Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF CASH FLOW VOLATILITY, GROWTH OPPORTUNITIES, AND DIVIDEND POLICY ON HEDGING DECISIONS

This study aims to examine the effect of Cash Flow Volatility, Growth Opportunities, and Dividend Policy on Hedging Decisions. This type of research is quantitative associative. With the population study consist of mining companies listed on the Indonesia Stock Exchange in 2016 – 2020. Meanwhile, the sampling technique used is purposive sampling. Hence, the number of samples obtained in this study as many as 13 companies with data obtained as much as 65 data. The analysis technique used is logistic regression analysis. This research is processed using e-views 10 software. The results revealed that Cash Flow Volatility, Growth Opportunities, and Dividend Policy have a significant effect on Hedging Decisions. Besides, Cash Flow Volatility and Growth Opportunity variables have a significant effect on Hedging Decisions, while Dividend Policy Variables have no effect on Hedging decisions.
Transforming Urban Fabric into Mobile Call Traffic Signatures

Many studies have shown how to process mobile network data with machine learning algorithms to infer land uses or predict mobile traffic behavior. However, there are few, if any, machine learning applications to help deploy new cells. Current forecasting models are designed to predict the traffic of existing cells based on the historical data they produced. In a previous work, we have proposed a method to predict the class activity of a future cell, given its modeled area, demographic and geographic features. In this paper, we extend it by estimating static hour-by-hour median weekly activity aggregated at base station level, based on the same static inputs. We tested several machine learning models including transformers, compared their results and showed that our method beats simple baselines.
A Road Intersection Control in Urban Intelligent Transportation Systems

The implementation of optimized management process for road intersections in Cooperative Intelligent Transport Systems (C-ITS) is highly recommended in order to reduce traffic jams and fuel consumption. Most of solutions are implemented on light controllers and they use various sensors in order to optimize intersection management. The use of vehicle communications, mainly I2V (Infrastructure to Vehicle), enhances the solutions since the vehicles continuously provide messages about their status (position, timestamp, speed, heading, etc.). In this paper, we suppose that a manager is embedded on a hotspot located at a road intersection. It will know the status of each road (thanks to requests sent by vehicles approaching the intersection) and will give the right to the most relevant vehicle to pass through the intersection (many vehicles could drive in the same time when they use concurrent roads). Our algorithm measures the weight of each lane and allows to non-crossing ones having the highest weight to traverse the intersection. The calculation of the weight is based on many parameters: the waiting time, the length of the road queue and some others. Our algorithm considers that the intersection topology as stable. The algorithm has been implemented on a simulator on three urban scenarios and our results show very promising performances, they show considerable enhancement in terms of computing time.
Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images

ABSTRACT In some remote sensing applications, such as unsupervised change detection, bitemporal multispectral images must be first aligned/harmonized radiometrically. For doing so, Many Relative Radiometric Normalization (RRN) algorithms exist; however, most suffer from misregistration problems and can only operate on geo/co-registered image pairs, while unregistered multispectral pairs are required. To tackle this situation, keypoint-based RRN methods were introduced, which can radiometrically calibrate unregistered/registered image pairs using keypoint matching algorithms. However, they ignore the spatial and spectral characteristics of spectral bands of input images, resulting in potential RRN errors. They also employ a linear mapping function for RRN modelling, which can not handle non-linear radiometric distortions. To address these limitations, this paper proposes a robust algorithm for RRN of bitemporal multispectral images, using a new extension of SURF detector for multispectral images, namely the Weighted Spectral Structure Tensor SURF (WSST-SURF), and a flexible Switching Regression (SR) model. Taking advantage of the tensor theory, WSST-SURF efficiently preserves both spatial and spectral information distributed over all bands of multispectral images for keypoint detection, resulting in extracting reliable inliers (or keypoints) for RRN. An adaptive SR model is introduced based on the normalized mutual information, accurately approximating a linear/non-linear relationship between inliers in multispectral images. Six unregistered multispectral image pairs captured by inter/intra remote sensing sensors were employed to validate the efficacy of the proposed method. The results indicate that adopting spectral tensor-based SURF methods in the RRN process exhibits better local and global performance than using the original SURF. Furthermore, the proposed method outperforms the existing conventional RRN methods in terms of accuracy and visual quality, indicating its competence for RRN of bitemporal multispectral images with high illumination, viewpoint, and scale differences.
Pathophysiological study of atorvastatin drug on toxicity induced by D-galactose in liver of experimental rats

