Miracle-dz/UniFAR-FTUs huggingface dataset:
A cycle‐jumping method for multicyclic Hubbert modeling of resource production

The amount of ultimately recoverable resources and/or timing of peak production have been the central purpose of numerous studies. One broadly applied method is Hubbert modeling, subsequently extended as multicyclic Hubbert modeling. This paper explores a modification to conventional multicyclic Hubbert modeling that we term “cycle‐jumping” wherein the overlap of multiple curves is limited and explicitly accounted for. The model is designed in a way that each curve is described by the same three parameters as a multicyclic Hubbert model, and every two curves are connected through an explicit transition. The transition width indicates the time of the shift from one curve to the next and is controlled by a weighting parameter for the respective curves. Cycle‐jumping provides a superior data fit compared to the conventional cycle‐addition model and, more important, reflects historical production data more realistically as socioeconomic and political factors important to resource production vary in time.
Combining Ability, Heterosis and Gene Action for Grain Yield and Its Related Traits of Some WA-CMS with Tester Lines of Rice (Oryza sativa L.)

An understanding the nature and relative extent of gene actions and combining ability is useful for a breeder to develop superior rice hybrids. In this association, an attempt was made to identify good general and specific combiners for selecting better parents and better cross combinations for developing high yielding and short duration rice hybrids. Four cytoplasmic male sterile lines viz., IR69625A, IR70368A, IR58025A and IR79575A were crossed with ten elite testers, 40 hybrids along with their fourteen parents were evaluated for grain yield and its related traits at the experimental farm of Rice Research and Training Center, Sakha, Kafr El-Sheikh, Egypt, during the rice-growing seasons of 2018 and 2019. The differences among genotypes, parents and crosses were highly significant for all studied traits. The specific combining ability (SCA) played the major role in determining inheritance traits, revealing that the largest part of the total genetic variability associated with most traits was a result of non-additive gene action. The parental line, IR58025A was demonstrated as a great general combiner for grain yield per plant and most of yield components and therefore could be utilized in future breeding programs to improve grain yield. HHZ12-SAL8-Y1-SAL1, HHZ5-Y7-Y2-SUB1 and HUA564 were seen as a good general combiner among testers for grain yield per plant and some studied traits. Four crosses, namely IR58025A / HHZ8-SAL9-DT2-Y2, IR70368A / WEED TOLERANT RICE1, IR70368A / HHZ12-SAL8-Y1-SAL1 and IR70368A / HHZ5-Y7-Y2-SUB1 exhibited significantly good specific combining ability effects for grain yield per plant. Hence, it can be seen that non-additive genetic effects are controlling the inheritance of the studied traits and indicated that selection for the traits inherited with this manner should be performed in the further generations.
The Conduct of Paediatric Surgery Collaborative Learning Cases Via an Online Platform During the COVID-19 Outbreak: Challenges and Lessons Learnt

Introduction A pedagogical approach termed as Collaboration Learning Cases (CLC) is conventionally conducted in a physical classroom (P-CLC) to encourage active learning in a shared dialogic space. The cessation of physical classroom activities as a result of the COVID-19 pandemic has paved the way for virtual classroom experiences. This study aims to compare the student and tutor experiences between P-CLC and virtual CLC (V-CLC). Methods There were eight clinical student subgroups and five paediatric surgery tutors involved in V-CLC. Microsoft Teams was the online platform chosen for V-CLC. The study employed an action research approach with mixed methods. Comparative analysis of the quantitative data across four different domains (content, facilitation, relevance and user interface) based on the 5-point Likert scale between P-CLC and V-CLC was performed. An inductive and iterative process of information collection, analysis and thematic classification was also employed for the qualitative data. Results The mean class size and student-to-facilitator ratio was higher in V-CLC. Both approaches were comparable for facilitator effectiveness (p=0.383) and being systematic and understandable (p=0.907). However, P-CLC had significantly higher scores in conduciveness, pace and relevance as compared to V-CLC. Student satisfaction scores were also higher in P-CLC [V-CLC: 78.6 (75.4-81.8) vs P-CLC: 87.6 (86.4-88.7), p<0.001]. Nonetheless, most students would still recommend V-CLC to their peers (p=0.494). Four main thematic areas such as platform-specific concerns, student engagement, interactivity and lesson pacing were identified for possible improvement. Tan J, Lee Y, Cheong Y, Nyo Y MedEdPublish https://doi.org/10.15694/mep.2020. 000261.1 Page | 2 Conclusion While V-CLC may not full replicate a physical classroom experience, it provides a viable alternative during the COVID-19 pandemic. Successful application of this virtual collaborative pedagogy model to other different contexts will require bespoke adjustments for optimal adaptation to local conditions.
Metabolic syndrome in patients with Schizophrenia: Relationship with socio-demographic & clinical variables

