Miracle-dz/UniFAR-FTUs huggingface dataset:
¿Las Características centrales del puesto son un antecedente de la Satisfacción laboral?: Un estudio con profesores universitarios

The job satisfaction takes up a central place in the consideration ́s work experience, which produces numerous researches and variety instruments of evaluation. From a practical point of view, research has focused on identifying the antecedents of satisfaction which will allow and facilitate the intervention. The aim of this paper is to analyze the role of work resources (main characteristics of job position) and sociodemographic variables (gender and type of contract) as antecedents of job satisfaction in the framework of the Labours demands and resources Model. The sample is made up of 39 academics from the University Jaume I (43.6 % are women). The average age of participants is 42.31 years (dt = 7.27). The contractual relationship can be: University P. Holder, P. Holder School, P. Contracted Doctor, Pic / Assistant Doctor Type I and Type II, Assistant and Associate P. labor. The instrument used is the adaptation of the Job Diagnostic Survey or Jds. Analysis shows statistically significant differences: gender in both satisfaction and some labor resources (Task identity and Importance of the tasks), and depending on the type of contractual relationship satisfaction in both Variety and skills. Regression analyzes show how the Contractual relationship, the Importance of the tasks and the Feedback explain the Job satisfaction of university professors. These data provide important information for possible interventions to facilitate and optimize the working conditions and thus the satisfaction of university professors. keywords: Work satisfaction, main characteristics of job position, work resources, Jds
Comparative cytogenetics of four endemic Capoeta (Teleostei: Cyprinidae) species from Anatolia, Türkiye

The genus Capoeta is an important taxon covering a wide distribution in Türkiye. However, only a few genetic studies on Capoeta species reported from Türkiye. There is no cytogenetical study in Capoeta aydinensis Turan, Küçük, Kaya, Güçlü & Bektaş, 2017, Capoeta bergamae Karaman, 1969, Capoeta erhani Turan, Kottelat & Ekmekçi, 2008 and Capoeta pestai (Pietschmann, 1933). Thus, in this study, we karyotyped through classical cytogenetic techniques (Giemsa staining, Ag-NORs, and C-banding) the four endemic Capoeta species. The diploid chromosome number invariably was 150 in the four species. However, chromosome morphologies in the karyotypes had some differences between them. The number of biarmed chromosomes in the karyotypes was higher in all studied species. Their karyotypes contained respectively: 54 metacentric, 42 submetacentric and 54 subtelo-acrocentric in C. aydinensis, 56 metacentric, 30 submetacentric and 64 subtelo-acrocentric in C. bergamae, 50 metacentric, 42 submetacentric and 58 subtelo-acrocentric in C. erhani and 44 metacentric, 40 submetacentric and 66 subtelo-acrocentric chromosomes in C. pestai. C-bands were on the pericentromeres of most chromosomes in the four species. Three chromosome pairs carry rDNA genes in all studied species. The chromosomal locations of these sites were varied between the species. This study provides new insights into the chromosomal data of the hexaploid cyprinids. Moreover, obtained cytogenetic results should be conclude the cytotaxonomy of the genus Capoeta that distributed in Türkiye.
The Effect of Parenting and Learning Processes on the Development of Children's Empathy

The effect of parenting and learning processes is a scientific literature review article. The purpose of writing this article is to build a hypothesis on the influence or role between variables that will be used in further research, within the scope of Islamic Education Management. The article writing method is the library research method, which is sourced from online media such as Google Scholar, PDF Drive and other academic online media. Qualitative descriptive analysis. The results of this article are: 1) Parenting has an effect on child empathy; 2) the learning process of children's empathy influences
Measuring the effectiveness of value-framing and message valence on audience engagement across countries

Changing public behaviour is an essential step for successful conservation, and can be achieved through effective use of message framing. However, its use in the conservation sector is not well-studied. We first performed a content analysis to assess what types of framing styles environmental non-governmental organisations (ENGOs) often employ for their social media posts. We then ran a real-world online fundraising campaign to examine the influence of value-framing (‘Intrinsic’ and ‘Extrinsic’) and message valence (‘Positive’ and ‘Negative’) on audience engagement with the advertisements, across five countries. Altogether, ENGOs generally used ‘Positive’ framing for their posts significantly more often than ‘Negative’, but did not use one type of value-framing more than the other. For the fundraising campaign, there were significant differences between countries’ engagement with the advertisements. However, click-through rates did not significantly differ when using types of value-framing nor message valence, and no donations were received to support the campaign. These results may show that message valence and value-framing alone have little influence on audience engagement, if any, at least in the context of social media. To enhance campaign success for the future, it is recommended that conservationists offer concrete information regarding fundraising outcomes, and activate social norms.
MAPPING FOREST INVENTORY ATTRIBUTES ACROSS CONIFEROUS, DECIDUOUS AND MIXEDWOOD STAND TYPES IN THE NORTHWEST TERRITORIES FROM HIGH SPATIAL RESOLUTION QUICKBIRD SATELLITE IMAGERY

