Extensive Testing Research Articles

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image and Video Forgeries

This paper introduces a hybrid architecture combining Long Short-Term Memory (LSTM) networks and an encoderdecoder model for the detection and localization of image and video forgeries. The proposed system leverages resampling features and LSTM cells to identify manipulation patterns such as splicing and retouching in multimedia content. By utilizing a combination of spatial and temporal features, the model achieves high precision in detecting forged regions. Extensive testing on diverse datasets demonstrates the robustness of the proposed method.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

The Polyglot’s Playground: Navigating KMP, Flutter, and React Native in Cross-Platform Ecosystems

Abstract: This white paper compares three prominent cross-platform mobile development frameworks: Kotlin Multiplatform (KMP), Flutter, and React Native. Through analysis of key features, performance, development efficiency, community support, UI/UX capabilities, and learning curves, this study aims to guide developers and decision-makers in selecting the most appropriate framework for their projects. The paper scrutinizes each framework’s strengths and limitations based on extensive literature review and empirical testing. Key findings reveal that KMP excels in native performance and code sharing, Flutter facilitates rapid development with rich UI capabilities, and React Native offers a gentle learning curve with extensive community support. The optimal choice among these frameworks ultimately depends on project requirements, team expertise, and development priorities. As cross-platform development evolves, this paper equips readers with insights to make informed decisions in the dynamic mobile app landscape

Sex differences in patients with acute myocardial infarction under 65 years of age: a Spanish cohort

Abstract Background Myocardial infarction (MI) clinical presentation and prognosis usually vary between men and women. Studying different age groups may be of interest. Aim Compare clinical characteristics and prognosis between men and women under 65 years old (y/o) with MI. Methods observational prospective study, including all patients admitted for MI between 2016 and 2023. Median follow-up was 43months[20-71]. Results Out of a total of 1064 patients, 436 were younger than 65 y/o (98 were women and 338 were men). The median age was similar in both sexes: 53.4[41-65] vs 54.2[43-65], p=0.24. A higher percentage of men were married (76.6 vs 59.2%, p<0.01), while a greater proportion of women were widowed (7.9 vs 1.3%, p<0.01). Men showed higher rates of permanent employment (71.4vs54.9%, p=0.01) and night shifts (58vs26.1%, p<0.01), whereas women had a higher unemployment rate (26.8vs15.2%, p=0.03) and reported higher levels of stress (70.3 vs 42.4%, p<0.01). The percentage of smokers was similar in both sexes (84.6% in men vs 75.8% in women, p=0.06), but there were more cocaine users in the male group (10.8vs1.4%, p=0.01). Women had a higher prevalence of autoimmune diseases (20.4 vs 6.2%, p<0.01), allergies (17.3 vs 5.6%, p<0.01), psychiatric illnesses (25.8 vs 9.9%, p<0.01), and migraines (15.6 vs 1.8%, p<0.01). Men had more history of previous coronary angioplasty (9.5 vs 3.1%, p=0.05). At admission, men presented with higher levels of creatine kinase (1175.2±1490.1 vs 766.5±1084.5 U/L, p=0.02), haemoglobin (15.2±1.6 vs 14±1.4 g/dl, p<0.01) and serum creatinine (1.2±1.4 vs 0.8±0.5 mg/dl, p<0.01), while women exhibited higher cholesterol levels (186.5±51.6 vs 174.9±44.5 mg/dl, p=0.03). Coronary dissection and myocardial infarction with non-obstructive coronary arteries were more frequent in women (14.3 vs 2.1%, p<0.01 and 27.6 vs 11.5%, p<0.01, respectively). Women also underwent additional tests at a higher rate, such as cardiac magnetic resonance (14.3 vs 4%, p<0.01) and vasospasm test (7.6vs1.9%, p=0.02). During hospitalization, men underwent drug-eluting stent implantation more frequently (66.7vs23.8%, p<0.01). At discharge, they were more likely to receive antiplateles (93.7vs84.5%, p<0.01), beta-blockers (81.7vs.71.1%, p=0.03), angiotensin-converting enzyme inhibitors (63.2vs49.5%, p=0.02) and statins (96.4vs84.5%, p<0.01). The rest of variables, which showed no significant differences, are in Table 1. Prognosis was similar in both sexes: death from any cause (HR 0.7, 95%CI[0.1-3.4]), cardiovascular readmission (HR 1.2, 95%CI[0.5-2.7]), reinfarction (HR 1.9, 95%CI[0.3-10.3]), stroke (HR 1, 95%CI[0.1-11.9]). Conclusion The clinical profile of men and women with MI is clearly different. The investigation into the underlying mechanisms of MI occasionally necessitates more extensive testing in women. Additionally, the treatment plans at discharge diverge between the two genders. Nevertheless, the prognosis appears to be similar for both sexes.

