Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Stress, Hopelessness and Thwarted Belongingness Predicts Suicidal Ideation among Youth of Kashmir

—“Suicide is when people harm themselves with the goal of ending their life, and they die as a result”. (National institute of Mental Health). Suicide is a global issue; over 700,000 people lost their lives due to suicide. Suicide is one of the leading causes of death among youth around the globe. (World Health Organization, [WHO], 2021). The state of Jammu and Kashmir, particularly Kashmir division has witnessed many atrocities since 1989. (H.A Wani et al., 2013). As a result mental and psychological health degradation has occurred. This mental and psychological affliction ultimately paved way for suicide among the people. The present study aims to assess suicidal ideation, inter-correlation between perceived stress, hopelessness, thwarted belongingness and suicidal ideation and thus assess the predictive power of factors under the study. The participants for the present study include youth of Kashmir between the age group of 15-19 olds. Results revealed that there is significant positive inter-correlation between the variables under the study. Furthermore, multiple regression analysis revealed that the independent variables significantly predict suicidal ideation. This indicates that the three factors under study have significant impact on suicidal ideation
Design of Quantitative Trading System Based on Data Mining Method under Software and High-Performance Computing

The size of funds managed by all hedge funds in the world has exceeded 2.7 trillion US dollars. The funds of various funds and asset management products managed by quantitative investment account for about 30% of the total global trading volume, and in various large stock exchanges around the world, various quantitative investment methods contribute nearly 50% volume of transactions. The construction of a quantitative trading strategy requires first statistical analysis of the information in the securities and futures market and then backtesting the quantitative model with historical data. In view of the practical application of quantitative trading, this study designs a quantitative trading system based on the data mining method. The main development tool used is the numerical computing software MATLAB, and four cores are designed: quantitative stock selection, strategy backtesting, time-series analysis, and portfolio management. The system supports modules for simple trading decisions. It abandons the traditional method of predicting the absolute value of the future price of stock index futures and adopts a new method of predicting the future price trend of stock index futures. This method avoids the huge impact of the accuracy of the absolute value of the prediction on the final investment in the traditional method and also reduces the high dependence of investors on the accuracy of the absolute value. This study also introduces the support-vector machine algorithm in data mining and the quantitative trading system model in data mining. The accuracy of investment transactions in the experiment is also simulated by using the support-vector machine.
Updating the Symmetric Indefinite Factorization with Applications in a Modified Newton's Method

In recent years the use of quasi-Newton methods in optimization algorithms has inspired much of the research in an area of numerical linear algebra called updating matrix factorizations. Previous research in this area has been concerned with updating the factorization of a symmetric positive definite matrix. Here, a numerical algorithm is presented for updating the Symmetric Indefinite Factorization of Bunch and Parlett. The algorithm requires only O(n/sup 2/) arithmetic operations to update the factorization of an n x n symmetric matrix when modified by a rank-one matrix. An error analysis of this algorithm is given. Computational results are presented that investigate the timing and accuracy of this algorithm. Another algorithm is presented for the unconstrained minimization of a nonlinear functional. The algorithm is a modification of Newton's method. At points where the Hessian is indefinite the search for the next iterate is conducted along a quadratic curve in the plane spanned by a direction of negative curvature and a gradient-related descent direction. The stopping criteria for this search take into account the second-order derivative information. The result is that the iterates are shown to converge globally to a critical point at which the Hessian is positive semi-definite. Computational results are presented which indicate that the method is promising.
On the simulations of aerosol pH in China using (v4.0) : sensitivities of aerosol pH and its temporal variations in haze episodes

Aerosol pH is a fundamental property of aerosols in terms of atmospheric chemistry and its impact on air quality, 15 climate and health. Precise estimation of aerosol pH in chemical transport models (CTMs) is critical forto aerosol modeling and thus influencing policy development that partially relies on results from model simulations. We reported WRF-Chem simulated PM 2.5 pH over China during a period with heavy haze episodes in Beijing, and exploreed the sensitivity of the modeled aerosol pH to factors including emissions of nonvolatile cations (NVCs) and NH 3 , aerosol phase state assumption , and heterogeneous production of sulfate. We findfound default WRF-Chem could predict spatial patterns of PM 2.5 pH over 20 China similar to other CTMs, but with generally lower pH values largely due to the underestimates of alkaline species (NVCs and NH 3 ) and the difference in thermodynamic NVCs 2+ 7.5% 2+ from a normalized (NMB) 5%.
On the Limitations of Continual Learning for Malware Classification

Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software, both malicious and benign, and over the course of the lifetime of a malware classifier, more than a billion samples can easily accumulate. Given the scale of the problem, sequential training using continual learning techniques could provide substantial benefits in reducing training and storage overhead. To date, however, there has been no exploration of CL applied to malware classification tasks. In this paper, we study 11 CL techniques applied to three malware tasks covering common incremental learning scenarios, including task, class, and domain incremental learning (IL). Specifically, using two realistic, large-scale malware datasets, we evaluate the performance of the CL methods on both binary malware classification (Domain-IL) and multi-class malware family classification (Task-IL and Class-IL) tasks. To our surprise, continual learning methods significantly underperformed naive Joint replay of the training data in nearly all settings -- in some cases reducing accuracy by more than 70 percentage points. A simple approach of selectively replaying 20% of the stored data achieves better performance, with 50% of the training time compared to Joint replay. Finally, we discuss potential reasons for the unexpectedly poor performance of the CL techniques, with the hope that it spurs further research on developing techniques that are more effective in the malware classification domain.
Psoriatic Dermal-Derived Mesenchymal Stem Cells Induced C3 Expression in Keratinocytes

Purpose: Our recent studies found a splice region mutation in C3 accompanied by a significantly increased C3 in psoriatic peripheral blood. Mesenchymal stem cells (MSCs) are a key immunological suppression cell. We further investigate the regulation of MSCs on C3 in psoriasis. Patients and Methods: We analyzed the C3 and its upstream S100A9, S100A8 and downstream MCP1 in psoriatic and control skin, and in normal human epidermal keratinocytes (NHEKs) co-cultured with psoriatic versus control dermal-derived mesenchymal stem cells (DMSCs) by mRNA, iTRAQ (isobaric tags for relative and absolute quantitative) and simple Western analysis. Results: The mRNA and Simple Western analysis showed that the expression of C3, S100A8 and S100A9 are upregulated in psoriatic lesion (C3: mRNA, 9.23-fold, p = 0.0092; protein, 3.56-fold, p = 0.0244. S100A8: mRNA, 28.35-fold, p = 0.0015; protein, 4.68-fold, p = 0.0215. S100A9: mRNA, 79.45-fold, p = 0.0066; protein, 12.42-fold, p > 0.05). Moreover, the iTRAQ showed that C3 and S100A9 were significantly increased in NHEKs after co-cultured with psoriatic DMSCs compared to that of control DMSCs (C3: 3.40-fold, p = 0, FDR = 0; S100A9: 2.30-fold, p = 9.86E-241, FDR = 6.50E-239), verified by Simple Western. However, the expression of S100A8 and MCP1 was slightly different between the two groups. Conclusion: Our results suggest that psoriatic DMSCs contribute to the increased C3 expression in psoriatic lesion via upregulating S100A9, providing the theoretical basis for the role of C3 and DMSCs in the pathogenesis of psoriasis.
Hydrodynamics of mass transfer and accretion in close binary systems with compact objects

OWEN, MICHAEL PARKER. Hydrodynamics of Mass Transfer and Accretion in Close Binary Systems With Compact Objects. (Under the direction of John M. Blondin) Most stars are formed in binary or multiple systems. Many of these stars will undergo some period of mass transfer at some point during their lifetimes. Hence it is useful to understand the dynamics of mass transfer in binary star systems in order to better understand the current population of stars in the Galaxy and their evolution. A natural class of objects for study are the close binaries that contain compact objects such as neutron stars and black holes. These systems are bright X-ray emitters, allowing us to study the circumstellar gas within them. We use numerical hydrodynamic modeling to study mass transfer processes in the high-mass X-ray binaries, including the evolutionary sequence between wind fed and disk fed systems, elliptical orbit X-ray binaries, and the global dynamics of LMC X-4 in 3D. We also investigate the properties of high resolution 3D accretion disks, including transport via global waves modes, the effects of tidal stream impact, and the fluid response of tilted disks. Hydrodynamics of Mass Transfer and Accretion in Close Binary Systems With Compact Objects
Microfabricated electrochemical sensors for combustion applications

