Search Results (721)

This study presents a new steganography method for message transmission based on style transfer and denoising diffusion probabilistic model (DDPM) techniques. Different types of object images are used to represent the messages and are arranged in order from left to right and top to bottom to generate a secret image. Then, the style transfer technique is employed to embed the secret image (content image) into the cover image (style image) to create a stego image. To reveal the messages, the DDPM technique is first used to inpaint the secret image from the stego image. Then, the YOLO (You Only Look Once) technique is utilized to detect objects in the secret image for the message decoding. Two security mechanisms are included: one uses object images for the message encoding, and the other hides them in a customizable public image. To obtain the messages, both mechanisms need to be cracked at the same time. Therefore, this method provides highly secure information protection. Experimental results show that our method has good confidential information transmission performance. Full article

This paper presents a cross-layer intrusion detection framework leveraging explainable artificial intelligence (XAI) and interpretability methods to enhance transparency and robustness in attack detection within the Internet of Medical Things (IoMT) domain. By addressing the dual challenges of compromised data integrity, which span both biosensor and network-layer data, this study combines advanced techniques to enhance interpretability, accuracy, and trust. Unlike conventional flow-based intrusion detection systems that primarily rely on transport-layer statistics, the proposed framework operates directly on raw packet-level features and application-layer semantics, including MQTT message types, payload entropy, and topic structures. The key contributions of this research include the application of K-Means clustering combined with the principal component analysis (PCA) algorthim for initial categorization of attack types, the use of SHapley Additive exPlanations (SHAP) for feature prioritization to identify the most influential factors in model predictions, and the employment of Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) to elucidate feature interactions across layers. These methods enhance the system’s interpretability, making data-driven decisions more accessible to nontechnical stakeholders. Evaluation on a realistic healthcare IoMT testbed demonstrates significant improvements in detection accuracy and decision-making transparency. Furthermore, the proposed approach highlights the effectiveness of explainable and cross-layer intrusion detection for secure and trustworthy medical IoT environments that are tailored for cybersecurity analysts and healthcare stakeholders. Full article

Fuzzy signature (${\mathsf{SIG}}_{\mathsf{F}}$) is a type of digital signature that preserves the core functionalities of traditional signatures, while accommodating variations and non-uniformity in the signing key. This property enables the direct use of high-entropy fuzzy data, such as biometric information, as the signing key. In this paper, we define the m-existentially unforgeable under chosen message attack ($\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$) security of fuzzy signature. Furthermore, we propose a generic construction of fuzzy signature, which is composed of a homomorphic secure sketch ($\mathsf{SS}$) with an error-recoverable property, a homomorphic average-case strong extractor ($\mathsf{Ext}$), and a homomorphic and key-shift* secure signature scheme ($\mathsf{SIG}$). By instantiating the foundational components, we present a $\mathfrak{m}{-\mathrm{EUF}-\mathrm{CMA}}^{\ast }$ secure fuzzy signature instantiation based on the Computational Diffie–Hellman (CDH) assumption over bilinear groups in the standard model. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

Linguistic steganography can be utilized to establish covert communication channels on social media platforms, thus facilitating the dissemination of illegal messages, seriously compromising cyberspace security. Synonym substitution-based linguistic steganography methods have garnered considerable attention due to their simplicity and strong imperceptibility. Existing linguistic steganalysis methods have not achieved excellent detection performance for the aforementioned type of linguistic steganography. In this paper, based on the idea of focusing on accumulated differences, we propose a two-stage synonym substitution-based linguistic steganalysis method that does not require a synonym database and can effectively detect texts with very low embedding rates. Experimental results demonstrate that this method achieves an average detection accuracy 2.4% higher than the comparative method. Full article

