Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Group Investigation Method and Local Wisdom to Enhance Students’ Writing Skill

The integration of local wisdom and teaching method in all language skills are important to preserve the culture and to insert the character values. This notion relates with this study in which the local wisdom and group investigation method are integrated in teaching writing. Group investigation is teaching method that build students’ team work. Besides, the aim of this study is to explore the effect of implementing group investigation method and local wisdom in improving students’ writing skill. The research method used is a quasi-experimental study (one group Pre-test - Post-test). The subject of this study is junior high school students. The results of T-test show that there is a significant difference of students’ writing skill before and after getting the treatment. Besides, integrating group investigation method and local wisdom is more effective to enhance students’ writing skill. Furthermore, this study gives theoretical, practical, and pedagogical implications in teaching writing. The novelty of this study is that this study uses interdisciplinary approach, especially in integrating the local wisdom discourse and group investigation method. The contribution of this study is to the language teachers especially those who concern with writing skills, on how to develop teaching method using cultural approach in creative way
Discovery of RC-752, a Novel Sigma-1 Receptor Antagonist with Antinociceptive Activity: A Promising Tool for Fighting Neuropathic Pain

Neuropathic pain (NP) is a chronic condition resulting from damaged pain-signaling pathways. It is a debilitating disorder that affects up to 10% of the world’s population. Although opioid analgesics are effective in reducing pain, they present severe risks; so, there is a pressing need for non-opioid pain-relieving drugs. One potential alternative is represented by sigma-1 receptor (S1R) antagonists due to their promising analgesic effects. Here, we report the synthesis and biological evaluation of a series of S1R antagonists based on a 2-aryl-4-aminobutanol scaffold. After assessing affinity toward the S1R and selectivity over the sigma-2 receptor (S2R), we evaluated the agonist/antagonist profile of the compounds by investigating their effects on nerve growth factor-induced neurite outgrowth and aquaporin-mediated water permeability in the presence and absence of oxidative stress. (R/S)-RC-752 emerged as the most interesting compound for S1R affinity (Ki S1R = 6.2 ± 0.9) and functional antagonist activity. Furthermore, it showed no cytotoxic effect in two normal human cell lines or in an in vivo zebrafish model and was stable after incubation in mouse plasma. (R/S)-RC-752 was then evaluated in two animal models of NP: the formalin test and the spinal nerve ligation model. The results clearly demonstrated that compound (R/S)-RC-752 effectively alleviated pain in both animal models, thus providing the proof of concept of its efficacy as an antinociceptive agent.
A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps.
Adaptive Path-Tracking Control With Passivity-Based Observer by Port-Hamiltonian Model for Autonomous Vehicles

A port-Hamiltonian model is suitable to represent the dynamics of many physical processes, which has an essential feature of underscoring a significance of an energy function, an interconnection pattern, and a dissipation for physical systems. Hence, adaptive path-tracking control with a passivity-based observer is designed by the port-Hamiltonian model for autonomous vehicles in this article. Firstly, a passivity-based observer of lateral velocity is established by designing a gain matrix to assign the interconnection and damping parts, which makes an observation error system transform into a desired port-Hamiltonian system and renders the observer passive. At the same time, it's convergence and stability analysis is given. Then, a port-Hamiltonian model with perturbations and actuator saturations is constructed to describe lateral vehicle dynamics in terms of tracking errors for path-tracking, and an L2-gain disturbance attenuation strategy is developed to overcome the adverse influence caused of external disturbances. Furthermore, the robust stability of the controller is proved in the presence of observation errors and external disturbances. Finally, simulations and experiments are performed to demonstrate the effectiveness of the proposed control strategy.
Autosomal Recessive Cerebellar Ataxias: New Acronyms, Old Eponyms, and the Butterfly Life Cycle

