Search Results (2,348)

This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using continuous signal strengths. To train and validate the model, the signal strengths are collected in several locations within various indoor environments. The positioning technique is adaptively selected based on the highest signal strength to avoid the interfering problem due to an excessively strong signal. If the signal strength exceeds a certain threshold, the location is determined using the proximity technique, which utilizes only the strongest signal instead of triangulation. In the experimental results, the proposed method demonstrated an average error of about 2.90 m, which is 34.2% better than a triangulation-based positioning method that does not utilize neural networks. Full article

The relationship between circulating metabolites and sarcopenia-related phenotypes remains unclear. We explored the causality between circulating metabolites and sarcopenia-related phenotypes. Instrumental variables for the human metabolome were derived from the recently published GWAS, which included 690 plasma metabolites. Summary statistics for four sarcopenia phenotypes (whole-body lean mass (WBLM), usual walking pace, appendicular lean mass (ALM), and handgrip strength (HGS)) (both sexes, males and females) were obtained from relevant GWASs. We used MR to evaluate the association between circulating metabolites and sarcopenia-related phenotypes. Colocalization analysis was utilized to determine whether two associated signals were consistent with a shared causal variant rather than the confounding effect of linkage disequilibrium. Subsequently, we explored associations between modifiable risk factors and sarcopenia-related metabolites to explore which metabolites may serve as potential intervention targets through lifestyle modification. Genetically predicted plasma levels of 95 known metabolites were associated with sarcopenia-related phenotypes, and 27 metabolites were supported by robust evidence of colocalization, among which 13 metabolites had a cross-sarcopenia effect. These metabolites primarily included acyl carnitines, amino acids and their derivatives, and phospholipids. Specifically, our analyses supported causal relationships between 23, 6, and 15 metabolites and ALM, HGS, and WBLM, respectively. Seven relevant metabolites might be associated with six modifiable factors. We identified 27 metabolite biomarkers with robust causal evidence for sarcopenia-related phenotypes, highlighting 13 metabolites with a cross-sarcopenia effect, and prioritized several metabolites as the potential interventional targets of lifestyle changes. Our study provided new insight into the etiology and prevention of sarcopenia. Full article

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection. Full article

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc. Full article

Gold nanoparticles (AuNPs) are highly attractive for applications in the field of biosensing, particularly for colorimetric nucleic acid detection. Their unique optical properties, which are highly sensitive to changes in their environment, make them ideal candidates for developing simple, rapid, and cost-effective assays. When functionalized with oligonucleotides (Au-nanoprobes), they can undergo aggregation or dispersion in the presence of complementary sequences, leading to distinct color changes that serve as a visual signal for detection. Aggregation-based assays offer significant advantages over other homogeneous assays, such as fluorescence-based methods, namely, label-free protocols, rapid interactions in homogeneous solutions, and detection by the naked eye or using low-cost instruments. Despite promising results, the application of Au-nanoprobe-based colorimetric assays in complex biological matrices faces several challenges. The most significant are related to the colloidal stability and oligonucleotide functionalization of the Au-nanoprobes but also to the mode of detection. The type of functionalization method, type of spacer, the oligo–AuNPs ratio, changes in pH, temperature, or ionic strength influence the Au-nanoprobe colloidal stability and thus the performance of the assay. This review elucidates characteristics of the Au-nanoprobes that are determined for colorimetric gold nanoparticles (AuNPs)-based nucleic acid detection, and how they influence the sensitivity and specificity of the colorimetric assay. These characteristics of the assay are fundamental to developing low-cost, robust biomedical sensors that perform effectively in biological fluids. Full article

