Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges by Ceray Aldemir, Tuğba Uçma Uysal

“…The study reveals that AI-driven solutions such as predictive analytics, fraud detection systems, and automated reporting significantly improve operational efficiency, transparency, and decision making. However, challenges such as algorithmic bias, data privacy issues, and the need for strong ethical guidelines still exist, and these could hinder the equitable use of AI. …”
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Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables by Feng Gao, Yage Xing, Jialong Li, Lin Guo, Yiye Sun, Wen Shi, Leiming Yuan

“…Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

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Explainable machine learning model and nomogram for predicting the efficacy of Traditional Chinese Medicine in treating Long COVID: a retrospective study by Jisheng Zhang, Yang Chen, Aijun Zhang, Yi Yang, Liqian Ma, Hangqi Meng, Jintao Wu, Kean Zhu, Jiangsong Zhang, Ke Lin, Xianming Lin

“…This study aims to develop an explainable machine learning (ML) model and nomogram to identify Long COVID patients who may benefit from TCM, enhancing clinical decision-making.MethodsWe analyzed data from 1,331 Long COVID patients treated with TCM between December 2022 and February 2024 at three hospitals in Zhejiang, China. …”
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Influence of the distortion type on the image quality assessment when reducing its sizes by D. G. Asatryan, M. E. Harutyunyan, Y. I. Golub, V. V. Starovoitov

“…It is shown that the average values of correlations for all images at three types of distortions are very high, while for the other two they are unacceptably low. …”
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Q-Band MMW Transmission Enabled by Joint Probabilistic Shaping and Precoding With MZM-Based OCS Modulation by Hengxin Yan, Jiaohao Bi, Dong Guo, Xinying Li, Xiaolong Pan, Hongwu Ge, Wei Wu, Chao Yu, Xiangjun Xin, Qi Zhang, Ran Gao, Haipeng Yao, Ze Dong, Zhipei Li, Huan Chang, Sitong Zhou

“…The proposed scheme exhibits a clear advantage in terms of nonlinear interference suppression and satisfies the hard-decision forward-error-correction (HD-FEC) threshold with a BER of 3.8 × 10^−3 in radio-over-fiber (RoF) systems.…”
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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

“…Resting data recorded during the UPDRS assessment were extracted and used to identify additional resting periods within the recordings through an automatic segmentation algorithm. At the end, for each of the selected arms, 90,000 data points were labeled based on the respective UPDRS 3.17 scores. …”
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A Simplified Gaussian Approach for Asphalt Crack Detection based on Deep Learning and RGB images by W. Darwish, W. Darwish, W. Ahmed, W. Ahmed

“…Cracks impact both the operational efficiency and safety of road pavements and significantly influence maintenance decisions. We propose a workflow to detect cracks using YOLOv9 deep learning algorithm combined with statistical analysis through principal component (PCA) and Gaussian distribution. …”
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Optimisation of Criminal Data Clustering Model using Information Gain by Prih Diantono Abda’u, Ratih Hafsarah Maharrani, Muhammad Nur Faiz, Oman Somantri

“…The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.…”
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Water Supply Scheduling for Cross-basin Reservoir Groups Based on Improved Scheduling Diagram by ZHAO Siqi, XU Wei, CHEN Si, WANG Sufan, YANG Yi

“…To exploit the scheduling potential of cross-basin reservoir groups by employing runoff information,this paper proposes an improved reservoir scheduling diagram to retain simplicity and intuitiveness.Taking the Xiajiankou,Fengyan,and Nanpeng reservoirs in Banan District of Chongqing as an example,it adopts the POA algorithm to build a joint scheduling model of water diversion and water supply for the cross-basin reservoir groups by a simulation-optimization approach.Meanwhile,three scenarios of the current situation scheduling,optimized scheduling with conventional scheduling diagrams,and optimized scheduling with improved scheduling diagrams are set up to evaluate the water diversion and supply performance of reservoirs and analyze the influence of uncertainty runoff information on the scheduling performance.The results are as follows:① The improved scheduling diagram performs best in the long series scheduling,and it can increase the annual average water supply of the reservoir group by 6.87% and reduce the abandoned water by 87.58% compared with the current situation scheduling;② In a dry year,the method can reduce 486.3×10⁴ m³ of water shortage and increase 477.6×10⁴ m³ of water for power generation based on the current situation scheduling;③ When considering the runoff information uncertainty,the water supply of the improved scheduling diagram is 3.37% lower than the ideal case but is still 5.96% more than the conventional optimization.The results are conducive to making scientific decisions and improving the water supply effect.…”
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Thermo-economic investigation and comparative multi-objective optimization of dual-pressure evaporation ORC using binary zeotropic mixtures as working fluids for geothermal energy... by Shahin Akbari, Shahin Faghiri, Ali Mehrparwar Zinjanabi, Mohamad Ali Bijarchi, Mohammad Behshad Shafii, Khashayar Hosseinzadeh

