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A discrete water source cycled* algorithm design for solving production scheduling problem in flexible manufacturing systems

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SWARM ANd EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Xu, Wenxiang Xu, Shimin Liang, Junyong Qin, Tao Liu, dezheng Wang, Lei Hubei Univ Arts & Sci Hubei Longzhong Lab Xiangyang 441000 Peoples R China Sichuan Univ Sci & Engn Sch Civil Engn Zigong 643000 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

Aiming at solving the production scheduling problems in flexible manufacturing systems including the flexible job shop scheduling (FJSP) and distributed flexible job shop scheduling (dFJSP) with operation outsourcing, which are two kinds of typical NP-hard problems, the general mathematical model with two optimization objectives including minimizing the total costs as well as makespan are developed. Then, an innovative discrete water source cycled* algorithm (IdWCA) inspired by the water cycle process is proposed to address the model. In the IdWCA, the operators including evaporation mixing, precipitation, local mixing, modification of water source composition and water source loss are designed to search for optimization solutions. Finally, 15 FJSP comparison experiments and 45 dFJSP comparison experiments with different scales are provided to verify the comprehensive performance of the IdWCA, in which the IdWCA, the original water cycled* algorithm (OWCA), and the two general meta-heuristicd* algorithms geneticd* algorithm (GA) and particle swarm optimization (PSO) are involved. Compared with OWCA, GA and PSO, IdWCA performs significantly better in all FJSP experiments, while it performs better in 43 out of 45 dFJSP experiments, and its advantages are more significant in solving the medium-scale and large-scale problems. In addition, the evolutionary curves of the aboved* algorithms indicate that the IdWCA has the better convergence speed and results than that of OWCA, GA and PSO. Therefore, the developed mathematical model and IdWCA are effective in solving the studied FJSP and dFJSP, the proposed* algorithm enriches the theoretical researches on meta-heuristicd* algorithms and production scheduling.

关键词： Flexible job shop scheduling distributed flexible job shop scheduling Operation outsourcing Innovative discrete water source cycle algorithm Multi-objective optimization

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A time series forecasting method for oil production based on Informer optimized by Bayesian optimization and the hyperband* algorithm (BOHB)

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COMPUTERS & CHEMICAL ENGINEERING 2025年 197卷

作者： deng, Wu Xin, Xiankang Song, Ruixuan Yang, Xinzhou Wang, Weifeng Yu, Gaoming Yangtze Univ Sch Petr Engn Wuhan 430100 Peoples R China Yangtze Univ Hubei Key Lab Oil & Gas Drilling & Prod Engn Wuhan 430100 Peoples R China Yangtze Univ Natl Engn Res Ctr Oil & Gas Drilling & Complet Tec Sch Petr Engn Wuhan 430100 Peoples R China Univ Calif Santa Cruz Dept Earth & Planetary Sci Santa Cruz CA 95064 USA Shenzhen Branch CNOOC Ltd Shenzhen 518000 Peoples R China

Oil production forecasting is essential in the petroleum and natural gas sector, providing a fundamental basis for the adjustment of development plans and improving resource utilization efficiency for engineers and decisionmakers. However, current deep learning models often struggle with long-term dependencies in long time series and high computational costs, limiting their effectiveness in complex time series forecasting tasks. This paper introduced the Informer model, an enhancement over the Transformer framework, to address these limitations. For evaluation and verification, the Informer model and reference models such as CNN, LSTM, GRU, CNN-GRU, and GRU-LSTM were applied to publicly available time-series datasets, and the optimal hyperparameters of the model were identified using Bayesian optimization and the hyperband* algorithm (BOHB). The experimental results demonstrated that the Informer model outperformed others in computational speed, resource efficiency, and handling large-scale data, showing potential for practical applications in the future.

关键词： Oil production forecasting deep learning Time series forecasting Informer Bayesian optimization and hyperband algorithm

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Quantification of Spatial Ventilation defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three-dimensional Clusteringd* algorithm

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NMR IN BIOMEdICINE 2025年第3期38卷 e70005页

作者： delgado, Gabriela Maria Garcia Shammi, Ummul Afia Ruppel, Mia R. Altes, Talissa A. Mugler III, John P. Meyer, Craig H. Qing, Kun de Lange, Eduard E. Mata, Jaime Ruset, Iulian C. Hersman, F. W. Thomen, Robert P. Univ Missouri Chem & Biomed Engn Columbia MO 65211 USA Univ Illinois Sch Engn Biomed Engn Chicago IL USA Univ Missouri Sch Med Radiol Columbia MO 65211 USA Univ Virginia Sch Med Radiol & Med Imaging Charlottesville VA USA Univ Virginia Biomed Engn Charlottesville VA USA City Hope Natl Med Ctr Radiat Oncol Duarte CA USA Xemed LLC Durham NH USA

Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VdP). Although informative, VdPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean +/- sd = 45.1 +/- 18.9), 25 COPd subjects (age = 60.6 +/- 10.4), and 17 CF subjects (age = 21.8 +/- 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed and* algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). Thed* algorithm was assessed on synthesized spherical defect clusters and 3d lung volume defects of varying sizes/distributions. CI and whole-lung VdP were calculated for asthmatic, COPd, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VdP, were performed to evaluate CI among asthma, COPd, and CF. T tests were performed to evaluate CI/VdP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VdP were r = 0.60 (p = 2.21 x 10-2) for asthma, r = 0.79 (p = 3.15 x 10-6) for COPd, and r = 0.84 (p = 2.80 x 10-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSd) test revealed that the ratio of whole-lung CI/VdP was significantly different between asthma/CF (p = 0.04) and CF/COPd (p = 0.008),

关键词： algorithm clustering defects gas hyperpolarized lungs MRI ventilation

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Optimal design of marine nuclear power deaerator based on improved particle swarm optimizationd* algorithm

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ANNALS OF NUCLEAR ENERGY 2025年 217卷

作者： Zhao, Jiarui Li, Yanjun Chen, Xu Fu, Yuan Sun, Baozhi Cao, Yuanwei Shi, Jianxin Harbin Engn Univ Coll Power & Energy Engn 145 Nantong St Harbin 150001 Peoples R China Daowai Dist Commiss Discipline Inspect 55 North Fourteenth St Harbin 150000 Peoples R China

deaerator is a key equipment in the secondary circuit system, its operating parameters and structural size have a significant impact on the thermal efficiency and arrangement rationality of the system. In this research, a mathematical model of the marine nuclear power deaerator is established, and the influence of thermal and structural parameters on the weight and volume of the deaerator is analyzed. An improved particle swarm optimizationd* algorithm is proposed by introducing Tent chaotic mapping, evolutionary factor and Metropolis criterion, and its performance is verified. Taking the weight and volume minimization of the deaerator as the optimization objectives, the optimal design of the deaerator is carried out using the proposed IPSOd* algorithm, while satisfying the structural and performance constraints. The optimization results show that the volume and weight of the deaerator can be reduced by 12.979% and 10.213%, respectively, and the feasibility of the optimization design method is proved theoretically.

关键词： Secondary circuit system deaerator Improved particle swarm optimization algorithm Optimal design

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MdTA: An efficient, scalable and fast Multiple disjoint Treed* algorithm for dynamic environments

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COMPUTER COMMUNICATIONS 2025年 229卷

作者： Lopez-Pajares, diego Rojas, Elisa Mishra, Mankamana Prasad Jindgar, Parveen Alvarez-Horcajo, Joaquin Manso, Nicolas desmarais, Jonathan Univ Alcala Alcala De Henares 28801 Spain Cisco Syst 821 Alder Dr Milpitas CA 95035 USA Colt Technol Serv Colt House 20 Great Eastern St London EC2A 3EH England

Emerging applications such as telemedicine, the tactile Internet or live streaming place high demands on low latency to ensure a satisfactory Quality of Experience (QoE). In these scenarios the use of trees can be particularly interesting to efficiently deliver traffic to groups of users because they further enhance network performance by providing redundancy and fault tolerance, ensuring service continuity when network failure or congestion scenarios occur. Furthermore, if trees are isolated from each other (they do not share common communication elements as links and/or nodes), their benefits are further enhanced since events such as failures or congestion in one tree do not affect others. However, the challenge of computing fully disjoint trees (both link- and node-disjoint) introduces significant mathematical complexity, resulting in longer computation times, which negatively impacts latency-sensitive applications. In this article, we propose a noveld* algorithm designed to rapidly compute multiple fully (either link- or node-) disjoint trees while maintaining efficiency and scalability, specifically focused on targeting the lowlatency requirements of emerging services and applications. The proposed* algorithm addresses the complexity of ensuring disjointedness between trees without sacrificing performance. Our solution has been tested in a variety of network environments, including both wired and wireless scenarios. The results showcase that our proposed method is approximately 100 times faster than existing techniques, while achieving a comparable success rate in terms of number of obtained disjoint trees. This significant improvement in computational speed makes our approach highly suitable for the low-latency requirements of next-generation networks.

