Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring Prisons in Europe: Understanding Perspectives of People in Prison and Prison Staff

Prison monitoring has been promoted by international human rights bodies as a way to support the prevention of torture and ill-treatment in prison. However, there has been very limited examination of the operation of prison-monitoring bodies, especially from the perspective of those most affected by their recommendations: those who live and work in prisons. This article draws on interviews conducted with people living and working in a Scottish and a Norwegian prison to explore how they perceive a long-standing prison-monitoring body, the European Committee for the Prevention of Torture and Inhuman or Degrading Treatment or Punishment (CPT). The article provides an examination of the benefit and utility of CPT recommendations from the point of view of people in prison and prison staff. The study further reveals that the effectiveness of CPT monitoring in prisons may be considered in terms aside from state implementation of recommendations and should include how people in prison see its work, its impact, its power to persuade, and its connection to the outside world. Through taking a prison-centered approach, the article contributes to wider discussions on whether and how human rights frameworks can alter experiences of punishment.
Musculoskeletal System Pain and Related Factors During Online Education in the COVID-19 Pandemic among Ankara University Faculty of Medicine Students, Turkey

Objectives: This study aims to determine the prevalence of musculoskeletal pain (MSP) and its related factors among Ankara University Faculty of Medicine students during online education in the coronavirus disease-2019 (COVID-19) pandemic and to investigate the effects of the MSP on students’ quality of life. Materials and Methods: This cross-sectional study was conducted among medical students who responded online questionnaire between April and July 2021. The frequency of MSP (Nordic Musculoskeletal Questionnaire), level of physical activity (International Physical Activity QuestionnaireShort Form), and depressive symptoms [Beck Depression Inventory (BDI)], and the health status [Short Form-36 Health Survey Questionnaire (SF-36)] of the students were evaluated. Results: A total of 233 students (65.7% female) with a mean age of 20.1±1.5 years responded to our survey. By individual body side, the most commonly affected body regions within one year were upper back (79.4%), neck (71.7%), lower back (69.5%), and shoulders (60.1%), respectively. Twelve-month MSP prevalence was higher in female students at the neck (p=0.01) and upper back (p=0.001). Body mass index, smoking, and time spent on technology-based devices were not related to MSP prevalence. Physical activity level was significantly lower in those with upper back and low back pain. BDI scores were found to be significantly higher in those with neck, upper back, lower back, and shoulder pain in the 7-day-period, compared to those without. The SF-36 scores of those with MSP was found to be significantly lower than those without. Conclusion: The prevalence of MSP was very high among medical students receiving online education during the COVID-19 pandemic. Depressive symptoms were more pronounced in students with MSP and it was evident that the MSP affected the quality of life negatively. Preventive measures should be considered to reduce MSP in this population.
Seismic Anisotropy Within the Subducting Northern Philippine Sea Plate, SW Japan, Using DONET Seafloor Observation Network

Anisotropy within subducted slabs carries important information about the deformation history of the slabs. Seismic waves propagating a long distance within a subducted slab can be utilized to invert for the slab anisotropy. The Dense Ocean Floor Network System (DONET) deployed near the Nankai trough are located in a suitable position to record seismic waves guided along the subducted northern Philippine Sea plate (nPSP). In this study, we perform the shear wave splitting analysis on local event data recorded by ocean bottom seismometers from DONET to measure the anisotropy within the subducting nPSP. The obtained fast axis is sub‐trench‐parallel with inferred strength of 1%–3% anisotropy, arising likely from the fossilized fabric formed during the seafloor spreading period and strengthened by trench‐parallel serpentinized hydrated faults. This result shows that the fossilized anisotropy of nPSP is preserved during the subduction.
High-Performance Bidirectional Chemical Sensor Platform Using Double-Gate Ion-Sensitive Field-Effect Transistor with Microwave-Assisted Ni-Silicide Schottky-Barrier Source/Drain

