Search Results (345)

Reliable individual identification and minimally invasive tracking are essential for monitoring threatened snake populations. A relict high-altitude population of Vipera ursinii ursinii was studied in the Maiella National Park (Central Apennines, Italy) during two field seasons (2024–2025) to (i) validate dorsal head photo-identification against unequivocal PIT-tag identities and (ii) test a novel, non-invasive telemetry method based on externally attached harmonic diodes detected with a RECCO^® harmonic direction finder (HDF). All analysed snakes were PIT-tagged and photographed under standardised conditions. Manual photo-identification based on dorsal cephalic scale counts was performed independently by four blinded operators. In parallel, software-assisted photo-identification was conducted with two independent programmes (Wild-ID and Hotspotter). Both methods were evaluated exclusively against PIT-tag-confirmed identities. Manual identification achieved moderate-to-high overall accuracy (0.77–0.91) but showed marked inter-operator variability. Software-assisted matching appeared more consistent: Hotspotter identified 75% of true recaptures at first suggestion (85% within the top six suggestions), while Wild-ID identified 56% at first suggestion (88% within the top six). Correct matches were primarily supported by the distinctive pholidosis of the dorsal head region, especially apical, intercanthal and parafrontal scales—which were highly diverse but independent of sex and age class in the studied population. Externally attached HDF diodes enabled repeated short-term relocations with detachments occurring within hours to several days and mostly associated with ecdysis. The method was minimally invasive, supporting its applicability for monitoring small-bodied animals with low-density populations and restricted ranges. Full article

Electric-propulsion (EP) orbit raising from geosynchronous transfer orbit (GTO) to geostationary Earth orbit (GEO) requires long-duration, continuously steered low thrust, for which small pointing deviations may accumulate over time, and practical execution is constrained by spacecraft attitude and momentum management. This study develops a cross-layer command-to-execution framework that couples mission-level thrust-command generation with smooth trajectory planning of an EP vectoring arm. At the orbit layer, an engineering-oriented mission-level transfer model with dominant J₂ secular correction is used to construct a time-tagged sequence of thrust magnitude and direction commands for the GTO–GEO transfer. At the execution layer, a 4-DOF revolute arm is modeled using Denavit–Hartenberg kinematics, and the desired thrust directions are mapped to feasible joint trajectories through a direction-only inverse-kinematics formulation cast as a constrained nonlinear least-squares problem with cross/dot residuals, smoothness regularization, and warm-start propagation. In numerical simulation, the GTO–GEO transfer is completed in approximately 278 days with Δv ≈ 3665 m/s, corresponding to a propellant consumption of 175 kg (spacecraft mass from 1800 kg to 1625 kg). The planned joint trajectories remain smooth over the full horizon, with maximum inter-sample variations of 1.84° and 1.04° for the major and minor motion groups, respectively. The numerical geometric thrust-direction tracking error in the kinematic mapping remains at the millidegree level, with a mean of 7.39 × 10⁻⁴° and a P95 of 0.00101°. The results demonstrate that the proposed cross-layer interface can generate executable, low-bandwidth joint commands while preserving high geometric consistency with the desired thrust directions in the numerical kinematic mapping sense, thereby providing a practical basis for implementation-oriented studies of EP orbit transfer with vectoring manipulators. Full article

Source camera identification on smartphones constitutes a fundamental task in multimedia forensics, providing essential support for applications such as image copyright protection, illegal content tracking, and digital evidence verification. Numerous techniques have been developed for this task over the past decades. Among existing approaches, Photo-Response Non-Uniformity (PRNU) has been widely recognized as a reliable device-specific fingerprint and has demonstrated remarkable performance in real-world applications. Nevertheless, the rapid advancement of computational photography technologies has introduced significant challenges: modern devices often exhibit anomalous behaviors under PRNU-based analysis. For instance, images captured by different devices may exhibit unexpected correlations, while images captured by the same device can vary substantially in their PRNU patterns. Current approaches are incapable of automatically exploring the underlying causes of these anomalous behaviors. To address this limitation, we propose a simple yet effective forensic analysis framework leveraging Exchangeable Image File Format (EXIF) metadata. Specifically, we represent EXIF metadata as type-aware word embeddings to preserve contextual information across tags. This design enables visual interpretation of the model’s decision-making process and provides complementary insights for identifying the anomalous behaviors observed in modern devices. Extensive experiments conducted on three public benchmark datasets demonstrate that the proposed method not only achieves state-of-the-art performance for source camera identification but also provides valuable insights into anomalous device behaviors. Full article

