MethodsX最新文献

{"title":"Heat4Future: A strategic planning tool for decarbonizing district heating systems","authors":"Nina Kicherer ,&nbsp;Pablo Benalcazar ,&nbsp;Peter Lorenzen ,&nbsp;Olessya Kozlenko ,&nbsp;Sadi Tomtulu ,&nbsp;Jan Trosdorff","doi":"10.1016/j.mex.2025.103222","DOIUrl":"10.1016/j.mex.2025.103222","url":null,"abstract":"<div><div>Strategic planning of future heat supply, particularly in the context of district heating systems, is essential for achieving a viable and cost-efficient energy transition. However, existing planning tools for district heating systems often require detailed data, which is frequently unavailable in the early stages of the planning process. In response to this challenge, this paper presents Heat4Future, a new planning tool designed to provide insights into potential decarbonized district heating systems for a given location using minimal input data.</div><div>The tool demonstrates the implementation of an innovative methodology for strategic planning of district heating systems. It uses a snapshot simulation model to configure the supply for a particular district heating system, taking into account the annual heat demand and user-specified heat sources. The tool comprises of four modules for calculating the cost-effective generation profile of the system. It is designed to generate detailed hourly profiles over an entire year for key parameters essential to the operation and planning of a DHS using a specified set of renewable energy sources, including heat load, generation, and storage profiles.<ul><li><span>•</span><span><div>Heat4Future provides an overview of the possibilities for a decarbonized district heating supply in a specific location.</div></span></li><li><span>•</span><span><div>The simulation tool contains four modules for calculating the system's generation profile: Weather and Environmental Data Module, Thermal Load Module, Buffer Thermal Energy Storage Module, and Strategic Heat Planning Module.</div></span></li><li><span>•</span><span><div>The tool is licensed under the MIT License and is available as an open-source repository on GitLab.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103222"},"PeriodicalIF":1.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytic study and statistical enforcement of extended beta functions imposed by Mittag-Leffler and Hurwitz-Lerch Zeta functions","authors":"Faten F. Abdulnabi ,&nbsp;Hiba F. Al-Janaby ,&nbsp;Firas Ghanim","doi":"10.1016/j.mex.2025.103206","DOIUrl":"10.1016/j.mex.2025.103206","url":null,"abstract":"<div><div>Special Function Theory is used in many mathematical fields to model scientific progress, from theoretical to practical. This helps efficiently analyze the newly expanded Beta class of functions on a complicated domain. We use Mittag-Leffler and Hurwitz Lerch zeta (HLZ) kernels to produce the Beta function using the convolution tool. This special function advances a statistical implementation research approach. This unique function also discusses and gives analytical benefits, including functional and summation relations, Mellin transformations, and integral representations. Additionally, many derivative formulae are obtained. The statistical implementation of expanded Beta distribution using the suggested beta function was also conducted. We use the extended Beta function to create the new extended ordinary hypergeometric function and Kummer function. Derivative formulae, integral representations, generating functions, and fractional derivatives are also investigated.<ul><li><span>•</span><span><div>Developed utilizing Mittag-Leffler and Hurwitz Lerch Zeta functions as kernels, delivering increased analytical and computational capabilities.</div></span></li><li><span>•</span><span><div>Comprises derivative formulae, integral representations, Mellin transformations, and generating functions, offering a comprehensive mathematical foundation.</div></span></li><li><span>•</span><span><div>Illustrates the use of the extended Beta function inside the Beta distribution, highlighting its statistical importance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103206"},"PeriodicalIF":1.