Search Results (48)

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

Object detection in complex scenarios such as construction sites, electric power operations, and resource exploration often suffers from low accuracy and frequent missed or false detections. To address these challenges, this study proposes a modified You Only Look Once version 8 nano (YOLOv8n)-based algorithm, termed YOLOv8n-ASA, for safety-helmet-wearing detection. The proposed method introduces structural asymmetry into the network to enhance feature representation and detection robustness. Specifically, an Adaptive Kernel Convolution (AKConv) module is incorporated into the backbone, in which asymmetric kernels are used to better capture features of irregularly shaped objects. The Simple Attention Module (SimAM) further sharpens the focus on critical regions, while the Asymptotic Feature Pyramid Network (AFPN) replaces the symmetric top–down fusion pathway of the traditional FPN with a progressive and asymmetric feature integration strategy. These asymmetric designs mitigate semantic gaps between non-adjacent layers and enable more effective multi-scale fusion. Extensive experiments demonstrate that YOLOv8n-ASA achieves superior accuracy and robustness compared to several benchmarks, validating its effectiveness for safety-helmet-wearing detection in complex real-world scenarios. Full article

A single drone collects data, but a fleet builds a complete picture, and this is the primary objective of this study. To address this goal, a swarm-based drone system has been designed in which multiple drones follow one another to collect data from diverse perspectives. Such a strategy demonstrates strong potential for use in critical fields such as search and rescue operations. This study introduces the first unified framework that integrates autonomous formation control, real-time object detection, and multi-source data fusion within a single operational UAV-swarm system. A high-fidelity simulation environment was built using Unreal Engine with the AirSim plugin, featuring a lightweight QR code tracking algorithm for inter-drone coordination. The drones were employed to detect vehicles from various angles in real time. Two types of experiments were conducted: the first used a pretrained YOLO model, and the second used a custom-trained YOLOv8-nano model, which outperformed the baseline by achieving an average detection confidence of 90%. Finally, the results from multiple drones were fused using various techniques including temporal, probabilistic, and geometric fusion methods to produce more reliable and robust detection results. Full article

To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection. Full article

The use of different nanoparticles (NPs) is increasing in a wide variety of everyday products. Nevertheless, most studies concerning NP risk assessment have evaluated exposure scenarios involving a single kind of NP. A stepwise study distinguishing between the effects resulting from exposure to one kind of NP and those resulting from different co-exposure scenarios to ${\mathrm{Al}}_2{\mathrm{O}}_3$ and ${\mathrm{CeO}}_2$ NPs at concentrations below acute toxicity was conducted with different analytical techniques. As a starting point, WST-1 viability assays were performed to assess whether the chosen exposure concentrations resulted in any acute loss of viability, which would hamper further insight into the cellular response to NP exposure. Then, data on NP dissolution and uptake were obtained via single-particle inductively coupled plasma–mass spectrometry (spICP-MS) and microwave-assisted ICP-MS. Additionally, time-of-flight secondary ion mass spectrometry (ToF-SIMS) was performed to check for differences in the biological response to the exposure scenarios at the single-cell level. It was found that the proposed combined techniques provide insight into changes in biological responses as well as cellular metal contents among the exposure scenarios. In this work, a comprehensive tiered analytical strategy for evaluating the biological responses to challenging exposure scenarios is provided. The results highlight the necessity of selecting situations more closely resembling real life—including concentrations below acute toxicity and potential interactions due to multiple NPs—when estimating potential health risks. These findings thus provide a foundation and an incentive for further research into the complex processes leading to the observed effects. Full article

The performance of an orthopedic procedure depends on several tandem functionalities. Such characteristics include materials’ surface properties and subsequent responses. Implant surfaces are typically roughened; this roughness can further be optimized to a specific morphology such as nanotubular roughness (ZrNTs) and the surfaces can further be used as static drug reservoirs. ZrNTs coatings are attracting interest due to their potential to improve the success rate of implant systems, by means of better physical affixation and also micro/nano physio-chemical interaction with the extracellular matrix (ECM). Effective control over the drug release properties from such coatings has been the subject of several published reports. In this study, a novel and simple approach to extending drug release time and limiting the undesirable burst release from zirconia nanotubes (ZrNTs) via structural modification was demonstrated. The latter involved fabricating a double-layered structure with a modulated diameter and was achieved by varying the voltage and time during electrochemical anodization. The structurally modified ZrNTs and their homogenous equivalents were characterized via SEM and ToF-SIMS, and their drug release properties were monitored and compared using UV–Vis spectroscopy. We report a significant reduction in the initial burst release phenomenon and enhanced overall release time. The simple structural modification of ZrNTs can successfully enhance drug release performance, allowing for flexibility in designing drug delivery coatings for specific implant challenges, and offering a new horizon for smart biomaterials based on metal oxide nanostructures. Full article

