Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithmic Theory of Markets and Their Application to Decentralized Markets

Broadly speaking, I hope to dedicate my PhD to improving our understanding of algorithmic economics with the ultimate goal of building welfare improving decentralized technology for markets. In the following pages, I describe how my past work has built on the existing literature to get closer to the goal of creating such technologies, and describe what research paths this work opens up for the rest of my PhD. I believe that my research has the potential to provide algorithmic solutions to problems in machine learning, optimization, and game theory, and can be used to improve the efficiency of online marketplaces.
Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach

Vehicle-to-Infrastructure (V2I) communication has provided a solution for the improvement of the traffic efficiency of smart city intersections. For example, turning maneuvers prediction at signalized intersections in a connected environment helps traffic command centers time traffic lights and dynamically predict traffic flow. However, the modeling methods used in existing research on this topic have some limitations, such as poor scalability and interpretability of machine learning. Thus, this study proposes a dictionary learning-based approach to predict turning maneuvers before the intersection. The proposed dictionary model estimates the LogDet divergence-based sparse inverse covariance matrix (LDbSICM) of driving behavior samples. The graphical lasso method is used to estimate the sparse inverse covariance matrix of the driving samples to construct a dictionary library of the maneuver behavior. The LogDet divergence is used to calculate the difference between each inverse covariance matrix. A driving simulator is utilized to collect experimental data consisting of turning left (TL), turning right (TR), and going straight (GS) behaviors to establish and evaluate the proposed model. The experimental results demonstrate that the proposed dictionary learning-based turning maneuver prediction model achieves 100% prediction accuracy for TL and GS and 97.2% for TR. The proposed model has substantial advantages over existing methods. The model can predict TL, TR, and GS in a connected environment 270, 280, and 290 m, respectively, before the intersection.
Interdependency and Priority of Critical Infrastructure Information (Case Study: Indonesia Payment System)

The sturdy and reliable payment system is one of the most important systems in the digitalization era, especially in the pandemic COVID-19 period. As part of Critical Infrastructure Information (CII), a strategy to protect the payment system is needed to reduce risks that may arise. But the author's best knowledge, in Indonesia, no reference describes the interdependency of the CII sector that could be used as input on strategy making for reducing the risk on all CII sectors which are influencing the payment system. This research uses the Fuzzy-based DANP (FDANP) Framework based on a multi-expert perspective on inter-sector influences to identify the interdependency and priority of the CII sector with a case study on the payment system. The contribution of this research is to provide information about the interdependency and priority of the CII sector. The findings of this research show that 5 sectors that have an influence on other sectors with a case study of the payment system and the information and communication technology sector, the energy and mineral resources sector, and the financial sector are the three major sectors that must be paid more attention to because they have an impact on many sectors.
A Platform for Holistic Embodied Models of Infant Cognition, and Its Use in a Model of Event Processing

Most computational models of infant cognition focus on selected components of the cognitive system. In this article, we present a model that is more holistic in aspiration: a computational model of a “whole baby,” incorporating a graphical simulation of the baby’s body, and a multicomponent brain model. The model is realistic and fast enough to interact in real time with human users playing a caregiver role. This allows us to directly compare the behavior of the simulated baby interacting with users with that of real babies interacting with parents via video chat using touchscreen devices. To illustrate the model, we present components of the cognitive model involved in processing events, and in representing events in working memory and long-term memory. We focus on the processing of motion events, where an object moves from one location to another. We model how the baby perceives such events, and also how the baby produces them, through motor actions. We also introduce our framework for recording and annotating interactions between real babies and their parents, and we describe a preliminary evaluation of our motion event model against manually identified motion events from these interactions.
Extracellular Vesicles from BMSCs Prevent Glucocorticoid-Induced BMECs Injury by Regulating Autophagy via the PI3K/Akt/mTOR Pathway