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing efficiency in photovoltaic hydrogen production: A comparative analysis of MPPT and electrolysis control strategies","authors":"Shen Yuong Wong,&nbsp;Jiawei Li","doi":"10.1016/j.mex.2025.103220","DOIUrl":"10.1016/j.mex.2025.103220","url":null,"abstract":"<div><div>With the rapid growth of photovoltaic installed capacity, photovoltaic hydrogen production can effectively solve the problem of electricity mismatch between new energy output and load demand. Photovoltaic electrolysis systems pose unique challenges due to their nonlinear, multivariable, and complex nature. This paper presents a thorough investigation into the control methodologies for such systems, focusing on both Maximum Power Point Tracking (MPPT) and electrolysis cell control strategies. Beginning with a comprehensive review of MPPT techniques, including classical, intelligent, optimization, and hybrid approaches, the study delves into the intricate dynamics of Proton Exchange Membrane Electrolysis Cells (PEMEL). Considering the nonlinear and time-varying characteristics of PEMEL, various control strategies such as Proportional-Integral-Derivative (PID), robust, Model Predictive Control (MPC), and Fault Tolerant Control (FTC) are analyzed. Evaluation metrics encompass stability, accuracy, computational complexity, and response speed. This paper provides a comparative analysis, encapsulating the strengths and limitations of each MPPT and PEM control technique.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103220"},"PeriodicalIF":1.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate sampling of undisturbed top sediment from grab sampler collected using aluminum tube and stainless-steel containers for shallow and deep-sea applications","authors":"Mutsumi Iizuka ,&nbsp;Atsuko Amano ,&nbsp;Takuya Itaki","doi":"10.1016/j.mex.2025.103213","DOIUrl":"10.1016/j.mex.2025.103213","url":null,"abstract":"<div><div>This study describes a sediment sampling protocol using a Kinoshita-type grab (K-grab) sediment sampler to collect and analyze microplastics (&lt;5 mm) and macroplastics (&gt;5 mm) in marine sediments. During the GB24 geological survey cruise aboard the <em>Bosei-maru</em>, 133 surface sediment samples were collected from depths of 20–800 m. The K-grab, equipped with a head-slide weight mechanism, enhanced sampling efficiency across various sediment types. For microplastics, stainless steel containers and J-shaped aluminum tubes minimized contamination while maintaining sample integrity. Macroplastics were separated using a 5 mm mesh and analyzed on board. Method verification confirmed high-spatial-resolution sampling with minimal contamination. These results demonstrate that the K-grab is a reliable tool for microplastic and macroplastic analysis, providing valuable data on plastic pollution in marine sediments.<ul><li><span>•</span><span><div>This study describes a sediment sampling protocol using a grab sampler to collect and analyze microplastics (&lt;5 mm) and macroplastics (&gt;5 mm) in marine sediments.</div></span></li><li><span>•</span><span><div>During the survey, 133 surface sediment samples were collected from depths of 20–800 m, with microplastics handled using J-shaped aluminum tubes and stainless steel containers to minimize contamination while maintaining sample integrity.</div></span></li><li><span>•</span><span><div>Macroplastics were separated using a 5 mm mesh and analyzed on board. Method verification confirmed high-spatial-resolution sampling with minimal contamination.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103213"},"PeriodicalIF":1.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyPortOptimization: A portfolio optimization pipeline leveraging multiple expected return methods, risk models, and post-optimization allocation techniques","authors":"Rushikesh Nakhate ,&nbsp;Harikrishnan Ramachandran ,&nbsp;Amay Mahajan","doi":"10.1016/j.mex.2025.103211","DOIUrl":"10.1016/j.mex.2025.103211","url":null,"abstract":"<div><div>This paper presents PyPortOptimization, an automated portfolio optimization library that incorporates multiple methods for expected returns, risk return modeling, and portfolio optimization. The library offers a flexible and scalable solution for constructing optimized portfolios by supporting various risk-return matrices, covariance and correlation matrices, and optimization methods. Users can customize the pipeline at every step, from data acquisition to post-processing of portfolio weights, using their own methods or selecting from predefined options. Built-in Monte Carlo simulations help assess portfolio robustness, while performance metrics such as return, risk, and Sharpe ratio are calculated to evaluate optimization results.<ul><li><span>•</span><span><div>The study compares various configured methods for each step of the portfolio optimization pipeline, including expected returns, risk-modeling and optimization techniques.</div></span></li><li><span>•</span><span><div>Custom Designed Allocator outperformed. For example, the Proportional Allocator's sharpe ratio of out-performed the expected average.</div></span></li><li><span>•</span><span><div>A caching system was implemented to optimize execution time.