Search Results (824)

Digital twin is a mathematical model that virtually represents a physical object or process and predicts its behavior at future time points. These simulation models enable a deeper understanding of tumorigenic processes and improve biomarker discovery in cancer research. Tumor microenvironment is marked by dysregulated signaling pathways, where kinases and phosphatases serve as critical regulators and promising sources for biomarker discovery. These enzymes operate within multiscale and context-dependent processes where spatial and temporal coordination determine cellular outcomes. Digital Twin technology provides a platform for multimodal and multiscale modeling of kinase and phosphatase processes at the patient-specific level. These models have the potential to transform biomarker validation processes, enhance the prediction of therapeutic responses, and support precision decision-making. In this review, we present the major alterations affecting kinases and phosphatase functions within the tumor microenvironment and their clinical relevance as biomarkers, and we address how digital twins in oncology can augment and refine each stage of the biomarker discovery pipeline. Introducing this emerging technology for cancer biomarker discovery will assist in accelerating its adoption and translation into precision diagnostics and targeted therapies. Full article

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions. Full article

Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance. The monocular vision-based subsystem implements the YOLOv8n algorithm to detect objects of daily use. Then, audio prompting conveys the detected objects’ information to the user, who selects their targeted object using a voluntary trigger decoded through real-time EEG classification. The target’s physical coordinates are extracted using ArUco markers, and a gradient descent-based path optimization algorithm (POA) guides a 3-DoF robotic arm to reach the target. The classification algorithm achieves over 85% precision and recall in decoding EEG data, even with coexisting physiological artifacts. Similarly, the POA achieves approximately 650 ms of actuation time with a 0.001 learning rate and 0.1 cm² error threshold settings. In conclusion, the study also validates the preliminary analysis results on a working physical model and benchmarks the robotic arm’s performance against human users, establishing the proof-of-concept for future assistive technologies integrating EEG and computer vision paradigms. Full article

Background: The increasing deployment of unmanned aerial vehicles (UAVs) for logistics in smart cities presents pressing challenges related to identity spoofing, unauthorized payload transport, and airspace security. Existing drone defense systems (DDSs) struggle to verify both drone identity and payload authenticity in real time, while blockchain-assisted solutions are often hindered by high latency and limited scalability. Methods: To address these challenges, we propose iBANDA, a blockchain- and AI-assisted DDS framework. The system integrates a lightweight You Only Look Once 5 small (YOLOv5s) object detection model with a Snowball-based Proof-of-Stake consensus mechanism to enable dual-layer authentication of drones and their attached payloads. Authentication processes are coordinated through an edge-deployable decentralized application (DApp). Results: The experimental evaluation demonstrates that iBANDA achieves a mean average precision of 99.5%, recall of 100%, and an F1-score of 99.8% at an inference time of 0.021 s, validating its suitability for edge devices. Blockchain integration achieved an average network latency of 97.7 ms and an end-to-end transaction latency of 1.6 s, outperforming Goerli, Sepolia, and Polygon Mumbai testnets in scalability and throughput. Adversarial testing further confirmed resilience to Sybil attacks and GPS spoofing, maintaining a false acceptance rate below 2.5% and continuity above 96%. Conclusions: iBANDA demonstrates that combining AI-based visual detection with blockchain consensus provides a secure, low-latency, and scalable authentication mechanism for UAV-based logistics. Future work will explore large-scale deployment in heterogeneous UAV networks and formal verification of smart contracts to strengthen resilience in safety-critical environments. Full article