Autosomal recessive cerebellar ataxias (ARCAs) encompass a heterogeneous group of rare degenerative and metabolic genetic diseases that may affect the cerebellum or its connections. 1 – 3 The pathophysiological mechanisms of these conditions can be related to dysfunction of mitochondrial metabolism (eg, Friedreich ataxia [FA] and autosomal recessive spastic ataxia of Charlevoix-Saguenay [ARSACS]) of DNA repair/genome stability (eg, ataxia telangiectasia [A-T] and ataxia with oculomotor apraxia) or complex lipid metabolism (eg, cerebrotendinous xanthomatosis [CTX] and patatin-like phospholipase domain-containing protein 6 [ PNPLA6 ] – related disorders). 1 – 3 The inclusion of this wide variety of disorders that may carry their own inter-and intrafamilial variability may lead to further phenotypical pleomorphism, as observed in hereditary neurological diseases, particularly those linked to movement disorders. As such, in 2016, Marras et al published new recommendations of the International Parkinson ’ s Disease and Movement Disorders Task Force. 3 In 2018,
Exploring Music to Learn Languages from an Intercultural Perspective

The interconnected and changing social, political and economic reality of the postmodern era pushes for a perspective of language teaching that addresses the intercultural dimension of communication in the EFL classroom. This need added to the persistence of ethnocentrism, hate discourses, and discrimination demand an active role of teachers and students as intercultural mediators. This goal can be achieved with the integration of the intercultural dimension in language teaching. Therefore, this study aimed to integrate the intercultural approach of language teaching through the exploration of the cultural meanings in Rhythm and Blues (r&b) music. In this way, data collection included a survey, focus group sessions, students’ artifacts and observations. Data were analyzed through thematic analysis and some themes explored were the cultural dimension of R&B, the affordances of music to discover other cultures, the students’ conception of culture, and the relevance of culture in EFL. Finally, findings showed that the students identified some cultural manifestations in r&b music and its potential to examine other cultures. Similarly, the participants discovered the importance of culture in language learning and its role to enhance their pedagogic experiences.
SPEECH/TEXT TO INDIAN SIGN LANGUAGE USING NATURAL LANGUAGE PROCESSING

Sign language is a way of communication that helps people exchange information by using hand and arm gestures, commonly used by individuals who have difficulty hearing. However, sign language isn’t universal, because impaired individuals from different countries use their corresponding sign languages. Using sign language, it allows us to communicate with impaired individuals including our loved ones, students in mainstream/deaf schools/colleges, locals and company owners, etc. Studies say learning sign language makes it simpler for a person to grasp lip-reading along with their native language. Most research has been done on Sign Language Translation/Recognition; different sign languages are translated into a common spoken language. However, the inverse is less, meaning limited research has been done on converting spoken languages to sign languages. Focusing on this matter, this study aims to translate speech/text into Indian Sign Language using the basics of Natural Language Processing.
Wild relatives of fruit trees in Syria: Genetic resources threatened by conflict

Wild relatives of fruit trees (WRFT) are highly valued for food and tradable products by rural communities, especially in low-income countries and as such are a vital resource for tree improvement. During periods of conflict, a lack of support and protection by national authorities may make WRFT vulnerable. In Syria, WRFT are at risk of extinction due to the ongoing crisis, which has limited efforts to conserve and propagate these unique genetic resources. We collected information about the current status and key threats to WRFT in northwest Syria from 50 agricultural experts using structured interviews. Our results show that many sites have experienced erosion, overgrazing and drought. To initiate in situ and ex situ conservation, the locations of WRFT exposed to deterioration were identified and mapped using GPS, and seeds from five genotypes per species were collected from each of ten species to be used for the establishment of an in situ and ex situ WRFT collection.
Newsroom Ideological Diversity and the Ideological Sorting of Journalists

In efforts to curb ideological bias in the news, citizens, academics, and journalists have highlighted the importance of newsroom ideological diversity. Using a large-scale survey of newspaper political journalists in the United States, we examine the diversity of ideological perspectives of political journalists across newspapers and communities and how ideological misalignments with the newsroom and the local community relate to those journalists’ employment decisions. We find political newspaper reporters regularly work for newspapers and in communities that do not mirror their own ideological perspectives. However, by following newspaper political journalists’ employment decisions over a three-year period, we also find that the ideological differences between those journalists and the newsroom where they work (but not ideological difference with the local community) are related to an increase in journalists’ desire to change jobs and in the likelihood they will actually seek other employment, either within journalism or outside of the profession.
Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.
My first six months (MF6M): industrial design students perceptions of their first semester experience of learner-centred design studios

