Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the operating cycle efficiency of the long-stroke slow stage under the changing ratio of the piston forward and backward stroke time

The paper presents theoretical studies on the operation of linear hydraulic drive by changing the ratio of the compression time (forward stroke) and the suction time (backward stroke). The influence of the drive operating mode on the working cycle efficiency is assessed.The paper presents theoretical studies on the operation of linear hydraulic drive by changing the ratio of the compression time (forward stroke) and the suction time (backward stroke). The influence of the drive operating mode on the working cycle efficiency is assessed.
Compact LWIR polarimeter for cirrus ice properties

A miniaturized long-wave InfraRed (LWIR) spectro-polarimeter is being developed as a prototype for the Compact Submm-Wave and LWIR Polarimeters (SWIRP) project. The polarimeter in development is a compact (20x20x40 cm) conical-scan instrument to measure the polarimetric radiation from ice cloud scattering at mm- submm (220 and 680 GHz) and IR (8.6, 11, and 12 m) bands. The LWIR polarimeter will provide a series of polarization measurements across the 8.5 - 12.5 micron band, measuring the full set of linear Stokes parameters (I, Q, U) as a function of wavelength. The spectro-polarimeter uses a combination of birefringent crystals, a Wollaston prism, a diffraction grating, and an uncooled microbolometer array to measure both the degree and angle of linear polarization across the spectral bandwidth by modulating the polarization flux in wavelength with a high order retarder.
Validation of active shape model techniques for intracochlear anatomy segmentation in computed tomography images

Abstract. Purpose Cochlear implants (CIs) have been shown to be highly effective restorative devices for patients suffering from severe-to-profound hearing loss. Hearing outcomes with CIs depend on electrode positions with respect to intracochlear anatomy. Intracochlear anatomy can only be directly visualized using high-resolution modalities, such as micro-computed tomography (μCT), which cannot be used in vivo. However, active shape models (ASM) have been shown to be robust and effective for segmenting intracochlear anatomy in large scale datasets of patient computed tomographies (CTs). We present an extended dataset of μCT specimens and aim to evaluate the ASM’s performance more comprehensively than has been previously possible. Approach Using a dataset of 16 manually segmented cochlea specimens on μCTs, we found parameters that optimize mean CT segmentation performance and then evaluate the effect of library size on the ASM. The optimized ASM was further evaluated on a clinical dataset of 134 CT images to assess method reliability Results Optimized parameters lead to mean CT segmentation performance to 0.36 mm point-to-point error, 0.10 mm surface error, and 0.83 Dice score. Larger library sizes provide diminishing returns on segmentation performance and total variance captured by the ASM. We found our method to be clinically reliable with the main performance limitation that was found to be the candidate search process rather than model representation. Conclusions We have presented a comprehensive validation of the ASM for use in intracochlear anatomy segmentation. These results are critical to understand the limitations of the method for clinical use and for future development.
Severity of Prior Coronavirus Disease 2019 is Associated With Postoperative Outcomes After Major Inpatient Surgery

Objective: To determine how the severity of prior history (Hx) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection influences postoperative outcomes after major elective inpatient surgery. Background: Surgical guidelines instituted early in the coronavirus disease 2019 (COVID-19) pandemic recommended a delay in surgery of up to 8 weeks after an acute SARS-CoV-2 infection. This was based on the observation of elevated surgical risk after recovery from COVID-19 early in the pandemic. As the pandemic shifts to an endemic phase, it is unclear whether this association remains, especially for those recovering from asymptomatic or mildly symptomatic COVID-19. Methods: Utilizing the National COVID Cohort Collaborative, we assessed postoperative outcomes for adults with and without a Hx of COVID-19 who underwent major elective inpatient surgery between January 2020 and February 2023. COVID-19 severity and time from infection to surgery were each used as independent variables in multivariable logistic regression models. Results: This study included 387,030 patients, of whom 37,354 (9.7%) were diagnosed with preoperative COVID-19. Hx of COVID-19 was found to be an independent risk factor for adverse postoperative outcomes even after a 12-week delay for patients with moderate and severe SARS-CoV-2 infection. Patients with mild COVID-19 did not have an increased risk of adverse postoperative outcomes at any time point. Vaccination decreased the odds of respiratory failure. Conclusions: Impact of COVID-19 on postoperative outcomes is dependent on the severity of illness, with only moderate and severe disease leading to a higher risk of adverse outcomes. Existing perioperative policies should be updated to include consideration of COVID-19 disease severity and vaccination status.
Regulatory mechanism of RNA binding motif protein 15‐mediated N6 methyladenosine modification in proliferation, invasion, and migration of colorectal cancer cells

