Search Results (20)

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis. Full article

Background: Intraoperative blood transfusion (IOBT) during liver transplantation (LT) has negative outcomes, and it has been shown that an increasing number of these procedures may no longer require IOBT. Regarding living donor liver transplantation (LDLT), the literature on the pre-transplant predictors of IOBT is quite heterogeneous and deficient. In this study, we reviewed our experience of IOBT among a homogenous cohort of adult right-lobe LDLTs. Methods: We conducted a retrospective analysis of prospectively collected data on adult LDLT recipients between January 2018 and October 2023. Two groups were constructed (No-IOBT vs. IOBT) for the exploration of pre- and intraoperative predictors of IOBT using univariate and multivariate analyses. An ROC curve analysis was applied to identify possible cut-offs. The one-year post-LDLT overall survival was compared using the Kaplan–Meier method. A p-value < 0.05 was considered statistically significant. Results: A total of 219 adult LDLT recipients were enrolled. The No-IOBT (n = 56) patients were mostly males (p = 0.016), with higher preoperative levels of HGB (p < 0.001), fibrinogen (p = 0.005), and albumin (p = 0.007) and a lower incidence of pre-transplant upper abdominal surgery (p = 0.017), portal vein thrombosis (p = 0.04), hepatorenal syndrome (p = 0.015), and ascites (p = 0.02) than the IOBT group (n = 163). The No-IOBT group had a shorter anhepatic phase (p = 0.002) and received fewer intravenous crystalloids (p = 0.001). In the multivariate analysis, the pre-transplant HGB (p < 0.001), fibrinogen (p < 0.001), and albumin (p = 0.04) levels were independent predictors of IOBT, showing the following cut-offs in the ROC curve analysis: HGB ≤ 11.5 (AUC: 0.800, p < 0.001), fibrinogen ≤ 125 (AUC: 0.638, p = 0.0024), and albumin ≤ 3.6 (AUC: 0.663, p = 0.0002). These were significantly associated with the No-IOBT group. The one-year overall survival of the No-IOBT and IOBT groups was 100% and 83%, respectively (p = 0.007). Conclusions: IOBT during LDLT is associated with inferior outcomes. The increased need of IOBT during LT can be predicted by evaluating serum levels of hemoglobin, albumin and fibrinogen before liver transplantation. Full article

The balise transmission system (BTS) is essential for train position sensing and safe operation. Transmission loss is a key parameter particularly required for the evaluation of systems. The eddy current loss (ECL), caused by the conductivity of debris, affects the transmission performance of the BTS when the balise is immersed in water. This study proposes an effective modeling for the BTS using S-parameters. Utilizing the electromagnetic coupling analysis in the near-field region, we derived an equivalent circuit with the frequency and conductivity of water taken into consideration. The S21 can be predicted accurately by using the proposed equivalent circuit. For validation, a BTS system was implemented and measured to compare with theoretically calculated results and electromagnetic simulation results in the main lobe zone. The measurement results, simulation, and calculation were in good agreement. Moreover, the modeling was used to predict the I/O characteristics of the balise. The power of the balise uplink FSK signal was measured in the water debris and found to be approximately 0.62 dB less than in air. These findings aligned well with theoretical predictions. Full article

Odontotermes formosanus (Shiraki) is a subterranean termite species known for causing severe damage to trees and structures such as dams. During the synergistic evolution of O. formosanus with pathogenic bacteria, the termite has developed a robust innate immunity. Termicin is a crucial antimicrobial peptide in termites, significantly contributing to the defense against external infections. Building upon the successful construction and expression of the dsRNA-HT115 engineering strains of dsOftermicin1 and dsOftermicin2 in our laboratory, this work employs the ultrasonic breaking method to establish an inactivated dsOftermicins-HT115 technological system capable of producing a substantial quantity of dsRNA. This approach also addresses the limitation of transgenic strains which cannot be directly applied. Treatment of O. formosanus with dsOftermicins produced by this method could enhance the virulence of both Bt and Bb to the termites. This study laid the theoretical groundwork for the development of novel termite immunosuppressants and for the advancement and application of termite biological control strategies. Full article