“…This contribution performs an energy, exergy, and exergoeconomic (3E) analysis of a dual-pressure evaporation organic Rankine cycle system employing twenty different binary zeotropic mixtures as working fluids for power production from a geothermal field. …”
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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…The aim of this study is to evaluate predictive strategies for the assessment of radiotracer uptake in pre-operative [99mTc]Tc-sestamibi scintigraphy ([99mTc] Tc-MIBI SPECT-CT) among PHP patients to identify individuals with a high probability of negative results, and to develop clinical decision-making tools. MATERIAL AND METHODS: Development and evaluation of logistic regression (LR), classification trees utilizing the classification and regression trees (CART) algorithm, random forest (RF), and boosted trees employing XGBoost (XGB) predictive models. …”
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Assessing an ovarian reserve and risk factors for premature ovarian failure as part of pre-abortion counseling for women under 40 planning to terminate own first pregnancy by L. V. Tkachenko, I. A. Gritsenko, K. Yu. Tikhaeva, N. I. Sviridova, I. S. Gavrilova, V. A. Dolgova

“…At the stage of pre-abortion counseling, it seems possible to influence a decision to keep pregnancy by identifying risk factors for premature ovarian failure (РОF), laboratory and ultrasound criteria for reducing ovarian reserve (OR).Aim: optimization of the pre-abortion counseling algorithm by introducing an assessment of OR. …”
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Assessing regional economic systems balance based on the dynamics of key social and economic indicators by A. A. Shatsky

“…The results of the analysis of the dynamics of key economic spheres development in the context of Russian regions have been presented, and the stable and unstable components of the RES have been identified. The algorithm for assessing the level of balance has been described, including the collection and analysis of statistical data, coefficients calculation and interpretation, and managerial decisions development based on the analysis results. …”
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Enhanced MCDM Based on the TOPSIS Technique and Aggregation Operators Under the Bipolar pqr-Spherical Fuzzy Environment: An Application in Firm Supplier Selection by Zanyar A. Ameen, Hariwan Fadhil M. Salih, Amlak I. Alajlan, Ramadhan A. Mohammed, Baravan A. Asaad

“…Moreover, a numerical example is provided in order to ensure that the presented model is applicable. By using the two algorithms, a comparative analysis of the proposed method with other existing ones is given in order to verify the feasibility of the suggested decision-making procedure.…”
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Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit by Heshan Cao, Junying Wei, Ping Hua, Songran Yang, Songran Yang

“…The primary outcome was the incidence of VAP post-ICU admission. The Boruta algorithm was used to select features prior to developing 10 ML models. …”
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Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices by Xie M, Liu Z, Dai F, Cao Z, Wang X

“…LightGBM demonstrated superior predictive performance (ranking score=54) without overfitting, identifying Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), NIHSS score, age, aggregate index of systemic inflammation (AISI), and platelet-to-lymphocyte ratio (PLR) as the top predictors.Conclusion: Our findings demonstrate that machine learning models exhibit strong predictive performance for SAP, with the LightGBM algorithm outperforming other approaches. The web-based prediction tool developed from this model provides clinicians with actionable insights to support real-time clinical decision-making.Keywords: stroke-associated pneumonia, machine learning, ischemic stroke…”
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The views of the general public on prioritising vaccination programmes against childhood diseases: A qualitative study. by Gemma Lasseter, Hareth Al-Janabi, Caroline L Trotter, Fran E Carroll, Hannah Christensen

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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…By leveraging machine learning and statistical learning techniques, we aim to develop a robust clinical decision support system for dental practitioners.MethodsThis research aims to predict the eruption of 3rd molars in the mandible by analyzing three parameters: the distance from the lower 2nd molar to the anterior border, the mesiodistal width of the third molar, and the distance from the apex of the root to the inferior border of the mandible. …”
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