This paper reports on a learning and teaching project investigating the experience of Industrial Design students in their first semester of study in three courses that were reimagined to increase students’ agency in shaping their learning. In this five year project, My First Six Months (MF6M), teachers aimed to create a learner-centred environment by using facilitative, rather than directive, teaching strategies in a studio-based context. Focus groups with students explored their experience of the courses and unearthed valuable insight into students’ expectations about learning at university and the design discipline. Students were overwhelmingly positive about collaborating in their projects, while diverse expectations about learning and teaching roles and bemusement about the different emphasis on assessment featured prominently in students’ reflections. Recommendations arising from their perceptions include providing an ongoing narrative about independent learning throughout each course and ensuring support and expectations for independent learning are more explicitly designed into the course delivery.
Nonparametric Rotations for Sphere-Sphere Regression

ABSTRACT Regression of data represented as points on a hypersphere has traditionally been treated using parametric families of transformations that include the simple rigid rotation as an important, special case. On the other hand, nonparametric methods have generally focused on modeling a scalar response through a spherical predictor by representing the regression function as a polynomial, leading to component-wise estimation of a spherical response. We propose a very flexible, simple regression model where for each location of the manifold a specific rotation matrix is to be estimated. To make this approach tractable, we assume continuity of the regression function that, in turn, allows for approximations of rotation matrices based on a series expansion. It is seen that the nonrigidity of our technique motivates an iterative estimation within a Newton–Raphson learning scheme, which exhibits bias reduction properties. Extensions to general shape matching are also outlined. Both simulations and real data are used to illustrate the results. Supplementary materials for this article are available online.
Psychosocial correlates of well-being among people who engage in online health-seeking behaviour

We examined the extent to which neuroticism, health anxiety, social anxiety, and social support predicted well-being in a sample of online users engaging in health-seeking behaviour. We used a cross-sectional online survey to recruit participants who engaged in online health-seeking behaviour. The study included 350 participants who were recruited online using social media platforms such as Reddit and Twitter. A multiple regression was conducted to examine the relationships between neuroticism, health anxiety, social anxiety, social support, and well-being. Participants completed a battery of measures on Survey Monkey consisting of a demographic questionnaire, International Personality Item Pool Big Five Markers, Leibowitz Social Anxiety scale, Short Health Anxiety Inventory, World Health Organisation Five Well-Being Index, and the Multidimensional Scale of Perceived Social Support. We found significant negative correlations between the indicated psychological variables and well-being, indicating that higher levels of neuroticism, social anxiety, and health anxiety were related to lower levels of well-being. We also found significant, positive correlations between the social support variables and well-being, indicating that more social support was correlated with better well-being. The results of the multiple regression demonstrate that neuroticism, health anxiety, family support, and friend support were significant predictors of well-being.
Perinatal Outcomes of Adolescent Pregnancies: A Retrospective Study

Objective: Adolescent pregnancies constitute a very important health issue for maternal and fetal with social and economic consequences. The purpose of this study is to analyse the perinatal outcomes of adolescent pregnancies in a training and research hospital. Materials and Methods: Perinatal outcomes for adolescents between the ages of 10 and 19 years old and adults between the ages of 20 and 30 years old between 2015 and 2019 were evaluated and compared retrospectively, using an obstetric and neonatal database from Van Training and Research Hospital. Results: Cesarean section (CS) rates were 19,4 % vs 30% in the adolescent and control groups, respectively. (p < 0.05). The rates of preterm delivery (PD), premature rupture of membranes (PROM), preeclampsia, and eclampsia were significantly higher among adolescents (p < 0.05). After adjusting for maternal age, gravida, parity, hemoglobin level, education, socioeconomic status, and pregnancy follow-up, the rates of NVD (aOR 2.004, 95% CI 1.739-2.309), PB (aOR 1.419, 95% CI 1.122-1.794), PROM (aOR 4.401, 95% CI 3.066-6.319) and low birth weight (< 2500 g; aOR 2.480, 95% CI 1.442-4.264) were elevated in the adolescent group. Conclusion: Adolescent pregnancy is associated with increased risk of PD, low birth weight, PROM, fetal mortality, and preeclampsia in our study. In this study, which evaluated adolescent pregnancies, maternal and fetal outcomes, risk of preeclampsia increased among maternal outcomes, and it was found to be associated with preterm birth, low birth weight, premature rupture of the membrane and increased fetal mortality from fetal outcomes. However, there are different results between studies because there are many factors affecting the results. More studies are needed to confirm these results. There is a need for more studies on adolescent pregnancies with important maternal and fetal outcomes not only for our country but also for many countries.
Factors Affecting Online Shopping of Purchasing Fashionable Clothes Among Adults in Klang Valley

