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The Michelangelo effect is a phenomenon that shows a reduction in perceived effort and an improvement in performance among both healthy subjects and patients when completing a motor task related to artistic stimuli, compared to performing the same task with non-artistic stimuli. It could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking 15 healthy subjects and 17 patients with a history of stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate for the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demand of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and the number of attempts (p = 0.007) needed to complete the task in the presence of artistic stimuli, accompanied by a reduction in the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated with visuospatial control than linguistic functions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The behavior of the grain with a specific shape in the flotation process. • The effect of particle size and shape properties on the recovery of valuable metals. • Separation of plastics is easy and applicable with the frother before metal flotation stages. • The coarse and platy particles are mostly observed in the float product. • The sink product has mainly locked and rounder particles. Since Random Access Memory (RAM), one of the main parts of computers contains a remarkable quantity of precious metals, applying flotation at the pre-concentration stage to recycle these metals can result in a more cost-effective, user-friendly, and environmentally friendly process compared to direct chemical methods. While the significance of physical characteristics like particle size and shape in the flotation process is well established, the impact of particle shape in the flotation process utilized in the recycling of end-of-life (EoL) RAMs hasn't yet been thoroughly investigated. To fill this gap, a two-stage coarse flotation approach is used for the selective recovery of plastic and valuable metallic particles for sustainable development. The particle geometry of metallic particles recovered by flotation was characterized by axis measurement on the images by optical microscope that allows us to distinguish particles of different sizes and colors that make up the sample and evaluated in terms of particle size distribution (PSD), elongation (E) and roundness (R) parameters. The results showed that after the plastic fraction is effectively removed, it is possible to produce pre-concentrated products with high metal content (more than 50 % Cu content at the 1st stage and 1800 g/t Au content at the 2nd stage using 900 g/t KAX) in an economical and environmentally friendly way. Thus, it was concluded that the gold and copper metallic particles in the reduced-size EoL RAM cards could be easily floated by attaching them to the air bubble with the help of the collector, thanks to their flat shape. [ABSTRACT FROM AUTHOR]

Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems. [ABSTRACT FROM AUTHOR]

Nowadays, smart card and password-based user authentication and key agreement schemes have become an integral mechanism to ensure only legitimate users get access to the data of wireless sensor networks (WSNs). In the recent past, lots of password-based multi-factor user authentication schemes have been developed by the researchers. However, most of them are not secure against various known attacks. Recently, two user authentication and key agreement schemes for WSNs are suggested by Chen and Chen (J Supercomput 77(12):13653–13675, 2021) and Qi and Chen (J Supercomput 77(12):13897–13910, 2021). They claim that their schemes are secure against all known attacks. We analyze the security of these schemes and demonstrate that these schemes are not secure against some common attacks (such as smart card lost attack) and do not provide common security features such as user untraceability and forward secrecy. By keeping all the merits of these schemes, we suggest a new provably secure user authentication and key agreement scheme using physically unclonable functions and elliptic curve cryptography. To verify the security and authentication properties of the suggested scheme, we use the well-known Proverif tool for formal security verification. We further prove its security informally. Finally, performance comparison with some recent schemes in terms of security features and computational cost is performed. Performance comparison results show the superiority of the suggested scheme over the existing schemes. [ABSTRACT FROM AUTHOR]

The emerging cloud infrastructure has escalated number of servers offering flexible and diverse remote services through public channels. However, user authentication in conventional single-server architecture necessitates multiple smart cards maintenance and passwords memorization to access different cloud servers. To address this limitation, researchers devised authentication protocols for multi-server architecture that offers scalable platform wherein users can access multiple servers with single registration. The multi-factor authentication protocols leverage biometric keys to bind users' physical characteristics with their identity, offering higher security than two-factor authentication protocols. However, the existing protocols for multi-server architecture are prone to replay, user impersonation, denial of service, server spoofing attacks and lack security functionalities such as user anonymity and untraceability, backward and forward secrecy, and session key security. Moreover, the incorporation of registration center (RC) to authenticate each pair of user-server in multi-server architecture can lead to computational bottleneck and single-point failure issues on RC. To overcome these security loopholes, we design a novel provably secure multi-factor elliptic curve cryptography (ECC) based authentication protocol for multi-server architecture with offline RC for cloud environment. The formal security analysis under widely accepted real-or-random (ROR) model and informal security analysis of proposed protocol demonstrate provision of security functionalities and resilience against potential security attacks. Furthermore, we adopt Scyther security verification tool to verify our protocol's correctness and security properties. The performance evaluation demonstrates that our protocol offers robust security functionalities with reasonable communication and computation overheads than state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