A new design for the miniaturization of an existing oxygen sensor is proposed based on the application of silicon microfabrication technologies to a cm sized O2 sensor demonstrated by Argonne National Laboratory and The Ohio State University which seals a metal/metal oxide within the structure to provide an integrated oxygen reference. The structural and processing changes suggested will result in a novel MEMS-based device meeting the semiconductor industry standards for cost efficiency and mass production. The MEMS design requires thin film depositions to create a YSZ membrane, palladium oxide reference and platinum electrodes. Pt electrodes are studied under operational conditions ensuring film conductivity over prolonged usage. SEM imaging confirms void formation after extended tests, consistent with the literature. Furthermore, hydrophilic bonding of pairs of silicon die samples containing the YSZ membrane and palladium oxide is discussed in order to create hermetic sealed cavities for oxygen reference. The introduction of tensile Si3N4 films to the backside of the silicon die generates bowing of the chips, compromising bond quality. This effect is controlled through the application of pressure during the initial bonding stages. In addition, KOH etching of the bonded die samples is discussed, and a YSZ membrane that survives the etching step is characterized by Raman spectroscopy.
Female bike riders’ clothing needs with the incorporation of wearable technology: Design and evaluation of wearers’ perception of smart clothing within the cradle-to-cradle design framework

The overall purpose of this study was to increase a general understanding of the mechanisms that determine how female bike riders’ clothing needs are met through the use of smart clothing, by empirically testing new smart clothing designs that incorporate wearable devices developed by the researcher within the cradle-to-cradle design framework. The specific objectives of this study were: (1) to identify important design criteria of bike wear for female bike riders under the frame of consumers’ functional-expressive-aesthetic needs along with their needs and desires for wearable technology; (2) to design and develop smart clothing for female bike riders in accordance with identified female bike riders’ expectations and needs within the cradle-to-cradle design framework; (3) to evaluate female bike riders’ perceived needs satisfaction and social acceptability of the proposed smart clothing by examining the relationships among perceived satisfaction of functional, expressive and aesthetic needs, and perceived social acceptability; and (4) to evaluate the marketability of proposed smart clothing by testing the hypothetical research model incorporating the following variables: perceived satisfaction of functional, expressive and aesthetic needs, perceived social acceptability, attitude, and purchase intention. The findings of this study were based on data collected from two different online surveys (Study 1 and Study 2) as well as a proposed smart bike jacket that included transformable functions developed by this researcher and used for Study 2 survey. For this study, females in large cities worldwide were considered a key segment of the population for studying female bike riders’ clothing needs incorporating wearable technologies. Using a purposeful and convenience sampling approach, the Study 1 sample was recruited from females aged 18 years and over living in the U.S. with bike riding experience, and were members of the “Transportation Alternative,” a
Development of Biomechanical Models for Describing Hand and Finger Placements in Handling Work Objects.

Self selected hand and upper limb postures to get, hold (8s), and put cylindrical objects with weights of 3.3, 20.0, 36.7, and 53.3 N located at elbow height were determined for 10 male and 10 female subjects. Use of overhand grasp decreased in favor of underhand grasp from 85% to get the lightest cylinders to 0% to get the heaviest cylinders. Subjects held lightest cylinders at elbow height in front of their body 20% of the time, and at mid-thigh height using a hook grip 80% of the time. Subjects held heaviest cylinders 60% of the time at shoulder height using a palm grip. The postures used to place objects were similar to the ones for grasping, but the overhand posture was used more frequent for heavier objects. A biomechanical analysis was performed so that logistic models could be developed to describe the influence of relative joint loads on posture selection. The data show that selection of alternative postures are motivated by relative loads as low as 24%.
Meridional-energy-transport extremes and the general circulation of Northern Hemisphere mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

Abstract. The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple spatial scales, from planetary to synoptic. We use the ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (k = 2–3) are key drivers of the meridional-energy-transport extremes, and planetary- and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (k = 4–6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that the weather regime structures associated with these heat wave events are typical for extremely large poleward-energy-transport events.
Multimode dynamics of terahertz quantum cascade lasers: Spontaneous and actively induced generation of dense and harmonic coherent regimes