Background: This study was designed to clarify the toxic effect of a high dose of atorvastatin and the palliative effect of low doses of atorvastatin on toxicity induced by D-galactose in the liver, kidney, and adrenal gland organs. The current study was conducted on 36 healthy adult male Wistar albino rats (Rattus norvegicus) who were sexually mature at the age of 8-10 weeks and weighed between 200 and 250 grams in the animal house of Pharmacy College/ Basrah University. Methods: These rats were selected and divided into six main groups of equal weight, each with six rats. The first group (control) was drenched in (1) mL of normal saline. The second group was drenched in a single dose of atorvastatin (150 mg per kg B.W), the third received D-galactose at a dose of (500 mg per kg B.W), and the fourth group was treated with both D-galactose and atorvastatin (10 mg per kg B.W), the fifth group was coadministered with both D-galactose and atorvastatin (20 mg per kg B.W), and the sixth group where rats cotreated with Dgalactose and atorvastatin (40 mg per kg B.W). The treatment was extended for 8 weeks.
Optimal Power Allocation and Optimal Linear Encoding for Parameter Estimation in the Presence of a Smart Eavesdropper

In this article, we consider secure transmission of a deterministic vector parameter from a transmitter to an intended receiver in the presence of a smart eavesdropper. The aim is to determine the optimal power allocation and optimal linear encoding strategies at the transmitter to maximize the estimation performance at the intended receiver under constraints on the estimation performance at the eavesdropper and on the transmit power. First, the A-optimality criterion is adopted by utilizing the Cramér-Rao lower bound as the estimation performance metric, and the optimal power allocation and optimal linear encoding strategies are characterized theoretically. Then, corresponding to the D-optimality criterion, the determinant of the Fisher information matrix is considered as the estimation performance metric. It is shown that the optimal linear encoding and optimal power allocation strategies lead to the same solution for this criterion. In addition, extensions of the theoretical results are provided to cases with statistical knowledge of systems parameters. Numerical examples are provided to investigate the optimal power allocation and optimal linear encoding strategies in different scenarios.
Effectiveness of Brand Image, Content Marketing, and Quality Product on Purchase Decision of J-GLOW Products

This study intends to investigate the relationship between brand image, content marketing, and product quality in influencing Surabaya consumers' decisions to purchase J-GLOW skin care and cosmetic items. This study employed a quantitative survey methodology. Multiple regression analysis was used to analyze an association between brand image and purchasing the J-GLOW product. The content marketing variable has no relationship with J-GLOW product purchase decisions. Meanwhile, product quality influences the choice of the J-GLOW skin care product. It is indicated by the fact that the F-count value is greater than the F-table value (22,763 3,10), and the actual value is less than the alpha value (0.000 0.05). It demonstrates that characteristics such as brand image (X1), content marketing (X2), and product quality (X3) have a significant impact on purchasing decisions. According to the findings of the study, the brand image (X1), content marketing (X2), and product quality (X3) variables had a 45.7% impact on the decision to purchase J-GLOW products. Additional factors influenced the remaining 54,3%.
Photoperiod and Temperature Effects on Root Cold Acclimation1

Hardiness of intact roots of Potentilla fruiticosa L. cv. Katharine Dykes and Picea glauca Voss were determined during the autumn. Both extended photoperiod and warm temperature interfered with root acclimation to cold. Seasonally short days and near freezing temperature were necessary for maximum rates of cold acclimation of roots.
Reinforcement Learning Based Misbehavior Detection in Vehicular Networks

Vehicle-to-everything (V2X) communication is contributing towards the realization of futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher in a new direction of intelligence and networking to achieve the goal of intelligent transport systems, which rely on the secure exchange of messages between vehicles and infrastructure. However, the transmission of false/incorrect data by malicious vehicles may cause serious damages on road safety. Therefore, it is crucial to detect safety-threatening incorrect information and mitigate potentially detrimental effects on road users. In this paper, we propose a reinforcement learning (RL)-based misbehavior detection approach for V2X scenarios. In our method, the RL-based detection model processes V2X data broadcast by vehicles as time-series at the roadside units, and classifies incoming data as misbehaving or genuine. We evaluate the proposed RL-based approach for detection of various attack types using an open-source dataset, and compare its performance against recent work in misbehavior detection. Our scheme is able to detect all types of misbehavior with a superior recall of 0.9970 and an F1 score of 0.9845, yielding a significant improvement over the benchmarks. Our research outcomes further reveal that misbehaving vehicles can be detected with a great accuracy of 0.9882 by exploiting real-time V2X information.
Microbial contamination, antimicrobial resistance and biofilm formation of bacteria isolated from a high-throughput pig abattoir