In the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities. [ABSTRACT FROM AUTHOR]

Conventional demand-prediction methods predominantly rely on past user behaviors to predict regular future transportation demands using acquired user preference data. Nevertheless, predicting unforeseen travel demands arising from bad weather or emergency events remains challenging owing to the absence of data on such future contingencies. This study introduces a method to predict travel demand by leveraging search history data, which potentially signal unforeseen travel requirements. We elucidate the correlation between the search count and integrated circuit (IC) card usage on an aggregate level. Subsequently, we propose a two-stage analytical technique to estimate the number of IC card usages based on route-search counts. Our findings demonstrate that the proposed model has superior accuracy, and the route-search count plays a pivotal role in predicting the number of IC card usages, especially unforeseen shifts in demand. [ABSTRACT FROM AUTHOR]

Blockchain technology has limitations in terms of scalability and throughput of transactions compared to other payment methods such as VISA and smart cards. But, Off-chain payment solves this issue by performing micro-transactions without communicating with the ledger. Similarly, Ethereum uses smart contracts to complete Off-chain transactions by constructing state and virtual channels. In this model, we develop an n-party payment system that uses state and virtual channels. The proposed model simultaneously executes different contract instances while they belong to other independent channels. Again, a virtual channel having n-intermediaries takes linear time to resolve payment channel disputes by transferring to the Blockchain. We introduce a global contract GSCC that solves the disputes in constant time in the worst case without including Blockchain. The dispute-solving process does not affect any intermediary node and ensures the intermediary is not losing coins. GSCC stores all the prior information of the active participants. But, GSCC has no communication overhead to other parties and ensures the execution of disputes with total conflict amount. The resultant model guarantees the execution of every contract, fair dispute resolution, and security balance. Our proposed theorems and zero-knowledge proof ensure the security of the n-party model. [ABSTRACT FROM AUTHOR]

During the COVID‐19 pandemic, the United States released the CDC‐issued vaccination card to document the status of immunizations. It eventually was used as a verification system to allow patrons to safely access public venues. This is/was apparently a simple printed piece of paper with minimal security features which made it an easy target for counterfeiters with little expertise required. Those unvaccinated who wished to enter areas that required proof of the COVID‐19 vaccine were soon finding alternative illegal methods to do so which led to public health concerns. This research was divided into two parts. Part A aimed to forensically identify the security features with a video spectral comparator and a stereomicroscope. Ten "known" blank cards were collected directly from dispensaries that administered the vaccine and 68 "unknowns" from individuals who received the COVID‐19 vaccine. Part B aimed to evaluate the authentication systems employed by countries outside the United States to propose a model on how the vaccination card could be improved and what security features should be included for future instances requiring proof of immunization. The results revealed the CDC document was produced with minimal security features making the document prone to counterfeiting. Furthermore, based on the information collected from other countries' vaccination certificates, it is recommended to develop a dual authentication system, with digital and printable aspects, that contains a QR code linked to a database. Other security features may include a non‐optical brightener substrate, security fibers, a watermarked logo, offset printing, and a geographical color‐coded system. [ABSTRACT FROM AUTHOR]

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), protocols with authentication and key agreement functions can enhance the security of the interaction between users and sensor nodes, guaranteeing the security of user access and sensor node information. Existing schemes have various security vulnerabilities and are susceptible to security attacks (e.g., masquerading user, password guessing, internal privilege, and MITT attacks), so they cannot meet the anonymity requirements or achieve forward security. To effectively improve the security performance of WSNs, an elliptic curve cryptography (ECC)-based three-factor authentication and key agreement scheme for WSNs is proposed. The scheme is based on the ECC protocol and combines biometrics, smart card and password authentication technology; uses a challenge/response mechanism to complete the authentication between users, gateways, and sensors; and negotiates a secure session key. The Burrows, Abadi and Needham logic for formal security analysis proves the correctness and security of the scheme, and the informal analysis of multiple known attacks proves that the scheme can resist various attacks and has high security characteristics. The feasibility of the scheme has been analysed and verified with the ProVerif tool. The efficiency analysis results show that the scheme is suitable for resource-constrained WSNs. [ABSTRACT FROM AUTHOR]

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative. [ABSTRACT FROM AUTHOR]

