Search Results (30)

Previous studies have suggested that the COVID-19 pandemic exposed children to an increased risk of greater body weight accumulation; however, the evidence found is limited to examining relatively short periods in children from Asian, European, and North American countries and, in most cases, using cross-sectional designs, while studies with longitudinal designs are scarce. To our knowledge, to date, no study involving Brazilian children has examined temporal trends in body weight during the pandemic period using a longitudinal approach. Objective: To report the weight status of children aged 6 to 11 years pre-, during, and post-school closures in response to restrictions imposed by the COVID-19 pandemic, using a six-year school-based longitudinal cohort design (2020–2025). Method: Weight status was analyzed using the body mass index and diagnostic criteria proposed by IOFT. Initial data collection took place in 2020 (baseline—pre-school closures), in 2021 and 2022 (pandemic period—school closures), and in 2023, 2024, and 2025 (post-pandemic period—after the reopening of schools). Results: The data collected confirmed that restrictions imposed to mitigate the adverse impact of the COVID-19 pandemic, including the full or partial closure of schools, substantially increased children’s weight above what would be expected for their gender and age. After two years of the pandemic period, 23% of children identified in the baseline pre-school closures with normal body weight migrated to overweight, while 34% of overweight children became obese. Data equivalent to the post-pandemic period showed signs of a reduction in the excess body weight accumulated during the pandemic; however, the prevalence rates of overweight and obesity remained significantly higher than pre-school closures. Conclusions: The findings suggest that the harmful effects contributing to the greater accumulation of body weight during the COVID-19 pandemic tended not to reverse spontaneously, even five years after its onset. Therefore, specific actions to prevent, combat, and control overweight and obesity are essential to avoid present and future adverse consequences for children’s health. Full article

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

Cloud computing is a virtualized and distributed computing model that provides resources and services based on demand and self-service. Resource failure is one of the major challenges in cloud computing, and there is a need for fault tolerance mechanisms. This paper addresses the issue by proposing a multi-objective radial kernelized federated learning-based fault-tolerant scheduling (MRKFL-FTS) technique for allocating multiple IoT requests or user tasks to virtual machines in cloud IoT-based environments. The MRKFL-FTS technique includes Cloud RAN (C-RAN) and Virtual RAN (V-RAN). The proposed MRKFL-FTS technique comprises four entities, namely, IoT devices, cloud servers, task assigners, and virtual machines. Each IoT device generates several service requests and sends them to the control server. At first, radial kernelized support vector regression is applied in the local training model to identify resource-efficient virtual machines. After that, locally trained models are combined, and the resulting model is fed into the global aggregation model. Finally, using a weighted round-robin method, the task assigner allocates incoming IoT service requests to virtual machines. This approach improves resource awareness and fault tolerance in scheduling. The quantitatively analyzed results show that the MRKFL-FTS technique achieved an 8% improvement in task scheduling efficiency and fault prediction accuracy, a 36% improvement in throughput, and a 14% reduction in makespan and time complexity. In addition, the MRKFL-FTS technique resulted in a 13% reduction in response time. The energy consumption of the MRKFL-FTS technique is reduced by 17% and increases the scalability by 8% compared to conventional scheduling techniques. Full article

Monitoring the variation of urban expansion is crucial for sustainable urban planning and cultural heritage management. This paper proposes an approach for the semantic segmentation of very-high-resolution (VHR) satellite imagery to detect the changes in urban sprawl in the surroundings of Chan Chan, a UNESCO World Heritage Site in Peru. This study explores the effectiveness of combining Mix Transformer encoders with U-Net architectures to improve feature extraction and spatial context understanding in VHR satellite imagery. The integration of active contour loss functions further enhances the model’s ability to delineate complex urban boundaries, addressing the challenges posed by the heterogeneous landscape surrounding the archaeological complex of Chan Chan. The results demonstrate that the proposed approach achieves accurate semantic segmentation on images of the study area from different years. Quantitative results showed that the U-Net-scse model with an MiTB5 encoder achieved the best performance with respect to SegFormer and FT-UNet-Former, with IoU scores of 0.8288 on OpenEarthMap and 0.6743 on Chan Chan images. Qualitative analysis revealed the model’s effectiveness in segmenting buildings across diverse urban and rural environments in Peru. Utilizing this approach for monitoring urban expansion over time can enable managers to make informed decisions aimed at preserving cultural heritage and promoting sustainable urban development. Full article

