MethodsX最新文献

{"title":"A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence in fog computing","authors":"Sneha Ojha ,&nbsp;Priyanka Paygude ,&nbsp;Amol Dhumane ,&nbsp;Snehal Rathi ,&nbsp;Vijaykumar Bidve ,&nbsp;Ajay Kumar ,&nbsp;Prakash Devale","doi":"10.1016/j.mex.2024.103053","DOIUrl":"10.1016/j.mex.2024.103053","url":null,"abstract":"<div><div>Recent advancements in cloud computing have heightened concerns about data control and privacy due to vulnerabilities in traditional encryption methods, which may not withstand internal attacks from cloud servers. To overcome these issues about the data privacy and control of transfer on cloud, a novel three-tier storage model incorporating fog computing method has been proposed. This framework leverages the advantages of cloud storage while enhancing data privacy. The approach uses the Hash-Solomon code algorithm to partition data into distinct segments, distributing a portion of it across local machines and fog servers, in addition to cloud storage. This distribution not only increases data privacy but also optimises storage efficiency. Computational intelligence plays a crucial role by calculating the optimal data distribution across cloud, fog, and local servers, ensuring balanced and secure data storage.<ul><li><span>•</span><span><div>Experimental analysis of this mathematical mode has demonstrated a significant improvement in storage efficiency, with increases ranging from 30 % to 40 % as the volume of data blocks grows.</div></span></li><li><span>•</span><span><div>This innovative framework based on Hash Solomon code method effectively addresses privacy concerns while maintaining the benefits of cloud computing, offering a robust solution for secure and efficient data management.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103053"},"PeriodicalIF":1.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700490","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":"Method for measuring the transpiration resistance of fruit and vegetables","authors":"Manfred Linke,&nbsp;Tuany Gabriela Hoffmann,&nbsp;Akshay D. Sonawane,&nbsp;Guido Rux,&nbsp;Pramod V. Mahajan","doi":"10.1016/j.mex.2024.103058","DOIUrl":"10.1016/j.mex.2024.103058","url":null,"abstract":"<div><div>This investigation explores the intricate relationship between postharvest quality losses in fruit and vegetables and the dynamic interplay of transpiration and respiration activities. It underscores the profound impact of inherent produce properties and postharvest environmental conditions on transpiration, inducing changes in both external appearance and internal quality, notably wilting. Despite their common use, produce-specific transpiration coefficients encounter limitations due to diverse assumptions in calculations. Surface conditions intricately link produce and air properties, necessitating a comprehensive understanding. Horticultural products, with high water content, undergo continuous water loss through transpiration, driven by the water potential difference between the product and ambient air. Transpiration encompasses tissue and boundary layer resistances, influenced by plant tissue properties and external factors. Fruits experiencing drought stress exhibit elevated tissue resistance, serving as a protective mechanism. Concurrently, boundary layer resistance, influenced by external parameters, significantly shapes postharvest behaviour. To address these complexities, a novel method developed allows separate analysis of produce properties, climate, and flow conditions. This innovative approach enhances the understanding of transpiration behaviour, providing a foundation for improved postharvest practices, technical configurations, and quality maintenance strategies.<ul><li><span>•</span><span><div>Direct method for tissue resistance and boundary layer resistance determination for fruit and vegetables.</div></span></li><li><span>•</span><span><div>Non-destructive method to optimize postharvest by using produce as a sensor to ensure quality.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103058"},"PeriodicalIF":1.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700486","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":"Deep learning-based classification of alfalfa varieties: A comparative study using a custom leaf image dataset","authors":"Yonis Gulzar ,&nbsp;Zeynep Ünal ,&nbsp;Tefide Kızıldeniz ,&nbsp;Usman Muhammad Umar","doi":"10.1016/j.mex.2024.103051","DOIUrl":"10.1016/j.mex.2024.103051","url":null,"abstract":"<div><div>Deep learning has profoundly impacted agriculture by enhancing the accuracy and efficiency of plant classification tasks. In particular, advanced models have significantly improved the ability to classify various plant species based on their visual features. This study focuses on classifying alfalfa plant varieties using deep learning techniques. We created a custom dataset comprising 1,214 images of three alfalfa varieties (Bilensoy-80, Diana and Nimet) cultivated under controlled conditions. Our comparative study involved several state-of-the-art models, including MobileNetV3, InceptionV3, Xception, VGG19, DenseNet121, ResNet101, and EfficientNetB3, to assess their performance in classifying these alfalfa varieties. We evaluated these models with various configurations: learning rates ranging from 0.1 to 0.000001, batch sizes of 8, 16, 32, and 64, and using dropout with a decay rate of 0.96 and decay steps of 1000. The results revealed that models trained with transfer learning generally achieved higher test accuracies. For instance, DenseNet121 achieved a test accuracy of 0.9945 when trained from scratch and 1.0000 with transfer learning, while EfficientNetB3 achieved a test accuracy of 0.9945 with both methods. The findings underscore the effectiveness of transfer learning in enhancing model performance for plant classification tasks.