Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning Approaches to Optimal Market Making

Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and ask prices in response to the market maker’s current inventory level and market conditions with the goal of maximizing a risk-adjusted return measure. This problem is naturally framed as a Markov decision process, a discrete-time stochastic (inventory) control process. Reinforcement learning, a class of techniques based on learning from observations and used for solving Markov decision processes, lends itself particularly well to it. Recent years have seen a very strong uptick in the popularity of such techniques in the field, fueled in part by a series of successes of deep reinforcement learning in other domains. The primary goal of this paper is to provide a comprehensive and up-to-date overview of the current state-of-the-art applications of (deep) reinforcement learning focused on optimal market making. The analysis indicated that reinforcement learning techniques provide superior performance in terms of the risk-adjusted return over more standard market making strategies, typically derived from analytical models.
Effects of Virtual Reality-Based Physical And Psychological Exercise On Depression In Stroke Patients : A Meta-Analysis

Background: Virtual Reality (VR) has been widely used in health-related fields. The aim of this study was to assess the effectiveness of virtual reality-based physical and psychological exercise on depression.Methods: A total of 752 patients with stroke from 11 randomized controlled trial (RCT) studies were included in this meta-analysis and the studies derived from 6 electronic databases searched from database inception to August 2021. For continuous results, the standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated to synthesize the effects.Results: The meta-analysis showed that compared with control group, VR-based interventions significantly decreased the depression scale score (SMD = −0.71, 95% CI: −1.32, −0.10) in the subjects. The results showed that the the patient age and total intervention time had a significant impact on the outcome of depression. The smaller the age and longer the intervention duration, the greater the impact on the outcome of depression.Conclusions: The current meta-analysis indicated the virtual reality-based physical and psychological exercise has a significant effect on depression in stroke patients compared to controls. The study showed an outstanding reduction in depression for aged under 60 participants and intervention longer than 8 weeks. At the same time, clinical studies of different ages and different intervention time also need further in-depth study.
Visibility Estimation in Complex, Real-World Driving Environments Using High Definition Maps

As the autonomy level of self-driving vehicles increases, they will be expected to operate safely in increasingly complex environments. During real-world driving, occlusions are inevitable. Therefore, the ability to accurately identify the visible and occluded regions surrounding an autonomous vehicle is crucial for safe operation. In this paper, a method for estimating visibility using 3D point clouds and road network maps is proposed. The proposed method projects the positions of the surrounding lanes, obtained from a road network map, and a 3D scan of the driving environment approximated from a point cloud map, onto depth images. The depth images are then compared in order to determine the visible and occluded regions of the driving environment from the specified viewpoint. Furthermore, a visibility ratio, which is a numerical value that encapsulates visibility information of a particular location, is proposed. The visibility ratio is calculated by dividing the visible area of interest by the total driving area relevant to that location. The proposed method was tested and found to be applicable in both simulated and real-world driving environments. Moreover, the experimental results show that the visibility ratio was representative of the actual visibility from particular locations.
Masticatory function in elderly compared to young adults

ABSTRACT Purpose To characterize the masticatory function of the elderly and to compare total amount of time, masticatory strokes and total mastication score among the elderly and young adults. Methods It is an observational, cross-sectional and analytical study. A total of 50 individuals participated, 25 elderly (mean age 66 years) and 25 young adults (mean age 22 years). The evaluation of mastication was performed by standardized filming of the usual mastication of a wheat flour biscuit. The masticatory type (alternated bilateral, simultaneous bilateral, preferential unilateral, chronic and anterior), masticatory score, total masticatory time and the total number of masticatory strokes were verified and compared between the elderly and young adults. Results The predominant masticatory pattern in the young adults was the alternated bilateral mastication (52%), while, in the elderly, the simultaneous bilateral mastication predominated (48%). The use of dental prostheses had a significant influence on the total mastication score; elderly presented greater masticatory time and greater amount of masticatory strokes; however, the total masticatory score was lower for this group. Conclusion The use of dental prosthesis has a significant influence on masticatory function. When compared to young adults, the elderly had a greater amount of time and masticatory strokes and a lower total mastication score.
Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

Abstract Gully erosion is an erosive process that contributes considerably to the shape of the earth’s surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naïve Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility.
GOLM1 Drives Colorectal Cancer Metastasis by Regulating Myeloid-derived Suppressor Cells

