The Effects of TRX-Based Movement Pattern Correction Exercises on the Landing Biomechanics and Balance of Female Volleyball Players Susceptible to Knee Injuries by Mohammad Kalantariyan, Niloufar Rahmani, Hadi Samadi

“…Objective Movement pattern correction exercises using TRX are a novel approach that has recently attracted the attention of researchers. Therefore, the purpose of this research is to investigate the effect of movement pattern correction exercises using TRX on the landing biomechanics and balance of female volleyball players prone to knee injuries. …”
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Validation of the MCNP model of formation of the background wires current of the self-powered neutron detectors of VVER-1000 by V. I. Borysenko, V. V. Goranchuk

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Development, Validity, and Reliability of Adolescent Motivation Inventory-Cerebral Palsy by Moslem Dehghanizadeh, Malahat Akbarfahimi, Armin Zareiyan, Farzaneh Yazdani, Mitra Khalafbeigi

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Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…Abstract Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. …”
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Applying the FAIR Principles to Open Educational Resources: A Semantic Similarity Approach to Improve Resource Discovery by Farzaneh Sadeghi, Auriol Degbelo, Carsten Kessler, Reza Zolnouri

“…Open educational resources (OER) are teaching, learning, or research resources freely available for use and reuse. …”
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Assessment of noise pollution in region 16 of Tehran by Fatema Rahimi, Abolghasem Sadeghi Niaraki, Mostafa Ghodousi

“…Measuring sound pollution is an essential step to control and manage its effects. Therefore in this research, zoning and modeling of noise pollution was carried out in one of the densely populated areas of Tehran, region 16. …”
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Improving time upscaling of instantaneous evapotranspiration based on machine learning models by Danni Yang, Shanshan Yang, Jiaojiao Huang, Shuyu Zhang, Sha Zhang, Jiahua Zhang, Yun Bai

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A Practical Sensor-to-Segment Calibration Method for Upper Limb Inertial Motion Capture in a Clinical Setting by Mhairi McInnes, Dimitra Blana, Andrew Starkey, Edward K. Chadwick

“…Inertial sensors have the potential to be a useful clinical tool because they can facilitate human motion capture outside the research setting. A major barrier to the widespread application of inertial motion capture is the lack of accepted calibration methods for ensuring accuracy, in particular the lack of a common convention for calculating the rotational offset of the sensors, known as sensor-to-segment calibration. …”
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CFD based neural network model framework for mixed convection in a lid-driven cavity with conductive cylinder by Md Muhtasim Fardin, Md Sadman Hossain, Mohammad Nasim Hasan

“…It is also revealed that substantial improvements in computational efficiency are achieved by the ANN model as it has reduced 82 % of computation time and 83 % of storage requirements to predict the results by maintaining mean absolute error below 0.1 %. The study provides an insight that ANN modelling could open a new dimension to the heat transfer research field and significantly reduce the requirement of time and resources to solve complex problems.…”
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A flexible and lightweight signcryption scheme for underwater wireless sensor networks by Sabir Shah, Nadeem Sarwar, Abdu Salam, Farhan Amin, Faizan Ullah, Asfandyar Khan, Isabel de la Torre, Mónica Gracia Villar, Helena Garay

“…Abstract Underwater wireless sensor networks (UWSNs) are a new research area gaining popularity. It has several key applications for instance; marine monitoring, surveillance, environmental sensing, etc. …”
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Development and evaluation of rapid, national-scale outdoor air pollution modelling and exposure assessment: Hybrid Air Dispersion Exposure System (HADES) by Calvin Jephcote, John Gulliver

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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

“…The goal of the research is to introduce a method for differentiating children with ADHD from those without the disorder by analyzing their EEG signals while they engage in a cognitive task. …”
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Influence of hydroacoustic noise from vessel on echo integration estimations of fish stocks and catchability of accounting trawl (on example of walleye pollock in the Okhotsk Sea) by Michail Yu. Kuznetsov, Vladimir I. Poljanichko, Igor A. Ubarchuk, Eugeny V. Syrovatkin

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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract In this research, advanced regression techniques are investigated for modeling intricate release patterns utilizing a high-dimensional dataset comprising more than 1500 spectrum-based variables and categorical inputs. …”
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A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection by Mondher Bouazizi, Kevin Feghoul, Shengze Wang, Yue Yin, Tomoaki Ohtsuki

“…Our proposed method reaches a 79.25% F1-score in AU detection for the main AUs, and 0.66 in AU intensity estimation Root Mean Square Error (RMSE). This performance shows that it is possible for researchers to share 3D landmarks, which are far less intrusive, instead of facial images while maintaining high accuracy in AU detection. …”
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Scaling up uncertain predictions to higher levels of organisation tends to underestimate change by James A. Orr, Jeremy J. Piggott, Andrew L. Jackson, Jean‐François Arnoldi

“…As an application we discuss the consequences of our theory for multiple‐stressor research. This empirical field focuses on interactions between stressors, defined as the error made by a prediction based on their observed individual effects. …”
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IELTSoC: enhanced image encryption using combined logistic and Tinkerbell maps with second order cellular automata for internet of things by Biswarup Yogi, Satyabrata Roy, Ajoy Kumar Khan, Umashankar Rawat, Mahesh Jangid, Pronaya Bhattacharya

“…The proposed scheme achieved an improvement of 10.87% in “Mean Squared Error" (MSE) over the existing techniques. Furthermore, it achieved a “Unified Average Changing Intensity" (UACI) value of 49.8788 in differential analysis. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…The ability to infer these variables through a singular measurable soil property, soil resistivity, can potentially improve sub-surface characterization. This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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Automated craniofacial biometry with 3D T2w fetal MRI. by Jacqueline Matthew, Alena Uus, Alexia Egloff Collado, Aysha Luis, Sophie Arulkumaran, Abi Fukami-Gartner, Vanessa Kyriakopoulou, Daniel Cromb, Robert Wright, Kathleen Colford, Maria Deprez, Jana Hutter, Jonathan O'Muircheartaigh, Christina Malamateniou, Reza Razavi, Lisa Story, Joseph V Hajnal, Mary A Rutherford

“…Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research.<h4>Conclusion</h4>This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. …”
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