Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate Change Risk Perception among Agriculture Students: the Role of Knowledge, Environmental Attitude, and Belief in Happening

Climate change has arisen due to "enhanced greenhouse effect", as a result of human activities and lifestyle. Mitigation of greenhouse gases is dependent on climate change risk perception. Therefore, the present study aimed to provide a conceptual model to determine risk perception and explore whether knowledge about causes and consequences of climate change influence individuals’ environmental attitudes, beliefs in the happening of climate change, and risk perception. The study was designed as a cross-sectional survey. The study sample consisted of 320 undergraduate students who were selected through random sampling. Structural Equation modeling showed that the relationship between knowledge of causes and consequences and risk perception was mediated by environmental attitude and beliefs. Respondents with higher knowledge about the causes and consequences of climate change are significantly more likely to consider climate change as a risk. Knowledge of causes and consequences has a direct effect on the belief that climate change is happening and on environmental attitude, and indirect effect on risk perception. Environmental attitude and belief have a significantly positive effect on risk perception. The suggested model could account for about half of the variance (49%) in risk perception. The results showed that the suggested framework is an effective tool for the prediction of risk perception.
Psychosocial Profile of Children of Unwed Mothers-An Indian Perspective

Unwed mother hood results in different psychosocial problems in children. Practice of devadasi systemwere young girls are consecrating to God towards perform the role of community entertainers, offering sexual services to their patrons leads to unwed mother hood in various rural villages in south India. These unwed mothers are deserted to bring up their children in absolute poverty, experience the stigma of being the child of unwed mother (Devadasi) and illegitimacy that results in various psychosocial problems among these children. Aim: Psychosocial profile children of unwed (devadasi) mothers, single parent and children with both parents. Methodology: A descriptive cum comparative study was conducted among 180 children (60 children each of devadasis, single parent and both parents category) of 10 to 16yrs, in 36 villages of Bagalkot District, Karnataka, India through simple random sampling. Results and discussion: A checklist on 15 psychosocial domains was developed, standardized and administered among the samples. The results revealed psychosocial problems among children in the areas of basic needs; family support; social support; personal safety, conflicts in family and child marriage. Conclusion: The psychosocial issues identified above were prevalent among the children in the population studied though there is no significant difference between children of unwed mothers( devadasis), single parent and both parents.
Jointly Non-negative, Sparse and Collaborative Representation for Image Recognition

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many image recognition tasks such as face recognition. In this paper, we propose a jointly Non-negative, Sparse and Collaborative Representation (NSCR) for image recognition. NSCR seeks a sparse and collaborative representation vector that represents each test image as a linear combination of training images, under a non-negative constraint. The non-negativity forces the SR and CR in our NSCR more discriminative for effective recognition. Based on the proposed NSCR, we propose a NSCR based classifier for image recognition. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR classifier outperforms the previous SR or CR based approaches, as well as state-of-theart deep ones, on diverse image recognition tasks. The code is available at https://github.com/csjunxu/NSCR.
GTP Cyclohydrolase 1/Tetrahydrobiopterin Counteract Ferroptosis through Lipid Remodeling

Ferroptosis is an iron-dependent form of regulated cell death linking iron, lipid, and glutathione levels to degenerative processes and tumor suppression. By performing a genome-wide activation screen, we identified a cohort of genes antagonizing ferroptotic cell death, including GTP cyclohydrolase-1 (GCH1) and its metabolic derivatives tetrahydrobiopterin/dihydrobiopterin (BH4/BH2). Synthesis of BH4/BH2 by GCH1-expressing cells caused lipid remodeling, suppressing ferroptosis by selectively preventing depletion of phospholipids with two polyunsaturated fatty acyl tails. GCH1 expression level in cancer cell lines stratified susceptibility to ferroptosis, in accordance with its expression in human tumor samples. The GCH1-BH4-phospholipid axis acts as a master regulator of ferroptosis resistance, controlling endogenous production of the antioxidant BH4, abundance of CoQ10, and peroxidation of unusual phospholipids with two polyunsaturated fatty acyl tails. This demonstrates a unique mechanism of ferroptosis protection that is independent of the GPX4/glutathione system.
Running head: STATE AND TRAIT ANGER PREDICTING CPE

Drawing on the specific emotion approach, and based on the emotional regulation theory and cognitive and activation perspectives on emotions, this study examined the differentiated impact of state and trait anger on creative process engagement (CPE) and the moderating influences of emotion reappraisal and suppression. Data were obtained from daily surveys (N = 422) of 98 employees from three consultancy companies. Hierarchical linear modelling analysis revealed that trait anger has a stronger impact on CPE than state anger does. Furthermore, the relationship between state anger and CPE is stronger when emotion reappraisal is lower, rather than higher, and the relationship between trait anger and CPE is also stronger when emotion suppression is lower, rather than higher.
Biosurveillance of Measles using Control Charts: A Case Study using National Capital Region Laboratory Confirmed Measles Counts from January 2009 to January 2014