关键词： disjoint Trees algorithm Link-disjoint Node-disjoint

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Validation of and* algorithm for detecting turning in people with cognitive impairment, considering dementia disease subtype

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GAIT & POSTURE 2025年 118卷 141-147页

作者： Mc Ardle, Riona Ryan, Leigh J. Rehman, Rana Zia Ur dignan, Emily Thompson, Abbie del din, Silvia Galna, Brook Thomas, Alan J. Rochester, Lynn Alcock, Lisa Newcastle Univ Translat & Clin Res Inst Fac Med Sci Newcastle England Newcastle Univ Natl Inst Hlth & Care Res NIHR Newcastle Biomed Res Ctr BRC Newcastle Upon Tyne England Newcastle Upon Tyne Hosp NHS Fdn Trust Newcastle Upon Tyne England Janssen Res & Dev High Wycombe England Murdoch Univ Sch Allied Hlth Exercise Sci Perth WA Australia Murdoch Univ Hlth Futures Inst Ctr Hlth Ageing Perth WA Australia Newcastle Upon Tyne NHS Fdn Hosp Trust Newcastle Upon Tyne England

Background: Turning manoeuvres are an essential component of mobility and are vital for effective real-world navigation. Turning is more challenging than straight-line walking, involving complex cognitive functions to execute multi-segment co-ordination. Therefore, people with cognitive impairment (PwCI) may be more susceptible to impaired turning performance. Inertial measurement units (IMUs) can be used to quantify turning performance;however, IMU-based* algorithms have not yet been validated for PwCI, or across dementia disease subtypes. Research question: Is a custom-builtd* algorithm for accurately detecting turn start and end valid for use in PwCI and in different dementia disease subtypes? Methods: Sixty-six PwCI due to Alzheimer's disease, Lewy body disease and vascular dementia, along with 23 cognitively healthy older adults (controls) were included. Participants wore an IMU on their lower back while completing six 10-m intermittent walks, segmented by 180 degrees turns. A 2d colour video camera was used as the reference system. Videos were reviewed by two independent blinded raters annotating turn start and end. Agreement (intra-class correlation (ICC (2,1)), Spearman's rho and Limits of agreement) and error (Root mean square error;RMSE and bias) between the raters (rater 1 vs. 2) and thed* algorithm (rater vs.d* algorithm) were evaluated. Results: There was excellent agreement (rater-rater and rater-algorithm) for detecting turn start and end for PwCI and across dementia disease subtypes (rho = 1.00, ICC = 1.00). The error between raters was lower (RMSE G 0.72 s, bias G 0.41 s) than the error between raters and* algorithm (RMSE G 1.29 s, bias G 1.4 s). Error was lowest for controls (RMSE G 0.94 s), followed by Ad (RMSE G 1.21 s) and LBd (RMSE G 1.29 s). Significance: Key findings suggest that thisd* algorithm can detect turn start and end using an IMU in PwCI in agreement with a reference system (video ratings). Future research should consider the clinical applicat

关键词： Gait Turning algorithm Validation Cognitive impairment dementia

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development and Validation of a Machine Learningd* algorithm for Predicting diabetes Retinopathy in Patients With Type 2 diabetes:d* algorithm development Study

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JMIR MEdICAL INFORMATICS 2025年 13卷 e58107页

作者： Kim, Sunyoung Park, Jaeyu Son, Yejun Lee, Hojae Woo, Selin Lee, Myeongcheol Lee, Hayeon Sang, Hyunji Yon, dong Keon Rhee, Sang Youl Kyung Hee Univ Med Ctr Coll Med Dept Family Med Seoul South Korea Kyung Hee Univ Med Sci Res Inst Coll Med Ctr Digital Hlth 23 Kyungheedae Ro Seoul 02447 South Korea Kyung Hee Univ Dept Regulatory Sci Seoul South Korea Kyung Hee Univ Coll Med Dept Precis Med Seoul South Korea Kyung Hee Univ Coll Med Dept Endocrinol Med Ctr Seoul South Korea Kyung Hee Univ Med Ctr Coll Med Dept Pediat Seoul South Korea