The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive approach to determine the maturity of the Hass avocado crop based on machine learning techniques. The approach consists of a low-cost, non-invasive bioimpedance spectroscopy system operating in the 1–10 kHz range, featuring a custom Analog Front End (AFE) and a tetrapolar surface probe to mitigate skin contact resistance, which collects data for predictive models of avocado maturity. To evaluate the quality of the approach, a longitudinal field study (n = 100) was conducted in a commercial orchard in Cundinamarca, Colombia, tracking complex impedance features—Magnitude, Phase Angle, Resistance, and Reactance—of tagged fruits over 8 weeks across four measurement timepoints. The predictive performance of a classical chemometric model (PLS-DA), non-linear classifiers (SVM, Random Forest), and a temporal Deep Learning (LSTM) architecture was compared using a Stratified Group K-Fold Cross-Validation scheme to prevent data leakage across fruits from the same tree. The 4-electrode configuration successfully isolated mesocarp impedance, identifying the 5–7.2 kHz band as the most sensitive to physiological maturation. In turn, the LSTM model achieved a mean accuracy of 92.0% and an AUC of 0.94, outperforming the other models by 4.0% in mean accuracy. The results demonstrate that modeling the temporal trajectory of impedance, rather than single-point measurements, improves harvest maturity classification in Hass avocados, providing a scalable, low-cost alternative to destructive testing. Full article

Pestiviruses, such as bovine viral diarrhea virus (BVDV) or classical swine fever virus (CSFV), are members of the family Flaviviridae and infect a broad range of species, causing significant economic losses in livestock. A unique feature of pestiviruses is the E^rns protein, which is part of the glycoprotein complex at the surface of the virion, but it is also secreted as an RNase that functions as an interferon (IFN) antagonist. This dual nature makes E^rns a particularly complex and multifunctional protein, highlighting its importance for understanding pestivirus biology. Bungowannah pestivirus (BuPV) was reported to exhibit high genetic plasticity, making it suitable for engineering recombinant tools. In this study, we generated a recombinant BuPV expressing green fluorescent protein (GFP) fused to the N-terminus of the E^rns protein from BVDV. The GFP-E^rns fusion was detected by fluorescence microscopy and remained stable across five serial passages. The recombinant virus infected all tested mammalian cell lines but replicated more slowly than the parental BuPV stock. RNase activity assays confirmed retention of enzymatic function. These results demonstrate stable expression, broad infectivity, and preserved activity of GFP-E^rns in the recombinant BuPV, indicating that this might be a useful tool for further investigations on pestivirus pathogenesis. Full article

To address the critical demand for real-time dynamic tracking of personnel in complex buildings during emergency rescue, a novel system was proposed integrating Back Propagation (BP) neural networks with Double-Sided Two-Way Ranging (DS-TWR) technology to achieve precise indoor localization and motionless detection. Comprising hardware (positioning base stations, tags, POE switches, routers, and a computer) and software (developed on LabVIEW), the system leverages the symmetric signal transmission of DS-TWR and the adaptive learning capability of BP neural networks to effectively mitigate multipath interference, enhancing positioning consistency and accuracy. Thresholds of time period and movement distance were set to determine whether the occupant was trapped. When tested in several common building structures, it demonstrated good stability and high accuracy—the average RMSE of the positioning system was within 0.012–0.018 m (static state) and 0.048–0.065 m (dynamic state). Furthermore, the system could real-time monitor and display the movement trajectory of each person, and automatically alarm when anyone was trapped in a fire scene. Hence, rescue measures can be taken timely according to the alarm information provided by the system, effectively ensuring the safety of personnel and improving the efficiency of fire rescue work. The proposed approach provides a symmetry-driven framework for intelligent building safety. Full article