This study proposes a bidirectional chemical sensor platform using ambipolar double-gate ion-sensitive field-effect transistors (ISFET) with microwave-assisted Ni-silicide Schottky-barrier (SB) source and drain (S/D) on a fully depleted silicon-on-insulator (FDSOI) substrate. The microwave-assisted Ni-silicide SB S/D offer bidirectional turn-on characteristics for both p- and n-type channel operations. The p- and n-type operations are characterized by high noise resistance as well as improved mobility and excellent drift performance, respectively. These features enable sensing regardless of the gate voltage polarity, thus contributing to the use of detection channels based on various target substances, such as cells, antigen-antibodies, DNA, and RNA. Additionally, the capacitive coupling effect existing between the top and bottom gates help achieve self-amplified pH sensitivity exceeding the Nernst limit of 59.14 mV/pH without any additional amplification circuitry. The ambipolar FET sensor performance was evaluated for bidirectional electrical characteristics, pH detection in the single-gate and double-gate modes, and reliability in continuous and repetitive operations. Considering the excellent characteristics confirmed through evaluation, the proposed ambipolar chemical sensor platform is expected to be applicable to various fields including biosensors. And through linkage with subsequent studies, various medical applications and precision detector operations for specific markers will be possible.
Higher education capitalism in Indonesia as a social problem: A case study in the digital age

Since the Indonesian state entered the reform era in 1998, several state universities that were centers of scientific development had their legal status changed by the government to become State-Owned Legal Entity University. From a sociological perspective, changes in legal status are commonly understood as institutional changes, which include changes in the norms that refer to governance, changes in governance procedures, and changes in organizational structure and governance. Some of the State Universities that have changed their legal status (institutions) are the Universitas Indonesia (UI), Universitas Gadjah Mada (UGM), Institut Pertanian Bogor (IPB), Institut Teknologi Bandung (ITB), Universitas Sumatera Utara (USU), Universitas Pendididikan Indonesia (UPI), and Universitas Airlangga (UNAIR). This article will describe whether, in today’s digital era, these changes cause social problems of their own, especially social problems related to inter-structural relationships in academic activities on campus. This study employed Mixed Methods Research and 100 students as a sample of this study. The results found the fact that most of the student respondents (55.42%) considered that social problems that arise in State-Owned Legal Entity University were related to the capitalism of higher education. This research also found the impact of institutional changes being State-Owned Legal Entity University is a change in the inter-structural relationship among the academic community in carrying out academic activities to become exploitative, hegemonic, and repressive. Therefore these changes in an inter-structural relationship were also considered as a social problem.
IMMERSIVE FEEDBACK IN FENCING TRAINING USING MIXED REALITY

During sports training, providing athletes with real-time feedback that is based on the automatic analysis of motion is both useful and challenging. In this work, a novel system that is based on mixed reality is proposed and verified. The system allows for immersive and real-time visual feedback in fencing training. Novel methods have been introduced for 3D blade tracking from a single RGB camera, creating weapon-action models by recording the actions of a coach and evaluating the trainee’s performance against these models. Augmented reality glasses with see-through displays are employed, and a method for coordinate mapping between the virtual and real environments is proposed; this will allow for the provision of real-time visual cues and feedback by overlaying virtual trajectories on the real-world view. The system has been verified experimentally in fencing bladework training (with the supervision of a fencing coach). The results indicate that the proposed system allows novice fencers to perform their exercises more precisely.
Fog Managed Data Model for IoT based Healthcare Systems

In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.
Philippine Development Response to Mitigate the COVID-19 Pandemic: A case study in Central Visayas