Comparative Analysis of Osteoarthritis Therapeutics: A Justification for Harnessing Retrospective Strategies via an Inverted Pyramid Model Approach

In this review, we seek to explore two distinct approaches to the clinical management of OA: a prospective approach, addressing primarily one’s genetic predisposition to OA and generating early intervention options, and the retrospective approach, aimed at halting or reversing OA progression post-symptom onset. The clinical management of OA remains challenging, largely due to the limited availability of preventative treatments and failure of existing therapies to modify or reverse the underlying pathophysiology. The prospective approach involves the identification of genetic markers associated with OA and utilizes in vitro and in vivo models to characterize the underlying disease mechanism. Further, this approach focuses on identifying genetic predispositions and unique molecular subtypes of OA to develop individualized treatment plans based on patient genotypes. While the current literature investigating this strategy has been notable, this approach faces substantial challenges, such as extensive time burdens and utilization of extensive genetic testing that may not be economically feasible. Additionally, there is questionable justification for such extensive investigations, given OA’s relatively low mortality rates and burden when contrasted with diseases like specific forms of cancer, which rely heavily on the prospective approach. Alternatively, the retrospective approach primarily focuses on intervention following symptom onset and aims to utilize novel therapeutics to slow or reverse the inflammatory cascade typically seen in disease progression. These treatments, like Hippo pathway inhibitors, have shown initial promise in halting OA progression and alleviating OA symptomology by modulating cellular processes to preserve articular cartilage. In comparison to the prospective approach, the retrospective strategy is likely more cost-effective, more widely applicable, and does not necessitate thorough and invasive genetic screening. However, this approach must still be weighed against the typical natural history of disease progression, which frequently results in total knee arthroplasty and unacceptable outcomes for 15–20% of patients. From a comparative analysis of these two approaches, this review argues that the retrospective strategy, with ideally lower time and economic burden and greater accessibility, offers a more reasonable and effective solution in the context of OA management. Using a similar approach to other management of chronic diseases, we suggest an “Inverted Pyramid” model algorithm, a structured research and development regimen that prioritizes generating widely effective therapies first, with subsequent refinement of treatments based on the development of patient resistance to these therapies. We argue that this strategy may reduce the need for total knee arthroplasty while improving patient outcomes and accessibility.

Impact of in-silico technologies in the development of high-risk medical devices on healthcare

Abstract This study examines the impact of in-silico technologies (IST), such as virtual cohorts and digital twins, on the development of high-risk medical devices within the healthcare sector. An iterative empirical approach combining a literature review, exploratory and in-depth interviews with stakeholders from academia, industry, regulators, healthcare professionals, and focus groups with patients across Europe, Japan, and the USA was employed to gain insight into the implications of IST on healthcare. The findings indicate that IST can reduce the time to market for medical devices, thereby facilitating faster, broader, and more equitable access for patients. The implementation of extensive early testing, optimized study designs and increased sample sizes in trials can facilitate the achievement of enhanced safety. The early identification of adverse effects can enhance the safety of medical devices and improve patient outcomes. Furthermore, IST may facilitate the inclusion of underrepresented groups, such as children, in clinical trials, thereby enhancing their representation in medical research. The reduction in costs associated with IST may facilitate innovation cycles. However, the impact on prices is contingent on competition. Furthermore, IST have the potential to reduce the need for animal testing. Nevertheless, several uncertainties and potential adverse effects have been identified. Physician training on new devices remains a bottleneck, which may limit the benefits of new products. Despite the advancements, a complete replacement of traditional clinical and preclinical trials with IST is not seen to be feasible. From a policy perspective, it is crucial to promote regulatory clarity by providing guidance for the usage of IST, to offer incentives for the adoption of IST, and to foster competition. These findings demonstrate the multifaceted potential benefits and challenges of IST, including enhanced safety, accessibility, and innovation in healthcare. Key messages • In-silico technologies can reduce time-to market of medical devices up medical device and improve safety for patients. • Challenges like physician training and regulatory guidance must be addressed to fully realize benefits of in silico technologies.