This paper aims to explore early outbreak detection methods for measles. Two methods adapted from statistical process control were modified and used to fit biosurveillance, namely Shewhart and Exponentially Weighted Moving Average (EWMA) charts. Seven variations of such control charts are proposed: two under Shewhart chart (normal-based and zero-inflated Poisson (ZIP)based) and five under EWMA charts (λs of 0.05, 0.10, 0.15, 0.20, and 0.25). To study the proposed charts, daily counts of laboratory confirmed cases of measles in the National Capital Region from 2009 until 2014 were utilized to characterize both the disease background and outbreak equations. During this time span, three measles outbreaks have transpired. The proposed charts, set at average time between false signals (ATFSs) of both one and two months, were evaluated and compared using performance metrics such as conditional expected delay (CED), proportion of true signals (PTS), proportions of detections in an outbreak (PDO), and probability of successful detection(PSD), computed from 500 sets of simulated data. It was found that ZIP-based Shewhart and EWMA with a λ of 0.05 work best for ATFSs of one and two months, respectively. Health-governing bodies may seek to explore the possible utilization of these charts to improve measles surveillance.
CRITICAL ANALYSIS AND MODELLING OF SMALL BUSINESS PERFORMANCE (CASE STUDY: SYRIA)

INTRODUCTION The health of small business sector is very important for the overall economic growth potential and future strength of an economy. There has been more written about small business growth in recent years than any other aspect of management. One of the main reasons is the contribution of expanding enterprises to economic development and unemployment reduction, which, generally, has attracted the attention of researchers and policy makers in many countries (Bernice and Meredith,1997). The existence of a strong small business sector is necessary for the boosting the economy. However, the transition of this sector to medium and large business sectors is as crucial to preserve the flow of new small businesses into the economy. In addition, such transition or growth will further reduce the unemployment rate and increase the number of products or services offered to the society. Hence, growth is considered to be synonymous with success. While a considerable amount is known about the factors that affect the success of small and medium-sized businesses, this knowledge continues to be imperfect and a large number of questions remain unanswered regarding the small business sector in developing countries (Cook, 2001). Since developing countries are expected to adopt different economic policies and they are occupying different phases of economic development, factors that determine the success of small businesses would vary accordingly. In this study, the attempt is made to investigate factors that might contribute to the success of small businesses in Syria. Additionally, the effort is made in the study to develop success predictive models using several performance measures. The remainder of the paper is organised as follows. The following section provides a summary of the status of the small business sector in Syria. Related literature will be reviewed in section three. Data collection and models development are described in section four. While the findings are discussed in section five, the conclusion is offered in the final section. The nature of the centralized Syrian economy has been a major obstacle to the growth of the private sector, hindering the expansion of the small business environment in Syria. The Syrian authorities have come to realize the importance of decentralizing the economy towards a real market economy by putting effort into encouraging and supporting entrepreneurship among the small business investors. According to Piasecki (1995), private sector development in a centralized economy is extremely critical and vital for a smooth transition to a market …
Populating a Linked Data-based Entity Name System

Data Integration is the problem of combining data in various data sources and providing a user with a unified view over these sources. Building an automatic data integration system that can process large, semi-structured data sources has emerged as an important problem. An automated data integration system requires automatic population of an Entity Name System (ENS). An ENS is a thesaurus for entities and is used to serve instance matching needs across data sources. Resource Description Framework (RDF) is a graph-based data model used to publish data on the Web as linked data. To build and populate an ENS over linked data, the fundamental problem of data matching needs to be solved. Traditionally, data matching concerned identifying pairs of logically equivalent entities across one or more structurally homogeneous data sources, and required a human in the loop. Additionally, most systems run on serial architectures. These assumptions cannot be expected to hold for linked data. Given the size and variety of data sources on Linked Open Data, a growing repository of linked data published under an open license, a data matching system must be scalable and account for multiple forms of heterogeneity in an automated fashion. The thesis is that building such a data matching system requires simultaneously resolving the three challenges of scalability, heterogeneity and automation. This dissertation proposal spans three pieces of work leading to a data matching system that will automatically link multi-domain semi-structured data sources in a shared-nothing parallel architecture. The first work addresses automation, and enables a key process in the data matching pipeline called blocking to be done using unsupervised learning, that is, without human involvement. The contribution is extended to implement a high-quality, fully unsupervised data matching system on structurally homogeneous relational database inputs. The second piece of work accommodates graph database inputs, thereby addressing heterogeneity. The third piece of work addresses scalability. Proposed is a fully unsupervised heterogeneous pipeline implemented in a MapReduce-based framework, and with strong run-time bounds. The pipeline is also extended to accommodate multiple domains. The final prototype will be implemented on cloud infrastructure and evaluated on real-world test suites already published as Linked Open Data. The prototype will then be used to populate an Entity Name System to serve instance matching needs in an actual data integration system designed for the Semantic Web.
Contamination by Persistent Organohalogen Compounds in Deep-sea Fishes from off the Pacifi c Coast of Northern Japan