Antibiotic contaminants such as tetracycline (TC) from agricultural production have become widely distributed and persistently accumulated in aquatic environments (rivers, lakes, and oceans), posing severe threats to ecological security and human health. This study developed a modified rice-straw-derived biochar through NaOH activation and ball-milling-assisted Fe₃O₄ loading, which simultaneously enhanced TC adsorption capacity and enabled magnetic recovery. The Box–Behnken design (BBD) response surface methodology was employed to optimize three key preparation parameters: ball-milling time (A, 39.95 min), frequency (B, 57.23 Hz), and Fe₃O₄/biochar mass ratio (C, 2.85:1), with TC adsorption capacity as the response value. The modified biochar was systematically characterized using SEM, BET, FTIR, XRD, and XPS, while adsorption mechanisms were elucidated through kinetic studies, isotherm analyses, and pH-dependent experiments. The results demonstrate that modification via ball-milling with Fe₃O₄ loading significantly enhanced the biochar’s tetracycline adsorption capacity. The maximum adsorption capacity of the modified biochar reached 102.875 mg/g, representing a 114.85% increase from the initial value of 47.882 mg/g observed for the pristine biochar. Furthermore, the modified biochar exhibited excellent stability, maintaining robust adsorption performance across a wide pH range. The primary adsorption mechanisms involved metal coordination complexation, supplemented by hydrogen bonding, π-π interactions, and pore filling. Full article

Foreign Object Debris (FOD) on airport pavements poses a serious threat to aviation safety, making accurate detection and interpretable scene understanding crucial for operational risk management. This paper presents an integrated multi-modal framework that combines an enhanced YOLOv7-X detector, a cascaded YOLO-SAM segmentation module, and a structured prompt engineering mechanism to generate detailed semantic descriptions of detected FOD. Detection performance is improved through the integration of Coordinate Attention, Spatial–Depth Conversion (SPD-Conv), and a Gaussian Similarity IoU (GSIoU) loss, leading to a 3.9% gain in mAP@0.5 for small objects with only a 1.7% increase in inference latency. The YOLO-SAM cascade leverages high-quality masks to guide structured prompt generation, which incorporates spatial encoding, material attributes, and operational risk cues, resulting in a substantial improvement in description accuracy from 76.0% to 91.3%. Extensive experiments on a dataset of 12,000 real airport images demonstrate competitive detection and segmentation performance compared to recent CNN- and transformer-based baselines while achieving robust semantic generalization in challenging scenarios, such as complete darkness, low-light, high-glare nighttime conditions, and rainy weather. A runtime breakdown shows that the enhanced YOLOv7-X requires 40.2 ms per image, SAM segmentation takes 142.5 ms, structured prompt construction adds 23.5 ms, and BLIP-2 description generation requires 178.6 ms, resulting in an end-to-end latency of 384.8 ms per image. Although this does not meet strict real-time video requirements, it is suitable for semi-real-time or edge-assisted asynchronous deployment, where detection robustness and semantic interpretability are prioritized over ultra-low latency. The proposed framework offers a practical, deployable solution for airport FOD monitoring, combining high-precision detection with context-aware description generation to support intelligent runway inspection and maintenance decision-making. Full article

Rye exhibits high diversity in grain coloration among small cereals, which is mainly linked to the presence of colored flavonoids synthesized in the outer layers of the kernel. This variability is not yet sufficiently described from colorimetric, cytological, and biochemical points of view. In this study, the localization of flavonoid pigments, anthocyanins and proanthocyanidins (PAs), was analyzed across different grain tissues in 26 rye lines with identified anthocyanin grain color genes. Grain coloration was objectively characterized using the CIELAB color coordinates and the GrainScan software for image analysis of individual grains. The distribution of anthocyanins and PAs was investigated through light microscopy and matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) on longitudinal and cross sections of the grains. The results revealed that violet-grained lines accumulate anthocyanins in the pericarp, while green-grained lines contain anthocyanins in the aleurone layer. MALDI-IMS confirmed the presence of specific anthocyanins: cyanidins in the pericarp of violet-grained lines and delphinidins in the aleurone layer of green-grained lines. All studied lines, except for the anthocyanin-less vi3 mutants, exhibited PAs in the brown-colored testa. Four main color groups of the rye grains (yellow, green, brown, and violet) could be clearly differentiated using the CIE color coordinate h°. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

