Search Results (68)

Contextualization: Lifestyle changes, such as smoking cessation, physical activity, and healthy eating, are essential for the treatment and prevention of cardiovascular diseases. The 2021 update of the European Society of Cardiology (ESC) guidelines highlights the need to investigate the adherence to the Mediterranean diet in patients who have suffered acute myocardial infarction (AMI). Objective: The aim of this study was to investigate the adherence to the Mediterranean diet and dietary 2021 ESC guidelines of individuals who have suffered AMI and to evaluate the sociodemographic and lifestyle factors associated with the adherence. Methods: A cross-sectional study was conducted using baseline data from participants prior to enrollment in the DICA-NUTS multicenter clinical trial. The dietary intake was assessed using a food frequency questionnaire and 24 h dietary recall. The nutrient intake was compared with the nutritional recommendations of the 2021 ESC guidelines, and the adherence to the Mediterranean diet was analyzed using a Mediterranean diet score. Furthermore, the analyses of the factors associated with the Mediterranean diet score and 2021 ESC were performed. Analyses were conducted using Stata Data Analysis and Statistical Software version 15.0. Values were considered statistically significant when the p-value < 0.05. Results: Among 488 participants aged ≥ 40 years, moderate adherence to the Mediterranean diet was observed obtaining an average of nine points, with a low intake of vegetables, fish, and cereals. The protein intake was adequate with 18% (interquartile range [IQR] 15.0–23.1) of the total energy value, and the saturated fat intake was high with 9.7% (IQR 7.3–12.7) of the total energy value, while the carbohydrate and total fat intake was adequate. According to the 2021 ESC guidelines, the fiber intake was low with more than 79% of the sample consuming less than 30 g per day. The multivariate analysis using the 2021 ESC dietary recommendations showed that older age (≥60 years) [odds ratio (OR) = 1.63; 95% confidential interval (CI) = 1.44–1.91], never smoking (OR = 1.34; 95% CI = 1.17–1.65), and higher education (OR = 1.37; 95% CI = 1.17–1.77) were correlated with an increased fruit and vegetable intake. Non-white, never-smokers, and former smokers were more likely to consume fish. The alcohol scores were higher in older age participants and women. The dairy scores increased with older age, while the cereal scores decreased. No significant association was found for legumes. Conclusions: According to the scores used, the diet of these individuals moderately reflects the Mediterranean characteristics. Analyzing the 2021 ESC dietary recommendations, the studied population was inadequate. This study found different factors associated with an adequate food intake in post-AMI patients. The highlight of this study was that older age is more likely to increase fruit, vegetable, and dairy intake. Full article

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications. Full article

Background: In Oman, primary healthcare physicians (PHPs) are often the first point of contact in the healthcare system. Understanding the prevalence and impact of stigma among these professionals is crucial to fostering a supportive work environment and promoting access to mental health care. This study evaluated mental health stigma and its association with help-seeking behaviors among PHPs in Muscat, Oman. Methods: A cross-sectional analytical study was conducted from March to May 2023 using cluster random sampling to recruit 191 PHPs. Participants completed a structured questionnaire that evaluated demographic and clinical characteristics, help-seeking behaviors, and perceived stigma. The PPSS developed for this study underwent expert review, pilot tests, and reliability analysis. Data were analyzed using descriptive statistics, Chi-square tests, and multivariate logistic regression, with a significance set at p < 0.05. Results: Most of the participants were women (78.5%), aged 30–39 years (49.7%), and Omani nationals (71.2%). More than half (57.6%) reported experiencing depressive episodes, yet only 21.8% sought professional help. High levels of stigma were associated with reluctance to seek professional mental health support, and 24.6% of participants preferred not to seek help at all. Those in the stigma group were significantly more likely to rely on family or friends for support (adjusted OR = 2.873; 95% CI = 1.345–6.138; p = 0.006). Common barriers to help-seeking included a lack of belief in the effectiveness of treatment (23.0%) and concerns about confidentiality (19.9%). Conclusions: Mental health stigma remains a widespread problem among primary healthcare physicians in Oman, influencing their behavior and preferences. Interventions to reduce stigma and address barriers to mental health care, such as enhanced confidentiality safeguards and treatment skepticism, are critical to improving physician well-being and healthcare delivery. This study can inform policy and training programs aimed at improving physician well-being and patient care. Full article

