Miracle-dz/UniFAR-FTUs huggingface dataset:
Ocular Axial Length Prediction Based on Visual Interpretation of Retinal Fundus Images via Deep Neural Network

Ocular axial length (AL) is an important property of eyes used for determining their health prior to surgery. Estimation of AL is also crucial while making artificial lenses to replace impaired natural lenses. However, accurate measurement of AL requires a costly and bulky benchtop optical system. The complex structural features of eyes can be captured by fundus images, which can be easily captured nowadays with portable cameras. Here, we suggest a deep learning method for predicting AL based on fundus images with evidence of decision. This visual interpretation of predictions is achieved by post-processing, separated from the training process, to ensure that the architecture can be freely designed. Through the visualization technique, discriminative regions on input images can be localized to demonstrate specific areas of interest for predictions. In the experiments, we found a significant relationship between the fundus images and AL with achieving a coefficient of determination (R2) of 0.67 and accuracy of 90%, within an error margin of $ \pm 1$ mm. Furthermore, visual evidence proves that the network uses consistent regions for predicting AL. The visual results of this study also point to a link between AL and biological structure of eyes, which paves the way for future research.
Effectiveness of DCFM on different mobility models

Simulation based network protocol testing requires that one model reality correctly. Mis-modelling can render results invalid. Hence, one must determine the context in which a solution is to be implemented for the results to be meaningful. In this work, we investigate whether the security augmentation technique DCFM (denial contradictions with fictitious nodes) for OLSR based mobile ad-hoc networks (MANET) is valid for multiple contexts. We accomplish this by running the node isolation attack and DCFM defense on a variety of mobility models, comparing the results. We show that for most models the technique performs well, while for a specific scenario (e.g. street based) it fails. Additionally we describe the infrastructure that was built to support the street based simulations.
Optimal End-to-End Path Selection Mechanism for CR-WMNs Based on Fuzzy Logic System

In Cognitive Radio Wireless Mesh Networks (CR-WMNs) the end-to-end path selection is more challenging due to its heterogeneous nature. To utilize the network resources efficiently, a cognitive user must find the best available path from the source node to the end node. The factors which may affect the path in CR-WMNs are mobility, bandwidth, throughput and number of users. In this paper, all these parameters have been considered in fuzzy logic based mechanism to select an end-to-end optimal path. In this technique, the mentioned parameters of all available paths are mapped in a fuzzy logic system to go through fuzzy rules to find the optimal path. The results emphasized on improvement of the path analyzing process and path selection. The proposed algorithm is simulated in Matlab. The simulation results demonstrate the efficiency of the fuzzy system.
Autonomous assembly of structures using pinning control and formation algorithms

In this paper, we propose an autonomous construction system for building structures composed of multiple mobile robots. We use formation control techniques to guide the mobile robots to the desired positions in the structure, and pinning control methodologies to guide the group of robots to desired mounting locations. We also present here a technique for modelling the system based on network control theory. We demonstrate how our network-based approach for autonomous assembly must communicate with the robots in a system architecture diagram. In order to validate our approach, we show a simulation trial of the assembly of a pyramidal structure with nine robots.
Simultaneous Radar Detection and Constrained Target Angle Estimation via Dinkelbach Algorithm

Simultaneous target detection and angle estimation with a multichannel phased array radar system is addressed in this paper. Starting from a linearized expression for the array steering vector around the beam pointing direction, the problem is cast as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target direction cosines displacements with respect to the array nominal steering. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion where a decision statistic leveraging the Maximum Likelihood Estimates (MLEs) of the parameters is compared to a detection threshold. If crossed, target presence is declared and MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view ML estimation requires the solution of a constrained fractional quadratic optimization problem whose optimal solution can be found via Dinkelbach's algorithm. The analysis of the proposed architecture is developed in terms of detection performance and angular estimation accuracy also in comparison with some counterparts available in open literature and benchmark limits.
Present State of the Art in Post Disaster Victim Localization

