Miracle-dz/UniFAR-FTUs huggingface dataset:
Unravelling the enigmas of the ‘silver sands’ in the Dutch/German/Belgian border area

Bright white sands consisting almost exclusively of quartz (sometimes called ‘silver sands’) occur throughout the world; those in Europe commonly date from the late Paleogene and early Neogene. They have a clearly sedimentary origin, and they may have originated in various types of environments, but precise data are lacking because sedimentary research into these deposits (that have a high economic value) has been scarce. It is most likely that diagenetic processes are largely responsible for their exceptional appearance, but it is highly unlikely that all silver sands were subject to the same diagenetic conditions. The precise origin of most of the silver sands is still enigmatic. In the case of the silver sands in the Dutch/German/Belgian border area, it appears that long-lasting in situ leaching by humic acids (resulting in an extremely low percentage of heavy minerals), in combination with differential cementation (and later partial dissolution of the cement), must be held responsible for the wide variety of the characteristics of these sands, including locally sharp boundaries with the underlying sands, lack of precipitates at the contact plane with the underlying sands, and the joint occurrence of strongly weathered and fresh specimens of the same heavy-mineral species.
Methodological aspects and standardization of PET radiomics studies

In oncology, Positron Emission Tomography (PET) is frequently performed for cancer staging and treatment monitoring. Metabolic active tumour volume (MATV) as well as total MATV (TMATV - including primary tumour, lymph nodes and metastasis) derived from PET images have been identified as prognostic factor or for evaluating treatment efficacy in cancer patients. To this end a segmentation approach with high precision and repeatability is important. Moreover, to derive TMATV, a reliable segmentation of the primary tumour as well as all metastasis is essential. However, the implementation of a repeatable and accurate segmentation algorithm remains a challenge. In this work, we propose an artificial intelligence based segmentation method based on textural features (TF) extracted from the PET image. From a large number of textural features, the most important features for the segmentation task were selected. The selected features are used for training a random forest classifier to identify voxels as tumour or background. The algorithm is trained, validated and tested using a lung cancer PET/CT dataset and, additionally, applied on a fully independent test-retest dataset. The approach is especially designed for accurate and repeatable segmentation of primary tumours and metastasis in order to derive TMATV. The segmentation results are compared with conventional segmentation approaches in terms of repeatability. In summary, the TF segmentation proposed in this study provided better repeatability than conventional segmentation approaches. Moreover, segmentations were accurate for both primary tumours and metastasis and the proposed algorithm is therefore a good candidate for PET tumour segmentation.
The roles of Klotho and FGF-23 in bipolar manic episode.

OBJECTIVE Bipolar disorder (manic episode) is an essential psychiatric disorder with unknown etiology, in which inflammation is considered to play a role. Klotho and FGF-23 are known to be associated with inflammation. Therefore, this study aimed to determine the link between Klotho and FGF-23 levels and bipolar disorder. PATIENTS AND METHODS In this study, 42 men with BD and 41 healthy controls were enrolled, followed up, and/or treated at the High-Security Forensic Psychiatry Clinic. Sociodemographic data form, Young Mania Rating Scale, and Hamilton Depression Rating Scale were applied to all participants. RESULTS Klotho and FGF-23 levels were significantly increased in patients with BD manic episodes. There was no correlation between Klotho and FGF-23 levels and clinical parameters. For Klotho and FGF-23, cutoff values of 69 and 1,646 yielded 67.4% sensitivity and 72.1% specificity and 81.4% sensitivity and 51.2% specificity, respectively. CONCLUSIONS Klotho and FGF-23 may play critical roles in the etiopathology of manic episodes and are potential candidate biomarkers for bipolar disorder. This relationship might contribute to the etiopathogenesis of the disease and determine its treatment. Anti-Klotho and anti-FGF-23 administration may be a future treatment for controlling the course of the disease.
Linguistic Exaptation in Arabic

: In contemporary years, there is an interesting linguistic topic that has been attracted by many scholars which is "linguistic exaptation", for example, "functional renewal" (Brinston and Stein, 1993), "semantic reanalysis" (Eckardt, 2011), and "revival" Ghazala (2014). "Linguistic exaptation" is described by those scholars but with different terms. This phenomenon is significant since languages are constantly losing some semantic contents of lexical words, but sometimes retaining the phonetic string to be reused with new semantic content. If languages have fixed linguistic system, there will be no sign of vitality in the language and no room for change. Therefore, the main goal of this study is to concentrate on whether the phenomenon of linguistic exaptation exists in Arabic or not. For the sake of the validity of the analysis and results, the method employed here is based on Arabic dictionaries, articles, and also published books. The results suggest that the phenomenon "linguistic exaptation" occurs in Arabic language and there are many Arabic examples that represent this phenomenon such as sjarah 'car', qitar 'train', dʒaridah 'newspaper', dʒwal 'mobile phone', ħ ‏ ari:m 'women', and hatif 'telephone'.
ANALYSIS OF SPEAKER ADAPTATION TECHNIQUES IN AUTOMATIC SPEECH RECOGNITION SYSTEMS USING DEEP NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS

