Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches by Ismail Shah, Hasnain Iftikhar, Sajid Ali

“…The estimation of these models is carried out by four different estimation methods, including ordinary least squares (O), Lasso (L), Ridge (R), and Elastic-net (E). …”
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Adaptive hybrid hyperparameter optimization with MRFO and Lévy flight for accurate melanoma classification by Shamsuddeen Adamu, Hitham Alhussian, Said Jadid Abdulkadir, Ayed Alwadin, Sallam O. F. Khairy, Hussaini Mamman, Shamsu Abdullahi, Saidu Yahaya, Aliyu Garba, Dahiru Adamu Aliyu, Muhammad Muntasir Yakubu, Daniel Tonye Oyefidein

“…When compared to MRFO, PSO, and GA, the proposed method improved ISIC accuracy by 0.40%, reduced PH $$ ^2 $$ 2 loss by over 95%, and converged up to 30% faster. …”
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Advances in Hosting Capacity Assessment and Enhancement Techniques for Distributed Energy Resources: A Review of Dynamic Operating Envelopes in the Australian Grid by Naveed Ali Brohi, Gokul Thirunavukkarasu, Mehdi Seyedmahmoudian, Kafeel Ahmed, Alex Stojcevski, Saad Mekhilef

“…This paper reviews state-of-the-art HC assessment methods, including deterministic, stochastic, time-series, and AI-based approaches. …”
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On the Synergy of IoMT Devices and Ceiling-Mounted Systems for Advanced Medical Data Analytics by Andreas Andreou, Constandinos X. Mavromoustakis, Evangelos K. Markakis, Athina Bourdena, George Mastorakis

“…Deep Reinforcement Learning (DRL) methods solve the resource allocation challenge under realistic constraints. …”
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Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. …”
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Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP... by Sana Qaiyum, Kashif Irshad, Mohamed E. Zayed, Salem Algarni, Talal Alqahtani, Asif Irshad Khan

“…Ground source heat pump (GSHP) has recently gained a great attention because of its efficient utilization of geothermal energy for building cooling and heating. …”
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Effect of Data Assimilation on Basin Evapotranspiration Simulation by YIN Jian, QIU Yuanhong, ZHANG Bin

“…The data assimilation significantly modifies the hydrological process simulation under the influence of extreme climate and human water use. Therefore, the method is of potential practical value and an alternative tool for hydrological process simulation.…”
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Microbial composition, assembly, and functional characteristics of generalized and specialized subcommunities under flooded paddy fields: long-term pesticide versus non-pesticide m... by Jing-Wen Wang, Jing-Wen Wang, Cai-Ting Han, Cai-Ting Han, Cai-Ting Han, Dong-Ming Wu, Dong-Ming Wu, Dong-Ming Wu, Wei Zhou, Wei Zhou, Miao Gao, Miao Gao, Yu-Shuang Li

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AI and Data Analytics in the Dairy Farms: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…It is timely to review modern technologies and data analytics methods for milk predictions in view of supporting decision-making in dairy farms. …”
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Rhizosphere-associated bacterial and fungal communities of two maize hybrids under increased nitrogen fertilization by Qing Liu, HongCui Dai, Hao Cheng, Guodong Shao, Liang Wang, Hui Zhang, Yingbo Gao, Kaichang Liu, Xiaomei Xie, Junhua Gong, Xin Qian, Zongxin Li

“…Nevertheless, the effects of heightened nitrogen fertilizer demand of these crops on the composition and assembly of soil microbial communities in agricultural production require further elucidation.MethodsIn this study, the effects of four nitrogen fertilizer managements on rhizosphere bacterial and fungal community assembly, co-occurrence network and function of two maize hybrids (LD981 and DH605) were compared.Results and discussionFindings revealed that the bacterial community was primarily shaped by deterministic processes, while stochastic processes played a pivotal role in fungal community assembly. …”
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Analyzing measles spread through a Markovian SEIR model by Yousef Alnafisah, M. A. Sohaly

“…We employ the state reduction method to simplify complex computations and develop a Mathematica-based algorithm to efficiently determine steady-state probabilities. …”
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Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model by Wen Nie, Yong-chang Liang, Lin Chen, Wei Shao

“…The results of the deterministic method of the numerical case and optimized method of the modified tank model matched well.…”
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Leading Degree: A Metric for Model Performance Evaluation and Hyperparameter Tuning in Deep Learning-Based Side-Channel Analysis by Junfan Zhu, Jiqiang Lu

“…To attain an effective generic side-channel evaluation metric, we investigate the deterministic component of power consumption, find that the elements of score vector under a key follow a linearly transformed chi-square distribution approximately, and some wrong key hypotheses usually with top scores provide great assistance in model performance evaluation, and finally we propose a new metric called Leading Degree (LD) as well as its simplified version LD-simplified for measuring model performance, which offers similar accuracy but much better generality and efficiency compared with the classical side-channel benchmark metric TGE1, and offers similar generality and efficiency but significantly better accuracy compared with recently proposed sidechannel metrics like Label Correlation and Cross Entropy Ratio. …”
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Multiobjective Transmission Network Planning considering the Uncertainty and Correlation of Wind Power by Yuan Hu, Zhaohong Bie, Yanling Lin, Guangtao Ning, Mingfan Chen, Yujie Gao

“…In order to consider the uncertainty and correlation of wind power in multiobjective transmission network expansion planning (TNEP), this paper presents an extended point-estimation method to calculate the probabilistic power flow, based on which the correlative power outputs of wind farm are sampled and the uncertain multiobjective transmission network planning model is transformed into a solvable deterministic model. …”
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Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios by HU Heng, QIN Jianru, LI Haibo, LIU Jiefeng

“…[Methods] To address this， this paper first constructs RE generation scenarios using Latin hypercube sampling （LHS） and modified k-means clustering， verifying their reserve feasibility， while transforming reserve-infeasible scenarios into extreme scenario sets. …”
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Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control by Ties van derHeijden, Miguel Angel Mendoza‐Lugo, Peter Palensky, Nick van deGiesen, Edo Abraham

“…Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. …”
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Wide-Range Variable Cycle Engine Control Based on Deep Reinforcement Learning by Yaoyao Ding, Fengming Wang, Yuanwei Mu, Hongfei Sun

“…To solve this problem, this paper adopts a deep reinforcement learning method based on a deep deterministic policy gradient algorithm, and it applies an action space pruning technique to optimize the controller, which significantly improves the convergence speed of network training. …”
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Pareto Optimal Solutions for Stochastic Dynamic Programming Problems via Monte Carlo Simulation by R. T. N. Cardoso, R. H. C. Takahashi, F. R. B. Cruz

“…This new idea is carried out by using Monte Carlo simulations embedded in an approximate algorithm proposed to deterministic dynamic programming problems. The new method is tested in instances of the classical inventory control problem. …”
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Variational Quantum Monte Carlo Solution of the Many-Electron Schrödinger Equation Based on Deep Neural Networks by Huiping Su, Hongbo Gao, Xinmiao Wang, Xi He, Da Shen

“…Therefore, it is crucial to find an effective numerical method. To solve this problem, this paper present a deep learning architecture, VMCNet, using the powerful computational efficiency of neural networks to improve the speed of numerical computation. …”
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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…This paper presents a reinforcement learning (RL)-based control strategy for the robotic disassembly of flexible elements. The proposed method focuses on low-level control, in which the precise manipulation of the robot is essential to minimize force and avoid damage during extraction. …”
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