Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Emotional and Behavioral Problems among School-Going Adolescents: A Cross-Sectional Study

Background: Adolescence is a period of several emotional, mental, and behavioral changes, and many adolescent problems are manifested in the form of emotional problems, conduct problems, hyperactivity, and peer-related problems. The objectives of the current study are to assess the prevalence of emotional and behavioral problems across gender and location. It analyzes the Strengths and Difficulties Questionnaire (SDQ) total score with sociodemographic details among school-going adolescents in Kollam district. Materials and Methods: A cross-sectional study of 600 school-going adolescents between Class VIII to XII from the rural and urban areas in Kollam district was carried out using a cluster sampling method. A self-reported SDQ was administered to the respondents. Results: The findings indicate that a little more than a tenth of school-going adolescents had emotional and behavioral problems and multiple regression analysis revealed a significant association in SDQ total score with the type of schools, settings, and socioeconomic status. Conclusion: Early detection, periodical assessment, and intervention in the schools with the aid of mental health professionals can help adolescents in coping with different issues and for a healthy transition into adulthood.
Compost Source and Rate Effects on Soil Macronutrient Availability Under Saint Augustine Grass and Bermuda Grass Turf

Compost application to turf grasses can increase availability of nutrients in soil and improve growth, but can potentially lead to accumulation of macronutrients in soil and contribute to leaching and runoff losses. The objectives of this study were to investigate the influence of compost source and application rate on concentrations of plant-available macronutrients in soil over 29 months after a one-time application to saint augustine grass [Stenotaphrum secundatum (Walt.) Kuntze] and Bermuda grass [Cynodon dactylon (L.) Pers. ] turf. Compost application increased soil organic C, P, Ca, and S concentrations by 3 months after addition, but further increases from 3 to 29 months were seldom observed. In contrast, NO3-N and K levels declined while Mg levels increased slightly from 3 to 29 months. Seasonal or cyclical patterns of soil macronutrient levels were apparent, as lower concentrations were observed during dormant stages of Bermuda grass growth in winter. Initial macronutrient concentrations of compost sources strongly influenced macronutrient dynamics in surface soil, while higher application rates resulted in higher levels of P, K, Ca, Mg, but not NO3-N and S. Higher levels of macronutrients in Bermuda grass than saint augustine grass turf suggested plant-mediated uptake and assimilation differed between turf grass species. Utilization of turf grass systems for compost application should take into account plant species composition and the related impacts of plant uptake. Macronutrient concentrations were significantly correlated with both total organic C and dissolved organic C (DOC). Formation of organic matter-cation complexes appeared to influence macronutrient dynamics in soil, and may contribute to leaching and runoff losses.
Family Psychoeducation as an Intervention Tool in the Management of Schizophrenia and the Psychological Wellbeing of Caregivers

Background: Caregivers need to be imparted with specialized skills to retain their psychological well-being and to manage the patient with schizophrenia effectively. Aim: This study aims to understand the role of family psychoeducation (FPE) in the management of schizophrenia and the well-being of caregiver. Materials and Methods: The sample included 40 caregivers and patients, 20 each assigned randomly in treatment group (psychoeducation given) and the control group. Pre and post assessment of psychological wellbeing (PWB), symptoms of the patient, and emotional regulation was done through the scales mentioned in the study and analyzed through analysis of variance. Results: Statistically significant improvement in emotional regulation of caregivers and patient (P = 0.05) and improvement of PWB in caregivers (P = 0.01) as well as significant reduction in symptoms of patients (P = 0.01) found in the treatment group. Conclusion: FPE was found to be effective in improving PWB of caregivers and effective management of a patient with schizophrenia.
Effect of Nonmagnetic Impurity in Nearly Antiferromagnetic Fermi Liquid: Magnetic Correlations and Transport Phenomena

In nearly antiferromagnetic (AF) metals such as high-Tc superconductors (HTSC's), a single nonmagnetic impurity frequently causes nontrivial widespread change of the electronic states. To elucidate this long-standing issue, we study a Hubbard model with a strong onsite impurity potential based on an improved fluctuation-exchange (FLEX) approximation, which we call the GV^I-FLEX method. This model corresponds to the HTSC with dilute nonmagnetic impurity concentration. We find that (i) both local and staggered susceptibilities are strongly enhanced around the impurity. By this reason, (ii) the quasiparticle lifetime as well as the local density of states (DOS) are strongly suppressed in a wide area around the impurity (like a Swiss cheese hole), which causes the ``huge residual resistivity'' beyond the s-wave unitary scattering limit. We stress that the excess quasiparticle damping rate caused by impurities has strong momentum-dependence due to non-s-wave scatterings induced by many-body effects, so the structure of the ``hot spot/cold spot'' in the host system persists against impurity doping. This result could be examined by the ARPES measurements. In addition, (iii) only a few percent of impurities can causes a ``Kondo-like'' upturn of resistivity ($d\rho/dT<0$) at low temperatures when the system is very close to the AF quantum critical point (QCP). The results (i)-(iii) obtained in the present study, which cannot be derived by the simple FLEX approximation, naturally explains the main impurity effects in HTSC's. We also discuss the impurity effect in heavy fermion systems and organic superconductors.
Comparative Factor Analysis and Experimental Validation of Low Power MAC Performance