To support more complex and robust online services, enterprise-class applications prefer to interconnect multiple servers as the pedestal to enhance the system's interoperability. However, the multiserver architecture always struggles to reconcile the trade-off between convenience and security, leaving users exposed to a variety of network attack threats. Existing security authentication schemes based on the Chebyshev Chaotic Map for multiserver architectures cannot provide three-factor (including password, biometric feature, and smart card) security. Therefore, we propose a novel Physical-Unclonable-Function-based Lightweight Three-Factor Authentication (PUF-LTA) scheme, which can achieve three-factor security. The PUF-LTA scheme mainly includes two components: (1) PUF-assisted registration and (2) lightweight mutual authentication with one-time interaction. During the PUF-assisted registration process, to defend against side-channel attacks on smart cards, the login credentials of users are XORed with the unique identifier generated by the PUF so that the adversary cannot obtain these secret login credentials. During the lightweight mutual authentication process, we combine the Chebyshev polynomial map and symmetric encryption/decryption to negotiate the session key between users and servers, which only needs one interaction. The security performance of PUF-LTA is theoretically proved by leveraging the random oracle model. In contrast with relevant multiserver authentication schemes, PUF-LTA is more efficient and suitable for resource-constrained multiserver environments because it can ensure secure three-factor authentication and support flexible biometrics and password updates with less computation cost. [ABSTRACT FROM AUTHOR]

A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers' alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems. [ABSTRACT FROM AUTHOR]

Ohmic heating is an emerging technology currently in high demand for application in various processes. In this research, a prototype of a multipurpose ohmic heating apparatus was successfully designed, fabricated, and tested. This apparatus was designed based on low-cost and versatile sensors and components available worldwide. Three independent chambers could be operated parallelly and individually with different treatments. Parameter data, i.e., voltage, electrical current, the temperature of heated material and environmental humidity-temperature, could be recorded by an embedded data logging system. The sensor had been tested and validated by comparing all the sensors used with commercial standard instruments. The result showed that all sensors had high measurement accuracy, indicated by very low mean absolute error (MAE) and mean absolute percentage error (MAPE), with R2 > 0.999. The performance test revealed that product temperature could be accurately maintained according to the set point temperature with a deviation value lower than 0.1 °C. The data logging system was able to record and store the parameter data in SD card memory for up to several days without interruption. The prototype of the ohmic heating apparatus could be an effective alternative to process many purposes such as pasteurisation, cooking, warming, and fermentation based on the ohmic heating principle. [ABSTRACT FROM AUTHOR]

In recent years, flexible and wearable electronics such as smart cards, smart fabrics, bio‐sensors, soft robotics, and internet‐linked electronics have impacted our lives. In order to meet the requirements of more flexible and adaptable paradigm shifts, wearable products may need to be seamlessly integrated. A great deal of effort has been made in the last two decades to develop flexible lithium‐ion batteries (FLIBs). The selection of suitable flexible materials is important for the development of flexible electrolytes self‐supported and supported electrodes. This review is focused on the critical discussion of the factors that evaluate the flexibility of the materials and their potential path toward achieving the FLIBs. Following this analysis, we present how to evaluate the flexibility of the battery materials and FLIBs. We describe the chemistry of carbon‐based materials, covalent‐organic frameworks (COFs), metal‐organic frameworks (MOFs), and MXene‐based materials and their flexible cell design that represented excellent electrochemical performances during bending. Furthermore, the application of state‐of‐the‐art solid polymer and solid electrolytes to accelerate the development of FLIBs is introduced. Analyzing the contributions and developments of different countries has also been highlighted in the past decade. In addition, the prospects and potential of flexible materials and their engineering are also discussed, providing the roadmap for further developments in this fast‐evolving field of FLIB research. [ABSTRACT FROM AUTHOR]

Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research. [ABSTRACT FROM AUTHOR]

There's a new fad among technology aficionados: voluntarily injecting radio frequency identification chips under their skin. Self-described "bio-hackers" reason that by having such microchips inserted, they can pay for purchases by just hovering their bare hand over a scanner at a checkout counter instead of swiping the magnetic strip of a credit card, inserting a card into a chip reader, or displaying a smartphone app (or doing something as old-fashioned as paying cash). At first glance, such consumer technology looks like a harmless whim, or the next logical step among those who favor piercings, or the supposed ultimate in convenience. But inserting identification microchips in humans would also seem to bear the seeds of a particularly intrusive form of surveillance, at a time when authorities in some parts of the world have been forcibly collecting DNA and other biological data – including blood samples, fingerprints, voice recordings, iris scans, and other unique identifiers – from all their citizens. This article explains some of the nuts and bolts of this hybrid human-machine product. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic drastically changed the way of living for billions of people and severe restrictions were implemented by governments around the world, affecting the travel patterns of all citizens. This article investigates how travel patterns changed in the Greater Copenhagen area of Denmark during the full two-year period covering 2020 and 2021, thus allowing for an analysis of both the short-term and medium-term impacts as society gradually reopened and restrictions were lifted. The analysis covers large-scale travel survey data as well as a segmentation clustering analysis of public transport smart card data. The results showed that impacts were strongly linked to changes in trip purpose and were thus not uniformly distributed throughout the public transport system. User segmentation analysis revealed that most users changed to less intense travel use of public transport. The results highlight important policy implications in terms of how to adapt service provision within a public transport network more efficiently. [ABSTRACT FROM AUTHOR]

