Search Results (2,279)

As power demand increases and the scale of power grids expands, accurately predicting transmission line temperatures is becoming essential for ensuring the stability and security of power systems. Traditional physical and statistical models struggle with complex multivariate time series, often failing to balance short-term fluctuations with long-term dependencies, and their prediction accuracy and adaptability remain limited. To address these challenges, this paper proposes a deep learning model architecture based on the Dynamic Adaptive Artificial Hummingbird Algorithm (DA-AHA), named the DA-AHA-CNN-LSTM-TPA (DA-AHA-CLT). The model integrates convolutional neural networks (CNNs) for local feature extraction, long short-term memory (LSTM) networks for temporal modeling, and temporal pattern attention mechanisms (TPA) for dynamic feature weighting, while the DA-AHA optimizes hyperparameters to enhance prediction accuracy and stability. The traditional artificial hummingbird algorithm (AHA) is further improved by introducing dynamic step-size adjustment, greedy local search, and grouped parallel search mechanisms to balance global exploration and local exploitation. Our experimental results demonstrate that the DA-AHA-CLT model achieves a coefficient of determination (R²) of 0.987, a root-mean-square error (RMSE) of 0.023, a mean absolute error (MAE) of 0.018, and a median absolute error (MedAE) of 0.011, outperforming traditional models such as CNN-LSTM and LSTM-TPA. These findings confirm that the DA-AHA-CLT model effectively captures the complex dynamic characteristics of transmission line temperatures, offering superior performance and robustness in full-time-step prediction tasks, and highlight its potential for solving challenging multivariate time-series forecasting problems in power systems. Full article

The Gambia’s coastline, known for its unique ecosystems and vital role in the country’s economy through fisheries, tourism, and agriculture, is becoming increasingly vulnerable as a result of the combined effects of climate change and human activity. This coastline sustains an important portion of the population by providing livelihoods and contributing to food security, as well as housing critical infrastructure including ports, urban areas, and tourism hubs. However, as climate change intensifies pre-existing vulnerabilities, such as increased sea-level rise, coastal erosion, and extreme weather events, these socio-economic assets are becoming more and more vulnerable. As a result, this study focused on investigating the physical vulnerability of the coastline in the context of climate change. The vulnerability assessment was conducted using the coastal vulnerability index approach, utilising a combination of oceanographic data, remote sensing, and field observations. The research outcomes supported the identification of key areas at risk and examined the contributing factors such as tidal ranges, storm surges, and human activities. The findings highlight the immediate and long-term threats to coastal communities, infrastructure, and natural habitats. Due to the vulnerability provided by geological and geomorphological factors, the average Coastal Vulnerability Index (CVI) score of 29 indicates a high level of exposure to coastal hazards from Buniadu Point to Barra. From Banjul to Cape Point, the average coastline dynamic rate is positive at 0.21 m/year, indicating some accretion. Despite this, the CVI score of 22 indicates significant vulnerability to coastal hazards from Bald Cape to Salifor Point. The study also explored potential mitigation and adaptation strategies to enhance coastal resilience to sea-level rise, coastal erosion, and flooding. Integrated and sustainable strategies were outlined to support policy-making and community-based initiatives towards safeguarding coastal regions of The Gambia against the backdrop of climate change. Full article

With the consequences of climate change becoming more urgent, there has never been a more pressing need for technologies that can help to reduce the carbon dioxide (CO₂) emissions of the most polluting sectors, such as power generation, steel, cement, and the chemical industry. This review summarizes the state-of-the-art technologies for carbon capture, for instance, post-combustion, pre-combustion, oxy-fuel combustion, chemical looping, and direct air capture. Moreover, already established carbon capture technologies, such as absorption, adsorption, and membrane-based separation, and emerging technologies like calcium looping or cryogenic separation are presented. Beyond carbon capture technologies, this review also discusses how captured CO₂ can be securely stored (CCS) physically in deep saline aquifers or depleted gas and oil reservoirs, stored chemically via mineralization, or used in enhanced oil recovery. The concept of utilizing the captured CO₂ (CCU) for producing value-added products, including formic acid, methanol, urea, or methane, towards a circular carbon economy will also be shortly discussed. Real-life applications, e.g., already pilot-scale continuous methane (CH₄) production from flue gas CO₂, are shown. Actual deployment of the most crucial technologies for the future will be explored in real-life applications. This review aims to provide a compact view of the most crucial technologies that should be considered when choosing to capture, store, or convert CO₂, informing future researchers with efforts aimed at mitigating CO₂ emissions and tackling the climate crisis. Full article

