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In view of the security problems of image encryption algorithms encoded by single DNA or RNA, to increase the randomness of the diffusion process and the uncertainty of the coding rules, we propose a combining dynamic RNA and DNA computing based chaotic image encryption algorithm, which has a more complicated encryption process for improving the security of the encryption algorithm and increases the difficulty of decoding. First, a new three-dimensional hyperchaotic map is proposed, which exhibits a rich set of dynamic behaviors. Second, the sequences generated by the proposed map are passed to NIST test with good randomness and implemented by digital signal processing hardware, which shows the feasibility of the proposed chaotic map for industrial applications. Second, the K-means algorithm is used to split the plaintext into two parts. Third, the chaotic sequence is used to displace and diffuse the two parts of the plaintext, respectively. Then, chaotic sequences were used to encode using dynamic DNA and RNA of these two parts, respectively. Then, the chaotic sequences were used to compute the dynamic DNA and RNA computing of these two parts, respectively. Finally, the cipher text is decoded accordingly. The experimental results show that compared with some related encryption algorithms, our method has higher security. [ABSTRACT FROM AUTHOR]

K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μ m of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μ m length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel. [ABSTRACT FROM AUTHOR]

A joint maintenance decision-making framework is proposed to optimise the long-term maintenance plan and lower the maintenance cost for offshore wind farms. The historical wind speed data are screened by using the method of k-means clustering, and Markov chains are established for the wind speed in different seasons. On this basis, the approach of Markov chain Monte Carlo is applied to simulate the distribution of repair vessel's waiting time for maintenance, where the impact of wind speed on maintenance availability is considered. Moreover, the components in wind turbines are divided into four states according to their effective ages, i.e. young, mature, old and failed, respectively. A maintenance decision model is established, with the objective to minimise maintenance cost. Besides, three types of opportunistic maintenance are considered, i.e. failure-based opportunistic maintenance (FBOM), event-based opportunistic maintenance (EBOM) and age-based opportunistic maintenance (ABOM), respectively. The enhanced elitist genetic algorithm (SEGA) is adopted to solve the optimisation problem. The results indicate that among the three types of opportunistic maintenance, ABOM can reduce maintenance cost more effectively, and it is more suitable for long-term maintenance plans of offshore wind farm. [ABSTRACT FROM AUTHOR]

Multi-tier sustainable supply chain relationships (MSSCR) are complex with involvement of multiple organisations, practices, objectives, and interconnectivities. These complexities make it difficult to effectively study MSSCR using traditional empirical statistical correlative research. In MSSCR the relationship between the focal firm and sub-supplier may be affected by the relationship between the focal firm and direct-supplier, and the relationship between the direct-supplier and the sub-supplier. This paper introduces a research methodology, based on K-Means clustering, rough set theory, and cluster membership to investigate complex relationships across multi-tier sustainable supply chain triads. Collaborative advantage variables including knowledge-sharing routines, relation-specific investments, complementary capabilities, effective governance mechanisms, and long-term commitment are conceptually presented to evaluate relationships among MSSCR practices and sustainability performance. Insights for practitioners and researchers are provided. Research insights can be gained by analytically providing alternative explanatory and equifinality relationships amongst variables and multi-pair organisations. The methodology can mitigate the endogeneity problem, such as omitted factors (variables), measurement error, even in small sample data situations. Directions for future research are discussed. [ABSTRACT FROM AUTHOR]

We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states. [ABSTRACT FROM AUTHOR]

BackgroundIndividuals confronting health threats may display an optimistic bias such that judgments of their risk for illness or death are unrealistically positive given their objective circumstances.PurposeWe explored optimistic bias for health risks using k-means clustering in the context of COVID-19. We identified risk profiles using subjective and objective indicators of severity and susceptibility risk for COVID-19.MethodsBetween 3/18/2020-4/18/2020, a national probability sample of 6,514 U.S. residents reported both their subjective risk perceptions (e.g., perceived likelihood of illness or death) and objective risk indices (e.g., age, weight, pre-existing conditions) of COVID-19-related susceptibility and severity, alongside other pandemic-related experiences. Six months later, a subsample (N = 5,661) completed a follow-up survey with questions about their frequency of engagement in recommended health protective behaviors (social distancing, mask wearing, risk behaviors, vaccination intentions).ResultsThe k-means clustering procedure identified five risk profiles in the Wave 1 sample; two of these demonstrated aspects of optimistic bias, representing almost 44% of the sample. In OLS regression models predicting health protective behavior adoption at Wave 2, clusters representing individuals with high perceived severity risk were most likely to report engagement in social distancing, but many individuals who were objectively at high risk for illness and death did not report engaging in self-protective behaviors.ConclusionsObjective risk of disease severity only inconsistently predicted health protective behavior. Risk profiles may help identify groups that need more targeted interventions to increase their support for public health policy and health enhancing recommendations more broadly.

