Search Results (2,857)

Increasing power density of electrical devices in electrified transportation is an irreversible trend which involves power electronic-type supply, higher voltage and temperature. However, fast converter-switch rise times, high modulation and carrier frequencies, harmonics, and increased design field and temperature constitute potential causes of accelerated electrothermal aging of insulation, especially if harmful phenomena, as partial discharges (PDs), incept. This paper focuses on solving issues related to PD monitoring under power electronics waveforms, dealing with effective and automatic tools for noise rejection and for the identification of the type of source generating PD, the latter being fundamental for quality control, diagnostic and condition maintenance. It is shown that innovative techniques are available, which allow PD to be measured even under fast switching (rise time) and high frequency, separating, in the time domain, PD pulses from switching noise. This approach can be carried out automatically by the PD detector software presented here, not requiring experts for measurement management and, thus, making it a feasible tool also for on-line PD monitoring and condition-based maintenance. PD monitoring results from accelerated aging tests on a motor under pulse-width modulation (PWM supply) are presented. In order to assess the insulation health condition, progressive degradation of the motor is quantified using a dynamic health index (DHI), primarily based on key PD parameters, i.e., PD magnitude, repetition rate, and likelihood of discharge type (surface or internal). The proposed DHI approach not only provides meaningful metrics for translating PD data into a diagnostic tool, but it also offers insights into residual life estimation and failure risk prediction. Full article

This manuscript presents the results of a health assessment of a tree stand at the historic manor park in Rzeczyca (Łódź Voivodeship, Poland), carried out as part of its revitalization process. The study aimed to preserve the relics of the historic landed estate garden and adapt the park to contemporary public functions through the development of a tree management plan based on specialized arboricultural diagnostics. A dendrological inventory covered 370 trees within an area of approximately 3 ha, documenting their spatial distribution, species composition, morphology, and health condition. The dominant species were Tilia cordata (21.9%), Fraxinus excelsior (19.7%), and Prunus spinosa (9.4%). Advanced technical examinations were performed on 87 selected trees using sonic tomography with the ArborSonic 3D device and biomechanical assessment. These methods enabled the evaluation of internal wood degradation and the structural stability of mature trees, supporting decisions regarding their preservation. The findings emphasize the importance of cooperation between landscape architects and qualified arborists in the revitalization of historic parks with mature tree stands. The study also highlights the need for standardized diagnostic procedures and regular monitoring to improve public safety, support conservation efforts, and reduce unnecessary tree removal in response to increasing climate-related risks. Full article

Calibration-free electrochemical sensing is increasingly promoted as a route to simpler, more deployable analytical devices. However, the term is used inconsistently, ranging from genuinely absolute measurement to factory-calibrated, ratiometric, self-referenced, drift-corrected or model-assisted operation. This review critically examines what calibration-free sensing can and cannot mean in electrochemical analysis. We argue that a strict claim requires that the reported measurand be obtained from an internally constrained physical, chemical or stoichiometric relationship, with the required parameters known, controlled or independently measured within an uncertainty framework. Potentiometric, amperometric, coulometric, impedimetric, biosensing and affinity-based approaches are compared to show where empirical calibration is removed and where it is shifted to fabrication, internal correction, model fitting, matrix correction or context-specific validation. Particular attention is given to coulometric and thin-layer systems, geometry-constrained devices, electrochemical aptamer-based sensors and self-diagnostic platforms. We propose a classification scheme, a decision tree and a minimum assumption map linking measurand definition, electrochemical signals, signal-to-result relationships, parameter sources, uncertainty, matrix transfer, reproducibility and independent-method agreement. The review provides a practical framework for distinguishing genuinely calibration-free measurements from calibration-reducing, conditionally calibration-free and drift-corrected strategies. Full article

This study examines likelihood-based estimation of the joint survival function $S(t_1,t_2)=Pr\{T^{\left(1\right)}>t_1,T^{\left(2\right)}>t_2\}$ for systems with two competing failure modes observed under right censoring. Rather than introducing a new distributional family, the study compares established dependence mechanisms within a common observed-data framework. Exponential and Weibull margins are combined with three types of dependence: Archimedean copulas, represented by the Gumbel and Clayton families; shared gamma frailty, used to model latent measurement-level heterogeneity; and Marshall–Olkin extensions, used to represent common shocks and simultaneous failures. The same observation scheme, likelihood construction, censoring design, and performance criteria are used across models. Model performance is evaluated through Monte Carlo simulation using bias, integrated mean squared error, and empirical coverage, and the workflow is illustrated with the Device G reliability data. The results show that ignoring dependence can distort joint survival estimates, especially under moderate or high censoring. They also show that copula, frailty, and Marshall–Olkin specifications can lead to different reliability assessments because they encode different stochastic mechanisms. The estimation workflow includes multi-start optimization and diagnostics for boundary solutions, Hessian stability, and irregular likelihood behavior. Full article