Natural and man-made disasters often trap victims inside rubble, who must be rescued within tight time-constraints. A number of technologies are available to track the victims, each having their own benefits and trade offs. This paper presents a review of literature on the technologies that are used for victim localization, and highlights some of the problems associated with their use in post disaster environments.
Factors affecting digital education during COVID-19: A statistical modeling approach

Worldwide governments have decided to temporarily closures of educational institutions in an attempt to minimize the spread of the COVID-19 Pandemic, which has forged significant challenges for the education community. The present study is from the digital education scenario during the COVID-19 lockdown to find out the factors affecting online learning. This study is exploratory from 1218 students who have been collected based on a structured questionnaire having a 5-point linear scale. Jamovi software has been used for data analysis and results demonstrate that there are three major factors like affordability, infrastructural, and training that affect online learning during the COVID-19. Besides, correlation analysis between these factors highlights the relationship among them. Linear regression has applied to know the impact of affordability and infrastructure on the training factor. Outcomes suggested that infrastructure has a negative impact but affordability has a positive impact on the training factor. In the present scenario, this study highlighted the importance of social distancing and digital education tools that should be adopted by schools and colleges.
Statistical Learning in Persons with Aphasia: A Critical Review of Literature

Statistical learning is the process of discovering patterns which are present in the input. Serial Reaction Time Task (SRTT) is commonly used to study statistical learning. Recently, many studies have been conducted in aphasia literature. But it is unclear about the magnitude of learning considering the learning conditions, methodological variations and consolidation of learning in persons with aphasia when compared to neurotypical individuals. This review discusses the learning differences based on certain parameters and also suggest the possible methodological innovations in SRTT to be adopted in aphasia research.
The mediating role of job satisfaction on compensation, work environment, and employee performance: Evidence from Indonesia

Job satisfaction has a pivotal role in improving employee performance. It provides enthusiasm and high motivation to employees to increase productivity. On the other hand, to improve employee performance, the organization provides compensation based on established standards and in an environment that is, sometimes, less conducive. This study purposes to examine the mediating role of job satisfaction on compensation, work environment, and employee performance at State Polytechnic colleges in Indonesia. This study used Partial Least Square (PLS) as data analysis. This study found that compensation has no impact on job satisfaction and employee performance. In addition, job satisfaction cannot mediate the compensation and employee performance. However, the work environment positively and significantly affects job satisfaction and employee performance. Job satisfaction has impact on work environment and employee performance. It also influences employee performance positively and significantly. Thus, job satisfaction directly or indirectly can be a mediator for the relationship between work environment and employee performance.
Market Power and Labor Market Trends ∗

Market power has been rising while wage polarization and wage growth have both slowed down in the United States since 2000. Over the same time period, routine labor share has continued downward trend whereas non-routine labor share has turned to decline from the previous rise, which results in the dramatic decline of aggregate labor share. I develop a general equilibrium model which incorporates the job polarization mechanism into monopolistic competition to explore the role of market power on these labor market trends. Comparative static analysis suggests rising market power qualitatively contributes to these trends. The calibrated model can quantitatively account for the trends with the magnitude of increase in markups close to the higher end of range in the literature. Empirically, I find that increases in industry concentration are associated with declines in aggregate labor share, declines in labor share across non-routine and routine occupations, and stagnation of growth in wages per efficiency unit of labor across both occupational groups.
UNDERSTANDING THE EUROPEAN UNION ’ S REGIONAL POTENTIAL IN LOW-CARBON TECHNOLOGIES