The Smart Card Reader (SCR) deployed in Nigeria 2015 and 2019 elections by the Independent National electoral Commission (INEC) for voter accreditation failed to eliminate voting by proxy. Thus, the Bimodal Voter Accreditation System (BVAS) was introduced. This revolutionary technology combines voter registration, accreditation, and results transmission in one device. INEC Voter Enrolment Device (IVED) enrolls new voters; the BVAS accredits voters on Election Day while the same device uploads election results to the INEC Result Viewing (IReV) Portal. Results show that the voter accreditation process is faster compared to SCR. BVAS has increased election participation, ensure equitable distribution of seats in the National Assembly, hence promoting inclusivity and sustenance of democracy in the country. This paper examines how BVAS works, how it protects election integrity, its drawbacks, and possible recommendation for future elections. Despite its drawbacks, technology is the answer to Nigeria electoral fraud. Its benefits exceed its drawbacks. [ABSTRACT FROM AUTHOR]

The identification of commuters' home and work locations is crucial for urban and transport planning because it enables a better understanding of the urban spatial structure and commuting flow. Various methods have been developed for home and job location identification; however, the accuracy, reliability, and sensitivity of these methods have not been thoroughly examined. This study aimed to compare three commonly used approaches—the staying time method, the trip frequency method, and the hidden Markov chain method (HMM)—in terms of their adaptability and sensitivity to different scales of data, advantages and disadvantages by using smart card data of Beijing in 5 weeks of 2016. Our results showed significant differences among the three methods in identifying actual commuters. The staying time method had the largest error, while HMM was more intelligent in the recognition result due to its combination with historical inbound and outbound passenger flow rules. Although the staying time method was simple and easy to implement, it was unable to fully reflect the data's characteristics. For larger amounts of sample data, the trip frequency method demonstrated faster processing efficiency; however, missing data had a significant impact on the results. Finally, the machine learning method was able to identify locations more intelligently than the other two approaches, although its algorithm's time complexity and resource consumption were very high. These findings provided new insights into the application of big data in urban spatial research and offered suggestions for selecting the most appropriate identification method based on data and scenarios. [ABSTRACT FROM AUTHOR]

Dedicated bus lane (DBL) is one of the effective measures to improve transit system efficiency and further promote transit priority. Then, the performance assessment of DBLs turns out to be an essential task for better reflecting the interests of all stakeholders. To this end, this paper presents a data-driven and multidimensional framework to objectively and accurately assess the detailed performance of DBLs from the perspective of passengers, government, and operators, respectively, in which multiple data sources (i.e., IC card and GPS data) and corresponding mining technologies are adopted to establish a series of criteria, enabling a before-and-after comparison for DBL opening. The proposed model has been applied to evaluate the efficiency of the DBL system in the city of Chongqing, China. The results reveal the benefits of opening DBLs, especially for government interests, in which DBLs use less public resources to expand more public welfare. But its role in strengthening transit system competitiveness is diminished, because the opening of DBLs did not significantly increase speed and volume. [ABSTRACT FROM AUTHOR]

Between the period from 2018 to the first semester of 2020, a total of 2.4 million students who owned the Kartu Indonesia Pintar (Indonesia Smart Card) were at risk of not being able to access the Program Indonesia Pintar due to delays in entering recipient data, misdirected targeting, and fund disbursement obstacles. During its implementation, there were issues with inaccurate data of Kartu Indonesia Pintar recipients, resulting in the beneficiaries not being entirely targeted correctly. According to Article 12 of Minister of Education and Culture Regulation No. 10 of 2020, schools, as educational units, are assigned the responsibility of managing the Smart Indonesia Program at the school level. This includes tasks such as proposing eligible student candidates for the Program based on criteria, monitoring and assisting the smooth process of receiving the benefits, and accommodating students who hold the Kartu Indonesia Pintar (KIP). However, schools faced difficulties in obtaining the eligibility data of potential Indonesia Pintar Program recipients through the school proposal format. The objective of this research is to determine the eligibility of potential Indonesia Pintar Program recipients using a combination of Support Vector Machine (SVM) and Simple Additive Weighting (SAW) techniques. The SVM model was trained using kernel tricks and parameter selection (C and gamma) based on data sourced from the DAPODIK application of students in SMK Negeri 1 Kota Bekasi for the academic years 2020/2021 to 2022/2023, with a dataset containing 2499 rows . This dataset was used to determine the eligibility of potential Indonesia Pintar Program recipients. The SAW method was then employed to calculate scores based on the weighting of attributes possessed by the students, which were subsequently ranked from the highest score. The Linear Kernel with parameter values C=0.1 and gamma=0.1 achieved an accuracy of 88.8% during the classification of test data, using 10% of the total dataset, which amounted to 250 rows of data. The combination of SVM and SAW methods resulted in the classification of eligibility for potential Indonesia Pintar Program recipients, ranked based on their scores using the weighted assessment with the SAW method. [ABSTRACT FROM AUTHOR]

