Miracle-dz/UniFAR-FTUs huggingface dataset:
Antecedents to firm performance during re-internationalization

There is a paucity of research on re-internationalization, where firms re-enter international operations after complete withdrawal from previous international operations. The extant literature is largely silent on what drives firm performance during re-internationalization. We conducted an empirical investigation of re-internationalized enterprises from India to identify key antecedents to firm performance during the re-internationalization phase. Data analysis using partial least squares structural equation modeling (PLS-SEM) indicates that initial internationalization experiences, presence of dynamic capabilities, and organizational commitment to internationalization positively contribute to re-internationalization performance. The findings have implications for firm strategies, organization systems, managerial attention to knowledge management, policies supporting subventions, and for future research into de-internationalization and re-internationalization. JEL Classification: F23
Origins, Diversity and Naturalization of Eucalyptus globulus (Myrtaceae) in California

Eucalyptus globulus is native to southeastern Australia, including the island of Tasmania, but is one of the most widely grown hardwood forestry species in the world and is naturalized on several continents. We studied its naturalization in California, where the species has been planted for over 150 years. We sampled 70 E. globulus trees from 53 locations spanning the entire range of the species in California to quantify the genetic variation present and test whether particular genotypes or native origin affect variation in naturalization among locations. Diversity and native affinities were determined based on six nuclear microsatellite markers and sequences from a highly variable chloroplast DNA region (JLA+). The likely native origin was determined by DNA-based comparison with a range-wide native stand collection. Most of California’s E. globulus originated from eastern Tasmania. Genetic diversity in California is greatly reduced compared with that of the native Australian population, with a single chloroplast haplotype occurring in 66% of the Californian samples. Throughout California, the degree of E. globulus naturalization varies widely but was not associated with genotype or native origin of the trees, arguing that factors such as local climate and disturbance are more important than pre-introduction evolutionary history.
AN ENHANCED WIRELESS NETWORK FOR LOW INCOME USERS

Information and communication technology present huge potentials for socio-economic development in under-served communities. Wireless network offers great opportunity for the provision of affordable Internet services and bridge the digital gap among low income earners. In this study, a wireless network that is scalable and easily manageable was designed and implemented to cover five rural communities. We have been monitoring and maintaining the network for more than three years. Over time signal attenuation has been experienced as a result of environmental changes such as trees creating obstacles. A mesh network at the access layer was deployed to enhance throughput and signal propagation. Data was collected and analyzed to assess network and end users performance. The results shows that the transmit/receive (Tx/Rx) rate of 300Mbps/300Mbps was observed and a throughput loss of 0.5Mbps on the network. The point-to-multipoint access layer design increased the throughput, enhance signal coverage and quality of service.
Sketched Stochastic Dictionary Learning for large‐scale data and application to high‐throughput mass spectrometry

Factorization of large data corpora has emerged as an essential technique to extract dictionaries (sets of patterns that are meaningful for sparse encoding). Following this line, we present a novel algorithm based on compressive learning theory. In this framework, the (arbitrarily large) dataset of interest is replaced by a fixed‐size sketch resulting from a random sampling of the data distribution characteristic function. We apply our algorithm to the extraction of chromatographic elution profiles in mass spectrometry data, where it demonstrates its efficiency and interest compared to other related algorithms.
Impacts of big data analytics management capabilities and supply chain integration on global sourcing: a survey on firm performance

Purpose This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration. Design/methodology/approach Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method). Findings The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration. Originality/value Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.
A Global Media Analysis of the Impact of the COVID-19 Pandemic on Chicken Meat Food Systems: Key Vulnerabilities and Opportunities for Building Resilience

Food systems are increasingly under threat, with climate, biological, economic or policy shocks and stressors occurring at an increasing frequency and scale. Their complex and fragile nature has become even more apparent during the COVID-19 pandemic. A systematic review of news articles published globally between December 2019 and April 2020 was conducted to describe the impacts of the COVID-19 pandemic on the chicken meat system and to identify key vulnerabilities and intervention points to build resilience. Most of the impacts identified were caused by a combination of the different mitigation measures implemented by the system actors such as movement restrictions rather than by the direct effects of the virus, thereby demonstrating the importance of interconnections and coordination in the system. Social media were found to have played a crucial role in amplifying, mitigating or mediating the impact of the pandemic. The findings highlight the importance of adopting a holistic approach that integrates the multiple dimensions of food systems for effective responses to systemic shocks.
COMPARATIVE EFFECTS OF RAISED BED AND TRADITIONAL FLAT BASIN ON WHEAT YIELD AND WATER PRODUCTIVITY UNDER EGYPTIAN CONDITIONS

