Advancing Open Science

Diagnosing faults in Permanent Magnet Synchronous Motors (PMSMs) is critical for ensuring their reliable operation, particularly in detecting internal short-circuit faults in the stator windings. These faults, such as inter-turn and inter-coil short circuits, can significantly affect motor performance and lead to costly downtime if not detected early. However, detecting these faults accurately, especially in the presence of operational noise and varying load conditions, remains a challenging task. To address this, a novel methodology is proposed for diagnosing and classifying fault severity in PMSMs using vibration and current data. The key innovation of the method is the combination of signal processing for both vibration and current data, to enhance fault detection by applying advanced feature extraction techniques such as root mean square (RMS), peak-to peak values, and spectral entropy in both time and frequency domains. Furthermore, a cooperative gain transformation is applied to amplify weak correlations between vibration and current signals, improving detection sensitivity, especially during early fault progression. In this study, the publicly available dataset on Mendeley, which consists of vibration and current measurements from three PMSMs with different power ratings of 1.0 kW, 1.5 kW, and 3.0 kW, was used. The dataset includes eight different levels of stator fault severity, ranging from 0% up to 37.66%, and covers normal operation, inter-coil short circuit, and inter-turn short circuit. The results demonstrate the effectiveness of the proposed methodology, achieving an accuracy of 96.6% in fault classification. The performance values for vibration and current measurements, along with the corresponding fault severities, validate the method’s ability to accurately detect faults across various operating conditions.

Accessible and inclusive community environments support physical activity and health equity for people with disabilities, yet gaps in design, maintenance, and communication limit safe, independent use. This statewide cross-sectional audit assessed park accessibility and usability and playground safety in publicly accessible, non-fee-based Delaware community parks with playgrounds. Fifty stratified sites were evaluated using the Community Health Inclusion Index and the America’s Playgrounds Safety Report Card by trained raters with strong interrater reliability. Descriptive analyses summarized accessibility, usability, communication, and safety features by county, with exploratory urban-suburban/micropolitan contrasts. Most sites provided wide, smooth paths, shade, and strong playground visibility, but foundational accessibility varied. Only 30% had a nearby transit stop, fewer than 10% of crossings included auditory or visual signals. Curb-ramp completeness was inconsistent, with detectable warnings frequently absent. Restrooms commonly lacked low-force doors or operable hardware, and multi-use trails often had obstacles or lacked wayfinding supports. Playground accessibility features were present at approximately two-thirds of sites, and 62% were classified as safe, although 10% were potentially hazardous or at-risk. Higher playground accessibility scores were strongly associated with lower life-threatening injury risk. Overall, gaps in transit access, pedestrian infrastructure, amenities, and communication support limit equitable, health-supportive park environments and highlight priority improvement areas.

The geometric precision of ballastless tracks critically determines the performance and safety of high-speed railways. Traditional manual fine adjustment methods remain labor-intensive, iterative, and sensitive to human expertise, making it difficult to achieve sub-millimeter accuracy and global consistency. To address these challenges, this paper proposes a virtual-model–enabled pre-adjustment framework for high-speed ballastless track construction. The framework integrates a dual-frame SLAM-based and multi-sensor measurement system based on RC-SLAM principles and a local attitude compensation model, enabling accurate 3D mapping and reconstruction of long-track segments under extended-range and GNSS-denied conditions typical of linear infrastructure scenarios. A constraint-based global optimization algorithm is further developed to transform empirical fine adjustment into a computable geometric control problem, generating executable adjustment configurations with engineering feasibility. Field validation on a 1 km railway section demonstrates that the proposed method achieves sub-millimeter measurement accuracy, improves adjustment efficiency by over eight times compared with manual operations, and reduces material waste by $2800–$7000 per kilometer. This paper demonstrates a previously unexplored execution-level workflow for long-rail fine adjustment, establishing a closed-loop paradigm from measurement to predictive optimization and paving the way for SLAM-driven, simulation-based, and multi-sensor–integrated precision control in next-generation railway construction.

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably.

Spermatogenesis is a tightly coordinated differentiation program that sustains male fertility while transmitting genetic and epigenetic information to the next generation. This review consolidates mechanistic evidence showing how RNA-centered regulation integrates with the epitranscriptome and three-dimensional (3D) genome architecture to orchestrate germ-cell fate transitions from spermatogonial stem cells through meiosis and spermiogenesis. Recent literature is critically surveyed and synthesized, with particular emphasis on human and primate data and on stage-resolved maps generated by single-cell and multi-omics technologies. Collectively, available studies support a layered regulatory model in which RNA-binding proteins and RNA modifications coordinate transcript processing, storage, translation, and decay; small and long noncoding RNAs shape post-transcriptional programs and transposon defense; and dynamic chromatin remodeling and 3D reconfiguration align transcriptional competence with recombination, sex-chromosome silencing, and genome packaging. Convergent nodes implicated in spermatogenic failure are highlighted, including defects in RNA metabolism, piRNA pathway integrity, epigenetic reprogramming, and nuclear architecture, and the potential of these frameworks to refine molecular phenotyping in male infertility is discussed. Finally, key gaps and priorities for causal testing in spatially informed, stage-specific experimental systems are outlined.

