Search Results (914)

This paper presents an experimental investigation into the flow characteristics and volumetric efficiency (η_vol) of a fixed-displacement external gear pump (GHD 17R) operating under coupled hydraulic parameters using an eco-friendly synthetic ester-based hydraulic fluid (48 mm²·s⁻¹ at 40 °C). Measurements were performed on a laboratory single-circuit hydraulic test rig across a rotational speed range of 500–2500 min⁻¹, operating pressure range of 2–10 MPa, and fluid temperature range of 30–60 °C. To eliminate flow fluctuations caused by structural vibrations at 1250 and 1750 min⁻¹, a 15% trimmed mean statistical filter was successfully implemented. A comparative sensitivity analysis—evaluating absolute, normalized, and relative significance—was developed and compared against a three-way analysis of variance (ANOVA) effect size model (η² and partial η²). The relative sensitivity approach identified rotational speed as the dominant parameter for direct hydraulic flow, accounting for 95.80% of total variation. Conversely, when evaluating volumetric efficiency, the proportional impact of speed was removed, revealing a balanced distribution of internal losses: rotational speed contributed 54.73%, fluid temperature 26.08%, and pressure 19.19%. The three-way ANOVA confirmed that all primary parameters and their cross-interactions had a statistically significant effect (p < 0.05). The findings scientifically demonstrate that temperature-induced viscosity collapse exhibits a stronger relative dynamic sensitivity on volumetric losses than pressure fluctuations within standard operating envelopes. The constructed multi-dimensional flow and efficiency maps provide practical input for advanced diagnostic tools, real-time thermal condition monitoring, predictive maintenance, and energy-optimized control schemes in modern fluid power systems using eco-friendly lubricants. Full article

Sustainable and reliable operation of water distribution systems requires timely detection of bursts and effective control of real water losses. The digitalisation of water utilities and the increasing deployment of smart monitoring infrastructures are enabling continuous monitoring and diagnostic support, but the development of operational anomaly detection tools remains constrained by scarce labelled events and by the lack of structured workflows for designing, testing and comparing alternative solutions. This study proposes a methodology for developing and benchmarking burst detection tools in water distribution systems. The framework integrates stochastic-hydraulic synthetic data generation to create or enrich labelled datasets, feature engineering to extract temporal and spatial descriptors from hydraulic signals, baseline modelling of normal system behaviour, residual generation, anomaly identification, and systematic performance evaluation. The methodology is applied to a real water distribution network partitioned into nine district metered areas, enabling a consistent comparison of alternative strategies for normal-behaviour modelling and anomaly detection. Results show that the forecasting-based approach, including multi-horizon prediction, produces more informative residuals than the reconstruction-based approach, while XGBoost provides the best overall trade-off between sensitivity and false alarms. These findings demonstrate the value of the proposed methodology for the data-driven development of operational burst detection tools in smart water distribution systems. Full article

The Functional Lumen Imaging Probe (EndoFLIP^®) system is an impedance-based technology providing real-time measurements of cross-sectional area (CSA) and intraballoon pressure across a luminal segment. Initially developed for esophagogastric junction (EGJ) evaluation in achalasia, the application of EndoFLIP has subsequently expanded to the assessment of both sphincteric and non-sphincteric regions throughout the gastrointestinal tract in a variety of clinical conditions, including eosinophilic esophagitis (EoE), post-fundoplication states, and gastroparesis. Its unique ability to be utilized in both the diagnostic and therapeutic settings, including the assessment of treatment response, makes it a valuable tool in the overall management of gastrointestinal motility disorders. The present review extends beyond a simple overview of EndoFLIP applications by providing a comprehensive evaluation of its performance in comparison with other diagnostic modalities, as well as the current knowledge gaps regarding its use. Study limitations and procedure-related aspects requiring further investigation are critically discussed, with the aim of supporting and guiding future research in this field. Full article