Background: Schizophrenia is an enduring severe mental illness with significant morbidity and early excess mortality. Metabolic syndrome (MetS) is a construct that aids psychiatrist to understand this constellation of risk factors leading to premature mortality. The current study was carried out with an aim to understand the association of metabolic syndrome in patients with schizophrenia and also to ascertain its relationship with socio-demographic & clinical variables. Materials and Methods: A cross sectional study was undertaken in patients suffering from Schizophrenia (F20) as per International Classification of Diseases (ICD-10. A total of one-hundred patients were selected by purposive random sampling after obtaining a written informed consent. A predesigned proforma was used to obtain the information regarding the sociodemographic variables, drug intake, anthropometric values and metabolic parameters. Metabolic syndrome was diagnosed as per the International Diabetes Federation2006 criteria. The data obtained was analysed using statistical package for social services (SPSS 20). Results: The association of metabolic syndrome in schizophrenia in the current study was 42%. The individual metabolic parameters were found to be higher in the MetS group than the non-MetS group (p=0.001). Majority of the schizophrenia patients with MetS were older (59.5% vs 44.8%; 31-50 years of age) and females (52.4% vs 36.2%; p=0.107) compared to those without MetS. Most of them were married (85.7% vs 56.9%; p=0.006), from urban areas (45.2% vs 19%; p=0.005), had lower educational qualification (p=0.01) in comparison to the non-MetS group. The MetS group had longer mean duration of schizophrenia (9.64±8.1 years vs 3.76±2.29 years), more hospitalizations (66.7% vs 53.4%; p=0.02) and higher medical comorbidity (diabetes-7.1% vs nil, hypertension-9.5% vs 1.7% and hypothyroidism-7.1% vs nil; p=0.006). With respect to psychotropic drugs usage; the use of Olanzapine (69% vs 46.6%; p=0.025), longer duration of treatment with Olanzapine (26.31 ± 33.9 months vs 8.98 ± 12.3; p=0.001) and Amisulpride (11.0 ± 21.68 months vs 3.48 ± 9.9; p=0.022) were significantly associated with MetS group than the group without MetS. On the contrary, the non-MetS group had more patients receiving risperidone (58.6%) than the MetS group. Conclusion: The study shows that the association of metabolic syndrome in schizophrenia patients is significantly in higher percentage and thereby underlines the importance of early detection and treatment of both the disorders simultaneously.
Application of the polynomial filtering algorithm to identification of the narrowband process parameters

A polynomial filtering algorithm has been implemented to identify the central frequency and correlation interval of a narrowband process. The efficiency of the polynomial filtering algorithm is carried out by comparing it with the potential accuracy calculated using the algorithm based on multi-alternative filtering. Recurrent relations are proposed for determining the lower bound of accuracy for estimating the parameters of a narrowband process. The possibility of using less computationally expensive procedures for calculating the lower bound of accuracy is investigated.
Biosynthesis of a Periodic Protein Containing 3-Thienylalanine: A Step Toward Genetically Engineered Conducting Polymers