: We present an extended study concerning the dynamics of dense and harmonic coherent regimes in quantum cascade lasers (QCLs) in a Fabry–Perot (FP) conﬁguration emitting in the terahertz (THz) spectral region. Firstly, we study the device in free running operation, reproducing the main features of the self-generated of optical frequency combs (OFCs) and harmonic frequency combs (HFCs) in this spectral range, commenting on the points of diﬀerence from the mid-infrared region, and ﬁnding excellent agreement with the most recent experimental evidences. Then, we analyze the THz-QCL dynamics under radiofrequency (RF) injection, with a focus on the eﬀect of the modulation of the current on the degree of locking of the system, and we perform a systematic investigation aimed to provide a procedure for the generation of train of pulses with short duration and high contrast. Furthermore, we extend our study to the generation of sequences of pulses with repetition frequency which is a multiple of the free-spectral range (FSR) of the laser cavity, reproducing harmonic mode-locking (HML) of the laser, a method which has been recently demontrated in experiments with THz-QCLs, and the results of which are particulary promising for a large stream of applications, ranging from optical communication to imaging.
The Planetary Nebula in the 500 Myr Old Open Cluster M37

We report confirmation of a large, evolved, bipolar planetary nebula and its blue, white dwarf central star as a member of the ∼500 Myr old Galactic open star cluster M37 (NGC 2099). This is only the third known example of a planetary nebula in a Galactic open cluster and was found via our ongoing program of identifying and studying planetary nebulae—open cluster associations. High confidence in the association comes from the consistent radial velocities and proper motions for the confirmed central star and cluster stars from Gaia, reddening agreement, and location of the planetary nebula well within the tidal cluster boundary. Interestingly, all three Galactic examples have bipolar morphology and likely Type-I chemistry, both characteristics of higher mass progenitors. In this case the progenitor star mass is in the midrange of ∼2.8 M ☉. It provides a valuable, additional point on the key stellar initial-to-final mass relation independent of cluster white dwarf estimates and also falls in a gap in the poorly sampled mass region. This planetary nebula also appears to have the largest kinematical age ever determined and implies increased visibility lifetimes when they are located in clusters.
Shifted Windows Transformers for Medical Image Quality Assessment

To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the image quality, their performance can still be improved in terms of accuracy. In this work, we approach this problem by using Swin Transformer, which improves the poor-quality image classification performance that causes the degradation in medical image quality. We test our approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR) and Left Ventricular Outflow Tract Classification problem on Cardiac MRI with a four-chamber view (LVOT). While we obtain a classification accuracy of 87.1% and 95.48% on the Object-CXR and LVOT datasets, our experimental results suggest that the use of Swin Transformer improves the Object-CXR classification performance while obtaining a comparable performance for the LVOT dataset. To the best of our knowledge, our study is the first vision transformer application for medical image quality assessment.
The Influence of β-decay Rates on r-process Observables

The rapid neutron capture process (r-process) is one of the main mechanisms whereby elements heavier than iron are synthesized, and is entirely responsible for the natural production of the actinides. Kilonova emissions are modeled as being largely powered by the radioactive decay of species synthesized via the r-process. Given that the r-process occurs far from nuclear stability, unmeasured beta-decay rates play an essential role in setting the timescale for the r-process. In an effort to better understand the sensitivity of kilonova modeling to different theoretical global beta-decay descriptions, we incorporate these into nucleosynthesis calculations. We compare the results of these calculations and highlight differences in kilonova nuclear energy generation and light-curve predictions, as well as final abundances and their implications for nuclear cosmochronometry. We investigate scenarios where differences in beta-decay rates are responsible for increased nuclear heating on timescales of days that propagates into a significantly increased average bolometric luminosity between 1 and 10 days post-merger. We identify key nuclei, both measured and unmeasured, whose decay rates directly impact nuclear heating generation on timescales responsible for light-curve evolution. We also find that uncertainties in beta-decay rates significantly impact age estimates from cosmochronometry.
Microscopic fluctuations in power-grid frequency recordings at the sub-second scale

Complex systems, such as the power grid, are essential for our daily lives. Many complex systems display multifractal behavior, correlated fluctuations and power laws. Whether the power-grid frequency, an indicator of the balance of supply and demand in the electricity grid, also displays such complexity remains a mostly open question. Within the present article, we utilize highly resolved measurements to quantify the properties of the power-grid frequency, making three key contributions: First, we demonstrate the existence of power laws in power-grid frequency measurements. Second, we show that below one second, the dynamics may fundamentally change, including a suddenly increasing power spectral density, emergence of multifractality and a change of correlation behavior. Third, we provide a simplified stochastic model involving positively correlated noise to reproduce the observed dynamics, possibly linked to frequency-dependent loads. Finally, we stress the need for high-quality measurements and discuss how we obtained the data analyzed here.