MethodsX最新文献

{"title":"Experimental and experiential recommendations for using the GreenFeed systems to measure gas flux in grazing and confined cattle","authors":"E.A. French ,&nbsp;M.R. Beck ,&nbsp;K.F. Kalscheur ,&nbsp;D.M. Jaramillo ,&nbsp;J.D. Derner ,&nbsp;C.A. Moffet ,&nbsp;B.W. Neville ,&nbsp;K.J. Soder ,&nbsp;R.C. O’Connor ,&nbsp;J.A. Koziel ,&nbsp;P. Vadas ,&nbsp;S. Moeller ,&nbsp;S.A. Gunter ,&nbsp;USDA-Agricultural Research Service, Enteric Methane Intervention Team (EMIT) Working Group","doi":"10.1016/j.mex.2025.103667","DOIUrl":"10.1016/j.mex.2025.103667","url":null,"abstract":"<div><div>Improving ruminant production efficiency is contingent on accurately measuring gas fluxes from individual animals in grazing and confined feeding environments The GreenFeed system (GFS) has been increasingly utilized by researchers to measure gas flux of ruminants in their production environment. However, there are wide inconsistencies in methodologies from laboratory-to-laboratory. The objective of this manuscript is to provide standardized recommendations for measuring individual animal gas fluxes of methane, carbon dioxide, oxygen, and hydrogen by the GFS, which are based on experiential and experimental evidence. The method includes:<ul><li><span>•</span><span><div>GFS management: Setup, maintenance, and calibration of sensors</div></span></li><li><span>•</span><span><div>Animal Recommendations: Number of animals to sample, training on use of the system, and bait feeds – including type, composition, and mass. Further, we address operational considerations of using the GFS in extensive grazing environments and confined feeding operations.</div></span></li><li><span>•</span><span><div>Data recommendations: pre-processing data, data cleaning, handling outliers, approaches for estimating individual animal gas fluxes, and uses of application performance interfaces in conjunction with R for statistical analyses.</div></span></li></ul>Increasing standardization in GFS management across experiments and laboratory groups is greatly needed. We hope that these recommendations based on our collective experience and experimental evidence will aid in the standardization of GFS methodologies.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103667"},"PeriodicalIF":1.9,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vocal features based Parkinson’s detection: An ensemble learning approach","authors":"Megha Chakole ,&nbsp;Sanjay Dorle ,&nbsp;Rahul Agrawal ,&nbsp;Priya Dasarwar ,&nbsp;Uma Yadav ,&nbsp;Rashmi Sharma","doi":"10.1016/j.mex.2025.103662","DOIUrl":"10.1016/j.mex.2025.103662","url":null,"abstract":"<div><div>Parkinson’s disease (PD) primarily affects the central nervous system. In 2019, over 8.5 million cases were reported, with numbers continuing to rise. This growing prevalence emphasizes the urgent need for early detection and preventive strategies. To resolve this, numerous methods have been introduced, one of them being machine learning technique. By employing deep learning methods on the large-scale datasets, the early prediction and detection of PD is possible. These methods should be precisely evaluated on the basis of vocal features and the best method to predict this neurodegenerative ailment is disclosed. The core objective of this research is to facilitate the medical centers by providing an optimal machine learning technique to early detect PD. In order to decide an ideal method, the renowned machine learning algorithms like Random Forest, K Nearest Neighbor, Naïve Bayes, Gradient Boosting and XGBoost are evaluated according to their performance. Gradient Boosting outperforms earlier results with high recall, low log loss, and overfitting resistance.</div><div>Vocal features proved to be valuable indicators for early-stage Parkinson’s detection.</div><div>The Gradient Boosting model has scored the highest in terms of all the mentioned parameters, showing a promising result for predicting the occurrence of PD.</div><div>Machine learning can play a significant role in supporting clinical diagnosis and decision-making.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103662"},"PeriodicalIF":1.