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data. [ABSTRACT FROM AUTHOR]

The continuous development of quantum computing necessitates the development of quantum-resistant cryptographic algorithms. In response to this demand, the National Institute of Standards and Technology selected standardized algorithms including Crystals-Dilithium, Falcon, and Sphincs+ for digital signatures. This paper provides a comparative evaluation of these algorithms across key metrics. The results indicate varying strengths and weaknesses for each algorithm, underscoring the importance of context-specific deployments. Our findings indicate that Dilithium offers advantages in low-power scenarios, Falcon excels in signature verification speed, and Sphincs+ provides robust security at the cost of computational efficiency. These results underscore the importance of context-specific deployments in specific and resource-constrained technological applications, like IoT, smart cards, blockchain, and vehicle-to-vehicle communication. [ABSTRACT FROM AUTHOR]

Cryptography can be thought of as a toolbox, where potential attackers gain access to various computing resources and technologies to try to compute key values. In modern cryptography, the strength of the encryption algorithm is only determined by the size of the key. Therefore, our goal is to create a strong key value that has a minimum bit length that will be useful in light encryption. Using elliptic curve cryptography (ECC) with Rubik's cube and image density, the image colors are combined and distorted, and by using the Chaotic Logistics Map and Image Density with a secret key, the Rubik's cubes for the image are encrypted, obtaining a secure image against attacks. ECC itself is a powerful algorithm that generates a pair of public and private elements, and we generate secret key values with the help of the above key pair. This algorithm is applied to smart cards, sensors, and wireless network security. Whereas, the test results indicate that the highest value for PSNR is 7.9641, while the highest values for UACI and NPCR are 35.3721 and 99.9992, respectively. The encoding and decoding speeds reach 0.1561 and 0.1532 seconds, respectively. [ABSTRACT FROM AUTHOR]

Ground‐level bus offers flexible services with relatively inexpensive fares and high‐level accessibility, thus is the dominant feeder mode for metro in the majority of metropolitan areas in China. Intermodal transfer between the metro and bus networks is therefore a crucial element in the successful operation of an integrated transit system. In this circumstance, a key challenge is lack of appropriate methodologies to evaluate the spatial–temporal disparities of intermodal transfers between metro and bus. To address this issue, this research aims to promote an existing two‐step floating catchment area (2SFCA) model by incorporating the temporal variability of service supply, demand, and travel time to provide a more realistic estimation of accessibility using smart card data and automatic vehicle location data. The proposed methodology was applied in a case study of the metropolitan area of Shanghai in the specific context of metro‐stop based accessibility to bus services. The results of the case study show that the daily fluctuations in output metro‐to‐bus transfer accessibility values are highly sensitive to temporal dynamics of transfer time, service supply, and demand. It is hoped the results output here could give planners and policymakers greater insight into spatiotemporal dynamics on transfer accessibility, and help to establish an effective and efficient integrated transit system. [ABSTRACT FROM AUTHOR]

This article presents the DTC-SVM approach for controlling a sensorless speed induction motor. To implement this approach, a practical prototype is built using a microcontroller, an embedded GPS module, and a memory card to collect real-time data during the driving route, such as road geographical data, speed, and time. These data are then utilized in the laboratory to implement the control law (DTC-SVM) on the electric vehicle. The d-q model of the induction motor is first presented to explain the requirements for calculating the rotor speed. Then, an adaptive model reference system speed estimator is developed based on the rotor flux, along with a controller and DTC-SVM strategy, which are implemented using the dSpace 1104 board to achieve the desired performance. The simulation results demonstrate satisfactory speed regulation with the proposed system. In this study too, an electronic differential system is modeled for the four wheels of an electric vehicle equipped with an integrated motor, all controlled by the DTC-SVM strategy. Vehicle speed and electrical vehicle steering angle variations, as well as wheel speeds estimated by code system, are verified using MATLAB/Simulink simulations. [ABSTRACT FROM AUTHOR]

