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A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence in fog computing 通过雾计算中计算智能的三层方案加强云存储中隐私保护的方法
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-19 DOI: 10.1016/j.mex.2024.103053
Sneha Ojha , Priyanka Paygude , Amol Dhumane , Snehal Rathi , Vijaykumar Bidve , Ajay Kumar , Prakash Devale
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.
  • 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.
  • 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.
由于传统加密方法存在漏洞,可能无法抵御来自云服务器的内部攻击,云计算的最新进展加剧了人们对数据控制和隐私的担忧。为了克服这些有关数据隐私和云端传输控制的问题,我们提出了一种结合雾计算方法的新型三层存储模型。该框架充分利用了云存储的优势,同时提高了数据的私密性。该方法使用哈希-所罗门码算法将数据划分为不同的部分,除云存储外,还将一部分数据分布在本地机器和雾服务器上。这种分布不仅提高了数据隐私性,还优化了存储效率。计算智能通过计算云、雾和本地服务器之间的最佳数据分布发挥了关键作用,确保了数据存储的均衡性和安全性。对这种数学模式的实验分析表明,随着数据块数量的增加,存储效率得到了显著提高,提高幅度从30%到40%不等。
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引用次数: 0
Method for measuring the transpiration resistance of fruit and vegetables 测量水果和蔬菜蒸腾阻力的方法
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-16 DOI: 10.1016/j.mex.2024.103058
Manfred Linke, Tuany Gabriela Hoffmann, Akshay D. Sonawane, Guido Rux, Pramod V. Mahajan
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.
  • Direct method for tissue resistance and boundary layer resistance determination for fruit and vegetables.
  • Non-destructive method to optimize postharvest by using produce as a sensor to ensure quality.
这项研究探讨了水果和蔬菜采后质量损失与蒸腾作用和呼吸作用的动态相互作用之间的复杂关系。它强调了农产品的固有特性和采后环境条件对蒸腾作用的深远影响,从而引起外观和内部质量的变化,尤其是萎蔫。尽管农产品专用蒸腾系数被普遍使用,但由于计算中的假设条件不同,该系数也存在局限性。表面条件将农产品和空气特性错综复杂地联系在一起,因此需要全面了解。园艺产品含水量高,在产品和环境空气水势差的作用下,会通过蒸腾作用不断失水。蒸腾作用包括组织阻力和边界层阻力,受植物组织特性和外部因素的影响。遭遇干旱胁迫的果实会表现出较高的组织阻力,这是一种保护机制。同时,边界层阻力受外部参数影响,对采后行为产生重大影响。为解决这些复杂问题,我们开发了一种新方法,可对农产品特性、气候和流动条件进行单独分析。这种创新方法增强了对蒸腾作用的理解,为改进采后实践、技术配置和质量维护策略奠定了基础。
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引用次数: 0
Deep learning-based classification of alfalfa varieties: A comparative study using a custom leaf image dataset 基于深度学习的紫花苜蓿品种分类:使用自定义叶片图像数据集进行比较研究
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-16 DOI: 10.1016/j.mex.2024.103051
Yonis Gulzar , Zeynep Ünal , Tefide Kızıldeniz , Usman Muhammad Umar
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.
  • 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.
  • 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.
  • 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.