Automatic fish length estimation is essential for modern aquaculture. Occlusion and body bended make accurate fish length estimation challenging in intensive aquaculture environments. Aiming at these issues, this study proposes a fish length estimation scheme based on cross-modal feature fusion Mask R-CNN (CMFF Mask R-CNN) and point cloud normalization segmentation transformation. To eliminate fish which are incomplete in binocular images due to occlusion and extract masks of fish which are complete in binocular images, a cross-modal feature fusion module is designed and embedded into Mask R-CNN to aggregate boundary features of fish from RGB and disparity into unified feature maps. The feature maps help remove incomplete fish and improve the accuracy of complete fish mask boundary. A fish length estimation algorithm based on point cloud normalization segmentation transformation is designed to reduce the length estimation error caused by bending. After plane and ellipse fitting transformation, the fish contour point cloud is then transformed into a unified space for K-means clustering segmentation. The sum of each segment is the fish length. Experimental results show that the mean relative error of the salmon length estimation is less than 5%. Moreover, the generalizability experiments show that the average relative error of the proposed method for the other freshwater fish species is less than 5%. This indicates that the proposed method for fish length estimation could be applied in aquaculture. [ABSTRACT FROM AUTHOR]

Machine Type Communication Devices for Machine-to-Machine (M2M) communication in 5G cellular networks have issues with scalability, quality of service (QoS), collisions, and delays in data transmission. M2M connectivity has become prevalent in the Internet of Things. The suggested MAC protocol for M2M communication using adaptive TDMA was designed to be scalable and power-efficient. To address the problems of collision, quality of service and scalability in M2M communication by presenting a Power-efficient MAC switching protocol with Adaptive Time Division Multiple Access (PMAC-ATDMA). There are three phases to this: grouping, dynamic MAC switching, and time slot allocation. Optimization Technique: The usage of the adaptive k-means algorithm with the HHO method for selecting MTC heads based on their power status and proximity to enhance network efficiency and reduce collision. Hybrid MAC Protocol Design: A dynamic switching mechanism between CSMA/CA and Carrier Sense Multiple Access/Collision Avoidance Reservation Protocol (CSMA/CARP) based on network density and device activity, aiming to optimize collision handling and energy consumption. ATDMA assigns time slots that are used for data transmission based on the size of the data and QoS requirements. Traditional TDMA's synchronization issue is solved by using the Markov chain model; this PMAC-ATDMA is simulated using a network simulator tool. Access delay, energy, collision likelihood, and successful packet transmissions are all taken into account throughout the evaluation process. [ABSTRACT FROM AUTHOR]

This paper introduces a novel similarity function that evaluates both the quantitative and qualitative similarities between data instances, named QQ-Means (Qualitative and Quantitative-Means). The values are naturally scaled to fall within the range of − 1 to 1. The magnitude signifies the extent of quantitative similarity, while the sign denotes qualitative similarity. The effectiveness of the QQ-Means for cluster analysis is tested by incorporating it into the K-means clustering algorithm. We compare the results of the proposed distance measure with commonly used distance or similarity measures such as Euclidean distance, Hamming distance, Mutual Information, Manhattan distance, and Chebyshev distance. These measures are also applied to the classic K-means algorithm or its variations to ensure consistency in the experimental procedure and conditions. The QQ-Means similarity metric was evaluated on gene-expression datasets and real-world complex datasets. The experimental findings demonstrate the effectiveness of the novel similarity measurement method in extracting valuable information from the data. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Vehicles (IoV) technology has led to a sharp increase in vehicle data. Traditional cloud computing is no longer sufficient to meet the high bandwidth and low latency requirements of IoV tasks. Ensuring the service quality of applications on in-vehicle devices has become challenging. Edge computing technology moves computing tasks from the cloud to edge servers with sufficient computing resources, effectively reducing network congestion and data propagation latency. The integration of edge computing and IoV technology is an effective approach to realizing intelligent applications in IoV.This paper investigates the deployment of vehicle edge servers in cloud-edge collaborative environment. Taking into consideration the vehicular mobility and the computational demands of IoV applications, the vehicular edge server deployment within the cloud-edge collaborative framework is formulated as a multi-objective optimization problem. This problem aims to achieve two primary objectives: minimizing service access latency and balancing server workload. To address this problem, a model is established for optimizing the deployment of vehicle edge servers and a deployment approach named VSPR is proposed. This method integrates hierarchical clustering and reinforcement learning techniques to effectively achieve the desired multi-objective optimization. Experiments are conducted using a real datasets from Shanghai Telecom to comprehensively evaluate the performance of workload balance and service access latency of vehicle edge servers under different deploy methods. Experimental results demonstrate that VSPR achieves an optimized balance between low latency and workload balancing while ensuring service quality, and outperforms SRL, CQP, K-means and Random algorithm by 4.76%, 44.59%, 40.78% and 69.33%, respectively. [ABSTRACT FROM AUTHOR]

