Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles

The objective of the current study was to design a suitable model to predict the cytotoxicity induced by SiO2 and TiO2 nanoparticles in different conditions using computational models. To achieve this, we employed various statistical approaches such as linear regression, as well as artificial neural networks and support vector machine (nonlinear models). The effective input parameters of the SiO2 nanoparticles were particle size, particle concentration, and cell exposure time. In the case of the TiO2 nanoparticles, the particle size and concentration served as input variables. Cell viability was considered the output response for both nanoparticles. The modeling was performed using both linear and non-linear methods. In addition, an external validation analysis was conducted to evaluate the predictability of the models by splitting the data into training and test data. The best models to predict cell viability were the models developed by artificial neural network. The results of this investigation indicate that non-linear models could be superior to linear models in predicting cell viability for SiO2 and TiO2 nanoparticles.
Self-Supervised 3D Human Mesh Recovery from a Single Image with Uncertainty-Aware Learning

Despite achieving impressive improvement in accuracy, most existing monocular 3D human mesh reconstruction methods require large-scale 2D/3D ground-truths for supervision, which limits their applications on unlabeled in-the-wild data that is ubiquitous. To alleviate the reliance on 2D/3D ground-truths, we present a self-supervised 3D human pose and shape reconstruction framework that relies only on self-consistency between intermediate representations of images and projected 2D predictions. Specifically, we extract 2D joints and depth maps from monocular images as proxy inputs, which provides complementary clues to infer accurate 3D human meshes. Furthermore, to reduce the impacts from noisy and ambiguous inputs while better concentrate on the high-quality information, we design an uncertainty-aware module to automatically learn the reliability of the inputs at body-joint level based on the consistency between 2D joints and depth map. Experiments on benchmark datasets show that our approach outperforms other state-of-the-art methods at similar supervision levels.
A New Method for Monitoring Video Traffic in Wireless Networks by Using Distortion-Resistant Framework

Broadband and wireless communication systems in this day and age are more vigorous and omnipresent than they used to be before. Video activity has turned into an issue these days because of the expansion in the utilization of wireless networks. Keeping up a decent nature of video is essential. The video quality is influenced by: 1) the distortion because of pressure at the source and 2) distortion because of both wireless channel initiated blunders and obstruction. Here, we are attempting to diminish distortion in video movement streaming over a wireless network. The present clients request astounding recordings to be conveyed consistently on their gadgets. In this paper, we examine directing arrangements to decrease video distortion on a conclusion to end premise. Customary and well known connection based directing measurements, for example, ETX cause high video distortion as they don't represent reliance over the connections of a way. Thus, video activity proceeds onto couple of ways causing distortion. To decrease the distortion in recordings and report outline misfortune in recordings, we construct a logical structure. A steering protocol for decreasing distortion in recordings is composed in view of the system's directing strategy. Reenactments are done to demonstrate the protocol outlined is effective in limiting video distortion.
MelSpectroNet: Enhancing Voice Authentication Security with AI-based Siamese Model and Noise Reduction for Seamless User Experience

Voice authentication has become critical for secure access control while achieving usability. Background noise and increased security requirements, however, continue to be problems. This paper presents MelSpectroNet, an innovative voice authentication system using Siamese neural network trained on over one million samples. It leverages mel spec-trograms for efficient feature extraction and employs noise reduction, enhancing reliability. The model achieves 96.62% test accuracy, demonstrating efficacy. Our methodology involves audio denoising, meticulous spectrogram preprocessing, a tailored Siamese architecture, and rigorous training. Testing demonstrates MelSpectroNet’s exceptional performance and ability to generalize. However, enhancing longitudinal accuracy by accounting for natural voice variations over time still needs exploration. Overall, MelSpectroNet pioneers highly accurate and usable voice authentication with enhanced security. It balances user convenience and stringent authentication needs. This research motivates further work to optimize these systems for diverse conditions while advancing security and inclusiveness.
Investigation of the Mediator Role of Authenticity Between Emotional Intelligence and Resilience Levels of University Students