Contrast-enhanced low-mechanical-index endoscopic ultrasound (CELMI-EUS) extends the diagnostic capabilities of endoscopic ultrasound by enabling real-time assessment of hepatic microvascularization and perfusion using intravenous ultrasound contrast agents. Owing to its close proximity to the liver and high spatial resolution, CELMI-EUS is particularly suited for the detection and characterization of small or otherwise inconspicuous focal liver lesions. This review summarizes the technical principles of CELMI-EUS, contrast agent usage, vascular phase interpretation, and standardized assessment of benign and malignant focal liver lesions, including the potential and provisional application of CEUS Liver Imaging Reporting and Data System (LI-RADS) terminology in patients at risk for hepatocellular carcinoma, while acknowledging that dedicated validation for CELMI-EUS is lacking. Typical enhancement patterns of common benign lesions such as hemangioma, focal nodular hyperplasia, hepatocellular adenoma, biliary hamartomas, and abscesses are discussed, as well as malignant entities including metastases, hepatocellular carcinoma, and cholangiocarcinoma. In addition, the role of CELMI-EUS in lesion detection, interventional guidance, and its limitations and pitfalls are addressed. Integrated into a multimodality imaging approach alongside transcutaneous CEUS and cross-sectional imaging, CELMI-EUS represents a valuable complementary tool that improves diagnostic confidence and supports optimized management of patients with focal liver lesions. Full article

Background/Objectives: Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are highly prevalent among African men. While single-nucleotide polymorphisms (SNPs) in KLK3, HNF1B, and JAZF1 are established risk markers in Western cohorts, their specific allele frequencies and diagnostic utility remain poorly characterized in African populations. This study evaluated the diagnostic accuracy, sensitivity, and cost-effectiveness of High-Resolution Melting (HRM) PCR for germline SNP genotyping in a cohort of Nigerian men. Methods: Peripheral blood buffy coat DNA was extracted from 31 male participants attending the urology clinic at Lagos University Teaching Hospital, classified into PCa (n = 13), BPH (n = 12), and healthy controls (n = 6). Real-time PCR and HRM analysis were optimized to genotype three specific risk variants: rs2735839 (KLK3), rs4430796 (HNF1B), and rs10486567 (JAZF1). Allelic discrimination was validated using sequence-verified, synthetic positive controls for homozygous wild-type and mutant variants. HRM analysis generated reproducible amplification and melting profiles that enabled clear discrimination of wild-type, heterozygous, and variant genotypes for all three loci. Results: The risk-associated alleles of KLK3 (G), HNF1B (C), and JAZF1 (T) occurred more frequently among patients with prostate cancer than among those with benign prostatic hyperplasia. Clinically, prostate cancer patients also presented with markedly higher PSA concentrations, older age at diagnosis, and higher Gleason scores, consistent with more aggressive disease. The optimized HRM workflow required less than two hours from amplification to genotype assignment and eliminated the need for post-PCR processing, making it suitable for routine molecular testing in resource-constrained laboratories. Conclusions: HRM-based genotyping of KLK3 (rs2735839), HNF1B (rs4430796), and JAZF1 (rs10486567) provides a highly reproducible, rapid, and low-cost molecular tool for mapping prostate disease risk variants. Because it requires no post-PCR processing, this closed-tube HRM workflow is ideally suited for resource-limited clinical settings to enhance early risk stratification and differentiate malignant PCa from benign BPH. Full article

Plasmonic biosensing technologies have emerged as powerful analytical tools for sensitive and label-free characterisation of biomolecular interactions and complex samples. The increasing demand for comprehensive molecular profiling has accelerated the development of multiplexing strategies that enable simultaneous analysis of multiple analytes and molecular interactions. This Feature Paper examines multiplexing through the complementary spatial, spectral, and temporal dimensions of multiplexing, together with their hybrid combinations and associated analytical trade-offs. Compared with other optical biosensing approaches, including interferometric, photonic, and fluorescence-based sensing platforms, plasmonic biosensors remain attractive owing to their combination of label-free detection, real-time interaction monitoring, sensitive interfacial analysis, and compatibility with multiplexed assay formats. This Feature Paper critically discusses current multiplexing strategies, focusing primarily on surface plasmon resonance (SPR), imaging SPR (SPRi), localised SPR (LSPR), surface-enhanced Raman scattering (SERS), and related nanoplasmonic biosensing approaches, together with recent advances in surface biofunctionalisation, antifouling interfaces, and molecular recognition strategies. Representative applications in biomedical diagnostics and non-clinical settings are highlighted, with examples such as liquid biopsy, glycoprofiling, extracellular vesicle profiling, and food and environmental analysis, alongside key challenges in reproducibility, standardisation, data interpretation, and clinical translation. In addition, selected non-plasmonic optical biosensing technologies are briefly discussed to position plasmonic biosensing within the broader landscape of multiplexed optical biosensing. This Feature Paper argues that the future of multiplexed plasmonic biosensing will depend less on further improvements in sensor performance than on robust, standardised analytical systems. Full article