This study aims to explore the regulatory mechanism of RNA binding motif protein 15 (RBM15) on the proliferation, invasion, and migration of colorectal cancer (CRC) cells. RBM15, KLF1, or SIN3A expression in CRC tissues and cells was detected by RT‐qPCR or Western blot. CRC cell functions were measured by CCK‐8, colony formation, and Transwell assays after RBM15 intervention. MeRIP and RIP measured N6 methyladenosine (m6A) and IGF2BP3 enrichment on KLF1 mRNA. ChIP and dual‐luciferase analyzed KLF1 enrichment on SIN3A promoter. Combined experiments verified the effect of KLF1/SIN3A on CRC cell functions. Lung/liver metastasis models were established to validate the effect of RBM15 on CRC in vivo. RBM15, KLF1, and SIN3A were highly expressed in CRC. RBM15 knockdown reduced the proliferation, invasion, and migration of CRC cells in vitro. Mechanistically, RBM15 facilitated KLF1 mRNA stability and expression through IGF2BP3‐dependent m6A modification, thus promoting KLF1 enrichment on the SIN3A promoter and activating SIN3A transcription. Overexpression of KLF1 or SIN3A reversed the inhibitory effect of RBM15 knockdown on CRC cells. In vivo experiments verified that RBM15 promoted tumorigenesis and lung/liver metastasis via KLF1/SIN3A axis. In conclusion, RBM15 stimulated CRC proliferation and metastasis by promoting the KLF1/SIN3A axis through IGF2BP3‐dependent m6A modification.
The impact of menopause education on quality of life among menopausal women: a systematic review with meta-analysis

Abstract A systematic review with meta-analysis was conducted to establish the impact of menopause health education on quality of life (QoL) among menopausal women. Research suggests that specific educational programs can support and enable women during the physical and emotional transition through menopause. The CINAHL, Medline, APA, Embase and Google Scholar databases were searched between 30 November 2021 and 9 January 2022 using the PRISMA guidelines. The Cochrane risk of bias tool was used to critically evaluate the included studies. Review Manager software was used to conduct the meta-analysis of suitable studies. Eight papers were eligible for this review. The participants were aged between 40 and 60 years, with diagnosis of menopause stemming from changes in the menstrual cycle to a last menstrual period of 7 years. Follow-up data were collected between 1 and 4 months post education. Meta-analysis of both the primary outcome (QoL) and secondary outcome (symptom control) demonstrated statistically significant improvements post intervention. Papers not suitable for meta-analysis were reviewed narratively; two papers assessing the primary outcome (QoL) demonstrated an improvement, but only one to a statistically significant level. Secondary outcomes revealed improvements, with all bar one paper doing so to statistical significance. Menopause health education demonstrated an improvement in both QoL and symptom control in menopausal women; however, given some weaknesses in the included studies, further research is justified. Limitations include participants’ level of education, geographical location, risk of bias, that only half of the papers addressed participant use of hormone replacement therapy and length of follow-up.
Nonparametric low-frequency Lévy copula estimation in a general framework

