Search Results (639)

Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To address this, we propose an AI- and IoT-driven monitoring and visualisation framework that integrates edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models to enable predictive anomaly detection, proactive alerting, and reduced downtime. This system leverages Prometheus, Grafana, and Mimir for data collection, visualisation, and long-term storage, while incorporating Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for anomaly prediction and fault classification. These AI modules are containerised and deployed at the edge or centrally, depending on tenant topology, with predicted risk metrics seamlessly integrated back into Prometheus. A one-month deployment across five MSP clients (500 nodes) demonstrated significant operational benefits, including a 95% reduction in downtime and a 90% reduction in incident resolution time relative to historical baselines. The system ensures secure tenant isolation via VPN tunnels and token-based authentication, while providing GDPR-compliant data handling. Unlike prior monitoring platforms, this work introduces a fully edge-embedded AI inference pipeline, validated through live deployment and operational feedback. Full article

Legionella pneumophila (L. pneumophila) infection is characterized by a wide spectrum of manifestations, from influenza-like illness to life-threatening atypical pneumonia with multiorgan failure. The aim of our study was the assessment of in vitro and ex vivo neutrophil activation in L. pneumophila infections, as well as the role of neutrophils’ peptides such as LL-37 in infection. The ability of neutrophils to form ex vivo extracellular traps (NETs) in response to bacterial infection was examined by immunofluorescence. In parallel, patients’ sera, as well as opsonized standard L. pneumophila strains, were used for in vitro activation of neutrophils from healthy individuals. The serum levels of interleukins were assessed using the LEGENDplexTM Multi-Analyte Flow Assay Kit. Furthermore, citrullinated cf-DNA as a marker of neutrophil extracellular traps (NETs) was detected in the serum of patients with acute infection. It was demonstrated that neutrophils released NETs in vitro and ex vivo upon L. pneumophila (interaction in an autophagy-independent manner. Notably, IL-1b was detected on NETs, but an antimicrobial peptide LL-37 was absent. The lack of antimicrobial activity failed to inhibit bacterial proliferation. In addition, in vitro and ex vivo NETs formation was observed during the Clarithromycin treatment. Those NETs were decorated with bioactive antimicrobial peptide LL-37, which inhibits bacterial proliferation. The findings provide evidence that neutrophils release NETs in vitro and ex vivo by expressing the IL1β protein in them. The lack of expression of the antimicrobial peptide LL-37 on the NETs demonstrates the inability of the cells to inhibit proliferation, and consequently the elimination of L. pneumophila. Clarithromycin plays a dual role in the elimination. Full article

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article

This review critically evaluates the performance of approximately 80 commercially available mobile detectors for explosive identification. The majority of devices utilize Ion Mobility Spectrometry (IMS), Fourier Transform Infrared Spectroscopy (FTIR), or Raman Spectroscopy (RS). IMS-based instruments, such as the M-ION (Inward Detection), typically achieve sensitivities at the ppt level, while other IMS implementations demonstrate detection ranges from low ppb to ppm. Gas Chromatography–Mass Spectrometry (GC–MS) systems, represented by the Griffin™ G510 (Teledyne FLIR Detection), provide detection limits in the ppb range. Transportable Mass Spectrometers (Bay Spec) operate at ppb to ppt levels, whereas Laser-Induced Fluorescence (LIF) devices, such as the Fido X4 (Teledyne FLIR Detection), achieve detection at the nanogram level. Quartz Crystal Microbalance (QCM) sensors, exemplified by the EXPLOSCAN (MS Technologies Inc. 8609 Westwood Center Drive Suite 110, Tysons Corner, VA, USA), typically reach the ppb range. Only four devices employ two orthogonal analytical techniques, enhancing detection reliability and reducing false alarms. Traditional colorimetric tests based on reagent–analyte reactions remain in use, demonstrating the continued relevance of simple yet effective methods. By analyzing the capabilities, limitations, and technological trends of current detection systems, this study underscores the importance of multi-technique approaches to improve accuracy, efficiency, and operational effectiveness in real-world applications. The findings provide guidance for the development and selection of mobile detection technologies for security, defense, and emergency response. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice. Full article