Identification of new instances of white matter hyperintensities in patients following TAVI (from the CAPITA study)

Abstract A third of patients diagnosed with symptomatic severe aortic valve stenosis and scheduled for transcatheter aortic valve implantation (TAVI) exhibit some level of cognitive impairment. The prospective CAPITA study aimed to systematically evaluate the impact of TAVI on cerebral and cognitive outcomes in patients undergoing the procedure. In this report, we focus on the identification of new incidence of white matter hyperintensities (WMH) and the correlation with changes in cognitive functioning. The CAPITA (CArdiac OutPut, Cerebral Blood Flow and Cognition In Patients With Severe Aortic Valve Stenosis Undergoing Transcatheter Aortic Valve Implantation) study is a prospective study of cerebral and cognitive outcomes following TAVI. Patients underwent magnetic resonance imaging (MRI) and extensive neuropsychological testing at baseline and three month follow-up. New cerebral infarcts were defined according to NeuroARC criteria. We examined the incidence of new WMH in terms of volume (Δ mL) and number on FLAIR MRI, and explored the correlation between new WMH and cognitive functioning. A total of 148 patients were included who underwent TAVI between August 2020 and October 2022. Mean age was 80.5±5.7 years, of whom 43% female with a median STS-PROM of 2.0% (IQR 1.4%-2.9%). Median baseline WMH comprised 127 (IQR 32-216) lesions, with a total volume of 13.1 (IQR 7.0-24.6) mL. At 3 months follow-up, the number of lesions increased to 164 (IQR 121-301) lesions, with a total volume of 13.3 (IQR 5.8-21.5) mL. Median WMH volume increase was 0.1 (IQR 0.0-0.3) ml (p=0.02). A total of 9 patients (9.5%) had an increase of 1 mL of WMH or more. At follow-up, 12% of patients exhibited cognitive decline of >0.5SD. These patient tended to have a greater increase in white matter hyperintensity volume (1.26±2.96 ml), compared to the 53% of patients with stable cognition (0.29±0.45 ml) and the 36% of patients with improved cognition (0.31±0.91 ml), (p=0.06). WMH lesions did not exhibit distribution patterns across various locations within the groups characterized by declined, stable, or improved cognition (Figure 1). WMH volumes were higher in patients who experienced postprocedural delirium than in patients without delirium (27.1±14.0 vs 15.0±12.5 mL, p<0.001). In this study, patients with severe symptomatic aortic valve stenosis undergoing TAVI, there was an overall increase of white matter hyperintensity volume between baseline and 3-month follow-up. Up to 12% of patients had cognitive decline, which was associated with new white matter hyperintensities on MRI.

Residual mitral leaflet length independently indicates left ventricular outflow tract obstruction in hypertrophic cardiomyopathy