The aim of this work was to assess the level of microbial contamination and resistance of bacteria isolated from a highthroughput heavy pig slaughterhouse (approx. 4600 pigs/day) towards antimicrobials considered as critical for human, veterinary or both chemotherapies. Samples, pre-operative and operative, were obtained in 4 different surveys. These comprised environmental sampling, i.e. air (ntotal = 192) and surfaces (ntotal = 32), in four different locations. Moreover, a total of 40 carcasses were sampled in two different moments of slaughtering following Reg. (CE) 2073/2005. Overall, 60 different colonies were randomly selected from VRBGA plates belonging to 20 species, 15 genera and 10 families being Enterobacteriaceae, Moraxellaceae and Pseudomonadaceae the most represented ones. Thirty-seven isolates presented resistance to at least one molecule and seventeen were classified as multi-drug resistant. Enterobacteriaceae, particularly E. coli, displayed high MIC values towards trimethoprim, ampicillin, tetracycline and sulphametoxazole with MICmax of 16, 32, 32 and 512 mg/L, respectively. Moreover, isolated Pseudomonas spp. showed high MIC values in critical antibiotics such as ampicillin and azithromycin with MICmax of 32 and 64 mg/L, respectively. Additionally, in vitro biofilm formation assays demonstrated that fifteen of these isolates can be classified as strong biofilm formers. Results demonstrated that a high diversity of bacteria containing antibiotic resistant and multiresistant species is present in the sampled abattoir. Considering these findings, it could be hypothesised that the processing environment could be a potential diffusion determinant of antibiotic resistant bacteria through the food chain and operators.
Pollination Effects on Fruit and Seed Development in ‘Comice’ Pear1

Cross-pollination increased fruit set, fruit size and seed content of ‘Cornice’ pear (Pyrus communis L.) in 3 large orchards without pollenizers. Supplemental self-pollination was equivalent to open pollination in its effect on fruit set and size. Low female fertility of ‘Comice’ could not be accounted for by early ovule degeneration or frost injury. Pollen transfer by bees was observed to be low in 2 orchards, with 36 and 41% of seedless fruit the result of open pollination. In comparison, only 6% of open-pollinated fruit were seedless in the orchard of greatest bee activity. Seedlessness was due to seed abortion during early fruit development. Classification of aborted seeds as to size at time of abortion revealed that in most fruits a minimal amount of seed growth occurs, although fruit retention does not require the presence of fully developed seeds. Fruit size was positively correlated with seed content, although this relationship was less pronounced in fruits with high seed count. The importance of seed development before abortion and of fully developed seeds in fruit set as well as fruit size is shown.
On the utilization of aircraft derived observations for operational meteorology and numerical weather prediction

This thesis analyses a new source of observations, Mode-Select Enhanced Surveillance (Mode-S EHS), obtained from reports exchanged between aircraft and air-traffic control. These reports contain the aircrafts speed, direction, altitude and Mach number. Observations of temperature and horizontal wind can be derived from the reports. However, Mode-S EHS processing reduces the reporting precision from 16-bit to 10-bit representation. We aim to understand the observation errors that are due to the reduced precision of Mode-S EHS reports, how accurately these derived observations represent vertical profiles of wind and temperature and the benefit they bring to convection-permitting NWP. We derive new models to estimate the observation errors and validate them using research grade instruments on board the Facility for Atmospheric Airborne Measurements. For the cases studied, the temperature observation error increases from 1.25 K to 2.5 K between an altitude of 10 km and the surface, due to its dependence on Mach number and Mode-S EHS precision. The zonal wind error is around 0.50 ms−1 and the meridional wind error is 0.25 ms−1. The horizontal wind is also subject to directionally dependent systematic errors. We aggregate Mode-S EHS reports from multiple aircraft to construct vertical pro- files of temperature and demonstrate their ability to resolve temperature inversions. However, there are large errors in the aggregated observations that are still dominated by the effects of reduced precision. We assess the benefits of Mode-S EHS for data assimilation in the Met Office convection-permitting NWP model. We find that assimilation of Mode-S EHS has a neutral impact. Using assimilation output statistics, we find that the observation uncertainties for AMDAR and Mode-S EHS horizontal wind are similar in magnitude, while for Mode-S EHS Mach temperature the diagnosed errors are similar to our new error model. Our new results may assist with utilising Mode-S EHS reports in operational forecasting.
Correlation between saturation magnetization and surface morphological features in Zn1−xCrxO thin films