Object detection in soccer scenes serves as a fundamental task for soccer video analysis and target tracking. This paper proposes WCC-YOLO, a symmetry-enhanced object detection framework based on YOLOv11n. Our approach integrates symmetry principles at multiple levels: (1) The novel C3k2-WTConv module synergistically combines conventional convolution with wavelet decomposition, leveraging the orthogonal symmetry of Haar wavelet quadrature mirror filters (QMFs) to achieve balanced frequency-domain decomposition and enhance multi-scale feature representation. (2) The Channel Prior Convolutional Attention (CPCA) mechanism incorporates symmetrical operations—using average-max pooling pairs in channel attention and multi-scale convolutional kernels in spatial attention—to automatically learn to prioritize semantically salient regions through channel-wise feature recalibration, thereby enabling balanced feature representation. Coupled with InnerShape-IoU for refined bounding box regression, WCC-YOLO achieves a 4.5% improvement in mAP@0.5:0.95 and a 5.7% gain in mAP@0.5 compared to the baseline YOLOv11n while simultaneously reducing the number of parameters and maintaining near-identical inference latency (δ < 0.1 ms). This work demonstrates the value of explicit symmetry-aware modeling for sports analytics. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

Background/Objectives: Adalimumab and Infliximab are biologics used to treat autoimmune diseases. Monitoring drug and anti-drug antibody (ADA) levels in patients helps optimize treatment. However, current quantitation methodologies for drug and total (free and drug-bound) ADAs often involve multi-step workflows. Automated systems can streamline the process. The i-Tracker chemiluminescent immunoassays (CLIA) are cartridge-based kits for quantifying serum levels of drugs such as Adalimumab, Infliximab, and associated ADAs. Herein, we aimed to establish performance characteristics of the i-Tracker Adalimumab, Infliximab, and total ADAs in serum on the random-access analyzer IDS-iSYS and to compare patient results with an electrochemiluminescent immunoassay (ECLIA)-based reference method. Methods: Remnant serum specimens, calibration material, or spiked serum were used to evaluate assay linearity, precision, functional sensitivity, and accuracy on the IDS-iSYS analyzer and to perform the method comparison. Results: The assays displayed linearity, accuracy, and up to 8% imprecision across clinically relevant analyte ranges. Compared to the reference method, the drug assays exhibited a strong linear fit (correlation coefficient > 0.95) with <±1.0 µg/mL mean bias. The total anti-Adalimumab assay demonstrated over 85% qualitative agreement. The total anti-Infliximab assay, however, showed higher detection rate of ADAs in Infliximab-treated patient specimens, yielding < 60% negative agreement with the reference method. Although i-Tracker total ADA assays exhibited drug sensitivity, they still detected ADAs in supratherapeutic drug concentrations. Conclusions: The i-Tracker assays demonstrated robust analytical performance, suggesting potential for clinical application. The method comparison underscored functional differences with the reference method, an important consideration when transitioning assay formats for monitoring Adalimumab- and Infliximab-treated patients. Full article

Background: Anti-dsDNA is an important biomarker for the diagnosis, prognosis, and monitoring of systemic lupus erythematosus (SLE). Although several assays for anti-dsDNA antibody detection are routinely used, standardization remains limited, and differences have been reported. This study aimed to compare five methods for anti-dsDNA antibody detection and to estimate their association with complement consumption. Methods: A total of 149 samples submitted for routine laboratory testing were collected and tested on five platforms: Crithidia luciliae indirect immunofluorescence test (CLIFT), addressable laser bead immunoassay (ALBIA), a high-avidity (HA) enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay (CIA), and a novel particle-based multi-analyte technology (PMAT). Complements C3 and C4 were available for a subset of the total samples. Results: Correlation between anti-dsDNA assays ranged from 0.94 (CIA and PMAT) to 0.65 (ALBIA and CLIFT). The AUC from the ROC analysis using CLIFT as a reference was 0.95 for PMAT, 0.94 for CIA, 0.93 for ELISA, and 0.86 for ALBIA. The highest sensitivity relative to CLIFT at a fixed specificity of 94.4% was 84.7% for CIA and ELISA, 76.3% for PMAT, and 42.4% for ALBIA. Correlation between anti-dsDNA and C3 ranged from −0.81 for ELISA to −0.51 for ALBIA. Conclusions: Different anti-dsDNA detection methods showed varying diagnostic performance and correlation and varying agreement with CLIFT and complement consumption. ELISA, CIA, and PMAT showed high correlation to each other and to CLIFT and were in strong concordance with low C3 levels. In contrast, ALBIA revealed lower clinical performance and correlation with CLIFT and complement consumption. Full article