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring. Full article

DC microgrids are important platforms for renewable energy integration, energy storage interaction, and bidirectional power exchange. In these systems, multi-phase interleaved parallel DC-DC converters are widely used as key energy-router interfaces, but open-circuit faults in power devices may lead to current imbalance, waveform distortion, ripple redistribution, and system instability. To improve fault warning and diagnosis under variable operating conditions, this paper proposes a time–frequency dual-branch efficient fully convolutional network (EPFCN). The proposed model takes synchronized multi-channel voltage/current signals and their FFT-domain representations as complementary inputs. The time-domain branch extracts transient waveform features, while the FFT-domain branch captures spectral variation and harmonic-related information. An efficient channel attention (ECA) module is introduced to enhance fault-sensitive channel responses while maintaining a lightweight structure. An RT-LAB hardware-in-the-loop platform is established to construct a multi-condition diagnostic dataset covering one normal state and nine fault states. Experimental results show that the proposed EPFCN achieves high diagnostic accuracy, strong noise robustness, clear feature separability, and feasible edge-side inference performance. The proposed method provides an effective data-driven solution for online fault warning and diagnosis of multi-phase interleaved converters in DC microgrids. Full article

Objective: This study aimed to evaluate the risk of medical device-related pressure injury (MDRPI) development among patients exposed to medical devices and to assess the clinical utility of the Medical Device-Related Pressure Injury Risk Assessment Scale (MDRPIS). Methods: This clinical assessment study included 132 patients receiving care in intensive care, palliative care, and home-care units. The MDRPIS total score ranges from 8 to 27, with scores of 8–12 indicating high risk, 13–21 indicating moderate risk, and 22–27 indicating low risk. The scale was used to assess MDRPI risk associated with life-sustaining medical devices. Its psychometric performance was evaluated through analyses of internal consistency, criterion validity against the Braden Scale, and diagnostic accuracy using receiver operating characteristic (ROC) analysis. Results: The MDRPIS demonstrated strong discriminative ability for identifying patients at risk of MDRPI, with an area under the curve (AUC) of 0.822. A cut-off score of ≤16 was identified as the optimal threshold for detecting high-risk patients. Patients with MDRPIS scores of 16 or lower had a significantly higher incidence of MDRPI than those classified as low risk (p < 0.001). Respiratory support devices, particularly non-invasive ventilation (NIV)/continuous positive airway pressure (CPAP) masks and tracheostomy flanges or securement devices, were identified as the most significant risk factors for injury development. The highest incidence of MDRPI was observed among patients in intensive care units, followed by those in palliative care and home-care settings, indicating a statistically significant concentration of device-related risk in high-acuity care environments (p < 0.05). Conclusions: Clinical settings, particularly intensive care and palliative care units, should incorporate the MDRPIS into routine risk assessment protocols to facilitate targeted preventive interventions, including prophylactic dressings and advanced fixation techniques for patients using high-risk devices such as NIV masks and tracheostomy securement systems. The systematic implementation of the MDRPIS may support more effective allocation of nursing resources and enhance patient safety by enabling the early identification and prevention of avoidable device-related pressure injuries. Furthermore, the findings indicate that an MDRPIS score of 16 or below represents a clinically meaningful threshold for initiating preventive interventions. Full article

Total RNA was isolated from sections of healthy testes and Leydig cell tumors of mixed-breed dogs using TMA Master II device. The RNA-seq libraries were sequenced on the Illumina platform. Following differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were applied with quality control obtained using FastQC and Trimmomatic. This analysis revealed 1500 transcripts, including 982 upregulated and 168 downregulated genes. The results demonstrated that a significant proportion of these differentially expressed genes are directly involved in the control of sex steroid production (CYP11A1, STAR, and 3β-HSD3B1) or tube formation, angiogenesis, and extracellular matrix remodeling in interstitial cells (ESM1, FGG, and VEGFA). Moreover, we identified the upregulation of transcripts responsible for neurotransmitter or neuroendocrine signaling (SLC6A4, GRIN2C, GABRB3) and cholesterol metabolism and its regulation (GPX3, MSMO1, DHCR24). These genes were strongly associated with the phosphatidylinositol-3-kinase (PI3K)-Protein Kinase B (Akt) cascade and extracellular matrix interactions, features shared with various malignancies. Alterations in estrogen and relaxin signaling appear to be distinctive, understudied mechanisms specific to canine Leydig cell tumors. Concurrently, downregulated genes (e.g., DMRTC2, SEMA3C, ALOX12) were linked with cell differentiation, signaling and immunoregulatory pathway suppression involved in tumorigenesis. A complex transcriptomic profile of canine Leydig cell tumors was developed, revealing a conserved oncogenic core shared in some aspects with human malignancies alongside unique species-specific alterations. Findings seem to be useful for identifying novel diagnostic biomarkers and targeted therapies in veterinary oncology, establishing canine reproductive tissues as a valuable comparative biomedical model for research in human. Full article

