Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Twenty-eight effective features were preferred, based on which the SVR model had <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula>, <i>RMSEP</i> and <i>RPD</i> scores of 0.9533, 0.4186, and 4.4186, respectively; the pH prediction performance was substantially improved. This study provides technical support for the scientific evaluation of silage quality.…”
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Pentraxin 3 Is a Predictor for Fibrosis and Arterial Stiffness in Patients with Nonalcoholic Fatty Liver Disease by Kadir Ozturk, Omer Kurt, Tolga Dogan, Alptug Ozen, Hakan Demirci, Fatih Yesildal, Murat Kantarcioglu, Turker Turker, Ahmet Kerem Guler, Yıldırım Karslioglu, Battal Altun, Ahmet Uygun, Sait Bagci

“…The aim of the present study was to investigate whether pentraxin 3 (PTX3) can be a new noninvasive marker for prediction of liver fibrosis in patients with NAFLD. …”
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Developing non-invasive molecular markers for early risk assessment of Alzheimer's disease by Tapas K. Sur, Tanmoy Mondal, Zarish Noreen, Jheannelle Johnson, Gail Nunlee-Bland, Christopher A. Loffredo, Brent E. Korba, Vijay Chandra, Siddhartha S. Jana, Bernard Kwabi-Addo, Sumit Sarkar, Somiranjan Ghosh

“…Emerging literature supports the importance of new molecular biomarkers and signature genes from blood to detect and predict AD in advance. This review examines the potential applications of these blood-based target biomarkers for early disease detection, co-morbid condition risk prediction, and treatment management of AD.…”
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Diagnostic accuracy of drooling, reluctance, oropharynx, others, and leukocytosis score as a predictor of mortality and complications following acute corrosive ingestion by Fawaz Poonthottathil, Soorya Suresh, Jamshed Nayer, Praveen Aggarwal

“…The AUROC of DROOL score in predicting overall complications was 0.932 (95% CI: 0.877–0.987) and the AUROC of DROOL score in predicting mortality was 0.864 (95% CI: 0.758–0.970). …”
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A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH) by Sogol Attaripour Esfahani, Nima Baba Ali, Juan M. Farina, Isabel G. Scalia, Milagros Pereyra, Mohammed Tiseer Abbas, Niloofar Javadi, Nadera N. Bismee, Fatmaelzahraa E. Abdelfattah, Kamal Awad, Omar H. Ibrahim, Hesham Sheashaa, Timothy Barry, Robert L. Scott, Chadi Ayoub, Reza Arsanjani

“…Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). <i>Results:</i> This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. …”
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Impact of introducing electric vehicles on ground-level O₃ and PM_2.5 in the Greater Tokyo Area: yearly trends and the importance of changes in the urban heat... by H. Hata, N. Mizushima, T. Ihara

“…We used a numerical weather prediction to estimate changes in the UHI effect in the Greater Tokyo Area (GTA) of Japan by introducing BEVs. …”
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Thinking on the Meteorological Services to Ensure the Development of a System for New Energy Sources by Yongchang ZHU, Xiaofeng ZHANG, Zhenghong CHEN, Dong YE, Juan TAN, Yong QIN, Zhaohui LIU, Zhiqi XU

“…The construction of a system for new energy sources puts forward new requirements for meteorological service, such as optimizing the overall layout of meteorological service support for the construction of clean energy bases, strengthening the accuracy of power generation forecast and climate prediction services, and carrying out meteorological service support for various scenarios such as the construction of new energy infrastructure, power generation, storage, transmission and consumption. …”
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Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review by Taseef Hasan Farook, Nafij Bin Jamayet, Johari Yap Abdullah, Mohammad Khursheed Alam

“…A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. …”
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Clinicopathological risk factors of oral second primary tumours by Jelena Karan, Miriam P. Rosin, Lewei Zhang, Denise M. Laronde

“…Methods: Patients with oral squamous cell carcinoma, carcinoma in situ, or severe dysplasia were accrued into the Oral Cancer Prediction Longitudinal study within one year post-curative treatment. …”
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Systematic review of the application of the Kidney Failure Risk Equation and Oxford classification in estimating prognosis in IgA Nephropathy by M. P. Toal, R. Fergie, M. P. Quinn, C. J. Hill, C. O’Neill, A. P. Maxwell

“…Both the KFRE and the IgAN Risk Prediction (IRP) tool (incorporating the OC) were highly accurate at predicting ESRD with a C-statistic of 0.90 and 0.91, respectively. …”
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Model-assisted analysis on the response of tomato fruit growth to source-sink ratio regulated by water and nitrogen by Huiping Zhou, Jinliang Chen, Shaozhong Kang

“…Compared to the original model simulation, MAE and RRMSE of fruit growth prediction decreased by up to 82.0 % and 79.9 %. The simulation efficiency of fruit fresh and dry weights was in the range of 77.4 %-96.4 % and 72.9 %-92.9 % by the modified model, respectively. …”
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Comparative analysis of hull steel corrosion in tropical marine atmosphere: Correlation between indoor simulations and outdoor exposures by Feng Gao, Caifu Yang, Weiyi Wang, Yajun Pang, Naipeng Zhou, Jian Li, Xiaobing Luo, Feng Chai