The extensive use of digital media among adolescents has raised concerns about its impact on emotional development and mental health. Understanding the psychological factors behind problematic digital media use is essential for effective prevention. This study examined whether the relationships between emotion regulation (negative and positive), compulsive use, cognitive preoccupation, and negative outcomes linked to digital media are consistent across sex and academic year. We used a cross-sectional design with 2357 adolescents (12–16 years old) from Compulsory Secondary Education. Participants completed validated self-report questionnaires assessing problematic digital media use, and associated consequences in four domains: internet, video games, social networking, and messaging. Four structural equation models (SEMs), each focused on a media type, tested whether these relationships remained stable across sex and academic year. All models showed good fit, and differences between groups were minimal, supporting valid comparisons. Results confirm that emotion regulation difficulties and problematic digital media use are consistently associated with negative outcomes in all adolescents, regardless of sex or academic level. Preventive strategies targeting emotional regulation and digital media behaviors may be broadly applied to reduce emotional and functional problems related to excessive media use. Full article

This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article

Dengue virus infection (DVI), a mosquito-borne arboviral infection, is prevalent in tropical and subtropical regions, including Bangladesh, where incidence has surged over the past three decades—particularly in urban and peri-urban areas. This study investigates the factors influencing DVI seropositivity among clinically suspected patients admitted to the selected hospitals of Savar, Dhaka, and Chattogram. Data were collected from 850 clinically suspected patients admitted to two hospitals in Savar, Dhaka, and two in Chattogram during 2019. Questionnaire responses and laboratory test results (NS1, IgM, and IgG) were analyzed using descriptive statistics, chi-square tests, and logistic regression. Out of 450 admissions in Savar, 330 tested positive, while Chattogram reported 145 positives from 400 cases. No significant differences were observed between regions in relation to hospital type, season, gender, or household preventive measures. In Savar, DVI status was significantly associated with season, mosquito net use, and patient contact. In Chattogram, household repellent use and patient contact were key factors. Diagnostic tests varied in detection capability. These findings can inform targeted intervention strategies and public health messaging, such as promoting personal protection measures and community awareness campaigns, particularly in high-incidence urban settings. However, further research across diverse geographic and socio-ecological contexts is needed to enhance the generalizability and policy relevance of these results. Full article

A rational exchange protocol is a type of e-commerce protocol that aims to maximize the participants’ own interests. The Buttyan model is commonly used to analyze the security of such protocols. However, this model has limitations in dealing with uncertainties and false messages in rational exchanges. To address these shortcomings, this paper proposes a formal analysis method based on Bayesian games. By incorporating participants’ types and beliefs, the Buttyan model is extended to enhance its ability to express uncertainties. Additionally, attack messages are introduced to simulate the potential fraudulent behaviors that participants may exploit through the security vulnerabilities in the protocol. Finally, the improved model is applied to conduct a formal analysis of a rational electronic contract signing protocol, and it is found that the protocol meets the usability requirements. The results show that this method can be effectively applied to the security analysis of rational exchange protocols, thereby enhancing the security of the e-commerce transaction process. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide. Its incidence has been increasing rapidly, alongside the growing epidemics of type 2 diabetes mellitus and overweight/obesity. Global population age has also been increasing in parallel, and predictions indicate there will be more than 2 billion persons aged over 65 by the year 2050. The interplay between MASLD and other health conditions of older persons has been a focus of recent research. In this narrative review, we aim to describe its prevalence; clinical and sociodemographic associations; and outcomes for older persons, all of which are of significant importance when considering public health messaging as well as screening and counselling individual older adults. Full article

Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. However, it is known that such convolution layers can only barely capture long-term spatial and temporal dependencies. To solve this problem, we propose Attention-DenseNet (ADNet), which effectively incorporates an attention module into STDenseNet to learn representations for long-term spatio-temporal patterns. Specifically, we explored the optimal positions and the types of attention modules in combination with STDenseNet. Our key findings are as follows: i) attention modules are very effective when positioned between the last dense module and the final feature fusion module, meaning that the attention module plays a key role in aggregating low-level local features with long-term dependency. Hence, the final feature fusion module can easily exploit both global and local information; ii) the best attention module is different depending on the spatio-temporal characteristics of the dataset. To verify the effectiveness of the proposed ADNet, we performed experiments on the Telecom Italia dataset, a well-known benchmark dataset for network traffic prediction. The experimental results show that, compared to STDenseNet, our ADNet improved RMSE performance by 3.72%, 2.84%, and 5.87% in call service (Call), short message service (SMS), and Internet access (Internet) sub-datasets, respectively. Full article