Our research identifies existing and potential specialisation in green technologies in European Union regions, and proposes an approach to identify policies that can help to realise this potential. Using the Organisation for Economic Cooperation and Development’s REGPAT database for regionalised patent data, we estimate the potential advantage European NUTS2 regions could have in 14 green technologies. We use network proximity between technologies and between regions to understand technological/regional clusters, and build the regressors for estimating potential regional advantage in specific technologies via zero-inflated beta regressions. We construct a dataset of lagged potentials and labour market, economic and demographic variables, and perform an elastic net regularisation to understand the association with current revealed advantages. Our approach indicates an association between technological advantage in green technologies in EU regions and participation rates in labour markets, sectoral employment in science and technology, general higher education, duration of employment, percentage of GDP spent on research and development (public and private), and other expenditure on R&D. If confirmed by causality tests, the established associations could help in designing horizontal economic policies to enable specific regions to realise their specialisation potential in specific green technologies. UNDERSTANDING THE EUROPEAN UNION’S REGIONAL POTENTIAL IN LOW-CARBON TECHNOLOGIES
College Admissions, Grading Policy, and Students’ Time Allocation Responses :The Case of South Korea

College admissions competition is zero-sum game where students do care about their relative ranking resulting in the overinvestment of resources and an inefficient market outcome as the game participants are facing prisoner’s dilemma. Can SAT and GPA grading policy reduce high school students’ college prep activities? This paper estimates causal effects of the nationwide education policy in 2005 which introduces coarsening SAT grade structure and more competitive grading of high school GPA in Korea on students’ time allocation behavior in a college admission game. I model a student’s welfare maximization behavior to investigate the policy effects on her college admission preparation activities. Empirical estimation is done by difference-in-differences method using repeated cross-sectional data. The result shows that the policy did not achieve the goal as the SAT taking students reduced leisure compared to the CSAT non takers. Top GPA students respond to the policy more sensitively by decreasing leisure and increasing self study because of the heterogeneous marginal productivity and motivation.
Properties of a Ubiquitous 29 kDa Au:SR Cluster Compound †

The broad, intense peak found near 29 kDa in the laser-desorption mass-spectral abundances of various aurothiol (Au:SR) cluster compounds has been used to optimize the preferential formation of the species in that mass range. Recrystallization gives enriched fractions, on the 10 mg scale, that in several cases appear free of species outside that mass range. Elemental analysis and X-ray photoelectron spectra (XPS) confirm the absence of elements other than Au, S, C, and H, while infrared and NMR (1H,13C) spectra are consistent with intact thio groups. The Au 4f7/2 XPS peak is only slightly shifted (∼0.2 eV) and broadened from that of bulk Au(0) metal, and intense optical absorption extends far into the infrared region (0.5 eV), consistent with a metallic Au core. Recrystallized samples of the R = C4, C6, and C12 materials readily form highly diffractive crystalline films, powders, and single crystals on the scale of 10 μm, consistent with perfect ordering in >100 nm grains, and a negligible amorphous conte.
IMPACT OF SOME GENETICALLY IMPROVED RHIZOBACTERIA IN CONTROLLING MELOIDOGYNE INCOGNITA AND TWO WEEDS INFECTING SOLANUM LYCOPERSICUM SEEDLINGS UNDER GREENHOUSE CONDITIONS

