Search Results (66)

This paper presents the design, development, and pilot deployment of Vitalia, an AR-gamified application targeting the formation of healthy habits in primary education children. Developed within the EU DUSE project, Vitalia integrates physical activity, nutritional education, and immersive storytelling into a gamified framework to promote sustained behavioral change. Grounded in evidence-based behavior change models and co-designed with health, nutrition, and physical activity experts, the system envisions high daily engagement rates and measurable knowledge improvements. The concept positions Vitalia as a scalable model for child-centric, ethically responsible digital health interventions, with the potential to be integrated into school curricula and public health strategies. Full article

In the past two decades, there has been a tremendous increase in demand for services requiring a high bandwidth, a low latency, and high data rates, such as broadband internet services, video streaming, cloud computing, IoT devices, and mobile data services (5G and beyond). Optical wireless communication (OWC) technology, which is also envisioned for next-generation satellite networks using laser links, offers a promising solution to meet these demands. Establishing a line-of-sight (LOS) link and initiating communication in laser links is a challenging task. This process is managed by the acquisition, pointing, and tracking (APT) system, which must deal with the narrow beam divergence and the presence of satellite platform vibrations. These factors increase acquisition time and decrease acquisition probability. This study presents a framework for evaluating the acquisition time of four different scanning methods: spiral, raster, square spiral, and hexagonal, using a probabilistic approach. A satellite platform vibration model is used, and an algorithm for estimating its power spectral density is applied. Maximum likelihood estimation is employed to estimate key parameters from satellite vibrations to optimize scan parameters, such as the overlap factor and beam divergence. The simulation results show that selecting the scan path, overlap factor, and beam divergence based on an accurate estimation of satellite vibrations can prevent multiple scans of the uncertainty region, improve target satellite detection, and increase acquisition probability, given that the satellite vibration amplitudes are within the constraints imposed by the scan parameters. This study contributes to improving the acquisition process, which can, in turn, enhance the pointing and tracking phases of the APT system in laser links. Full article

Diagnosis and intervention at the earliest stages of cancer are imperative for maximizing patient recovery outcomes and substantially increasing survival rates and quality of life. Recently, to facilitate cancer diagnosis, volatile organic compounds (VOCs) have shown potential with unique characteristics as cancer biomarkers. Various insects with sophisticated sensitivities of odor can be quickly and readily trained to recognize such VOCs using olfactory-linked skills. Furthermore, the approach to analyzing VOCs can be made using electronic noses, commonly referred to as e-noses. Using analytical instruments like GC-MS, LC-MS/MS, etc., chemical blends are separated into their constituent parts. The significance of odorant receptors in triggering neural responses to ambient compounds has received great attention in the last twenty years, particularly in the investigation of insect olfaction. Sensilla, a sophisticated olfactory neural framework, is regulated by a neuronal receptor composed of neuronal, non-neuronal, extracellular lymphatic fluid with an effectively generated shell. This review provides an in-depth exploration of the structural, functional, and signaling mechanisms underlying odorant sensitivities and chemical odor detection in the excretory products of cancer patients, addressing current challenges in VOC-based cancer diagnostics and innovative strategies for advancement while also envisioning the transformative role of artificial olfactory systems in the future of cancer detection. Furthermore, the article emphasizes recent preclinical and clinical advancements in VOC applications, highlighting their potential to redefine early diagnostic approaches in oncology. Full article

The last few decades have witnessed the rapid development of passive backscatter technologies, which envision promising cost-efficient ambient Internet of Things (IoT) for various applications, such as distributed solar sensor networks. However, limited by the harmonic interference caused by the conventional frequency-shifting-based backscatter control methods, existing backscatter communication technologies cannot support the growing scale of the network. To tackle this issue, we propose a harmonic interference resilient frequency-shifting technique to compress the harmonics during backscatter communication. Different from conventional backscatter tags that shift the frequency with square waves with a constant pulse width, we dynamically modify the pulse width of the square wave to compress different parts of the harmonic waves. Furthermore, we propose a lightweight communication coding algorithm to enhance the compatibility of our system with backscatter applications. We implement the system with off-the-shelf components and conduct comprehensive experiments to evaluate the performance. The results demonstrate our harmonic interference resilient backscatter system can compress the harmonic interference and reduce the BER (bit error rate) by 70%. Full article