Colorectal cancer (CRC) is the most common digestive neoplasms worldwide, metastasis and recurrence still account for the leading cause for the high mortality rate, but the exact mechanisms remain unclear. More and more evidence has indicated that the deregulation of GOLM1 plays a crucial role in cancer progression. Here, we reported a novel role of GOLM1 in promoting CRC metastasis. In this study, the expression of GOLM1 was detected in human CRC cohort. The function of GOLM1 in CRC metastasis was analyzed by in vivo cecum orthotopic model. We found that the expression of GOLM1 was significantly increased in CRC tissues than adjacent nontumor. Overexpression GOLM1 can promote CRC immune escape and metastasis by recruiting of myeloid-derived suppressor cells (MDSCs) at the same time. PF-04136309, a small molecule and specific inhibitor of CCR2 can largely suppressed GOLM1-mediated CRC metastasis. These results suggest that GOLM1 can promote CRC metastasis and is a prognostic biomarker in human CRC.
Biological Diversity in Aerated Facultative Lagoon Treating Kraft Cellulose Effluent Through Bioaugmentation

Background: This study analyzes the microbiological diversity in an aerated facultative lagoon system with volumetric organic loading rates of 0.2 and 0.6 kgCODm-3d-1 treating effluent from the kraft pulp industry through bioaugmentation. The samples for the study of biological diversity were taken from a laboratory-scale lagoon at steady state treating kraft pulp effluent and operated with 0.2 and 0.6 kgCODm-3d-1 for 120 days. This analysis was performed by identifying the 16s DNA sequencing, through DNA extraction, polymerase chain reaction and agarose gel electrophoresis at 1%. Next, the autochthonous bacteria were named through statistical similarity obtained from the National Center for Biotechnology Information database. The lagoon performance was assessed based on the removal efficiency of specifics compounds.Results: In the biomass samples collected at steady state, 9 and 12 species of bacteria were identified and the species Bacillus cereus, Bacillus thuringiensis and Paenibacillus glucanolyticus found in this matrix presented significant removal of the parameters in the kraft effluent. In the treatment, it was possible to observe that the removal of organic matter above 50% for chemical demand and above 95% for biochemical demand. The specific compounds were not significantly removed, but this is a characteristic of biological treatments. Conclusions: We found that the three referred species show great promise in the removal of specific parameters in a lagoon biological treatment system using bioaugmentation.
A YOLO-v4-Based Risk Detection Method for Power High Voltage Operation Scene

Power accidents may occur during the period of power operation, which threatens the safety of power operators. It is important to supervise the power operation scene and the safety of workers. Traditional monitoring method for power operation relies on camera and manual operation, which is tedious, labour-intensive and error-prone. To solve this problem, this paper proposes a risk detection method of highvoltage power operation scene based on YOLO v4. Firstly, it selects a specific object “hand-held high-voltage joystick” as the identification object of the high-voltage operation scene. Then the index Degree of Intersection (DoI) is defined to describe the degree of overlap between two bounding boxes. Finally, a safety function is constructed as a risk indicator according to safety requirements. The effectiveness of the proposed method is verified based on tensorflow 12.0 and TITAN RTX platform. The model mAP value reached 91.26%, and the detection speed reached 13f/s, which greatly helps the real-time safety monitoring of the power operation field.
The Emission Mechanism of Gold Nanoclusters Capped with 11-Mercaptoundecanoic Acid, and the Detection of Methanol in Adulterated Wine Model

The absorption and emission mechanisms of gold nanoclusters (AuNCs) have yet to be understood. In this article, 11-Mercaptoundecanoic acid (MUA) capped AuNCs (AuNC@MUA) were synthesized using the chemical etching method. Compared with MUA, AuNC@MUA had three obvious absorption peaks at 280 nm, 360 nm, and 390 nm; its photoluminescence excitation (PLE) peak and photoluminescence (PL) peak were located at 285 nm and 600 nm, respectively. The AuNC@MUA was hardly emissive when 360 nm and 390 nm were chosen as excitation wavelengths. The extremely large stokes-shift (>300 nm), and the mismatch between the excitation peaks and absorption peaks of AuNC@MUA, make it a particularly suitable model for studying the emission mechanism. When the ligands were partially removed by a small amount of sodium hypochlorite (NaClO) solution, the absorption peak showed a remarkable rise at 288 nm and declines at 360 nm and 390 nm. These experimental results illustrated that the absorption peak at 288 nm was mainly from metal-to-metal charge transfer (MMCT), while the absorption peaks at 360 nm and 390 nm were mainly from ligand-to-metal charge transfer (LMCT). The PLE peak coincided with the former absorption peak, which implied that the emission of the AuNC@MUA was originally from MMCT. It was also interesting that the emission mechanism could be switched to LMCT from MMCT by decreasing the size of the nanoclusters using 16-mercaptohexadecanoic acid (MHA), which possesses a stronger etching ability. Moreover, due to the different PL intensities of AuNC@MUA in methanol, ethanol, and water, it has been successfully applied in detecting methanol in adulterated wine models (methanol-ethanol-water mixtures).
Challenges Facing Libyan Pharmacists During the Drug Dispensing Process