Biological control is considered as an alternative strategy to chemical tool for controlling either nematodes or weeds. So, two pot experiments were carried out to evaluate the role of two native isolated bacteria Bacillus cereus, Pseudomonas aeruginosa and their three fusant strains namely (fusant7, fusant 20 and fusant 35) as biocontrol agents against root knot nematode Meloidogyne incognita and two weeds Portulaca oleracea and Echinochloa crus-galli infecting Solanum lycopersicum. Revealed results on nematode population, weeds and S. lycopersicum seedlings growth were recorded seven weeks after nematode inoculation. The results exhibited that, all bacterial strains suspension at the rates of (1.25x107 and 2.50x107cuf/ml) significantly suppressed M. incognita parameters, decreased fresh and dry weights of weeds and improved S. lycopersicum growth. In general, undiluted concentration of all bacterial suspensions was more effective than diluted one. Additionally, fusants were more effective than their parents in reducing nematode population and controlling both weeds. Combination of two strains was more effective than the individual application in controlling nematode and P. oleracea weed. But, it was less effective in controlling E. crus-galli weed. P. aeruginosa was more effective than B. cereus in suppress nematode and weeds parameters. The fusant 7 was more effective than all other treatments in suppressing M. incognita J2 in soil, galls and eggmasses /root system by 91.30%, 92.50% and 90.91% reduction, respectively as compared to control. Also, fusant 7 was the highly effective strain in decreasing the fresh and dry-weights of E. crus-galli, by 75.87% and 75.55%, respectively and of P. oleracea, by 84.68% and 86.17% consecutively as compared to unweeded control treatment. This in turn reflected on S. lycopersicum seedlings by improving growth parameters. In conclusion, the genetically improved bacterial strains could be used as biological alternative safe method to chemical pesticides for suppressing root knot nematode reproduction and weed seedlings growth.
Seasonal Rainfall Forecasts for the Yangtze River Basin in Summer 2019 from an Improved Climate Service

Philip E. BETT , Nicola MARTIN , Adam A. SCAIFE , Nick DUNSTONE , Gill M. MARTIN , Nicola GOLDING , Joanne CAMP , Peiqun ZHANG , Chris HEWITT , Leon HERMANSON , Chaofan LI , Hong-Li REN , Ying LIU , Min LIU 8 1 Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK 2 Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK 3 College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK 4 Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China 5 University of Southern Queensland, Toowoomba, Australia 6 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 7 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China 8 Wuhan Regional Climate Centre, Wuhan 430074, China
Solving a Random Asymmetric TSP Exactly in Quasi-Polynomial Time w.h.p

Let the costs $C(i,j)$ for an instance of the Asymmetric Traveling Salesperson Problem (ATSP) be independent copies of an random variable $C$ that (i) satisfies $\Pr(C\geq x)=1-x+O(x^2)$ as $x\to 0$ and (ii) has an exponential tail. We describe an algorithm that solves ATSP exactly in time $e^{\log^{2+o(1)}n}$, w.h. p.
Blood biochemical profiles of dairy cows and their calves from Bulgarian Black and White Cattle Breed

Teneva, A. , Hristov, K. & Stoimenov, G. (2020). Blood biochemical profiles of dairy cows and their calves from Bulgarian Black and White Cattle Breed. Bulg. J. Agric. Sci., 26 (4), 890–893 This study examined the activities of aspartate aminotransferase (ASAT), alanine aminotrans-ferase (ALAT), alkaline phosphatase (ALP), acid phosphatase (ACP), albumin (Alb), total protein (TP), creatin-phosphokinase (CPK), total lipid (TL), calcium (Ca) in the plasma of 304 dairy Holstein breed cows and their calves. The cows were held in an intensive farm breeding premises. Their meal consisted of ordinary alfalfa, silage, hay and concentrates with energy and protein supplements. The Student t-test showed significant difference (P < 0.05) in mean of the ASAT, ALAT, ALP, ACP, Alb, TP, TL, Ca between cows and calves of the breed. Mean values of the ASAT, ALAT, Alb, TP, TL and Ca was higher in cow group than in calves group (P < 0.05), while ALP and ACP was lower in cow in comparison to the calves group (P < 0.05).
GPU Accelerated Limit Order Book Modeling Pipeline

Modeling ﬁnancial processes is present for a long time by econometric and mathematical models, however, today’s technology enables data-driven algorithms to be applied in this ﬁeld. This includes models such as artiﬁcial neural networks, which can be executed on massively parallel hardware, such as Graphical Control Units (GPUs). These technologies allow running models quicker than before, but preprocessing of data is mostly done in a traditional way - e.g on the Central Processing Units (CPUs). In this work, a GPU accelerated pipeline is proposed, which aims to apply the GPUs parallel execution mode not just on the model execution, but the data preprocessing stages as well. The pipeline handles a speciﬁc type of ﬁnancial data – limit order books – and handles data collection, order book preprocessing, data normalisation, batching into training samples and training of deep neural networks. Measurements of the performance and inference are taken and compared with benchmarking meth-ods.
How Effective are Matching Schemes in Enticing Low-income Earners to Save More for Retirement? Evidence from a National Scheme