Sustainability rating systems (SRSs) have emerged as indispensable frameworks for advancing the environmental, social, and economic sustainability of road infrastructure. Despite their growing adoption, their integration as authoritative tools within infrastructure planning and development remains limited. This study provides a comprehensive evaluation of eight leading SRSs—CEEQUAL, Greenroads, GreenLITES, GreenPave, I-LAST, INVEST, BE2ST-in-Highways, and Envision—focusing on their structural frameworks, criteria weightings, adherence to the three pillars of sustainability, and alignment with international benchmarks such as ISO, EN, and ASTM standards. By considering the three pillars of sustainability, the analysis of the eight SRSs reveals a disproportionate focus on environmental well-being (43%) and social well-being (42%), with economic well-being receiving minimal emphasis (15%). Furthermore, this study identifies notable deficiencies in the integration of critical international standards, including ISO, EN, and ASTM, which constrains the comprehensiveness and global applicability of these frameworks. Key findings suggest that the current SRSs inadequately address the principles of a circular economy, risk management, and social equity, highlighting areas for methodological enhancement. This review offers critical insights for researchers, policy makers, and practitioners seeking to refine sustainability rating systems for road infrastructure. By consolidating existing knowledge and proposing methodological advancements, this study contributes to the evolution of SRSs into comprehensive, globally relevant tools for sustainable infrastructure development. Full article

This article presents selected issues related to the reliability of the power supply for electronic security systems (ESSs) used in smart buildings (SBs). ESSs operate in diverse environmental conditions and are responsible for the safety of lives, property and the natural environment of SB users. The operational tasks of ESSs in SBs require a continuous power supply from various sources, including renewable energy sources. The authors conducted an analysis of the power supply for selected ESSs used in SBs, which enabled the development of a power supply model. For the proposed model, the authors designed a proprietary graph of the ESS operational process, taking into account power supply implementation. Considering the operational indicators for the analysed ESSs, such as repair and failure rates, a computer simulation was performed. The simulation allowed the determination of the reliability of the ESS power supply within the considered redundancy configuration of additional energy sources, which can be utilised during the design phase. The reliability analysis of the power supply and the determination of rational parameters conducted in the article are crucial for achieving all the functionalities of ESSs in SBs, as envisioned during the design process. The article is divided into six chapters, structured to address the topics sequentially: an introduction to the state of the issue, a critical literature review, an analysis of the power supply for selected ESSs, implementation of renewable energy sources, the development of a proprietary model and operational graph, a computer simulation and conclusions. Full article

