Blockchain Technology in Supply Chain Management Prospects and Challenges for Implementation by Peshattiwar Atish, P Mohanraj, A Anand Gerald, S Dharmalingam, Tiwari Mohit, G Swarnalakshmi

“…In response, this research presents a next-gen blockchain framework leveraging hybrid blockchain architectures, AI-integrated smart contracts, green consensus algorithms, and cross-platform interoperability strategies to bridge the gaps of the existing systems. …”
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Iris: A Next Generation Digital Pathology Rendering Engine by Ryan Erik Landvater, Ulysses Balis

“…Further, it is able to buffer and compute high-fidelity reduction-enhancements for viewing low-power cytology with increased visual quality at a rate of 100–160 μs per slide tile, and with a cumulative median buffering rate of 1.36 GB of decompressed image data per second. …”
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Gamified Adaptive Approach Bias Modification in Individuals With Methamphetamine Use History From Communities in Sichuan: Pilot Randomized Controlled Trial by Danlin Shen, Jianping Jiao, Liqun Zhang, Yanru Liu, Xiang Liu, Yuanhui Li, Tianjiao Zhang, Dai Li, Wei Hao

“…Incorporating gamified designs and adaptive algorithms in CBM training may address this issue and enhance engagement and effectiveness. …”
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Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems by Prashant Nene, Dolly Thankachan

“…The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems. • Integrated Intelligence Stack: Combines RL-ENN, T-STFREP, FL-DEO, GNNHSCO, and Q-GAN-ESO into a unified architecture for real-time control, forecasting, decentralized optimization, network routing, and synthetic scenario generation. • Real-Time, Scalable, and Privacy-Preserving: Enables adaptive energy dispatch, federated optimization without compromising data privacy, and graph-based power routing, making it suitable for large-scale, smart grid deployments. • Proven Long-Term Performance: Achieved significant improvements over traditional methods (GA, PSO) with 27.5 % lower NPC, 18.2 % reduction in COE, and 30.2 % increase in battery life, validated using 30 years of meteorological data.…”
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Low-overhead defect-adaptive surface code with bandage-like super-stabilizers by Zuolin Wei, Tan He, Yangsen Ye, Dachao Wu, Yiming Zhang, Youwei Zhao, Weiping Lin, He-Liang Huang, Xiaobo Zhu, Jian-Wei Pan

“…Abstract To make practical quantum algorithms work, large-scale quantum processors protected by error-correcting codes are required to resist noise and ensure reliable computational outcomes. …”
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Planificación y optimización asistida por computadora de secuencias de ensamble mecánico. by L. L. Tomás García

“…The generated assembly sequences are preprocessed and optimized for the assembly Process Planning using Genetic Algorithms. This approach integrates the geometric and technological information of the assembly process, which allows reducing the number of elements and sequences to be processed with the consequent processing time and cost reduction.…”
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Absorbing Aerosol Effects on Hyperspectral Surface and Underwater UV Irradiances from OMI Measurements and Radiative Transfer Computations by Alexander Vasilkov, Nickolay Krotkov, Matthew Bandel, Hiren Jethva, David Haffner, Zachary Fasnacht, Omar Torres, Changwoo Ahn, Joanna Joiner

“…The satellite UV surface irradiance algorithms combine satellite retrievals of extraterrestrial solar irradiance, cloud/surface reflectivity, aerosol optical depth, and total column ozone with radiative transfer computations. …”
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A clustering-based multi-setting overcurrent protection approach with optimisation and experimental validation for distribution networks by Feras Alasali, Mohamed Salem, Haytham Y. Mustafa, Hassen Loukil, Naser El-Naily, Abdelaziz Salah Saidi, William Holderbaum

“…This work involves integrating clustering-based topology grouping with bio-inspired optimisation techniques, specifically the Water Cycle Algorithm (WCA) and the Transit Search Algorithm (TSA), to assign optimised Time Multiply Settings (TMS) values for each relay group, ensuring fast and coordinated protection without reliance on continuous communication or centralised control. …”
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Information support for decision making in problem situation description by S. F. Lipnitsky

