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ACNFP Advice on the safety of UV-treated A. Bisphorus Mushroom Powder with increased vitamin D2 as a novel food
No full textShadow-Analyzer: An Efficient Neural Networks-based Ghost Objects Detection for Autonomous Vehicles
Get PDFSeveral studies have been conducted to investigate the security and reliability of object detection systems in Autonomous Vehicles (AVs), which rely on sensors such as cameras and Light Detection and Ranging (LiDAR). These studies demonstrate the low hurdle for adversaries to execute spoof attacks on LiDAR signals, deceiving non-existing objects (ghosts) as real objects in the surroundings. However, existing approaches to detect such attacks primarily depend on 3D point clouds to analyze LiDAR signals and decide whether an object is real or spoofed, thereby requiring additional processed data. Since decisions in AVs must be made in real-time to ensure safe driving and minimize the risk of exposing road users to danger, reducing the processed data without negatively impacting the reliability and precision of object detection is a significant factor in meeting real time requirements. Furthermore, reducing the required data to achieve reliable and accurate detection enables efficient edge data processing, thus optimally utilizing available computing power in proximity. To this end, this paper introduces Shadow-Analyzer, a technique that leverages a reduced 2D dataset derived from the 3D point cloud to detect ghost object attacks. The results obtained indicate that the Shadow-Analyzer effectively identifies ghost objects with an accuracy of up to 99.17%, achieving this within an average processing time of 5.9 milliseconds. These findings suggest that our solution fulfils the criteria for real-time detection and significantly improves the standard performance of AVs
Smart sensing and anomaly detection for microalgae culture based on LoRaWAN sensors and LSTM autoencoder
Get PDFThe increasing demand for sustainable aquaculture solutions has accelerated interest in microalgae as an alternative fish feed source. This study presents a novel integration of Long-Range Wide Area Network (LoRaWAN) sensors and a Long Short-Term Memory (LSTM) autoencoder model to enable real-time monitoring and anomaly detection in 300L outdoor microalgae cultivation. A solar-powered Internet of Things (IoT) sensor and LoRaWAN system were developed to monitor key water quality parameters such as pH, water temperature, electrical conductivity (EC), and oxidation-reduction potential (ORP). ORP was identified as a sensitive alternative for monitoring microalgae growth dynamics and contamination. The LSTM autoencoder, trained on normal ORP data, effectively distinguished abnormal culture patterns based on reconstruction errors, achieving high precision (1.000), recall (0.944), F1-score (0.971), and AUC-ROC (1.000). The addition of a moving average filter improved data stability for model training of filtered sensor signals. A bespoke visual reconstruction error heatmap further simplified anomaly interpretation for end-users. This framework advances smart aquaculture by enabling timely intervention, supporting sustainable microalgae production, and aligning with global food security and environmental sustainability goals
Exploring hospital physician relationships in HRD through POS lens: an integrated literature review
No full textPurposeThe purpose of the paper is to examine how perceived organizational support (POS) for physicians is situated within different hospital physician relationship (HPR) employment structures and to explore human resource development (HRD) implications within these relationships.Design/methodology/approachThis paper focuses on research in the western context using an integrated literature review methodology, which includes employed (“Employed”), affiliated (“Affiliated”) and Locum tenens physicians [“Locum(s)]” who work in hospitals.FindingsPOS constructs, under the broader purview of organizational support theory (OST), provided evidence for hospital leadership to find intentional ways to support their hospitals physicians within all HPR models. Our review also revealed the strategies adopted by hospitals to align and influence physician practice behaviors, which were researched using economic and/or noneconomic constructs. The findings indicated stronger or isolated alignment with hospitals where the physicians practiced.Research limitations/implicationsThe paper focuses on Employed, Affiliated and Locums HPRs in western healthcare contexts. When available, distinctions are made with respect to the US or Europe. Limitations to our review include (a) limited research within healthcare contexts on organizational support, and (b) limited literature about Affiliated and Locums hospital physician populations in the US and Europe written in English.Practical implicationsThough hospital physicians are integral to patient care and economic efficiencies in hospitals, not much is known about how hospital physicians perceive organizational support within different HPRs, and what construct contributions are made to hospital system performance. Accordingly, this study contributes to the HRD literature in the healthcare context starting with confirming HPRs from a POS perspective and extending findings to theories of employee alignment that can contribute to organizational performance. The findings of this study serve HRD theory and practice. Theorists may use this to pursue HPR-related inquiries to explore HRD’s influence in creating hospital policies and culture that make physicians feel supported and satisfies the hospital’s financial and patient care-related goals. Practitioners may use the findings of this article to inform quality improvement and organization development efforts.Social implicationsHospital physicians are an integral workforce that provides critical patient care across the world. Understanding how organizations can support physicians has a ripple effect from hospitals to communities at large.Originality/valueThis review contributes toward the development of a conceptual model of organizational support for HRD that can help physicians and their hospital employees within different HPRs to reach mutual goals of support, understanding, integration (clinical, economic and noneconomic) and physician alignment evidenced by improved patient care and organizational performance
State of the Art in Measuring Frailty in Patients With Heart Failure: from Diagnosis to Advanced Heart Failure
Get PDFPurpose of Review: This review aims to present the current state of the art in measuring frailty in patients with heart failure (HF), covering the entire spectrum from diagnosis to advanced stages of the disease. Frailty is a critical factor that significantly impacts outcomes in heart failure, and accurate assessment is essential for guiding treatment and improving prognosis. Recent Findings: Frailty is increasingly recognized as a key determinant of morbidity and mortality in HF patients. Various tools are available for assessing frailty, but there is no consensus on the optimal method. The assessment of frailty needs to be multidimensional, incorporating physical, cognitive, and social domains. Early detection of frailty, coupled with personalized interventions, has the potential to improve patient outcomes. Summary: Integrating routine frailty assessments into the clinical care of heart failure patients is essential for optimizing treatment. Future research should focus on standardizing frailty assessment tools and integrating innovative technologies, such as artificial intelligence, to enhance the precision and applicability of these assessments in clinical practice
GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios
Get PDFThe advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with = 0.05, representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications
Handbook on methods in restorative justice research
No full textHow do we research restorative justice in ways that align with its core values? This groundbreaking handbook is the first to systematically explore research methods in restorative justice, addressing ethical dilemmas, interdisciplinary approaches, and methodological innovations. Featuring contributions from leading scholars, the book examines qualitative, quantitative, and participatory methods, reflecting on challenges unique to the field. It provides practical guidance for researchers and practitioners alike, offering insights into victim-offender encounters, justice policies, and community-based initiatives. Essential reading for those studying or working in restorative justice, this book advances a much-needed conversation on how we study its evolving practices
Is Malware Detection Needed for Android TV?