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103211"},"PeriodicalIF":1.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT","authors":"Jayapradha J ,&nbsp;Su-Cheng Haw ,&nbsp;Naveen Palanichamy ,&nbsp;Kok-Why Ng ,&nbsp;Senthil Kumar Thillaigovindhan","doi":"10.1016/j.mex.2025.103201","DOIUrl":"10.1016/j.mex.2025.103201","url":null,"abstract":"<div><div>In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network.<ul><li><span>•</span><span><div>In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant.</div></span></li><li><span>•</span><span><div>The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103201"},"PeriodicalIF":1.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced load balancing techniques using MIMO fuzzy logic: A panel distribution case study at state polytechnic of Malang","authors":"Ika Noer Syamsiana,&nbsp;Harry Hassidiqi,&nbsp;Wijaya Kusuma,&nbsp;Anang Dasa Novfowan,&nbsp;Arwin Datumaya Wahyudi Sumari,&nbsp;Chandra Wiharya","doi":"10.1016/j.mex.2025.103197","DOIUrl":"10.1016/j.mex.2025.103197","url":null,"abstract":"<div><div>The issue of unbalance in electrical distribution systems is a significant challenge that requires effective management to ensure stability, reliability, and safety. Load imbalance can result in overheating of transformers and other electrical equipment, reducing their operational life and increasing the risk of failure, even leading to power outages.</div><div>A study employed fuzzy logic to address the problem, achieving load balancing through the utilization of the Sugeno Fuzzy Logic method. The objective of this research is to make a significant contribution to improving the efficiency, reliability, and scalability of the power distribution system, with the ultimate goal of maximizing the use of electrical equipment.</div><div>It will facilitate the implementation of more intelligent and adaptive decision-making processes. The method is as follows:<ul><li><span>•</span><span><div>The fuzzy approach used a multi-input multi-output (MIMO) system with rule-base 3 × 3 × 3 matrix.</div></span></li><li><span>•</span><span><div>The Sugeno method was selected due to its utilization of a constant mathematical function. This approach offers the benefit of straightforward computation, which can enhance the system's speed and efficiency.</div></span></li><li><span>•</span><span><div>The results showed that the initial load imbalance was 30.86 %, reduced to 5.59 % after the application of load balancing, this is following the IEEE std 446–1995 which allows the maximum load imbalance percentage to be 5–20 %.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103197"},"PeriodicalIF":1.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From lab to real-life: A three-stage validation of wearable technology for stress monitoring","authors":"Basil A. Darwish ,&nbsp;Shafiq Ul Rehman ,&nbsp;Ibrahim Sadek ,&nbsp;Nancy M. Salem ,&nbsp;Ghada Kareem ,&nbsp;Lamees N. Mahmoud","doi":"10.1016/j.mex.2025.103205","DOIUrl":"10.1016/j.mex.2025.103205","url":null,"abstract":"<div><div>Stress negatively impacts health, contributing to hypertension, cardiovascular diseases, and immune dysfunction. While conventional diagnostic methods, such as self-reported questionnaires and basic physiological measurements, often lack the objectivity and precision needed for effective stress management, wearable devices present a promising avenue for early stress detection and management. This study conducts a three-stage validation of wearable technology for stress monitoring, transitioning from controlled experimental data to real-life scenarios. Using the controlled WESAD dataset, binary and five-class classification models were developed, achieving maximum accuracies of 99.78 %±0.15 % and 99.61 %±0.32 %, respectively. Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP) were identified as reliable stress biomarkers. Validation was extended to the SWEET dataset, representing real-life data, to confirm generalizability and practical applicability. Furthermore, commercially available wearables supporting these modalities were reviewed, providing recommendations for optimal configurations in dynamic, real-world conditions. These findings demonstrate the potential of multimodal wearable devices to bridge the gap between controlled studies and practical applications, advancing early stress detection systems and personalized stress management strategies.<ul><li><span>•</span><span><div>Stress detection methods were validated using multimodal wearable data in controlled (WESAD) and real-life (SWEET) datasets.