Abstract Background Due to its dynamic character, the diagnosis of left ventricular outflow tract obstruction (LVOTO) in hypertrophic cardiomyopathy (HCM) often requires extensive exercise testing. Mitral valve alterations represent structural parameters that are not affected by load or contractility and influence left ventricular outflow tract (LVOT) gradients. The residual portion of the mitral valve extending past its coaptation (RML) has been postulated as a necessity to exhibit LVOTO. Purpose This study aims to assess the impact of RML length on the likelihood of LVOTO in HCM patients. Methods This is a cross-sectional, multi-center, registry-based analysis conducted at two HCM referral centers. The study included HCM patients with valid standardized transthoracic echocardiographic examinations. Blinded investigators performed post-processing echocardiographic analyses. LVOTO was defined as resting or dynamic peak LVOT gradients ≥30 mmHg. Results Among 270 HCM patients studied (43% women, mean age 58±14 years), 131 patients (49%) exhibited LVOTO, with 76 (28%) having undergone septal reduction therapy (SRT). Patients with obstructive HCM exhibited a more pronounced end-diastolic interventricular septum (IVSd) thickness (2.2±0.4 vs. 1.8±0.5 cm; p<0.001) and higher left ventricular ejection fraction (LVEF) (65±9 vs. 62±10%; p=0.009) compared to those without LVOTO (nHCM). Obstructive HCM patients demonstrated longer anterior (29±4 vs. 26±4 mm; p<0.001), posterior (23±4 vs. 20±4 mm; p<0.001), and residual mitral leaflets (11±3 vs. 7±3 mm; p<0.001) than nHCM patients. Multivariable logistic regression analysis adjusting for age, IVSd thickness, LVEF, anterior, and posterior mitral leaflet length identified RML length as an independent predictor of LVOTO [OR=1.47 (95% CI 1.30-1.66); p<0.001]. The area under the receiver-operating characteristic curve for RML length in identifying LVOTO was 0.82 (95% CI 0.77-0.87). RML length ≥9 mm demonstrated 73% sensitivity and 77% specificity in identifying obstructive HCM patients. Notably, these results remained statistically significant in sensitivity analyses selectively performed in HCM patients who had previously undergone SRT as well as in those who were SRT-naïve, respectively. Conclusions This study indicates RML length as an independent predictor of LVOTO in HCM patients. Moreover, RML length demonstrated high diagnostic accuracy in identifying LVOTO among HCM patients. Prospective studies are warranted to assess the added value of RML length in the diagnostic work-up of HCM patients.Figure:Diagnostic Accuracy of RML

Applications of AI in public health: Improving access to high-quality evidence syntheses

Abstract Issue Using the best available evidence is crucial for effective public health practice. Health Evidence™ provides access to high-quality synthesis evidence relevant to public health. As the volume of peer-reviewed published literature grows, maintaining a database of this magnitude is increasingly resource-intensive. Artificial intelligence (AI) can be used to reduce the maintenance burden thereby ensuring decision makers can easily access high-quality synthesized public health evidence. These innovative strategies may be transferable to enhance efficient maintenance of large curated databases of published literature. Description of the problem In 2020, The Health Evidence™ team conducted extensive training and testing of a supervised machine learning application to explore the accuracy of AI-assisted reference de-duplication and relevance screening. Finding promising results, the team implemented these AI-assisted strategies in August 2020. To assess the impact on the overall screening burden and time saved, implementation data was analyzed between November 2020 to 2023. Results For these 3+ years, AI assisted de-duplication and relevance screening was applied to 394,903 search results. The AI assisted de-duplication application removed 31% (n = 123,903) of references as duplicates. From the remaining reference sets (n = 272,253), the AI assisted screening application removed 70% (n = 190,966) of references as not relevant. Quality assurance spot testing found minimal classification errors (n = 1). In total, AI assisted approaches reduced the need for manual screening by 80%, saving approximately 626 hours of manual screening time over three years (or approximately 17 hours/month). Lessons With the reality of limited public health resources, continued access to high-quality synthesis evidence is critical. Innovative strategies using AI-assisted applications improves the feasibility of maintaining a large database of quality-appraised public health synthesis evidence. Key messages • Access to high quality synthesis evidence is critical for evidence-informed decision making. • Artificial Intelligence can be used to efficiently identify evidence syntheses relevant for public health.

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.

Using the Discrete Wavelet Transform for Lossy On‐the‐Fly Compression of GPU Fluid Simulations

ABSTRACTHigh‐performance computing in fluid dynamics frequently confronts substantial memory demands, especially in large‐scale applications. Data compression techniques can alleviate these memory constraints, but introduce new challenges. This paper introduces an innovative on‐the‐fly low‐overhead lossy compression technique tailored for GPU‐based fluid simulations, utilizing the discrete wavelet transform (DWT). The technique is applicable to any numerical scheme where the data is stored on a regular grid and the time step is computed using a stencil. Our approach significantly diminishes memory requirements, achieving up to a 10‐fold long‐term reduction on a D3Q27 simulation, while minimally impacting the simulation accuracy. The methodology is built around careful design choices to achieve a satisfactory compression ratio/speed trade‐off. It effectively maintains mass conservation and accurately preserves essential discontinuities in simulations. Extensive testing with a D3Q27 Lattice‐Boltzmann method (LBM) simulation on a single GPU has shown that large‐scale grids can be processed with minimal impact on the simulation accuracy and acceptable compression times. This compression technique demonstrates a robust capability to handle memory limitations in fluid simulations, opening the door to more complex and larger‐scale simulations.