Conducting vegetation inventories that describe the state of forests in the Northwest Territories (NWT) is challenging due to the large geographic extent of forest resources that are not easily accessible. The federal (Natural Resources Canada, Canadian Space Agency) and territorial (Government of NWT) governments recently completed a satellite-based land cover map of the forested areas of the NWT in the Earth Observation for the Sustainable Development of Forests (EOSD) project with Landsat Thematic Mapper data. Within the framework of the EOSD land cover map, this project is modeling and mapping four forest inventory attributes (stand height, crown closure, stand volume and aboveground biomass) from panchromatic and multispectral Quickbird satellite imagery. The image derivatives from Quickbird used to derive inventory attributes depends on whether the stand types were predominately comprised of coniferous, deciduous or mixedwood species. For coniferous species, image tree shadows derived from the panchromatic Quickbird image were used to compute the shadow fraction, of which image estimates of the four inventory attributes were within 10 to 15 percent of independent field measurements. Due to differences in crown morphology between coniferous and deciduous stands, image shadow fraction was not a good predictor of the forest inventory attributes for deciduous and mixedwood species. A first-order texture measure based on variance from the panchromatic image resulted in image models for deciduous species with adjusted R 2 values that ranged from 0.50 to 0.62. Estimating forest structure from mixedwood stands were more difficult, and required a combination of both panchromatic and multispectral image bands as estimators of the forest inventory variables.
System-Level Validation of Radiated Noise Source Characterization Using Only Near-Field Magnitude Information

To characterize the radiated noise sources in the radio-frequency interference simulations, a novel equivalent dipole source extraction method was proposed by our group previously. An iteration algorithm and the genetic algorithm work together to reconstruct an equivalent source using near-field magnitude information only while minimizing the number of dipoles needed. In this letter, the previously proposed equivalent dipole extraction algorithm is extended to efficiently extract an equivalent source of harmonics. Furthermore, the algorithm is verified using the actual product instead of simplified test boards. A rigorous and systematic validation process is proposed and conducted, which ensures the robustness and credibility of the extraction algorithm for future applications.
Flooding hazard mapping for Poyang Lake Region with remote sensing and water level records

Flooding is a natural calamity that can caused great loss. Flooding hazard mapping is an effective tool for managing flooding to reduce the destructivity. In this paper, we suggested a new method to map the flooding hazard for alluvial plain with multi-temporal remote sensing images and lake level records. 9 images gained at different date for Poyang Lake Region were used to pickup the inundation extent with different lake level, and the annual and monthly exceedance probability of lake level for Poyang was estimated by sorting all lake level records. The boundary of inundation extent from remote sensing images was regarded as isolines valued as exceedance probability for the lake level happened on the image taken time. With the interpolation process provided by ArcGIS software, the flooding hazard were mapped for Poyang Lake Region.
Analyzing Effects of Social Media User’s Influence on Contents Caching in ICN

In recent years, with the rapid growth of Social Networking Service (SNS), Information-Centric Networking (ICN) has been emerged as a promising network architecture for mitigating the traffic generated by SNS users. To fully utilize potential of ICN as a sort of the cache network, we have to carefully design the caching strategy and the cache-replacement policy while considering both of features of social network among SNS users and those of cache network. Therefore, in this paper, we assume that ICN is introduced as a content distribution infrastructure for SNS, and extensively analyze the characteristics of the socially-aware ICN. In particular, we focus on the influential user called influencer, which is one of typical features of social networks, and investigate the effect of the selection of influential users and their ratio on the content caching in ICNs. Our key findings are summarized as follows. First, the effectiveness of the content caching is dominated by the centrality measures to determine influential users. Second, on determining influential users, it is enough to sample at most 30% of the social network rather than its entire structure. Third, to gain the benefit of the content caching, we have to carefully select the ratio of influential users on the social network whose degree distribution follows power-law.
Pharmacogenomics of Dementia: Personalizing the Treatment of Cognitive and Neuropsychiatric Symptoms

Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer’s disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these multifactorial disorders involves complex interactions of various environmental and (epi)genetic factors and requires multiple forms of pharmacological intervention, including anti-dementia drugs for cognitive impairment, antidepressants, antipsychotics, anxiolytics and sedatives for behavioral and psychological symptoms of dementia, and other drugs for comorbid disorders. The pharmacotherapy of dementia patients has been characterized by a significant interindividual variability in drug response and the development of adverse drug effects. The therapeutic response to currently available drugs is partially effective in only some individuals, with side effects, drug interactions, intolerance, and non-compliance occurring in the majority of dementia patients. Therefore, understanding the genetic basis of a patient’s response to pharmacotherapy might help clinicians select the most effective treatment for dementia while minimizing the likelihood of adverse reactions and drug interactions. Recent advances in pharmacogenomics may contribute to the individualization and optimization of dementia pharmacotherapy by increasing its efficacy and safety via a prediction of clinical outcomes. Thus, it can significantly improve the quality of life in dementia patients.
The case of banana industry in Cameroon

Purpose The purpose of the social Life Cycle Assessment (LCA) method is to predict the social impacts on people caused by the changes in the functioning of one product chain throughout its life cycle. Changes in health status are very important experiences for people. The aim of this paper is to build a pathway between changes in economic activity generated by the functioning of a product chain and the changes in health status of the population in the country where the economic activity takes place. Methods Empirical and historical factors suggest that increased economic activity through growth in income leads to improvements in the health of a country’s population. This empirical relationship is well known in economics as the Preston curve. Using this relationship, we design a pathway for social LCA impact assessment. This pathway may be used to explain or predict the potential impact caused by the modification of one product sector upon the health of a population. The Preston relationship usually is calculated for a cross section of countries. We assess wheth
IMPLEMENTING COLLABORATIVE GOVERNANCE: COLLABORATION BETWEEN ACTORS IN ORGANIZING BANYUWANGI FESTIVAL

The Banyuwangi Festival is the flagship of Banyuwangi tourism on a national level. In addition, the presence of the Banyuwangi Festival also supports the growth of tourist visits to the Banyuwangi area. The success of the Banyuwangi Festival is inseparable from the presence of two significant events, namely the Banyuwangi Ethno Carnival (BEC) and the Gandrung Sewu event. The success of organizing these two significant events cannot be separated from the multi-actor collaboration between the Banyuwangi local government and the private sector to culturalists. This article answers questions about the Collaborative Governance process in the Banyuwangi Festival Program, which consists of the BEC event with the Hybrid Culture theme and Gandrung Sewu with the Traditional theme. This study uses a qualitative comparative method. In Collaborative Governance at the BEC event, the Banyuwangi government cooperates with Dinan Fariz Event Organizer as a private party who provides ideas in terms of conceptual to various detailed elements that support the implementation of a modern carnival. Meanwhile, the Blambangan Arts Council from the Cultural side determines the theme of traditional culture and the party that maintains traditional standards that must be maintained in the implementation of BEC. At the Gandrung Sewu event, the Banyuwangi local government invites cultural cooperation to determine all aspects of culture in the Gandrung Sewu event. Meanwhile, schools and art studios were invited to collaborate to involve their students in the Gandrung Sewu performance. The success of organizing two significant events, namely BEC and Gandrung Sewu, summarized in the Banyuwangi Festival program, cannot be separated from the collaboration of multi-actors who have successfully played their roles by the functions and synergies that have been built between all actors involved.
Relationship between Parental Concerns about Social–Emotional Reciprocity Deficits and Their Children’s Final ASD Diagnosis

Concerns raised by parents regarding their child’s development are compatible with the child’s final diagnosis of autism spectrum disorder. A better understanding of the relationship between parental concerns and a final diagnosis of autism spectrum disorder is therefore critical. In the current study, we compared the frequencies of parental concerns related to DSM-5 criteria for autism spectrum disorder between pair-matched groups with and without a final diagnosis of autism spectrum disorder and determined which parental concerns predicted a final diagnosis of autism spectrum disorder. The cohort included 80 participants (48–154 months of age, IQ > 70) assessed for a possible autism spectrum disorder diagnosis. Parental concerns were retrieved from the free-description portion of the introductory questions of the Autism Diagnostic Interview-Revised and analyzed to assess whether they corresponded to any of the seven DSM-5 criteria for ASD. The two groups only differed in the frequency of parental concerns relating to deficits in social–emotional reciprocity. Parents of children diagnosed with autism spectrum disorder were four times as likely to report deficits in social–emotional reciprocity. This finding highlights the significance of parental concerns regarding deficits in social–emotional reciprocity in predicting a final diagnosis of autism spectrum disorder.
Adaptive clustering algorithm based on improved marine predation algorithm and its application in bearing fault diagnosis