Synthetic Aperture Radar (SAR) has the advantage of continuous observation throughout the day and in all weather conditions, and is used in a wide range of military and civil applications. Among these, the detection of ships at sea is an important research topic. Ships in SAR images are characterized by dense alignment, an arbitrary orientation and multiple scales. The existing detection algorithms are unable to solve these problems effectively. To address these issues, A YOLOV8-based oriented ship detection and classification method using SAR imaging with lightweight receptor field feature convolution, bottleneck transformers and a probabilistic intersection-over-union network (R-LRBPNet) is proposed in this paper. First, a CSP bottleneck with two bottleneck transformer (C2fBT) modules based on bottleneck transformers is proposed; this is an improved feature fusion module that integrates the global spatial features of bottleneck transformers and the rich channel features of C2f. This effectively reduces the negative impact of densely arranged scenarios. Second, we propose an angle decoupling module. This module uses probabilistic intersection-over-union (ProbIoU) and distribution focal loss (DFL) methods to compute the rotated intersection-over-union (RIoU), which effectively alleviates the problem of angle regression and the imbalance between angle regression and other regression tasks. Third, the lightweight receptive field feature convolution (LRFConv) is designed to replace the conventional convolution in the neck. This module can dynamically adjust the receptive field according to the target scale and calculate the feature pixel weights based on the input feature map. Through this module, the network can efficiently extract details and important information about ships to improve the classification performance of the ship. We conducted extensive experiments on the complex scene SAR dataset SRSDD and SSDD+. The experimental results show that R-LRBPNet has only 6.8 MB of model memory, which can achieve 78.2% detection accuracy, 64.2% recall, a 70.51 F1-Score and 71.85% mAP on the SRSDD dataset. Full article

This paper aims to analyze and define incentives for the implementation of modern technology and digitalization of the Food Supply Chain (FSC) in the function of sustainability of the food retail sector of the Western Balkans (WB) region. The survey method was applied to a sample of 255 employees. We tested the importance of certain indicators for the implementation of the digitalization process, such as the application of Blockchain Technology (BT), the use of modern IT solutions for traceability, the implementation of the Internet of Things (IoT), the introduction of Artificial Intelligence (AI), development of a system for electronic food placement, implementation of standards, measures, and procedures for regulating the digitalization process, continuous training of employees and economic and financial measures and incentives. A special segment of research deals with the impact of the implemented digitalization process on the sustainability of food placement. The research was conducted among employees of SMEs, large-scale business entities, and retail chains. The research results showed significant deviations from the mentioned incentives to the digitalization process depending on the size of the FSC participants. The work has practical implications because the obtained results show the FSC management, trade policy makers, and competent institutions (ministries, chambers of commerce, professional associations) what measures to apply in order to improve a more efficient implementation of the digitalization process of food placement and lay the foundation for the sustainability of the FSC. Guidelines for future research are outlined in the paper. Full article

The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker’s ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker’s ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks. Full article

The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m. Full article

Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people’s daily lives. However, IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. This research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories: normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes the following models: shallow neural networks (SNN), decision trees (DT), bagging trees (BT), k-nearest neighbor (kNN), and support vector machine (SVM). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was used to improve the learning models’ accuracy. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4–99.9% recorded for the classification process. Full article

During 2006–2008, a pipeline was buried in Vallona lagoon in the Northern Adriatic Sea (Italy). A Before-During-After environmental monitoring programme was scheduled to monitor possible alterations. Bioaccumulation of metal(loid)s, BTs (butyltins) and HMW-PAHs (High Molecular Weight Polycyclic Aromatic Hydrocarbons), and biological responses (Condition index, air Survival—LT_50, Acetylcholinesterase, Micronuclei—MN, acyl-CoA oxidase, catalase, malondialdehyde—MDA, and the total oxyradical scavenging capacity—TOSCA) were investigated in Manila clams (Ruditapes philippinarum) from November 2005 to June 2015. In opera (IO) results showed higher levels of HMW-PAHs (73 ± 13 ng/g), BTs (90 ± 38 ng Sn/g) and increasing levels of Pb (6.7 ± 0.7 mg/kg) and Zn (73.6 ± 6.08 mg/kg) probably linked to works. Other contaminant alterations, especially metal(loid)s, before (AO) and after (PO) the burial, were attributed to a general condition of the area and mostly unrelated to works. In addition, LT₅₀, MN and TOSCA showed alterations, probably due to hotspots occurring in IO. TOSCA and MDA increases, right after the burial, were considered delayed responses of IO, whilst other biological responses detected later were connected to the general condition of the area. Comparisons between results of Principal Component Analyses (PCAs) highlighted partial overlapping of AO and IO, whilst PO differed only for contaminants. Visual correlations between PCAs highlighted the biomarkers’ latter response. Full article