Addressing the pressing need to extend the communication range of RF RFID tag antennas, this paper introduces a novel UHF RFID tag antenna technology based on resonant superstrate regulation using a Split-Ring Resonator (SRR). First, a finite element model of the UHF RFID folded dipole antenna was constructed based on the tag chip’s port impedance. Subsequently, a Two-element SRR resonant superstrate was employed to enhance the dipole antenna’s gain through “resonance and near-field coupling” technology. A folded dipole antenna gain-enhancing SRR resonant superstrate unit was designed, and a multi-parameter joint optimization method was adopted to obtain the optimal SRR resonant superstrate configuration for regulating the dipole antenna. Near-field coupling technology was used to design SRR resonant superstrate elements that enhance the folded dipole antenna’s gain. A multi-parameter joint optimization method was employed to obtain the optimal structural parameter set for the SRR resonant superstrate-controlled dipole antenna. Finally, simulations and experimental measurements of the RFID antenna performance revealed that: within the 920–925 MHz band, the maximum measured forward reading distance enhancement reached 62.1%. The research findings significantly enhance the practical performance of UHF RFID tags in complex environments, enabling more stable and efficient long-range identification in applications such as logistics tracking, asset management, and smart warehousing. Full article

To reduce manual operation and enhance the intelligence of the high-altitude maintenance wall-climbing robot during its operation, path planning and autonomous navigation need to be implemented. Due to non-uniform magnetic adhesion between the wall-climbing robot and the steel plate, often caused by variations in steel thickness or surface pitting, the wall-climbing robot may experience motion deviations and deviate from its planned trajectory. In order to obtain the actual deviation from the expected trajectory, it is necessary to accurately locate the wall-climbing robot. This allows for the generation of precise control signals, enabling trajectory correction and ensuring high-precision autonomous navigation. Therefore, this paper proposes an external visual localization system based on a pan–tilt laser tracker unit. The system utilizes a zoom camera to track an AprilTag marker and drives the pan–tilt platform, while a laser rangefinder provides high-accuracy distance measurement. The robot’s three-dimensional (3D) pose is ultimately calculated by fusing the visual and ranging data. However, due to the limited tracking speed of the pan–tilt mechanism relative to the robot’s movement, we introduce an Extended Kalman Filter (EKF) to robustly predict the robot’s true spatial coordinates. The robot’s three-dimensional coordinates are periodically compared with the predefined route coordinates to calculate the deviation. This comparison generates closed-loop control signals for the robot’s movement direction and speed. Finally, based on the LoRa communication protocol, closed-loop control of the robot’s movement direction and speed are achieved through the upper-level computer, ensuring that the robot returns to the predefined track. Extensive comparative experiments demonstrate that the localization system achieves stable localization with an accuracy better than 0.025 m on a 6 m × 2.5 m steel structure surface. Based on this high-precision positioning and motion correction, the robot’s motion deviation is kept within 0.1 m, providing a reliable pose reference for precise motion control and high-reliability operation in complex structural environments. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

The Pacific sleeper shark (Somniosus pacificus) is a long-lived, deep-water, sub-polar species that exhibits flexible foraging strategies, likely combining scavenging with active predation on a broad range of prey, yet their role in marine food webs and impact on commercial species remain undetermined. Tracking the location of Pacific sleeper sharks in Alaskan coastal waters is extremely challenging given the predominantly aphotic depths that these sharks occupy, often in spatially constrained and critically under-sampled regions: deep, steep-flanked, convoluted fjords of Prince William Sound (PWS). From the first ever, year-long depth and temperature records recovered from archiving pop-up satellite-linked transmitters (n = 7), we characterized the residence distributions, depth, and thermal habitat for sharks within the PWS fjords and identified seasonal and temporal variation in habitat use. Depths recorded from the seven sharks ranged from 3 to 572 m, and pop-up tag locations suggested a high degree intra-annual residency within western PWS. Ambient water temperatures ranged from 2.65 to 11.1 °C, with little deviation from the median of 5.9 °C. Seasonal patterns emerged within and across individuals relative to the variation in vertical movements, ambient temperatures, and horizontal movements that could reflect resource-oriented strategies. The high degree of residency combined with extensive use of the water column facilitates the use of physically recoverable, high-resolution behavioral and environmental samplers on Pacific sleeper sharks. This adaptive sampling using Pacific sleeper sharks as platforms of opportunity may in turn enable the use of Pacific sleeper sharks as climate and ecosystem sentinels. Full article

