Search Results (95)

Visual secret sharing (VSS) schemes can enhance the security of image transmission over networks. Conventional VSS schemes often generate meaningless shares, which can raise suspicion among potential attackers. To address this issue, this paper proposes a novel VSS scheme that integrates information hiding techniques with quick response (QR) codes to generate meaningful shares. The first $n-1$ shares are encoded as standard QR codes, while the n-th share is embedded into a grayscale carrier image using a reversible information hiding method, ensuring the carrier remains visually meaningful. During transmission, the $n-1$ QR codes and the hidden image are distributed. At the receiver end, the hidden n-th share is extracted losslessly from the carrier image using the $n-1$ QR codes, and the original secret image is perfectly reconstructed by bitwise XORing all n shares. Experimental results demonstrate the feasibility, security, and visual quality of the proposed scheme. Full article

Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these challenges, it is crucial to develop an effective, secure, and reliable payment system. This research presents a comprehensive study on payment card fraud detection using deep learning techniques. The introduction highlights the significance of a strong financial system supported by a quick and secure payment system. It emphasizes the need for advanced methods to detect fraudulent activities in card transactions. The proposed methodology focuses on the conversion of a comma-separated values (CSV) dataset into quick response (QR) code images, enabling the application of deep neural networks and transfer learning. This representation enables leveraging pre-trained image-based architectures by encoding numeric transaction attributes into visual patterns suitable for convolutional neural networks. The feature extraction process involves the use of a convolutional neural network, specifically a residual network architecture. The results obtained through the under-sampling dataset balancing method revealed promising performance in terms of precision, accuracy, recall, and F1 score for the traditional models such as K-nearest neighbors (KNN), Decision Tree, Random Forest, AdaBoost, Bagging, and Gaussian Naïve Bayes. Furthermore, the proposed deep neural network model achieved high precision, indicating its effectiveness in detecting card fraud. The model also achieved high accuracy, recall, and F1 score, showcasing its superior performance compared to traditional machine learning models. In summary, this research contributes to the field of payment card fraud detection by leveraging deep learning techniques. The proposed methodology offers a sophisticated approach to detecting fraudulent activities in card payment systems, addressing the growing concerns of identity theft and payment security. By deploying the trained model in an Android application, real-time fraud detection becomes possible, further enhancing the security of card transactions. The findings of this study provide insights and avenues for future advancements in the field of payment card fraud detection. Full article

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide. Full article

Background/Objectives: Food insecurity and mental health are two important issues affecting college students, and their incidence have increased since the coronavirus disease (COVID-19) pandemic. The objective of this study was to examine the association between food insecurity and the mental health outcomes among college students in the Bronx, NY. Methods: The study utilized a cross-sectional design. Data were collected from 710 undergraduate students, using a self-administered survey with a quick response (QR) code. The survey consisted of sociodemographic questions, the US Household Food Security Survey Module (ten-item questionnaire), and the Depression, Anxiety, and Stress Scale (DASS-21). Data were analyzed using Statistical Package for the Social Sciences (SPSS), Version 29. Results: Results of the study indicated that 53% of the participants were food insecure, 47.4% had high depressive symptoms, 46.2% had high anxiety, and 50.6% had high stress levels. Multiple logistic regression revealed the reciprocal association between food insecurity and mental health. Students who were stressed (p = 0.022) were likely to experience food insecurity, while those who were food insecure were more likely to experience stress (p = 0.007) and depression (p < 0.021). Students who identified as Black (p = 0.021) and had lower income (p = 0.031) were more likely to be food insecure. Conclusions: This research shows that food insecurity worsens mental health, and mental health worsens food insecurity. However, it was not possible for us to establish causality between the two variables. Full article

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

Electronic quick response (e-QR) codes provide access to real-time sensor data using smartphone readers and internet connectivity. Printed electronics and hybrid integration on flexible substrates is a promising solution for wide-scale and low-cost deployment of sensor systems. This paper presents a 21 × 21-pixel e-QR display implemented on black Kapton using hybrid additive and subtractive microfabrication techniques. The process flow for the double-sided circuit allows for layer alignment using multiple fiducial markers. The steps include inkjet printing of tracks on both sides of the substrate, laser-cut via holes, stencil-aided via filling, solder paste dispensing, and final integration of discrete surface-mount components by semi-automatic pick-and-place. Full article