Policies play a crucial role in ensuring the welfare of a country's economy, its public health, and its social welfare safety nets in times of crisis. This article would like to identify the different policies employed by some of the ASEAN member countries, albeit heavily putting emphasis on Philippine's response to the pandemic. Countries have employed more or less identical policies as per standard protocol by the World Health Organization. However, policies that allowed for the capacitating of a country's facilities; such as but not limited to acquisition of equipments and more testing kits, and that of personnel are more effective in drastically slowing down the spread of the pandemic. The study has also found that there have been challenges, flaws, and certain difficulties to the implementation of policies. Most of the policies identified that target beneficiaries of identified vulnerable sectors are not sustainable. Some of the policies only aim to be band-aid (temporary) solutions , most of which only allowing at most two months of either financial assistance or in kind from the government, which is truly not enough to have outlasted a lockdown that lasted for almost 16 months. Finally, a draconic response or strong man policy has been found to have varying effects. It has also been found that such a response if misguided can lose its purpose and can be used as a means to crack down critics of a regime and those who are critical of its efficacy in its response to the pandemic.
Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features

A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.
Speaker Gender Recognition Based on Deep Neural Networks and ResNet50

Several speaker recognition algorithms failed to get the best results because of the wildly varying datasets and feature sets for classification. Gender information helps reduce this effort since categorizing the classes based on gender may help lessen the impact of gender variability on the retrieved features. This study attempted to construct a perfect classification model for language-independent gender identification utilizing the Common Voice dataset (Mozilla). Most previous studies are doing manual extracting characteristics and feeding them into a machine learning model for categorization. Deep neural networks (DNN) were the most effective strategy in our research. Nonetheless, the main goal was to take advantage of the wealth of information included in voice data without requiring significant manual intervention. We trained the deep learning network to choose essential information from speech spectrograms for the classification layer, performing gender detection. The pretrained ResNet 50 fine-tuned gender data successfully achieved an accuracy of 98.57% better than the traditional ML approaches and the previous works reported with the same dataset. Furthermore, the model performs well on additional datasets, demonstrating the approach’s generalization capacity.
Identifying QTLs for Grain Size in a Colossal Grain Rice (Oryza sativa L.) Line, and Analysis of Additive Effects of QTLs

Grain size is an important component of quality and harvest traits in the field of rice breeding. Although numerous quantitative trait loci (QTLs) of grain size in rice have been reported, the molecular mechanisms of these QTLs remain poorly understood, and further research on QTL observation and candidate gene identification is warranted. In our research, we developed a suite of F2 intercross populations from a cross of 9311 and CG. These primary populations were used to map QTLs conferring grain size, evaluated across three environments, and then subjected to bulked-segregant analysis-seq (BSA-seq). In total, 4, 11, 12 and 14 QTLs for grain length (GL), grain width (GW), 1000-grain weight (TGW), and length/width ratio (LWR), respectively, were detected on the basis of a single-environment analysis. In particular, over 200 splicing-related sites were identified by whole-genome sequencing, including one splicing-site mutation with G>A at the beginning of intron 4 on Os03g0841800 (qGL3.3), producing a smaller open reading frame, without the third and fourth exons. A previous study revealed that the loss-of-function allele caused by this splicing site can negatively regulate rice grain length. Furthermore, qTGW2.1 and qGW2.3 were new QTLs for grain width. We used the near-isogenic lines (NILs) of these GW QTLs to study their genetic effects on individuals and pyramiding, and found that they have additive effects on GW. In summary, these discoveries provide a valuable genetic resource, which will facilitate further study of the genetic polymorphism of new rice varieties in rice breeding.
Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System

Risk analysis and scenario testing are two of the core activities carried out by economists at central banks. With the increasing adoption of machine learning to enhance decision-support systems, and the amount of collected data spiking, institutions provide countless use-cases for the application of these innovative technologies. Consequently, in recent years, the term sup-tech has entered the financial jargon and is here to stay. In this paper, we address risk assessment from a central bank’s perspective. The uptrending number of involved banks and institutions raises the necessity of a standardised risk methodology. For that reason, we adopted the Risk Assessment Methodology (RAS), the quantitative pillar from the Supervisory Review and Evaluation Process (SREP). Based on real-world supervisory data from the Portuguese banking sector, from March 2014 until August 2021, we successfully model the supervisory risk assessment process, in its quantitative approach by the RAS. Our findings and the resulting model are proposed as an Early Warning System that can support supervisors in their day-to-day tasks, as well as within the SREP process.
Misophonia in Urological Patients: Our Experience of Management