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Lifestyle Nursing in Promoting Planetary Health: A Scoping Review Protocol","authors":"Anabela Coelho ,&nbsp;Marta Sofia Catarino ,&nbsp;Vanessa Cardoso Monteiro ,&nbsp;Florbela Bia ,&nbsp;Maria do Céu Marques ,&nbsp;Mónica Couto Antunes","doi":"10.1016/j.mex.2025.103659","DOIUrl":"10.1016/j.mex.2025.103659","url":null,"abstract":"<div><div>This scoping review aims to explore and map the existing scientific evidence on how lifestyle nursing contributes to the promotion of planetary health. Lifestyle nursing encompasses interventions related to healthy behaviour change, including physical activity, nutrition, stress management, and sustainable practices. Planetary health, in turn, refers to the interconnectedness of human well-being and the health of natural systems.</div><div>The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and be reported according to the PRISMA-ScR guidelines. Relevant literature will be identified through comprehensive searches in databases such as CINAHL Ultimate, MEDLINE, Complementary Index, BASE (Bielefeld Academic Search Engine), Directory of Open Access Journals (DOAJ), Psychology and Behavioural Sciences Collection, and ScienceDirect, along with grey literature sources. Studies involving nurses or nursing interventions that address individual lifestyle, and environmental sustainability will be included. The results will provide a synthesis of current knowledge, highlight gaps in the literature, and inform future research and nursing practice related to sustainability and global health.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103659"},"PeriodicalIF":1.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating commonsense knowledge with GPT embeddings for emotion classification","authors":"Uma Yadav ,&nbsp;Priya Dasarwar ,&nbsp;Deepak Asudani","doi":"10.1016/j.mex.2025.103656","DOIUrl":"10.1016/j.mex.2025.103656","url":null,"abstract":"<div><div>Recognizing emotions in text is still hard, especially in real life where little differences and hidden indications are widespread. Because they don't rely enough on either contextual understanding or external knowledge, traditional models typically miss the deeper layers of human emotion. This research suggests a new paradigm based on fusion that combines contextual semantics with common sense knowledge to improve emotion classification. We use GPT-based embeddings and external knowledge graphs like ConceptNet and COMET to help us grasp emotions in text both via experience and through meaning. Our technique makes important contributions by:</div><div>• Includes both contextual meaning and common sense reasoning to find emotions more accurately.</div><div>• It connects surface-level language clues with hidden emotional states.</div><div>• It makes it easier to sort emotions into seven main groups: Joy, Sadness, Anger, Fear, Surprise, Disgust, and Neutral.</div><div>A thorough examination of the GoEmotions dataset shows that our model does far better than current baselines at classifying multiple emotions. Combining commonsense and contextual aspects makes things easier to understand and more reliable, especially when dealing with indirect or unclear emotional expressions. Our results show how important it is to combine semantic knowledge with human-like experiential reasoning to make affective computing more accurate and useful in more situations.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103656"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum-enhanced LSTM for predictive maintenance in industrial IoT systems","authors":"Sudharson K ,&nbsp;Varsha S ,&nbsp;Santhiya R ,&nbsp;Rajalakshmi D","doi":"10.1016/j.mex.2025.103653","DOIUrl":"10.1016/j.mex.2025.103653","url":null,"abstract":"&lt;div&gt;&lt;div&gt;An innovative solution for predictive maintenance in IIoT systems combining quantum computing with the proficiency of LSTM neural networks is proposed by us. Our concept is guided by a hybrid quantum-classical architecture to facilitate quantum computing to exploit high-dimensional industrial sensor measurements while preserving crucial temporal relationships through particular quantum channels. Through the combination of the representational ingenuity of quantum circuits, along with the sequence-based modelling of classical LSTMs, QE-LSTM is uniquely positioned to handle complicated time series coming out of industrial sensors. At the heart of our methodology are the following unique elements:&lt;ul&gt;&lt;li&gt;&lt;span&gt;•&lt;/span&gt;&lt;span&gt;&lt;div&gt;A collaborative framework integrating quantum and classical technologies allowing for the quantum computer to manage the complex analysis of high dimensional sensor data in the industry.&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;•&lt;/span&gt;&lt;span&gt;&lt;div&gt;Quantum channel designs were aimed at minimizing temporal dependencies in temporal series industrial measurements, thereby maximizing the quality of sequential analysis.