Search Results (4,324)

This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users’ walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model’s efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys. Full article

Electrical data analysis based on smart grids has become a fundamental tool used by electrical grid stakeholders to understand the energy consumption patterns of users, although many proposals in this area do not consider reactive energy as another source of useful information regarding distribution costs and threats to the grid. In this regard, the analysis of reactive energy patterns can become an extremely useful addition to existing electrical data analysis frameworks. This work shows the application of a series of clustering techniques over measurements of both active and reactive energy consumption measured for end users from the Colombian electrical network, including an analysis of the efficiency of the network measured by calculating the ratio of active energy to total consumption (power factor) per user. This allows a detailed characterization of users to be compiled, based on the identification of different active and reactive energy consumption behaviors, which could help grid operators to improve overall grid management and to increase the efficiency of their reactive energy compensation strategies. Full article

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management. Full article

In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson’s disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington’s Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. Full article

Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body’s internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule’s movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule’s trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization. Full article

The proliferation of the Internet of Things (IoT) has worsened the challenge of maintaining data and user privacy. IoT end devices, often deployed in unsupervised environments and connected to open networks, are susceptible to physical tampering and various other security attacks. Thus, robust, efficient authentication and key agreement (AKA) protocols are essential to protect data privacy during exchanges between end devices and servers. The previous work in “Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT” proposed a novel AKA scheme for secure IoT environments. They claimed their protocol offers comprehensive security features, guarding against numerous potential flaws while achieving session key security. However, this paper demonstrates through logical and mathematical analyses that the previous work is vulnerable to various attacks. We conducted a security analysis using the extended Canetti and Krawczyk (eCK) model, which is widely employed in security evaluations. This model considers scenarios where an attacker has complete control over the network, including the ability to intercept, modify, and delete messages, while also accounting for the potential exposure of ephemeral private keys. Furthermore, we show that their scheme fails to meet critical security requirements and relies on flawed security assumptions. We prove our findings using the automated validation of internet security protocols and applications, a widely recognized formal verification tool. To strengthen attack resilience, we propose several recommendations for the advancement of more robust and efficient AKA protocols specifically designed for IoT environments. Full article

In recent years, shared e-scooters have become increasingly popular as a mode of transportation in urban areas. Shared e-scooters have emerged as a convenient and sustainable transportation option in urban areas, providing users with a flexible and efficient way to travel short distances within a city. Many service providers and local municipalities are interested in implementing shared e-scooter operational models. However, determining which operating model to prefer and what the service areas will be is a significant problem. We aimed to solve the implementation of three different operational models, the site selection problem of station locations, and service areas for Erzurum, the metropolitan city in this study. As shared e-scooter is quite a new transportation mode; information collected to assess the operational models’ sustainability performance may be indeterminate and vague. In this study, the Geographic Information System (GIS)-based hybrid multi-criteria decision-making (MCDM) method is proposed for the solution of implementation, site selection, and service areas problems of three different shared e-scooter operational models. To this end, a four-step scientific and strategic solution approach is developed: (i) the identification and detailed explanation of 5 main and 24 sub-criteria, (ii) the weighting of criteria through the Analytical Hierarchical Process (AHP), Multi-Influencing Factor (MIF), and Best–Worst Method (BWM) in order to increase the sensitivity and robustness of the study, (iii) obtaining a suitability map for the solution of implementation, site selection, and service areas problems of operational models, and (iv) assigning shared e-scooter stations and analyzing their performance levels with COmplex PRoportional ASsessment (COPRAS). The results show that, in Erzurum, the central three districts are the most suitable for service areas. The paper’s solution methodology can help service providers and policymakers invest in sustainable shared e-scooter operational models, even in situations of high uncertainty. Full article

In households, waste growth has become a critical global issue. This study aims to extend our understanding of promoting pro-environmental behavior by exploring the role of trust and perceived justice in households’ waste recycling practices. Trust and social justice are context-sensitive; studying them in different contexts provides new knowledge on motivating recycling motivation. The research is based on semi-structured interviews (n = 40) on household recycling practices in Latvia. Research results demonstrate that a lack of institutional trust and perceived social injustice concerning household waste recycling practices appear in several contexts: trust in an individual’s capacity to recycle, trust in their peers’ capacity to recycle, trust in the efficiency of the recycling system, trust in the capacity of the waste management company to recycle, trust in waste management policy, and trust in information on recycling. Lack of trust in waste management is linked to perceived social injustice as waste management policy is criticized for not being accessible and putting too much responsibility on households, and this can serve as a critical barrier to household recycling. This study reveals which elements of the waste management system are related to a higher risk of social injustice and lack of trust in the perspective of households, thus signaling where changes in the system or providing end-users more information are needed to make the waste-sorting process more efficient. The findings of the study indicated a few directions for further improvements in the waste management system: ensuring the financial availability of recycling services, reassessment of the responsibilities of the stakeholders, and working toward raising awareness of the recycling and waste management system. Full article