Efficient virtual machine (VM) placement is vital for optimizing the performance of cloud data centers. While recent studies have addressed this challenge, many have overlooked the heterogeneity of cloud environments and the importance of scalability. This paper introduces a novel multi-objective algorithm designed specifically for VM placement in heterogeneous and large-scale cloud data centers. Our approach leverages the K-means algorithm to group VMs based on demand characteristics. Subsequently, a multi-reward reinforcement learning algorithm is employed to allocate these VMs to physical hosts. Despite its simplicity, the proposed method demonstrates exceptional efficiency. Simulation results reveal that our approach significantly outperforms established algorithms such as GMPR, GRVMP, FFD, NSGA-II, RLVMP, and BFD. Key performance metrics include the number of active devices, energy consumption, resource utilization (CPU and memory), VM migrations, and adherence to service level agreements, highlighting the superiority of our method. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns and the need for sustainable energy sources have driven the researchers into alternative fuels. The study employs advanced statistical methods, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS)-Gaussian Mixture Model (GMM) and k-Means Cluster Analysis. By integrating these techniques, the research aims to identify patterns and trends that lead to optimized fuel formulations. The performance metrics and emission parameters are outcomes parameters. ANFIS prediction models identified the Gaussian membership function as the most accurate, prompting its integration with GMM. The study demonstrates that the addition of Aluminium oxide nanoparticles significantly enhances the BTE, while reducing Hydrocarbon (HC) emissions. The optimal input combination was found to be Load is 100.00 %, Blend percentage is 45.71 %, NPC is 27.14, Hydrogen supply is 8.57 for which ideal outcomes are BTE 35.14 %, BSEC 1.07, HC 80.71 ppm, and NOx 128.57 ppm. • Addition of Aluminium oxide enhance the performance characteristics of biodiesel. • Hydrogen infusion was found to further improve combustion efficiency. • The GMM outperformed k-means clustering in accurately capturing performance outcomes. • ANFIS prediction models identified the Gaussian membership function as most accurate. [ABSTRACT FROM AUTHOR]

Susceptibility mapping has been an effective approach to manage the threat of debris flows. However, the sample heterogeneity problem has rarely been considered in previous studies. This paper is to explore the effect of sample heterogeneity on susceptibility mapping and propose corresponding solutions. Two unsupervised clustering approaches including K-means clustering and fuzzy C-means clustering were introduced to divide the study area into several homogeneous regions, each region was processed independently to solve the sample heterogeneity problem. The information gain ratio method was used to evaluate the predictive ability of the conditioning factors in the total dataset before clustering and the homogeneous datasets after clustering. Then the total dataset and the homogeneous datasets were involved in the random forest modeling. The receiver operating characteristic curves and related statistical results were employed to evaluate the model performance. The results showed that there was a significant sample heterogeneity problem for the study area, and the fuzzy C-means algorithm can play an important role in solving this problem. By dividing the study area into several homogeneous regions to process independently, conditioning factors with better predictive ability, models with better performance and debris flow susceptibility maps with higher quality could be obtained. [ABSTRACT FROM AUTHOR]

A clustering-based framework that can select a smaller number of ground motions from the entire ground motion set for collapse fragility estimation of structures while preserving the accuracy is proposed in this study. The bisecting k-means clustering algorithm is applied to the feature space that we construct. The construction is done by using spectral acceleration over a certain period interval to obtain a small number of ground motions until the fragility curve converges. The efficiency and applicability of the proposed framework is successfully demonstrated through a generic set of single-degree-of-freedom systems and six moment-resisting frame buildings having from 1 to 20 stories. The results indicate that collapse fragilities estimated by a smaller number of ground motions selected by using the proposed framework converge to a near optimal value. [ABSTRACT FROM AUTHOR]

One of the most recent areas of research in agriculture is the detection along with classification of diseases from plant leaf images. Since rice is the most widely consumed staple food worldwide, it is crucial to increase paddy production's quality and quantity. In the production of paddy, early detection of pests and diseases at various growth stages is very important. Utilising image processing scheme to spot diseases of agricultural plants will lessen the need for farmers to safeguard agricultural products. The use of VGG-16 along with a Convolutional Neural Network (CNN) for the classification and recognition of paddy leaf diseases is proposed in this work. The rice plant leaves images taken from the kaggle dataset is utilised for the purpose of image acquisition. The Gaussian filter is used in pre-processing. The clustering technique is utilised for the segmentation of the diseased part, the normal part, and the surroundings. The proposed model is then utilised for disease classification. This study classifies 10 categories of paddy images. VGG16, InceptionV3, MobileNetV2, and ResNet-152, among other transfer learning methods, are evaluated and contrasted with the experimental results. The outcome accuracy of the proposed model achieves of nearly 99.9%. [ABSTRACT FROM AUTHOR]