Signcryption is a highly efficient approach to simultaneously achieving message confidentiality and authentication in Human-Centered IoT (HC-IoT) systems. HCIoT is a new field of study that links various aspects of life such as smart cards, business, e-commerce, healthcare, and sensitive private data. A number of intelligent systems favor human intervention to start automated tasks. A number of smart devices have a social effect in that they should be capable of changing their functional model based on the behavior of different humans. It has boosted the development of information exchange over the IoT and enabled networks. It encompasses cellular, vehicular, and human healthcare by utilizing middleware. Currently, group signcryption schemes are gaining widespread popularity in HC-IoT environments. HC-IoT is useful in electronic cash systems, lightweight devices, multi-server networks, and more. However, most signcryption schemes use bilinear pairing that is computationally intensive, and there is a need for more efficient signcryption schemes. In order to solve this issue, this paper introduces an efficient Certificateless Group-oriented Signcryption (CGS) scheme using Quantum Chebyshev Chaotic Maps (QCCM) without employing bilinear pairing. The proposed QCCM-CGS scheme's standout feature is that any group signcrypter can signcrypt a text/information with the group manager (GM) and then present it to the verifier for verification. By using the public conditions of the group, the verifier approves the validity of the signcrypted text and cannot connect the signcrypted text to the conforming signcrypter. However, a legitimate signcrypted text cannot be generated even by the GM or any signcrypter of that group unaccompanied. In situations where there is a legal disagreement, such as non-repudiation of the signature, the GM can reveal the identity of the signcrypter. The presented scheme is adequately secured from the indistinguishably chosen ciphertext attack. The computationally challenging problem, QCCM, is used as the foundation for the construction of traceability, unforgeability, unlinkability, and security. Lastly, the security review of the projected scheme clearly shows significant consistency, and it can be easily deployed in vulnerable security applications. [ABSTRACT FROM AUTHOR]

Determination of transport demand is one of the key factors to solve many transportation problems. Existing methods for obtaining information about passenger mobility have significant shortcomings; currently, methods based on the collection, integration and analysis of big data (Urban computing, big data) are being increasingly used. Within the framework of this approach, a methodology has been developed for determining the passenger traffic by public transport from the operations of validating electronic travel tickets: smart cards, transport cards, magnetic cards, mobile phones or other electronic devices (Electronic Gadgets), their details are recorded in the Automated Fare Collection (AFC). In this work we have taken into account that the passenger can travel one, two or more segments before paying for the trip. On some routes, payment is made at the end of the trip. The article presents a methodology based on defining and evaluating the set of acceptable variants of the connectedness of the passenger trips' sequence, which takes into account many factors that influence the choice of travel routes by the passenger. For example, unlike existing solutions, the possibility of paying for travel at any point of the route is taken into account, not necessarily immediately after boarding the vehicle. Approbation of the considered methodology was carried out on the data of the Krasnoyarsk public transport system for April 2019. It has been proved that passenger traffic from the validation of electronic travel tickets allows us to estimate the parameters of public transport demand within the limits of acceptable statistical errors. [ABSTRACT FROM AUTHOR]

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods. [ABSTRACT FROM AUTHOR]

This paper presents a secure face recognition system with advanced template protection schemes for Cyber–Physical–Social Services (CPSS). The implementation of the proposed system consists of five components. The initial step performs image preprocessing, where it detects the facial region from the captured image using the Tree-Structured Part Model (TSPM). The second phase involves feature extraction, where it utilizes the Scale Invariant Feature Transform (SIFT) descriptor to extract features from small patches of the preprocessed images, forming a collection of feature descriptors. The collection of feature descriptors is then clustered using the K-means clustering algorithm, returning the centers of K-clusters that serve as the vocabulary of a dictionary. Finally, a histogram is generated using the vocabularies and frequencies, referred to as the "Bag of Visual Words (BoVW)". Using this dictionary and a feature learning technique called Sparse Representation Coding (SRC), followed by Spatial Pyramid Mapping (SPM), the system generates feature vectors from training/testing image samples. In the third component, the modified FaceHashing technique is applied to the original feature vectors, generating cancelable feature vectors. The fourth component employs a Bio-Cryptographic technique to preserve the cancelable feature vectors in a database. Lastly, the fifth component utilizes a multi-class linear SVM classifier on the decrypted and query-cancellable feature vector to classify users. The system evaluates its performance using FERET and CASIA-FaceV5 benchmark databases, providing 100% identification accuracy for 200-dimensional cancelable feature vectors. The performance and security comparisons demonstrate the superiority of the proposed system over existing methods. • Encrypted domain-based recognition system. • Modification of the FaceHashing technique up to three levels of security. • Two additional prime factors in the RSA cryptosystem. • Access control via OTP-based smart card. • Original feature vectors kept offline. [ABSTRACT FROM AUTHOR]