The fast growth of internet facility and services has provided another great marketplace for clothes shopping in the era of globalization. Moreover, online shopping also become very famous currently. The way people purchase, evaluate products and services, and conduct business has changed because of technological improvements, notably in the information and communication technology fields. Fashionable clothes play an essential role, and many adult customers like to purchase through online shopping. Several factors can impact adult consumers purchasing fashionable clothes. The main aim of this study is to determine the factors affecting the online shopping of purchasing fashionable clothes among adults. In addition, there is also an investigation of the relationships between the independent variables which are privacy/security, website design, customer service, and product variety, and the dependent variable which is online shopping of purchasing fashionable clothes among adults. Data gathering which is primary data was collected through a survey questionnaire with 402 respondents with a response rate of 89 percent who have experience purchasing clothes through online stores. The target population consists of adult consumers from various backgrounds in Klang Valley. Besides that, convenience sampling of non-probability sampling was used for data collection. Based on the research results, there are four independent variables which are privacy/security, website design, customer service, and product variety had been established that have a positive and significant relationship with online shopping of purchasing fashionable clothes among adults according to the coefficient value, all variables are reported positively. Furthermore, there is a significant moderate positive relationship between privacy/security, website design, customer service and online shopping of purchasing fashionable clothes among adults. Then, there is a significant high positive relationship between product variety and online shopping of purchasing fashionable clothes among adults. where r = 0.762, p < 0.001. As the finding of this study, it can help online clothes store sellers to understand and build up suitable strategies and methods to gain profit and market share efficiently and effectively.
Achieving High-Performance Solution-Processed Thin-Film Transistors by Doping Strong Reducibility Element Into Indium-Zinc-Oxide

Adding an appropriate amount of suitable elements is an effective way to suppress In-Zn-O (IZO) carriers. The influence of different doping concentration Ca incorporation on In-Zn-O oxide-based thin film transistors (Ca-IZO TFTs) was investigated theoretically and experimentally. First-principles calculations show that as the doping concentration of Ca increases, the IZO bandgap gradually widens. Moreover, low-concentration doping of Ca does not significantly reduce carrier density and mobility. A novel high-performance Ca-IZO film has been experimented by a low-cost sol-gel process. And the best Ca-IZO TFT device was achieved when the mole ratio of Ca was 3%. The slight Ca-doped In-Zn-O TFT showed a high on/off current ratio ~106, a high field-effect saturated mobility of 1.4 cm2/Vs, and a positive threshold voltage $(V_{\mathrm{ th}})$ of 7.7 V.
A Completed Model of Local Ternary Pattern for Face Recognition

In this paper, a novel face description method called a completed model of local ternary pattern (CLTP) for face representation and recognition. Firstly, the original face image is divided into small blocks from which the local difference value sign-magnitude transform was computed. Extracting the histogram statistical characteristics of each block by CLTP_P, CLTP_N and CLTP_M, and information entropy of the sub blocks are calculated separately. Then the histograms of all the blocks are linked to get the CLTP feature to be used as the final face descriptor. Finally, . the k- nearest neighbor classifier with chi-square is used for classification and recognition. Experimental results on AR and Yale face databases show that the proposed method has good face representation under different facial expression, partial occlusion, different illumination. Also the recognition rate is higher than LTP and variations of LTP method.