Core samples analysed through CT scans provide crucial reservoir information for reservoir development, encompassing porosity, permeability, pore size, grain distribution, grain sphericity and roundness, and distribution of their mineralogical assemblages. While 3D CT scans offer detailed insights, their availability is limited. This paper introduces a novel machine learning-based workflow for extracting reservoir properties from 2D CT scan images. The k-means clustering algorithm was applied to segregate and separate the constituent assemblages of the samples and consequently estimating effective porosity, clay volume, and individual pore area of the core samples. Subsequently, we employ a Monte-Carlo simulation on the Kozeny-Carman equation to predict low, mid, and high-case permeabilities of the core samples. The sphericity and roundness of the grains were estimated from the grain assemblage. Furthermore, grain sphericity and roundness were analysed using spectral clustering, another machine learning based algorithm in order to cluster the grains based on these attributes, which help in understanding the depositional transport mechanism of the grains. The feasibility of the proposed method was tested over the sandstone samples from the Lower Fulmar formation (Jurassic) and Leman formation (Permian) of the Anglo-Dutch and Central Graben basins in the North Sea. The results from the study show good correlation and inline with experimental results performed on the core samples. The methodology demonstrated is unique and has novel applications including simulating fluid flow behaviour in porous and permeable media of different constituent assemblages. [ABSTRACT FROM AUTHOR]

Transportation networks in the United States contain more than 615,000 bridges. Federal regulations require routine inspections of these infrastructure systems at least every 24 months. However, recent studies have suggested that this approach is costly, inefficient, and, in some cases, risky. An essential element in developing customizable alternatives for condition assessment plans is the design of precise and robust forecasting models for bridge deterioration, capable of sustaining acceptable predictive accuracy even when operating with small subsets of data. The overarching objective of this research is to introduce a novel forecasting method for bridge deterioration conditions, augmented with an exploration mechanism to identify a subset of bridges crucial for maintaining the predictive power of the model in subsequent years. The proposed method comprises two main components. Firstly, a forecasting model for bridge deterioration conditions was designed, formulated, and developed based on a novel framework for ordinal extreme gradient boosting. Secondly, an exploration mechanism is employed using a new multidimensional clustering method that integrates K-means clustering and dynamic time wrapping. The proposed method is applied using historical condition assessment data from bridges in the three neighboring states of New York, New Jersey, and Connecticut. The outcomes validate the performance of the proposed method, demonstrating that the forecasting model accurately predicts future bridge conditions. Additionally, the clustering component successfully identifies a small subset of bridges essential for maintaining the predictive power of the forecasting model. The findings of this study will assist transportation agencies in utilizing their bridge inspection resources more efficiently and in customizing their condition assessment operations based on bridge characteristics and expected deterioration levels. [ABSTRACT FROM AUTHOR]

Advances in autonomous transportation technologies have profoundly influenced the evolution of daily commuting and travel. These innovations rely heavily on seamless connectivity, facilitated by applications within intelligent transportation systems that make effective use of vehicular Ad- hoc Network (VANET) technology. However, the susceptibility of VANETs to malicious activities necessitates the implementation of robust security measures, notably intrusion detection systems (IDS). The article proposed a model for an IDS capable of collaboratively collecting network data from both vehicular nodes and Roadside Units (RSUs). The proposed IDS makes use of the VANET distributed denial of service dataset. Additionally, the proposed IDS uses a K-means clustering method to find clear groups in the simulated VANET architecture. To mitigate the risk of model overfitting, we meticulously curated test data, ensuring its divergence from the training set. Consequently, a hybrid deep learning approach is proposed by integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. which results in the highest training, testing, and validation accuracy of 99.56, 99.49, and 99.65% respectively. The results of the proposed methodology is compared with the existing state-of-the-art in the same domain, the accuracy of the proposed method is raised by maximum of 4.65% and minimum by 0.20%. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). Materials and methods: Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. Results: Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. Conclusions: A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes. [ABSTRACT FROM AUTHOR]

In the current era, the relentless advancement of information technology necessitates efficient information acquisition, which relies on proper data processing. To address the challenges in data organization, data mining emerges as a pivotal solution. This study aims to delve into various methodologies for data grouping. Employing a survey approach, the research scrutinizes journals published from 2020 to 2024. The findings illuminate prevalent techniques, algorithms, and software tools utilized in similar research domains. Notably, the study reveals that the predominant approach entails clustering via K-Means leveraging RapidMiner. This insight underscores the significance of employing robust methodologies and tools to streamline data processing and analysis in the contemporary information landscape. By elucidating the prevalent techniques and tools, this study contributes to enhancing understanding and fostering advancements in data mining practices, thereby facilitating more efficient data utilization and decision-making processes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy. [ABSTRACT FROM AUTHOR]

To address the protracted blood transportation time prevalent in contemporary urban settings, we proposed a location-routing optimization problem tailored to the distribution of blood within intricate road networks. This involved a comprehensive assessment that encompassed the judicious selection of sites for both stations and blood centers, coupled with the meticulous planning of delivery routes for unmanned aerial vehicles (UAVs) that orchestrate the transportation of blood. First, a model was formulated to minimize the overall cost, including transportation expenses, costs associated with the site, and other relevant costs related to blood transportation vehicles coordinated by UAVs. Subsequently, a two-stage hybrid heuristic algorithm was designed based on the distinctive characteristics of the problem at hand. Moreover, an enhanced k-means algorithm was employed to generate clustering schemes, utilizing the centroid method to address the challenge of location selection for delivery sites effectively. A genetic algorithm enhanced with adaptive operators was employed to address the challenging large-scale NP-hard problem associated with route planning in intricate urban road networks. The results indicated that, compared to the traditional blood delivery model using vehicles, the total blood transportation cost decreased by 12.65% and the overall delivery time was reduced by 37.5% with the adoption of drone-assisted delivery; ultimately, case and sensitivity analyses were conducted to investigate the impact of variables including the number of blood transportation vehicles, UAVs, driver wages, and unit costs of blood transportation vehicles on the location-routing problem. [ABSTRACT FROM AUTHOR]