Protein engineering provides an approach to materials synthesis in which macromolecular architecture is subject to precise control. Proteins synthesized biologically from a genetic template by the protein synthesis machinery are monodisperse with respect to molecular weight, and they are stereochemically pure, as only L-amino acids are used. Chemical composition and sequence are controlled, as the covalent structure of the chain is encoded by the template messenger RNA. We reasoned that genetic engineering technology could be used to create conducting materials by incorporation of electroactive amino acids, which could subsequently be cross-linked (or grafted) to produce extended conjugated systems. The role of the protein backbone in such materials would be to control the spatial organization of the conducting layers or domains. We report here the successful synthesis of a recombinant protein containing 3-thienylalanine (3-TA, 1) in place of phenylalanine (2).
Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario

Abstract The whole world is embroiling the pandemic situation caused by COVID-19, which is spreading across all countries. As of mid-May, COVID-19 continues to increase the number of people affected and the number of deaths in each country. Each country’s administrations concerned are making endless efforts to maintain public health, mental health and to regulate the rate of illness of COVID-19. Analysis of COVID-19 data using the machine learning paradigm is becoming a major interest of the researcher in these situations. Several researchers analyzed data from COVID-19 to predict infection, death, cured persons in the future, which may lead to the planning of each country’s regulatory authority to maintain the public health of its people. The machine learning algorithm provides more accurate results when the data size is large due to the lower number of data sets available to COVID-19, making the most accurate predictions a challenging task to implement the machine learning algorithm. This paper was essentially designed to predict the active rate, the death rate, and the cured rate in India by analyzing the data of COVID-19. There are three models of machine learning Support Vector Machine (SVM), Prophet Forecasting Model, and Linear Regression Model for predicting active rate, death rate and cured rate. Prophet Forecasting Model has been shown to be the best predictive method for predicting active rate, death rate and cured rate compared to SVM and Linear Regression when the vast uncertain and small data sets.
Emergency response with mobile geosocial sensing in the post‐app era

Disaster and emergency management is inherently a spatial problem. In case of disaster, the use of real‐time location‐based social media posts within the spatial decision support processes has great potential so that resource distribution can be planned, damage areas can be determined, the amount of damage can be estimated, and evacuation and/or help routes can be defined. Crowdsourced data and collaborative platforms like OpenStreetMap and Twitter can be widely employed for near‐real‐time mapping to support emergency management. After a disaster occurs, the aim is to quickly assess and triage damage, injury, and loss of life, and respond with medicine, food, water, housing, and basic infrastructure. In the scope of the study, in a similar manner, a solution, called WITNER, is proposed, designed, and developed. The difference is that WITNER is a cloud‐native geo‐enabled solution. The value of WITNER lies in the fact that it acts only as a mediator in translating help requests posted on a social media platform into spatially readable form, while keeping original social media post requests intact and accessible. The main purpose of WITNER is to help more people and save more lives in case of emergency response, with the intention of preventing the same help request locations being concurrently targeted by multiple relief, search, and rescue efforts. Moreover, it reveals more viable and less costly help requests that might take less time for potential help providers to assist with.
Rapid Prediction of Different Wood Species Extractives and Lignin Content Using Near Infrared Spectroscopy

Abstract The feasibility of using Fourier transform near infrared spectroscopy (FT-NIR) to rapidly determine the lignin and extractive content of various wood species (including softwoods and hardwoods) was investigated. Partial Least Square regression analyses were performed to describe the relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. The selection of relevant wavenumbers combined with the appropriate data pre-processing methods produced satisfactory prediction models. The test statistics (R2 , RMSECV, RMSEP, RPD) improved compared with the models over the wave number range 7500 cm−1 to 4000 cm−1. Automatic selection was superior to manual selection. The predicted lignin and extractive content models, using the full cross-validation in the appropriate wave number ranges (in cm−1) of 5450.1 to 4246.7, 6102.1 to 4597.7, 6252.4 to 4246.7, and 6252.4 to 6098.1 using the spectral data preprocessing methods of the straight-line subtraction, minimum–maximum normalization, and first derivative + vector normalization, were established. The high R2 values were 0.9838, 0.9809, and 0.9625, respectively. The low RMSECV values were 0.425%, 0.452%, and 0.185%, respectively. RPD values were 7.86, 7.25, and 5.17, respectively. Predictions were very good, with R2 of 0.9775, 0.9751, and 0.9521; RMSEP of 0.418%, 0.403%, and 0.206%; and RPD of 6.78, 6.7, and 4.57 for the lignin, 1% sodium hydroxide extractive, and ethanol-benzene extractive models, respectively.
When Ignoring Negative Feedback Is Functional: Presenting a Model of Motivated Feedback Disengagement

