Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling enterprise location choice decision behavior

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.
Tradeoffs and synergies among ecosystem services, biodiversity conservation, and food security in coffee agroforestry

Concerns over the capacity of the world’s existing agricultural land to provide food for the global population under climate change and continued biodiversity loss have set the stage for a prevailing narrative of inherent tradeoffs with agricultural production. However, a strict focus on increasing production can undermine attempts to build more sustainable and equitable food systems. Coffee, a major export crop of tropical countries, offers a unique opportunity to examine how management practices can drive a variety of outcomes for food security, ecosystem services, and biodiversity conservation. Our study examined this intersection to identify tradeoffs and synergies using compiled data from Puerto Rico. At the island level, we analyzed data on coffee yield and area sown under shade or sun management. At the farm level, we analyzed management variables (percent shade cover, maximum canopy height, ground cover, and crop richness), non-provisioning ecosystem service variables (total farm carbon storage, soil organic carbon storage, coffee plant carbon biomass, and hurricane resistance and resilience), and biodiversity variables (ant, bird, and lizard richness and abundance). At the island level, we found that area sown was the most significant predictor of yield, suggesting no obvious tradeoff between yield and shade in coffee farms. At the farm level, canopy cover was negatively correlated with ground cover and positively correlated with crop richness, suggesting a synergy between agroforestry and food security. We detected mostly synergies resulting from agroforestry management and no tradeoffs among ecosystem services and biodiversity. Shade canopy cover significantly increased total carbon storage, coffee plant biomass, hurricane resistance and bird species richness. Shade canopy height had a similar positive effect on total farm carbon storage while crop richness had a positive effect on farm resilience following Hurricane Maria. Ground cover was positively associated with soil carbon storage and pest-controlling lizard abundance. Tradeoffs related to agroforestry management included an inverse relationship between ground cover and hurricane resistance, and greater dominance of an invasive ant species in farms with higher shade canopies. We discuss implications of practicing agroforestry principles in this smallholder coffee system and highlight opportunities for maximizing biodiversity conservation, ecosystem services, and food security.
Limits of Individual Consent and Models of Distributed Consent in Online Social Networks

Personal data are not discrete in socially-networked digital environments. A user who consents to allow access to their profile can expose the personal data of their network connections to non-consented access. Therefore, the traditional consent model (informed and individual) is not appropriate in social networks where informed consent may not be possible for all users affected by data processing and where information is distributed across users. Here, we outline the adequacy of consent for data transactions. Informed by the shortcomings of individual consent, we introduce both a platform-specific model of “distributed consent” and a cross-platform model of a “consent passport. ” In both models, individuals and groups can coordinate by giving consent conditional on that of their network connections. We simulate the impact of these distributed consent models on the observability of social networks and find that low adoption would allow macroscopic subsets of networks to preserve their connectivity and privacy.
Effect of Emotional Support on Perceived Stress among Hospitalized High Risk Pregnant Women

Article History Received: 05.05. 2018 Accepted: 27.05. 2018 Published: 30.08. 2018 Abstract: Women hospitalized with high risk pregnancy are exposed to many stressors such as separation from their families, unsuitable and noisy environment, hospital routine, lack of privacy. Stress has negative effect on maternal, fetal and neonatal outcomes such as abortion, preterm birth and low birth weight babies. One group time series research design. A purposive sample of 45 high risk pregnant women. High risk in-patient (unit 21) at El Manial University Hospital. AMaternal Interviewing Questionnaire, BPerceived Stress Scale, CHospital Stressors Assessment Sheet. Study revealed statistical significant difference in stress level before and after intervention due to emotional support (X2 41.627, P .000*) and there is statistical significant difference between stress level and maternal complications (X2 49.828, P .023*), but there is no statistical significant difference between stress level and neonatal birth weight, neonatal complications(X2 18.280, P .107*), time and mode of delivery. Women hospitalized with high risk pregnancy need not only medical treatment but also to give attention to their psychological status and provide emotional support from health care providers.
NDS_A_243521 215..222