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce DistilLoc: a cross-knowledge distillation framework that transfers knowledge from an RTT-based teacher model to an RSSI-based student model. By applying a teacher–student architecture, where the RTT model (teacher) trains the RSSI model (student), DistilLoc enhances RSSI-based localization with the accuracy and robustness of RTT without requiring RTT data during deployment. At the core of DistilLoc, the FNet architecture is employed for its computational efficiency and capacity to capture complex relationships among RSSI signals from multiple access points. This enables the student model to learn a robust mapping from RSSI measurements to precise location estimates, reducing computational demands while improving scalability. Evaluation in two cluttered indoor environments of varying sizes using Android devices and Google WiFi access points, DistilLoc achieved sub-meter localization accuracy, with median errors of 0.42 m and 0.32 m, respectively, demonstrating improvements of 267% over conventional RSSI methods and 496% over multilateration-based approaches. These results validate DistilLoc as a scalable, accurate solution for indoor localization, enabling intelligent, resource-efficient urban environments that contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities). Full article

In this study, a Wi-Fi system with a tethered balloon is proposed for the teleoperation of vehicular forestry machines. This system was developed to establish a Wi-Fi communication for stable teleoperation in a timber harvesting site. This system consisted of a helium balloon, Wi-Fi nodes, a measurement system, a global navigation satellite system (GNSS) antenna, and a wind speed sensor. The measurement system included a GNSS module, an inertial measurement unit (IMU), a data logger, and an altitude sensor. While the helium balloon with the Wi-Fi system was 60 m in the air, the received signal strength indicator (RSSI) was measured by moving a Wi-Fi receiver on the ground. Another GNSS set was also utilized to collect the latitude and longitude data from the Wi-Fi receiver as it traveled. The developed Wi-Fi system with a tethered balloon can create a Wi-Fi zone of up to 1.9 ha within an average wind speed range of 2.2 m/s. It is also capable of performing the teleoperation of vehicular forestry machines with a maximum latency of 185.7 ms. Full article

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. Full article

Agricultural water scarcity has become a global issue. Optimizing irrigation water quality and effectively utilizing non-conventional water resources are essential strategies to alleviate pressure on agricultural water use and achieve sustainable development. This study employed Italian lettuce as the test crop to explore the effects of magnetization treatment (M) at a magnetic field strength of 0.2 T and various irrigation water sources (T) on its growth. The following six treatments were established: fresh water irrigation (M0T1), recycled water irrigation (M0T2), saline water irrigation (M0T3), magnetized fresh water irrigation (M1T1), magnetized recycled water irrigation (M1T2), and magnetized saline water irrigation (M1T3). The results showed that the magnetization treatment increased the electrical conductivity (EC), power of hydrogen (pH), and dissolved oxygen (DO) of the three water sources compared to the non-magnetized treatment. Furthermore, magnetized irrigation with fresh water, recycled water, and saline water increased the contents of nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) in lettuce. It also led to increases in the contents of soluble proteins (by 9.27% to 22.25%), soluble sugars (by 13.45% to 20.50%), and vitamin C (VitC) (by 4.18% to 19.33%) in lettuce. Additionally, it enhanced the above-ground fresh weight of lettuce (by 9.36% to 8.81%) and water productivity (WPc) (by 5.85% to 10.40%), while reducing water consumption. Among these treatments, magnetized fresh water irrigation was the most effective in improving quality, fresh weight, and WPc, followed by magnetized recycled water. Gene expression analysis revealed that differentially expressed genes (DEGs) were primarily enriched in metabolic pathways such as the MAPK signaling pathway—plant, phytohormone signaling, and cysteine and methionine metabolism. In summary, magnetized irrigation significantly enhanced DO levels in irrigation water, along with the fresh weight, quality, and WPc of lettuce, demonstrating its effectiveness as an efficient method for agricultural irrigation. Full article