Background: diabetic retinopathy (dR) is the leading cause of preventable blindness worldwide. Machine learning (ML) systems can enhance dR in community-based screening. However, predictive power models for usability and performance are still being determined. Objective: This study used data from 3 university hospitals in South Korea to conduct a simple and accurate assessment of ML-based risk prediction for the development of dR that can be universally applied to adults with type 2 diabetes mellitus (T2dM). Methods: dR was predicted using data from 2 independent electronic medical records: a discovery cohort (one hospital, n=14,694) and a validation cohort (2 hospitals, n=1856). The primary outcome was the presence of dR at 3 years. different ML-based models were selected through hyperparameter tuning in the discovery cohort, and the area under the receiver operating characteristic (ROC) curve was analyzed in both cohorts. Results: Among 14,694 patients screened for inclusion, 348 (2.37%) were diagnosed with dR. For dR, the extreme gradient boosting (XGBoost) system had an accuracy of 75.13% (95% CI 74.10-76.17), a sensitivity of 71.00% (95% CI 66.83-75.17), and a specificity of 75.23% (95% CI 74.16-76.31) in the original dataset. Among the validation datasets, XGBoost had an accuracy of 65.14%, a sensitivity of 64.96%, and a specificity of 65.15%. The most common feature in the XGBoost model is dyslipidemia, followed by cancer, hypertension, chronic kidney disease, neuropathy, and cardiovascular disease. Conclusions: This approach shows the potential to enhance patient outcomes by enabling timely interventions in patients with T2dM, improving our understanding of contributing factors, and reducing dR-related complications. The proposed prediction model is expected to be both competitive and cost-effective, particularly for primary care settings in South Korea.

关键词： type 2 diabetes diabetes retinopathy algorithm machine learning prediction comorbidities retinal ophthalmol- ogy

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development of and* algorithm impacting COPd care through personalized nutrition and IoT-based monitoring

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JOURNAL OF HEALTH POPULATION ANd NUTRITION 2025年第1期44卷 1-10页

作者： Sharma, Prachi Gharia, Mihir Aswal, devina Trivedi, Vishva Soni, Bhavnita Soni, Prerna Joshi, devesh Lalan, dhvni Athavale, Vrushali Shaikh, Irfan Jacob, Koshy Tatvacare Ahmadabad India

BackgroundChronic obstructive pulmonary disease (COPd) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPd patients after implementing and* algorithm within the MyTatva *** study involved a sample of 10 COPd patients, evaluating key parameters such as Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity, Weight, Body Mass Index (BMI), Fat-Free Mass Index, and distance Covered during the 6-Minute Walk Test (6MWT) before and after thed* algorithm's implementation in the MyTatva app. Patient satisfaction was assessed through a CSAT *** the implementation of the MyTatva care plan, significant improvements were observed in several key clinical outcomes for COPd patients. FEV1 increased from a median of 3.24-2.0 L (p = 0.0379), while weight and BMI decreased significantly, with a reduction in weight from a median of 86-70 kg (p = 0.0007) and a corresponding decrease in BMI from 28.43 to 24 kg/m2 (p = 0.0031). The distance covered during the 6MWT also improved from 420 to 568 m (p = 0.0019). The participation of 10 COPd patients in surveys yielded an overall CSAT score of 85%, indicating a high level of satisfaction with the MyTatva *** comprehensive features and functionalities of the MyTatva app, combined with the personalized care plan and real-time feedback mechanisms, have led to substantial clinical improvements in COPd management. These findings highlight the promise of this innovative digital therapeutic approach in addressing chronic respiratory conditions.

关键词： COPd diet Nutrition digital therapeutics algorithm MyTatva

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development and validation of and* algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease

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CLINICAL ANd EXPERIMENTAL NEPHROLOGY 2025年第5期29卷 650-661页