1Medical Physiology Unit, Madda Walabu University Goba Referral Hospital, School of Medicine, Bale Goba, Ethiopia; 2Public Health Department, Madda Walabu University Goba Referral Hospital, School of Health Sciences, Bale Goba, Ethiopia Background: Diversified foods are considered key indicators of a balanced diet. Consumption of a minimum of four from the seven food groups is described as a minimum for dietary diversity. Nearly two-thirds of malnutrition-related child mortality is due to inappropriate feeding practice during the first two years of life. In Ethiopia, only five percent of children aged 6–23 months received a minimum diversity diet. Therefore, this study was intended to assess the predictors of minimum diversified diet feeding practice among mothers having children aged 6–23 months, in Goba Town, Southeast Ethiopia. Methods: A community-based survey was employed in Bale-Goba town, Southeast Ethiopia from April to May 2018. A total of 517 study subjects were selected using systematic random sampling technique. A pretested interviewer administered questionnaire was used to collect the data. Ethical clearance was obtained from Madda Walabu University, Goba Referral Hospital. Data were entered to EpiData3.02 and analyzed using SPSS version 20, and the association between dependent and independent variables was assessed using bivariable and multiple logistic regression. Statistical significance was considered with 95% confidence interval and p-value of less than 0.05. Results: The proportion of children receiving minimum dietary diversity was 39.8% (95% CI 35.52, 44.08). Postnatal care visit (AOR=1.9 95% CI 1.3, 2.8) and attending growthmonitoring follow-up (AOR=1.5 95% CI 1.001, 2.2) were independent predictors, statically significant with dependent variable. Conclusion: Almost forty percent received minimum dietary diversity among the study subjects. Attending postnatal visit and having growth-monitoring follow-up were factors associated with minimal meal frequency practice. Encouraging mothers to attend postnatal care visits and frequently bring their children to growth-monitoring follow-up is highly recommended.
Interactions between sensory prediction error and task error during implicit motor learning

Implicit motor recalibration allows us to flexibly move in novel and changing environments. Conventionally, implicit recalibration is thought to be driven by errors in predicting the sensory outcome of movement (i.e., sensory prediction errors). However, recent studies have shown that implicit recalibration is also influenced by errors in achieving the movement goal (i.e., task errors). Exactly how sensory prediction errors and task errors interact to drive implicit recalibration and, in particular, whether task errors alone might be sufficient to drive implicit recalibration remain unknown. To test this, we induced task errors in the absence of sensory prediction errors by displacing the target mid-movement. We found that task errors alone failed to induce implicit recalibration. In additional experiments, we simultaneously varied the size of sensory prediction errors and task errors. We found that implicit recalibration driven by sensory prediction errors could be continuously modulated by task errors, revealing an unappreciated dependency between these two sources of error. Moreover, implicit recalibration was attenuated when the target was simply flickered in its original location, even though this manipulation did not affect task error – an effect likely attributed to attention being directed away from the feedback cursor. Taken as a whole, the results were accounted for by a computational model in which sensory prediction errors and task errors, modulated by attention, interact to determine the extent of implicit recalibration. Author’s summary What information does the brain use to maintain precise calibration of the sensorimotor system? Using a reaching task paired with computational modeling, we find that movements are implicitly recalibrated by errors in predicting both the sensory outcome of movement (i.e., sensory prediction errors) as well as errors in achieving the movement goal (i.e., task errors). Even though task errors alone do not elicit implicit recalibration, they nonetheless modulate implicit recalibration when sensory prediction error is present. The results elucidate an unappreciated interaction between these two sources of error in driving implicit recalibration.
KRISP: Supplemental Material

DBPedia First we extracted DBPedia [1]. DBPedia is actually a set of datasets collected from Wikipedia articles and tables. For our knowledge graph we used the October 2016 crawl of Wikipedia.1 For our DBPedia edges we used the following files: Article Categories, Category Labels, DBPedia Ontology, Instance Types, Instance Types Sdtyped Dbo, Mappingbased Objects, and Person Data. Next we wrote string parsers and regular expressions to translate these triplets into lowercase multi-word english expressions. This involved extracting the category words from the hyperlink: e.g., “<http://dbpedia.org/resource/Tadeusz Borowski>” would be extracted as “tadeusz borowski”. We will provide the parsing code and the processed final dbpedia files when we release the code for this paper. Before final filtering, this knowledge source contains 24, 685, 703 edges.
Human Computer Interaction Feedback Based-On Data Visualization Using MVAR and NN

In the situation of pandemic with people being ill, contagious, or suffering from long-term effects, touchless human computer interfaces, and especially brain computer interfaces (BCI), could provide humans with safe communication technologies as well as user-centric systems for rehabilitation. Hence, this work studies at first the representation of electroencephalogram (EEG) signals in a data space which is appropriate for an efficient data processing and a user-friendly interpretation. Then, we propose 3D data visualizations mapping the human brain activity into that data space. The developed representations have been successfully tested within a BCI framework through 3D data visualizations the user can see and interact with in real time.
Earthquake Prediction using Hybrid Machine Learning Techniques