The incidence of postoperative residual curarisation remains unacceptably high. We assessed whether an educational intervention on perioperative neuromuscular block management can reduce it. In this multicentre, cluster randomised crossover trial, centres were allocated to receive an educational intervention either in a first or a second period. The educational intervention consisted of a lecture about neuromuscular management key points, including quantitative neuromuscular monitoring and use of reversal agents. The lecture was streamed to allow repetition. Additionally, memory cards were distributed in each operating theatre. The primary outcome was postoperative residual curarisation in the PACU. Secondary outcomes were frequency of quantitative neuromuscular monitoring, use of reversal agents, and incidence of postoperative pulmonary complications during hospital stay. Measurements were performed before randomisation and after the first and the second period. The effect of the educational intervention was estimated using multivariable mixed effects logistic regression models. We included 2314 subjects in 34 Spanish centres. Postoperative residual curarisation incidence was not affected by the educational intervention (odds ratio [OR] 0.90 [95% confidence interval {CI}: 0.51–1.58]; P =0.717 and 1.30 [0.73–2.30]; P =0.371] for first and second time-period interaction). The educational intervention increased the quantitative neuromuscular monitor usage (OR 2.04 [95% CI: 1.31–3.19]; P =0.002), the use of reversal agents was unchanged (OR 0.79 [95% CI: 0.50–1.26]; P =0.322), and the incidence of postoperative pulmonary complications decreased (OR 0.19 [95% CI: 0.10–0.35]; P <0.001). An educational intervention on perioperative neuromuscular block management did not reduce the incidence of postoperative residual curarisation nor increase reversal, despite increased quantitative neuromuscular monitoring. Sugammadex reversal was associated with reduced postoperative residual curarisation. The educational intervention was associated with a decrease in postoperative pulmonary complications. NCT03128151. [ABSTRACT FROM AUTHOR]

The Mass Rapid Transit (MRT) is one of the major modes of public transportation in Singapore. Understanding the mobility patterns of MRT passengers has implications for improving transportation efficiency. As a city‐state with a high population density, Singapore provides a representation of balanced urban dynamics that informs smart urban planning. In this paper, we investigated and visualized (using both static maps and dynamic web map applications) the spatiotemporal characteristics of Singapore's MRT commuting patterns before the COVID‐19 pandemic (January 2020) and during the first outbreak (May 2020) and the Omicron wave of the pandemic (February 2022), using MRT smart card data. We also investigated the relationship between the passenger flows of individual MRT stations and the nearby land use types. Our results showed that the spatial patterns of Singapore's MRT commuters match the polycentric urban structure. In addition to central areas, several regional centres were identified as passenger hotspots in multiple time periods. Furthermore, during the outbreak of the pandemic, especially in the period of the 'circuit breaker', there was a major decline in MRT passenger flows and a decrease in average MRT commuting distances during weekend/holiday peak hours. Lastly, correlations between passenger flows of MRT stations and the proportion of nearby land use types have been identified. [ABSTRACT FROM AUTHOR]

Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual's own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling. [ABSTRACT FROM AUTHOR]

In the majority of metropolitan areas in China, the ground-level bus is the dominating feeder mode for metro systems. For enhancing the intermodal integration, it is necessary to evaluate the transfer accessibility between metro and bus considering the spatiotemporal dimensions with finer resolution. This study developed a methodology to identify the metro-to-bus transfer desert by considering the spatiotemporal dynamics on both service supply and passenger demand based on smart card data and automatic vehicle location data. In order to provide a more accurate and detailed depiction of results and stronger policy implications, this research aggregates to a higher spatial resolution (i.e., metro stops), instead of utilizing census tracts or administrative zones used in available research. The proposed methodology was implemented in the case study of the metropolitan area of Shanghai. Our empirical analysis highlights transfer service deficiency existing across the metropolitan area of Shanghai throughout the day, and varies both on spatially and temporally. Peak hours are characterized by better transfer accessibility levels on average but worse performances on metro stops with high transfer volume compared with off-peak hours. Because the developed methodology makes use of widely available input data, policymakers and transit agencies could replicate such gap analysis of local transit services in other contexts around the world. [ABSTRACT FROM AUTHOR]