In cluster analysis, determining the number of clusters is an important issue because there is less information about the most appropriate number of clusters in the real problem. Automatic clustering is a clustering method that automatically finds the most appropriate number of clusters and divides instances into the corresponding clusters. In this paper, a novel automatic clustering algorithm based on the improved marine predator algorithm (IMPA) and K-means algorithm is proposed. The new IMPA utilizes refracted opposition-based learning in population initialization, generates opposite solutions to improve the diversity of the population and produces more accurate solutions. In addition, the sine-cosine algorithm is incorporated to balance global exploration and local development of the algorithm for dynamic updating of the predator and prey population positions. At the same time, the Gaussian-Cauchy mutation is combined to improve the probability of obtaining the globally optimal solution. The proposed IMPA is validated with some benchmark data sets. The calculation results show that IMPA is superior to the original MPA in automatic clustering. In addition, IMPA is also used to solve the problem of fault classification of Xi*an Jiaotong University bearing data. The results show that the IMPA has better and more stable results than other algorithms such as the original MPA, whale optimization algorithm, fuzzy C-means and K-means in automatic clustering.
Magnetofossils: Relicts and Records of Deep Time and Space

Magnetofossils are magnetic nanoparticles that represent the fossil remains of microorganisms that biomineralize magnetic minerals in a genetically controlled manner. Most magnetofossils found in the geologic record are produced by magnetotactic bacteria, which use them for navigating within their living environment. Magnetofossils can be identified using a combination of magnetic and imaging techniques. A common attribute of magnetofossils, although not pervasive, is that they are arranged in chains, which determines their specific magnetic properties. Magnetofossil signatures have been reported from ancient rocks to modern sediments and even in extraterrestrial materials. They provide a window into biomineralization, past environments, and ancient magnetic fields, as well as supplying fuel for questions on the origin of life in the Solar System.
Enhancing Similar Business Group Recommendation through Derivative Criteria and Web Crawling

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.
Studying sediment transport in mountain rivers by mobile and stationary RFID antennas

Sediment transport is an important factor in the assessment of natural hazards, but due to the complexity of the systems the predictability is very restricted, especially in mountain rivers. Thus, ac- curate data from natural streams are essential to assess and develop current approaches. Tracing tech- niques are widely used to study the processes in sediment transport. Recently, the Radio Frequency Iden- tification (RFID) method was successfully employed in various streams using mobile antennas to determine the displacement of single transponders-tagged particles. We have developed an antenna sys- tem that can be permanently installed in the channel bed to continuously record the tagged pebbles pass- ing over it. In this study, we discuss the advantages and limitations of using such tracer systems for moni- toring bedload transport in steep channels. We illustrate the application of the mobile antenna system using data from the mountain stream Erlenbach and the stationary antenna system using data from field and laboratory experiments. Both methods complement other methods used to investigate the mechanics of sediment transport in steep mountain rivers.
Forecasting Market Price of Stock Using Artificial Neural Network

Stocks represent the ownership stake in a company and reflect its financial performance and contributions to the economy. Company stocks are divided into shares. Analysing stock market decisions is a complex process involving factors such as price trends, market demand, company stability, rumours, brand reputation, and venture capital. It is crucial to have access to vital information that allows investors to understand and assess the associated risks. While various methods like technical analysis, time series analysis, and statistical analysis attempt to predict stock prices, none of these methods are consistently reliable. Therefore, artificial neural networks, a branch of Artificial Intelligence, offer a promising approach to identify both known and hidden patterns in data. The process involves two phases: training and prediction. The Backpropagation algorithm is utilized for the training session, while a Multilayer Feedforward Network serves as the model for price prediction. This forecasting is based on multiple parameters derived from company data.
An Analysis of the Effect of Invariance on Generalization in Neural Networks

Invariance is often cited as a desirable property of machine learning systems, claimed to improve model accuracy and reduce overﬁtting. Empirically, invariant models often generalize better than their non-invariant counterparts. But is it possible to show that invariant models provably do so? In this paper we explore the effect of invariance on model generalization. We ﬁnd strong Bayesian and frequentist motivations for enforcing invariance which leverage recent results connecting PAC-Bayes generalization bounds and the marginal likelihood. We make use of these results to perform model selection on neural networks.