Twelve species of deep-sea fi shes collected in 2005 from off the Pacifi c coast of northern Japan from depths ranging from 400̶1000 m were analyzed for persistent organochlorine compounds (OCs), including PCBs and organochlorine pesticides and brominated fl ame retardants (BFRs) such as polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane isomers (HBCDs). Among OCs, concentrations of DDTs and PCBs (up to 23000 and 12400 ng/g lipid wt, respectively) were the highest and other OCs were approximately in the order of CHLs > HCHs ≥ HCB. PBDEs and HBCDs were also detected in almost all the deep-sea fi shes. The present study is the foremost to report the occurrence of BFRs in deep-sea fi shes from the North Pacifi c region. Although magnitude of contamination by these organohalogen compounds in most of the fi sh species analyzed was comparable or lower than that reported in shallow-water and deep-sea fi shes from Japanese coastal waters, snubnosed eels accumulated some hydrophobic compounds like PCBs, DDTs and PBDEs at extremely high concentrations. Signifi cant positive correlations found between δ15N (‰) and PCBs, DDTs and PBDEs suggest the high biomagnifi cation potential of these contaminants in food web. Nevertheless, large variation in δ13C (‰) values observed between the species indicate multiple sources of carbon in the food web and specifi c accumulation of hydrophobic organohalogen compounds in benthic dwelling species like snubnosed eel. It is noteworthy that no signifi cant declining trends of PCBs, HCB and HCHs levels were observed in fi shes collected in 1995 and 2005, suggesting continuous input of the contaminants into the environment. The results obtained in this study highlight the usefulness of deep-sea fi shes as sentinel species to monitor the deep-sea environment.
Determinants of Income Class in Philippine Households: Evidence from the Family Income and Expenditure Survey 2009

We developed a model that can classify households based on their demographic profile and household assets. A methodology is then provided in the use of this model in identifying householdbenefeciaries for some targetted interventions. Using the 2009 Family Income and Expenditure Survey (FIES), five income clusters were identified. In a generalized logistic regression model, geolocation of households, urbanity, employment status and education of household head, presence of some assets, and nature of housing units can significantly identify income class of a household.
Copula-Based Vector Autogressive Models for Bivariate Cointegrated Data

The copula method is well applied in finance and actuarial science but its application in economic studies is limited and its use in the cointegration framework virtually nil. This paper explores the use of copula method to analyze the remaining dependence after a cointegration relationship is modeled. Specifically, simulated data is used to characterize the behavior of the dependence parameter estimates of several copulas fitted to the distribution of the residuals after cointegrated Vector Autoregressive (VAR) and Vector Error-Correction Mechanism (VECM) models are fitted, as well as evaluate the forecasting ability of the copula-based models. The Clayton, Frank, Gaussian, Gumbel and Plackett copulas are used and are compared on the basis of bias, root mean square error (RMSE) and maximum likelihood. The density forecasting ability of the copula-based VAR and VECM is then compared with that of standard models via conditional Kullback-Leibler Information Criterion (KLIC) divergence measure using simulated and empirical data. The simulation results indicate that the copula-based models generally have better density forecasting ability than standard VAR and VECM models, a finding that is supported in the application of a copula-based VAR to empirical data.
Effects of Level and Particle Size of Date Fruit Press Cake on Batter Rheological Properties and Physical and Nutritional Properties of Cake

Date Press Cake (DPC) is a by-product of date fruit juicing, which has remained mostly underutilized in food products. The main objective of this research was to investigate the viability of adding DPC in cake formulation. Thus, different levels (0, 10, 20, 30, and 40%) and particle sizes (210 μm= DPC210 and 500 μm= DPC500) of DPC were used in cake formulation. The results showed that DPC had higher fiber, ash, and fat content than cake flour. Increasing the level and reducing the particle size of DPC increased batter consistency, firmness, stickiness, cohesiveness and viscosity. Inclusion of DPC increased cake density, reduced pH and crust moisture content, but these were not affected by DPC particle size. The samples became harder and less cohesive as the level of DPC increased. DPC210 resulted in softer and less cohesive cakes compared to the DPC500. Addition of DPC improved the antioxidant content of the cakes, particularly when DPC210 was added. Overall, the sample produced with 10% DPC210 had acceptable sensory characteristics.
Magical Science: Discriminating Science and Pseudoscience in Media Messaging with Undergraduate Students