Development and validation of the four-item Concise Aging adults Smell Test to screen for olfactory dysfunction in older adults.

Olfactory dysfunction (OD) in the elderly is common and associated with numerous comorbidities, yet often underrecognized. This study sought to develop an instrument for widespread screening of this condition. Demographic factors, comorbidities, and olfactory-related questions were examined in a focus group (N=20) and development cohort (N=190). The 4-item Concise Aging adults Smell Test (4-CAST) screening instrument was then developed to predict OD on Sniffin Sticks testing. An independent validation cohort (N=147) was then examined to confirm ability of the 4-CAST to predict OD on Smell Identification Test 40 (SIT40). The most important variables for predicting olfactory loss in older subjects were age, type II diabetes status, Visual Analog Scale (VAS) of overall rating of smell, and VAS of impact of smell upon safety. In the development cohort, the 4-CAST model yielded predictive probability (area under the curve) of 0.805 (p<0.001) for predicting the bottom 25th percentile on Sniffin Sticks. Results were similar in the validation cohort, as the 4-CAST accurately classified 83.8% subjects (area under the curve [AUC] 0.789, p<0.001) in the bottom 25th percentile of SIT40 testing. The 4-CAST was able to accurately classify 89.4% of subjects with anosmia. The 4-CAST is a quick screening instrument for predicting OD in older adults and has similar performance characteristics in independent populations using the two most common validated olfactory tests. Given the high prevalence of unrecognized olfactory loss in this population, this can be a useful tool for practitioners to determine which subjects may benefit from more extensive olfactory testing.

Bilateral Idiopathic Neuroretinitis

Background/Objectives: Neuroretinitis (NR) is a rare inflammatory condition characterized by sudden vision loss, optic disc edema and macular star appearance predominantly affecting individuals in their third and fourth decades of life. Methods: This paper describes the case of 33-year-old Caucasian man with no significant medical history complaining of decreased vision for about a week. Results: The ophthalmological exam revealed best-corrected visual acuity (BCVA) for the right eye (RE) of 0.8 (decimal notation) and of 0.9 for the left eye (LE). Intraocular pressure (IOP) was 20 mmHg in RE and 18 mmHg in LE. Slit-lamp examination of both eyes (OU) showed no evidence of intraocular inflammation in the anterior chamber or vitreous cavity. Examination of the posterior pole of the right eye showed bilateral papilledema with an incomplete macular fan pattern. Conclusions: Despite extensive laboratory tests, including serologic and imaging investigations, a definitive etiology remained unclear. It is very important to differentiate NR from other optic nerve disorders, requiring careful clinical evaluation and observation of the evolution of symptoms.

Dynamic Response of a Rotor Supported on Hybrid Bearings in Air, Water, and Liquid Nitrogen: Measurements and Comparisons to Predictions