The understanding of smart payment industry in the financialization under digital transformation will be the clue to capture the future directions and dynamics of modern capitalism economy. Is there any differences in smart payment industry according to capitalist economy condition among Daegu, Cardiff, and Nanjing ? By answering to this research question through comparative analysis of 3 regions based on qualitative interview method, this study found out as follows. First, there are different payment industry context among 3 regions; Daegu, card payment majority; Cardiff, transition from card payment to smart payment; Nanjing, Smart payment majority. Second, Economic contexts of regions could give direct impacts of the development of mobile payment industry such as the lock in the card industry at Daegu in South Korea, or the well-developed smart delivery platform of E-commerce at Nanjing in China, or the long history of capitalist economy which had increased the labor condition of laborer at Cardiff in Wales. Third, from the qualitative interview researches on 3 regions, gave 2 additional grounded theories such as the future direction of smart payment industry, and the double locked-in the card industry at Daegu in South Korea. [ABSTRACT FROM AUTHOR]

With the continuous progress of the Internet of Things (IoT) technology, IoT devices have gradually penetrated all aspects of life. However, due to rapid data growth, IoT devices with limited memory resources cannot store massive data. Cloud computing is an Internet-centric network that can provide security services and data storage. The combination of IoT devices with cloud computing considerably promotes resource sharing, facilitates users' access to information at any time, and provides users with corresponding cloud computing services on time. Because the information transmitted through public channels is easily intercepted, tampered with, and eavesdropped on by malicious attackers. As a result, users' and servers' private information are disclosed. Numerous scholars have proposed different authentication protocols in this environment to protect the communications between users and servers. Amin et al. devised a smart card based authentication protocol. Unfortunately, Kang et al. demonstrated that their protocol was insecure. Huang et al. discovered Kang et al.'s improvement also has security flaws and then designed a protocol to enhance security. In this paper, we first show that Huang et al.'s protocol cannot resist privileged insider and temporary value disclosure attacks. Afterward, we propose an enhanced protocol based on their protocol. Finally, we use formal/informal security analysis to demonstrate the security of the improved protocol. The comparison results are indicated that our protocol has lower computational and communication costs under the same security level. [ABSTRACT FROM AUTHOR]

The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data. [ABSTRACT FROM AUTHOR]

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept 'polycentric city' has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London. [ABSTRACT FROM AUTHOR]

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept 'polycentric city' has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London. [ABSTRACT FROM AUTHOR]

Smart card data have received increasing interest from transport researchers for travel behavior investigation. However, the utility of smart card data has arguably yet to be exploited in the spatio-temporal context of interpreting the essential properties of passenger groups. Before building socially responsible data models, considering ethics issues of what is right, just and fair is able to minimize negative impacts of technology. This work demonstrates a multi-phase methodology to illustrate the essential properties of passenger groups with crowd mobility portraits that are presented by several words in combination with visualized maps. In particular, we propose an efficient ranking algorithm to figure out trajectories mostly contributing to the city-wide mobility patterns and construct time-varying land functions to textualize trajectories and obtain interpretable crowd mobility portraits. Experiments show that our method outperforms the state-of-the-art methods with an accuracy improvement of 8%. Case studies of real-world data also confirm the effectiveness of crowd mobility portraits. [ABSTRACT FROM AUTHOR]

The term "user persona" recently has become more popular and can reflect the characteristics and needs of each user. To analyze the individual characteristics of each passenger in order to better implement targeted transportation policies, a method to mine travel profiles from each passenger based on their smart card transaction records is proposed, mainly mining six labels of passengers: activity, loyalty, whether they are stable commuters, where they live, where they work, and how they prefer to travel. A case study was conducted in the Huitian area of Beijing, the largest community in Asia, which demonstrated the high practicality and accuracy of the passenger profiling method. The results show that 33.06% of passengers were given the stable commuter label and were more likely to have high activity and loyalty labels, whereas 66.94% of other passengers were more likely to have low activity and loyalty labels; 88.9% of passengers were given the residence label and 67.71% of stable commuters received the workplace label, with an accuracy rate of over 90% according to a travel survey. In addition, the application of passenger labels during the design of demand responsive transit (DRT) was discussed to illustrate how to use the labels to improve the efficiency of DRT. The proposed passenger profiling method is applicable to the data mining of passenger travel labels in a simple and accurate way, and can help public transport service providers and researchers to study individual passenger characteristics and provide a theoretical basis for transit network planning and personalization measures. [ABSTRACT FROM AUTHOR]