Pharmacists are supposed to deliver services, like counseling, dispensing, and managing patients care plans. During daily routine they face problems that can hinder their job, e.g., work overload stress, or unorganized drug shelves, and unclear written prescription, which may lead to errors during drug dispensing. These errors are traumatic for the pharmacist and patient; therefore, it is crucial to reduce them and any circumstances that leads to them. In this study, we aimed to study some of the problems that face Libyan pharmacists in their daily routine and prevent them from performing their main roles as community pharmacists. An online cross-sectional survey of pharmacists in Libya was conducted over a period of four months. This online questionnaire was built using the “Google forms” application and was distributed to participants using social network groups of pharmacists. 182 pharmacists responded to this questionnaire. Almost (45%)of the respondents found it difficult to educate patients on their medicines and any related health issues. Meanwhile, the bulk majority (82%) stated that the major problem that faced them was getting a prescription written with very bad handwriting. About (59.4%) of the responding pharmacists stated that they did not have enough time to educate patients about their medications during drug dispensing. In conclusion, this study reveals some of the main challenges that face pharmacists during their daily work. Generally, it shows that it is necessary to make pharmacists aware of social and environmental factors that may complicate their work. This can be done by educating practicing pharmacists about “good pharmacy practices” through workshops and continuous learning programs. Additionally, regulating authorities should also play a role in establishing policies and laws that regulate the design of retail pharmacies.
Perceptions of open‐source software developers on collaborations: An interview and survey study

With the emergence of social coding platforms, collaboration has become a key and dynamic aspect to the success of software projects. In such platforms, developers have to collaborate and deal with issues of collaboration in open‐source software development. Although collaboration is challenging, collaborative development produces better software systems than any developer could produce alone. Several approaches have investigated collaboration challenges, for instance, by proposing or evaluating models and tools to support collaborative work. Despite the undeniable importance of the existing efforts in this direction, there are few works on collaboration from perspectives of developers. In this work, we aim to investigate the perceptions of open‐source software developers on collaborations, such as motivations, techniques, and tools to support global, productive, and collaborative development. Following an ad hoc literature review, an exploratory interview study with 12 open‐source software developers from GitHub, our novel approach for this problem also relies on an extensive survey with 121 developers to confirm or refute the interview results. We found different collaborative contributions, such as managing change requests. Besides, we observed that most collaborators prefer to collaborate with the core team instead of their peers. We also found that most collaboration happens in software development (60%) and maintenance (47%) tasks. Furthermore, despite personal preferences to work independently, developers still consider collaborating with others in specific task categories, for instance, software development. Finally, developers also expressed the importance of the social coding platforms, such as GitHub, to support maintainers, and contributors in making decisions and developing tasks of the projects. Therefore, these findings may help project leaders optimize the collaborations among developers and reduce entry barriers. Moreover, these findings may support the project collaborators in understanding the collaboration process and engaging others in the project.
Um Algoritmo Genético para Seleção de Portfólio de Investimentos com Restrições de Cardinalidade e Lotes-Padrão

O modelo quadrático de Markowitz desempenhou um papel fundamental no problema da seleção de portfólio de investimentos, contudo, já não atende às necessidades da complexidade do mercado atual. A adição de restrições que aproximam o modelo à situação real enfrentada pelos investidores, tornam o problema NP-Completo. Assim, este artigo apresenta um Algoritmo Genético que busca maximizar o retorno de um investimento, dado um patamar aceitável de risco. Após a otimização do portfólio com base em dados históricos, faz-se uma previsão para o ano posterior. Os resultados superam a performance do íIndice Bovespa, destacando os efeitos da diversiﬁcação de acordo com o número de ativos e com o capital disponível para investimento.
Chronic tophaceous gout

A condition of purine metabolism, gout is characterised by sporadic bouts of acute inflammation, pain, swelling and redness of joint. It is usually preceded by hyperuricemia related to increased uric acid synthesis or reduced excretion which leads to monosodium urate deposits in soft tissues and synovial joints, known as tophi.
Machine Learning-Based Predictions of Benefits of High Thermal Conductivity Encapsulation Materials for Power Electronics Packaging