“…As an implementation of the proposed model, the following algorithms have been developed: an algorithm for creating a dictionary of communicative fragments; algorithms for creating fragment-slot templates for sentences, texts and subject areas; an algorithm of user information support. …”
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Segmentation in object-oriented encoding and multi-view images transmission by V. Yu. Tsviatkou

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Evaluation of the angular coordinates in survey radar stations with subsystems for spatial noise compensation by S. V. Kozlov, Vu. Thanh Ha

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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

“…Therefore, this paper proposes a user energy service demand prediction model based on feature selection. The methodology includes introducing a sampling algorithm to solve the class imbalance problem in the data on the basis of analysing the user energy service data, reducing the dimensionality of the data based on an autoencoder to ensure efficient clustering of the K-mean algorithm, constructing a feature selection algorithm based on a lightweight gradient lifting machine to filter the effective features and improve the training efficiency of the prediction model, and establishing a bidirectional long- and short-term memory neural network multi-label predicting model based on an attentional mechanism to refine the user’s energy service demand. …”
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Mechanical Ventilation Strategies in Buildings: A Comprehensive Review of Climate Management, Indoor Air Quality, and Energy Efficiency by Farhan Lafta Rashid, Mudhar A. Al-Obaidi, Najah M. L. Al Maimuri, Arman Ameen, Ephraim Bonah Agyekum, Atef Chibani, Mohamed Kezzar

“…Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. …”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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矿用自卸车盘式制动器热固耦合研究 by 解淑英

“…In order to further study the dump truck disc brake thermal-structural coupling characteristics,by analyzing the major impact parameter of brake pressure,brake initial speed,plate/sheet friction coefficient and the equivalent moment of inertia on the disc brake performance and using orthogonal experiment principle,the GA-BP network training sample is formed,a GA-BP network model with traditional genetic algorithm BP neural network combined with the introduction of disc brakes thermal-structural coupling finite element predictive model is proposed.The study results show that for the temperature and stress time history results,the overall trend of the training results and finite element simulation results are basically the same,but in the individual condition curve,there are some differences in local curves.Aiming at the specific conditions,the main characteristics of the temperature-time history is basiclly predicted by the GA-BP Network,the data extreme values of stress time history prediction effect and other major information are consistent with the finite element results.The training network has higher approximation performance and better prediction performance,the maximum error of prediction and simulation is only 8%,the computational accuracy is higher.…”
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A Method of Communication Delay Compensation for Urban Transit SystemBased on Long-term and Short-term Memory by HUANG Zihao, LI Hongbo, ZHANG Chao, XU Dongsheng

“…After measuring the communication parameters in a 4G communication test, the communication delay induced error is calculated and compared with the prediction method. The result shows that the prediction algorithm can reduce communication delay induced error by 21.8% and packet loss induced error by 25.8% ~ 26.9%, which can provide more accurate real-time train power information and make real-time improvement for energy flow more feasible.…”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy by Kausalya Neelavara Makkithaya, Wei-Chung Chen, Chun-Chieh Wu, Ming-Chi Chen, Wei-Hsun Wang, Jackson Rodrigues, Ming-Tsang Wu, Nirmal Mazumder, I-Chen Wu, Guan-Yu Zhuo

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Developments in solar-driven desalination: Technologies, photovoltaic integration, and processes by Mohamed El-Sayed M. Essa, Hemdan S. El-sayed, Elwy E. El-kholy, Mohammed Amer, Mahmoud Elsisi, Uzair Sajjad, Khalid Hamid, Hilmy El-sayed Awad

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Research trends and prospects in global riverine carbon fluxes: A bibliometric analysis by Wei Fang, Mohd Remy Rozainy Mohd Arif Zainol, Xianbao Zha, Pingping Luo

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