Ultrasound imaging has emerged as a pivotal diagnostic modality in contemporary medical practice, offering a noninvasive, cost-effective, and radiation-free alternative to advanced radiological examinations such as computed tomography and magnetic resonance imaging. This descriptive research paper examines the expanding role of ultrasound technology in clinical decision-making and its potential to reduce the dependence on more complex and expensive imaging modalities. Drawing upon a comprehensive review of recent literature, the study investigates the clinical contexts in which ultrasound serves as a primary diagnostic tool, the technological advancements that have enhanced its diagnostic accuracy, and the implications of its expanded use for patient safety, healthcare economics, and workflow efficiency. The findings indicate that point-of-care ultrasound, in particular, has demonstrated significant promise in emergency medicine, obstetrics, musculoskeletal assessment, and abdominal evaluation, frequently providing sufficient diagnostic information to preclude the need for further imaging. The paper concludes that strategic integration of ultrasound into clinical protocols can substantially decrease unnecessary referrals for advanced imaging, thereby optimizing resource allocation and minimizing patient exposure to ionizing radiation.

According to the World Health Organization (WHO), arthritis is the biggest cause of disability worldwide. It is also one of the most common inflammatory conditions affecting the musculoskeletal system. Synovial fluid is often present in the joint area and serves as a lubricant, which is an essential function. The fluctuation in density of synovial fluid may be used to detect arthritis. This might be done to assess the severity of the disease. This work provides a non-invasive approach for measuring changes in synovial fluid density based on the electrical bio-impedance idea. The purpose of this study approach is to measure change. The current research studied the parameters estimated by bioelectric impedance spectroscopy (BIS) in persons with healthy knees and knees with osteoarthritis (OA). A comparison was done between the two groups of people. Because tissue may be represented as both resistance and capacitive reactance, bio-impedance values are calculated using the voltage drop in relation to the joint tissue area. When the frequency and voltage are held constant, the resistance and reactance values change from a healthy individual to a patient with arthritis. This happens even when everything else stays the same. DOI : https://doi.org/10.52783/pst.3128

The study analyses smart data quality management models of the AI-based financial decision-making frameworks, overcoming the essential drawbacks of the traditional, manual ones. By considering secondary mixed process, the investigation also summarises the current literature, empirical evidence, and actual case studies to assess the use of AI-based data quality control, data governance, and scalability structures to improve the quality of decisions. The key trends that have been discovered include time-consuming, unstable, and imprecise traditional practice, but AI-moderated automation increases the detection of anomalies by up to 90%, compliance, and real-time financial decision-making. JP Morgan and Capital One Case Evidence Case studies in both financial institutions affirm that the current deep data quality paradigms, managed lifecycle, and embedded in data, are necessary to support trustworthy and sustainable AI-based financial decision-making.

This study establishes a high-fidelity experimental benchmark for the canonical problem of stress concentration at a circular hole in a finite-width aluminum plate. By applying a formal Photoelastic Experimental Hybrid Method (PEHM) framework, we integrate quantitative reflection photoelasticity, comprehensive uncertainty quantification, and direct finite element analysis (FEA) validation. A bonded PhotoStress® coating and reflection polariscope were used to measure full-field strains under incremental uniaxial tension (5-25 kN). The mean experimental stress concentration factor (SCF) was determined to be K_t = 2.50, with an expanded uncertainty (95% confidence) of ±0.05. A complementary 3D FEA model predicted a SCF of 2.65, showing a 6% deviation which is critically analyzed in the context of experimental limitations and model assumptions. This work successfully bridges established experimental techniques and modern computational validation needs, demonstrating that rigorous photoelastic methodology can generate reliable quantitative data essential for calibrating models of more complex structural details.