Oleg Banyra1,2, Oxana Jourkiv3, Oleg Nikitin4, Iryna Ventskivska5, Zvenyslava Kechur6, Viacheslav Didkovskiy7 1Department of Urology, St. Paraskeva Medical Centre, Lviv, Ukraine, 2Department of Surgery, 2nd Lviv Municipal Polyclinic, Lviv, Ukraine, 3Child and Family Counseling Group, P.L. C. , Fairfax, VA, United States of America, 4Department of Urology, Bogomolets National Medical University, Kyiv, Ukraine, 5Department of Gynecology, Bogomolets National Medical University, Kyiv, Ukraine, 6Psychiatrist, Psychotherapeutist, St. Paraskeva Medical Centre, Lviv, Ukraine, 7Department of Otorhinolaryngology, Bogomolets National Medical University, Kyiv, Ukraine Case report
Environmental Fate of Toxic Volatile Organics from Oil Spills in the Niger Delta Region, Nigeria

Over the years, the environmental degradation of ecological resources from crude oil pollution and its human health impacts is receiving more global attention. The utilization of environmental models capable of predicting the fate, transport, and toxicity of chemicals in spilt crude oil can provide essential knowledge required to deal with the complexity associated with the fate of volatile petroleum chemicals in the environment. This paper explores the environmental fate of toxic volatile organics from an oil spill in the Niger Delta Region of Nigeria. Results from the literature implicated sabotage and operational failures from pipelines as primary causes of crude oil spillages. The generation of a fugacity model using EPI Suite™ revealed that Koc values greatly influence the behavior of BTN. Benzene, Toluene, and Naphthalene (BTN) were partitioned into three compartments based on organic-carbon partitioning coefficient (Koc). The organic-carbon partitioning coefficient (Koc) was computed as a function of soil-water distribution coefficient (Kd) and percentage organic matter (%OM). Koc was used to determining the possible risk posed on delicate ecological resources. Aquatic toxicology estimation using Ecological Structural Activity Relationship revealed that all chemicals were not toxic even at over-estimated Koc values. This research established the usefulness of screening level environmental modeling tools in assessing ecological risk and hence helpful in developing site-specific models for monitoring chemicals in the environment, which can assist governments, policymakers, and industries in designing appropriate regional disaster management plans.
Overexpression of HLH4 Inhibits Cell Elongation and Anthocyanin Biosynthesis in Arabidopsis thaliana

In plants, many basic helix-loop-helix (bHLH) transcription factors are involved in controlling cell elongation. Three bHLH proteins, PACLOBTRAZOL RESISTANCE1 (PRE1), Cryptochrome Interacting Basic Helix-loop-helix 5 (CIB5), and Arabidopsis ILI1 binding bHLH1 (IBH1) form a triantagonistic system that antagonistically regulates cell elongation in a competitive manner. In this study, we identified a new player, HLH4, related to IBH1, that negatively regulates cell elongation in Arabidopsis thaliana. Overexpression of HLH4 causes dwarf and dark green phenotypes and results in the downregulation of many key regulatory and enzymatic genes that participate in the anthocyanin biosynthetic pathway. HLH4 interacts with CIB5 and PRE1. By interacting with CIB5, HLH4 interferes with the activity of CIB5, and thus inhibiting the transcription of cell elongation-related genes regulated by CIB5, including EXPANSINS8 and 11 (EXP8 and EXP11) and indole-3-acetic acid 7 and 17 (IAA7 and IAA17). The interference of HLH4 on CIB5 is counteracted by PRE1, in which these bHLH proteins form a new tri-antagonistic system.