ABSTRACT Let X be a d-dimensional Lévy process. Given the low-frequency observations , , the dependence structure of the jumps of X is estimated. In general, the Lévy measure ν describes the average jump behaviour in a time unit. Thus the aim is to estimate the dependence structure of ν by estimating the so-called Lévy copula of ν. We generalise known one-dimensional low-frequency techniques to construct a Lévy copula estimator based on the above-mentioned n observations and prove , , uniformly on compact sets bounded away from zero with the rate of convergence that is optimal in a certain sense. This convergence holds under quite general assumptions which also include Lévy triplets with non-vanishing Brownian part and ν of arbitrary Blumenthal–Getoor index .
Natural History and Phenotypic Spectrum of GAA‐FGF14 Sporadic Late‐Onset Cerebellar Ataxia (SCA27B)

Heterozygous GAA expansions in the FGF14 gene have been related to autosomal dominant cerebellar ataxia (SCA27B‐MIM:620174). Whether they represent a common cause of sporadic late‐onset cerebellar ataxia (SLOCA) remains to be established.
Isolation and characterization of a cold‐active, detergent‐stable protease from Serratia sp. TGS1

Psychrophiles are cold‐adapted microorganisms living in cold regions and are known to generate cold‐active enzymes such as proteases, lipases, and peptidases. These types of enzymes are a major part of the market of the food and textile sector. This study aimed to isolate and characterize the cold‐active and detergent‐stable, extracellular protease from psychotrophic bacteria Serratia sp. TGS1 (OQ654005). Protease was purified by gel permeation chromatography using Sephadex G‐75. The specific activity of the purified protease was 250 U/mg at 15°C, with a purification fold of 5.68 and a percentage yield of 60%. The cold active protease was stable within a temperature range of 5–30°C and a pH range of 6–10. Ca+2 and Mg+2 enhanced its activity while chelators like ethylenediaminetetraacetic acid inhibited cold active protease, showing it as metalloprotease in nature. The enzyme was sensitive to Cu+2, Zn+2, and Hg+2, and the proteolytic activity decreased upon treatment with heavy metals. The molecular weight of the protease was estimated to be 47 kDa using sodium dodecyl sulfate (SDS)‐polyacrylamide gel electrophoresis. Proteins within a specific range of molecular weight possess desirable properties for industrial enzyme use. By working on a specific range, the researchers intended to examine an enzyme to examine its specific characteristics. The purified protease showed high stability to detergents like SDS, Tween 20, Tween 60, and Triton X. The maximum velocity Vmax and Km values were 59.90 mg/min/mL and 1.53 mg/mL, respectively. The obtained protease exhibited an interesting activity at a broad range of pH (6–10) and stability at low temperatures (5–30°C) and detergents. Such enzymatic features of versatile and potent cold‐active enzymes enhance their industrial applications to meet food, dairy, and laundry requirements.
ISPRS Journal of Photogrammetry and Remote Sensing

Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classi ﬁ cation, we propose a probabilistic graphical model for automatic classi ﬁ cation of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is ﬁ nally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random ﬁ elds to obtain locally continuous and globally optimal classi ﬁ cation results. To evaluate the e ﬀ ectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is e ﬃ cient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classi ﬁ cation correctness, speci ﬁ cally for small objects such as cars and pole-like objects.
Regular and chaotic motions of the Chaplygin sleigh with periodically switched location of nonholonomic constraint

We consider motions of the Chaplygin sleigh on a plane supposing that the nonholonomic constraint is located periodically turn by turn at each of three legs supporting the sleigh. We assume that at switching on the constraint the respective element (“knife-edge”) is directed along the local velocity vector and becomes fixed relatively to the sleigh for a certain time interval till the next switch. Differential equations of the mathematical model are formulated and analytical derivation of a 2D map for the state transformation on the switching period is provided. The dynamics takes place with conservation of the mechanical energy. Numerical simulations show phenomena characteristic to nonholonomic systems with complex dynamics. In particular, on the energy surface attractors may occur responsible for regular sustained motions settling in domains of prevalent area compression by the map. In addition, chaotic and quasi-periodic regimes take place similar to those observed in conservative nonlinear dynamics.
Organic Aerosols in Anoxic and Oxic Atmospheres of Earth-like Exoplanets: VUV-MIR Spectroscopy of CHON Tholins