Background: The increasing prevalence of chronic respiratory conditions has led to a greater reliance on home respiratory devices such as Continuous Positive Airway Pressure machines and oxygen concentrators. These devices are essential in managing conditions like obstructive sleep apnea and chronic obstructive pulmonary disease. Understanding patient experiences with these devices is vital for improving adherence and overall satisfaction. Non-invasive ventilation is widely recognized as the gold standard for treating OSA. It functions by delivering a continuous flow of air through a mask, preventing airway collapse during sleep. While NIV can significantly enhance sleep quality and reduce daytime drowsiness, adherence remains a significant challenge. It has been reported that nearly 30% of patients discontinue NIV therapy within the first year, primarily due to discomfort, nasal congestion, and feelings of claustrophobia. Such discontinuation not only affects the individual’s health but can also lead to long-term complications associated with untreated sleep apnea, including cardiovascular disorders and impaired cognitive function. While NIV machines and oxygen concentrators are vital in managing chronic respiratory conditions, patient experiences vary widely based on numerous factors, including device comfort and cultural perceptions. This research will address the factors for patient experience and satisfaction with the use of non-invasive home respiratory therapy devices. Methodology: A quantitative, cross sectional study design using online survey technique distributed among participants as data collection methods in a tertiary hospital. We will utilize two validated questionnaires in one form: the standard method for analyzing System usability scale (SUS) and the S3-NIV questionnaire. Usability will be assessed using the SUS, developed by Brooke in 1986, which has demonstrated reliability and validity in numerous studies. The S3-NIV questionnaire will also be used to assess patient experience. Results: Total of 197 participants, with a mean age of 62.0 years (SD = 10.5) and a balanced gender distribution (55% male, 45% female). Participants reported an average device usage duration of 2.5 years (SD = 1.2). The System Usability Scale revealed a high overall mean score of 78.5 (SD = 12.3), with approximately 75% of participants scoring above 70, indicating strong perceived usability and ease of operation. Patient satisfaction, as measured by the S3-NIV questionnaire, was notably high, with 85% of participants reporting being "satisfied" or "very satisfied." While 70% found device setup easy, and 80% found daily operation easy, maintenance presented a more varied challenge. The devices had a generally positive impact on daily life for 60% of participants, though 25% reported a neutral impact and 15% a negative one. A weak but statistically significant positive correlation ($r = 0.45, p < 0.05$) was found between perceived usability and overall patient satisfaction. Importantly, no statistically significant correlations were identified between demographic factors (age, gender, duration of use) and either usability or satisfaction scores, suggesting a robust user experience across diverse patient profiles. Conclusion: This study investigated patient experiences and satisfaction with non-invasive home respiratory therapy devices. It found high levels of perceived usability and overall satisfaction, with participants generally finding the devices easy to set up and operate. A statistically significant positive correlation between usability and satisfaction highlighted the importance of user-centered design for positive patient outcomes and enhanced quality of life. Despite these positive perceptions, device maintenance was identified as an area needing improvement, suggesting a need for simplified designs and enhanced patient education from manufacturers and healthcare providers. The varied impact of devices on daily life also indicates the importance of a holistic approach to patient care, beyond just device functionality. DOI : https://doi.org/10.52783/pst.2541

The dynamics of mass transport and convective heat in a magneto hydrodynamic nanofluid passing through a permeable medium on top of an extended surface while being subjected to a magnetic field are investigated in this publication. Chemical processes, thermal radiation, viscosity dissipation, and heat creation are all included in the extensive analytical model. We discuss in detail some of the governing properties, such as heat generation, thermal radiation, magnetic fields, chemical processes, porosity, and viscous dissipation. Additionally, the volumetric distribution of nanoparticles at the boundary interface is proposed to be controllable. This investigation focuses on two distinct types of nanofluids: Cu-H2O and Al2O3-H2O. The problem is theoretically framed in terms of a system of nonlinear differential equations. These are then solved numerically by combining the shooting method with the fourth-order Runge-Kutta methodology. Our results show good agreement with those that have been previously published in the academic literature. DOI : https://doi.org/10.52783/pst.2766

Background: Herbal products, botanical products, or phytomedicines are all terms that refer to plant-based products that are utilized for the management of diseases or preserve health. Heart disease is a generic word that refers to a variety of chronic & acute medical problems that affect one or more components of the heart. Aims of study: This research aimed to determine the effect of various concentrations of Onosma tinctoria plant on blood lipids and atherosclerosis index in laboratory mice. Materials and Methods: Twenty-Four Sprague-Dawley albino mice, aged ten weeks, with a weight of 150 ±10, were split into four groups: the 1st was a control negative nourished on a basal diet alone, while the other three groups nourished on basal diet plus different levels of Onosma tinctoria plant (5%,10%, 15%) for 28 days. The experiment concluded with a blood sample & biochemical examination. Results: F.S.VLDL-c, and Atherogenic index recorded the greatest value for 5 % Onosma group, whereas the lowest value in the 15%Onosma group after 4 weeks. But F.S.LDL-c , F.S. Cholesterol , S.GPT and S.GOT recorded the highest value in 5% group, but the lowest value was recorded 15% Onosma group after 4 weeks. Conclusion: Onosma tinctoria may enhance vascular health and diminish the risk of cardiovascular disease by improving the lipid profile & elevating blood concentrations of antioxidants. DOI : https://doi.org/10.52783/pst.2361