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Daily 7Be activity concentrations data across 21 global locations between 2010 and 2017 from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) were analyzed using multifractal formalism. The multifractal detrended fluctuation analysis revealed that 7Be distribution across the 21 locations are multifractal (0.17<α<0.66) with a wide range of fractal exponents. The observed multifractality was found to be statistically significant except at two locations (RN45 and RN47). The multifractal strength (α) and Holder’s exponent (α0) were used to group the locations into 3 clusters with K-means algorithm. The relationship between 7Be and five drivers (Southern Oscillation Index — SOI, North Atlantic Oscillation — NAO, Total Sunspot number — Tot_SN, Northern hemisphere Sunspot number — NH_SN and Southern hemisphere sunspot number — SH_SN) was investigated using multifractal detrended cross-correlation analysis. The multifractal cross-correlation between 7Be and drivers was found to be 0.06–0.21(SOI), 0.08–0.23 (NAO), 0.04–0.27 (Tot_SN), 0.05–0.25 (NH_SN) and 0.04–0.27 (SH_SN). NAO was found to be the strongest driver of 7Be. The location, RN16 in Yellowknife Canada, showed strong cross-correlation with the five drivers. [ABSTRACT FROM AUTHOR]

Spectrum Resource Efficient Medium Access Control (SRE-MAC) is proposed in this work to effectively and efficiently improve access to the communication channels of a WBAN system. Initially, Sensor nodes are collected from the human figure such as pulses, body temperature, and oxygen level. Utilize a multi-objective-based clustering algorithm to categorize sensor nodes based on a range of criteria, such as energy level, proximity to the central processing unit, similarity of physiological data, and QoS from the collected data. From it, the clustering-based K-Means Algorithm & Multi objective is presented. For choosing the best cluster heads, a Sparrow Guided Teaching Learning Optimization (SG-TLO) algorithm is used. Use the Contention Phase (CP) and variable size TDMA slot allotments of the SRE-MAC protocol to distribute spectrum resources among each cluster. Utilize the SG-TLO to identify the best spectrum allocation that balances energy consumption within each cluster, maximizes channel utilization, and reduces interference. After spectrum allocation and optimization, the Transmission Phase (TP) of the SRE-MAC protocol is used by the sensor nodes within each cluster to connect with the central processing unit. The nodes switch to sleep mode during periods of inactivity to save energy and transmit their health packets during their specific assigned time slots. Data from all clusters are acquired by the CPU, which also aggregates and analyses the data. Results of data analysis are used in a kind of context, including research, healing determination, and supervision of healthcare. Utilize performance metrics, such as network throughput, energy consumption, communication reliability, and interference levels, to assess the effectiveness of the clustering and spectrum allocation algorithms. [ABSTRACT FROM AUTHOR]

Motion sickness (MS) is a common physiological response that often occurs when individuals are exposed to environments with repeated acceleration stimuli. MS results from a mismatch between the vestibular system and visual and proprioceptive inputs. As a crucial organ for sensing acceleration stimuli, the vestibular system is closely related to the onset of MS. However, the complex pathogenesis of MS has led to its diagnosis primarily relying on subjective questionnaires, with a lack of objective indicators for evaluation. To identify objective indicators for evaluating MS, we conducted rotating chair stop experiments based on the principle of vestibulo-ocular reflex with 65 volunteers, obtaining their nystagmus slow-phase velocity and related time constants. Additionally, we conducted detailed MS questionnaires with these volunteers to assess their MS susceptibility. Through correlation analysis, we explored whether the nystagmus slow-phase velocity and related time constants significantly correlated with the MS questionnaire scores. The results showed significant positive correlations between the maximal nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and the nystagmus duration with the MS questionnaire scores in the 65 volunteers. These results indicated that these nystagmus parameters could serve as objective indicators for assessing MS susceptibility. Using K-Means clustering analysis, we classified MS susceptibility into categories I, II, III, and IV, and conducted K-Means clustering analysis on the corresponding nystagmus slow-phase velocity, cupula time constant, velocity storage time constant, and nystagmus duration. The magnitude and range of these indicators at different levels was quantified, offering objective and quantitative indicators for the clinical diagnosis of MS. [ABSTRACT FROM AUTHOR]