The accuracy of automatic speech recognition (ASR) systems can be affected by changes in training and testing environments. In the context of adaptation, narrowing the model-to-dataset discrepancy for a certain speaker or channel is quite effective. Two of the most widely used ASR methods nowadays are deep neural networks and Gaussian mixture models (GMMs). GMM-HMM has been a standard approach in ASR systems for decades. Speaker adaption is especially helpful to AMs in this particular subgroup. Efforts have been made to help this group in several ways. DNN-HMM AMs, on the other hand, have lately beaten GMM-HMM models in ASR tasks. These AMs, on the other hand, frequently have to retrain their accents, which can be difficult for them. As a result, many GMM model modification processes do not apply to DNNs. An explanation of GMM models, as well as ways for increasing speaker adaption, is the primary purpose of this study Domain Adaptation Challenge unsupervised domain adaptation goal data might be collected using DNNs as well (DAC). An out-of-domain system's speaker recognition performance is improved by more than 25 percent by using a DNN trained on data from outside the domain.
ANALYSIS OF THE EFFECT OF GAMIFICATION ON CUSTOMER LOYALTY OF THE USE OF THE ONLINE TRANSPORTATION APPLICATION

The purpose of this paper is to see what factors influence customer loyalty in the use of the Gojek online transportation application. Then the data collection was carried out by distributing questionnaires through the google form media to 410 respondents who were users of the Gojek application in the Jabodetabek area. All data that has been collected is processed using the Smart PLS 3.0 application. Based on the analysis conducted, the results show that the gamification variable, namely Entertainment, has an influence on Hedonic Value, as well as the Intimacy and Novelty variables have a significant influence on Hedonic Value and Utilitarian Value, while the Trendiness variable has a significant influence on Hedonic Value but does not have a significant effect on Hedonic Value. influence on Utilitarian Value. Then Hedonic Value and Utilitarian Value have a significant effect on Satisfaction and Satisfaction has a significant influence on Continuance Intention. Then Hedonic Value, Satisfaction, and Continuance Intention have a significant influence on Loyalty. Meanwhile, Utilitarian Value has no effect on Loyalty.
Outcome of Self Efficacy of Tuberculosis-Infection Prevention Among Patients Recieveing Anti-Retroviral Therapy in Tertiary Hospitals in Ogun State, Nigeria

: Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis Human Immunodeficiency Virus (HIV) infection stands out as the most significant risk factor for tuberculosis. Public health education intervention is effective preventive strategy against tuberculosis infection, improvement of people’s knowledge of self-efficacy of tuberculosis infection risk factors led to a decrease in prevalence rate of the infection. The study was a descriptive survey utilizing 80-item validated questionnaire to collect information on Knowledge of tuberculosis prevention practices among people living with HIV currently enrolled in two facilities providing free comprehensive HIV care and treatment services in Ogun State Nigeria. Percentages, means and standard deviations analyses were conducted at p=0.05 significance level. The age range of participants was 20 to 56 years with a mean ± standard deviation (SD) of 42.73±2.93 years. The finding from the study revealed that there was no significant differences in knowledge of preventive practices of TB among patients (p=2.40±1.65). In other to boost the influence of knowledge of HIV paitients prevention practices towards tuberculosis infection across target population , there is need for more awareness and health education intervention on prevention practices
OpenMP Application Proﬁling – State of the Art and Directions for the Future

OpenMP is a successful approach to writing threaded parallel applications. This article describes the state of the art in performance proﬁling OpenMP applications, covering vendor performance tools and platform independent techniques. The features of the OpenMP proﬁler ompP are described in detail and an outlook of future directions in this area is given.
REAL-TIME RAY TRACING REFLECTIONS AND SHADOWS IMPLEMENTATION USING DIRECTX RAYTRACING

In traditional 3D games, techniques such as environment mapping and shadow mapping were used to simulate reflections and shadows due to the high computational load of ray tracing. However, recent advancements in technology have made real-time ray tracing possible, allowing for higher quality reflections and shadows compared to traditional methods. This paper proposes DirectX Raytracing (DXR) to achieve high-quality real-time reflections and shadows. To reduce the computational load of real-time ray tracing, we use the G-buffer from deferred rendering to compute only the information required for shadows and reflections, which is then combined to generate the final color. This paper verifies the effectiveness of our approach by comparing the performance of a DXR-based program with images produced using Unreal Engine 4's ray tracing capabilities. The results show that the proposed method provides high-quality graphics while minimizing computational load.
Observing the Arctic Ocean carbon cycle in a changing environment