The fluttering of leaves under wind fields significantly impacts the efficiency and precision of agricultural spraying. However, existing spraying technologies often overlook the complex mechanisms of wind–leaf interactions. This study integrates the fine-tuned Segment Anything Model 2 with multi-dimensional dynamic behavior analysis to provide a systematic approach for investigating leaf fluttering under wind fields. First, a segmentation algorithm based on Principal Component Analysis was employed to eliminate background interference in leaf fluttering data. The results showed that the segmentation algorithm achieved an Intersection over Union (IoU) ranging from 98.2% to 98.7%, with Precision reaching 99.0% to 99.5%, demonstrating high segmentation accuracy and reliability. Building on this, experiments on leaf segmentation and tracking in dynamic scenarios were conducted using the SAM2-FT model. The results indicated that SAM2-FT effectively captured the dynamic behavior of leaves by integrating spatiotemporal information, achieving Precision and AP50/% values exceeding 97%. Its overall performance significantly outperformed mainstream YOLO-series models. In the analysis of dynamic response patterns, the Hilbert transform and time-series quantification methods were introduced to reveal the amplitude, frequency, and trajectory characteristics of a leaf fluttering under wind fields across three dimensions: area, inclination angle, and centroid. This comprehensive analysis highlights the dynamic response characteristics of leaves to wind field perturbations. Full article

Due to the high mobility and low cost, unmanned aerial vehicle (UAV)-mounted edge computing (UMEC) provides an efficient way to provision computing offloading services for Internet of Forestry Things (IoFT) applications in forest areas without sufficient infrastructure. Multiple IoFT applications can be consolidated into fewer UAV-mounted servers to improve the resource utilization and reduce deployment costs with the precondition that all applications’ Quality of Service (QoS) can be met. However, most existing application placement schemes in UMEC did not consider the dynamic nature of the aggregated computing resource demand. In this paper, the resource allocation and application placement problem based on fine-grained cooperative overbooking in UMEC is studied. First, for the two-tenant overbooking case, a Two-tenant Cooperative Resource Overbooking (2CROB) scheme is designed, which allows tenants to share resource demand violations (RDVs) in the cooperative overbooking region. In 2CROB, an aggregated-resource-demand minimization problem is modeled, and a bisection search algorithm is designed to obtain the minimized aggregated resource demand. Second, for the multiple-tenant overbooking case, a Proportional Fairness-based Cooperative Resource Overbooking (PF-MCROB) scheme is designed, and a bisection search algorithm is also designed to obtain the corresponding minimized aggregated resource demand. Then, on the basis of PF-MCROB, a First Fit Decreasing-based Cooperative Application Placement (FFD-CAP) scheme is proposed to accommodate applications in as few servers as possible. Simulation results verify that the proposed cooperative resource overbooking schemes can save more computing resource in cases including more tenants with higher or differentiated resource demand violation ratio (RDVR) thresholds, and the FFD-ACP scheme can reduce about one third of necessarily deployed UAVs compared with traditional overbooking. Thus, applying efficient cooperative overbooking in application placement can considerably reduce deployment and maintenance costs and improve onboard computing resource utilization and operating revenues in UMEC-aided IoFT applications. Full article

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation. Full article

Bovine serum albumin (BSA) hydrogels are non-immunogenic, low-cost, biocompatible, and biodegradable. In order to avoid toxic cross-linking agents, gellan was oxidized with NaIO₄ to obtain new functional groups like dialdehydes for protein-based hydrogel cross-linking. The formed dialdehyde groups were highlighted with FT-IR and NMR spectroscopy. This paper aims to investigate hydrogel films for biomedical applications obtained by cross-linking BSA with oxidized gellan (OxG) containing immobilized β-cyclodextrin–curcumin inclusion complex (β-CD–Curc) The β-CD–Curc improved the bioavailability and solubility of Curc and was prepared at a molar ratio of 2:1. The film’s structure and morphology were evaluated using FT-IR spectroscopy and SEM. The swelling degree (Q%) values of hydrogel films depend on hydrophilicity and pH, with higher values at pH = 7.4. Additionally, the conversion index of -NH₂ groups into Schiff bases increases with an increase in OxG amount. The polymeric matrix provides protection for Curc, is non-cytotoxic, and enhances antioxidant activity. At pH = 5.5, the skin permeability and release efficiency of encapsulated curcumin were higher than at pH = 7.4 because of the interaction of free aldehyde and carboxylic groups from hydrogels with amine groups from proteins present in the skin membrane, resulting in a better film adhesion and more efficient curcumin release. Full article

In this work, the input–output finite-time stability (IO-FTS) of a class of continuous-time switched systems characterized by uncertainties and subjected to external disturbances is studied under asynchronous switching by means of the sliding mode control (SMC) method. The IO-FTS poses a finite-time constraint problem, which involves addressing two main issues: firstly, ensuring that the state trajectory of the switched system reaches the given sliding mode surface within the specified time, and secondly, achieving IO-FTS for the closed-loop switched system during asynchronous switching. To address these issues, we apply a partitioning strategy and construct asynchronous sliding mode controllers with adjustable parameters to ensure the reachability of the system’s state trajectory within a finite time. Subsequently, we employ a multiple Lyapunov function (MLF) approach to provide sufficient conditions that ensure IO-FTS during the whole phase $[0,T]$ for the resulting switched system. Additionally, we analyze the asynchronous characteristics concerning the reachability phase of the designed sliding surface, considering the system’s switching properties. Finally, we demonstrate the efficacy of the proposed approach with a numerical example. Full article