The majority of the KP-based copy-move forgery (CMF) detection techniques have a high computational cost due to their large feature descriptor sets and numerous KPs. Furthermore, the accuracy of the results may be impacted by the identified KPs not spreading over all regions of the image and the classical clustering approaches not efficiently classifying the feature space into cluster space. This article therefore attempts to employ both KAZE & fast hessian matrix (FHM) techniques for identifying KPs that spread over the entire image region, SURF for evaluating feature descriptors, Network based Dimensionality Reduction (NDR) for reducing the dimension of each feature descriptor and Self-Organizing Map (SOM) for clustering the feature vectors for avoiding sub-optimal clusters. It portrays the superior performances of the proposed forgery detection scheme on a standard image database “MICC-F220” and medical records like fundas images. [ABSTRACT FROM AUTHOR]

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. [ABSTRACT FROM AUTHOR]

Resting-state functional MRI (fMRI) has revealed functional changes at the cortical level in degenerative cervical myelopathy (DCM) patients. The aim of this study was to systematically integrate static and dynamic functional connectivity (FC) to unveil abnormalities of functional networks of DCM patients and to analyze the prognostic value of these abnormalities for patients using resting-state fMRI. In this study, we collected clinical data and fMRI data from 44 DCM patients and 39 healthy controls (HC). Independent component analysis (ICA) was performed to investigate the group differences of intra-network FC. Subsequently, both static and dynamic FC were calculated to investigate the inter-network FC alterations in DCM patients. k-means clustering was conducted to assess temporal properties for comparison between groups. Finally, the support vector machine (SVM) approach was performed to predict the prognosis of DCM patients based on static FC, dynamic FC, and fusion of these two metrics. Relative to HC, DCM patients exhibited lower intra-network FC and higher inter-network FC. DCM patients spent more time than HC in the state in which both patients and HC were characterized by strong inter-network FC. Both static and dynamic FC could successfully classify DCM patients with different surgical outcomes. The classification accuracy further improved after fusing the dynamic and static FC for model training. In conclusion, our findings provide valuable insights into the brain mechanisms underlying DCM neuropathology on the network level. [ABSTRACT FROM AUTHOR]

Background: High-need, high-cost (HNHC) patients are a major focus of international healthcare reform. However, research on HNHC children in China remains limited. This study aims to classify HNHC pediatric patients, analyze the differences among groups, and explore the factors influencing HNHC status. Methods: Data were obtained from a retrospective observational cohort of hospitalized children in Shanghai, China from 2017 to 2023. K-means clustering, one of the unsupervised learning algorithms, was employed to classify patients according to their HNHC status. Descriptive statistical analysis and the Kruskal-Wallis H test were used to describe and test the differences among different groups, with the logit regression models to analyze the predictors. Results: 688,131 hospitalized children were classified into three groups: 1,871 persistent HNHC, 32,539 episodic HNHC, and 653,721 non-HNHC. Significant differences were observed among these groups. Persistent HNHC patients have significantly higher costs and longer HNHC durations compared to episodic and non-HNHC patients, who were more likely to be aged 30 days to 1 year or 13–18 years, female with only one type of health insurance, and leukemia was the most prevalent and costly disease. They exhibited distinct healthcare utilization patterns, including emergency admissions, higher surgery rates, longer hospital stays, more frequent hospitalizations, and a preference for tertiary and specialized hospitals in city centers. Multiple influencing factors of persistent HNHC versus episodic HNHC and non-HNHC were identified. Conclusion: This study provides valuable insights into the classification, characteristics, and influencing factors of persistent, episodic, and non-HNHC hospitalized children in China. Persistent HNHC patients warrant targeted interventions to improve health outcomes and reduce healthcare costs. Enhanced medical coverage for key diseases, high-quality healthcare services tailored to their needs, and early interventions are crucial. [ABSTRACT FROM AUTHOR]

Background and purpose: The immune response following aneurysmal subarachnoid hemorrhage (aSAH) can exacerbate secondary brain injury and impact clinical outcomes. As the immune response after aSAH is a dynamic process, we aim to track and characterize immune cell trajectories over time to identify patterns associated with various clinical outcomes. Methods: In this retrospective single-center study of patients with aSAH, we analyzed immune cell count trajectories, including neutrophil, monocyte, and lymphocyte counts, collected from day 1 to day 14. These trajectories were classified into four distinct clusters utilizing the k-means longitudinal clustering method. A comprehensive multivariable analysis was performed to explore the associations of these immune cell clusters with various clinical outcomes. These outcomes included a Modified Rankin Scale score (mRS) of 3 to 6, indicative of poor functional outcomes, along with complications including shunt dependency, vasospasm, and secondary cerebral infarction. Results: In this study, 304 patients with aSAH were analyzed. The trajectories of immune cell counts, including neutrophils, monocytes, and lymphocytes, were successfully categorized into four distinct clusters for each immune cell type. Within neutrophil clusters, both persistent neutrophilia and progressive neutrophilia were associated with poor functional outcomes, shunt dependency, and vasospasm, with resolving neutrophilia showing a lesser degree of these associations. Within monocyte clusters, early monocytosis was associated with vasospasm, whereas delayed monocytosis was associated with shunt dependency. Within lymphocyte clusters, both early transient lymphopenia and early prolonged lymphopenia were associated with poor functional outcomes. Conclusion: Our study demonstrates that distinct immune cell trajectories post-aSAH, identified through unsupervised clustering, are significantly associated with specific clinical outcomes. Understanding these dynamic immune responses may provide key insights with potential for future therapeutic strategies. [ABSTRACT FROM AUTHOR]