深度学习通过提高植物分类任务的准确性和效率,对农业产生了深远影响。特别是,先进的模型大大提高了根据视觉特征对各种植物物种进行分类的能力。本研究的重点是利用深度学习技术对紫花苜蓿植物品种进行分类。我们创建了一个自定义数据集,其中包括在受控条件下栽培的三个紫花苜蓿品种(Bilensoy-80、Diana 和 Nimet)的 1,214 张图像。我们的比较研究涉及多个最先进的模型,包括 MobileNetV3、InceptionV3、Xception、VGG19、DenseNet121、ResNet101 和 EfficientNetB3,以评估它们在对这些紫花苜蓿品种进行分类时的性能。我们用不同的配置对这些模型进行了评估:学习率从 0.1 到 0.000001 不等;批量大小为 8、16、32 和 64;使用衰减率为 0.96、衰减步数为 1000 的 dropout。结果显示,使用迁移学习训练的模型通常能获得更高的测试准确率。例如,DenseNet121 从零开始训练时的测试准确率为 0.9945,而采用迁移学习后的测试准确率为 1.0000,而 EfficientNetB3 采用这两种方法后的测试准确率均为 0.9945。研究引入了一个独特的数据集,该数据集由在受控条件下栽培的三个紫花苜蓿品种(Bilensoy-80、Diana 和 Nimet)的 1214 张图像组成,为推进植物分类研究提供了宝贵的资源。-该研究比较了几种最先进的深度学习模型(MobileNetV3、InceptionV3、Xception、VGG19、DenseNet121、ResNet101 和 EfficientNetB3)在不同超参数配置下的性能,展示了不同架构在紫花苜蓿植物品种分类中的有效性。
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引用次数: 0
Evaluative comparison of machine learning algorithms for stutter detection and classification 用于口吃检测和分类的机器学习算法的评估比较
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-14 DOI: 10.1016/j.mex.2024.103050
Ramitha V, Rhea Chainani, Saharsh Mehrotra, Sakshi Sah, Smita Mahajan
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.
  • 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).
  • The SEP-28k dataset is used to perform a comparative analysis of different machine learning models.
  • The research examines the impact of key acoustic features on model accuracy while addressing challenges such as class imbalance.
口吃是一种神经发育性语言障碍,由于不自主的停顿和声音重复而导致说话中断。口吃对心理有深远影响,会影响社会交往和职业发展。自动检测语音记录中的口吃事件可以帮助语言治疗师或语言病理学家跟踪口吃患者(PWS)的流利程度。它还有助于改进现有的口吃患者语音识别系统。本文利用 SEP-28k 数据集进行比较分析,以评估各种机器学习模型在对五种流畅性障碍类型(即延时、插话、词语重复、声音重复和块状)进行分类时的性能。-这项研究的重点是自动检测语音记录中的口吃事件,为语音治疗师提供支持,并改进口吃患者(PWS)的语音识别系统。
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引用次数: 0
Optimized Methyl methacrylate embedding of small and large undecalcified bones. 优化甲基丙烯酸甲酯包埋大小未钙化骨骼。
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-13 DOI: 10.1016/j.mex.2024.103046
Jackie A. Fretz, Nancy W. Troiano
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:
  • Updates previously published MMA embedding protocols to reflect utilization of stabilized acrylamides (over the unstabilized reagents of the past)
  • Outlines the techniques that are important for embedding both small (mus), medium (rattus), and large (porcine, lagomorph, human) histological samples.
  • 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
甲基丙烯酸甲酯(MMA)塑料包埋作为一种对骨骼进行处理和组织学评估的技术由来已久。与石蜡相比,它的优势在于不需要对组织进行脱钙处理就能观察到细胞细节,从而保留了有关未矿化骨质以及骨骼矿化程度的重要信息。它还能通过保留骨中的荧光标签,进行动态组织形态分析。硬组织的有效浸润对于骨骼的处理和制作高质量的切片至关重要,这样才能在另一端获得可用的可量化组织学结果。该技术:-更新了之前发布的 MMA 包埋方案,以反映稳定丙烯酰胺的使用情况(而不是过去的非稳定试剂)-概述了包埋小型(麝香)、中型(鼠)和大型(猪、袋鼠、人)组织学样本的重要技术。
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引用次数: 0
A comprehensive study of fractal clustering and firefly algorithm for WSN Deployment: Implementation and outcomes 针对 WSN 部署的分形聚类和萤火虫算法的综合研究:实施与成果
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-10 DOI: 10.1016/j.mex.2024.103030
Neha Sharma , Vishal Gupta
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.