Get PDFThe smart TV ecosystem is rapidly expanding, allowing developers to publish their applications on TV markets to provide a wide array of services to TV users. However, this open nature can lead to significant cybersecurity concerns by bringing unauthorized access to home networks or leaking sensitive information. In this study, we focus on the security of Android TVs by developing a lightweight malware detection model specifically for these devices. We collected various Android TV applications from different markets and injected malicious payloads into benign applications to create Android TV malware, which is challenging to find on the market. We proposed a machine learning approach to detecting malware and evaluated our model. We compared the performance of nine classifiers and optimized the hyperparameters. Our findings indicated that the model performed well in rare malware cases on Android TVs. The most successful model classified malware with an F1-Score of 0.9789 in 0.1346 milliseconds per application
Effects of hearing intervention on falls in older adults: Findings from a secondary analysis of the ACHIEVE randomized controlled trial
Get PDFBackgroundHearing loss is highly prevalent among older adults and has been associated with an increased likelihood of falling. We aimed to examine the effect of a hearing intervention on falls over three years among older adults in a secondary analysis of the ACHIEVE study.MethodsACHIEVE is an unmasked randomized trial (ClinicalTrials.gov: NCT03243422) of adults aged 70-84 years with untreated hearing loss and without substantial cognitive impairment. Participants were recruited at four US community sites from two study populations: an ongoing observational study of cardiovascular health (ARIC) or de novo. Participants were randomized 1:1 to a hearing intervention (audiologic counselling and provision of hearing aids) or health education control (didactic education and enrichment activities covering chronic disease prevention topics). Self-reported falls were assessed at baseline and annually and analysis was by intention to treat.FindingsBetween November 9 2017 and October 25 2019, 3004 participants were screened for eligibility and a total of 977 (238 from ARIC and 739 de novo) participants were randomized, with 490 in the intervention group and 487 in the control group. Participants had a mean age of 76.8 years (SD=4.0), 523 were female (53.5%), 112 were Black (11.5%), and 858 were White (87.8%). In adjusted analyses, the intervention group had a 27% reduction in the average number of falls compared to control (mean falls over 3-years [95% confidence interval]: intervention 1.45 [1.28, 1.61], control: 1.98 [1.82, 2.15], difference: -0.54, [-0.77, -0.31]). The 3-year protective effect of hearing intervention was consistent across both the ARIC and de novo study populations.InterpretationHearing intervention versus control was associated with a reduction in the average number of falls over 3-years in older adults. Continued follow up of ACHIEVE participants will enable examination of the longer-term effects of a hearing intervention on falls. Funding US National institutes of Healt
A Practical Distortion Simulator for Screen-Shooting Resilient Image Watermarking in Consumer Electronic Applications
No full textNowadays screen-shooting resilient watermarking still remains as a predictive and challenging area of research for proactive data protection in consumer electronic applications. Present deep learning-based methodologies embrace end-to-end frameworks and devise specialized noise layer to simulate the distortions introduced in cross-media transmission between consumer electronics. Typically, the noise layer is designed as a deterministic image-to-image network to simulate the transformation from the clean images to the screen-captured ones, overlooking the randomness of real-world distortions, which makes it challenging to satisfy the demands of various consumer electronics and application scenarios. To address this issue, we conceptualize the screen-shooting channel as an image degradation model and accordingly develop the Resolver-Simulator framework (ReSim). The involved screen-shooting simulator is designed as a conditional image-to-image network to learn the exact degradation function. By taking advantage of the pre-trained parameter resolver, the noise components can be disentangled to compose the instance sets. Then the set sampling strategy is adopted to obtain the noise instances for realistic screen-shooting simulation for unseen images. Experimental results demonstrate that the proposed scheme outperforms the previous arts in terms of real-world robustness as well as exhibits high computational efficiency to enable real-time security solutions in consumer devices
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