Mortars taken from the 16th century Venetian Fortress of Bergamo (Italy) were characterized (binder-concentrated fractions and aggregate fractions as well as bulk samples) with a multi-analytical approach using X-ray diffraction (XRD), inductively coupled plasma optical emission spectrophotometry (ICP-OES), optical microscopy (OM), differential scanning calorimetry (DSC) and thermogravimetric analysis (TG). The results showed the presence of calcite, hydrocalumite and hydrotalcite-type compounds, brucite, aragonite, plombierite and a large fraction of amorphous phases (ranging between 14 and 27 wt%) in the binder. Quartz and carbonate-rich sands were used as aggregates. The mortar is a Mg-rich material containing 4–5 wt% brucite. No evidence of magnesite or hydromagnesite was found in any sample, although these phases are frequently detected in the binder of buildings from the Renaissance period that are located in Northern Italy. The large average amount (12–13 wt%) of reactive silicate, such as Mg-containing phyllosilicates that can react with lime, and the presence of carbonate-containing hydrocalumite and hydrotalcite indicate hydraulic interactions between lime and reactive silicate aggregates. The CO₂/H₂O_bound ratio, evaluated from the weight loss referred to the finer fraction (<63 μm), ranges from 1.99 to 2.55, which suggests that the walls of Bergamo were constructed using lime-based mortar with hydraulic properties. Full article

The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations. Full article

Carbon emissions in the building materialization stage are highly significant and concentrated. Quantification at this stage is essential for assessing carbon reduction potential, guiding energy-saving strategies, and supporting China’s “dual carbon” goals in the construction sector. Distinct from conventional environmental and energy economics analytical approaches, the building carbon emissions in the materialization stage (BCEMS) in 30 provinces of China from 2010 to 2021 were calculated using multi-source data, and the characteristics of their spatio-temporal evolution were analyzed. The key influencing factors were identified using a geographic detector, and their spatial heterogeneity was analyzed with the Geographically and Temporally Weighted Regression (GTWR) model from a geographical analysis perspective. The results indicated the following: (1) From 2010 to 2021, BCEMS exhibited a trend of an “initial increase followed by a decrease and subsequent fluctuation”, with an average annual growth rate of 4.28%. Building materials were the largest contributor to BCEMS, particularly cement and steel. Spatially, the emissions displayed a pattern of “higher in the east, lower in the west”. High–high-agglomeration areas remained stable over time, primarily in Zhejiang and Fujian provinces, while low–low-agglomeration areas were concentrated in Xinjiang. (2) Single-factor detection revealed that fixed assets, population density, and the liabilities of construction enterprises were the dominant factors driving the emissions’ spatial evolution. Two-factor interaction detection identified the economic society and the construction industry as the key influencing domains. (3) The economic development level and the total population showed a positive correlation with BCEMS, with the effect intensity increasing from west to east. The urbanization level and fixed assets also generally showed a positive correlation with BCEMS; however, their effect intensity initially increased positively from west to east and then turned into a negative enhancement. The findings provide references for implementing regionally differentiated carbon reduction measures and promoting green and low-carbon urban transformation in China’s construction industry. Full article