Climate warming is especially pronounced in the Arctic, which has led to decreased sea-ice coverage and substantial permafrost thawing. These changes have a profound impact on the carbon cycle that directly affects the air–sea exchange of carbon dioxide (CO2), possibly leading to substantial feedback on atmospheric CO2 concentration. Several recent studies have indicated such feedback but the future quantitative impact is very uncertain. To minimize these uncertainties, there is a need for extensive field studies in order to achieve both a better process understanding as well as to detect probable trends in these processes. In this contribution, we describe a number of processes that have been reported to be impacted by climate change and suggest a coordinated international observational programme for their study.
PD-L1/PD-L2 de�ciency alters numbers and types of microglia and transcriptomic landscape of neural retina

Background To investigate the effects of PD-1 ligands signaling pathway on microglia and transcriptomic expression and function of neural retina. Methods PD-L1/PD-L2 de�cient mice were generated by Cyagen Biosciences Inc. (Suzhou, China). We performed quantitative and qualitative analyses of retinal microglia using mouse retinal western blotting and immuno�uorescence experiments. The transcriptomic sequencing of neural retina was performed and analyzed by OE Biotech Co. Ltd. (Shanghai, China). The morphology of neural retina in PD-L1/PD-L2 de�cient mice was evaluated using haematoxylin and eosin (HE) staining and in vivo optical coherence tomography (OCT). The visual evoked potential (VEP) test and measuring the photo negative response (PhNR) test assist in approaching changes in visual function in mice.
The risk of irritable bowel syndrome in patients with metabolic syndrome(cid:0)a population-based, 5-year follow-up cohort study

Purpose Few studies have reported the associations between metabolic syndrome (MetS) and irritable bowel syndrome (IBS). The purpose of this study was to investigate the incidence of IBS in patients with MetS during a 5-year follow-up period and to evaluate potential risk of IBS. Methods A retrospective cohort study was conducted, and 5104 subjects who underwent physical examination in the People's Hospital of Xinjiang Uygur Autonomous Region in October 2014 were randomly selected and divided into MetS group, pre-metabolic syndrome (Pre-MetS) group and healthy control (HC) group. The clinical datas and the incidence of IBS were recorded and analyzed.
TRUST EVALUATION MODEL FOR SOCIAL INTERNET OF THINGS USING RESILIENT APPROACH

Social Internet of Things is a trend in the technology which allows to the add objects to the network through which communication is possible using unique object relationship and ability to transfer the data in a network. Internet of Things is able to achieve more efficiency in decision making, Social internet of things is a subset of Internet of Things that establishes the relationship with other objects for effective communication and can improve the scalability, trust, resource management using social trust computing. Many existing models are not dynamic in nature in proving the trust with objects and user interaction and decision making process is not identifiable, the proposed Resilient Based Social Internet of Things model increases performance of evaluation with various attributes like information gain, resilience of the system, cooperativeness and trustworthiness. In SIoT trustworthiness is very important in defining reliability in user communications and interactions. The proposed experiments shows the significant improvement in the trust model for the AppClassNet data set and social internet of things data set in order to segregate trust and untrusts effectively in the network model with 92% information gain and high resilience by comparing with existing model.
HYBRIDML: FAULTY NODE DETECTION USING HYBRID LEARNING MODEL FOR DISTRIBUTED DATA CENTRES

The distributed systems are very effective when it deals with massive data processing. Nowadays, entire world generates high-dimensional data such as audio, video, image etc. To process such extensive data at a minimum is hard for a stand-alone machine, and this is a big challenge for the computer system to evaluate such data. The distributed framework is the solution for the process of such extensive data. Still, during the execution, some faulty or straggler nodes can increase the overall computation time to process data. However, to detect such straggler nodes, from large distributed systems are mandatory before assigning jobs to VM. Early identification of such faulty node can future save the overall computation time. In this paper, we proposed a hybrid machine learning model for detecting faulty nodes in large distributed machines using collaboration of reinforcement and supervised machine learning. The large Virtual Machine (VM’s) log data has been collected from the distributed environment and proceeded with reinforcement learning algorithm for module training and supervised machine learning for module testing. According to extracted features, reinforcement learning encompasses an activation function that generates the label for the respective node, whether healthy or faulty. In the testing phase, the natural world VM’s log data has been collected and evaluated with supervised machine learning classifiers. Several machine learning classification algorithms have evaluated and acquired the results. The SVM provides higher accuracy over the other machine learning classifiers with our reinforcement learning model.
Examining the Extent of and Determinants of SDG Reporting