Traditional wood anatomy provides the gold standard for timber identification, yet its reliance on centralized laboratory infrastructure severely limits its efficacy during real-time field inspections. This study addresses a critical research question: How can physical xylotheque resources, national timber extraction registries, and edge-computing computer vision be integrated into a cohesive framework to enable robust, forensic-level wood identification at field control stations? To resolve this, we implemented a three-tier methodology: first, we audited historical records from Ecuador’s Forest Administration System (SAF) encompassing 129 commercial timber species; second, we conducted a gap analysis using the Wood Anatomy Laboratory and Xylotheque (LAMX) repository (510 cataloged samples, 2267 histological preparations) to secure botanically validated references; and third, we leveraged a curated database of high-resolution digital cross-section captures (4900 images) to evaluate CNN architectures via k-fold cross-validation and a standard 70/15/15% training/validation/testing split. Benchmarking demonstrated that the lightweight MobileNetV2 architecture achieved a global accuracy of 94.04% and an F1-score of 0.976. External field validation conducted across commercial timber yards in Ibarra confirmed an offline inference latency of just 145 ms on mid-range Android devices, proving the framework’s operational transparency and low-cost scalability. Furthermore, Explainable AI analysis using Class Activation Maps (Grad-CAM) provided visual evidence indicating that the neural network targeted diagnostic xylotomic features (vessel distribution and axial parenchyma), minimizing reliance on external environmental noise. In conclusion, this study demonstrates that hybridizing physical taxonomic reference collections with targeted edge AI models provides a scalable, transparent, and low-cost solution that successfully bridges academic research and active forest law enforcement in tropical regions. Full article

The modernization of traditional Chinese medicine (TCM) is significantly impeded by the elusive material basis of its meridian system and by a lack of objective, quantitative diagnostic standards. Recent breakthroughs in photonic technologies and optoelectronic chips offer transformative paradigms to address these systemic bottlenecks. This review systematically evaluates the complete academic and engineering chain of “Photonic TCM,” spanning fundamental mechanisms, optical diagnostics, advanced therapeutics, and core chip-level technologies. Specifically, we analyze how ultra-weak photon emission (UPE), two-photon microscopy, and infrared thermography can objectify meridian dynamics and acupuncture pathways. For clinical translation, laser acupuncture has emerged as a robust, non-invasive modality for managing disorders such as chronic pain and insomnia, supported by cumulative evidence-based data. At the device level, vertical-cavity surface-emitting laser (VCSEL)-based photonic computing chips enable ultrafast herbal medicine recognition, while flexible optoelectronics and lab-on-a-chip systems lay the technical groundwork for wearable neuromodulation. Crucially, this review concludes that the Photonic TCM paradigm is transitioning from isolated clinical validation to integrated engineering implementation. We identify biological tissue scattering and parameter heterogeneities as the primary bottlenecks. To navigate these challenges, we propose that the field’s future should converge toward edge-computing-driven wearable closed-loop systems and multi-dimensional optical big data ecosystems. Ultimately, these technological trajectories will steer TCM from an empirical discipline toward a data-driven, precise, and standardized medical science. Full article

Nanozymes, nanomaterials that mimic enzymatic activity, offer superior stability, tunability, and lower production costs compared to natural enzymes. To date, most nanozyme-based point-of-care (PoC) diagnostic systems have relied on oxidation reactions, such as oxidation of 3,3′,5,5′-tetramethylbenzidine, which often suffer from limited substrate stability and high background signal. This study investigates reduction reactions, particularly those involving tetrazolium salts, as an alternative route for signal generation in PoC devices. For this purpose, monometallic and bimetallic gold, palladium, and platinum nanoparticles were synthesized via chemical reduction using poly(vinyl alcohol) as a stabilizing agent. The resulting nanoparticles were uniform in size and morphology. Their catalytic performance was confirmed through the reduction of 4-nitrophenol. The tetrazole salts were selected as promising substrates for application in PoC settings and further explored by examining the nanozyme-based reduction of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT). The nanozymes catalyzed the reduction of MTT in the presence of sodium borohydride, producing a distinct colorimetric signal under selected conditions. The effects of reducing agent concentration, buffer pH, and potential interferents were evaluated, with performance suitable for PoC devices achieved at basic pH and low borohydride concentration. Interference studies showed negligible MTT reduction in the presence of physiological levels of ascorbic acid, human serum albumin, and 10% concentration of human serum. Finally, a proof-of-concept lateral flow assay demonstrated successful signal generation through nanozyme-catalyzed MTT reduction. Results establish tetrazolium salts as suitable substrates for nanozyme-enhanced PoC diagnostics and highlight reduction-based chromogenic systems as a viable alternative to traditional oxidation-based assays. Full article