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables the estimation of the ionospheric FR angle (FRA), and consequently the total electron content, across most global regions (including the extensive ocean areas) using spaceborne FP SAR measurements. The accuracy of FRA estimation, however, is highly sensitive to noise interference. This study addresses denoising in FRA retrieval based on the Bickel–Bates estimator, with a specific focus on noise reduction methods built upon the adaptive Goldstein filter (AGF) that was originally designed for radar interferometric processing. For the first time, three signal-to-noise ratio (SNR)-based AGFs suitable for FRA estimation are investigated. A key feature of these filters is that their SNRs are all defined using the amplitude of the Bickel–Bates estimator signal rather than the FRA estimates themselves. Accordingly, these AGFs are applied to the estimator signal instead of the estimated FRAs. Two of the three AGFs are developed by adopting the mathematical forms of SNRs and filter parameters consistent with the existing SNR-based AGFs for interferogram. The third AGF is newly proposed by utilizing more general mathematical forms of SNR and filter parameter that differ from the first two. Specifically, its SNR definition aligns with that widely used in image processing, and its filter parameter is derived as a function of the defined SNR plus an additionally introduced adjustable factor. The three SNR-based AGFs tailored for FRA estimation are tested and evaluated against existing AGF variants and classical image denoising methods using three sets of FP SAR Datasets acquired by the L-band ALOS PALSAR sensor, encompassing an ocean-only scene, a plain land–ocean combined scene, and a more complex land–ocean combined scene. Experimental results demonstrate that all three filters can effectively mitigate noise, with the newly proposed AGF achieving the best performance among all denoising methods included in the comparison.

Background: Primary Biliary Cholangitis (PBC) requires early diagnosis and specialized management to prevent progression to cirrhosis. This study evaluates the awareness levels of Turkish physicians from various specialties regarding the clinical features, diagnostic criteria, and current treatment protocols of PBC. Methods: A multi-regional cross-sectional survey was conducted with 269 physicians across Türkiye. Knowledge levels were assessed through a 28-item instrument covering epidemiology, diagnosis and therapy. Data distribution was non-normal (Skewness: −1.296, Kurtosis: 2.857), necessitating the use of the Kruskal–Wallis H test and Dunn–Bonferroni post hoc procedure for inter-group comparisons. Internal consistency was confirmed with a Cronbach’s alpha of 0.80. Results: The overall mean awareness score was 62.6%. Item-level analysis revealed a near-universal understanding of the non-mandatory role of liver biopsy in diagnosis (99.1%) yet identified a critical knowledge gap regarding second-line therapies, particularly the use of steroids (6.8%). Significant disparities were observed among specialties (p < 0.001). Gastroenterologists (Median: 91.07%) and gastroenterology fellows (Median: 85.71%) exhibited significantly higher proficiency compared to general practitioners (64.29%) and family medicine residents (67.86%). Internal medicine specialists outperformed primary care providers, while no significant differences were found among other subgroups after Bonferroni adjustment. Conclusions: Professional specialization is the primary determinant of PBC awareness. While core diagnostic knowledge is stable, significant gaps exist in pharmacological management among non-specialists. Targeted medical education for primary care physicians is essential to ensure timely referral and optimize patient outcomes.

Weight gain has been associated with integrase strand transfer inhibitors (INSTIs) in real-world studies; however, the causality of this relationship is unclear. Thus, we examined the effects of the INSTI, Dolutegravir (DTG), on human adipose cells in vitro and the reversibility of these effects by switching to a protease inhibitor, Darunavir (DRV). We established cultures of human adipose stem cells (ASCs) and newly differentiated adipocytes from individuals without HIV. For adipocytes, cells were exposed to DTG or DRV for 7 days, after which cells were maintained or switched to another ART. Experiments examining ASCs and the effects on adipogenesis initiated exposure during proliferation and continued throughout differentiation. Adipogenic outcomes included triglyceride content, gene expression, and adipokine secretion. Metabolic outcomes included lactate production, lipolysis, and oxygen consumption rates. DTG suppressed the secretion of adiponectin and leptin, and this was reversed following the switch to DRV in adipocytes without the altered expression of adipogenic genes. DTG exposure increased markers of endoplasmic reticulum stress, elevated lactate production, and suppressed oxygen consumption in ASCs. Exposure to DTG during differentiation lowered triglyceride accumulation and adiponectin secretion without altering the expression of adipogenic markers. Thus, DTG exposure resulted in changes in adipocyte function consistent with the progression of metabolically adverse phenotypes, and these effects were reversible.