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Optimized Cardiovascular Disease Prediction Using Clustered Butterfly Algorithm
No full textCardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI repository is used for our experiments. The experiments are divided into three sets: the first set involves the RKM clustering technique, the next set evaluates the classification outcomes, and the last set validates the performance of the proposed hybrid model. The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97. This result is comparatively better than other combinations of optimization techniques. In addition, this approach effectively enhances data segmentation, optimization, and classification performance
Impact of Ramadan Intermittent Fasting on Cardiovascular Health and Disease
No full textIntermittent fasting (IF) is a dietary approach involving alternating periods of fasting and eating, which has garnered attention for its potential health benefits and flexibility. This chapter examines the efficacy of various IF protocols, such as alternate-day fasting (ADF) and time-restricted feeding/eating (TRF/E), in improving health markers among different populations, including those at increased risk of cardiovascular diseases (CVD). While the practice of fasting can enhance longevity and reduce chronic illness indicators, like oxidative stress and inflammation, individual responses may vary depending on specific protocols and demographic factors. The chapter emphasizes the significance of Ramadan fasting, a form of IF practiced by millions globally, showcasing its impact on lipid profile, blood pressure, and overall cardiovascular health. Challenges associated with fasting during Ramadan, particularly for individuals predisposed to CVD, are discussed alongside recommended health assessments. Additionally, the chapter highlights potential metabolic and circadian rhythm disturbances induced by altered eating patterns. Overall, IF, including Ramadan fasting, appears to facilitate better health outcomes, particularly in weight management and cardiovascular risk reduction, warranting further investigation to substantiate these findings across diverse populations
Integrative Techniques to Alleviate Abiotic Stress in Plants Using Plant Growth Promoting Bacteria and Fungi: Mechanisms, Interactions, and Applications
Full text linkThe association between visit-to-visit variability in risk factors and incident CVD: A Post-hoc analysis of the Multi-Ethnic Study of Atherosclerosis
No full textOur aim is to investigate the association between visit-to-visit variability of nine risk factors and incident cardiovascular disease (CVD) in a large multi-ethnic population cohort study. We used the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. We included individuals with no previous history of CVD, with at least three repeated measurements on each risk factor including total cholesterol, HDL, LDL, non-HDL, triglyceride, Chol/HDL ratio, Diastolic Blood Pressure (DBP), Systolic Blood Pressure (DBP), and Body mass index (BMI). Visit-to-visit variability was estimated via the variability independent of the mean (VIM). A Cox proportional hazards model was used to estimate the association between visit-to-visit variability and the hazard of developing CVD. There was a statistically significant association between visit-to-visit variability in SBP, BMI, HDL and the rate of incident CVD. This rate was higher in individuals with high visit-to-visit variability for SBP [HR: 1.28, 95% CI: 1.01-1.63, P = 0.04]; BMI [HR: 1.58, 95% CI: 1.25-2.00, P \u3c 0.001]; and HDL [HR: 1.3, 95% CI: 1.03-1.65, P = 0.025], compared to those with low visit to-visit variability. Our findings suggest that visit-to-visit variability in some CVD risk factors could be independently associated with incident CVD and may be useful to clinicians in risk stratification
Managing Digital Transformation in the Healthcare Sector: Big Data Influencing Patient Outcomes, Costs, and Efficiency Improvement
No full textThe fourth industrial revolution and digital transformation, followed by the broad utilization of various technological devices, have revolutionized all segments of society and business, including the healthcare sector. The technology-enabled proliferation of big data from various sources opened new data collection and processing options. The present study aims to identify the important technologies in smart health care that utilize big data and provide a better understanding of how big data can assist patients and physicians. A comprehensive literature review is conducted to identify the areas of application of big data in health care and map them to the P4 principles of health care: predictive, preventive, participatory, and personalized medicine. The study is descriptive and qualitative and based on an analysis of secondary data sources. The paper’s main contribution is providing a systematic review of the current applications of big data analytics in health care from the perspective of P4 principles of health care. Additionally, the paper provides an overview of the current state of digital transformation in the healthcare sector, analyzes big data sources in health care, and explores the current challenges and future perspectives of smart healthcare solutions concerning patients’ outcomes, costs, and efficiency. Finally, the paper concludes with practical recommendations and recommendations for further research
A Novel Slice Reconfiguration Method for Achieving QoS Guaranteeing and OPEX Saving in 5G Networks
No full textNetwork slicing is recognized as one effective method to provide tailored services for various vertical industries in 5G and beyond. However, the traditional static slice configuration pattern struggles to address the issue of time-varying service loads. Thus leading to the degraded quality of service (QoS) and inefficient network resource utilization. Slice reconfiguration paves the way for achieving QoS-guaranteed and cost-efficient management of network slices by adjusting slice configurations when service load varies. In this paper, we propose a slice reconfiguration (SR) framework that enables network service providers (NSPs) to perform vertical/horizontal scaling operations to cope with the challenges brought by time-varying service load. We first model the slice reconfiguration as an mixed-integer programming problem to minimize NSP\u27s service operating expenses (OPEX). However, its inherent non-convexity challenges direct optimization. Consequently, we reformulate it into a mixed-integer quadratically constrained programming-based SR problem (MIQCP-SR) enabling provably exact solutions. Given the prohibitive computational complexity of MIQCP-SR, we further develop one two-step slice reconfiguration (TS-SR) method: first, determine the function deployment, and second, execute the resource allocation. Extensive simulations show that our MIQCP-SR and TS-SR can effectively adapt the slice configuration to the changed service load. Compared with static configuration based on peak loads, MIQCP-SR and TS-SR reduce OPEX by 38% while still guaranteeing QoS requirements. Additionally, TS-SR outperforms other baselines and achieves 270 times faster solving speed than MIQCP-SR with only a 0.7% increase in cost