Contrary to popular belief, negative feedback occasionally hinders performance improvements. Investigations targeting this feedback-performance gap usually rest on two assumptions: (a) Feedback recipients want to improve their performance (have an improvement goal), and (b) feedback recipients engage with the negative feedback. We argue that people sometimes disengage from negative feedback for hedonic-goal attainment (to feel good). To explain such functional feedback disengagement, we conceptualize feedback processing from an emotion-regulation perspective, the model of motivated feedback disengagement. We posit that feedback-induced negative affect may render hedonic goals more salient than improvement goals, motivating emotion regulation. After forming the intention to regulate their emotions, feedback recipients select and implement an emotion-regulation strategy. We consider two common engagement strategies (reappraisal and feedback focus) and two common disengagement strategies (distraction and feedback removal). These strategies differentially impact recipients’ affect and feedback processing. Strategy-, person-, and situation-related factors influence strategy choice. Feedback processing is cyclical and dynamically unfolds over time. The model provides novel directions for future investigations and practical implications for stakeholders in negative-feedback contexts.
Detection of PCR products amplified from DNA of epizootic pathogens using magnetic nanoparticles and SERS

Here we present a novel approach using surface-enhanced Raman scattering (SERS) spectroscopy for the sequence-specific detection of DNA utilizing magnetic nanoparticles (MNPs) for the enrichment of the target molecules. To achieve fast and efficient binding of longer DNA strands, e.g. PCR products, the hybridization procedure is performed in solution. To further purify and enrich the DNA strands of interest, MNPs are used for their separation. Following the binding of the target DNA, a dye-modified, short synthetic ssDNA is hybridized, which serves as label for the SERS detection. The SERS spectra are used to identify the bound molecules. The applicability of this approach was first tested with short synthetic oligonucleotides to evaluate its specificity. Afterward, the system was applied to detect PCR products amplified from DNA of specific agents of epizootic diseases. Sequences of the bacterium Mycoplasma mycoides subspecies mycoides small colony type (MmmSC), causing contagious bovine pleuropneumonia (CBPP) were used as PCR targets. To demonstrate the multiplexing capability of SERS, the simultaneous detection of three different PCR products labeled with three dyes was performed. Copyright © 2010 John Wiley & Sons, Ltd.
Application of Nanoparticles in Electrochemical Sensors and Biosensors

The unique chemical and physical properties of nanoparticles make them extremely suitable for designing new and improved sensing devices, especially electrochemical sensors and biosensors. Many kinds of nanoparticles, such as metal, oxide and semiconductor nanoparticles have been used for constructing electrochemical sensors and biosensors, and these nanoparticles play different roles in different sensing systems. The important functions provided by nanoparticles include the immobilization of biomolecules, the catalysis of electrochemical reactions, the enhancement of electron transfer between electrode surfaces and proteins, labeling of biomolecules and even acting as reactant. This minireview addresses recent advances in nanoparticle-based electrochemical sensors and biosensors, and summarizes the main functions of nanoparticles in these sensor systems.
Antecedents of Student Loyalty Within Universities in North Sumatra Province, Indonesia