Organic hazes can drastically impact the characterization and detection of exoplanet atmospheres. We explore the effects of the transition from an anoxic to oxic atmosphere on the molecular structure of organic aerosols, by producing aerosols in N2:CO2:CH4-rich plasmas with increasing CO2/CH4 gas ratios. We performed a spectroscopic study on the resulting aerosol analogs, CHON-rich tholins, from the vacuum-ultraviolet (VUV) to the mid-infrared (MIR), i.e., the 0.13–10 μm range. VUV spectroscopy revealed the presence of π–π* and n–π* transitions in the 200–500 nm range in all samples. These are attributed to electronic transitions in amine groups. As the CO2/CH4 ratio increases, new bands emerge that can be attributed to electronic transitions in hydroxyl and carboxyl functional groups. MIR spectroscopy showed that the molecular structure of oxidized aerosols is dominated by the CO2/CH4 ratio. Band deconvolution revealed the oxygenation of the organic matrix via the formation of oxygenated functional groups, including amide, hydroxyl, and carbonyl groups. For the most oxidized aerosols, absorption is greatest in the 0.13–0.3 μm and 6–10 μm regions. Some of the oxidized samples were further irradiated with VUV photons, resulting in the formation of π–π* bands in the 200–250 nm region and a decrease in the band intensities below 200 nm, attributed to amine and nitrile losses. We discuss how oxidized aerosols could provide a detectable (bio-)signature in exoplanet atmospheres.
MANEMOS. The Trigger Situations Inventory. Validation of a Measure Helping Relapse Prevention in the Treatment of Alcohol Use Disorders

Abstract Background: Relapse Prevention (RP) is a cognitive-behavioral approach that aims to identify situations at high risk of relapse and to support patients with alcohol use disorders (AUDs) in developing coping strategies for the maintenance of desired behavioral changes. Based on this framework, the present study aims to assess and validate the psychometric properties of the MANEMOS, a 24-item inventory of alcohol relapse triggering situations. Methods: The internal consistency and factor structure of the MANEMOS were analyzed in a sample of 313 (27.7% women) in-patients and out-patients, attending alcohol treatment programs in Italian addiction treatment facilities. Results: The results support the internal reliability and validity of the measure. A confirmative factor analysis has revealed the existence of eight distinct dimensions measuring relapse risk situations: namely, Pleasant emotions, Unpleasant emotions, Craving, Conflicts with others, Occasions, Social Pressure, Personal Control, and Physical Discomfort. The patients’ assessment of the riskiness of the situation showed some significant differences depending on gender and on type of treatment received. Conclusions: The findings indicate that MANEMOS is a valid measure for identifying and reflecting on patients’ high relapse-risk situations. This easy and flexible assessment measure may have important implications for prevention and clinical intervention.
Leveraging Multiyear, Geospatial Social Media Data for Health Policy Evaluations: Lessons From the Philadelphia Beverage Tax