A new scheme for simple asymmetric bivariate copulas and applications
No full textBivariate copulas play a central role in modeling the dependence structure between two random variables and serve as a fundamental tool in various applied fields. In this article, we develop a new theoretical framework aimed at constructing simple asymmetric bivariate copulas of the form C(u, v) = uv [ϕ(v) + u(1 − ϕ(v))], (u, v) ∈ [0, 1]2. This framework relies on a tuning univariate function to achieve the desired asymmetry. We study this pioneering scheme, emphasizing its theoretical foundations, and illustrating it with several examples. More precisely, we establish important properties of the proposed copulas and derive analytical expressions for concordance measures such as Spearman’s rho, Kendall’s tau, Gini’s gamma, and Blomqvist’s beta. In addition, we investigate the estimation procedure for the dependence parameter using the maximum likelihood approach. Finally, we conduct a simulation study to evaluate the performance of the proposed estimator. A real climatological dataset from the city of Abu Dhabi is used to demonstrate the applicability of the proposed copulas, with very convincing results
CheatGuard: A cybersecurity inspired anti-cheating platform for higher education
No full textAcademic integrity aims to ensure that students’ achievements are the result of their own efforts and knowledge. However, academic dishonesty continues to evolve, becoming increasingly difficult to detect and mitigate—especially with the rise of sophisticated, AI-driven tools. The lack of a universal knowledge base and standardized guidelines for addressing emerging forms of misconduct further complicates enforcement, leaving educators with limited resources to effectively identify and respond to violations. This research addresses these challenges by proposing CheatGuard, a centralized online anti-cheating platform with incident reporting capabilities. Inspired by MITRE’s ATT&CK framework used in cybersecurity, CheatGuard catalogs a wide range of cheating techniques reported by institutions—from plagiarism to unauthorized collaboration during exams. For each assessment category, it also provides corresponding detection and prevention methods. CheatGuard’s knowledge base is continuously updated using an AI agent that gathers data from online sources. Tactics, techniques, and procedures related to academic dishonesty are structured similarly to those in MITRE ATT&CK, helping institutions systematically select appropriate countermeasures. This paper presents the conceptual architecture of CheatGuard, explains how it can be accessed and used by educators, and explores its potential to combat academic misconduct in the age of Artificial Intelligence
Uncovering the efficacy of a natural homemade sunscreen in protection from ultraviolet radiation
Full text linkIn Australia, skin cancer has the highest incidence of all cancer types, where Therapeutic Goods Association-approved, broad-spectrum sunscreens are recommended to prevent skin carcinogenesis. Commercial sunscreen ingredients, however, have been associated with negative impacts on human health, animal health and the environment. Together, the perceived harmful effects of commercial sunscreens have driven a trend towards home formulation of natural sunscreens, recipes for which are widely available online. Scientific evidence to support the efficacy of natural sunscreens, however, is lacking. We tested the efficacy of a natural homemade sunscreen (NHSS) published online by a wellness blogger with the aim to determine its photoprotective properties, beyond its ability to protect against erythema, compared to a commercially available SPF50 + sunscreen. The NHSS contained almond oil, coconut oil, shea butter, beeswax, red raspberry seed oil, carrot seed oil and zinc oxide. Skin explants were treated with either a commercial SPF50 + sunscreen, NHSS or base lotion prior to ultraviolet irradiation. Skin explants were assessed using immunohistochemistry for the levels of UVR-induced DNA damage in the form of cyclobutane pyrimidine dimers and 8-oxo-7, 8-dihydro-2’-deoxyguanosine, as well as for sunburn cells and epidermal thickness. We demonstrate herein that NHSSs can reduce UVR-induced DNA damage and epidermal thickness, but do not effectively protect against the generation of sunburn cells. In comparison, SPF50 + sunscreen provided effective protection against all investigated parameters. These factors, however, are markers of short-term UVR-induced damage and there is as yet no evidence for NHSSs in prevention of skin carcinogenesis. Therefore, we recommend the continued use of TGA-approved commercial sunscreens for sun protection. Further studies are required to test water resistance, variation in homemade formulation, shelf life, and protection against skin carcinogenesis in a chronic UVR model
Strengthening Security in Pharmaceutical Healthcare: Harnessing Blockchain for Reliable Detection of Counterfeit Drugs and Mitigating Dispensing Errors
No full textMaintaining drug authenticity and quality across the pharmaceutical supply chain is a major challenge. Counterfeit medicines and prescription errors threaten patient safety and public health. To address this, we propose a blockchain-based framework using Ethereum, smart contracts, and QR codes. The system ensures secure monitoring from manufacturing to end-user delivery. Each drug is linked to a unique QR code enabling real-time tracking and ownership transfer. Pharmacies use QR verification to reduce human errors during medicine dispensing. Blockchain ensures transparency, data integrity, and resistance to tampering or hacking. Stakeholders benefit from improved trust and verifiable supply chain transactions. The framework enhances traceability and helps eliminate counterfeit medicines. Ultimately, it promotes a safer, more transparent, and trustworthy pharmaceutical ecosystem