This study proposes an integrated agility assessment system that combines Millimeter-Wave (MMW) radar, Ultra-Wideband (UWB) ranging, and Mixed Reality (MR) technologies to quantitatively evaluate athlete performance with high accuracy. The system utilizes the fine motion-tracking capability of MMW radar and the immersive real-time visualization provided by MR to ensure reliable operation under low-light conditions and multi-object occlusion, thereby enabling precise measurement of mobility, reaction time, and movement distance. To address the challenge of player identification during doubles testing, a one-to-one UWB configuration was adopted, in which each base station was paired with a wearable tag to distinguish individual athletes. UWB identification was not required during single-player tests. The experimental protocol included three specialized agility assessments—Table Tennis Agility Test I (TTAT I), Table Tennis Doubles Agility Test II (TTAT II), and the Agility T-Test (ATT)—conducted with more than 80 table tennis players of different technical levels (80% male and 20% female). Each athlete completed two sets of two trials to ensure measurement consistency and data stability. Experimental results demonstrated that the proposed system effectively captured displacement trajectories, movement speed, and reaction time. The MMW radar achieved an average measurement error of less than 10%, and the overall classification model attained an accuracy of 91%, confirming the reliability and robustness of the integrated sensing pipeline. Beyond local storage and MR-based live visualization, the system also supports cloud-based data uploading for graphical analysis and enables MR content to be mirrored on connected computer displays. This feature allows coaches to monitor performance in real time and provide immediate feedback. By integrating the environmental adaptability of MMW radar, the real-time visualization capability of MR, UWB-assisted athlete identification, and cloud-based data management, the proposed system demonstrates strong potential for professional sports training, technical diagnostics, and tactical optimization. It delivers timely and accurate performance metrics and contributes to the advancement of data-driven sports science applications. Full article

Material tracking and localization are key applications of Industry 4.0 in manufacturing process control. Traditional approaches—such as barcode or QR code identification and RTLS-based localization using RF/UWB, 5G or GPS–require a large and complex infrastructure. As an alternative, this paper proposes an IoT-based solution that combines short-range Bluetooth Low Energy (BLE) communication with LPWAN LoRaWAN networks. Hybrid solutions using LoRaWAN and BLE technologies already exist, but pure localization based on BLE tags can lead to ambiguous asset identification in geometrically dense scenarios. Our paper aims to solve this problem with an alternative concept called Associated LoRaWAN Sensors (ALSs). An ALS enables logical grouping and integration of heterogeneous LoRaWAN sensors, providing their own data or directly scanning BLE tags. Sensor data can be combined and supplemented with new information, data, and events, supported by application logic (use case). Although ALS represents a general concept that could be applicable to various use cases (such as warehouse monitoring, object tracking), our paper will focus mainly on material tracking and validation in manufacturing. For this purpose, we designed a specific ALS model that integrates a classic LoRaWAN BLE sensor with an additional LoRaWAN magnetic contact sensor. The magnetic contact switch can provide validation of exact position, in addition to localization by BLE tag. Experimental validation using BLE tags (Trax 10229) and LoRaWAN sensors (IoTracker3, Milesight WS301) demonstrates the usability of the ALS model in typical industrial scenarios. We also measured RSSI and evaluated the accuracy of tag localization (3 × 25 = 75 tests) for the worst-case scenario: material validation on a machine with a BLE tag distance of ~0.5 m. While the traditional approach showed up to a 20% failure rate, our ALS model avoided the issue of incorrect accuracy. An additional magnetic switch in ALS confirmed that the correct carrier with the associated tag is attached to the machine and eliminated incorrect localization. The results confirm that a hybrid model based on BLE and LoRaWAN scanning can reliably support material localization and validation without the need for dense RTLS infrastructures. Full article