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs). Full article

During their life cycle, plants constantly respond to environmental changes. Abiotic stressors affect the photosynthetic and respiratory processes of plants. Reactive oxygen species (ROS) are produced during aerobic metabolism and play an important role as regulatory mediators in signaling processes, activating the plant’s protective response to abiotic stress and restoring “oxidation-reduction homeostasis”. Cells develop normally if the rates of ROS production and the ability to neutralize them are balanced. To implement oxidation-reduction signaling, this balance must be disrupted either by an increase in ROS concentration or a decrease in the activity of one or more antioxidant systems. Under abiotic stress, plants accumulate excessive amounts of ROS, and if the ROS content exceeds the threshold amount dangerous for living organisms, it can lead to damage to all major cellular components. Adaptive resistance of plants to abiotic stressors depends on a set of mechanisms of adaptation to them. The accumulation of ROS in the cell depends on the type of abiotic stress, the strength of its impact on the plant, the duration of its impact, and the recovery period. The aim of this review is to provide a general understanding of the processes occurring during ROS homeostasis in plants, oxidation-reduction processes in cellular compartments in response to abiotic stress, and the participation of ROS in signaling processes activating adaptation processes to abiotic stress. Full article

The mechanisms of CD95 (Fas/APO-1)-mediated extrinsic apoptotic pathway in cancer cells have been extensively studied. The majority of human cells express CD95, but not all these cells can induce extrinsic apoptosis. Accumulating evidence has shown that CD95 is a multifunctional protein, and its stimulation can also elicit non-apoptotic or even survival signals. It has become clear that under certain cellular contexts, due to the various checkpoints, CD95 activation can trigger both apoptotic and non-apoptotic signals. The crosstalk of death and survival signals may occur at different levels of signal transduction. The strength of the CD95 stimulation, initial levels of anti-apoptotic proteins, and posttranslational modifications of the core DISC components have been proposed to be the most important factors in the life/death decisions at CD95. Successful therapeutic targeting of CD95 signaling pathways will require a better understanding of the crosstalk between CD95-induced apoptotic and cell survival pathways. In this review, in order to gain a systematic understanding of the crosstalk between CD95-mediated apoptosis and non-apoptotic signaling, we will discuss these issues in a step-by-step way. Full article

This article is about better wireless network connectivity. The main goal is to provide wireless service to several use cases and scenarios that may not be adequately covered today. Some of the considered scenarios are home connectivity, street-based infrastructure, emergency situations, disaster areas, special event areas, and remote areas that suffer from problematic/inadequate network and possibly power infrastructure. A target system that we consider for such scenarios is that of an energy-efficient self-backhauled base station (also called a “portable access point—PAP”) that is mounted on a drone to aid/expand the land-based network. For the wireless backhaul link of the PAP, as well as for the fronthaul of the street-mounted base station, we consider newly built multi-active/multi-passive parasitic antenna arrays (MAMPs). These antenna systems lead to increased range/signal strength with low hardware complexity and power needs. This is due to their reduced number of radio frequency chains, which decreases the cost and weight of the base station system. MAMPs can show a performance close to traditional multiple input/multiple output (MIMO) systems that use as many antenna elements as RF chains and to phased arrays. They can produce a directional beam in any desired direction with higher gain and narrow beamwidth by just tuning the load values of the parasitic elements. The MAMP is designed based on radiation conditions which were produced during the research to ensure that the radiation properties of the array were good. Full article

Reliable paleoflood proxies can help reconstruct past flood variation patterns. Here, we investigated the grain-size data of a 63 cm core retrieved from Lake Chaonaqiu, western Chinese Loess Plateau, in order to build a long time-series of flood occurrence from sedimentology that extends the period of instrumental data. Our results indicate that three parameters (mean, standard deviation and grain-size ratio of 16–63/2–16 μm) are sensitive to hydrodynamic changes in Lake Chaonaqiu, which are further linked to high-energy inflow associated with high-intensity rainfall or flood events. These three parameters’ variations were well correlated with the precipitation records reconstructed from tree-rings and historical documents in neighboring regions and overlapped with 109 historical flood events from historical documents in counties around the lake for the past 300 years. Therefore, we propose that the grain size in the sediments of Lake Chaonaqiu is a reliable paleoflood proxy. The sensitivity of flood signals to grain size may be related to the precipitation and vegetation cover in the catchment of the lake, which are further linked to the strength of the East Asian summer monsoon. Full article

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively. Full article