Background: Given the severe economic burden that citrus greening disease imposes on fruit farmers and related industries, rapid and accurate disease detection is particularly crucial. This not only effectively curbs the spread of the disease, but also significantly reduces reliance on manual detection within extensive citrus planting areas. Objective: In response to this challenge, and to address the issues posed by resource-constrained platforms and complex backgrounds, this paper designs and proposes a novel method for the recognition and localization of citrus greening disease, named the HHS-RT-DETR model. The goal of this model is to achieve precise detection and localization of the disease while maintaining efficiency. Methods: Based on the RT-DETR-r18 model, the following improvements are made: the HS-FPN (high-level screening-feature pyramid network) is used to improve the feature fusion and feature selection part of the RT-DETR model, and the filtered feature information is merged with the high-level features by filtering out the low-level features, so as to enhance the feature selection ability and multi-level feature fusion ability of the model. In the feature fusion and feature selection sections, the HWD (hybrid wavelet-directional filter banks) downsampling operator is introduced to prevent the loss of effective information in the channel and reduce the computational complexity of the model. Through using the ShapeIoU loss function to enable the model to focus on the shape and scale of the bounding box itself, the prediction of the bounding box of the model will be more accurate. Conclusions and Results: This study has successfully developed an improved HHS-RT-DETR model which exhibits efficiency and accuracy on resource-constrained platforms and offers significant advantages for the automatic detection of citrus greening disease. Experimental results show that the improved model, when compared to the RT-DETR-r18 baseline model, has achieved significant improvements in several key performance metrics: the precision increased by 7.9%, the frame rate increased by 4 frames per second (f/s), the recall rose by 9.9%, and the average accuracy also increased by 7.5%, while the number of model parameters reduced by ${0.137\times 10}^7$. Moreover, the improved model has demonstrated outstanding robustness in detecting occluded leaves within complex backgrounds. This provides strong technical support for the early detection and timely control of citrus greening disease. Additionally, the improved model has showcased advanced detection capabilities on the PASCAL VOC dataset. Discussions: Future research plans include expanding the dataset to encompass a broader range of citrus species and different stages of citrus greening disease. In addition, the plans involve incorporating leaf images under various lighting conditions and different weather scenarios to enhance the model’s generalization capabilities, ensuring the accurate localization and identification of citrus greening disease in diverse complex environments. Lastly, the integration of the improved model into an unmanned aerial vehicle (UAV) system is envisioned to enable the real-time, regional-level precise localization of citrus greening disease. Full article

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks. Full article

An ambitious per capita GDP target has been envisioned by the Chinese government since 2020 to project its sustainable economic growth rate by 2035. Can China fully achieve its goal? This is a question worth investigating. By inserting relevant TABLO modules of the final goods trade, the intermediate goods trade, and factor-strengthening technology spillovers, along with technology absorption thresholds effects of the global value chain, this study builds a global recursive dynamic computational general equilibrium (CGE) model on the basis of GTAP-RD. This approach enables us to consider total factor productivity (TFP) development through the “dual circulation” system, which was pointed out by the Chinese government as the only way for further growth. We simulate China’s technological progress under eight scenarios and use the latest GTAP Version 11 production and trade data (released in April 2023) for 141 countries and regions. The main conclusions are as follows: (1) If China maintains its trade opening policy, the 2035 vision goal can be achieved, with external circulation being more important than internal circulation. (2) The economic growth impacts of external and internal circulation function relatively independently. FDI offers a somewhat stronger synergistic effect on intermediate goods trade compared to final goods trade and consumption. (3) We find that the Regional Comprehensive Economic Partnership is the most important strategic partner for China. (4) FDI is not an effective way to lift the productive services sector’s TFP, and it is more realistic for China to open up the productive services market more widely. (5) China–US decoupling has an enormous global impact, and the United States is always the country that loses the most, with Europe being the group of countries that benefits when there is a large increase in TFP in the US. This study is entirely original in terms of its model structure, simulations, scenarios, and shocks. It aims to fill the gap of extending the application of the CGE model to specific issues, thereby making contributions and supplements to the three theories discussed in the article too. The limitation of this paper lies in the CGE linear description feature, which is concise and elegant and has the characteristics of extrapolation and long-term absorption of disturbances. However, it tends to overlook the randomness, non-convergence, and significant structural disturbances that may occur in future reality. Full article

In the envisioned 6G internet of things (IoT), visible light communication (VLC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. However, when working as the access point, VLC has to be connected with the backbone network via other wire communication solutions. Typically, power line communication (PLC) is viewed as an excellent match to VLC, which is capable of providing both a power supply and backbone network connection. Generally, the integration of PLC and VLC is taken into consideration for the above hybrid system for channel characteristics analysis. However, almost all current works focus on hybrid PLC and VLC, based on a conventional Lambertian optical beam configuration, and fail to address the applications of hybrid PLC and VLC based on distinct optical beam configurations. To address this issue, in this paper, the channel characteristics of hybrid PLC and VLC, based on distinct optical beam configurations, are explored and illustrated. Numerical results show that, for a central position of the receiver, compared with an achievable rate of about 194 Mbps for hybrid PLC and VLC with a baseline Lambertian optical beam configuration, the counterparts of a hybrid channel based on Rebel and NSPW optical beams are about 173.4 Mbps and 222.4 Mbps. Moreover, the effect of azimuth rotation is constructed and estimated for hybrid PLC and VLC, adopting a typical rotating asymmetric beam configuration. Full article