Unilateral spatial neglect (USN) is a neuropsychological syndrome characterized by a reduced awareness of, and attention toward, stimuli located in the contralesional space. The left side of the space is most frequently affected, due to the higher incidence of the disorder following right-hemisphere lesions, particularly within the territory of the right middle cerebral artery and, less commonly, the right anterior cerebral artery. Although USN can also occur following left-hemisphere damage, it is typically less severe and tends to resolve more quickly. USN is a common consequence of acquired brain injury, especially of vascular origin, and significantly impairs a patient’s functional abilities and quality of life. Its high incidence in the acute phase after stroke, together with its potential persistence into the chronic stage, makes early diagnosis and timely rehabilitation essential. In this narrative review, we provide an overview of current assessment tools and rehabilitation approaches for post-stroke USN, with a focus also on recent advances and emerging therapeutic strategies. To illustrate diagnostic and therapeutic decision making, a representative case of a patient admitted to a Stroke Unit is reported, illustrating the application of assessment methods in a real-world setting. The aim of this review is to offer clinicians practical tools and evidence-based strategies to support a more effective and individualized management of patients affected by this complex and disabling condition. Full article

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R² Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance. Full article

Ultrasound is a fundamental diagnostic imaging tool in veterinary medicine that allows for the noninvasive, real-time evaluation of abdominal organs. However, reference ultrasound data for neotropical marsupials is scarce, which hinders diagnostic interpretation of ultrasound images of these species. The present study aimed to describe the morphological and biometric ultrasound characteristics of the abdominal organs of white-eared opossums (Didelphis albiventris) treated at a wildlife rehabilitation center in southern Brazil. Sixteen clinically stable opossums, with an average body mass of 1.4 kg, underwent abdominal ultrasonography using a multifrequency linear transducer (7–12 MHz). We analyzed parameters such as shape, contour, echogenicity, echotexture, and anatomical relationships between adjacent structures, in addition to taking biometric measurements of different abdominal organs. Most structures presented characteristics similar to those described in small domestic dogs and cats. However, species-specific particularities were observed, including decreased renal corticomedullary distinction, slightly coarser renal and prostatic echotexture, and a smaller abdominal aorta diameter. It is concluded that abdominal ultrasonography is an effective method for the anatomical evaluation of white-eared opossums. Full article

Invasive fungal infections, including candidemia and invasive aspergillosis, remain significant causes of morbidity and mortality in at-risk patient populations. Rapid, accurate diagnosis is critical for timely antifungal therapy and improved clinical outcomes. We evaluated two commercial molecular assays, the CandID Multiplex PCR and AspID Real-Time PCR, for detection of clinically relevant Candida and Aspergillus species, respectively, in at-risk patients in Paraguay. The CandID assay demonstrated strong agreement with blood culture in detecting candidemia. The AspID assay showed high concordance with galactomannan testing, particularly in bronchoalveolar lavage specimens, supporting its role as a complementary diagnostic tool. Our findings underscore the value of integrating molecular diagnostics into routine fungal diagnostic workflows to improve early detection of invasive fungal infections. Full article

Abies koreana Wilson (Korean fir) and Abies nephrolepis (Trautv. Ex Maxim.) Maxim. (Khingan fir) are ecologically and economically significant coniferous species in East Asia. However, morphological similarities and hybridization complicate species identification, affecting conservation, forestry management, and commercial activities. Here, we developed a species-specific DNA marker using single nucleotide polymorphisms (SNPs) within the mitochondrial nad5 intron 1 region to discriminate between A. koreana and A. nephrolepis. In particular, PCR amplification and sequencing analyses of candidate drought and heat stress-responsive genes as well as mitochondrial nad5 intron 1 and nad5 intron 4 revealed a species-specific T-to-G substitution in nad5 intron 1. Allele-specific primers were designed, and competitive allele-specific labeled light emission technology was employed for SNP detection. The primers were validated using real-time PCR, achieving high specificity and reliability. Overall, the molecular diagnostic tool offers a practical solution for accurate species identification, aiding conservation efforts and sustainable resource management. Full article