Business financing involves supplying funds or capital to initiate, expand, or maintain a business. This study investigates entrepreneurial funding in South Africa, emphasizing microloans, venture capital, and gender-sensitive grants as tools to facilitate inclusive business growth. Using a qualitative desktop research methodology, this study relies on policy documents, institutional reports, and peer-reviewed studies to assess how these funding strategies tackle access barriers for marginalized populations, specifically women, youth, and rural entrepreneurs. Guided by Access to Finance Theory, Gender Finance Theory, and Innovation Ecosystems Theory, this study indicates that microloans offer immediate funding for informal businesses but show minimal long-term effects without additional assistance. Venture capital promotes rapid innovation, yet it is predominantly based in urban regions and unattainable for underrepresented populations. Grants that address gender issues foster equity but are obstructed by institutional fragmentation and insufficient scale. The results highlight the necessity for unified financing frameworks that merge financial and non-financial assistance, facilitating scalable and inclusive business ventures. Policy suggestions involve aligning public financing tools with the National Integrated Small Enterprise Development Masterplan, integrating gender-sensitive budgeting frameworks, and utilizing digital financial platforms to enhance access. Future studies should utilize mixed-methods or longitudinal approaches to assess the ongoing developmental effects of coordinated financing models within the South African setting. Full article

Accurate and robust calibration of multifocal plenoptic cameras is essential for high-precision 3D light field reconstruction. In this work, we propose a blur feature-guided cascaded calibration for the plenoptic camera. First, white images at different aperture values are used to estimate the high-confidence center point and radius of micro-images, and the defocus theory is used to estimate the initial values of the intrinsic parameters. Second, the gradient value is introduced to quantify the degree of blurring of the corner points, which are then divided into three types: clear, semi-clear, and blurred. Furthermore, a joint geometric constraint model of epipolar lines and virtual depth is constructed, and the coordinates of the semi-clear and blurred corner points are optimized in a step-by-step manner by using the clear corner point coordinates. The micro-image center ray projection equation is then devised to assist in the optimization of the microlens array core parameters and establish blur-adaptive credibility weights, thereby constructing a global nonlinear optimization. Finally, the proposed method is tested on both simulated and captured datasets, and the results exhibit superior performance when compared with the established methods described by Labussière, Nousias, and Liu. The proposed method excels in corner feature extraction, calibration accuracy of both internal and external parameters, and calibration sensitivity when applied to multifocal-length light field cameras, highlighting its advantages and robustness. Full article

This study presents the synthesis and physicochemical characterization of coordination compounds formed between chrysin, a natural flavonoid, and transition metal ions: Mn(II), Co(II), and Zn(II). The complexes were obtained under mildly basic conditions and analyzed using elemental analysis, thermogravimetric analysis (TGA), silver-assisted laser desorption/ionization mass spectrometry (SALDI-MS), FT-IR spectroscopy, and ¹H NMR. The spectroscopic data confirm that chrysin coordinates as a bidentate ligand through the 5-hydroxyl and 4-carbonyl groups, with structural differences depending on the metal ion involved. The mass spectrometry results revealed distinct stoichiometries: 1:2 metal-to-ligand ratios for Mn(II) and Co(II), and 1:1 for Zn(II), with additional hydroxide coordination. Biological assays demonstrated that Co(II) and Mn(II) complexes exhibit enhanced antibacterial and anti-biofilm activity compared to free chrysin, particularly against drug-resistant Staphylococcus epidermidis, whereas the Zn(II) complex showed negligible biological activity. Full article