&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;•&lt;/span&gt;&lt;span&gt;&lt;div&gt;&lt;div&gt;Under ODS hindcasting, QE-LSTM improved F1 by 4–5 percentage points on SECOM and reduced RMSE and NASA Score on C-MAPSS; trends were consistent on IMMD (&lt;span&gt;&lt;span&gt;Table 1&lt;/span&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;Table 2&lt;/span&gt;&lt;/span&gt;).&lt;/div&gt;&lt;div&gt;&lt;span&gt;&lt;span&gt;&lt;p&gt;&lt;span&gt;Table 1&lt;/span&gt;. &lt;!--&gt;Performance comparison across datasets.&lt;/p&gt;&lt;/span&gt;&lt;/span&gt;&lt;div&gt;&lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;th&gt;Dataset&lt;/th&gt;&lt;th&gt;Model&lt;/th&gt;&lt;th&gt;Accuracy&lt;/th&gt;&lt;th&gt;Precision&lt;/th&gt;&lt;th&gt;Recall&lt;/th&gt;&lt;th&gt;F1&lt;/th&gt;&lt;th&gt;AUC&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;SECOM&lt;/td&gt;&lt;td&gt;LSTM&lt;/td&gt;&lt;td&gt;0.864&lt;/td&gt;&lt;td&gt;0.842&lt;/td&gt;&lt;td&gt;0.809&lt;/td&gt;&lt;td&gt;0.825&lt;/td&gt;&lt;td&gt;0.902&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;CNN-LSTM&lt;/td&gt;&lt;td&gt;0.878&lt;/td&gt;&lt;td&gt;0.862&lt;/td&gt;&lt;td&gt;0.824&lt;/td&gt;&lt;td&gt;0.842&lt;/td&gt;&lt;td&gt;0.914&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;QE-LSTM (sim)&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.904&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.892&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.861&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.876&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.938&lt;/strong&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;QE-LSTM (hardware)&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;0.896&lt;/td&gt;&lt;td&gt;0.881&lt;/td&gt;&lt;td&gt;0.850&lt;/td&gt;&lt;td&gt;0.865&lt;/td&gt;&lt;td&gt;0.930&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;IMMD&lt;/td&gt;&lt;td&gt;LSTM&lt;/td&gt;&lt;td&gt;0.906&lt;/td&gt;&lt;td&gt;0.883&lt;/td&gt;&lt;td&gt;0.862&lt;/td&gt;&lt;td&gt;0.872&lt;/td&gt;&lt;td&gt;0.943&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;CNN-LSTM&lt;/td&gt;&lt;td&gt;0.913&lt;/td&gt;&lt;td&gt;0.891&lt;/td&gt;&lt;td&gt;0.869&lt;/td&gt;&lt;td&gt;0.880&lt;/td&gt;&lt;td&gt;0.949&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;QE-LSTM (sim)&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.928&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.908&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.888&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.898&lt;/strong&gt;&lt;/td&gt;&lt;td&gt;&lt;strong&gt;0.960&lt;/strong&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div&gt;&lt;div&gt;QE-LSTM (sim) vs LSTM F1 deltas: SECOM &lt;strong&gt;+5.1 pp&lt;/strong&gt;, IMMD &lt;strong&gt;+2.6 pp&lt;/strong&gt;; paired &lt;em&gt;t&lt;/em&gt;-test &lt;em&gt;p&lt;/em&gt; &lt; 0.01.&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;span&gt;&lt;span&gt;&lt;p&gt;&lt;span&gt;Table 2&lt;/span&gt;. &lt;!--&gt;RUL prediction performance metrics.&lt;/p&gt;&lt;/span&gt;&lt;/span&gt;&lt;div&gt;&lt;table&gt;&lt;thead&gt;&lt;tr&gt;&lt;th&gt;Metric&lt;/th&gt;&lt;th&gt;Classical LS","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103653"},"PeriodicalIF":1.9,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AAB-FusionNet: A real-time object detection model for UAV edge computing platforms","authors":"Chi Kien Ha,&nbsp;Hoanh Nguyen,&nbsp;Long Ho Le","doi":"10.1016/j.mex.2025.103654","DOIUrl":"10.1016/j.mex.2025.103654","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) often operate under stringent resource constraints while requiring real-time object detection, which can lead to failures in cluttered backgrounds or when targets are small or partially occluded. To address these challenges, we introduce AAB-FusionNet, a real-time detection model specifically designed for UAV edge computing platforms. At its core is the Adaptive Attention Block (AAB), which employs an Adaptive Saliency-based Attention (ASA) mechanism to highlight the most discriminative tokens while a lightweight MBConv sub-layer refines local spatial features. This saliency-driven framework ensures the network remains focused on critical cues despite complex aerial imagery. To further boost performance, AAB-FusionNet utilizes a Multi-layer Feature Fusion Network that integrates three key components: Attentive Inverted Bottleneck Aggregation (AIBA) to restore significant details at multiple scales, DySample for preserving spatial fidelity during feature alignment, and the Dual-Attention Noise Mitigation (DNM) module to suppress environmental noise through complementary channel and spatial attention. Experiments on diverse aerial datasets confirm that AAB-FusionNet achieves robust detection, especially for small or partially occluded objects, while offering real-time inference on low-power hardware. Overall, AAB-FusionNet effectively balances accuracy, computational efficiency, and adaptability, making it ideally suited for UAV scenarios demanding fast, reliable object detection and robust and consistent performance.