Chronic pain is a prevalent public health concern in the United States, frequently managed with over-the-counter (OTC) painkillers without professional medical supervision. This study investigates household-level patterns of over-the-counter painkiller use utilizing a nationally representative dataset from NielsenIQ, focusing on how reported health conditions, whether self-identified or professionally diagnosed, affect purchasing behaviors. By linking consumer purchase data with self-reported ailment information, this study analyzed painkiller expenditures across different ailment types and demographic groups. Results show that over-the-counter painkiller purchases were highly symptom-driven, particularly for headache-related products, which were the most frequently purchased category across all household types. Nearly one-third of single-member households purchased over-the-counter painkillers for headaches, regardless of diagnosis type, indicating a strong role of perceived need in driving behavior. Females and older individuals more frequently reported ailments, with consistently higher proportions across both pain-related and other conditions. Nonetheless, a notable share of households reported over-the-counter painkiller use without any reported ailments. The findings suggest that diagnostic status plays a limited role in determining over-the-counter painkiller usage, emphasizing the need for improved public health messaging around safe self-medication. These insights can inform targeted education, labeling regulations, and policy interventions to support safer and more equitable pain management practices at the population level. Full article

This article focuses on graffiti and street art analysis in green areas from Romanian cities. Whether it is about the invasion or anticipated integration of urban artworks in green areas, in recent years, the phenomenon of urban art has become undoubtedly visible and finds its place among other components of artistic interventions. This study aims to diagnose various forms and expressions of urban art in the parks of several case study cities from Romania and to evaluate the urban art rapport with the parks’ organization. The methods used combine field research to identify spots with urban art, typologies based on different criteria, documentation for primary or supplementary explanations about the reality identified in the field, and results mapping using GIS tools. This study’s main findings are in relation to the location and preferred surfaces in the investigated parks for graffiti and street art works; hence, the main types of urban art and the messages transmitted. The results obtained highlight the idea that there are differences and gaps in terms of urban art phenomenon evolution reflected in diverse approaches of urban art and different balances that impact the parks’ image. Full article

This study proposes a derivative-based, carrierless pulse position modulation (PPM) scheme utilizing a voltage-controlled oscillator (VCO) and a monostable multivibrator. In contrast to conventional PPM systems that rely on reference carriers or complex demodulation methods, the proposed architecture simplifies signal generation by directly modulating the time derivative of the message signal. The modulated signal, when processed through standard analog demodulators, inherently yields the derivative of the original message. This behavior is first established through theoretical derivations and then confirmed by simulations and circuit-level experiments. The proposed method includes a differentiator feeding into a VCO, followed by a monostable multivibrator to generate a carrierless PPM waveform. Experimental validation confirms that, under all tested demodulation approaches—integrator-based, PLL-based, and quasi-FM—the recovered output aligns with the differentiated message signal. The integration of this output to retrieve the original message was not performed to maintain focus on verifying the modulation principle. Additionally, the study aimed to ensure the consistency of derivative recovery. Signal-to-noise ratio (SNR) expressions for each demodulator type are presented and discussed in the context of their relevance to the proposed system. Limitations and directions for further study are also identified. Full article

Green advertising messages often face challenges of abstraction and outcome ambiguity. To address this, we apply the framing effect theory to explore how concrete versus abstract expressions in green advertising interact with consumer perceptions. Drawing on the Stereotype Content Model (SCM), we propose a congruence framework: concrete messages align with competence appeals, while abstract messages align with warmth appeals. Through two experiments, we demonstrate that such congruence significantly enhances green purchase intention. Experiment 1 establishes the interaction effect between message framing (concrete vs. abstract) and appeal type (competence vs. warmth), revealing that concrete–competence and abstract–warmth pairings outperform mismatched conditions. Experiment 2 further validates advertising attitudes as a mediator and product involvement as a moderator, clarifying boundary conditions. These findings advance the theoretical understanding of framing effects in sustainability communication and offer actionable strategies for marketers: aligning message specificity (concrete/abstract) with appeal dimensions (competence/warmth) can amplify consumer engagement, particularly when tailored to product contexts. Full article