无线传感器网络(WSN)已在各种应用领域得到广泛应用,并成为从偏远和难以接近的地区收集数据的可靠技术。无线传感器网络对于环境监测、监控和智能基础设施等现实世界的应用非常重要。网络覆盖、连通性和节能是 WSN 部署的重要因素。无线传感器网络(WSN)面临着能源消耗最小化、覆盖范围最大化和网络寿命改善等大量关键挑战。在现实世界的许多应用中,传感器节点都受到能源限制,并部署在资源有限的环境中。因此,低能耗对于延长网络寿命至关重要。同时,为了保证 WSN 的性能,必须确保以较低的能耗和全覆盖的方式进行数据传输。这些挑战是这项工作的核心重点,需要可扩展的高效部署策略。本文对 WSN 部署的优化技术进行了全面调查研究,以提高网络性能和资源利用率。本文还提出了一种名为 "基于分形聚类的萤火虫部署算法 "的新算法,该算法特别适用于 WSN 中传感器节点的部署。所提出的混合方法利用分形聚类和萤火虫优化算法的原理,制定了轻量级、高能效和增强型的优化部署策略。首先,该算法利用分形聚类技术将感兴趣的区域划分为具有相似属性的区域。这种聚类确定了传感器节点密度要求较高的区域--需要关键响应的事件或数据流量较高的区域。该算法通过虚拟萤火虫来表示每个聚类。萤火虫算法是一种受生物启发的蜂群智能优化技术,其灵感来源于萤火虫的闪光行为,萤火虫在输入参数空间中随机移动,以找到有利的部署配置。本文通过使用 MATLAB2020 对提出的算法进行仿真,并与其他部署策略进行比较,验证了该算法的效率。该分析表明结果很有希望。
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引用次数: 0
Validation of reference genes for cardiac RT-qPCR studies spanning the fetal to adult period 验证从胎儿期到成年期心脏 RT-qPCR 研究的参考基因
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-09 DOI: 10.1016/j.mex.2024.103042
Karthikeyan Bose, Samantha Louey, Sonnet S. Jonker
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.
  • 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.
  • 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.
  • 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.
许多用作 mRNA 表达研究内部对照的基因在发育过程中并不稳定(会发生变化)。我们通过 RT-qPCR 确定了 12 个候选参考基因(ACTB、GAPDH、H3-3A、HYAL2、PPIA、RNA18S1、RPL32、RPL37A、RPL41、RPLP0、RPS15 和 YWHAZ)的 mRNA 表达。根据结合了 BestKeeper、比较 delta Ct、GeNorm 和 NormFinder 等计算算法的 RefFinder,RPL32、RPL37A、HYAL2、ACTB 和 GAPDH 是最稳定的参考基因,尽管它们在所有年龄段都没有变化。-我们确定了绵羊从胎儿到新生儿再到成年的心脏基因表达研究中最稳定的参考基因组合;这些步骤适用于确定其他器官、其他物种以及参考基因不稳定性较高的时期的 mRNA 研究的内部对照。
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引用次数: 0
A systematic scanning method to locate cryptic terrestrial species 定位隐蔽陆生物种的系统扫描方法
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-07 DOI: 10.1016/j.mex.2024.103038
Rachel Findlay-Robinson , Davina L. Hill
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 Muscardinus avellanarius. 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.