The purpose of this study was to analyze the antecedents of student loyalty which included Servicescape University, relationship quality, university image, and student satisfaction. The research was conducted in three universities with Islamic atmosphere under Al Washliyah Foundation in Medan, North Sumatra Province, Indonesia. Data analysis in this study includes descriptive analysis and Structural Equation Modeling (SEM). The results showed that: (1) Servicescape university influences university image, student satisfaction, but does not influence student loyalty; (2) Servicescape university through university image influences student loyalty, (3) Servicescape university through student satisfaction influences student loyalty, (4) Relationship quality influences university image, satisfaction Students, but does not influence student loyalty, (5) Relationship quality through university image influences student loyalty, (6) Relationship quality through student satisfaction influences student loyalty, (7) University image influences student loyalty, and (8). Student satisfaction influences student royalty. The findings of this study suggest that the antecedent of student loyalty in universities with Islamic atmosphere which was examined in this research includes four factors: Servicescape university, quality relationship, university image, and students’ satisfaction.
Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

Algorithmic management is used to govern digital work platforms such as Upwork or Fiverr. However, algorithmic decision-making is often non-transparent and rapidly evolving, forcing workers to constantly adapt their behavior. Extant research focuses on how workers experience algorithmic management, while often disregarding the agency that workers exert in dealing with algorithmic management. Following a sociomateriality perspective, we investigate the practices that workers develop to comply with (assumed) mechanisms of algorithmic management on digital work platforms. Based on a systematic content analysis of 12,294 scraped comments from an online community of digital freelancers, we show how workers adopt direct and indirect “anticipatory compliance practices”, such as undervaluing their own work, staying under the radar, curtailing their outreach to clients and keeping emotions in check, in order to ensure their continued participation on the platform, which takes on the role of a shadow employer. Our study contributes to research on algorithmic management by (1) showing how workers adopt practices aimed at “pacifying” the platform algorithm; (2) outlining how workers engage in extra work; (3) showing how workers co-construct the power of algorithms through their anticipatory compliance practices.
Derived verbs with suffix -ang in Balinese: syntactic and semantic analysis

This paper accounts for the suffix -ang in Balinese and it focuses on its syntactic and semantic representation. Using I Madé’s Sugianto’s Ki Bari Gajah, a one hundred fifty-page Balinese novel and informants as the data, and applying the RRG theory by Van Valin and Randy (1999) other thoughts of the experts of Balinese, it was found out that -ang functioning as a transitivizing suffix can attach to noun, adjective, adverbs and verbs and imply various syntactic structures and semantic representations. Suffix -ang attached to the base in imperative sentences express no meaning. In this case, it is just used to imply that the sentence is in the form of imperative. Like other languages, English for example, one derived verb with -ang may be used transitively or intransitively.
Post-1980s shift in the sensitivity of tundra vegetation to climate revealed by the first dendrochronological record from Bear Island (Bjørnøya), western Barents Sea

Tundra vegetation dynamics are an excellent indicator of Arctic climate change. In many places in the Arctic, greening of tundra has been observed since the 1980s due to rapid increases in temperature. However, in some areas the opposite process has taken place in connection with a reduction in biomass production. The spatial patterns of tundra ‘greening’ and ‘browning’ constitute important issues in the contemporary analysis of polar ecosystems. The aim of our study was to assess recent tundra vegetation dynamics on the basis of changes in annual growth ring widths of the polar willow. Bjørnøya (Bear Island), located in the western part of the Barents Sea is an important site in the transition zone between the high and low Arctic. No dendrochronological studies have been conducted to date due to the island’s isolation, which makes access very difficult. In 2012 and 2016, 43 samples of Salix polaris Wahlenb. were taken from the south-eastern part of Bear Island. An average chronology of the 29 most closely correlated measurement series was then compiled, covering 95 years (1922–2016); however, the time span 1946–2016 was used for the climate-growth analysis. Beginning in the mid-1980s, an increase in the width of annual increments was observed, whereas over the last decade (since 2005) the growth rate has declined rapidly. Simple correlation analyses showed that temperatures in spring and summer had the positive influence on the radial growth of the polar willow; however, the results of the moving correlation analysis made it possible to conclude that this relationship is more complex and time-dependent. Sensitivity of radial growth to temperature was strongest in the years 1955–2005, whereas the decrease in the strength of positive correlation with temperature since 2005 has been accompanied by a significant increase in the importance of summer precipitation.