Abstract Modern liquid rocket engine turbopumps implement hybrid bearings, which combine hydrostatic and hydrodynamic fluid film principles, in compact and lightweight units known for their superior reusability, durability, and reliability. This study aims to provide extensive and reliable test data for the purpose of anchoring and benchmarking predictive tools for these bearings. This paper provides comprehensive dynamic response measurements of a rotor weighing 10.40 kg and approximately 60 mm in diameter at the bearing locations. The test rotor is supported on two hybrid journal bearings with a bearing length to bearing diameter ratio of approximately 0.42 and a bearing radial clearance to rotor radius ratio of around 0.0017. The bearings are tested with air, water, and liquid nitrogen, serving as surrogate test fluids for common propellants in liquid rocket engines. Rotor run-up and speed coast-down tests are conducted to measure shaft motion responses. The results clearly demonstrate that the rotordynamic characteristics of the test rotor supported on the hybrid journal bearings largely rely on properties of the test fluids and their feeding conditions. Notably, when air is pressurized into the test bearings, the shaft motion responses differ markedly from those observed with water and liquid nitrogen, primarily due to significantly lower stiffness and damping coefficients. Furthermore, rotor speed coast-down tests for each test fluid exhibit notable differences in coast-down speed over time characteristics, depending on the test fluid properties. The predicted rotor imbalance responses exhibit excellent correlation with the measurement data. The steady increase of demand and growth of the fluid film bearing technology for reusable rocket engines require accurate bearing design tools, the extensive component testing, and the implementation of the technology. The validated and developed bearing predictive models from previous research demonstrate well the characteristics of bearings under various operating fluids conditions. The findings and database presented in this work contribute not only to comprehending the performance of hybrid bearings but also to understanding and enhancing the dynamic characteristics of rotor systems supported on hybrid bearings.

Comparison of two different stretching strategies to improve hip extension mobility in healthy and active adults: a crossover clinical trial

ObjectiveTo improve hip flexor flexibility, the commonly used hip extension stretch focuses on advancing the pelvis forward in a half-kneeling position, targeting only hip extension. This study aimed to compare the effectiveness of this traditional stretch with a new technique incorporating posterior pelvic tilt.MethodsA randomized crossover clinical trial was conducted with in 26 healthy, physically active individuals (52 hips), aged 22.50 ± 4.10 years. Each participant performed both the hip extension stretch and the posterior pelvic tilt stretch (26 hips), with one technique applied to each limb: the posterior pelvic tilt stretch on one limb and the hip extension stretch on the other limb (26 hips). The reactive hip flexor test and active knee extension test were measured pre and post stretching techniques. A repeated measures ANOVA was performed to assess differences between groups.ResultsThere was a significant group-by-time interaction with a medium effect size for the variable related to reactive hip flexor force (F = 4.775, p = 0.034, η2p = 0.087). The posterior pelvic tilt stretch had a statistically significant mean difference of 4.85 N·m (p = 0.003; 95%CI: 1.74; 7.96) compared with the hip extension stretch (mean difference = 0.06 N·m (p = 0.969; 95%CI-3.05; 3.17). No significant differences were found for active knee extension (p > 0.05).ConclusionsThe results showed that the posterior pelvic tilt stretching technique was effective in reducing the hip reactive flexor force when compared to conventional hip extension stretch.

A study on the impact of high-speed rail on the consumption structure of urban and rural residents in China.

Consumption is crucial to individual well-being and national economic development. This study investigates whether high-speed rail (HSR) influences consumption expenditure (CE) and consumption structure (CS) of urban and rural Chinese citizens. Using panel data from 2003 to 2019 and econometric models, this study finds that: (1) HSR significantly increases CE for both urban and rural residents, promotes CS upgrades in rural areas, but inhibits CS upgrades in urban areas. These results remain robust after extensive testing. (2) HSR's impact on urban consumption is relatively focused and singular, whereas its effect on rural consumption is dispersed and extensive. Additionally, the impact of HSR on consumption exhibited significant delays and regional characteristics. (3) Mediation analysis reveals that HSR significantly enhances urban and rural CE and facilitates rural CS upgrades through market, price, and income effects. However, it also triggers housing price increases, impeding urban CS upgrades. This study provides important references for the government to optimize transportation infrastructure investments, promote balanced economic development between urban and rural areas, and enhance residents' well-being.