  • A systematic search method enabled location of cryptic terrestrial species
  • The use of PIT tags allows detection with minimal disturbance
在研究野生动物时,必须考虑到研究技术可能产生的有害影响,特别是如果研究技术可能会影响动物的行为或能量消耗。许多小型陆生物种占据着隐蔽的栖息地,对其特征和位置可能知之甚少。要研究这些栖息地,研究人员必须能够确定它们的位置,但也必须考虑到生物可能受到的干扰及其影响。在这里,我们开发并测试了一种新颖的非侵入式方法,用于定位被动集成应答器(PIT)标记的榛睡鼠(Muscardinus avellanarius)的隐蔽冬眠巢穴。通过使用功能强大的 PIT 标签扫描仪和系统搜索技术,确定了九只野生冬眠睡鼠的位置。相机捕捉器的记录显示,在探测到休眠鼠后,外部没有休眠鼠活动,这表明干扰最小。此外,在搜索过程中,还在森林地面上发现了 11 个已不在睡鼠体内的 PIT 标签。这项研究展示了小型哺乳动物无线电追踪等技术之外的一种非侵入性替代方法,并强调了 PIT 标签在识别个体之外的潜在研究用途,特别是在了解精细尺度的栖息地选择方面。
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引用次数: 0
Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis 利用矩不变式量化细菌形状,可在细胞解痉过程中区分菌群
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-06 DOI: 10.1016/j.mex.2024.103036
Braulio Gutiérrez–Medina
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 Escherichia coli 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.
  • Moment invariants enable quantitative description of bacterial morphology in two dimensions, and estimation of volume
  • We apply the moment invariants framework to describe changes in bacterial shape during plasmolysis
  • The proposed methodology shows suitability to distinguish among cellular populations.
分析细胞的几何形状是了解细胞运动、发育和对外部刺激做出反应的基础。矩不变式框架可量化细胞的形状和大小,但其对杆状细菌的适用性尚未得到探讨。在这项研究中,我们利用力矩不变式来评估大肠杆菌细胞在受到高渗冲击时,在质解过程中细胞形状变化的程度(投影面积和体积)。随着外部盐分(NaCl)条件的增加,特征细胞尺寸描述符的宽度、长度和面积显示出系统性的减少--除了在高盐分条件下,一小部分细胞显示出膜破裂的迹象。我们利用这些二维结果来估计解痉过程中的细胞体积,发现了一个最小体积,该体积不会随着盐浓度的增加而进一步缩小。接下来,我们计算了伸长率和离散度,这两个指标分别量化了细胞形状的伸长程度或与椭圆的差异。在离散度方面,我们观察到在高盐条件下分布出现了长尾。此外,尽管存在宽广的分布,但使用伸长-离散图可以区分解痉细胞和正常细胞。我们应用矩不变式框架来描述解痉过程中细菌形状的变化。
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引用次数: 0
Smart charge-optimizer: Intelligent electric vehicle charging and discharging 智能充电优化器:智能电动汽车充电和放电
IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES Pub Date : 2024-11-06 DOI: 10.1016/j.mex.2024.103037
Archana Y. Chaudhari , Prashant B. Koli , Surbhi D. Pagar , Reena S. Sahane , Kalyani D. Kute , Priyanka M. Abhale , Akanksha J. Kulkarni , Abhilasha K. Bhagat
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.
  • This research aims to Couple Long Short-Term Memory with Integer Linear Programming
  • Applying Q-learning to minimize the peak to-average ratio of grid load through effective peak shaving and valley filling
  • Minimizing EV charging costs for users while respecting their mobility needs
电动汽车的快速发展给电力系统的可靠性带来了风险。然而,电动汽车的高用电量将导致电网变压器过载。要减少电动汽车对电网的负面影响,就必须采取行之有效的充放电调度策略。为了减少电网变压器过载,本文探讨了两种智能充放电调度策略。第一种是长短期记忆与整数线性规划(LSTM-ILP),第二种是Q-learning。LSTM-ILP 的目标是最大限度地减少充放电调度延迟。Q-learning 方法利用强化学习来确定与电动汽车充电状态和电网需求相关的最佳行动方案。本研究旨在将长短期记忆与整数线性规划相结合--应用 Q-learning 通过有效的削峰填谷,最大限度地降低电网负荷的峰均比--在尊重用户移动需求的同时,最大限度地降低用户的电动汽车充电成本。
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引用次数: 0
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