<ul><li><span>•</span><span><div>Incorporates an Adaptive Saliency-based Attention mechanism to emphasize critical visual cues.</div></span></li><li><span>•</span><span><div>Introduces a Multi-layer Feature Fusion Network for detail restoration, feature alignment, and noise mitigation.</div></span></li><li><span>•</span><span><div>Demonstrates real-time, high-accuracy detection on low-power UAV platforms, particularly for small or occluded targets.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103654"},"PeriodicalIF":1.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modal optical granulometry: An easy-to-use methodology to quantitatively determine particle size in consolidated and inaccessible deposits","authors":"L.A. Rodríguez-Sedano ,&nbsp;D. Sarocchi ,&nbsp;J.A. Montenegro-Ríos ,&nbsp;F. Castillo-Rivera ,&nbsp;G. Moreno-Chávez ,&nbsp;A.J. Ortiz-Rodríguez","doi":"10.1016/j.mex.2025.103651","DOIUrl":"10.1016/j.mex.2025.103651","url":null,"abstract":"<div><div>This work presents a methodology called Modal Optical Granulometry (MOG), designed to quantitatively analyze particle size in consolidated or inaccessible deposits. This optical, non-invasive, low-cost method enables the generation of grain size profiles from digital photographs without the need for physical sampling. MOG is based on stereological principles, which involves measuring the intersections between superimposed horizontal lines and clasts in calibrated images. The technique consists of three stages: (1) photographing or filming the outcrop with a visible scale in the field, (2) image analysis using software such as Image Pro Plus or free alternatives, and (3) data processing using a custom-designed Excel spreadsheet. In addition to general granulometric analysis, the method enables the construction of Vertical Granulometric Profiles (VGP) and the analysis of Longitudinal Grain Size Evolution (LGE) along the deposit, providing valuable information on the dynamics of pyroclastic flows, lahars, and other sedimentary processes. The methodology is validated through a case study at Colima Volcano (Mexico), demonstrating its effectiveness in identifying depositional units and subtle textural variations. The technique also shows potential applications in other fields, such as, economic geology, and hydrological connectivity studies, due to its ability to deliver accurate and representative data in an accessible manner.</div><div>Bullet points:</div><div>Optical methodology for quantitative particle size analysis in volcaniclastic deposits</div><div>Non-invasive technique suitable for inaccessible or consolidated outcrops</div><div>Easy-to-use, robust, low-cost optical granulometry method.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103651"},"PeriodicalIF":1.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The AI-DRM protocol to enhance the lifetime of wireless sensor network","authors":"Santosh Anand,&nbsp;Anantha Narayanan V","doi":"10.1016/j.mex.2025.103649","DOIUrl":"10.1016/j.mex.2025.103649","url":null,"abstract":"<div><div>Energy is a major research challenge in wireless sensor networks since it is placed in an area that is inaccessible to humans. In the current study, nodes send data to their neighboring nodes at any distance using the same energy level. Smaller distances require less energy to transmit to adjacent nodes, creating a strong research gap. High-distance transmissions require more energy. The node must tailor its transmission energy to distance, not fixed energy. The best transmission power for communication is determined via the neural network-based machine learning technique, which is based on the propagation model and network properties, such as the node density, residual energy, and energy harvesting rate. In this work, sensor nodes transmit information to their neighboring nodes via the multiple linear regression model for dynamic radio tuning with the FRIIS propagation model, and the simulation records the node's energy consumption. Compared with the four recent best current methods that increase the W.S.N. lifetime, the proposed protocol is better and uses less power. The proposed AI-DRM protocol has sufficient residual energy to transmit the packet until 1403 rounds, which is higher than those of two recent energy-efficient protocols, the ARORA and the EACHS-B2SPNN protocols.