Background/Objectives: Food-insecure individuals are at risk for poor health outcomes, including substandard sleep health. A possible association of food insecurity with sleep regularity has not been explored, and factors contributing to the relationship between food insecurity and sleep are not well understood. This cross-sectional study explored the relationship between food insecurity and sleep regularity and identified specific nutrients that mediated the association. Methods: This study used dietary intake, interview, physical examination, actigraphy, and laboratory data from NHANES 2011–2014 to assess the possible correlation between food insecurity and sleep in a sample of U.S. adults (n = 6730). Mediation analysis was conducted to determine specific serum biomarkers and intake of nutrients that indirectly contributed to the relationship. Results: Food insecurity was negatively correlated with sleep regularity. Dietary intake of fiber; vitamins A, B1, B2, C, E, and K; β-carotene; zinc; copper; and potassium and serum concentrations of palmitoleic acid had significant indirect effects on this association. The low/marginal food security group under-consumed vitamin K, and the very low food security group under-consumed vitamin K and zinc. Among food-insecure, income-eligible adults, those who received benefits from food assistance programs consumed significantly less fiber and β-carotene and exhibited significantly higher serum concentrations of palmitoleic acid than non-participants. Conclusions: Food insecurity predicted sleep regularity, and this relationship was mediated by dietary intake and serum concentrations of specific nutrients, underscoring the role of nutrition security when evaluating potential health impacts for adults experiencing food insecurity. Full article

For public security purposes, distributed surveillance systems are widely deployed in key areas. These systems comprise visual sensors, edge computing boxes, and cloud servers. Resource scheduling algorithms are critical to ensure such systems’ robustness and efficiency. They balance workloads and need to meet real-time monitoring and emergency response requirements. Existing works have primarily focused on optimizing Quality of Service (QoS), latency, and energy consumption in edge computing under resource constraints. However, the issue of task congestion due to insufficient physical resources has been rarely investigated. In this paper, we tackle the challenges posed by large workloads and limited resources in the context of surveillance with visual sensors. First, we introduce the concept of virtual nodes for managing resource shortages, referred to as virtual node-driven resource scheduling. Then, we propose a convex-objective integer linear programming (ILP) model based on this concept and demonstrate its efficiency. Additionally, we propose three alternative virtual node-driven scheduling algorithms, the extension of a random algorithm, a genetic algorithm, and a heuristic algorithm, respectively. These algorithms serve as benchmarks for comparison with the proposed ILP model. Experimental results show that all the scheduling algorithms can effectively address the challenge of offloading multiple priority tasks under resource constraints. Furthermore, the ILP model shows the best scheduling performance among them. Full article

Background: Household food insecurity (HFI) is a serious public health concern in Lebanon. Adverse mental health issues have been reported among food insecure households in addition to physical and nutritional complications. Caregivers in food insecure families tend to adopt different coping mechanisms to mitigate the effects of food insecurity (FI) on their children. Objective: This cross-sectional observational study aimed to explore the relationship between FI, maternal depression, child malnutrition, and differential coping mechanisms adopted by mothers. Methods: A total of 219 women were enrolled in this study; FI was assessed using the household food insecurity assessment (HFIAS), maternal depression using the patient health questionnaire (PHQ-9), and their children’s nutritional status through recall of anthropometric measurements. Pearson’s correlations and logistic regressions were performed to evaluate the associations between HFI, maternal depression, and children’s nutritional status. Results: A strong positive correlation between HFI and maternal depression (p = 0.001) and children’s nutritional status (p = 0.008) was shown. Logistic regressions revealed that being food secure decreased the risk of maternal depression (OR = 0.328, 95% CI 0.125–0.863, p = 0.024), while it did not predict children’s nutritional status. Eight main themes related to coping mechanisms were identified. Conclusions: This study highlights the understudied relationship between food insecurity and maternal depression, showing an increased prevalence of HFI among residents in Lebanon with a positive correlation with increased maternal depression. Further investigation is warranted to better explore how to mitigate the negative impact of food insecurity on mental health, maternal nutritional needs, and Infant and Young Child Feeding (IYCF) practices in Lebanon. Full article