Osteonecrosis of the femoral head (ONFH) is a common clinical disease with a high disability rate. Injury of bone microvascular endothelial cells (BMECs) caused by glucocorticoid administration is one of the important causes of ONFH, and there is currently a lack of effective clinical treatments. Extracellular vesicles derived from bone stem cells (BMSC-EVs) can prevent ONFH by promoting angiogenesis and can inhibit cell apoptosis by regulating autophagy via the PI3K/Akt/mTOR signaling pathway. The present study aimed to investigate the effect of extracellular vesicles derived from bone marrow stem cells (BMSC) on a glucocorticoid-induced injury of BMECs and possible mechanisms. We found that BMSC-EVs attenuated glucocorticoid-induced viability, angiogenesis capacity injury, and the apoptosis of BMECs. BMSC-EVs increased the LC3 level, but decreased p62 (an autophagy protein receptor) expression, suggesting that BMSC-Exos activated autophagy in glucocorticoid-treated BMECs. The protective effects of BMSC-EVs on the glucocorticoid-induced injury of BMECs was mimicked by a known stimulator of autophagy (rapamycin) and could be enhanced by co-treatment with an autophagy inhibitor (LY294002). BMSC-EVs also suppressed the PI3K/Akt/mTOR signaling pathway, which regulates cell autophagy, in glucocorticoid-treated BMECs. In conclusion, the results indicate that BMSC-EVs prevent the glucocorticoid-induced injury of BMECs by regulating autophagy via the PI3K/Akt/mTOR pathway.
Comparison of the Goal-Oriented Movements Between Healthy and Injured Leg in Patients with a Unilateral Ankle Sprain

Background: Balance assessment is a very important part of rehabilitation for people with unilateral ankle sprain. Biodex balance analyzer is one of the devices used for assessing balance. Using oriented movements is a method of this device, thus we decided to compare these movements in both healthy and unhealthy feet. Methods: This article was a cross sectional study. In this study 35 men and women aged from 19 - 34 with unilateral ankle sprain were participated. First they stood on one leg on the Biodex device with open eyes and were then asked to look at the screen in front of them and move the cursor to specific points on the screen and with due to attention to the time and overall points, the device allocated a number to each foot, which a shorter time and straighter path leaded to better results. Results: The results showed a significant difference between the healthy and injured legs of the participants in terms of the overall score of the stability index, especially, in the anterior, medial and lateral points (P < 0.05). Conclusions: According to the results, that the goal-oriented movements in the anterior, medial, and lateral directions in the injured leg are significantly different from the healthy leg, it can be assumed that these directions are more affected in the injured leg. Therefore, it is recommended to pay attention to the injured directions in the retraining and rehabilitation programs of patients with lateral ankle sprains.
ICOSL Stimulation by ICOS-Fc Accelerates Cutaneous Wound Healing In Vivo

Background: ICOS and its ligand ICOSL are immune receptors whose interaction triggers bidirectional signals that modulate the immune response and tissue repair. Aim: The aim of this study was to assess the in vivo effects of ICOSL triggering by ICOS-Fc, a recombinant soluble form of ICOS, on skin wound healing. Methods: The effect of human ICOS-Fc on wound healing was assessed, in vitro, and, in vivo, by skin wound healing assay using ICOS−/− and ICOSL−/− knockout (KO) mice and NOD-SCID-IL2R null (NSG) mice. Results: We show that, in wild type mice, treatment with ICOS-Fc improves wound healing, promotes angiogenesis, preceded by upregulation of IL-6 and VEGF expression; increases the number of fibroblasts and T cells, whereas it reduces that of neutrophils; and increases the number of M2 vs. M1 macrophages. Fittingly, ICOS-Fc enhanced M2 macrophage migration, while it hampered that of M1 macrophages. ICOS−/− and ICOSL−/− KO, and NSG mice showed delayed wound healing, and treatment with ICOS-Fc improved wound closure in ICOS−/− and NSG mice. Conclusion: These data show that the ICOS/ICOSL network cooperates in tissue repair, and that triggering of ICOSL by ICOS-Fc improves cutaneous wound healing by increasing angiogenesis and recruitment of reparative macrophages.
Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation

Classical matrix completion methods focus on data with stationary latent structure and hence are not effective in missing value imputation when the latent structure changes with time. This paper proposes a dynamic nonlinear matrix completion (D-NLMC) method, which is able to recover the missing values of streaming data when the low-dimensional nonlinear latent structure of the data changes with time. The paper provides an efficient approach to updating the nonlinear model dynamically. D-NLMC incorporates the information of new data and remove the information of earlier data recursively. The paper shows that the missing data can be estimated if the change of latent structure is slow enough. Different from existing online or adaptive low-rank matrix completion methods, D-NLMC does not require the local low-rank assumption and is able to adaptively recover high-rank matrices with low-dimensional latent structures. Note that existing high-rank matrix completion methods have high-computational costs and are not applicable to streaming data with varying latent structures, which fortunately can be handled by D-NLMC efficiently and accurately. Numerical results show that D-NLMC outperforms the baselines in real applications.
Polariscopy with optical near-fields.

Polarisation analysis of light-matter interactions established for propagating optical far-fields is now extended into an evanescent field as demonstrated in this study using an attenuated total reflection (ATR) setup and a synchrotron source at THz frequencies. Scalar intensity E2, rather than a vector E-field, is used for absorbance analysis of the s- and p-components of the linearly polarised incident light. Absorption and phase changes induced by the sample and detected at the transmission port of the ATR accessory revealed previously non-accessible anisotropy in the absorption-dispersion properties of the sample probed by the evanescent optical near-field. Mapping of the sample's anisotropy perpendicular to its surface by the non-propagating light field is validated and the cos2 θ absorbance dependence was observed for the angle θ, where θ = 0° is aligned with the sample's surface. A four-polarisation method is presented for the absorbance mapping and a complimentary retardance spectrum is retrieved from the same measurement of the angular dependence of transmittance in structurally complex poly-hydroxybutyrate (PHB) and poly-L-lactic acid (PLLA) samples with amorphous and banded-spherulite (radially isotropic) crystalline regions. A possibility of all 3D mapping of anisotropy (polarisation tomography) is outlined.
Adaptive bilateral matching for decoder-side motion vector refinement in video coding

This paper presents an adaptive bilateral matching technique for decoder-side motion vector refinement in video coding. It allows encoder to choose not only the conventional bilateral matching mode with symmetric motion vector difference but also the asymmetric alternatives. To study the efficiency of the proposed technique, the proposed method is integrated in the Versatile Video Coding Test Model 11.0. The experimental result reports an overall of −2.78% luma Bjontegaard Delta rate for the random-access configurations. The compression efficiency on top of the Enhanced Compression Model that beyond the VVC capability are also reported.
EFFECTIVENESS OF PHYSIOTHERAPY INTERVENTION ON FUNCTIONAL ACTIVITIES OF LOWER LIMBS IN A PATIENT WITH POTT’S SPINE

Spinal Tuberculosis (TB) is also known as a disorder that typically leads to a progressive onset of neurological deficits. The anterior part of the vertebral body adjacent to the subchondral plate is the usual involvement site which occurs most often in the lower thoracic vertebrae. Vertebral collapse is a potential consequence of this disease and if this happens anteriorly, anterior wedging results, leading to kyphotic spine deformity. The present case report is of a 30-year-old male diagnosed with Potts spine, presented with neurological deficits and pain. He underwent 4 weeks of physiotherapy primarily focussing on lower limb functional activities. He was under medical management for same. After 4 weeks patient showed good clinical response towards therapy and there was a significant improvement in his range, strength and transitions involving lower limbs. Through this case report we want to conclude that medical and physiotherapy management should go on hand in hand to gain optimal functional recovery in patients with Pott’s Spine.
Professional burnout, stress and job satisfaction of nursing staff at a university hospital