<ul><li><span>•</span><span><div>The study introduced a unique dataset consisting of 1214 images of three alfalfa varieties (Bilensoy-80, Diana, and Nimet) cultivated under controlled conditions, providing a valuable resource for advancing plant classification research.</div></span></li><li><span>•</span><span><div>The research compared the performance of several state-of-the-art deep learning models (MobileNetV3, InceptionV3, Xception, VGG19, DenseNet121, ResNet101, and EfficientNetB3) with various hyperparameter configurations, demonstrating the effectiveness of different architectures for classifying alfalfa plant varieties.</div></span></li><li><span>•</span><span><div>The study highlighted the superior performance of transfer learning in plant classification tasks, with models like DenseNet121 and EfficientNetB3 achieving near-perfect accuracy, underscoring its potential to significantly enhance model accuracy and efficiency in agricultural applications.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103051"},"PeriodicalIF":1.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700491","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":"Evaluative comparison of machine learning algorithms for stutter detection and classification","authors":"Ramitha V,&nbsp;Rhea Chainani,&nbsp;Saharsh Mehrotra,&nbsp;Sakshi Sah,&nbsp;Smita Mahajan","doi":"10.1016/j.mex.2024.103050","DOIUrl":"10.1016/j.mex.2024.103050","url":null,"abstract":"<div><div>Stuttering is a neuro-developmental speech disorder that interrupts the flow of speech due to involuntary pauses and sound repetitions. It has profound psychological impacts that affect social interactions and professional advancements. Automatically detecting stuttering events in speech recordings could assist speech therapists or speech pathologists track the fluency of people who stutter (PWS). It will also assist in the improvement of the existing speech recognition system for PWS. In this paper, the SEP-28k dataset is utilized to perform comparative analysis to assess the performance of various machine learning models in classifying the five dysfluency types namely Prolongation, Interjection, Word Repetition, Sound Repetition and Blocks.<ul><li><span>•</span><span><div>The study focuses on automatically detecting stuttering events in speech recordings to support speech therapists and improve speech recognition systems for people who stutter (PWS).</div></span></li><li><span>•</span><span><div>The SEP-28k dataset is used to perform a comparative analysis of different machine learning models.</div></span></li><li><span>•</span><span><div>The research examines the impact of key acoustic features on model accuracy while addressing challenges such as class imbalance.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103050"},"PeriodicalIF":1.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700489","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":"Optimized Methyl methacrylate embedding of small and large undecalcified bones.","authors":"Jackie A. Fretz,&nbsp;Nancy W. Troiano","doi":"10.1016/j.mex.2024.103046","DOIUrl":"10.1016/j.mex.2024.103046","url":null,"abstract":"<div><div>Methyl methacrylate (MMA) plastic embedding has been long established as a technique for the processing and histological assessment of bones. It provides the added benefit over paraffin in that it does not require decalcification of the tissue in order visualize the cellular detail, thus preserving vital information about the amount of unmineralized osteoid present in addition to the degree of mineralization in the bone. It also allows for the incorporation of dynamic histomorphometric analysis through the retention of fluorescent labels incorporated into the bone. Efficient infiltration of hard tissue is essential to the processing of bones and producing quality slides suitable for achieving usable quantifiable histology out the other end. This technique:<ul><li><span>•</span><span><div>Updates previously published MMA embedding protocols to reflect utilization of stabilized acrylamides (over the unstabilized reagents of the past)</div></span></li><li><span>•</span><span><div>Outlines the techniques that are important for embedding both small (<em>mus</em>), medium (<em>rattus</em>), and large (porcine, lagomorph, human) histological samples.</div></span></li><li><span>•</span><span><div>Updates the clearing and infiltration processes utilized and validates quality of the sample preparation though histological staining to confirm preservation of cellular detail, mineralization information, and enzymatic activity</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103046"},"PeriodicalIF":1.