Health professionals are trusted information sources and could be valuable for improving climate change health literacy. Few studies address teaching patients about health risks associated with climate change, and no studies have focused on the medical office waiting room as a teaching site for populations from heat-vulnerable neighborhoods. We gave adult patients in primary care office waiting rooms printed teaching materials about heat-related illnesses. We asked them to read these at home and then complete an online confidential survey concerning their preferences among teaching methods and their preferences for communication during health emergencies. Ninety-one surveys were received from patients residing in heat-vulnerable neighborhoods. Patients liked receiving information in waiting rooms. Printed brochures were favored statistically by patients, but other teaching methods that are feasible for waiting rooms also rated well, including single-page printed fliers, posters, and video screens. Digital options were far less favored. We conclude that printed teaching materials may improve decisions that impact human health. The medical office waiting room appears to be an accepted, time-efficient, and effective site to communicate knowledge on climate change and health. Additionally, medical offices could play a role supporting government agencies to communicate with patients during weather-related health emergencies. Full article

Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed to achieve real-time and accurate classification. SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. An initial classification is conducted based on the first K packets during the classification process. If the prediction meets the predefined probability threshold, processing stops; otherwise, additional packets are received. This progressive approach dynamically adjusts the required packets, enhancing classification efficiency. Experimental results show that traffic can be effectively classified by using only the initial K packets. Moreover, on most datasets, the classification time is reduced by more than 70%. Unlike existing methods, SwiftSession enhances the classification speed while ensuring classification accuracy. Full article

Mental health statistics come with numerous challenges, beginning with data integrity. Ensuring data accuracy and reliability is essential, especially if these datasets are to be used for advanced analysis or research. Additionally, privacy concerns heavily impact the management of mental health data. Protecting the privacy and confidentiality of individuals—especially those with personal or sensitive information—is paramount in system development. Robust protocols should be implemented to prevent unauthorized access and potential breaches. Another critical issue is bias in the training data, which can arise from the underrepresentation of certain demographic groups or the overrepresentation of others. Reducing bias within these datasets is essential to enhance the fairness and accuracy of the models and algorithms they support. Research on mental health classification using machine learning techniques, particularly PCA and logistic regression, is significant because it has the potential to improve decision-making in mental health care. Full article

Background/Objectives: Climate change increases the vulnerability of regions, communities, and individuals, stressing the urgent requirement to prepare health professionals in alignment with the Sustainable Development Goals. To foster sustainable and resilient communities, it is essential to integrate disaster preparedness into health education and practice. By equipping nurses with essential knowledge and skills, healthcare systems can be better prepared to respond to the challenges of climate change and disasters, contributing to safer and more resilient urban environments. This study aims to identify the factors that determine nurses’ preparedness for disaster situations. Methods: A cross-sectional online survey was conducted through digital platforms among 230 nurses across the Azores to assess their preparedness for disaster management. A structured questionnaire, adapted from the validated Disaster Preparedness Evaluation Tool (DPET©), was administered to registered nurses affiliated with the Nursing Council. The sample was chosen for convenience, and data were analyzed using IBM^® SPSS Statistics, employing descriptive statistics, Cronbach’s alpha for measuring internal consistency, independent samples t-tests, and one-way ANOVA for comparative analysis. Ethical oversight was obtained from the Ethics Committee of the Azores, ensuring confidentiality and the voluntary nature of participation. Results: The findings revealed concerns about nurses’ disaster response, with 70% of participants indicating low ability. While knowledge was relatively strong (M = 5.50, SD = 1.07), practical competence showed a moderate score (3.51, SD = 1.797). Additional education is necessary in areas such as family preparation (M = 2.58, SD = 1.569), patient management during responses (M = 3.43, SD = 1.312), disaster-specific knowledge (M = 2.95, SD = 1.45), and recovery management (M = 2.53, SD = 1.363). Conclusions: Despite increased knowledge and awareness of climate change and its global impact, there is a need for more meaningful nursing interventions to minimize the impact of climate change on the community. This study highlights that nurses have an in-depth knowledge of communities’ needs, which puts them in a unique position to influence disaster preparation and response. By developing these strategies, nurses contribute significantly to communities’ resilience in climate change, improving society’s ability to respond and adapt to these challenges. Full article

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount. Full article

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability. Full article

Addictions undermine the quality of life of individuals and their families. They generally lead to misadjustment that affects many areas (psychological, family, social, and occupational) and, in the most severe cases, can end in helplessness, loneliness, or early death. The self-esteem of the person with addictions tends to be reduced and this limits their chances of recovery. Through this research, we tried to test the effect of a self-esteem workshop on 58 people with addictions to different substances. A self-esteem workshop was carried out with people who were part of a therapeutic community belonging to the province of Pontevedra (Spain). Several working groups were established and received sessions over a period of two and a half months. Participation was voluntary and the anonymity and confidentiality of the participants were guaranteed. To assess self-esteem, the Coopersmith Self-Esteem Scale Short Form, or Form B, was used at the beginning and at the end of the workshop. The results confirm the existence of statistically significant differences in the Self-Esteem Scale in relation to the pre-treatment and post-treatment assessments: F₍₁₎ = 106.44, p = 0.000, η² = 0.655. The self-esteem workshop resulted in a significant change in this construct and contributed to the psychological improvement of people with addictions. Full article