Purpose: The purpose of this study was to find out what influences Sustainable Development Goals (SDG) reporting and to what extent. Particularly, we concentrated on 40 nations from various areas that actively participate in SDG reporting. Design/Methodology/Approach: The World Bank database, SGI Networking, and Sustainable Governance Indicators were some of the sources used to compile the data that was used to study this, which covered the years 2016 to 2022. Several regression methods, including Pooled OLS, Fixed Effect, and Random Effect, were used in the study to investigate the connection between reporting SDGs and indicators of sustainable governance. Additionally, we ran moderation experiments to evaluate the influence of the populace and religion as moderating factors. Findings: The findings imply that management, shareholders, and the market have a strong incentive to expand investment in sustainable practices to boost profits. Furthermore, our findings have significant policy implications for the sector, highlighting the necessity of incorporating SDG reporting into business models. Implications/Originality/Value: Our study has consequences for both academics and business. The importance of sustainable governance indicators in the context of SDG reporting and their potential influence on financial performance are both highlighted by our study.
FRACTURE CALCANEUM PLATING VS CLOSE REDUCTION

Background: Calcaneal injuries, a common type of fractures, account for about 60% of tarsal bone defects. [9] Higher intensity injuries, such as those caused by falls or vehicle accidents, frequently results in calcaneal fractures. Approximately 75% of all calcaneal fractures patients are the intra-articular variety. The tongue and joint depression forms of evacuated intra-articular calcaneal fractures were classified through Essex-Lopresti. Aimed at intra-articular tongue-type calcaneal ruptures, closed reduction is the best option since the fracture block is substantial as well as may be abridged with ease by utilizing expertise in technological aspects. One of the most often utilized least invasive methods is the sinus tarsi route. This study's main objective was to contrast fracture calcaneum plating with close reduction. Aims and Objective: To find efficacy between the plating and close reduction in the management of calcaneal fracture. Materials and Methods: A retrospective cohort study included 58 patients who received percutaneous cannulated screw (PCS) fixation or minimally invasive longitudinal approach (MILA) after closed reduction for traumatic fracture. Preoperative management, surgical procedures, and post-operative management were the same for both groups. Operative time, hospital stay time, preoperative time, and blood loss were measured, as well as the calcaneus' dimensions and angles. The study aimed to compare the outcomes of PCS and MILA groups. Results: The PCS group (13.2 mL) saw less loss of blood than the MILA group (17.9 mL). In comparison to the MILA group, the PCS group's mean OT was shorter (61.2 vs. 85.9 min). The PCS group's mean POT was 1.7 days shorter than that of the MILA group (2.7 days). The major complication seen in PCS and MILA groups is total hind foot pain 7.7% and 9.4% respectively. The total complication rate is higher in MILA (21.9%) compared to PCS (7.7%) group. While the AOFAS hindfoot scores among both groups exhibited an upward trend, the VAS ratings were decreasing. The PCS group's postoperatively VAS scores were lower compared to the MILA group. In the PCS and MILA groups, there were no discernible variations in the AOFAS hindfoot scores at any of the follow-up intervals of 1, 3, 6, and 12 months, as well as at the final follow-up. Conclusion: The findings of this study suggest that while plate fixation may be a more effective treatment than screw fixation for calcaneal fractures, PCS fixation may have certain advantages over MILA.
DYNAMIC LABOR SUPPLY WITH TAXES: THE CASE OF ITALIAN COUPLES

Labor force participation rate among married women in Italy is particularly low. In order to better understand the role played by the tax and benefit system on this phenomenon, we build and estimate a structural dynamic life-cycle model of household labor supply, saving, and consumption behavior. The model features several sources of heterogeneity in the characteristics of the members of the couple and it incorporates most of the fiscal rules which have an effect on the net incomes of the agents. The parameters of the model are estimated using cross-sectional and longitudinal data for the 2004-10 period. We use the estimated model to simulate a few counterfactual policies and study their effect on labor supply and poverty. In this version of our work we present some preliminary estimates and simulations.
Prognostic Prediction Models for Postoperative Patients with Stage I to III Colorectal Cancer: A Retrospective Study Based on Machine Learning Methods

Background To utilize the patient, tumor, and treatment features and compare the performance of machine learning algorithms, develop and validate models to predict overall, disease-free, recurrence-free, and distant metastasis-free survival, and screen important variables to improve the prognosis of patients in clinical settings. Methods More than 1,000 colorectal cancer patients who underwent curative resection were grouped according to 4 survival times (further categorized by 3-and 5-year) and divided into training sets and testing sets (9:1). Each 3-catergory survival time was predicted by 4 machine learning algorithms. The area under the receiver operating characteristic curve (AUC) and average precision (AP) were our accuracy indicators. Vital parameters were screened by multivariate regression models. To achieve better prediction of multi-categorized survival times, we performed 10-fold cross-validation except for the recurrence-free survival model (5-fold cross-validation). We iterated 1000 times after hyperparameter optimization.