With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively. Full article

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. Full article

In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the evaluation of rice seedling planting conditions. Therefore, in order to build a seedling insertion condition detection system, this study proposes an approach based on the combination of image processing and deep learning. The image processing stage is primarily applied to seedling absence detection, utilizing the centroid detection method to obtain precise coordinates of missing seedlings with an accuracy of 93.7%. In the target recognition stage, an improved YOLOv8 Nano network model is introduced, leveraging deep learning algorithms to detect qualified and misplaced seedlings. This model incorporates ASPP (atrous spatial pyramid pooling) to enhance the network’s multiscale feature extraction capabilities, integrates SimAM (Simple, Parameter-free Attention Module) to improve the model’s ability to extract detailed seedling features, and introduces AFPN (Asymptotic Feature Pyramid Network) to facilitate direct interaction between non-adjacent hierarchical levels, thereby enhancing feature fusion efficiency. Experimental results demonstrate that the enhanced YOLOv8n model achieves precision (P), recall (R), and mean average precision (mAP) of 95.5%, 92.7%, and 95.2%, respectively. Compared to the original YOLOv8n model, the enhanced model shows improvements of 3.6%, 0.9%, and 1.7% in P, R, and mAP, respectively. This research provides data support for the efficiency and quality of transplanting machine operations, contributing to the further development and application of unmanned field management in subsequent rice seedling cultivation. Full article

The improvement of fast-charging capabilities for lithium-ion batteries significantly influences the widespread application of electric vehicles. Fast-charging performance depends not only on materials but also on the battery’s inherent structure and the heterogeneity of the electrode reaction. Herein, we utilized advanced imaging techniques to explore how the internal structure of cylindrical batteries impacts macroscopic electrochemical performance. Our research unveiled the natural 3D structural non-uniformity of the electrodes, causing heterogeneity of electrode reaction. This non-uniformity of reaction exhibited a macro–meso–micro-scale feature in four dimensions: the exterior versus the interior of the electrode, the middle versus the sides of the cell, the inside versus the outside of the cell, and the surface versus the body of the electrode. Furthermore, the single-coated side of the anode demonstrated notably faster reaction than the double-coated sides, leading to the deposition of island-like lithium during fast charging. These discoveries offer novel insights into multi-scale fast-charging mechanisms for commercial batteries, inspiring innovative approaches to battery design. Full article

The accumulation of lipid droplets (LDs) and ceramides (Cer) is linked to non-alcoholic fatty liver disease (NAFLD), regularly co-existing with type 2 diabetes and decreased immune function. Chronic inflammation and increased disease severity in viral infections are the hallmarks of the obesity-related immunopathology. The upregulation of neutral sphingomyelinase-2 (NSM2) has shown to be associated with the pathology of obesity in tissues. Nevertheless, the role of sphingolipids and specifically of NSM2 in the regulation of immune cell response to a fatty acid (FA) rich environment is poorly studied. Here, we identified the presence of the LD marker protein perilipin 3 (PLIN3) in the intracellular nano-environment of NSM2 using the ascorbate peroxidase APEX2-catalyzed proximity-dependent biotin labeling method. In line with this, super-resolution structured illumination microscopy (SIM) shows NSM2 and PLIN3 co-localization in LD organelles in the presence of increased extracellular concentrations of oleic acid (OA). Furthermore, the association of enzymatically active NSM2 with isolated LDs correlates with increased Cer levels in these lipid storage organelles. NSM2 enzymatic activity is not required for NSM2 association with LDs, but negatively affects the LD numbers and cellular accumulation of long-chain unsaturated triacylglycerol (TAG) species. Concurrently, NSM2 expression promotes mitochondrial respiration and fatty acid oxidation (FAO) in response to increased OA levels, thereby shifting cells to a high energetic state. Importantly, endogenous NSM2 activity is crucial for primary human CD4⁺ T cell survival and proliferation in a FA rich environment. To conclude, our study shows a novel NSM2 intracellular localization to LDs and the role of enzymatically active NSM2 in metabolic response to enhanced FA concentrations in T cells. Full article