False base station (FBS) attacks exploit the attach window before the network authenticates to the device. Existing User Equipment (UE)-side detectors typically need either labeled attack data, which is scarce and does not generalize to unseen attacks, or models too heavy for the resource budget of a smartphone or embedded endpoint. This study presents a lightweight UE-side detector built on a first-order Markov chain over a four-tuple state of packet type, direction, message identifier, and access-network type. A single counting pass fits the 119 KB chain, and thresholds are derived from normal traffic, so no attack labels are consulted. The capture path requires root and Qualcomm modem diagnostic access. Attacks surface as low-probability transitions, rare field values, and anomalous pacing, fused into a per session verdict with per-message attribution. On 192 commercial, testbed, and public LTE and 5G captures, the detector flags 51 of 53 attacks at an F1 of 88.70% in leakage-free leave-one-session-out evaluation and 96.23% once calibration covers the scored sessions. In five-fold cross-validation its F1 of 86.21% trails the strongest supervised baselines by margins that are not statistically significant, and it records the lowest latency (0.46 ms) and smallest working set (8.8 MB) among the eleven detectors benchmarked. Full article

Biosensors are rapidly emerging as a pivotal technology with far-reaching implications in fields such as medical diagnostics, environmental analysis and pharmaceutical research. Among the various biosensing platforms, Graphene Field-Effect Transistor (GFET) biosensors have attracted considerable interest due to their exceptional sensitivity, potential for cost-efficient fabrication, and compatibility with scalable manufacturing processes. This work computationally addresses sensing mechanisms and design strategies associated with GFET-based biosensors, with a focus on the influence of electrolyte gating on device performance, tackling the role of graphene’s quantum capacitance and testing the electrical detection of β2-microglobulin as a case study. Molecular dynamics is used to rationalize the details of the physisorption of a single biomolecule onto the graphene surface, while finite element method simulations are employed to evaluate device sensitivity and figure of merit. Results reveal that incorporating quantum capacitance into the model leads to a Sensitivity-over-FWHM_min figure of merit exceeding 100 L/g being achievable for a β2-microglobulin concentration of 0.001 g/L. These computational outcomes highlight the relevance of quantum-electrostatic effects in GFET biosensor performance and suggest potential routes towards the optimization of graphene-based electronic biodetector engineering. Full article

While skeletal imagery appears across various ancient Andean traditions, the Wari Empire (c. 600–1000 CE) developed a uniquely standardized and widespread skull motif—the uma tullu—distributed throughout its former territory. Through an analysis of 63 artifacts spanning ceramic, textile, and metal, this study identifies key diagnostic markers of the motif: the representation of the metopic suture and the application of red pigment. By cross-referencing these stylistic features with bioarchaeological data, the research posits that the uma tullu served as a central communicative device. In the absence of a formal script, this motif may have encoded imperial values and ancestral cult practices, facilitating ideological expansion and state identity. Ultimately, this work demonstrates how standardized iconography functioned as a system of graphic communication and ideological cohesion in the Middle Horizon Andes. Full article

Transbronchial lung cryobiopsy (TBLC) is increasingly used as a minimally invasive approach for tissue acquisition in the evaluation of interstitial lung disease (ILD), serving as an alternative to surgical lung biopsy (SLB) within multidisciplinary diagnostic pathways. Despite its growing adoption, variability in diagnostic yield and complication rates highlight the importance of procedural technique, probe selection, and freezing parameters. This narrative review summarizes the current landscape of TBLC, with emphasis on factors that influence diagnostic performance and safety, including procedural considerations involving endobronchial balloon blockade (EBB), radial probe endobronchial ultrasound (RP-EBUS), and cone-beam computed tomography (CBCT) for biopsy localization and airway management. Much of the existing experience is based on conventional cryoprobes, including 2.4 mm and 1.9 mm devices, typically used with freezing times of several seconds. While these approaches have defined the current role of TBLC in ILD, outcomes remain variable across centers, prompting continued refinement of procedural strategies to improve consistency. More recently, attention has expanded to include a broader range of smaller cryoprobe sizes—1.7 mm and 1.1 mm. Overall, this review provides a framework for understanding contemporary TBLC practice and highlights key areas where further study is needed to better define optimal technique and improve consistency in clinical outcomes. Full article