This study examined the mediating effect of authenticity on the relationship between emotional intelligence and university students' resilience levels. While the concept of emotional intelligence is explained as the ability to control and manage emotions, resilience is defined as coping with negative situations and staying psychologically sound, and authenticity is the state of being original. The sample of the study consists of a total of 419 university students, 317 girls and 102 boys, aged between 18 and 25. Emotional Intelligence Trait Scale-Short Form, Short Psychological Resilience Scale, and Authenticity Scale were used as data collection tools. Data were analyzed with Pearson product-moment correlation, Confirmatory factor analysis, and Structural equation modeling. The results obtained from the study showed that there were positive significant relationships between emotional intelligence and resilience, emotional intelligence and authenticity, and authenticity. After this analysis, the mediating effect of authenticity between emotional intelligence and psychological resilience was examined. In addition, it was concluded that authenticity has a partial mediating effect between emotional intelligence and psychological resilience.
PERSONAL FINANCIAL PLANNING FOR THE RETIREMENT OF GEN Y IN PHRAYA PRASIT COMMUNITY, DUSIT DISTRICT, BANGKOK

The objectives of this research were: 1) to study the level of personal financial planning for Gen Y's ability to live in retirement in Phraya Prasit community, Dusit District, Bangkok, 2) to compare the level of personal financial planning for Gen Y to be able to live in retirement age in Phraya Prasit Community, Dusit District, Bangkok classified by personal factors. The sample group used in this research was 200 people living in Phraya Prasit community, Dusit District, Bangkok more than 5 years and over to whom were born during 1997-1982, with ages between 25-40 years old. Statistics used to analyze the data were percentage, mean, standard deviation, t-test, and analysis of variance. The statistical significance was determined at the 0.05 level. The results of the study showed that most of the people who live in Phraya Prasit Community, Dusit District, Bangkok with ages between 25-40 years and have lived in the area for 5 years or more were females with ages between 36-40 years old, they were singles, most of them were students, having a bachelor's degree
Arthroscopic treatment of massive rotator cuff tears involving subscapularis

Introduction. Massive rotator cuff tears represent a major challenge in shoulder surgery. Contemporary arthroscopic techniques allow for repair in majority of cases. In the setting of a massive rotator cuff tear, engagement of subscapularis plays a significant role in function deterioration and is a risk factor for pseudoparalysis. Materials and methods. We present our experience and clinical results with arthroscopic repair of massive rotator cuff tears engaging subscapularis in 32 patients, followed for 12,5 months. Results. Complete repair was performed in 25 (78.1%) and partial repair in 7 (21,9%) of the cases with significant improvement in active forward flexion and good to excellent functional result in 68,8%, according to Constant score. Acute pseudoparalysis was successfully reversed in 8 out of 9 cases (88,8%). Transient neurologic complication was observed in 1 case (3%). Conclusion. Subscapularis plays an important role in massive rotator cuff tears and should always be repaired. Arthroscopic repair of massive rotator cuff tears engaging subscapularis is safe and efficient with good clinical results during one year follow up. While complete repair of all torn tendons with secure reattachment to the tuberosities is the preferred surgical treatment, repair of subscapularis and lower portion of infraspinatus is crucial for normal rotator cuff function. Careful patient selection and individually tailored surgery is important for successful treatment.
Economic Education, Digital Literacy and Intention to Invest Among Students: The Mediating Role of Financial Attitudes