OBJECTIVES: to describe the social and work characteristics of the nursing staff at a tertiary hospital in the Public Health Service of Andalucia, to assess the degree of professional professional burnout and job satisfaction of those professionals and to study the possible relation between the professional burnout variables and the stress and job satisfaction levels on the one hand and social and employment variables on the other.METHOD: descriptive and cross-sectional study in a sample of 258 baccalaureate and auxiliary nurses. As research instruments, an original and specific questionnaire was used to collect social and employment variables, the Maslach Burnout Inventory, the Nursing Stress Scale and the Font-Roja questionnaire. Descriptive, inferential statistics and multivariate analysis were applied.RESULTS: average scores were found for professional stress and satisfaction, corresponding to 44,23 and 65,46 points, respectively. As regards professional burnout, an average score was found on the emotional exhaustion subscale; a high score for depersonalization and a low score for professional accomplishment. Studies are needed to identify the scores on these subscales in health organizations and to produce knowledge on their interrelations.
Detection and Classification of Diabetic Retinopathy Using Artificial Intelligence Algorithms

Diabetic Retinopathy (DR) is considered as a sight-threatening complication of diabetes mellitus, the primary cause of blindness among working-age individuals. Ophthalmologists use fundus images to diagnose diabetic retinopathy and measure its severity by observing retinal lesions with high accuracy. However, diagnosing DR manually from fundus images requires a high level of expertise and effort from professional ophthalmologists. Early diagnosis of diabetes helps in saving the patient’s eye and preventing possible risky complications. In this context, the current paper proposed a model aims to detect DR using image processing and deep learning methods. A fully automatic diagnosis system that exceeds manual techniques to avoid misdiagnosis, reducing time, effort and cost were presented through this paper. A publicly available dataset of fundus images was used to apply the current paper’s proposed neural network model and transfer learning models, to classify each image into one of the five diabetic retinopathy stages. The simple proposed model achieved an accuracy of 66.68% in predicting the right label of the image. On the other hand, the second approach of fine-tuning the pre-trained models achieved higher testing accuracy (ranging from 93.13% to 100%,), which exceeds the current state-of-the-art results.
Therapeutic Effects of Quetiapine and 5-HT1A Receptor Agonism on Hyperactivity in Dopamine-Deficient Mice

Some diseases that are associated with dopamine deficiency are accompanied by psychiatric symptoms, including Parkinson’s disease. However, the mechanism by which this occurs has not been clarified. Previous studies found that dopamine-deficient (DD) mice exhibited hyperactivity in a novel environment. This hyperactivity is improved by clozapine and donepezil, which are used to treat psychiatric symptoms associated with dopamine deficiency (PSDD). We considered that DD mice could be used to study PSDD. In the present study, we sought to identify the pharmacological mechanism of PSDD. We conducted locomotor activity tests by administering quetiapine and drugs that have specific actions on serotonin (5-hydroxytryptamine [5-HT]) receptors and muscarinic receptors. Changes in neuronal activity that were induced by drug administration in DD mice were evaluated by examining Fos immunoreactivity. Quetiapine suppressed hyperactivity in DD mice while the 5-HT1A receptor antagonist WAY100635 inhibited this effect. The number of Fos-positive neurons in the median raphe nucleus increased in DD mice that exhibited hyperactivity and was decreased by treatment with quetiapine and 5-HT1A receptor agonists. In conclusion, hyperactivity in DD mice was ameliorated by quetiapine, likely through 5-HT1A receptor activation. These findings suggest that 5-HT1A receptors may play a role in PSDD, and 5-HT1A receptor-targeting drugs may help improve PSDD.
Clinical Application of Next-Generation Sequencing in Patients With Breast Cancer: Real-World Data