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations. Full article

Shiga toxin-producing Escherichia coli (STEC) and Listeria monocytogenes are routinely responsible for severe foodborne illnesses in the United States. Current identification methods utilized by the U.S. Food Safety Inspection Service require at least four days to identify STEC and six days for L. monocytogenes. Adoption of long-read, whole genome sequencing for food safety testing could significantly reduce the time needed for identification, but method development costs are high. Therefore, the goal of this project was to use NanoSim-H software to simulate Oxford Nanopore sequencing reads to assess the feasibility of sequencing-based foodborne pathogen detection and guide experimental design. Sequencing reads were simulated for STEC, L. monocytogenes, and a 1:1 combination of STEC and Bos taurus genomes using NanoSim-H. At least 2500 simulated reads were needed to identify the seven genes of interest targeted in STEC, and at least 500 reads were needed to detect the gene targeted in L. monocytogenes. Genome coverage of 30x was estimated at 21,521, and 11,802 reads for STEC and L. monocytogenes, respectively. Approximately 5–6% of reads simulated from both bacteria did not align with their respective reference genomes due to the introduction of errors. For the STEC and B. taurus 1:1 genome mixture, all genes of interest were detected with 1,000,000 reads, but less than 1x coverage was obtained. The results suggested sample enrichment would be necessary to detect foodborne pathogens with long-read sequencing, but this would still decrease the time needed from current methods. Additionally, simulation data will be useful for reducing the time and expense associated with laboratory experimentation. Full article

Titanium dioxide (TiO₂) nanoparticles (NPs) are the subject of numerous studies and controversies on the risks they could pose to the environment and human health. When in contact with biological tissues, NPs can sometimes be challenging to precisely localize within subcellular structures (typically around 0.1 µm) when they exist as isolated NPs, particularly when using the SIMS approach. Indeed, the chemical signals produced by isolated NPs are very low, so they can be confused with background signals. This was the motivation behind our development of a new strategy for correlating TEM/SIMS to detect TiO₂ NPs in close proximity to cutaneous corneocytes. For this purpose, we initially developed a new tool for TEM and SIMS image registration based on a non-rigid image-deformation-enabling image overlay. Combining SIMS and TEM data through this overlay enhances NP localization’s precision. Secondly, we developed an algorithm based on the statistical analysis of multiplane SIMS images to denoise them. As a result, background noise was reduced, illuminating the low yet specific signals from isolated NPs. Finally, this new correlative approach enables the precise 3D localization of isolated NPs within the analyzed volume. We consider this method a breakthrough for subcellular-scale NP localization. Full article

Nowadays, with the rocketing of computational power, advanced numerical tools, and parallel computing, multi-scale simulations are becoming applied more and more to complex multi-physics industrial processes. One of the several challenging processes to be numerically modelled is gas phase nanoparticle synthesis. In an applied industrial scenario, the possibility to correctly estimate the geometric properties of the mesoscopic entities population (e.g., their size distribution) and to more precisely control the results is a crucial step to improve the quality and efficiency of the production. The “NanoDOME” project (2015–2018) aims to be an efficient and functional computational service to be applied in such processes. NanoDOME has also been refactored and upscaled during the H2020 Project “SimDOME”. To prove its reliability, we present here an integrated study between experimental data and NanoDOME’s predictions. The main goal is to finely investigate the effect of a reactor’s thermodynamic conditions on the thermophysical history of mesoscopic entities along the computational domain. To achieve this goal, the production of silver nanoparticles has been assessed for five cases with different experimental operative conditions of the reactor. The time evolution and final size distribution of nanoparticles have been simulated with NanoDOME by exploiting the method of moments and population balance model. The validation is performed by comparing NanoDOME’s calculations with the experimental data. Full article