The pandemic has raised the students’ activities in finding income, ranging from entrepreneurship to investing intention. This study aims to determine the role of economic education and digital literacy on student investment interest in universities during the Covid-19 period. In the analytical model proposed, it is assumed that the financial attitude variable is a mediating variable in education and financial knowledge in investment intentions. Ajzen’s Theory of Planned Behavior and Bandura’s Social Learning Theory was developed for the research model. The research adopted quantitative methods using survey research data from saturated sampling techniques. The participants in this study were students of Universitas Negeri Jakarta who are members of the Indonesian stock exchange investment gallery organization. From the 234 proposed questionnaires, 203 questionnaires were returned and filled out completely for data analysis. The study results confirmed the three hypotheses and rejected the four proposed hypotheses. Economic education affects student investment intentions and significantly affects financial attitudes. However, digital literacy affects students’ investment intentions but fails to shape investment intentions through financial attitudes. Another finding is that economic education and digital literacy have no effect on shaping financial attitudes, but financial attitudes influence students’ investment intentions during the pandemic. Keywords: economic education, digital literacy, intention to invest, financial attitudes, covid-19 period
Diagnosis of Retinal Diseases from OCT Images using Deep Learning Algorithms

OCT is a non-invasive test that provides ophthalmologists with cross-sectional images of the retinal layer of the eye, enabling them to make diagnoses based on the layers of the retina. As a result, it is a crucial modality for the identification and measurement of disorders and anomalies of the retina. For each patient, OCT produces several images, therefore ophthalmologists must spend a lot of time analyzing the data. In this paper, four categories—such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal—are proposed for machine learning models that classify OCT pictures of patients. There are two distinct models put forth. Convolutional neural networks (CNNs) with three layers are used in one, whereas those with seven layers are used in the other to create binary classifiers, several CNNs are modified as feature extractors, including VGG16, ResNet50, and DenseNet121. With 0.927 accuracy, 0.927 sensitivity, and 0.932 specificity, the suggested model that uses VGG16 for CNV vs. Other classes, DME vs. Other classes, Drusen vs. Other classes, and Normal vs. Other classes exhibit the best performance among them. The accuracy of the Normal class binary classifier is 0.947. These findings indicate that they could serve as an additional reader for ophthalmologists.
RESEARCH ARTICLE Association of Oral Contraceptives Use and Lung Cancer Risk among Women: an Updated Meta-analysis Based on Cohort and Case-control Studies

Background: Previous studies on the association of oral contraceptives (OC) use and lung cancer generated inconsistent findings. The aim of this study was to confirm any definite correlation between OC use and lung cancer risk. Methods: Publications were reviewed and obtained through PubMed and EMBASE databases literature search up to November, 2013. Reference lists from retrieved articles were also reviewed. The language of publication was restricted to English. A meta-analysis was performed to evaluate the association by calculating pooled odds ratios (ORs) and 95% confidence intervals (CIs). Results: A total of 14 studies consisting of 9 case-control studies and 5 cohort studies were finally included in this meta-analysis. There was no significant association observed between OC use and lung cancer risk in the overall analysis (OR=0.91; 95% CI=0.81-1.03). There was a significant protective effect in Europe (OR=0.74; 95% CI=0.60-0.91) and a borderline significant protective effect with an adenocarcinoma histology (OR=0.90; 95% CI=0.80-1.01) in subgroup analyses. No association was observed for methodological quality of study, study design, smoking status and case number of study. Conclusion: This meta-analysis suggests that OC use is not likely to be associated with the risk of lung cancer at all. While a significant protective effect of OC use on lung cancer was observed in Europe, interpretation should be cautious because of the potential biases of low-quality studies. At the same time, more attention should be paid to the possible association of OC use with adenocarcinoma of lung. Our findings require further research, with well-conducted and large-scale epidemiological studies to confirm effects of OC use on lung cancer.
Challenges and Opportunities in Developing Tailored Pain Management Strategies for Liver Patients