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total. Full article

Compared to other identity verification systems applications, vein patterns have the lowest potential for being used fraudulently. The present research examines the practicability of gathering vascular data from NIR images of veins. In this study, we propose a self-supervision learning algorithm that envisions an automated process to retrieve vascular patterns computationally using unsupervised approaches. This new self-learning algorithm sorts the vascular patterns into clusters and then uses 2D image data to recuperate the extracted vascular patterns linked to NIR templates. Our work incorporates multi-scale filtering followed by multi-scale feature extraction, recognition, identification, and matching. We design the ORC, GPO, and RDM algorithms with these inclusions and finally develop the vascular pattern mining model to visualize the computational retrieval of vascular patterns from NIR imageries. As a result, the developed self-supervised learning algorithm shows a 96.7% accuracy rate utilizing appropriate image quality assessment parameters. In our work, we also contend that we provide strategies that are both theoretically sound and practically efficient for concerns such as how many clusters should be used for specific tasks, which clustering technique should be used, how to set the threshold for single linkage algorithms, and how much data should be excluded as outliers. Consequently, we aim to circumvent Kleinberg’s impossibility while attaining significant clustering to develop a self-supervised learning algorithm using unsupervised methodologies. Full article

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R²) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans. Full article

Despite the extensive research successes and continuous developments in modern medicine in terms of diagnosis, prevention, and treatment, the lack of clinically useful disease-modifying drugs or immunotherapeutic agents that can successfully treat or prevent neurodegenerative diseases is an ongoing challenge. To date, only one of the 244 drugs in clinical trials for the treatment of neurodegenerative diseases has been approved in the past decade, indicating a failure rate of 99.6%. In corollary, the approved monoclonal antibody did not demonstrate significant cognitive benefits. Thus, the prevalence of neurodegenerative diseases is increasing rapidly. Therefore, there is an urgent need for creative approaches to identifying and testing biomarkers for better diagnosis, prevention, and disease-modifying strategies for the treatment of neurodegenerative diseases. Overexpression of the endogenous α-synuclein has been identified as the driving force for the formation of the pathogenic α-synuclein (α-Syn) conformers, resulting in neuroinflammation, hypersensitivity, endogenous homeostatic responses, oxidative dysfunction, and degeneration of dopaminergic neurons in Parkinson’s disease (PD). However, the conformational plasticity of α-Syn proffers that a certain level of α-Syn is essential for the survival of neurons. Thus, it exerts both neuroprotective and neurotoxic (regulatory) functions on neighboring neuronal cells. Furthermore, the aberrant metastable α-Syn conformers may be subtle and difficult to detect but may trigger cellular and molecular events including immune responses. It is well documented in literature that the misfolded α-Syn and its conformers that are released into the extracellular space from damaged or dead neurons trigger the innate and adaptive immune responses in PD. Thus, in this review, we discuss the nonintuitive plasticity and immunogenicity of the α-Syn conformers in the brain immune cells and their physiological and pathological consequences on the neuroimmune responses including neuroinflammation, homeostatic remodeling, and cell-specific interactions that promote neuroprotection in PD. We also critically reviewed the novel strategies for immunotherapeutic delivery interventions in PD pathogenesis including immunotherapeutic targets and potential nanoparticle-based smart drug delivery systems. It is envisioned that a greater understanding of the nonintuitive immunogenicity of aberrant α-Syn conformers in the brain’s microenvironment would provide a platform for identifying valid therapeutic targets and developing smart brain delivery systems for clinically effective disease-modifying immunotherapeutics that can aid in the prevention and treatment of PD in the future. Full article

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework. Full article