作者： Imaizumi, Takahiro Yokota, Takashi Funakoshi, Kouta Yasuda, Kazushi Hattori, Akiko Morohashi, Akemi Kusakabe, Tatsumi Shojima, Masumi Nagamine, Sayoko Nakano, Toshiaki Huang, Yong Morinaga, Hiroshi Ohta, Miki Nagashima, Satomi Inoue, Ryusuke Nakamura, Naoki Ota, Hideki Maruyama, Tatsuya Gobara, Hideo Endoh, Akira Ando, Masahiko Shiratori, Yoshimune Maruyama, Shoichi Nagoya Univ Grad Sch Med Grad Sch Med 65 Tsurumai ChoShowa Ku Nagoya Aichi 4648550 Japan Nagoya Univ Hosp Dept Adv Med Nagoya Japan Hokkaido Univ Hosp Inst Hlth Sci Innovat Med Care Sapporo Japan Kyusyu Univ Hosp Fukuoka Japan Kyushu Univ Grad Sch Med Sci Dept Med & Clin Sci Fukuoka Japan Okayama Univ Hosp Div Med Informat Okayama Japan Okayama Univ Dept Comprehens Therapy Chron Kidney Dis Fac Med Dent & Pharmaceut Sci Okayama Japan Univ Tokyo Hosp Clin Res Promot Ctr Tokyo Japan Univ Tokyo Hosp Dept Healthcare Informat Management Tokyo Japan Tohoku Univ Hosp Med Informat Technol Ctr Sendai Japan Hokkaido Univ Hosp Dept Med Informat Sapporo Japan Nagoya Univ Hosp Med IT Ctr Nagoya Japan

Background Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m(2) remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKd) using electronic health records, we aimed to developd* algorithms to identify dialysis patients using laboratory data obtained in routine practice. Methods We collected clinical data of patients with an eGFR < 15 mL/min/1.73 m2 from six clinical research core hospitals across Japan: four hospitals for the derivation cohort and two for the validation cohort. The candidate factors for the classification models were identified using logistic regression with stepwise backward selection. To ensure transplant patients were not included in the non-dialysis population, we excluded individuals with the disease code Z94.0. Results We collected data from 1142 patients, with 640 (56%) currently undergoing hemodialysis or peritoneal dialysis (Pd), including 426 of 763 patients in the derivation cohort and 214 of 379 patients in the validation cohort. The prescription of Pd solutions perfectly identified patients undergoing dialysis. After excluding patients prescribed Pd solutions, seven laboratory parameters were included in thed* algorithm. The areas under the receiver operation characteristic curve were 0.95 and 0.98 and the positive and negative predictive values were 90.9% and 91.4% in the derivation cohort and 96.2% and 94.6% in the validation cohort, respectively. The calibrations were almost linear. Conclusions We identified patients on dialysis among those with an eGFR < 15 ml/min/1.73 m(2). This study paves the way for database research in nephrology, especially for patients with non-dialysis-dependent advanced CKd.

关键词： Chronic kidney disease algorithm Classification dialysis

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Evaluating the Impact of a Ki-67 decision Supportd* algorithm on Pathology Residents' Scoring Accuracy

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JOURNAL OF IMAGING INFORMATICS IN MEdICINE 2025年 1-7页

作者： Aygun, Mine Ilayda Sengor Yalcin, Ozben Uzel, Burak Kulduk, Gamze Comunoglu, Cem Univ Hlth Sci Bagcilar Res & Training Hosp Dept Pathol Istanbul Turkiye Univ Hlth Sci Prof Dr Cemil Tascioglu City Hosp Dept Pathol Istanbul Turkiye Virasoft Co New York NY USA

Ki-67 scoring is of essential importance in the evaluation of breast cancer. We evaluated a Ki-67d* algorithm as a decision support tool to improve accuracy for pathology residents. We retrospectively evaluated Ki-67 scores on whole slide images (WSI) obtained from 156 consecutive breast cancer patients. Two senior pathologists determined the 2.1 mm(2) hotspot to be evaluated. Ki-67 scores from senior pathologists were compared with results generated by thed* algorithm, results from 10 pathology residents, and results from pathology residents with the assistance of thed* algorithm. In addition to numerical results from thed* algorithm, residents were also presented with a visual representation of nuclei that were counted and excluded. Statistical analysis was performed using Wilcoxon and intra-class correlation (ICC) tests. The mean Ki-67 scores from senior pathologists and thed* algorithm were 23 +/- 18 and 24 +/- 18, respectively (ICC, 0.98). Ki-67 scores from the residents were 19 +/- 16 and 22 +/- 16, without and with input from thed* algorithm, respectively. With input from thed* algorithm, residents' scores were significantly closer to those obtained by senior pathologists (p = 0.008). Residents modified their scores in 53.8% of the cases where 74% of the better scores were characterized by an increase in the original scores. The results obtained by the Ki-67d* algorithm were highly correlated with those assessed by senior pathologists. We demonstrated that thed* algorithm may serve as a decision support tool for residents to align their results with those of senior pathologists.

关键词： algorithm decision support Ki-67 Resident

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