The complexity of power systems has accelerated and this has emphasized the use of sophisticated maintenance strategies that would help in enhancing reliability in power systems, minimize down-time as well as reduce operational costs. Conventional corrective and preventive maintenance strategies have been found inadequate in managing the dynamism of large power grids that are increasingly incorporating smart grids, renewable energy sources and smart-incorporated devices. Predictive maintenance has become an even better replacement since it allows one to predict failing equipment to handle before they fail and conduct it proactively. The analysis of Big Data is fundamentally relevant in meeting this paradigm since it offers the ability to collect, analyze and extract meaning of large amounts of heterogenous data collected using a variety of sources including IoT, SCADA systems, and smart meters. This paper explores the ways by which predictive maintenance in the new power systems can be reinforced through Big Data Analytics. It presents the framework that combines superior data collection, scalable storage systems, and machine learning model to help detect faults and predict system health. Analyzing the impacts that Big Data-based predictive maintenance has to offer, the paper focuses on three positive dimensions of the change, including increased resilience of systems, economic feasibility, and sustainability of operations. By overcoming the problems of extensibility, instant response, and accuracy in the decision making, the suggested solution allows Big Data Analytics to become one of the main pillars of smart future power system management. DOI :https://doi.org/10.52783/pst.2349

This Novel-shaped Frequency Reconfigurable Antenna (FRA) is introduced in this study for wireless network applications. High Frequency Structure Simulator (HFSS) is used to design and optimize the FRA. The FRA is designed on an FR-4 epoxy substrate with relative permittivity of 4.4. The designed FRA has small dimensions of 30x16x1.6 mm3. The designed FRA has three different operating stats, at (1)2.45 GHz (Bluetooth), 5.48 GHz (WLAN), (2)3.38 GHz (5G – n78), (3)6.10 GHz (Wi-Fi 6E) which can be accessed by optimal placing of RF PIN diode on the FRA design. The antenna can be used in WiMAX, WLAN, 5G, and 6GHz wireless networks applications. DOI : https://doi.org/10.52783/pst.2151

This study addressed the critical inefficiency of siloed risk management approaches across U.S. industries, where fragmented methodologies for fraud detection (healthcare), AML monitoring (finance), and fiscal forecasting (public sector) incur annual losses exceeding $100 billion. We developed a unified analytical framework integrating machine learning (Isolation Forest, LSTM), network analysis (GNNs), and econometric modeling (ARIMA) to enable cross-sector risk interoperability. The methodology processed: (1) 500,000 anonymized Medicare claims (CMS/RADV), (2) synthetic FinCEN SARs networks emulating money laundering patterns, and (3) CBO macroeconomic indicators, evaluated through multi-criteria validation (precision-recall, MAE, PageRank centrality). Results demonstrated significant improvements over sector-specific baselines: fraud detection achieved 89.7% recall (Δ+27.4%, p<0.01) with SHAP analysis revealing claim frequency and provider networks as top predictive features; AML precision increased by 32.7% through transaction graph clustering (modularity=0.83); fiscal forecast errors reduced by 29.5% via hybrid LSTM-ARIMA modeling. The framework’s interpretability was validated through three lenses: (a) clinical relevance of detected Medicare fraud patterns (OIG audit alignment), (b) AML network topology consistency with FinCEN typologies, and (c) fiscal shock responsiveness within CBO confidence intervals. Economic simulations projected $12.5B annual savings from integrated implementation (ROI 3.6:1), though legacy system integration costs varied by sector (public: +47% vs banking: +71%). The study’s scientific contribution is threefold: (1) a validated protocol for cross-domain risk variable harmonization, (2) demonstration of interpretable AI’s superiority in regulated environments (SHAP-driven false positive reduction), and (3) quantification of cyber-physical risk couplings (fraud-AML volatility r=0.52±0.03). These advancements provide policymakers with a replicable template for national risk infrastructure modernization, particularly in API standardization and adaptive control deployment. DOI :https://doi.org/10.52783/pst.2090