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700487","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 comprehensive study of fractal clustering and firefly algorithm for WSN Deployment: Implementation and outcomes","authors":"Neha Sharma ,&nbsp;Vishal Gupta","doi":"10.1016/j.mex.2024.103030","DOIUrl":"10.1016/j.mex.2024.103030","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs) have been highly utilized and defensible technology in diverse application areas for data gathering from remote and hard-to-approach regions. Wireless Sensor Networks are substantially important for the real-world applications such as environmental monitoring, surveillance, and smart infrastructure. Network coverage, connectivity and energy savings are significant factors in the WSN deployment. Wireless sensor networks (WSNs) undergo a great deal of crucial challenges such as minimize energy consumption, maximize coverage, and network lifetime improvement. Sensor nodes are energy constrained and deployed in resource-constrained environments for many real-world applications. Low energy usage is hence crucial to prolong network life. Meanwhile, to guarantee the performance of a WSN, it is crucial to ensure data transmission with less energy consumption and full coverage. These challenges are the central focus of this work, requiring scalable and efficient deployment strategies. In this paper, a complete survey study on optimization technique for deployment of WSN to improve network performance and resource utilization is offered. The paper also suggests a new algorithm named as Fractal Clustering Based Firefly Deployment Algorithm which is particularly designed for the deployment of sensor nodes deployed in WSNs. The proposed hybridize method uses the principles of fractal clustering and firefly optimization algorithm to make light-weight, energy efficient and enhanced optimized deployment strategy. To start with, the algorithm makes use of a fractal clustering technique to partition an area of interest into regions that have similar attributes. This clustering determines the areas that are needed to have higher sensor node density requirements — regions where events requiring a critical response or data traffic are high. The algorithm represents each cluster by a virtual firefly. The firefly algorithm is a biologically-inspired swarm intelligence optimization technique, inspired by the flashing behavior of fireflies which stochastically moves through input parameter space to find favorable deployment configurations. In this paper, the efficiency of the algorithm is verified by simulating the proposed algorithm using MATLAB2020 and comparing it with other deployment strategies. This analysis shows promising results.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103030"},"PeriodicalIF":1.6,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700488","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":"Validation of reference genes for cardiac RT-qPCR studies spanning the fetal to adult period","authors":"Karthikeyan Bose,&nbsp;Samantha Louey,&nbsp;Sonnet S. Jonker","doi":"10.1016/j.mex.2024.103042","DOIUrl":"10.1016/j.mex.2024.103042","url":null,"abstract":"<div><div>Many genes used as internal controls for mRNA expression studies are unstable (change) over development. This study determined an approach to validate reference genes for mRNA studies spanning the fetal period to adulthood in sheep hearts.<ul><li><span>•</span><span><div>We determined the mRNA expression of 12 candidate reference genes (ACTB, GAPDH, H3-3A, HYAL2, PPIA, RNA18S1, RPL32, RPL37A, RPL41, RPLP0, RPS15, and YWHAZ) via RT-qPCR. Per RefFinder, which incorporates computational algorithms by BestKeeper, comparative delta Ct, GeNorm, and NormFinder, RPL32, RPL37A, HYAL2, ACTB and GAPDH were the most stable reference genes, although none were unchanged across all ages.</div></span></li><li><span>•</span><span><div>Systematical calculation of the geometric means of 3 reference genes revealed the combination of HYAL2, RPL32, and RPL37A was unchanged across the 5 fetal, neonatal, and adult ages.</div></span></li><li><span>•</span><span><div>We determined the most stable combination of reference genes for cardiac gene expression studies in sheep from fetus to newborn to adult; these steps are applicable to determine internal controls for mRNA studies in other organs, other species, and periods in which reference gene instability is high.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103042"},"PeriodicalIF":1.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655309","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 systematic scanning method to locate cryptic terrestrial species","authors":"Rachel Findlay-Robinson ,&nbsp;Davina L. Hill","doi":"10.1016/j.mex.2024.103038","DOIUrl":"10.1016/j.mex.2024.103038","url":null,"abstract":"<div><div>When studying wild animals, consideration must be given to potential detrimental effects of the study technique, particularly if techniques may affect behaviour or energy expenditure. Many small terrestrial species occupy cryptic habitats, the characteristics and locations of which may be poorly understood. To study these habitats, researchers must be able to locate them, but must also consider the potential for disturbance of the organisms and the impacts this may have. Here, we developed and tested a novel, non-invasive method of locating the cryptic hibernation nests of passive integrated transponder (PIT) tagged hazel dormice <em>Muscardinus avellanarius</em>. The use of a powerful PIT tag scanner combined with a systematic search technique resulted in the location of nine wild hibernating dormice. Camera trap recordings indicated no external dormouse activity following detections, indicating minimal disturbance. In addition, eleven PIT tags no longer inside a dormouse were detected on the forest floor during searches. This study demonstrates a non-invasive alternative to techniques such as radio-collaring for small mammals, and highlights potential uses of PIT tags in research beyond identification of individuals, particularly in understanding fine-scale habitat selection.