Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing by Po-Tong Wang, Chiu Wang Tseng, Li-Der Fang

“…This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>E</mi><mn>00</mn></msub></mrow></semantics></math></inline-formula>): 0.70 ± 0.08, <i>p</i> < 0.001), advanced attention mechanisms improving feature extraction efficiency by 58.3%, and a Bayesian optimization framework ensuring robust parameter tuning. …”
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Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare by D. Rubidha Devi, S. K. UmaMaheswaran, Sandhya Tarar, Abu Sarwar Zamani, Durgaprasad Gangodkar, A. Prabhu Chakkaravarthy, IssahAbubakari Samori

“…The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. …”
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A CNN-LSTM-Based Model to Forecast Stock Prices by Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang

“…And then, we adopt LSTM to predict the stock price with the extracted feature data. …”
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Superconductivity of electron-doped chalcohydrides under high pressure by Yu Du, Zefang Wang, Hanyu Liu, Guoji Liu, Xin Zhong

“…Here, we introduce Li into binary chalcohydrides and investigate ternary Li-M-H (M = S, Se, and Te) compounds using the state-of-the-art structure prediction method in conjunction with first-principles calculations. …”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. …”
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Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks by Eun‐Young Ji, Yong‐Jae Moon, Eunsu Park

“…These results show that our model can improve the global TEC prediction ability of the IRI‐2016. Our study suggests a sufficient possibility to generate DeepIRI global TEC maps in near real time if IRI is generated in time. …”
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Experimental Investigation on the Load-Carrying Capacity of Steel-to-Laminated Bamboo Dowel Connection I: Single Fastener with Slotted-In Steel Plate under Tension by Zhaoyan Cui, Liuhui Tu, Ming Xu, Zhongfan Chen, Qingfeng Xu

“…It presents a better prediction for the load-carrying capacity of steel-to-laminated bamboo dowel connections with slotted-in steel plate.…”
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Urinary bacteriophage cooperation with bacterial pathogens during human urinary tract infections supports lysogenic phage therapy by Mahmood Almosuli, Anna Kirtava, Archil Chkhotua, Lia Tsveniashvili, Nina Chanishvili, Sumaiya Safia Irfan, Emily Ng, Hope McIntyre, Adam J. Hockenberry, Robyn P. Araujo, Weidong Zhou, Ngoc Vuong, Barbara Birkaya, Lance Liotta, Alessandra Luchini

“…Using human urine, model organisms, mass spectrometry, gene expression analysis, and the phage phenotype prediction model BACPHLIP, the results corroborated our hypotheses at the functional protein and gene levels. …”
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Height of Mining-Induced Fractured Zones in Overlying Strata and Permeability of Rock with Nonpenetrative Fractures by Yu Liu, Qimeng Liu, Wenping Li, Youbiao Hu

“…The results of the study are helpful to the prediction of the potential loss of phreatic water and the determination of the mining thickness.…”
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Simulation of the Lower Head Boiling Water Reactor Vessel in a Severe Accident by Alejandro Nuñez-Carrera, Raúl Camargo-Camargo, Gilberto Espinosa-Paredes, Adrián López-García

“…Then, it is important to have a detailed model in order to predict the behavior of the reactor vessel lower head in a severe accident. …”
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From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR–FTIR and Machine Learning by Aubrey Barney, Václav Trojan, Radovan Hrib, Ashley Newland, Jan Halámek, Lenka Halámková

“…In this proof-of-concept study, ATR–FTIR was combined with machine learning methods, which are effective in detecting and differentiating fentanyl in samples, to explore whether nail samples are distinguishable from individuals who have used fentanyl and those who have not. PLS-DA and SVM-DA prediction models were created for this study and had an overall accuracy rate of 84.8% and 81.4%, respectively. …”
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Data-Driven Approach to Evaluate the Level of Service (LOS) of Demand-Responsive Transport for the Disabled (DRTD) with an ANFIS Algorithm by Seohyeon Park, Sooyeon Park, Hosik Choi, Do-Gyeong Kim

“…This study developed a model for predicting the waiting time of Demand-Responsive Transport for Disabled (DRTD) with irregular spatiotemporal characteristics in real time. …”
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Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings by Biplov Paneru, Bipul Thapa, Bishwash Paneru

“…Additionally, we develop a Flask-based application, demonstrating the practical applicability of our R-CNN model for real-time sentiment prediction.…”
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New approach to strongly coupled N $$ \mathcal{N} $$ = 4 SYM via integrability by Simon Ekhammar, Nikolay Gromov, Paul Ryan

“…We present a new analytic prediction for a coefficient in the strong coupling expansion of the conformal dimension for the lowest trajectory at a given twist L. …”
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A Perspective Review on Numerical Simulations of Hemodynamics in Aortic Dissection by Wan Naimah Wan Ab Naim, Poo Balan Ganesan, Zhonghua Sun, Kok Han Chee, Shahrul Amry Hashim, Einly Lim

“…Due to the limitations of cardiac imaging modalities, numerical simulations have been widely used for the prediction of disease progression and therapeutic outcomes, by providing detailed insights into the hemodynamics. …”
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Heterosis: current advances in the search for molecular mechanisms by M. N. Shapturenko, L. V. Khotyleva

“…With the advent of molecular markers great efforts were made to identify genomic regions causing heterotic response and clarify prospects of using information about molecular divergence of parental forms as a criterion for the prediction of F1 performance. Despite some achievements, the effec-tiveness of both molecular divergence and prospective heterotic QTL for practical goals was limited, confirming that genetic heterogeneity is necessary, but not sufficient to produce perfect phenotype. …”
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Comprehensive Study of E-Bike Braking Dynamics: Modeling, Simulation, and Experimental Validation by Mutasim Salman, Shivam Chaturvedi, Wencong Su

“…This model will be used for detection and isolation of braking system components faults as well as prediction of their failure. The model includes bike and passive rider dynamics, wheel dynamics, the mechanical brake linkages and the tire-road friction interaction including tire slip. …”
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Dynamic graph structure and spatio-temporal representations in wind power forecasting by Zang Peng, Dong Wenqi, Wang Jing, Fu Jianglong

“…However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. …”
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Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters by Jan Kleine Deters, Rasa Zalakeviciute, Mario Gonzalez, Yves Rybarczyk

“…The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.…”
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Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach by M. Benalla, B. Achchab, H. Hrimech

“…A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.…”
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