Due to the increasing number of applications for various purposes, including commercial, scientific, environmental, and military ones, Underwater Wireless Sensor Networks (UWSNs) have recently attracted substantial attention from academia and enterprises in research and development. Monitoring pollutants, tactical surveillance, tsunami alerts, and offshore drilling are some important applications. Due to acoustic transmission disruptions brought on by extreme noise, extraordinarily lengthy propagation delays, a high bit error rate, a constrained bandwidth, and interference, efficient sensor communication in UWSNs is a difficult challenge. Therefore, designing efficient communication among sensors and sinks is one of the fundamental research themes in UWSNs. This paper proposes an energy-efficient optimal cluster head (CH)-based routing in UWSNs. The proposed methodology consists of three stages namely, cluster formation, CH selection, and priority-based routing. In this study, initially, the clusters are formed using a k-means clustering algorithm. Then, the CHs are selected using the Adaptive Honey Badger optimization (AHBO) algorithm, which is used to reduce energy consumption and delay. AHBO is a combination of a honey badger, Levy flight, and genetic algorithm operators. After the CH selection process, data packets are transferred toward the base station through autonomous underwater vehicles (AUVs). The efficiency of the proposed approach is analyzed based on different metrics and performance compared with the different methods. [ABSTRACT FROM AUTHOR]

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation. [ABSTRACT FROM AUTHOR]

The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may contribute to dermatological care disparities in patients with darker skin phototypes, including the misdiagnosis of wound healing progression and escalated dermatological disease severity. This study introduces (1) an optical sensor measuring reflected light across 410–940 nm, (2) an unsupervised K-means algorithm for skin phototype classification using broadband optical data, and (3) methods to optimize classification across the Near-ultraviolet-A, Visible, and Near-infrared spectra. The differentiation capability of the algorithm was compared to human assessment based on FSPC in a diverse participant population (n = 30) spanning an even distribution of the full FSPC scale. The FSPC assessment distinguished between light and dark skin phototypes (e.g., FSPC I vs. VI) at 560, 585, and 645 nm but struggled with more similar phototypes (e.g., I vs. II). The K-means algorithm demonstrated stronger differentiation across a broader range of wavelengths, resulting in better classification resolution and supporting its use as a quantifiable and reproducible method for skin type classification. We also demonstrate the optimization of this method for specific bandwidths of interest and their associated clinical implications. [ABSTRACT FROM AUTHOR]

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals' lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas. [ABSTRACT FROM AUTHOR]

GARCH models can contain outliers, such as Additive Level Outliers (ALO) and/or Additive Volatility Outliers (AVO), so a robust parameter estimation is used to overcome the presence of outliers. This study uses Quasi-Maximum Likelihood Estimation (QMLE) and M-estimation to estimate the parameters of the GARCH model containing outliers. Hierarchical and K-means clustering algorithms are then used to cluster time-series data based on distances calculated from these parameter estimates. The proposed approach applies hierarchical and K-means clustering using distances derived from QMLE and M-estimation results for GARCH (1,1) models containing outliers for clustering with simulation data and a case study using stock data listed in the Indonesia Stock Exchange. The results show that hierarchical and K-means clustering with distance between two GARCH (1,1) models based on M-estimation produces good clusters with smaller C index than QMLE estimations for simulation data and the case study of stock data in Indonesia. [ABSTRACT FROM AUTHOR]

Breathing patterns (respiratory kinematics) contain vital prognostic information. This dimension of physiology is not captured by conventional vital signs. We sought to determine the feasibility and utility of quantifying respiratory kinematics. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We extracted 34 features based on an explainable correspondence with well-established breathing patterns. K-means clustering revealed that respiratory kinematics had three dimensions apart from the respiratory rate. We represented these dimensions using respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of the kinematic measures revealed two profiles consistent with the established clinical constructs of labored and unlabored breathing. In logistic regression, the labored breathing profile improved model discrimination for critical illness beyond the Sequential Organ Failure Assessment (SOFA) score (AUROC 0.77 v/s 0.72; p = 0.02). These findings quantitatively confirm the prior understanding that the respiratory rate alone does not adequately represent the complexity of respiratory kinematics; they demonstrate that high-dimensional signatures of labored breathing can be quantified in routine practice settings, and they can improve predictions of clinical deterioration. [ABSTRACT FROM AUTHOR]