Bitter cherries (Prunus avium var. sylvestris Ser.) represent a valuable raw material in the traditional Eastern European food industry with high potential within the horticultural chain and circular economy in the context of global food security due to exceptional nutritional properties. The present study was carried out in the period 2022–2024 and had as its main purpose the evaluation of the fruit quality and production indices of some bitter cherry cultivars suitable for the technological norms specific to industrial processing. Five bitter cherry cultivars (C₁-Amaris, C₂-Amar Maxut, C₃-Amar Galata, C₄-Silva, C₅-Amara) were studied and analyzed in terms of fruit quality—morpho-physiological and organoleptic traits, and physical and chemical parameters—and general productivity—tree vigor, fruiting, and yield indices. The results highlighted a wide variability in the physical characteristics of bitter cherries, with an average weight between 3.3 and 4.9 g and the color of the skin varying from yellow with redness to dark red and blackish. Regarding the chemical attributes, antioxidant activity was relatively higher in fruits with a more intense bitter taste (89.3 μg Trolox·g⁻¹ f.w for C₂ and 89.1 μg Trolox·g⁻¹ f.w. for C₄ and C₅), a fact also found in the content total of polyphenols (with a maximum value of 743.2 mg GAE·100 g⁻¹ f.w at C₂). Total soluble solids content had an average value of 20.51°Brix and titratable acidity of 0.85 g malic acid·100 g⁻¹ f.w. The influence of local environmental factors on the productivity of bitter cherry cultivars was highlighted by significant statistical differences (p < 0.05) between cultivars. Thus, the resistance to frost in the full flowering phenophase had an average value of 86.69%, and regarding the resistance to fruit cracking, the highest percentage was found in C₁, with 99.79% unaffected fruits. The productivity index per tree had an average value of 0.24 kg per cm² trunk cross-section area. The physico-chemical properties of the fruits and the productivity of bitter cherry cultivars support the possibility of their efficient use in processing and the food industry, yielding high-quality products with nutraceutical value. Full article

The increasing incorporation of Industrial Internet of Things (IIoT) devices into critical industrial operations and critical infrastructures necessitates robust security measures to safeguard confidential information and ensure dependable connectivity. Particularly in Cyber Physical Systems (CPSs), IIoT system security becomes critical as systems become more interconnected and digital. This paper introduces a novel Lightweight Industrial IoT Authentication (LI2A) method as a solution to address security concerns in the industrial sector and smart city infrastructure. Mutual authentication, authenticated message integrity, key agreement, soundness, forward secrecy, resistance to a variety of assaults, and minimal resource consumption are all features offered by LI2A. Critical to CPS operations, the approach prevents impersonation, man-in-the-middle, replay, eavesdropping, and modification assaults, according to a security study. The method proposed herein ensures the integrity of CPS networks by verifying communication reliability, identifying unauthorized message modifications, establishing a shared session key between users and IIoT devices, and periodically updating keys to ensure sustained security. A comprehensive assessment of performance takes into account each aspect of storage, communication, and computation. The communication and computing capabilities of LI2A, which are critical for the operation of CPS infrastructure, are demonstrated through comparisons with state-of-the-art systems from the literature. LI2A can be implemented in resource-constrained IIoT devices found in CPS and industrial environments, according to the results. By integrating IIoT devices into critical processes in CPS, it is possible to enhance security while also promoting urban digitalization and sustainability. Full article