Magical beliefs and paranormal thinking exist in the public and amongst university students. Researchers have suggested that media can influence such beliefs. A 2012 study suggested pseudoscientific rationales can influence acceptance of reported paranormal phenomena. Using a paranormal belief survey and controlled experiment this work explores the paranormal beliefs and test the effects of three versions of a supernatural news article on undergraduate professional students. One version of the story presented a simple news story, another same with a pseudoscientific rationale, and another gave a discrediting scientific critique. Results confirmed that many students do hold magical beliefs but discriminated between scientific and pseudoscientific narratives. However, pre-existing paranormal beliefs were associated with an increased likelihood of students finding paranormal reports scientific, believable and credible.
Microbiological Quality of Raw Milk at Selected Chilling Centers in Anuradhapura District of Sri Lanka

Milk easily undergoes microbial contamination and spoilage because it is a rich biological fluid which contains all necessary nutrients for animals. This study was carried out to evaluate the microbiological quality and safety of cow milk at different sampling points; selected chilling centers, milk transportation vessels and farmers in Anuradhapura district of Sri Lanka. A total of 11 chilling centers were selected for the study and classified them as low and high risk centers. Total 80 samples were tested for total bacterial count/standard plate count (SPC) and total coliform count (TCC) and analyzed by using Poisson Regression in GENMOD Procedure in SAS. Descriptive statistics of standard plate count and total coliform count data were obtained using Minitab 17. Results revealed that higher microbial counts for SPC and TCC were observed in chilling centers, transportation vessels and farmers with compared to the standards. The SPC and TCC showed a significant difference (p<0.05) between low and high risk chilling centers, low and high risk farmers, chilling centers and transportation vessels and chilling centers and farmers. In conclusions, microbial contamination occurs at every point of milk handling as expected. The contamination of milk is started at the farmer level and leads to poor quality milk. Thus, proper awareness programs should be carried out effectively to educate the dairy farmers on clean milk production.
An Exponentially Weighted Moving Average Control Chart for Zero-Truncated Poisson Processes: A Design and Analytic Framework with Fast Initial Response Feature

Inspired by the capability of exponentially-weighted moving average (EWMA) charts to balance sensitivity and false alarm rates, we propose one for zero-truncated Poisson processes. We present a systematic design and analytic framework for implementation. Further, we add a fast initial response (FIR) feature which ideally increases sensitivity without compromising false alarm rates. The proposed charts (basic and with FIR feature) were evaluated based on both in-control average run length (ARL0) to measure false alarm rate and out-of-control average run length (ARL1) to measure sensitivity to detect unwanted shifts. The evaluation process used a Markov chain intensive simulation study at different settings for different weighting parameters (ω). Empirical results suggest that for both scenarios, the basic chart had: (1) exponentially increasing ARLs as a function of the chart threshold L; and (2) ARLs were longer for smaller ωs. Moreover, the added FIR feature has indeed improved ARL1 within the range of 5% 55%, resulting to quicker shift detections at a relatively minimal loss in ARL0. These results were also compared to Shewhart and CUSUM control charts at similar settings, and it was observed that the EWMA charts generally performed better by striking a balance between higher ARL0 and lower ARL1. These advantages of the EWMA charts were more pronounced when larger shifts in the parameter λ happened. Finally, a case application in monitoring hospital surgical out-of-controls is presented to demonstrate its usability in a real-world setting.
Laboratory experiments on the improvement of rockfill materials with composite grout

Dam deformation should be strictly controlled for the construction of 300 m-high rockfill dams, so the rockfill materials need to have low porosity. A method of using composite grout is proposed to reduce the porosity of rockfill materials for the construction of high rockfill dams. The composite grout is a mixture of fly ash, cement and sand with the properties of easy flow and post-hardening. During the process of rolling compaction, the grout admixture sprinkled on the rockfill surface will gradually infiltrate into the inter-granular voids of rockfill by the exciting force of vibratory roller to reduce the porosity of rockfill. A visible flowing test was firstly designed to explore the flow characteristics of composite grout in porous media. Then, the compressibility, shear strength, permeability and suffusion susceptibility properties of composite grout-modified rockfill are studied by a series of laboratory tests. Experimental results show that the flow characteristics of composite grout are closely related to the fly ash content, the water-to-binder ratio, the maximum sand size and the content of composite grout. The filling of composite grout can effectively reduce the porosity of rockfill materials, as well as increase the compression modulus of rockfill materials, especially for loose and gap-graded rockfill materials. Composite grout-modified rockfill tends to have greater shear strength, larger suffusion erosion resistance, and smaller permeability coefficient. The composite grout mainly plays the roles of filling, lubrication and cementation in rockfill materials.