Purpose Next-generation sequencing (NGS)-based tumor panel testing has been reimbursed by the Korean government since 2017. We evaluated the use of NGS-based tumor panel testing in real-world clinical practice, focusing on molecular profiling (MP)-guided breast cancer treatment. Methods A total of 137 breast cancer patients underwent NGS panel testing between December 2017 and July 2020 at Seoul National University Bundang Hospital (SNUBH). Samples from patients were profiled using an in-house SNUBH pan-cancer panel. Sixty-four patients were profiled on SNUBH Pan_Cancer v1.0, targeting 89 genes, while 73 patients were profiled on SNUBH Pan_Cancer v2.0, targeting 546 genes. Results Breast cancer subtypes included hormone receptor+/human epidermal growth factor receptor 2 (HER2)− (n = 87), triple-negative (n = 44), and HER2+ (n = 6). Most patients had locally advanced or metastatic cancers (92%). Approximately 92% (126/137) of the patients had significant genomic alterations (tiers I and II), and 62% (85/137) had targetable genomic alterations. The most common targetable genomic alterations were PIK3CA (39%) and ESR1 mutations (9%), followed by ERBB2 (7%), PTEN (7%), BRCA2 (6%), and BRCA1 mutations (4%). Of the 81 patients with locally advanced/metastatic breast cancer with targetable genomic alterations, 6 (7.4%) received MP-guided treatments, including PARP inhibitor (n = 4), ERBB2-directed therapy (n = 1), and PI3K inhibitor (n = 1). Among these 6 patients, 4 participated in clinical trials, 1 underwent treatment at their own expense, and 1 received drugs through an expanded access program. The remaining 66 patients (81%) with targetable genomic alteration did not receive MP-guided treatment due to lack of matched drugs and/or clinical trials, poor performance status, and/or financial burden. Conclusion NGS panel testing allowed MP-guided treatment in only 4.7% (6/127) of patients with advanced breast cancer in a real-world setting. The availability of matched drugs is critical for the realistic implementation of personalized treatment.
Determinants of Online Behavior Among Jordanian Consumers: An Empirical Study of OpenSooq

Aim/Purpose: This study identifies the elements that influence intentions to purchase from the most popular Arabic online classifieds platform, OpenSooq.com. Background: Online purchasing has become popular among consumers in the past two decades, with perceived risk and trust playing key roles in consumers’ intention to purchase online. Methodology: A questionnaire survey was conducted of Internet users from three Jordanian districts to investigate how they used the OpenSooq platform in their e-commerce activities. In total, 202 usable responses were collected, and the data were analyzed with PLS-SEM for hypothesis testing and model validation. Contribution: Though online trading is increasingly popular, the factors that impact the behavior of consumers when purchasing high-value products have not been adequately investigated. Therefore, this study examined the factors affecting perceived risk, and the potential impact of privacy concerns on the perceived risk of online smartphone buyers. The study framework can help explore online behavior in various situations to ascertain similarities and differences and probe other aspects of online buying. Findings: Perceived risk negatively correlates with online purchasing behavior and trust. However, privacy concern and perceived risk, transaction security and trust, and trust and online purchasing behavior exhibited positive correlations. Recommendations for Practitioners: Customers can complete and retain online purchases in a range of settings illuminated in this study’s methods and procedures. Moreover, businesses can manage their IT arrangements to make Internet shopping more convenient and build processes for online shopping that allow for engagement, training, and ease of use, thus improving their customers’ online purchasing behavior. Recommendation for Researchers: Given the insight into the understanding and integration of variables including perceived risk, privacy issues, trust, transaction security, and online purchasing behavior, academics can build on the groundwork of this research paradigm to investigate underdeveloped countries, particularly Jordan, further. Impact on Society: Understanding the characteristics that influence online purchasing behavior can help countries realize the full potential of online shopping, particularly the benefits of safe, fast, and low-cost financial transactions without the need for an intermediary. Future Research: Future research can examine the link between online purchase intent, perceived risk, privacy concerns, trust, and transaction security to see if the findings of this study in Jordan can be applied to a broader context in other countries.