Machine learning (ML)-based predictive techniques are used in conjunction with a game-theoretic approach to predict the thermal behavior of a power electronics package and study the relative influence of encapsulation material properties and thermal management in influencing hotspot temperatures. Parametric steady-state and transient thermal simulations are conducted for a commercially available 1.2 kV/444 A SiC half-bridge module. An extensive databank of 2592 (steady-state) and 1200 (transient) data points generated via numerical simulations is used to train and evaluate the performance of three ML algorithms (random forest, support vector machine and neural network) in modeling the thermal behavior. The parameter space includes the thermal conductivities of the encapsulant, baseplate, heat sink and cooling conditions deployed at the sink; the parametric space covers a variety of materials and cooling scenarios. Excellent prediction accuracies with R2 values > 99.5% are obtained for the algorithms. SHAP (Shapley Additive exPlanations) dependence plots are used to quantify the relative impact of device and heat sink parameters on junction temperatures. We observe that while heatsink cooling conditions significantly influence the steady-state junction temperature, their contribution in determining the junction temperature in dynamic mode is diminished. Using ML-SHAP models, we quantify the impact of emerging polymeric nanocomposites (with high conductivities and diffusivities) on hotspot temperature reduction, with the device operating in static and dynamic modes. Overall, this study highlights the attractiveness of ML-based approaches for thermal design, and provides a framework for setting targets for future encapsulation materials.
Vitamin D Levels of Pediatric Intensive Care Patients

Objective: By measuring serum 25-hydroxyvitamin D (25-OHVitD) levels of the patients in Pediatric Intensive Care Unit (PICU), vitamin D supplementation is aimed to those who have been determined to have vitamin D deficiency. Furthermore, it is aimed to compare the vitamin D levels of only acute disease patients with acute disease added to the chronic disease in PICU. Methods: 327 patients (Group A) aged in the range of 1 month to 18 years, that have been hospitalized in PICU of İnönü University TurgutÖzal Medicine Centre from January 2015 to June 2016, and 90 healthy subjects as a control group (Group B) were included. Group A was divided into two; patients with only acute disease (A1=125 patients) and patients with acute disease added to the chronic disease (A2=202 patients). Demographic information and 25-OHVitD, Ca, P, ALP levels of all cases were recorded retrospectively. Results: Mean of the 25-OHVitD level was 20.9±16.4 ng/dl in Group A; 25.7±17.2 ng/dl in Group A1;17.9±15.2 ng/dl in Group A2 and 25.9±14.4 ng/dl in Group B, respectively. Vitamin D deficiency was 55% in Group A, 43.2% in Group A1, 62.4% in Group A2 and 40% in Group B. In addition, vitamin D insufficiency was 16% in Group A, 17.6% in Group A1, 14.9% in Group A2 and 20% in Group B. On the other hand, vitamin D sufficiency was 29% in Group A, 39.2% in Group A1, 22.8% in Group A2, and 40% in Group B. The increase of age and presence of chronic illness were the determining factors of vitamin D level. Conclusion: It has been found that, the incidence of vitamin D deficiency increases in prevalence, as age increases. The patients in PICU, especially those who have chronic illness, were found to have vitamin D deficiency more frequent. It is concluded that elimination of vitamin D deficiency may contribute to the treatment of the disease for the patients in PICU and the children with chronic illness.
Stochastic and dynamic routing with flexible deliveries for an e-grocer

The quality of the delivery service is a crucial asset for an e-grocer to create and maintain a loyal customer-base. With the rapid market growth of e-grocers over the last decade, there is an urgent need for e-grocer specific routing systems. Although stochastic and dynamic routing models are studied for a wide range of applications, e-grocer specific models are missing in the literature. This paper investigates the concept of flexible deliveries, which introduces differentiated time window sizes. This creates the possibility for real-time re-optimization of the sequence of customers in a trip in order to improve the on-time delivery performance. The potential of flexible deliveries is investigated by means of computational experiments in which historic trip instances from the Dutch e-grocer Picnic are used. It is shown that, when re-optimization is activated, on-time delivery performance is improved and this benefit is significant when flexible deliveries represent at least 10% of the deliveries. When 10% of the deliveries are flexible, the number of late deliveries can be reduced by up to 18% and the number of extreme late deliveries $\mathbf{(\geq\ 15\ {min\ late})}$ up to 27%. This improved on-time delivery performance comes at the cost of a maximum of 2% increase in the average time spent per delivery.