Chronic liver illnesses pose a substantial worldwide health challenge, with various causes that span from viral infections to metabolic problems. Individuals suffering from liver problems frequently face distinct difficulties in pain control, requiring a customized strategy that takes into account both the fundamental disease and the complexities of liver function. The liver, a vital organ responsible for metabolic control and detoxification, is pivotal in multiple physiological processes. Chronic liver illnesses, such as cirrhosis and non-alcoholic fatty liver disease (NAFLD), are marked by a gradual process of inflammation and fibrosis, resulting in reduced liver function. These disorders often come with pain, varying from internal discomfort to intense abdominal pain, which impacts the quality of life and general well-being of patients. The review explores the complex aspects of pain perception in liver illnesses, including inflammation, modified neuronal signaling, and the influence of comorbidities. It highlights the significance of a detailed comprehension of the pain experience in individuals with hepatic conditions for the implementation of successful pain management treatments. In addition, the review emphasizes the difficulties involved in treating pain in this group of patients, such as the possible complications linked to commonly prescribed pain relievers and the necessity for collaboration between hepatologists, pain specialists, and other healthcare professionals. Moreover, it examines new possibilities in the domain, such as the significance of innovative pharmacological substances, non-pharmacological treatments, and personalized medicine strategies designed for specific patient characteristics. This study thoroughly analyzes the difficulties and possibilities involved in creating personalized pain management approaches for individuals with liver conditions. Its purpose is to guide physicians, researchers, and healthcare providers, enabling them to implement more efficient and patient-focused interventions. As our comprehension of liver-related pain progresses, the potential for enhancing the quality of life for persons with chronic liver disorders through tailored pain management measures becomes more and more encouraging.
Tribology Characteristic of Ball Bearing SKF RB-12.7/G20W using SAE 5W-30 Lubricant with Carbon-Based Nanomaterial Addition

The thermophysical, rheological, and tribological properties in lubricant are essential for preserving the engines, power steering systems, and engine components from damage caused by friction and wear. The incorporation of nano-sized particles into lubricating oils has been reported to enhance these properties. Therefore, this study explores the impacts resulting from the incorporation of graphene, multiwalled carbon nanotubes (MWCNT), double-walled carbon nanotubes (DWCNT), and single-walled carbon nanotubes (SWCNT) at a quantity of 0.04 grams into the SAE 5W-30 base oil. These nanoparticles were synthesized using a two-step method, resulting in nanolubricants. The addition of carbon-based nanomaterials increases the density and decreases the viscosity of the base lubricant. The flow of nanolubricants exhibits non-Newtonian shear thinning behavior during the test at elevated temperatures. The results of the four-ball tribo-tester test reveal the increase of Coefficient of Friction (CoF) values and the extent of wear scar area. Further, the Scanning Electron Microscopy (SEM) analysis was employed to observe the wear morphology of the nanolubricants on the SKF RB-12.7/G20W ball bearing. Its results indicate a prevalence of groove-type wear as compared to conventional lubricants.
Spatio-Temporal Signatures for Video Copy Detection

The number of copied videos is growing rapidly on television broadcast networks as well as on the world wide web. The existing copy detection methods resort either to image techniques or to video ones. We propose a spatio-temporal signature for the automatic detection of video extracts, based on the evolution of gray level centroids along time. We have obtained good results for a base of more than 50 Gb of data and for numerous tests of robustness. Our algorithm is robust to changes in contrast and brightness, zooms, modification of frame rate, a logo superimposition on the image, etc.
University of Memphis Digital Commons University of Memphis Digital Commons

This is a report on the LIDA architecture, a work in progress that is based on IDA, an intelligent, autonomous, "conscious" software agent that does personnel work for the US Navy. IDA uses locally developed cutting edge artificial intelligence technology designed to model human cognition. IDA's task is to find jobs for sailors whose current assignments are about to end. She selects jobs to offer a sailor, taking into account the Navy's policies, the job’s needs, the sailor's preferences, and her own deliberation about feasible dates. Then she negotiates with the sailor, in English via iterative emails, about job selection. We use the word "conscious" in the sense of Baars' Global Workspace Theory (Baars, 1988, 1997), upon which our architecture is based.
Memristor Synapses for Neuromorphic Computing