The primary methods for hydrogen transportation include gaseous storage and transport, liquid hydrogen storage, and transport via organic liquid carriers. Among these, pipeline transportation offers the lowest cost and the greatest potential for large-scale, long-distance transport. Although the construction and operation costs of dedicated hydrogen pipelines are relatively high, blending hydrogen into existing natural gas networks presents a viable alternative. This approach allows hydrogen to be transported to the end-users, where it can be either separated for use or directly combusted, thereby reducing hydrogen transport costs. This study, based on the GERG-2008 equation of state, conducts experimental tests on the compressibility factor of hydrogen-doped natural gas mixtures across a temperature range of −10 °C to 110 °C and a pressure range of 2 to 12 MPa, with hydrogen blending ratios of 5%, 10%, 20%, 30%, and 40%. The results indicate that the hydrogen blending ratio, temperature, and pressure significantly affect the compressibility factor, particularly under low-temperature and high-pressure conditions, where an increase in the hydrogen blending ratio leads to a notable rise in the compressibility factor. These findings have substantial implications for the practical design of hydrogen-enriched natural gas pipelines, as changes in the compressibility factor directly impact pipeline operational parameters, compressor characteristics, and other system performance aspects. Specifically, the introduction of hydrogen alters the compressibility factor of the transported medium, thereby affecting the pipeline’s flowability and compressibility, which are crucial for optimizing and applying the performance of hydrogen-enriched natural gas in transportation channels. The research outcomes provide valuable insights for understanding combustion reactions, adjusting pipeline operational parameters, and compressor performance characteristics, facilitating more precise decision-making in the design and operation of hydrogen-enriched natural gas pipelines. Full article

The proliferation of Internet of Things (IoT) devices and big data has catalyzed the emergence of data markets. Regulatory and technological frameworks such as International Data Spaces (IDS) have been developed to facilitate secure data exchange while integrating security and data sovereignty aspects required by laws and regulations, such as the GDPR and NIS2. Recently, novel attack vectors have taken a toll on many enterprises, causing significant damage despite the deployed security mechanisms. Hence, it is reasonable to assume that the IDS may be just as susceptible. In this paper, we conduct a STRIDE threat analysis on IDS to assess its susceptibility to traditional and emerging cybersecurity threats. Specifically, we evaluate novel threats such as Man-in-the-Middle (MitM) attacks, compromised end-user devices, SIM swapping, and potential backdoors in commonly used open-source software. Our analysis identifies multiple vulnerabilities, particularly at the trust boundary (TB) between users and the IDS system. These include the traditionally troublesome Denial of Service (DoS) attacks, key management weaknesses, and the mentioned novel threats. We discuss the hacking techniques, tools, and associated risks to the IDS framework, followed by targeted mitigation strategies and recommendations. This paper provides a framework for performing a STRIDE-based threat analysis of the IDS. Using the proposed methodology, we identified the most potent threats and suggested solutions, thus contributing to the development of a safer and more resilient data space architecture. Full article

In this paper, the need for a quantum computing approach is analyzed for IoT applications using the 5G resource spectrum. Most of the IoT devices are connected for data transmission to end users with remote monitoring units, but there are no sufficient data storage units, and more data cannot be processed at minimized time periods. Hence, in the proposed method, quantum information processing protocols and quantum algorithms are integrated where data transmissions are maximized. Further, the system model is designed in such a way for checking the external influence factors that prevent the IoT device from transmitting data to end users. Therefore, with corresponding signal and noise power, it is essential to process the transmissions, thereby increasing data proportions at end connectivity. Once quantum computations are performed, then it is crucial to normalize IoT data units, thus establishing control over entire connected nodes that create a gateway for achieving maximum throughput. The combined system model is tested under four cases where the comparative outcomes prove that with reduced queue reductions of 12%, it is possible to achieve a maximum throughput of 99%. Full article

In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry. Full article

Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for household electricity consumption, grow into common problems across countries. Residential load forecasting can assist utility companies in determining effective electricity pricing structures and demand response operations, thereby improving renewable energy utilization efficiency and reducing the share of thermal power generation. However, due to the randomness and uncertainty of user load data, forecasting residential load remains challenging. According to prior research, the accuracy of residential load forecasting using machine learning and deep learning methods still has room for improvement. This paper proposes an improved load-forecasting model based on a time-localized attention (TLA) mechanism integrated with LSTM, named TLA-LSTM. The model is composed of a full-text regression network, a date-attention network, and a time-point attention network. The full-text regression network consists of a traditional LSTM, while the date-attention and time-point attention networks are based on a local attention model constructed with CNN and LSTM. Experimental results on real-world datasets show that compared to standard LSTM models, the proposed method improves R² by 14.2%, reduces MSE by 15.2%, and decreases RMSE by 8.5%. These enhancements demonstrate the robustness and efficiency of the TLA-LSTM model in load forecasting tasks, effectively addressing the limitations of traditional LSTM models in focusing on specific dates and time-points in user load data. Full article

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes. Full article

In this study, the mechanochemical preparation of nanocrystalline CaF₂:Sm³⁺ by ball milling calcium acetate hydrate, samarium (III) acetate hydrate, and ammonium fluoride is reported. The photoluminescence of the as-prepared CaF₂:Sm³⁺ shows predominantly Sm^3+ 4G_5/2 → ⁶H_J(J = 5/2, 7/2, 9/2, and 11/2) f-f luminescence, but intense electric dipole allowed 4f⁵5d (T_1u) → 4f^6 7F₁ (T_1g) luminescence by Sm²⁺ was generated upon X-irradiation. In comparison with the co-precipitated CaF₂:Sm³⁺, the conversion of Sm³⁺$\to$ Sm²⁺ in the ball-milled sample upon X-irradiation is significantly lower. Importantly, the present results indicate that the crystallite size and X-ray storage phosphor properties of the lanthanide-doped nanocrystalline CaF₂ can be modified by adjusting the ball milling time, dopant concentration and post-annealing treatment, yielding crystallite sizes as low as 6 nm under specific experimental conditions. Full article