</div></span></li><li><span>•</span><span><div>Commercial wearable technologies were reviewed, offering insights into their applicability for practical stress monitoring.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103205"},"PeriodicalIF":1.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction","authors":"R. Ramani ,&nbsp;S. Edwin Raja ,&nbsp;D. Dhinakaran ,&nbsp;S. Jagan ,&nbsp;G. Prabaharan","doi":"10.1016/j.mex.2025.103210","DOIUrl":"10.1016/j.mex.2025.103210","url":null,"abstract":"<div><div>Recent trendy applications of Artificial Intelligence are Machine Learning (ML) algorithms, which have been extensively utilized for processes like pattern recognition, object classification, effective prediction of disease etc. However, ML techniques are reasonable solutions to computation methods and modeling, especially when the data size is enormous. These facts are established due to the reason that big data field has received considerable attention from both the industrial experts and academicians. The computation process must be accelerated to achieve early disease prediction in order to accomplish the prospects of ML for big data applications. In this paper, a method named “Associative Kruskal Wallis and MapReduce Poly Kernel (AKW-MRPK)\" is presented for early disease prediction. Initially, significant attributes are selected by applying Associative Kruskal Wallis Feature Selection model. This study parallelizes polynomial kernel vector using MapReduce based on the significant qualities gained, which will become a significant computing model to facilitate the early prognosis of disease. The proposed AKW-MRPK framework achieves up to 92 % accuracy, reduces computational time to as low as 0.875 ms for 25 patients, and demonstrates superior speedup efficiency with a value of 1.9 ms using two computational nodes, consistently outperforming supervised machine learning algorithms and Hadoop-based clusters across these critical metrics.<ul><li><span>•</span><span><div>The AKW-MRPK method selects attributes and accelerates computations for predictions.</div></span></li><li><span>•</span><span><div>Parallelizing polynomial kernels improves accuracy and speed in healthcare data analysis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103210"},"PeriodicalIF":1.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta","authors":"Nurtiti Sunusi ,&nbsp;Ankaz As Sikib ,&nbsp;Sumanta Pasari","doi":"10.1016/j.mex.2025.103202","DOIUrl":"10.1016/j.mex.2025.103202","url":null,"abstract":"<div><div>Air pollution poses a significant challenge to public health and the global environment. The Industrial Revolution, advancing technology and society, led to elevated air pollution levels, contributing to acid rain, smog, ozone depletion, and global warming. Poor air quality increases risks of respiratory inflammation, tuberculosis, asthma, chronic obstructive pulmonary disease (COPD), pneumoconiosis, and lung cancer.</div><div>In this context, developing reliable air pollution forecasting models is imperative for guiding effective mitigation strategies and policy interventions. This study presents a daily air pollution prediction model focusing on Jakarta's sulfur dioxide (SO₂) and carbon monoxide (CO) levels, leveraging a hybrid methodology that integrates Clustering Large Applications (CLARA) with the Fuzzy Time Series Markov Chain (FTSMC) approach.</div><div>The analysis revealed five distinct clusters, with medoid selection refined iteratively to ensure stabilization. A 5 × 5 Markov transition probability matrix was subsequently constructed for modeling the data. Predicted values for SO₂ and CO in Jakarta using the CLARA-FTSMC hybrid method showed strong alignment with the actual data. Forecasting accuracy results for SO₂ and CO in Jakarta, based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), showed excellent performance, underscoring the efficacy of the CLARA-FTSMC hybrid approach in predicting air pollution levels.<ul><li><span>•</span><span><div>The CLARA-FTSMC hybrid method demonstrates high effectiveness in analyzing large datasets, addressing the limitations of previous hybrid clustering fuzzy time series methods.</div></span></li><li><span>•</span><span><div>The number of fuzzy time series partitions is optimally determined based on clustering results obtained through the gap statistic approach, ensuring robust partitioning.</div></span></li><li><span>•</span><span><div>The forecasting accuracy of the CLARA-FTSMC hybrid method, evaluated using MAE and RMSE, showed excellent performance in predicting daily air pollution levels of SO₂ and CO in Jakarta.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103202"},"PeriodicalIF":1.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}