We analyze and experimentally validate the throughput capacity of duty cycled wireless networks in general and state-of-the art MAC protocols in particular. This enables a comparison of the energy eﬃciency of the protocols, which differ by multiples from up to 17x (for low traﬃc) to 2x (for high traﬃc), the latency of these protocols, and the tradeoﬀ between these two metrics. We also evaluate the impact of speciﬁc protocol design factors on performance. Notably, we ﬁnd that one extant protocol has the best energy eﬃciency and is latency-competitive across a broad range of network conﬁgurations and traﬃc. 1
Potential anti-arteriovenous malformations effect of sirolimus on angiogenesis induced by maxillofacial venous hypertension

Background: This study aimed to explore the possible role and mechanism of venous hypertension (VH) in the occurrence and development of arteriovenous malformations (AVMs) and to study the therapeutic effect of VH regulation on AVMs to provide insight into AVM pathogenesis and explore new treatments.Methods: Maxillofacial VH models were established by cervical vascular anastomosis. Osteogenesis and angiogenesis in the model rats were analysed by imaging and histology, and the inhibitory effects of intraperitoneal (ip) sirolimus injection on angiogenesis were detected. The effect of sirolimus on the biological behaviour of human umbilical vein endothelial cells (HUVECs) cultured in vitro and mechanism of sirolimus were detected by cytology.Result: The local microvessel density increased, the vessels showed tortuous dilation, and mRNA and protein levels of angiogenesis-related genes were increased in venous hypertension environment. These phenomena were alleviated or eliminated by ip injection of a sirolimus solution. Sirolimus inhibited proliferation, tubule formation, migration, invasion, angiogenesis-related gene transcription and translation, and phosphorylation of mammalian target of rapamycin (mTOR) and its downstream substrate proteins S6K1 and 4EBP1.Conclusions: Abnormal angiogenesis caused by VH is related to abnormal activation of the mTOR pathway, which is important in regulating local angiogenesis; sirolimus inhibited hypertension-induced excessive angiogenesis in vivo and in vitro through the mTOR signalling pathway. This study provides a basis for further mechanistic study of AVM formation and insight into sirolimus as a possible target drug for AVM management.
The Moderating Influence of the Strength of Racial Identity on the Relationship Between Teacher-Student Racial Similarity-Dissimilarity and Classroom Engagement

This research, titled ‘The moderating influence of the strength of racial identity on the relationship between teacher-student racial similarity-dissimilarity and classroom engagement’, was conducted by Md Enamul Kabir, a graduate student in the Department of Communication Studies at Minnesota State University, Mankato as a requirement for completing a Master of Arts degree in August 2020. The purpose of this quantitative study was to understand how the strength of racial identity moderates the effects of the teacher-student racial similarity and dissimilarity on the engaging behavior of students with their instructors in United States classrooms. This study questioned the prevalent assumption that similarity and dissimilarity predicted the nature of interaction and established the following primary hypothesis: the effect of similarity and dissimilarity in racial identity between teacher and students on the level of classroom engagement will depend on the students’ strength of social identification with race. 114 students participated in an online survey which was administered through Qualtrics. The results showed that the moderating effect was significant, but there was not enough evidence to support the effect at high and low levels of identification.
Shoreline change analysis along the coast of Bandar Abbas city, Iran using remote sensing images

Article History: Received: 06 Nov. 2020 Accepted: 23 Dec. 2020 Coastal cities are among the most important and sensitive regions in the world. They are constantly affected by marine and coastal processes such as waves, currents, and other geological-physical parameters such as sedimentation and deposition. These factors constantly change the shoreline. Thus, evaluation and management at coastal area are very important. In this study, the rate of shoreline changes in the coastal area of Bandar Abbas, south of Iran, was investigated using remote sensing technique and DSAS tools. Landsat 8, 7 and, 5 satellite and Sentinel-2A satellite images were used to detect the rate of changes. Images from the years 1990 to 2020 were selected with 5-year time-interval. Using the NSM, SCE, EPR, and LRR statistical indexes of the DSAS tool, erosion and accretion rates were calculated in about 50 km of shoreline length. According to the EPR index Nakhl e Nakhoda jetty and Shoor River estuary show the maximum and minimum rate of changes, with amount of +31.07 m/yr and +4.83 m/yr, respectively. The average rate of changes was calculated as +12.34 m/yr. We recognized this part of the shoreline as the most sensitive area and suggested that any further development in this area should be undertaken obsessively. Shoreline of urban area of Bandar Abbas generally shows positive rate of change less than +5 m/yr, with the average rate of +2.35 m/yr, which suggests development in this area is in slow pace. In general, only 4% of the shoreline of is detected with high accretion (20.5 to 31.5 m/year) and about 53% is recognized as low accretion (0.5 to 10.5 m/year).
Preliminary Evidence of Improvement in Adolescent and Young Adult Cancer Survivors’ Brain Health Following Physical Activity: A Proof-of-Concept Sub-Study