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, the positioning system can only achieve two-dimensional (2D) positioning and requires the assistance of inertial measurement units (IMU). When the LED is not captured or decoding fails, the system’s positioning error increases further. Thus, we propose a novel three-dimensional (3D) visible light positioning system based on image sensors for various environments. Specifically, (1) we use IMU to obtain the receiver’s state and calculate the receiver’s 2D position. Then, we fit the height–size curve to calculate the receiver’s height, avoiding the coordinate iteration error in traditional 3D positioning methods. (2) When no LED or decoding fails, we propose a firefly-assisted unscented particle filter (FA-UPF) algorithm to predict the receiver’s position, achieving high-precision dynamic positioning. The experimental results show that the system positioning error under a single LED is within 10 cm, and the average positioning error through FA-UPF under no light source is 6.45 cm. Full article

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. Full article

Background and objectives: Neurosurgical intervention on the newborn’s developing brain is a risk factor for neurodevelopmental disorders (NDDs). These patients necessarily require regular, coordinated follow-up. The ventricular subgaleal shunt (VSGS) technique has been used since 2013 at Rouen University Hospital. Like any change in practice, this technique must be evaluated. In this paper, we describe the population of patients with hydrocephalus treated by VSGS, the complications associated with the procedure, and the outcome of these patients at two and six years old. Methods: This study was an observational, descriptive, retrospective, single-center study. Children included were those less than three months old with hydrocephalus treated by VSGS at Rouen University Hospital from January 2013 to December 2023. Data were anonymized and collected using EDSaN software. A descriptive analysis was performed. Results: Thirty-two patients were included in our study. Of these, 22 (69%) were born prematurely; 16 (50%) of these 22 had postnatal intraventricular hemorrhage (IVH) requiring treatment with VSGS. A total of three patients (13.6%) died within the first year of life; twenty-four patients (75%) required definitive shunting. Twenty-two patients were over 2 years old in our study. Only 10 of them acquired the ability to walk (45%). Cerebral palsy was present in 10 (45%) patients. Fifteen patients were over 6 years old; thirteen (87%) attended school, but six (40%) had special needs (the need of an assistant, or part-time schedule). In our study, only 24 patients (82%) were followed by a pediatrician trained in neurodevelopment at Rouen University Hospital, and 27 (93%) were followed by a neurosurgeon. Conclusions: This study describes all patients with hydrocephalus treated by VSGS at Rouen University Hospital between January 2013 and December 2023, as well as their complications and their neurological outcomes. The follow-up of these children at risk of NDDs is essential. Full article

Pteridium aquilinum (bracken fern) poses a global threat to biodiversity and to the health of both animals and humans due to its toxic metabolites and aggressive ecological expansion. In northern Spain, particularly in regions of intensive livestock farming, these risks may be exacerbated, calling for urgent assessment and monitoring strategies. In this study, we implemented a multidisciplinary approach to evaluate the toxicological and ecological relevance of P. aquilinum through four key actions: (a) quantification of pterosins A and B in young fronds (croziers) using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS); (b) analysis of in vivo genotoxicity of aqueous extracts using Drosophila melanogaster as a model organism; (c) a large-scale survey of local livestock farmers to assess awareness and perceived impact of bracken; and (d) the development and field application of a drone-based mapping tool to assess the spatial distribution of the species at the regional level. Our results confirm the consistent presence of pterosins A and B in croziers, with concentrations ranging from 0.17 to 2.20 mg/g dry weight for PtrB and 13.39 to 257 µg/g for PtrA. Both metabolite concentrations and genotoxicity levels were found to correlate with latitude and, importantly, with each other. All tested samples exhibited genotoxic activity, with notable differences among them. The farmer survey (n = 212) revealed that only 50% of respondents were aware of the toxic risks posed by bracken, indicating a need for targeted outreach. The drone-assisted mapping approach proved to be a promising tool for identifying bracken-dominated areas and provides a scalable foundation for future ecological monitoring and land management strategies. Altogether, our findings emphasize that P. aquilinum is not merely a local concern but a globally relevant toxic species whose monitoring and control demand coordinated scientific and policy-based efforts. Full article