Axillary lymph node management in early-stage breast cancer has undergone a fundamental transformation over the past three decades, shifting from routine radical clearance to precision-guided de-escalation. Ultrasound (US) has emerged as the central imaging modality in this paradigm shift, serving as the first-line tool for preoperative nodal assessment, guidance for biopsy and clip placement, monitoring of response to neoadjuvant chemotherapy (NAC), and localization of marked nodes for targeted axillary dissection (TAD). This review systematically examines the evolution of axillary management in early-stage breast cancer through a US-centric lens. We summarize the historical transition from Halstedian axillary lymph node dissection (ALND) to sentinel lymph node biopsy (SLNB), critically appraise the landmark trials—Z0011, AMAROS, OTOASOR, and SOUND—that have progressively de-escalated axillary surgery, and detail the role of US in preoperative staging (including conventional B-mode, superb microvascular imaging, contrast-enhanced US, elastography, and AI-assisted diagnostics), US-guided biopsy and node marking, and post-NAC TAD. We further explore the integration of US with liquid biopsy, multigene profiling, and molecular subtype-guided systemic therapy to enable individualized decision-making. Finally, we propose a stepwise US-centric clinical algorithm and discuss future directions, including AI-powered real-time interpretation, imaging-omics, and US-guided targeted therapy. This review provides a comprehensive framework for incorporating ultrasound as a key component of multidisciplinary decision-making—alongside pathological, surgical, radiotherapeutic, and molecular inputs—in contemporary axillary management. Full article

In recent years, nanopore technology has enhanced analyte detection, enabled higher resolution and achieved single-molecule sensing capability. Aptamer-conjugated nanopore sensing technology combines the high specificity of aptamers with the single-molecule resolution of nanopores. By anchoring aptamers to biological, solid-state or hybrid nanopores, target binding events produce distinct electrical signatures that allow sensitive and label-free detection. This approach enables real-time monitoring of small molecules, proteins, and even pathogens, with promising applications in diagnostics, drug screening, environmental monitoring, etc. Hybrid biological/solid state devices produce robust signals and are suitable for PoC applications. The aptamers “magic bullets” have also been exploited to develop single-molecule antigen detection using nanopores, which offers a promising alternative for accurate virus testing to contain their transmission. Chemical conjugation of aptamers to nanopore interfaces improves selectivity for peptides/amino acids and expands robustness for practical samples. Aptamer-based nanopipettes offer high analytical precision by enabling label-free, real-time detection of target molecules in ultra-small sample volumes. This review maps aptamer–nanopore integration across biological, solid-state, and hybrid platforms. It also explores various types of aptamers integrated into nanopore platforms that cater to precise, single-molecule recognition, paving the way for highly sensitive, portable diagnostics and next-generation therapeutic monitoring tools. Full article

Evaluating wearable assistive robots in real-world caregiving is challenging because temporally aligned and repeatable A/B comparisons are rarely available. Conventional evaluations assume that assist-on and assist-off trials are comparable in task content, posture, and movement timing. However, caregivers adjust their posture and timing across human–human interactions and task sequences. This study proposes a causally inspired diagnostic framework based on DBN/SCM-inspired time-series modeling and movement-fixed counterfactual estimation. We represented the multimodal observations using intervention, robot state, movement context, EMG, and context variables. Node-specific relationships were approximated using Attention-based Sparse Variational Gaussian Process regressors. We evaluated the framework at three levels of environmental complexity. These levels comprised controlled trunk flexion, partially controlled bed-to-wheelchair transfer, and real-world caregiving. The proposed framework is intended as a diagnostic counterfactual evaluation tool rather than as a method for strict causal identification. Across experiments, one-step EMG prediction accuracy alone was insufficient to identify intervention-sensitive models. In controlled validation, the selected movement-decoupled robot-only model reproduced an EMG-reducing response consistent with the controlled A/B reference. When fitted to the partially controlled transfer data, the selected structural specification identified an EMG-increasing response in supported contexts. In the real-world caregiving case study, the global assist-mediated response (AMR) was near zero despite a positive pooled A/B difference. However, the stratified analysis identified localized supported responses. The near-zero AMR indicates that the pooled difference was not reproduced through the modeled assist intervention–robot state–EMG pathway under fixed movement context. This result should not be interpreted as evidence of overall device ineffectiveness. These findings suggest that context-fixed counterfactual diagnosis can help interpret assistive responses under increasing environmental complexity. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article