The Internet of Battlefield Things is a newly born cyberphysical system and, even though it shares a lot with the Internet of Things and with ad hoc networking, substantial research is required to cope with its scale and peculiarities. This article examines a fundamental problem pertaining to the routing of information, i.e., the calculation of a backbone network. We model an IoBT network as a network with multiple layers and employ the concept of domination for multilayer networks. This is a significant departure from earlier works, and in spite of the huge literature on the topic during the past twenty years, the problem in IoBT networks is different since these networks are multilayer networks, thus making inappropriate all the past, related literature because it deals with single layer (flat) networks. We establish the computational complexity of our problem, and design a distributed algorithm for computing connected dominating sets with small cardinality. We analyze the performance of the proposed algorithm on generated topologies, and compare it against two—the only existing—competitors. The proposed algorithm establishes itself as the clear winner in all experiments concerning the dominating set from a size-wise and an energy-wise perspective achieving a performance gain of about 15%. Full article

Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described. Full article

This article presents the design of a low-cost Wireless Body Sensor Network (WBSN) for monitoring vital signs including a low-cost smart wristwatch that contains an ESP-32 microcontroller and three sensors: heart rate (HR), blood pressure (BP) and body temperature (BT), and an Internet of Things (IoT) platform. The vital signs data are processed and displayed on an OLED screen of the patient’s wristwatch and sent the data over a wireless connection (Wi-Fi) and a Cloud Thing Board system, to store and manage the data in a data center. The data can be analyzed and notified to medical staff when abnormal signals are received from the sensors based on a set parameters from specialists. The proposed low-cost system can be used in a wide range of applications including field hospitals for asymptotic or mild-condition COVID-19 patients as the system can be used to screen those patients out of symptomatic patients who require more costly facilities in a hospital with considerably low expense and installation time, also suitable for bedridden patients, palliative care patients, etc. Testing experiments of a 60-person sample size showed an acceptable accuracy level compared with standard devices when testing with 60 patient-samples with the mean errors heart rate of 1.22%, systolic blood pressure of 1.39%, diastolic blood pressure of 1.01%, and body temperature of 0.13%. According to testing results with 10 smart devices connected with the platform, the time delay caused by the distance between smart devices and the router is 10 s each round with the longest outdoor distance of 200 m. As there is a short-time delay, it does not affect the working ability of the smart system. It is still making the proposed system be able to show patient’s status and function in emergency cases. Full article

This paper describes a practical approach to the transformation of Base Transceiver Stations (BTSs) into scalable and controllable DC Microgrids in which an energy management system (EMS) is developed to maximize the economic benefit. The EMS strategy focuses on efficiently managing a Battery Energy Storage System (BESS) along with photovoltaic (PV) energy generation, and non-critical load-shedding. The EMS collects data such as real-time energy consumption and generation, and environmental parameters such as temperature, wind speed and irradiance, using a smart sensing strategy whereby measurements can be recorded and computing can be performed both locally and in the cloud. Within the Spanish electricity market and applying a two-tariff pricing, annual savings per installed battery power of 16.8 euros/kW are achieved. The system has the advantage that it can be applied to both new and existing installations, providing a two-way connection to the electricity grid, PV generation, smart measurement systems and the necessary management software. All these functions are integrated in a flexible and low cost HW/SW architecture. Finally, the whole system is validated through real tests carried out on a pilot plant and under different weather conditions. Full article

Internet of Things (IoT) applications play a relevant role in today’s industry in sharing diagnostic data with off-site service teams, as well as in enabling reliable predictive maintenance systems. Several interventions scenarios, however, require the physical presence of a human operator: Augmented Reality (AR), together with a broad-band connection, represents a major opportunity to integrate diagnostic data with real-time in-situ acquisitions. Diagnostic information can be shared with remote specialists that are able to monitor and guide maintenance operations from a control room as if they were in place. Furthermore, integrating heterogeneous sensors with AR visualization displays could largely improve operators’ safety in complex and dangerous industrial plants. In this paper, we present a complete setup for a remote assistive maintenance intervention based on 5G networking and tested at a Vodafone Base Transceiver Station (BTS) within the Vodafone 5G Program. Technicians’ safety was improved by means of a lightweight AR Head-Mounted Display (HDM) equipped with a thermal camera and a depth sensor to foresee possible collisions with hot surfaces and dangerous objects, by leveraging the processing power of remote computing paired with the low latency of 5G connection. Field testing confirmed that the proposed approach can be a viable solution for egocentric environment understanding and enables an immersive integration of the obtained augmented data within the real scene. Full article