Background: Cognitive impairment is common among adolescent and young adult (AYA) cancer survivors. Physical activity (PA) may help mitigate cognitive impairment post-treatment by positively impacting two indicators of general brain health: fractional anisotropy (FA) and functional connectivity (FC). As part of a two-arm, mixed-methods pilot randomized controlled trial (RCT), this sub-study was designed to provide preliminary proof-of-concept evidence for the effects of PA on FA and FC among AYA cancer survivors post-treatment to help inform decisions about proceeding to larger trials. Methods: AYA cancer survivors who had completed cancer treatment and who were enrolled in a larger pilot RCT comparing a 12-week PA intervention to a waitlist control group, were invited to participate in this sub-study. Sub-study participants completed diffusion tensor imaging and resting-state functional magnetic resonance imaging prior to randomization and post-intervention. Data were analyzed with descriptive statistics, independent component analysis, and paired sample t-tests. Results: Post-intervention, participants showed increases in FA of the bilateral hippocampal cingulum, left anterior corona radiata, middle cingulum, left anterior thalamic radiation, and left cerebellum. A decrease in overall FC of the default mode network and increases in the cerebellar and visual networks were also noted post-intervention (p < .05). Conclusion: Results provide preliminary evidence for the possible positive effects of PA on FA and FC among AYA cancer survivors post-treatment. On the basis of these results, larger trials assessing the effects of PA on specific brain health indicators, as captured by FA and FC, among AYA cancer survivors are appropriate and warranted.
Dissecting Language’s Effect on Bottleneck Models

Language has been shown to be helpful on improving models’ ability to generalize on unseen abstract concepts. However, it’s still unclear why and how language can help the model to generalize. Inspired by the results in Learning with Latent Language (L3) [1], we aim to answer several crucial questions about language’s role on facilitating learning and improve L3’s performance on few-shot classiﬁcation where models needs to learn how to quickly adapt to unseen tasks by learning from a set of similar tasks. We ﬁrst demonstrate that the accurate descriptions of the spatial relationships can massively improve the models’ performance on few-shot classiﬁcation by providing correct guidance encoded in natural language. To improve model’s performance, we focus on two directions: 1) enhancing model’s visual reasoning via providing more informative language guidance on spatial relationships, 2) enhancing model’s ability to fuse different modality. Our results demonstrated that 1) we can achieve comparable classiﬁcation performance by using a simple concept retrieval mechanism 2) a more trainable image model can dramatically improve the classiﬁcation accuracy.
Circular Polarization for Self-Interference Cancellation in Single Channel Full-Duplex

—Self-interference occurred due to implementation of single channel full-duplex, where the transmit signal interfere the receive signal in the same node. The previous work [2] has been studied the passive cancellation, but those techniques only against the direct path. Then proposed a method using directional antenna with circular polarization to solve self-interference issue. The use of different circular polarization type on transmitter and receiver gives better quality against self-interference. In free space loss channel, using this method obtain polarization loss factor (PLF) as 0 means self-interference totally eliminated. However, AWGN channel gives effect that change polarization shape that results PLF not same as 0. The alpha - comparison between side lobe and main lobe power at directional antenna- makes impact on system performance. For obtaining bit error rate (BER) 10 -3 needs SINR 17.04 dB, 16.29 dB, and 16.05 dB with alpha value 10 -0.5 , 10 -1 , and 10 -2.5 .
Low expression of AQP9 and its value in hepatocellular carcinoma