Blood Pressure (BP) is considered an essential factor that provides information regarding cardiovascular function. Regular monitoring of the BP is required for proper healthcare maintenance that avoids the high risk of life due to high and low BP. Several methods were devised for the estimation of BP, but the estimation accuracy is still a challenging task. Hence this research introduces an efficient BP estimation technique using the Fact Finding Instructor (FFI) based clustering method by considering the speech signal of the patients. An efficient BP extraction technique is introduced using the FFI Optimization algorithm an integration of the mannerism of the fact finder that identifies the suspect who commits the criminal offense and, with the instructor with good knowledge, these make the trainee more efficient. The detection and suspect's arrest contain two phases, the fact-finding phase and the chasing phase. Initially, the speech signal is collected from the database and pre-processed for removing noise and artifacts. Then feature extraction is used for the minimization of the computation overhead that generates a feature vector. The clustering of BP is employed with the k-means clustering algorithm and the proposed FFI optimization algorithm. The FFI Optimization algorithm provides a fast convergence rate due to the fact-finding phase and provides accurate detection of the suspect's location along with that the clustering of classes of patients' BP by considering the feature of the speech signal. The clusters formed using the FFI optimization algorithm are combined with the K-means clustering, by multiplying the clusters the BP estimation is implemented on three criteria Low BP, Normal, and, High BP. Finally, the output generated by both the clustering operations is multiplied together for the estimation of the BP. The performance of the proposed method is evaluated using the metrics like Davies Bouldin score, Homogeneity score, Completeness score, Jacquard Similarity score, Silhouette score, and Dunn's Index which acquired the improvement rate of 0.98, 0.96, 0.96, 0.98, 0.95, and 0.98 for training percentage 90, respectively to the existing Teaching Learning Based Optimization(TLBO) clustering technique. [ABSTRACT FROM AUTHOR]

Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. We present an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and k -means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representation of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures. [ABSTRACT FROM AUTHOR]

Trilobite cephalic shape disparity varied through geological time and was integral to their ecological niche diversity, and so is widely used for taxonomic assignments. To fully appreciate trilobite cephalic evolution, we must understand how this disparity varies and the factors responsible. We explore trilobite cephalic disparity using a dataset of 983 cephalon outlines of c. 520 species, analysing the associations between cephalic morphometry and taxonomic assignment and geological Period. Elliptical Fourier transformation visualised as a Principal Components Analysis suggests significant differences in morphospace occupation and in disparity measures between the groups. Cephalic shape disparity peaks in the Ordovician and Devonian. The Cambrian–Ordovician expansion of morphospace occupation reflects radiations to new niches, with all trilobite orders established by the late Ordovician. In comparison, the Silurian–Devonian expansion seems solely a result of within-niche diversification. Linear Discriminant Analyses cross-validation, average cephalon shapes, and centroid distances demonstrate that, except for Harpida and the Cambrian and Ordovician Periods, order and geological Period cannot be robustly predicted for an unknown trilobite. Further, k-means clustering analyses suggest the total dataset naturally subdivides into only seven groups that do not correspond with taxonomy, though k-means clusters do decrease in number through the Palaeozoic, aligning with findings of decreasing disparity. Trilobite cephalic shape disparity decreases through the Palaeozoic, with peaks in the Ordovician and Devonian caused by different underlying mechanisms, though occurrence data cannot generally be predicted for trilobites using just cephalic shape. [ABSTRACT FROM AUTHOR]

With advances in technology and growing user needs, satellite communication is changing over time. Efficiently managing resources across diverse satellite coverage areas can be significantly enhanced through the application of Artificial Intelligence (AI) and Machine Learning (ML). The intended project seeks to employ fundamental machine learning algorithms, including Weighted k-means and Gaussian Mixture Model (GMM), to cluster user demands within a designated geographical region. This approach aims to optimize the utilization of satellite bandwidth in a multi-beam satellite system. A comparison of the results obtained using two techniques indicates improved performance of the GMM in terms of allocating the user demands. To ensure effective coverage, the clusters are transformed into shapes that match the coverage areas of the satellite. A technique called Voronoi Tessellation is used to create irregular polygons, and then these polygons are approximated as ellipses to cover the clusters more effectively. [ABSTRACT FROM AUTHOR]

This study examines the intricate relationship between national happiness and sustainability by analyzing data from the 2024 World Happiness Report (WHR) and the Sustainable Development Goal (SDG) Index across 74 countries. Employing multidimensional scaling (MDS) and K-means clustering, we investigate the interplay of economic, social, and environmental factors that contribute to subjective well-being. Our results indicate that high-income countries, particularly those characterized by secular–rational values, tend to achieve elevated scores in both happiness and sustainability. In contrast, lower-income nations exhibit diverse cultural value systems that significantly influence their happiness and sustainability outcomes. This analysis identifies distinct clusters of countries with unique governance and cultural profiles, underscoring the inadequacies of traditional economic indicators in measuring societal well-being. By integrating sustainability metrics into happiness assessments, we propose a comprehensive framework for understanding and promoting human happiness. These findings offer critical insights for policymakers aiming to address pressing global challenges, including inequality and health disparities. [ABSTRACT FROM AUTHOR]

At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles. [ABSTRACT FROM AUTHOR]

Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse's location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies. [ABSTRACT FROM AUTHOR]