and Zhengqi porridge on immune function reconstruction in HIV/AIDS patients with Qi deficiency (补虚正气粥对艾滋病气虚型患者免疫功能重建的影响研究)... by WEI Yan (魏艳), LI shu (李树), YUAN Hongxia (袁洪霞), ZHANG Ying (张英), LIU Xiaoli (刘小丽), LIU Lingling (刘玲玲)

“…Methods Sixty (Human Immunodeficiency Virus/ Acquired Immunodeficiency Syndrome)HIV/AIDS patients admitted to the center for Disease Control and prevention in Huichuan District of Zunyi City were randomly divided into control group and treatment group by random number table. …”
Get full text

Comparative Analysis of Carbon Density Simulation Methods in Grassland Ecosystems: A Case Study from Gansu Province, China by Luyao Wu, Jiaqiang Du, Xinying Liu, Lijuan Li, Xiaoqian Zhu, Xiya Chen, Yue Gong, Yushuo Li

“…Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). …”
Get full text

Effect of Acupotomy Intervention on Fibrosis of Rectus Femoris in Rabbits with Knee Osteoarthritis by LIU Jing, ZENG Weiquan, LIN Qiaoxuan, LU Liming, GUO Zexing, LIU Hong, ZHANG Liangzhi, XIU Zhongbiao

“…Objective: To observe the effects of acupotomy intervention on the fibrosis of the rectus femoris in rabbits with knee osteoarthritis from the histological and molecular level, and to reveal the possible mechanism of the needle knife in the treatment of knee osteoarthritis.MethodsTwenty-four healthy male New Zealand rabbits were randomly divided into blank group, model group and acupotomy group used by random number table method, with 8 rabbits in each group. …”
Get full text

Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…A two-layer ensemble model is built to quantitatively estimate leaf nitrogen using the stacking ensemble learning (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. …”
Get full text

Impacts of plant root traits and microbial functional attributes on soil respiration components in the desert-oasis ecotone by Jinlong Wang, Jinlong Wang, Jinlong Wang, Guanghui Lv, Guanghui Lv, Guanghui Lv, Jianjun Yang, Jianjun Yang, Jianjun Yang, Xuemin He, Xuemin He, Xuemin He, Hengfang Wang, Hengfang Wang, Hengfang Wang, Wenjing Li, Wenjing Li, Wenjing Li

“…Concomitantly, the Ra and Rh components exhibited a similar trend throughout the study period, with Rh emerging as the dominant driver of Rs. Utilizing random forest modeling, we unearthed significant associations between microbial taxonomic and functional features and Rs components. …”
Get full text

Clinical Efficacy of Repetitive Transcranial Magnetic Stimulation Combined with Cognitive Training on Patients with Post Stroke Cognitive Impairment： A Meta-Analysis by LIU Changxing, GUO Xinyi, CHANG Xiang

“…ObjectiveTo evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training on patients with post stroke cognitive impairment (PSCI) by Meta-analysis.MethodsData were searched and retrieved from the databases of PubMed, Embase, The Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data, and Chinese Science and Technology Periodical Database (VIP). The randomized controlled trials (RCTs) of rTMS combined with cognitive training for the treatment of patients with PSCI were included, and the retrieval time was from database inception to June 2023. …”
Get full text

Partially Isolated Dual Work Function Gate IGZO TFT With Obviously Reduced Leakage Current for 3D DRAMs by Yunjiao Bao, Gangping Yan, Lei Cao, Chuqiao Niu, Qingkun Li, Guanqiao Sang, Lianlian Li, Yanzhao Wei, Xuexiang Zhang, Jie Luo, Yanyu Yang, Gaobo Xu, Huaxiang Yin

“…Moreover, the pronounced novel structure has mitigated parasitic capacitance, thereby leading to a notable 47.7% reduction in write latency within dynamic-random-access-memory (DRAM) circuits. The relevant operation mechanism is carefully demonstrated and verified by the simulation of the electric field and potential barrier results by technical computer-aided design (TCAD). …”
Get full text

Prevalence of post-acute sequelae of SARS-CoV-2 infection in people living with HIV: a systematic review with meta-analysisResearch in context by Dimitra V. Pouliopoulou, Nicole Billias, Joy C. MacDermid, Erin Miller, Kelly K. O'Brien, Kieran L. Quinn, Monali S. Malvankar-Mehta, Tiago V. Pereira, Angela M. Cheung, Fahad Razak, Saverio Stranges, Pavlos Bobos

“…We performed meta-analysis of proportions to synthesise prevalence estimates using logit transformation and a sensitivity analysis using mixed-effects logistic regression. We used random-effects meta-analyses to summarize the odds ratio (OR) of developing Long COVID in adults living with HIV compared to adults living without HIV and conducted a sensitivity analysis including only studies with covariate-adjusted estimates that was planned a-priori. …”
Get full text

The Antiobesity Effect of GLP-1 Receptor Agonists Alone or in Combination with Metformin in Overweight /Obese Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Ana... by Xiaorui Lyu, Taibiao Lyu, Xue Wang, Huijuan Zhu, Hui Pan, Linjie Wang, Hongbo Yang, Fengying Gong

“…The random-effect model was used to pool data extracted from the included literature. …”
Get full text

Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes by Kian Draper, Ben Li, Muzammil Syed, Farah Shaikh, Abdelrahman Zamzam, Batool Jamal Abuhalimeh, Kharram Rasheed, Houssam K. Younes, Rawand Abdin, Mohammad Qadura

“…Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. …”
Get full text

Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning by F. Bortolussi, H. Sandström, F. Partovi, F. Partovi, J. Mikkilä, P. Rinke, P. Rinke, P. Rinke, P. Rinke, M. Rissanen, M. Rissanen

“…We then trained two machine learning methods on these data: (1) random forest (RF) for classifying if a pesticide can be detected with CIMS and (2) kernel ridge regression (KRR) for predicting the expected CIMS signals. …”
Get full text