“…As an efficient alternative, laboratory-based accelerated corrosion tests provide a means to predict the corrosion behavior and service life of materials under long-term outdoor exposure. …”
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Assessment of RANS Models for Milli-Channel Turbulent Flow in Drip Irrigation Emitter by Qi Feng, Qingzheng Li, Yaojun Li, Xuefeng Qiu, Jiandong Wang, Xingfa Huang

“…Although both RNG and RKE using SWF are viable for hydraulic performance prediction under high-flow rate conditions, the deviation of predicted flow rate reaches up to 25.46% under low-flow rate conditions. …”
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The Role of Coal Mechanical Characteristics on Reservoir Permeability Evolution and Its Effects on CO2 Sequestration and Enhanced Coalbed Methane Recovery by Hao Han, Shun Liang, Yaowu Liang, Xuehai Fu, Junqiang Kang, Liqiang Yu, Chuanjin Tang

“…The coupled thermo-hydro-mechanical (THM) model is established and then validated by primary production data, as well as being applied in the prediction of CO2/N2-ECBM recovery. The results show that the harder coal seam is beneficial to primary production, while the softer coal seam results in greater CO2/N2-ECBM recovery and CO2 sequestration. …”
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A new approach for evaluating maize transgressive segregants and their three-way cross potential in the S4 convergent breeding population by Nuniek Widiayani, Muhammad Fuad Anshori, Nasaruddin Nasaruddin, Muh Farid, Ifayanti Ridwan, Abd. Haris Bahrun, Muhammad Azrai, Amin Nur, Purnama Isti Khaerani, Willy Bayuardi Suwarno, Karlina Syahruddin, Naeem Khan, Majed A. Alotaibi, Mahmoud F. Seleiman

“…Based on the resulting study, the combination of ratio analysis, path analysis, best linear unbiased prediction, relative fitness, and selection indices is an objective approach to assessing the genetic potential of the S4 TS. …”
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Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu, Yao Chen

“…To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). …”
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Correlation between Cortical Excitability and Hand Function in Patients with Subacute Cerebral Infarction by Ling CHEN, Junling WANG, Dongfeng HUANG

“…Objective:To observe the cortical activation characteristics of cerebral motor prediction in patients with subacute cerebral infarction in the palm grasping task, and to explore the correlation between cortical excitability and hand function in patients with cerebral infarction.Methods:A total of twenty-five patients with subacute cerebral infarction who were hospitalized in the first affiliated hospital of Sun Yat-sen University from January 2017 to March 2018 were selected as the experimental group, and twenty-five healthy individuals matched with the gender and age of the experimental group were selected as the control group.Event related potentials were used to collect the electroencephalogram signals of the two groups in the palm grasping task, and then analyzed the latencies and amplitudes of the contingent negative variation(CNV)in the cerebral cortex before motor activation.Action Research Arm Test(ARAT)was used to assess the function of hand, and the correlation between CNV amplitude and ARAT score was analyzed.Results:Compared with the control group, the CNV latencies in the midline region of the experimental group were significantly shorter during the left hand grasp and right hand grasp, and the CNV amplitude were statistically significantly increased in the midline, bilateral frontal lobe and primary motor area(M1)(<italic>P</italic>&lt;0.05).There was no significant difference in CNV latency between left hand grasp and right hand grasp in the experimental group(<italic>P</italic>&gt;0.05), with the CNV amplitude of Cz electrode in midline statistically significantly greater than that of C4 electrode in contralateral M1(<italic>P</italic>&lt;0.05).There was moderate correlation between the CNV amplitude and ARAT in the non-lesioned frontal lobe, M1 and midline during right hand grasp in the experimental group(F4: <italic>r</italic>=0.510; C4: <italic>r</italic>=0.442; Fz: <italic>r</italic>=0.428).There was moderate correlation between the CNV amplitude and ARAT in the non-lesioned frontal lobe, M1 and midline during left hand grasp in the experimental group(F4: <italic>r</italic>=0.422; C4: <italic>r</italic>=0.496; Fz: <italic>r</italic>=0.446).Conclusion:The neural mechanism of motor anticipation in patients with subacute cerebral infarction is abnormal, the time of brain motor preparation is prolonged, and the non-lesioned frontal lobe and M1 are hyperactivity compensatory during motor anticipation.There was moderate correlation between the cortical excitability and hand dysfunction.Therefore, CNV may be a neural indicator of the severity of hand dysfunction in patients with cerebral infarction.…”
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Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease by David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Daniel Kling, Maria Lerm, Mika Gustafsson

“…We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn’s disease.DiscussionIn this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. …”
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Late-onset first epileptic seizure and cerebral small vessel disease: role of juxtacortical white matter lesions by Adrian Nasca, Adrian Nasca, Leo Sokolovič, Leo Sokolovič, Oliver Koprda, Patrick Haage, Patrick Haage, Thorsten Schmidt, Juraj Kukolja, Juraj Kukolja

“…WML segmentation was performed on FLAIR images using the SPM based automated lesion prediction algorithm of the LST toolbox and careful manual adjustment. …”
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