Concerns over the adequacy of low and middle-income earner contributions to retirement plans have led governments to introduce targeted matching schemes. In this study, we examine the effects of a simple and generous Australian scheme using administrative tax-filer data, exploiting longitudinal changes in eligibility and match rates. We find small increases in the proportion who contribute and bunching at the eligible maximum, but lower average contributions because the matching payment displaces contributions of high contributors. Contributions through unmatched channels are also crowded out. These findings highlight the difficulties of targeting matching schemes and question the merits of simplifying them.
Digital Transformation of Global Value Chains and Sustainable Post-Pandemic Recovery

This perspective paper examines the impact of the COVID-19 pandemic on global production and trade from the perspective of global value chains. Particular attention is paid to the transmission mechanisms and the role of digital technology in sustainable post-pandemic economic recovery. It argues that emerging technologies will be a driver of the global economic recovery, while the challenge to sustainable and inclusive global development will be significant, especially with regard to inequality and job creation. Also discussed are policy implications, to ensure this recovery is inclusive and sustainable, not leaving any country or people behind.
Traditional Bulgarian foods in the Horeca sector as a factor of choice for the tourist destination

Stankov, S. , Fidan, H. , Toskov, G. , Dimitrova, E. & Nikovska, K. (2019). Traditional Bulgarian foods in the Horeca sector as a factor of choice for the tourist destination. Bulgarian Journal of Agricultural Science, 25(4), 654–660 In recent years, the culinary culture expressed in food preparation, marketing and consumption, as elements of the Horeca model, has established as one of the main components for the fortifi cation of touristic destinations. The traditional cuisine has an important place in the growing structure of tourism. The objective of this study is to determine the infl uence of traditional Bulgarian cuisine in the choice of a tourist destination. The methodology of this study involved conducting surveys and the data are collected from local and foreign tourists (n = 150) who visit three of the main cities in Bulgaria (Sofi a, Varna, and Plovdiv). The results of the study showed that the destination selection decision could be infl uenced by many factors as the local cuisine (local production of traditional foods) (88%), natural and historical resources of the country (76%), and cultural heritage (52%). Among the factors affecting tourist decision to consume traditional foods are determined: the welldefi ned destination image, local cuisine accessibility, eating and drinking habits of destination residents. Practical applications: this study contributes to the marketing potential for the tourism industry, regional development, and hospitality business, exploring the local food as a key element of the Horeca sector for choosing a tourist destination.
PROBLEMS PERCEIVED BY FARMERS OF NORTH-EAST INDIA IN ADOPTING IMPROVED AGRICULTURAL PRACTICES FOR MITIGATING THE ADVERSE EFFECTS OF CLIMATE CHANGE

A study was conducted at Kohima district of Nagaland state in north eastern part of India. The state is vulnerable and sensitive to all the climate change related issues. Kohima district has been administratively divided into four Rural Development blocks, out of which one block was selected purposively for study area. A well structured and pre-tested interview schedule was developed to collect data from 300 respondents. The selection of the respondents was done based on proportional random sampling technique. This study aimed to know the problems perceived by farmers in adopting improved agricultural practices for mitigating the adverse effects of climate change. The results revealed that more numbers of farmers perceived problems like “lack of knowledge to avail the loan, monetary support and subsidies from banks and development departments”, “poor economic condition of the farmers”, “lack of knowledge about suitable environmental friendly technologies”, lack of farm machineries suited for hilly terrain”, “high cost involved in improved agricultural practices” etc.