A Local Macroscopic Conservative (LoMaC) Low Rank Tensor Method for the Vlasov Dynamics

In this paper, we propose a novel Local Macroscopic Conservative (LoMaC) low rank tensor method for simulating the Vlasov-Poisson (VP) system. The LoMaC property refers to the exact local conservation of macroscopic mass, momentum and energy at the discrete level. This is a follow-up work of our previous development of a conservative low rank tensor approach for Vlasov dynamics (arXiv:2201.10397). In that work, we applied a low rank tensor method with a conservative singular value decomposition to the high dimensional VP system to mitigate the curse of dimensionality, while maintaining the local conservation of mass and momentum. However, energy conservation is not guaranteed, which is a critical property to avoid unphysical plasma self-heating or cooling. The new ingredient in the LoMaC low rank tensor algorithm is that we simultaneously evolve the macroscopic conservation laws of mass, momentum and energy using a flux-difference form with kinetic flux vector splitting; then the LoMaC property is realized by projecting the low rank kinetic solution onto a subspace that shares the same macroscopic observables by a conservative orthogonal projection. The algorithm is extended to the high dimensional problems by hierarchical Tuck decomposition of solution tensors and a corresponding conservative projection algorithm. Extensive numerical tests on the VP system are showcased for the algorithm’s efficacy.

Real-Time Control of Thermal Synchronous Generators for Cyber-Physical Security: Addressing Oscillations with ANFIS

This paper introduces a novel real-time ANFIS controller, specifically designed for thermal synchronous generators, to mitigate the risks associated with cyber-physical attacks on power systems. The controller integrates the dynamic model of the turbine’s thermomechanical components, such as the boiler and heat transfer processes, within the synchronous generator. In contrast to previous studies, this model is designed for practical implementation and addresses often-overlooked areas, including the interaction between electrical and thermomechanical components, real-time control responses to cyber-physical attacks, and the incorporation of economic considerations alongside technical performance. This study takes a comprehensive approach to filling these gaps. Under normal conditions, the proposed controller significantly improves the management of industrial turbines and governors, optimizing existing control systems with a particular focus on minimizing generation costs. However, its primary innovation is its ability to respond dynamically to local and inter-area power oscillations triggered by cyber-physical attacks. In such events, the controller efficiently manages the turbines and governors of synchronous generators, ensuring the stability and reliability of power systems. This approach introduces a cutting-edge thermo-electrical control strategy that integrates both electrical and thermomechanical dynamics of thermal synchronous generators. The novelty lies in its real-time control capability to counteract the effects of cyber-physical attacks, as well as its simultaneous consideration of economic optimization and technical performance for power system stability. Unlike traditional methods, this work offers an adaptive control system using ANFIS (Adaptive NeuroFuzzy Inference System), ensuring robust performance under dynamic conditions, including interarea oscillations and voltage deviations. To validate its effectiveness, the controller undergoes extensive simulation testing in MATLAB/Simulink, with performance comparisons against previous state-of-the-art methods. Benchmarking is also conducted using IEEE standard test systems, including the IEEE 9-bus and IEEE 39-bus networks, to highlight its superiority in protecting power systems.

Tailored IrO2@ZIF-67 nanocomposites for efficient oxygen evolution reaction: Insights into catalyst design and performance enhancement

The pursuit of effective electrocatalysts for the oxygen evolution reaction (OER) is crucial for advancing sustainable energy technologies. Efficient OER catalysts play a vital role in the development of water splitting systems, which are fundamental for producing hydrogen—a clean and renewable energy source. The synthesis of IrO2 nanoparticles embedded in ZIF-67 nanostructures has been explored to improve the performance of OER electrocatalysts. The resulting IrO2@ZIF-67 composite catalyst demonstrates a remarkable overpotential of 220 mV and 290 mV at current densities of 10 and 50 mA cm⁻2, alongside a low Tafel slope of 58 mV dec⁻1, significantly outperforming the pristine ZIF-67 catalyst. Integrating IrO2 nanoparticles into the ZIF-67 matrix significantly enhances both the catalytic activity and durability of the material. Extensive electrochemical testing reveals that the IrO2@ZIF-67 catalyst maintains exceptional stability, operating consistently for over 50 h at a current density of 50 mA cm⁻2 without significant degradation. This enhanced performance is attributed to the synergistic effects between the highly active IrO2 nanoparticles and the robust ZIF-67 framework, which collectively facilitate efficient charge transfer and improve structural integrity during prolonged OER operation. These findings highlight the potential of IrO2@ZIF-67 nanostructures as a promising electrocatalyst for sustainable water splitting applications, providing a pathway towards the development of more efficient and durable OER catalysts.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.