This research proposes two earthquake prediction models using seismic indicators and hybrid machine learning techniques in the region of southern California. Seven seismic indicators were mathematically and statistically calculated depending on pervious recorded seismic events in the earthquake catalogue of that region. These indicators are namely, time taken during the occurrence of n seismic events (T), average magnitude of n events (M_mean), magnitude deficit that is the difference between the observed magnitude and expected one (ΔM), the curve slope for n events using inverse power law of Gutenberg Richter (b), mean square deviation for n events using inverse power law of Gutenberg Richter (η), the square root of the released energy during T time (DE1/2) and average time between events (μ). Two hybrid machine learning models are proposed to predict the earthquake magnitude during fifteen days. The first model is FPA-ELM, which is a hybrid of the flower pollination algorithm (FPA) and the extreme learning machine (ELM). The second is FPA-LS-SVM, which is a hybrid of FPA and the least square support vector machine (LS-SVM). These two models' performance is compared and assessed using four assessment criteria: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Percent Mean Relative Error (PMRE). The simulation results showed that the FPA-LS-SVM model outperformed the FPA-ELM, LS-SVM, and ELM models in terms of prediction accuracy. Keywords—Extreme learning machine; least square support vector machine; flower pollination algorithm; earthquake prediction
Serum and salivary obestatin concentrations in the diagnosis of polycystic ovary syndrome

Aim: : To measure obestatin levels in the blood and saliva samples of normal-weight patients with polycystic ovary syndrome (PCOS) in comparison with normal-weight healthy controls, and to determine whether there were relationships between blood and/ or salivary obestatin levels and other measured parameters. Materials and Methods: Fifteen healthy women and 15 patients with PCOS, all of which had normal weight, were included in the study. Participants’ age, height, weight, menstrual characteristics, hormone levels, body mass index, waist/hip ratio, and modified Ferriman-Gallwey (FG) scores were recorded. Obestatin levels were measured in both fasting blood and saliva samples. Homeostasis Model Assessment (HOMA-IR) was used to predict insulin resistance. Results: In the PCOS group, menstrual cycle duration and FG scores were significantly higher (P-value, <0.001, <0.001, respectively). The levels of luteinizing hormone, total testosterone and androstenedione were significantly higher in the PCOS group than in the control group (P-value, 0.001, 0.009, 0.004, respectively). In the PCOS group, blood obestatin level was 1265.2 ± 221.9 pg/ml, salivary obestatin level was 3095.33 ± 310.2 pg/ml; whereas the control group demonstrated lower levels of 939.66 ± 72.3 pg/ml and 2611.20 ± 217.1 pg/mL, respectively. However, no statistically significant difference was found between the PCOS and control groups when comparing obestatin levels in blood or saliva (P-value, 0.218, 0.369, respectively). No correlation was found between blood and salivary obestatin levels in either group. Finally, obestatin levels were not associated with any of the other measured parameters. Conclusion: Our results suggest that increased levels of obestatin, albeit in a small amount that would not be statistically significant in normal weight patients with PCOS, may have significant effects on weight control in these patients. In addition, our findings show that saliva sample can be used as an alternative to blood sample in the measurement of obestatin level in patients with PCOS.
Physiologic specialization of Puccinia triticina Erikss. and effectiveness of Lr genes in Bulgaria

Ivanova, V. , Velikova, S. & Nikolova, D. (2021). Physiologic specialization of Puccinia triticina Erikss. and effectiveness of Lr genes in Bulgaria. Bulg. J. Agric. Sci., 27 (3), 555–561 Leaf rust caused by Puccinia triticina Erikss. is the most common diseases on wheat in Bulgaria. Monitoring on the pathogen population is carried out annually, and the information concerning the changes occurring in it and the data on the efficiency of the genes for resistance are helpful for the breeders. Different systems for pathotype identification are used in different parts of the world. This investigation considers the pathogen variability of Puccinia triticina in Bulgaria during 2015-2016. Twenty isogenic lines were involved, which were developed on the basis of cultivar Tatcher; the identification of the pathotypes is presented according to the North American nomenclature. One hundred twenty-nine isolates were analyzed, which were collected from seven different agro ecological zones of Bulgaria, and 42 phenotypically different pathotypes were identified. Three pathotypes were predominant in 2015: PKTTS (29%), PHTTS (13%) and MKTTS (11.6%). The most widespread pathotype in 2016 was TKTTN (16.6%). The distribution of the pathotypes over agro ecological zones was uneven. The efficiency of the genes for resistance was investigated and it was determined that genes Lr 9 and Lr 19 maintained their full efficiency, and genes Lr 22А, Lr 22В, Lr 28, Lr 41, Lr 43 and Lr 47 were with high efficiency.
Key role of interaction between dislocations and hydrogen-vacancy complexes in hydrogen embrittlement of aluminum: discrete dislocation plasticity analysis