The effect of chromium doping on the defect-induced ferromagnetism of Zn1−xCrxO films was investigated in this study. X-ray diffraction and x-ray photoelectron results confirm Cr substitution at Zn sites in the ZnO host lattice. A parabolic dependence of the saturation magnetization on the stoichiometric variable x was observed. Photoluminescence and magnetic results indicate that magnetic moments in Zn1−xCrxO films stem from Zn vacancies on the film surface or at grain boundaries. As the variation in saturation magnetization is consistent with that in surface morphology, the ferromagnetism in Zn1−xCrxO films is sensitive to the morphological modification caused by Cr doping.
The Empirical Hierarchical Bayes Approach for Pathway Integration and Gene-Environment Interactions in Genome-Wide Association Studies

Complex diseases such as cancer result from a complicated interplay of multiple genetic and environmental factors. To unveil their genetic component, the simple analysis of single-nucleotide polymorphisms (SNP) as done in genome-wide association studies (GWAS) is not sufficient. Complementary approaches considering the complexity of diseases, such as the incorporation of biological pathway information or detection of gene-environment interaction, are necessary. In this thesis we focus on an empirical hierarchical Bayes model proposed for the integration of external information into genome-wide association studies. We use the approach to incorporate biological pathway information and provide a new test for gene environment interaction (GxE) by adapting the method for that purpose. In an application, we furthermore integrate pathway information with GxE interaction effects by two different strategies. The integration of pathway information by the empirical hierarchical Bayes approach in a GWAS for Rheumatoid Arthritis, characterized by an extremely high number of strong association signals, supports the simple single SNP results, while in an application to four lung cancer GWAS it leads to higher consistency of results between the different studies. In both cases, the strength of the pathway integration approach is supported by the biological plausibility of the results. In comprehensive simulation studies, our new empirical hierarchical Bayes approach for GxE interaction outperforms other GxE methods, having high power to identify GxE interactions in the presence of population-based gene-environment associations (G-E). In our real data application to the lung cancer GWAS with smoking as environmental factor, the method leads to similar results as the powerful case-only test, with the latter in general accompanied by false positive results given population-based G-E associations. However, in the particular data sets, no strong G-E association effects with smoking are observed. By additional integration of pathway information with GxE effects, the consistency of results between the studies increases. Due to the biological plausibility of the results, good candidates for further investigations are identified.
Identification of Clones within Four Tree Fruit Species by Pollen Exine Patterns1

Twenty sample clones of peach (Prunus persica [L.] Batsch) and ten clones each of nectarine (P. persica var. nectarina [Ait.] Maxim.), European plum (P. domestica L.), sweet cherry (P. avium [L.] L.) and apple (Malus domestica Borkh.) were compared by scanning electron microscope (SEM) for characteristics of their pollen exine. The 4 taxa were separated into 3 groups by pollen grain size. Exine differences distinguished apple and sweet cherry pollens, which had similar size, and separated clones within each of the species. Peach and nectarine clones varied within the same ranges of pollen size and exine characteristics. Within P. persica, two patterns of ridging and the relative frequency, depth of impression, and size of pores categorized the clones individually. Plum exine patterns were distinct from those of other tree fruits, but ridging and pore characteristics of the exine separated plum clones into distinct categories. Apple cultivars, which had the narrowest ridges, were separated by the abundance and placement of pores in the exine. Sweet cherry clones also were distinguished by exine characteristics.
An Instantaneous Torque Model and the Practical Caculation Method for Permanent Magnet Synchronous Machines

This paper introduces an instantaneous torque model for permanent magnet synchronous machines(PMSMs). Compared to the previous studies, we denotes the torque directly with the flux linkages and currents to fully consider the magnetic saturation based on the actual ABC windings. An approximation calculation method is adopted to resolve the torque model. Then an in-wheel motor is established in the finite element method software to compare our original torque model with the previous study. The simulation results presented demonstrate not only the accuracy of the instantaneous torque model but the feasibility of our calculation method as well.
Chiefly Patronage and Schooling

Abstract In developing countries, indigenous chiefly systems can be a main driver of patronage, potentially hampering development. This paper explores how chiefly patronage shapes people’s incentives for schooling in Fiji. I develop a model to show how chiefly status can influence a household's child schooling decision, depending on whether the patronage operates in labor markets. I employ a triple-difference approach by combining the novel direct measures of chieftaincy from my original survey data with 1970 independence and 1987 coups as natural experiments, and gender. I show that indigenous chieftaincy interacted with British colonial rule to affect employment, and thus schooling, after independence for males but not females in chiefly networks. After this patronage weakened under post-coup reforms, males in the networks temporarily increased secondary schooling to compensate for their weakened advantage. The majority of male Fijians outside the networks were less encouraged to make schooling investments over time. As such, chiefly patronage persistently distorted male secondary schooling, thereby augmenting the outperformance of female education.