Background: Adoptive cell therapy using genetically engineered recombinant T cell receptors (rTCRs) expressed in T cells (TCR-T cell therapy) provides precision targeting of cancer cells expressing tumor-associated or tumor-specific antigens recognized by the rTCRs. Standardized analytical tools are lacking to easily quantify receptor expression. Methods: To overcome this hindrance, a universal tagging system (UniTope & TraCR) was designed consisting of a minimal peptide epitope (UniTope) inserted into the constant region of the rTCR α or β chain and a high-affinity monoclonal antibody (TraCR) specific to this tag. Detailed biophysical, biochemical, and functional assays were performed to evaluate rTCR expression, folding, pairing, and antigen recognition, as well as antibody performance, using the UniTope & TraCR System. Results: Tagged rTCRs were stably expressed in human T cells with surface densities comparable to untagged rTCRs. The TraCR antibody bound UniTope with nanomolar affinity and no detectable cross-reactivity was observed for endogenous proteins expressed by human cells of diverse origin, importantly, including T cells of the natural T cell repertoires of multiple human donors. Functional assays confirmed that UniTope-tagged rTCRs preserved their antigen-specific cytokine secretion and cytolytic activity upon antigen-specific stimulation. The UniTope & TraCR System enabled robust detection of rTCR-expressing T cells by flow cytometry, and rTCR protein expression by Western blot or immunoprecipitation, supporting the quantitative assessment of receptor copy number and structural integrity. Conclusions: The UniTope & TraCR System provides a modular, construct-agnostic platform for monitoring engineered rTCRs, integrated into TCR-T cell therapies currently in development. Full article

RFID (Radio Frequency Identification) technology has become an essential instrument in numerous industrial sectors, enhancing process efficiency and streamlining operations, allowing for the automated tracking of goods and equipment without the need for manual intervention. Nevertheless, the deployment of industrial IoT systems necessitates the establishment of complex sensor networks to enable detailed multi-parameter monitoring of items. Despite these advancements, challenges remain in item-level sensing, data analysis, and the management of power consumption. To mitigate these shortcomings, this study presents a holistic AI-assisted, semi-passive RFID-integrated multi-sensor system designed for robust food quality monitoring. The primary contributions are threefold: First, a compact (45 mm ∗ 38 mm) semi-passive UHF RFID tag is developed, featuring a rechargeable lithium battery to ensure long-term operation and extend the readable range up to 10 m. Second, a dedicated IoT cloud platform is implemented to handle big data storage and visualization, ensuring reliable data management. Third, the system integrates machine learning algorithms (LSTM) to analyze sensing data for real-time food quality assessment. The system’s efficacy is validated through real-world experiments on food products, demonstrating its capability for low-cost, long-distance, and intelligent quality control. This technology enables low-cost, timely, and sustainable quality assessments over medium and long distances, with battery life extending up to 27 days under specific conditions. By deploying this technology, quantified food quality assessment and control can be achieved. Full article

The development of autonomous train systems relies heavily on machine learning (ML) models, which in turn depend on large, high-quality annotated datasets for training and evaluation. The railway domain lacks adequate public datasets and efficient annotation tools. To address this gap, we present Labels4Rails, a tool designed specifically for the annotation of railway scenes. It captures track topology, switch states including switch directions, and informational tags regarding the images’ content and leverages consistent camera perspectives and the fixed track geometries inherent to railways for annotation efficiency. We used Labels4Rails to create the L4R_NLB reference dataset from Norwegian railway footage. The dataset contains 10,253 annotated images across four seasons, including 1415 switch annotations. Both the tool and dataset are publicly available. Full article