With the development of experimental techniques and characterization methods at the microscopic scale, a high concentration of hydrogen-induced vacancies and their clusters have been detected in a large variety of metals and alloys charged with hydrogen, which is supposed to play an important role in hydrogen-induced fracture process. In hydrogen environment, vacancies and their clusters can trap a certain number of H atoms to form vacancy-H complexes or clusters. To investigate the role of hydrogen-induced vacancies and their clusters in brittle-like failure in hydrogen environment, multi-scale modelling was performed in this work, with a particular attention on the interaction between dislocations and vacancy-H complexes. Firstly, the H-enhanced vacancy formation due to H-induced reduction in vacancy formation energy in Al was modelled by first principles calculations, and a hydrogen-influenced vacancy concentration model was proposed. Then, to capture the interaction between gliding edge dislocations and vacancy-H complexes and clusters, atomistic simulations were performed, and a mesoscale model quantitatively describing the pinning effect of vacancy-H complexes and clusters on the gliding edge dislocations was obtained. Finally, these quantitative models were introduced into a newly developed XFEM-based discrete dislocation plasticity scheme. With this scheme, mode-I crack propagation was simulated, where dislocation emission from crack-tip, dislocation evolution and cohesive crack propagation were considered, with a particular attention to the key role of the hydrogen-influenced dislocation evolution in the brittle-like failure of the H-charged metals. Based on these multi-scale simulations, a pinning effect of H-enhanced vacancies and vacancy-H complexes on edge dislocations was identified, which was proposed to be responsible for the brittle-like failure of Al in hydrogen environment. This pinning effect presents a good agreement with the hydrogen-restricted dislocation mobility observed in in situ fracture experiments.
Applying the Nash Equilibrium to Constructing Covert Channel in IoT

Although many different types of covert channels have been suggested in the literature, there are little work in directly applying game theory to building up covert channel. This is because researchers have mainly focused on tailoring game theory for covert channel analysis, identification, and covert channel problem solving. Unlike typical adaptation of game theory to covert channel, we show that game theory can be utilized to establish a new type of covert channel in IoT devices. More specifically, we propose a covert channel that can be constructed by utilizing the Nash Equilibrium with sensor data collected from IoT devices. For covert channel construction, we set random seed to the value of sensor data and make payoff from random number created by running pseudo random number generator with the configured random seed. We generate � × � (� ≥ 2 , � ≥ 2 ) matrix game with these generated payoffs and attempt to obtain the Nash Equilibrium. Covert channel construction method is distinctly determined in accordance with whether or not to acquire the Nash Equilibrium.
Hy-Fi: Aggregation of LiFi and WiFi using MIMO in IEEE 802.11

We present Hy-Fi, a system which combines light fidelity (LiFi) and radio based on the WiFi physical layer waveform by using the MIMO features available in IEEE 802.11-compliant commodity chip sets. Hy-Fi is based on two key ideas. First, we use inexpensive COTS hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing to aggregate LiFi and radio signals at the physical layer. The system was implemented as a prototype and evaluated in a small testbed. Experimental results show that our approach offers robustness against signal blockage (Shadowing) and external interference in both, the optical and RF channels. Moreover, the two media (LiFi and WiFi) can be aggregated to double the capacity in the best case.
Instability of Employment Careers and Union Dissolution. A Complex Micro-level Relation

The present study contributes to a more comprehensive understanding of the relation between employment instability and union dissolution. To address the oversights of previous research, we disentangle the effect of employment instability on union dissolution by: (i) considering not only the effect of joblessness, but also the type of employment contract; (ii) evaluating both status and the accumulation of instability over the life course; and (iii) detecting variation across gender and generations. We focus on Italy, applying event-history techniques to the 2009 and 2016 Italian Multipurpose Survey on Family and Social Subjects, observing cohorts from 1950 to 1980. Our results suggest that the effect of employment instability on union dissolution is gender-specific: joblessness and limited-time employment are facilitators for men’s dissolution, while joblessness is an inhibitor for women’s dissolution. Nonetheless, these differences weaken across recent cohorts as gender equality gains relevance in the labor market. We also highlight the crucial role of the persistence of instability in unpacking the association between employment instability and union dissolution.
Aggregate Output Measurements: A Common Trend Approach

We analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. We study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. We also develop an R measure of common trend observability that determines the severity of misspecification. Finally, we apply our framework to US quarterly data on GDP and GDI, obtaining an improved aggregate output measure.