<ul><li><span>1.</span><span><div>The AI-based dynamic transmission power protocol tunes the sensor nodes using a propagation model.</div></span></li><li><span>2.</span><span><div>Prediction of lifetime of WSN</div></span></li><li><span>3.</span><span><div>Effective utilization of all sensor nodes<span><span>¹</span></span></div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103649"},"PeriodicalIF":1.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hypercomplex neural networks: Exploring quaternion, octonion, and beyond in deep learning","authors":"Raghavendra M Devadas ,&nbsp;Vani Hiremani ,&nbsp;Preethi ,&nbsp;Sowmya T ,&nbsp;Sapna R ,&nbsp;Praveen Gujjar","doi":"10.1016/j.mex.2025.103644","DOIUrl":"10.1016/j.mex.2025.103644","url":null,"abstract":"<div><div>Hypercomplex Neural Networks (HNNs) represent the next frontier in deep learning, building on the mathematical theory of quaternions, octonions, and higher-dimensional algebras to generalize conventional neural architectures. This review synthesizes cutting-edge methods with their theoretical bases, architectural advancements, and primary applications, tracing the development of hypercomplex mathematics and its implementation in computational models. We distil key advances in quaternion and octonion networks, highlighting their ability to provide compact representations and computational efficiency. Particular attention is given to the unique challenge of non-associativity in octonions—where the order in which numbers are multiplied affects the result—requiring careful design of network operations. The article also discusses training complexity, interpretability, and the lack of standardized frameworks, alongside comparative performance with real- and complex-valued networks. Future directions include scalable algorithm construction, lightweight architectures through tensor decompositions, and integration with quantum-inspired systems using higher-order algebras. By presenting a systematic synthesis of current literature and linking these advances to practical applications, this review aims to equip researchers and practitioners with a clear understanding of the strengths, limitations, and potential of HNNs for advancing multidimensional data modelling.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103644"},"PeriodicalIF":1.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modified Gompertz model and its MATLAB implementation for microbial growth performance assessment","authors":"Loyal Murphy,&nbsp;Q․Peter He,&nbsp;Jin Wang","doi":"10.1016/j.mex.2025.103642","DOIUrl":"10.1016/j.mex.2025.103642","url":null,"abstract":"<div><div>To systematically assess the growth performance of different methanotrophs, microalgae and their cocultures, this work presents an improved four-parameter Zwietering modification of the Gompertz model (4Z model) to extract biologically relevant information using batch growth data. The 4Z model was based on the three-parameter Zwietering modification of the original Gompertz model, with a constant term added to address the discrepancy between model predictions and measurements for the initial period of growth data. The 4Z model provided excellent fits to the batch growth data of different monocultures and cocultures. However, the parameters in the 4Z model are different from the commonly used maximum growth rate and delay time, making interpretation of the results challenging. To facilitate the assessment of different strains, we follow the two-step procedure to extract biologically significant parameters:</div><div>1. Estimate the four parameters in the 4Z model using the whole batch growth trajectory.</div><div>2. Use the 4Z model prediction of early-stage growth data to estimate the biologically significant parameters in the commonly used exponential growth model.</div><div>The estimated biologically significant parameters (maximum growth rate, delay time, and carrying capacity) enabled an unbiased assessment of different strains.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103642"},"PeriodicalIF":1.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}