Background: Privacy in healthcare is a fundamental right essential to maintain patient confidentiality and trust. Community pharmacies in Saudi Arabia (SA) play a critical role in the healthcare system by providing accessible services and serving as initial points of contact for medical advice. However, the open nature of these settings poses significant challenges in maintaining patient privacy. Methods: This cross-sectional study used electronic surveys distributed across various online platforms. The target sample included Saudi adults, with a sample size of 385 participants to achieve 80% statistical power at a 95% confidence interval. The survey comprised demographic questions and sections evaluating perceptions of privacy, the importance of privacy, and personal experiences regarding privacy in community pharmacies. Descriptive statistics and logistic regression models were used for the analysis. Results: A total of 511 responses were obtained. The mean age was 33.5 years, with an almost equal distribution of males (49.71%) and females (50.29%). Most participants held a bachelor’s degree or higher (78.67%). Privacy perceptions varied, with only 9.0% strongly agreeing that there was a private space for consultations, while 64.0% felt that the design of community pharmacies did not adequately consider patient privacy, and 86.9% reported that conversations could be overheard. Privacy concerns were notable, with almost one-half of the participants (47.6%) having concerns about privacy and 56.6% doubting the confidentiality of their health information. Moreover, 17.6% reported being asked for unnecessary personal information when buying medication, and 56.2% admitted to avoiding discussing a health problem with the pharmacist due to privacy concerns. Experiences of privacy breaches were reported by 15.7% of respondents. Logistic regression analysis revealed that the availability of private space in the pharmacy and patients feeling that the pharmacy respects their privacy were associated with a lower likelihood of avoiding discussions with pharmacists due to privacy concerns (OR = 0.758, CI = 0.599–0.0957 and OR = 0.715, CI = 0.542–0.945 respectively) Conversely, greater privacy concerns and previous privacy breaches significantly increased the likelihood of avoiding discussions with pharmacists in the community pharmacy (OR = 1.657, CI = 1.317–2.102 and OR = 4.127, CI = 1.886–9.821 respectively). Conclusions: This study highlights the significant concerns regarding privacy practices in community pharmacies in SA. Thus, there is a need for standards to improve privacy in community pharmacies, such as mandating the need for private consultation areas and enhanced staff training on handling privacy-related issues. Addressing the issue of privacy is crucial for maintaining patient trust, improving healthcare service quality, and ensuring effective patient–pharmacist interactions. Full article

The use of physical unclonable functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on ring oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. The proposed architecture improves the hardware efficiency with two main objectives. On the one hand, we perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, we undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized intellectual property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness, and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a true random number generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4096 bits using an implementation of the PUF/TRNG module that requires only an array of $4\times 4$ configurable logic blocks (CLBs) to accommodate the RO bank. Full article

Data security and privacy have become essential due to the increasingly advanced interconnectivity in today’s world, hence the reliance on cryptography. This paper introduces a new algorithm that uses a novel hybrid Tent–May chaotic map to generate pseudo-random numbers, as well as block encryption. We design a robust S-box by combining the Tent and May Maps, which yields a chaotic system with improved cryptographic properties. This S-box is a critical cryptographic primitive that significantly improves encryption security and leverages the strengths of both maps. The encryption process involves two key steps: block-wise substitution and permutation. First, we divide the image into $16\times 16$ blocks, then substitute each pixel with the $8-byte$ key and S-box. Next, we convert the encrypted image back into vector form, reorganize it using the permutation vector based on the subgroups of $S_{16}$, and finally return it to its original form. This approach greatly improves block cipher security when used, especially to protect medical images by guaranteeing their confidentiality and noninterference. Performance measures like PSNR, UACI, MSE, NCC, AD, SC, MD, and NAE prove how immune our method is to various cryptographic and statistical attacks, making it more accurate and more secure than the existing techniques. Full article

Current data trading systems only support plaintext or unaudited private transactions. To overcome these, we present a confidential batch payment scheme with integrated auditing for enhanced data trading security. We use Castagnos–Laguillaumie (CL) homomorphic encryption and batch zero-knowledge proofs to construct the scheme. The scheme reduces decryption complexity and ciphertext length while enabling malicious model operations. In addition, it supports efficient batch payments to multiple recipients and includes features for payment statistic analysis and auditing. Experimental results indicate that the system efficiently handles encryption, decryption, and auditing tasks, completing each operation in an average of 0.89, 1.55, and 1.55 milliseconds, respectively. Full article

This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use. Full article