Background/Objectives: Carbapenem-resistant Gram-negative bacilli (CR-GNB) pose a growing challenge to public health worldwide due to limited treatment options. This cross-sectional study investigated the characteristics of CR-GNB isolated from clinical specimens in Lagos, Nigeria. Methods: Gram-negative bacilli (GNB) and clinical data were obtained from three multi-specialist private hospitals between March and June 2023. The GNB were identified using the Analytical Profile Index (API) and investigated for CR-GNB by disk diffusion. Antimicrobial resistance patterns and carbapenemase gene data for presumptive carbapenemase-producing Gram-negative bacilli (CP-GNB) were analyzed using Vitek-2 and polymerase chain reaction (PCR). Results: Of 317 GNB, 29.0% (n = 92) were CR-GNB. Significantly higher numbers of CR-GNB were reported from the intensive care unit and oncology department (p = 0.009). Of all CR-GNB, 17 isolates (18.5%) were classified as presumptive CP-GNB. In this subgroup, resistance rates of ampicillin/sulbactam (100.0%) and trimethoprim/sulfamethoxazole (100.0%) were highest. Ten (10) CP-GNB were confirmed, representing 3.15% of all GNB tested. Seven isolates of New Delhi Metallo-β-lactamase (bla_NDM) were found among P. aeruginosa, K. pneumoniae, E. coli, and A. baumannii. The bla_NDM was identified in strains classified as extensively drug-resistant (XDR) and pandrug-resistant. Conversely, the bla_KPC was detected solely in multidrug-resistant and XDR strains. Conclusions: Emerging CR-GNB, specifically CP-GNB, in Nigeria emphasize the need for specific therapeutic management of infected patients. Antimicrobial stewardship and long-term surveillance efforts must be implemented in healthcare settings, as well as improved, accelerated microorganism identification techniques. Full article

Detection of cancer biomarkers at the earliest stages of disease progression is commonly assumed to extend the overall quality of life for cancer patients as the result of earlier clinical management of the disease. Therefore, there is an urgent need for the development of standardized, sensitive, robust, and commonly available screening and diagnostic tools for detecting the earliest signals of neoplastic pathology progression. Recently, a new paradigm of cancer control, known as multi-cancer detection (MCD), evolved, which measures the composition of cancer-related molecular analytes in the patient’s fluids using minimally invasive techniques. In this respect, the “Holy Grail” of cancer researchers and bioengineers for decades has been composing a repertoire or molecular sensing probes that would allow for the diagnosis, prognosis, and monitoring of cancer diseases via their interaction with cell-secreted and cell-associated cancer antigens and biomarkers. Therefore, the current trend in screening and detection of cancer-related pathologies is the development of portable biosensors for mobile laboratories and individual use. Phage display, since its conception by George Smith 40 years ago, has emerged as a premier tool for molecular evolution in molecular biology with widespread applications including identification and screening of cancer biomarkers, such as Circulating Cellular Communication Network Factor 1 (CCN1), an extracellular matrix-associated signaling protein responsible for a variety of cellular functions and has been shown to be overexpressed as part of the response to various pathologies including cancer. We hypothesize that CCN1 protein can be used as a soluble marker for the early detection of breast cancer in a multi-cancer detection (MCD) platform. However, validated probes have not been identified to date. Here, we screened the multi-billion clone landscape phage display library for phages interacting specifically with immobilized CCN1 protein. Through our study, we discovered a panel of 26 different phage-fused peptides interacting selectively with CCN1 protein that can serve for development of a novel phage-based diagnostic platform to monitor changes in CCN1 serum concentration by liquid biopsy. Full article

To address the limitations of traditional single-point detection methods in monitoring the sulfur content of ship fuel (FSC), which are inadequate in meeting the regulatory demands of high-traffic ports, this study proposes an integrated analytical approach based on artificial intelligence. This approach synthesizes multi-source heterogeneous data, including historical fuel testing records, Automatic Identification System (AIS) trajectory data, ship and operator profiles, technical specifications, fuel supply chain documentation, fundamental ship attributes and so on. Following rigorous data cleaning and preprocessing procedures, a refined dataset comprising 3046 records collected between 2017 and 2024 from the Port of Ningbo was utilized. Initially, multiple linear regression analysis was con-ducted to identify key factors influencing sulfur emissions, resulting in an R² value of 0.67. Based on these findings, a deep neural network model was developed using TensorFlow to enable real-time estimation of FSC and classification of compliance risk levels. The results indicate that the proposed method exhibits high estimated accuracy and robustness. An AI-based intelligent monitoring module, developed based on this research, has been integrated into the ship exhaust gas detection system at the Port of Ningbo. This module enables real-time analysis of inbound ships and intelligent identification of potentially non-compliant ships, thereby significantly improving the precision and efficiency of port regulatory operations. This study not only contributes to the theoretical framework for ship fuel compliance monitoring but also provides a practical and scalable technical solution for intelligent port governance. Full article