<ul><li><span>•</span><span><div>A systematic search method enabled location of cryptic terrestrial species</div></span></li><li><span>•</span><span><div>The use of PIT tags allows detection with minimal disturbance</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103038"},"PeriodicalIF":1.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655307","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":"Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis","authors":"Braulio Gutiérrez–Medina","doi":"10.1016/j.mex.2024.103036","DOIUrl":"10.1016/j.mex.2024.103036","url":null,"abstract":"<div><div>The analysis of geometrical cell shape is fundamental to understand motility, development, and responses to external stimuli. The moment invariants framework quantifies cellular shape and size, although its applicability has not been explored for rod-shaped bacteria. In this work, we use moment invariants to evaluate the extent of cell shape change (projected area and volume) during plasmolysis, as <em>Escherichia coli</em> cells are subjected to hyperosmotic shock. The characteristic cell size descriptors width, length and area show systematic decrease as external salt (NaCl) conditions increase—except for high salt, where a small population of cells shows evidence of membrane rupture. We use these two-dimensional results to estimate cell volume during plasmolysis, finding a minimum volume that is not reduced further with increase in salt concentration. Next, we computed elongation and dispersion, metrics that quantify how cell shape is stretched out or differs from an ellipse, respectively. For dispersion, we observe the development of a long tail for the distribution at high salt. Moreover, the use of elongation-dispersion plots enables distinction of plasmolyzed and normal cells despite the presence of broad distributions. Altogether, a protocol is provided to evaluate bacterial shape, highlighting a set of metrics that help distinguish among bacterial populations.<ul><li><span>•</span><span><div>Moment invariants enable quantitative description of bacterial morphology in two dimensions, and estimation of volume</div></span></li><li><span>•</span><span><div>We apply the moment invariants framework to describe changes in bacterial shape during plasmolysis</div></span></li><li><span>•</span><span><div>The proposed methodology shows suitability to distinguish among cellular populations.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103036"},"PeriodicalIF":1.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655312","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":"Smart charge-optimizer: Intelligent electric vehicle charging and discharging","authors":"Archana Y. Chaudhari ,&nbsp;Prashant B. Koli ,&nbsp;Surbhi D. Pagar ,&nbsp;Reena S. Sahane ,&nbsp;Kalyani D. Kute ,&nbsp;Priyanka M. Abhale ,&nbsp;Akanksha J. Kulkarni ,&nbsp;Abhilasha K. Bhagat","doi":"10.1016/j.mex.2024.103037","DOIUrl":"10.1016/j.mex.2024.103037","url":null,"abstract":"<div><div>The important steps toward a low-carbon economy and sustainable energy future is switch to Electric Vehicles(EVs).The rapid development of EVs has brought a risk to reliability of the electrical system. However, the high electricity consumption of EVs will lead to the overload of power grid transformers. Strategies for scheduling charging and discharging that work are essential to reducing the negative grid effects of EVs. In order to reduce the overload of power grid transformers, this paper explores two strategies for intelligent charging and discharging scheduling. The first one is Long Short-Term Memory coupled with Integer Linear Programming(LSTM-ILP)and the second one is Q-learning. The LSTM-ILP aims to minimize the charging and discharging schedules delay. The Q-learning method makes use of reinforcement learning to ascertain the best course of action for EVs in relation to their state-of-charge and the demand on the grid. The outcomes of this research show that both strategies are successful in lowering the peak-to-average ratio of the grid and lessening the influence of EV charging demands.<ul><li><span>•</span><span><div>This research aims to Couple Long Short-Term Memory with Integer Linear Programming</div></span></li><li><span>•</span><span><div>Applying Q-learning to minimize the peak to-average ratio of grid load through effective peak shaving and valley filling</div></span></li><li><span>•</span><span><div>Minimizing EV charging costs for users while respecting their mobility needs</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"13 ","pages":"Article 103037"},"PeriodicalIF":1.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700458","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}