The rapid development of sixth-generation (6G) mobile broadband networks and Internet of Things (IoT) applications has led to significant increases in data transmission and processing, resulting in severe traffic congestion. To better allocate network resources, predicting network traffic has become crucial. However, satellite networks face global imbalances in IoT traffic demand, with substantial variations in satellite density and load distribution within the same constellation. These disparities render traditional traffic prediction algorithms inadequate for dynamically changing satellite network topologies. This paper thoroughly examines the impact of adaptive time stepping on the prediction of dynamic traffic load. Particularly, we propose a high-speed traffic prediction method that employs machine learning and recurrent neural networks over the 6G Space Air Ground Integration Network (SAGIN) structure. In our proposed method, we first investigate a variable step size-normalized least mean square (VSS-NLMS) adaptive prediction method for transforming time series prediction datasets. Then, we propose an adaptive time stepping-Gated Recurrent Unit (ATS-GRU) algorithm for real-time network traffic prediction. Finally, we compare the prediction accuracy of the ATS-GRU algorithm with that of the fixed time stepping-Gated Recurrent Unit (FTS-GRU) algorithm and compared the prediction results of three different step sizes (FSS, VSS, and ATS) based on normalized least mean square (NLMS). Numerical results demonstrate that our proposed scheme can automatically choose a suitable time stepping to track and predict the traffic load curve with acceptable accuracy and reasonable computational complexity, as its time stepping dynamically adjusts with the traffic. Full article

A food traceability system (FTS) can record information about processes along a production chain to determine their safety and quality. Under the Internet of Things (IoT) concept, the communication technologies that support FTSs act as platforms for mass access to information with limited security. However, the integrity of the collected data is not immune to security attacks. This paper proposes a point-to-point information transmission path with no edges or access boundaries (no intermediaries) to transmit data with integrity. This route is possible thanks to the architectural articulation of a hardware device (sensor BIoTS) at the perception layer, with the Blockchain architecture at the application layer. This pairing makes an ecosystem with the ability to trace and certify in parallel the products, the supply chain processes, and the data recorded in it possible. The design of the security testing ecosystem is based on the theoretical and technical principles of cybersecurity. It is executed through mathematical models that define the probability of attacks’ success against the transmitted data’s integrity. The security tests performed allow for establishing that this BIoTS information transmission route is unlikely to suffer from transmission vulnerabilities and that it is not prone to security attacks against integrity. This work paves the way toward fully integrating Blockchain technology in dedicated IoT architectures. Full article

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of $2^{2200}$. The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application. Full article

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements. Full article

In this study, a carboxylated carbon nanotube-grafted Ag₃PO₄@AgIO₄ (CNT-COO⁻/Ag₃PO₄@AgIO₄) composite was synthesized through an in situ electrostatic deposition method. The synthesized composite was characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), diffuse reflectance spectroscopy (DRS), and energy-dispersive X-ray spectroscopy (EDS). The electron transfer ability of the synthesized composite was studied using electrochemical impedance spectroscopy (EIS). The CNT-COO⁻/Ag₃PO₄@AgIO₄ composite exhibited higher activity than CNT/Ag₃PO₄@AgIO₄, Ag₃PO₄@AgIO₄, and bare Ag₃PO₄. The material characterization and the detailed study of the various parameters thataffect the photocatalytic reaction revealed that the enhanced catalytic activity is related to the good interfacial interaction between CNT-COO and Ag₃PO₄. The energy band structure analysis is further considered as a reason for multi-electron reaction enhancement. The results and discussion in this study provide important information for the use of the functionalized CNT-COOH in the field of photocatalysis. Moreover, providinga new way to functionalize CNT viadifferent functional groups may lead to further development in the field of photocatalysis. This work could provide a new way to use natural sunlight to facilitate the practical application of photocatalysts toenvironmental issues. Full article

This paper presents the design of an arc-shaped near-isotropic self-complementary antenna for spectrum sensing application. An arc-shaped dipole with horizontal and vertical arms is used to achieve a near isotropic radiation pattern. The radiation pattern improved by adjusting the horizontal and vertical arm lengths. Simulated and experimental results show that the proposed antenna has an impedance bandwidth of 146% (2.4–18.4 GHz) for VSWR ≤ 2 with a good radiation pattern. In order to quantify the antenna performance, antenna gain variation, bandwidth, efficiency, and size have been compared with previously reported designs. It is shown that the proposed arc-shaped antenna can achieve nearly isotropic radiation patterns with a maximum radiation efficiency of 92%. The isotropic performance of the antenna has been characterized by observing the radiation pattern and solid angle. The FR4 substrate is used as a dielectric with relative permittivity 4.4 and loss tangent of 0.02. (${\epsilon }_r$ = 4.4, h = 1.6 mm) The simulated and measured results are in good comparison, and the proposed design is a suitable candidate for spectrum sensing. Full article