Neuromorphic computing, which imitates the principle behind biological synapses with a high degree of parallelism, has recently emerged as a promising candidate for novel and sustainable computing technologies. The first step toward realizing a massively parallel neuromorphic system is to develop an artificial synapse capable of emulating synapse functionality, such as analog modulation, with ultralow power consumption and robust controllability. We begin this chapter with a simple description of neuromorphic systems and memristor synapses. Further, we introduce and evaluate the state-of-the-art neuromorphic hardware technology in terms of novel functional materials and device architectures toward the implementation of fully neuromorphic computers, which have been extensively explored in recent years. Finally, we briefly describe artificial neural networks based on memristor synapse in forms of crossbar arrays.
Seismogenic deformation between the Sierran microplate and Oregon Coast block, California, USA

The Sierran microplate is a northwest-translating block entrained in distributed motion east of the Pacific plate. To the north, the Oregon coast block (OCB) moves northward within the hanging wall of the Cascadia subduction zone, above the obliquely converging Juan de Fuca plate. Analysis of GPS velocity data indicates that relative motion between the rigid Sierran and OCB microplates is characterized by several millimeters per year of dextral shear directed ∼N70°W, which is distinct from and counterclockwise to macroscopic dextral shear in the Walker Lane east of the Sierran microplate. We present a new analysis of focal mechanisms from small earthquakes in an 80-km-wide zone that spans the geodetically defined Sierran-OCB boundary to evaluate patterns of distributed deformation. We find that the direction of macroscopic dextral shear in this region is parallel to Sierran-OCB motion derived from GPS data. The seismogenic deformation is consistent with postulated dextral shear within an incompletely studied west-northwest–trending zone of faults and lineaments that traverses the northern Sierra Nevada; the faults and lineaments terminate westward against Quaternary folds in the northern Sacramento Valley. Active deformation at the Sierran-OCB boundary accommodates the relative motion of the bounding microplates and probably does not represent discrete transfer of Walker Lane motion to the Cascadia subduction zone in a restraining left step across the northern Sierra and Sacramento Valley.
Projected trust region method for stochastic linear complementarity problems

ABSTRACT: In this paper, we consider the method for solving a kind of general stochastic linear complementarity problems. Based on Fischer-Burmeister function, we transform this kind of general stochastic linear complementarity problems into nonsmooth equations and present a projected trust region method to solve the nonsmooth equations. The global convergence of this method is also given. Finally, the numerical results are reported.
IZPR: Instant Zero Knowledge Proof of Reserve

We present a non-interactive and public verifier scheme that allows one to assert the asset of a financial organization instantly and incrementally in zero knowledge with high throughput. It is enabled by the recent breakthrough in lookup argument, where the prover cost can be independent of the lookup table size after a pre-processing step. We extend the cq protocol [21] and develop an aggregated non-membership proof for zero knowledge sets. Based on it, we design a non-intrusive protocol that works for pseudo-anonymous cryptocurrencies such as BTC. It has O ( n log( n )) prover complexity and O (1) proof size, where n is the platform throughput (instead of anonymity set size). We implement and evaluate the protocol. Running on a 56-core server, it supports 1024 transactions per second.
Couëtoux Milone Brendel Doghmen Teytaud While RAVE

In the last decade, Monte-Carlo Tree Search (MCTS) has revolutionized the domain of large-scale Markov Decision Process problems. MCTS most often uses the Upper Confidence Tree algorithm to handle the exploration versus exploitation trade-off, while a few heuristics are used to guide the exploration in large search spaces. Among these heuristics is Rapid Action Value Estimate (RAVE). This paper is concerned with extending the RAVE heuristics to continuous action and state spaces. The approach is experimentally validated on two artificial benchmark problems: the treasure hunt game, and a real-world energy management problem.