Quick-response (QR) codes are widely used for information transmission. However, their black-and-white dot structure often lacks visual appeal. Therefore, we developed novel graphic QR codes that remove traditional corner finder patterns and conceal data within redesigned modules. These codes can be scanned, positioned, and decoded directly in software, while paper-based versions can be instantly decoded by overlaying a transparency with printed finder patterns. Experimental results across varying grayscale levels and devices confirmed reliable readability. The proposed QR codes provide enhanced esthetics and security, offering a new direction for future QR code applications. Full article

QR codes (Quick Response) were originally developed by the automotive industry to enable rapid data exchange and have since evolved into versatile tools for commercial applications, such as linking to products or websites. However, the scope of their adoption has expanded into sensitive domains including financial, corporate, and governmental sectors. In order to address increasing security concerns, this work proposes a novel three-layer protection scheme. First, data confidentiality is ensured through encryption—in this study, symmetric AES (Advanced Encryption Standard) encryption is used as an example, though any encryption algorithm can be employed. Second, a multiplexing technique is employed to integrate two independent dichromatic QR codes into a single printed chromatic structure. Third, the recovery of each dichromatic code is achieved through the controlled incidence of specific wavelengths, not only providing improved channel separation but also functioning as a physical access control mechanism. This physical layer restricts unauthorized reading. Full article

As climate change concerns, urban congestion, and environmental degradation intensify, cities prioritise cycling as a sustainable transport option to reduce CO₂ emissions and improve quality of life. However, rampant bicycle theft and poor security infrastructure often deter daily commuters and tourists from cycling. This study explores how advanced security measures can bolster sustainable urban mobility and tourism by addressing these challenges. A mixed-methods approach is utilised, incorporating primary survey data from Slovenia and secondary data on bicycle sales, imports and thefts from 2015 to 2024. Findings indicate that access to secure parking substantially enhances users’ sense of safety when commuting by bike. Regression analysis shows that for every 1000 additional bicycles sold, approximately 280 more thefts occur—equivalent to a 0.28 rise in reported thefts—highlighting a systemic vulnerability associated with sustainability-oriented behaviour. To bridge this gap, the study advocates for an innovative security framework that combines blockchain technology and Non-Fungible Tokens (NFTs) with encrypted Quick Response (QR) codes. Each bicycle would receive a tamper-proof QR code connected to a blockchain-verified NFT documenting ownership and usage data. This system facilitates real-time authentication, enhances traceability, deters theft, and builds trust in cycling as a dependable transport alternative. The proposed solution merges sustainable transport, digital identity, and urban security, presenting a scalable model for individual users and shared mobility systems. Full article

Vegetable intake among military Service Members (SMs) is well below public health guidelines, with only 12.9% meeting the Dietary Guidelines for Americans (DGAs). Low vegetable consumption negatively impacts diet quality as measured by the Healthy Eating Index (HEI), and poses risks to health and performance. Given the high physical and mental demands of military life, improving diet quality, including through increased vegetable intake, is crucial for optimizing health and readiness. Providing meal kits may help improve vegetable intake by reducing access-related barriers for SMs living or working on a military base. Furthermore, the addition of spices and herbs is a readily modifiable accompanying approach to address taste-related barriers and increase intake that has shown promise in other populations with poor diet quality. Background/Objectives: This study aimed to evaluate whether heat-and-serve meal kits with spices and herbs could increase vegetable intake and liking among active-duty SM by simultaneously targeting barriers to healthy eating and modifiable sensory factors. Methods: Conducted at Naval Support Activity Bethesda, the study randomly distributed heat-and-serve meal kits (n = 400) featuring either spiced (n = 200) or plain versions (n = 200) of four vegetables (broccoli, carrots, cauliflower, and kale). Each kit contained a quick response (QR) code for participants to upload post-consumption photos and rate vegetable liking on a nine-point Likert scale. Food photography (SmartIntake^®) was used to estimate vegetable consumption. Paired t-tests were used to determine differences between the intake of plain and spiced vegetables. Results: Intake of the heat-and-serve vegetables was very high for both the spiced and plain preparations (1.73 out of 2 cups, 87%). There was minimal difference (p = 0.87) between the consumption of spiced (1.75 cups) and plain (1.725 cups) vegetables, suggesting that both were well accepted. Overall, convenient and accessible meal options, alongside sensory-driven strategies, appear to improve some barriers to vegetable consumption in SM populations. Conclusions: Future studies should explore long-term outcomes and adaptability across different military environments, while considering additional factors, including convenience and time constraints, that influence dietary choices in the military. Full article

Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications. Full article

Construction 4.0 is propelling the construction sector towards a digital, automated, and sustainable framework. This paper reviews advancements in automation and digitalization within the steel construction industry, framed by the principles of Construction 4.0. An analysis of the existing literature indicates that previous review studies have explored the technologies and concepts associated with Construction 4.0. However, none have consolidated these technologies and concepts (T&C) specifically within the context of the steel construction industry to evaluate their impact on steel manufacturing and assembly processes which was the main criterion for article selection. Therefore, this paper aims to consolidate various Construction 4.0 technologies and concepts to explore their integration into the steel construction industry. Based on data from the Web of Science and Scopus, a thorough screening process identified 56 out of 161 articles for analysis regarding their applicability to the steel construction industry. The evolution of various technologies in the steel construction industry has been examined over the years, starting with the initial references to each technology. In addition to discussing the advancements of these technologies and their influence on contemporary digitalization and automation within the steel sector, the authors seek to identify which T&C are most commonly utilized in manufacturing and assembly processes. The graphical results of this review indicate that each type of T&C can serve as a tool for quality control throughout the manufacturing and assembly processes. However, it is noteworthy that most research remains concentrated on enhancing material tracking and identification during these stages of production. Full article

The analysis and identification of the genetic diversity of plant germplasm resources and varieties are crucial for plant breeding. DNA fingerprinting using genomic molecular markers is crucial for precisely identifying germplasm resources. In this study, the SLAF-seq was performed using 47 germplasm resources of the Wanzhou tea plant population and 5 common cultivated varieties from the Yunnan, Sichuan, and Fujian provinces. A total of 7,447,940 SNPs were identified from 1,641,569 SLAF tags with an averaged sequencing depth of 11.73 (Q30 94.93% and GC 41.37%), which were used to analyze the population composition and genetic diversity. Results showed a large degree of genetic diversity and genetic variation among the samples. The cluster analysis showed that the tea plant population was categorized into three groups, indicating that these germplasms could not be fully classified by their geographical origin, and the linkage disequilibrium analysis indicates that the population resources of the XJ production area are more modern in evolution. A total of 371 uniformly distributed SNP loci were selected and successfully employed to construct the first SNP fingerprint and quick response code (QR code) for tea resources in Wanzhou. These findings offer new insights for genotyping, classifying, and identifying germplasm and genetic resources in the breeding of the Wanzhou tea population. Full article

The COVID-19 pandemic has precipitated several challenges, prompting the Indonesian government to enact rules aimed at minimizing direct contact to mitigate the spread of COVID-19, which has also affected transactional activities. Transactions conducted using a digital wallet represent a technological advancement that facilitates a cashless society lifestyle. Bank Indonesia established the Quick Response Code Indonesian Standard (QRIS) as a QR Code standard for digital payments using Electronic Money-Based (EU) servers, electronic wallets, or Mobile Banking. This study aims to identify the elements that affect consumer willingness to convert from cash payments to the QRIS during the COVID-19 epidemic. This study collected data through an online survey, distributing a 17-item questionnaire to QRIS users, yielding 568 valid responses. This research used a modified version of the Push-Pull-Mooring theory and an adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, concentrating on consumers’ intentions to transition from cash payments to QRIS utilization. This study employed the Hybrid SEM-ANN methodology with the SmartPLS and IBM SPSS Statistics 27 applications for data analysis. This investigation had 11 hypotheses, of which 4 were accepted. The findings indicated that alternative attractiveness, trust, critical mass, and traditional payment habits significantly influenced the intention to transition from cash payments to QRIS payments during the COVID-19 pandemic. Full article

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. Full article