The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios. To maximize the attack effectiveness, large and dispersed attack camouflages are often employed, which makes the camouflages overly conspicuous and reduces their visual stealth. The core issue is how to use minimal and concentrated camouflage to maximize the attack effect. Addressing this, our research focuses on developing more subtle and efficient attack methods that can better evade detection in practical settings. Based on these principles, this paper proposes a local 3D attack method driven by a Maximum Aggregated Region Sparseness (MARS) strategy. In simpler terms, our approach strategically concentrates the attack modifications to specific areas to enhance effectiveness while maintaining stealth. To maximize the aggregation of attack-camouflaged regions, an aggregation regularization term is designed to constrain the mask aggregation matrix based on the face-adjacency relationships. To minimize the attack camouflage regions, a sparseness regularization is designed to make the mask weights tend toward a U-shaped distribution and limit extreme values. Additionally, neural rendering is used to obtain gradient-propagating multi-angle augmented data and suppress the model’s detection to locate universal critical decision regions from multiple angles. These technical strategies ensure that the adversarial modifications remain effective across different viewpoints and conditions. We test the attack effectiveness of different region selection strategies. On the CARLA dataset, the average attack efficiency of attacking the YOLOv3 and v5 series networks reaches 1.724, which represents an improvement of 0.986 (134%) compared to baseline methods. These results demonstrate a significant enhancement in attack performance, highlighting the potential risks to real-world object detection systems. The experimental results demonstrate that our attack method achieves both stealth and aggressiveness from different viewpoints. Furthermore, we explore the transferability of the decision regions. The results indicate that our method can be effectively combined with different texture optimization methods, with the average precision decreasing by 0.488 and 0.662 across different networks, which indicates a strong attack effectiveness. Full article

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors. Full article

The Low Earth Orbit (LEO) small satellites are extensively used for global connectivity to enable services in underpopulated, remote or underdeveloped areas. Their inherent broadcast nature exposes LEO–terrestrial communication links to severe security threats, which always reveal new challenges. The secrecy performance of the satellite-to-ground user link in the presence of a ground eavesdropper is studied in this paper. We observe both scenarios of the eavesdropper’s channel state information (CSI) being known or unknown to the satellite. Throughout the analysis, we consider that locations of the intended and unauthorized user are both arbitrary in the satellite’s footprint. On the other hand, we analyze the case when the user is in the center of the satellite’s central beam. In order to achieve realistic physical layer security features of the system, the satellite channels are assumed to undergo Gamma-shadowed Ricean fading, where both line-of-site and scattering components are influenced by shadowing effect. In addition, some practical effects, such as satellite multi-beam pattern and free space loss, are considered in the analysis. Capitalizing on the aforementioned scenarios, we derive the novel analytical expressions for the average secrecy capacity, secrecy outage probability, probability of non-zero secrecy capacity, and probability of intercept events in the form of Meijer’s G functions. In addition, novel asymptotic expressions are derived from previously mentioned metrics. Numerical results are presented to illustrate the effects of beam radius, satellite altitude, receivers’ position, as well as the interplay of the fading or/and shadowing impacts over main and wiretap channels on the system security. Analytical results are confirmed by Monte Carlo simulations. Full article

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities. Full article

The potential of inland valleys to enhance food security and improve agricultural resilience to climate change in Africa is constrained by a limited understanding of their hydrological functioning and inadequate water management. In order to synthesize knowledge on hydrological responses in inland valley areas, this work reviewed 275 studies from tropical Sub-Saharan Africa (SSA). Data from the literature search were collected from Scopus™, ScienceDirect™, Web of Science™, Google Scholar™, and doctoral theses repositories such as ZEF, HAL, and Theses.fr, covering studies published from the inception of these databases through 31 May 2023. Our approach involved, firstly, a bibliometric analysis of all papers to gain insights into research trends and interests. Secondly, we performed a quantitative synthesis of results from 66 studies examining stream flows in a set of 79 inland valleys to better understand factors that govern runoff dynamics in these environments. Correlative analyses and clustering methods were applied to identify potential links between runoff and watershed physical parameters. The findings highlight the varied responses of inland valleys over both time and space, influenced by a combination of catchment drivers. The correlation matrices between hydrological indices and physical parameters indicate a strong relationship among runoff and a range of parameters, of which the most significant are rainfall (R² = 0.77) and soil silt content (R² = 0.68). Challenges in accurately spatializing information related to potential determining components of the water cycle, such as groundwater dynamics and soil moisture, seem to have limited the exploration of interactions between river flow, soil moisture, and groundwater. Future works should prioritize the development of accurate and user-friendly hydrological models that balance complexity and data availability to enhance the understanding of inland valley behavior at fine scales and consolidate food security in Africa. Full article

The lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae) and khapra beetle, Trogoderma granarium E. (Coleoptera: Dermestidae) are primary stored-grain insect pests. Differences in certain biological and physical parameters of both pest species and wheat genotypes were investigated under laboratory conditions. Zinc (Zn)-biofortified (Zincol-2016 and Akbar-2019) and conventional (Arooj-2022, Nawab-2021, Dilkash-2021, Bhakkar Star-2019) wheat genotypes were used in this study. Zn-biofortified genotypes outperformed the conventional ones, with significant differences observed in fecundity, percent adult emergence, total developmental duration, percent grain damage, and weight loss of both insect species. The results further revealed that the fecundity of R. dominica and T. granarium were lowest on Akbar-2019 in both the free-choice test (42.50 and 33.17) and no-choice test (35.50 and 32.50), respectively. Similarly, percent adult emergence of both insect species was also lowest on Akbar-2019 in both the free-choice test (69.78 and 70.28%) and no-choice test (67.38 and 70.71%). The total developmental period also showed significant variation among the tested genotypes. The longest developmental period was recorded in Akbar-2019, i.e., 44.33 and 58.83 days, for R. dominica and T. granarium, respectively. Similarly, percent grain damage (13.23 and 10.33%) and weight loss (3.62 and 2.12%) were found to be minimum in Akbar-2019 for both pest species, respectively. Additionally, a positive correlation was observed between grain moisture content and damage parameters, suggesting that the higher moisture content may aggravate the percent grain damage and weight loss. These findings indicate that the nutritional qualities of Zn-biofortified wheat genotypes negatively affected the development of both insect species; thus, it can be an efficacious approach not only for ensuring food security but also for protecting grains against storage pests. Full article

Background and Objectives: A degenerative joint disease that primarily affects elderly individuals, osteoarthritis (OA) causes pain, decreased mobility, and a lower quality of life. Procaine is regarded as a “veteran” medicine due to its extensive clinical use, although it remains a molecule of interest, as researchers are uncovering new biological and pharmacological effects through innovative experimental methods. This study evaluates the efficacy of the “procaine complex”, developed in our country, in alleviating pain and improving functionality in elderly individuals with osteoarthritis of the knee and hip. Materials and Methods: We conducted an assessment of a longitudinal short-term study involving 177 patients aged 65 and older, who were randomly divided into two groups. One group received physical therapy and “procaine complex” periarticular injections (n = 101), while the other group received just physical therapy (n = 76). We assessed pain using a visual analog scale (VAS), in addition to functional evaluations using the Lequesne Index, Activities of Daily Living (ADL), and Instrumental ADL (IADL) scores. We evaluated these through a CGA (complex geriatric assessment), the walk test, “Up and Go” test, Mini Mental State (MMSE) and Geriatric Depression Scale (GDS) for cognitive status. We analyzed all the data from this study using PSPP v3 software. Results: The procaine complex treatment group exhibited a significant reduction in pain (p < 0.001) and improvement in daily activities (p < 0.001) relative to the control group. However, there was no notable difference in walking test scores (p = 0.171). No substantial detrimental effects were identified. The procaine complex did not surpass physical therapy in reducing depressive disorders, but both groups showed some enhancement in this regard. Conclusions: This study demonstrates an innovative approach to pain management by integrating periarticular “procaine complex” injections with physical therapy. This provides elderly individuals experiencing osteoarthritis pain and functional limitations with a secure and efficacious alternative to surgery, or may diminish years of disability. Full article