Context: Public reactions to health policies are vital to understand policy sustainability and impact but have been elusively difficult to dynamically measure. The 2021 launch of the Twitter Academic Application Programming Interface (API), allowing for historical tweet analyses, represents a potentially powerful tool for complex, comprehensive policy analyses. Objective: Using the Philadelphia Beverage Tax (implemented January 2017) as a case study, this research extracted longitudinal and geographic changes in sentiments, and key influencers in policy-related conversations. Design: The Twitter API was used to retrieve all publicly available tweets related to the Tax between 2016 and 2019. Setting: Twitter. Participants: Users who posted publicly available tweets related to the Philadelphia Beverage Tax (PBT). Main Outcome: Tweet content, frequency, sentiment, and user-related information. Measures: Tweet content, authors, engagement, and location were analyzed in parallel to key PBT events. Published emotional lexicons were used for sentiment analyses. Results: A total of 45 891 tweets were retrieved (1311 with geolocation data). Changes in the tweet volume and sentiment were strongly driven by Tax-related litigation. While anger and fear increased in the months prior to the policy's implementation, they progressively decreased after its implementation; trust displayed an inverse trend. The 50 tweeters with the highest positive engagement included media outlets (n = 24), displaying particularly high tweet volume/engagement, and public personalities (n = 10), displaying the greatest polarization in tweet sentiment. Most geo-located tweets, reflecting 321 unique locations, were from the Philadelphia region (55.2%). Sentiment and positive engagement varied, although concentrations of negative sentiments were observed in some Philadelphia suburbs. Conclusions: Findings highlighted how longitudinal Twitter data can be leveraged to deconstruct specific, dynamic insights on public policy reactions and information dissemination to inform better policy implementation and evaluation (eg, anticipating catalysts for both heightened public interest and geographic, sentiment changes in policy conversations). This study provides policymakers a blueprint to conduct similar cost and time efficient yet dynamic and multifaceted health policy evaluations.
Computational homogenization of histological microstructures in human prostate tissue: Heterogeneity, anisotropy and tension‐compression asymmetry

Human prostatic tissue exhibits complex mechanical behaviour due to its multiphasic, heterogeneous nature, with hierarchical microstructures involving epithelial compartments, acinar lumens and stromal tissue all interconnected in complex networks. This study aims to establish a computational homogenization framework for quantifying the mechanical behaviour of prostate tissue, considering its multiphasic heterogeneous microstructures and the mechanical characteristics of tissue constituents. Representative tissue microstructure models were reconstructed from high‐resolution histology images. Parametric studies on the mechanical properties of the tissue constituents, particularly the fibre‐reinforced hyper‐elasticity of the stromal tissue, were carried out to investigate their effects on the apparent tissue properties. These were then benchmarked against the experimental data reported in literature. Results showed significant anisotropy, both structural and mechanical, and tension‐compression asymmetry of the apparent behaviours of the prostatic tissue. Strong correlation with the key microstructural indices such as area fractions of tissue constituents and the tissue fabric tensor was also observed. The correlation between the stromal tissue orientation and the principal directions of the apparent properties suggested an essential role of stromal tissue in determining the directions of anisotropy and the compression‐tension asymmetry characteristics in normal human prostatic tissue. This work presented a homogenization and histology‐based computational approach to characterize the apparent mechanical behaviours of human prostatic or other similar glandular tissues, with the ultimate aim of assessing how pathological conditions (e.g., prostate cancer and benign prostatic hyperplasia) could affect the tissue mechanical properties in a future study.
Liquidus temperature nonlinear modeling of silicates $SiO_2-R_2O-RO$

The liquidus temperature is an important parameter in understanding the crystalline behavior of materials and in the operation of blast furnaces. Its modeling can be carried out by linear and nonlinear methods through data, considering the artificial neural network a modeling method with high efficiency because it presents the theorem of universal approximation and with that better performances and possibility of greater oscillations. The best linear model and the best nonlinear model were modeled by structural parameters and presented a good numerical approximation, thus demonstrating that mathematical modeling can be performed using structural arguments and also showing a dimensionality reduction method for modeling a thermophysical property of the materials.
Decay of MHD Turbulence before the Final Period for Four-point Correlation in a Rotating System

Abstract: The aim of this study is to determine decay of magnetic field fluctuations in MHD turbulence for fourpoint correlation in a rotating system before the final period. Two, three and four point correlation equations have been obtained and the set of correlation equations is made determinate by neglecting the quintuple correlations in comparison to the third and fourth order correlation terms. The correlation equations are converted to spectral form by taking their Fourier-transforms. Finally, integrating the energy spectrum over all wave numbers. The energy decay of magnetic field fluctuations for four-point correlations in a rotating system is obtained and is shown graphically in the text.