Background The mortality rate for liver cancer is high worldwide. The etiology of liver cancer has altered with the high incidence rate of non-alcoholic fatty liver disease (NAFLD) although effective vaccination strategies have been developed. Therefore, it is important to discover new biomarkers for diagnosis and prognosis. Aquaporin 9 (AQP9) has been reported in some cancers, especially in liver cancer, although its role in this malignancy remains to be clarified. In this study, we conducted a bioinformatics analysis to clarify the function of AQP9 in liver cancer. Methods Immunohistochemistry, real-time qPCR, western blot analysis were applied to detect AQP9 expression in tissue samples or cells. Online databases were used to analyze the correlation of AQP9 expression and clinical factors. LinkedOmics and gene set enrichment analysis (GSEA) were used to analyze the functional network of AQP9 in hepatocellular carcinoma (HCC). Four authoritative databases were used to predict the candidate microRNAs that bind to AQP9. Finally, we used the Tumor Immune Estimation Resource (TIMER) to assess the correlation of AQP9 and immune cell infiltration in HCC. Results All analysis were revealed AQP9 is significantly decreased in HCC tissues and cells. AQP9 was negatively correlated with different tumor stage, grade, and weight, as well as lymph node metastasis, sex, and histological subtypes. AQP9 can be used to predict the prognosis of HCC patients. GSEA revealed that AQP9 was significantly involved in most significant hallmark pathways. LinkedOmics was used to analyze the relationship of AQP9 with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Mechanistically, mir-23a-3p and mir-330-3p may downregulate AQP9 expression in HCC. AQP9 was found to be specifically correlated with immune cell infiltration and play a major role in the liver cancer microenvironment. Conclusions In this study, we found that AQP9 was significantly decreased in HCC, with low AQP9 levels indicating a poor outcome. GSEA analysis and LinkedOmics revealed that AQP9 was significantly involved in the most significant hallmarks pathways. Mir-23a-3p and mir-330-3p may inhibit AQP9 expression in HCC. Our results also suggest that AQP9 is important in tumor immunity in the liver cancer.
Global value chains, private governance and multiple end-markets: insights from Kenyan leather

This article analyses how the private governance of global value chains (GVCs) varies across multiple end-markets. This is explored through a two-stage mixed-methods analysis of Kenya’s participation in leather value chains serving Europe, China, India and the COMESA region. We first draw on transaction-level customs data to analyse private governance in terms of the stability of buyer–supplier interactions and presence of intermediaries. We then interrogate these results through supplier interviews. Our article highlights the combined role of product specifications and trust in shaping private governance, and heterogeneity of GVCs across the global North and South, as well as within the South. It further questions commonly held assumptions that lower quality products (generally characterising Southern end-markets) are necessarily governed by market-based coordination mechanisms. We therefore challenge links established in the GVC literature between product standards and private governance.
Efficient Security Solutions for IoT Devices

The Internet of Things (IoT) is a technological innovation that has revolutionized society. The IoT will forever change the way we use simple things that do very little things to smart, fully capable things. IoT devices can process and automate everyday household and workplace tasks through simple sensors. Yet despite the benefits of these devices, they are vulnerable to violations such as privacy issues and security breaches. This paper aims to provide a clearer understanding of the IoT and current threats to it by explaining why IoT devices are susceptible to attack. Moreover, the technologies used in the IoT are examined, as well as the different communication layers of the IoT and their functioning. The findings reveal that IoT devices are prone to many software and hardware vulnerabilities, not to mention the challenges that come with IoT. Solutions to these challenges are proposed, notably through the use of anomaly-based intrusion detection systems, which are critical components of network security. Using machine learning (ML) to detect potential attacks is recommended. Many proposed anomaly-based detection systems use different ML algorithms and techniques. However, there is no standard benchmark to compare these in terms of power consumption. A benchmark that measures both accuracy and power consumption to calculate and evaluate each algorithm’s implementation is proposed. Keywords—Efficient; IoT; Systems on a Chip (SoC); ML; Network
Risk and Return Trade-off in the U.S. Treasury Markets

This paper characterizes the risk-return trade-off in the U.S. Treasury markets. We propose a discrete-time no-arbitrage term structure model, in which bond prices are solved in closed form and the conditional variances of bond yields are decomposed into a short-run component and a long-run component, each of which follows a GARCH-type process. Estimated using Treasury yields data from January 1962 to August 2007, our model simultaneously matches the conditional volatility dynamics and the deviation from the expectations hypothesis in the data. We find that a higher short-run volatility component of bond yields significantly predicts a higher future excess return, above and beyond the predictive power of the yields. The long-run volatility component does not predict bond excess returns.
TOWARDS HIERARCHICAL IMPORTANCE ATTRIBUTION: EXPLAINING COMPOSITIONAL SEMANTICS FOR NEURAL SEQUENCE MODELS

The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase compositions. To explain how the model handles semantic compositions, we study hierarchical explanation of neural network predictions. We identify non-additivity and context independent importance attributions within hierarchies as two desirable properties for highlighting word and phrase compositions. We show some prior efforts on hierarchical explanations, e.g. contextual decomposition, do not satisfy the desired properties mathematically, leading to inconsistent explanation quality in different models. In this paper, we start by proposing a formal and general way to quantify the importance of each word and phrase. Following the formulation, we propose Sampling and Contextual Decomposition (SCD) algorithm and Sampling and Occlusion (SOC) algorithm. Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms. Our algorithms help to visualize semantic composition captured by models, extract classification rules and improve human trust of models. Project page: this https URL