Field experiments were carried out at Sakha Agricultural Research Station in Egypt to compare the effect of the newly developed raised bed technique with different bed widths (RB130, RB100, RB80 cm) on wheat productivity to the conventional flat basin (FB) method during 2019/2020. The statistical analyses were carried out using a randomized complete block design. According to the results of statistical analysis, using RB130 cultivation gave the highest significant averages of water-saving (21.81%) and the highest averages of wheat 1000 kernels weight (50.05 g) over the FB method. However, no significant differences were found between RB130 and FB method, which produced the highest averages of grain yield, straw yield, and biological yield. Wheat sowing on FB gave the highest significant average of grain yield, straw yield, and biological yield than RB100, RB80 at 5% level of significance, but there were no significant differences in the number of kernels per spike, kernel weight spike, and harvest index ratio between wheat sowing on FB and RB130, RB100 or RB80. There were no significant differences in the number of kernels per spike, kernel weight spike, number of spikes per m2, straw yield, biological yield, and harvest index ratio at 5% level of significance between wheat sowing on RB130, RB100, RB80. But wheat sowing on RB130 produced the highest significant average of crop water productivity and grain yield than RB80 at 5%. Finally, according to the study findings, using raised beds cultivation resulted in saving irrigation water by 15~21% compared to a flat basin.
Probabilistic linguistic q‐rung orthopair fuzzy Generalized Dombi and Bonferroni mean operators for group decision‐making with unknown weights of experts

In this paper, we develop a multicriteria group decision‐making methodology with probabilistic linguistic q‐rung orthopair fuzzy sets (PLqROFSs). The benefit of choosing PLqROFSs is that they consider the simultaneous occurrence of stochastic and nonstochastic uncertainty and so are superior to probabilistic hesitant fuzzy sets, linguistic intuitionistic fuzzy sets, and linguistic Pythagorean fuzzy sets. To develop the methodology, we first propose two types of operators, namely, probabilistic linguistic q‐rung orthopair fuzzy weighted Generalized Dombi operator which gives the flexibility of choosing the parameter values and probabilistic linguistic q‐rung orthopair fuzzy weighted Generalized Dombi Bonferroni mean operator which can consider the interrelationships between criteria. Since both the subjective and objective weights of experts are hardly considered in the current PLqROFSs‐based research, so in our proposed methodology, we deploy the thought of consistency and similarity between the experts to calculate the subjective and objective weights, respectively, of the experts and as a result the evaluation results do not get malformed. For measuring the weights of criteria, the gray correlation coefficient of the assessment value of criteria is used to reflect the similarity between the criteria and its reference value. To exhibit the applicability of the proposed operators, we provide a case study on biomass feedstock selection. Moreover, to make sure that our model is stable, we have investigated a sensitivity analysis of parameters. At the end, we make a comparison of our approach to different existing schemes.
Color Texture Description with Diagonal Local Binary Patterns Using a New Distance Metric for Impressive Image Retrieval

Abstract-With the reliability and dependence of the world over the digitalization of information in all spectrums of professional and personal life, the volume of digital data is increasing exponentially. With such a huge database, retrieving information, and images from the storage media pose serious challenges. In this paper, a diagonal local binary pattern for color images (DLBPC) and a new distance metric are proposed for the retrieval of data with high accuracy and speed. The descriptor employs the deviceindependent L*a*b* color space. The discriminative quality of DLBPC is enhanced by combining local features with the (color moment) global feature of the image for effective image retrieval. To assess the efficacy, the suggested system was implemented on challenging datasets: Wang -1K and GHIM-10K. The analysis findings demonstrate the efficacy of the suggested system over other reported systems.
Development of Brief Rating Scales for Progress Monitoring Internalizing Behavior

Prevalence estimates suggest that up to 20% of students in schools experience significant internalizing behaviors that impact behavioral, social, or academic functioning. School-based interventions have great potential to promote student mental health; however, validated and feasible brief assessments are needed to progress monitor students’ responses to these supports. The purpose of the current study was twofold: to (a) develop and validate teacher-completed brief rating scales for progress monitoring internalizing concerns in elementary-age students and (b) determine the reliability of the resultant measures. First, item content was generated and subjected to evaluation by two panels of school-based consumers and researchers. Within the second phase of development, exploratory and confirmatory factor analyses were used to reduce the initial number of items and ensure that the items were indicators of one latent factor. Teachers in Grades K–3 (N = 307) each completed ratings for one randomly selected student in their classroom. Results of factor analysis for each scale indicated one-factor solutions for the 4-item Anxious/Depressed (ω = .88) and 4-item Socially Withdrawn (ω = .87) scales.
Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning

Abmarl is a package for developing Agent-Based Simulations and training them with MultiAgent Reinforcement Learning (MARL). We provide an intuitive command line interface for engaging with the full workflow of MARL experimentation: training, visualizing, and analyzing agent behavior. We define an Agent-Based Simulation Interface and Simulation Manager, which control which agents interact with the simulation at each step. We support integration with popular reinforcement learning simulation interfaces, including gym.Env (Brockman et al., 2016) and MultiAgentEnv (Liang et al., 2018). We leverage RLlib’s framework for reinforcement learning and extend it to more easily support custom simulations, algorithms, and policies. We enable researchers to rapidly prototype MARL experiments and simulation design and lower the barrier for pre-existing projects to prototype Reinforcement Learning (RL) as a potential solution.
Transient Analysis of Multiphase Transmission Lines Located above Frequency-Dependent Soils

This paper evaluates the influence of frequency-dependent soil conductivity and permittivity in the transient responses of single- and double-circuit transmission lines including the ground wires subjected to lightning strikes. We use Nakagawa’s approach to compute the ground-return impedance and admittance matrices where the frequency-dependent soil is modeled using Alípio and Visacro’s model. We compare some elements of these matrices with those calculated by Carson’s approach which assumes the frequency constant. Results show that a significant difference can be obtained in high resistive soils for these elements in impedance and admittance matrices. Then, we compute the transient responses for single- and double-circuit lines with ground wires located above soils of 500, 1000, 5000, and 10,000 Ω·m considering the frequency constant and frequency-dependent parameters generated for two lightning strikes (subsequent stroke and Gaussian pulse). We demonstrate that the inclusion of frequency dependence of soil results in an expressive reduction of approximately 26.15% and 42.75% in the generated voltage peaks in single- and double-circuit lines located above a high-resistive soil. These results show the impact of the frequency-dependent soils that must be considered for a precise transient analysis in power systems.
The role of self-efficacy in HIV treatment adherence and its interaction with psychosocial factors among HIV positive adolescents in transition to adult care in Kenya

ABSTRACT Adolescents and young adults (15–24 yrs.) have poorer HIV clinical outcomes than adults. Despite this, there is minimal data on individual-level factors such as self-efficacy towards antiretroviral adherence among perinatally infected adolescents living with HIV in sub-Saharan Africa. Our study examined the interaction between antiretroviral treatment adherence self-efficacy and other psychosocial factors among adolescents receiving care in Nairobi, Kenya. We enrolled perinatally infected Adolescent Living with HIV (ALWHIV) 16–19 yrs. who were accessing care routinely at the HIV clinic. We measured self-reported ART adherence (7-day recall) and defined optimal adherence as >95%, and conducted a regression analysis to identify independently associated factors. Mediation analysis explored interactions between the psychosocial variables. We enrolled 82 ALWHIV median age 17 (IQR 16,18) who had been on ART for a median age of 11 yrs. (IQR 7,13). Sixty-four per cent (52) of the ALWHIV reported optimal adherence of >95%, and 15% reported missing doses for three or more months. After controlling for the other covariates, self-esteem, high viral load and an adherence level > 95% were significantly associated with adherence self-efficacy. Self-esteem was significantly associated with adherence self-efficacy and social support (p < 0.001 and p = 0.001), respectively. The paramed test indicated that the association between self-efficacy and adherence was mediated by self-esteem with a total effect of OR 6.93 (bootstrap 95% CI 1.99–24.14). Adherence self-efficacy was also mediated by self-esteem in developing adherence behavior. Interventions focused on increasing adherence among ALWHIV should include self-esteem building components.Key words : Adolescents, Adherence self -efficacy, Transition , perinatal,
CNN‐based deep learning technique for improved H7 TLI with grid‐connected photovoltaic systems

In this article, a three‐phase transformerless inverter (TLI) for a solar photovoltaic (PV) system connected to a high‐power grid are proposed, which has advantages of better performance and lower cost. The primary concern about the TLI is fluctuations in the common‐mode voltage, which impacts switching frequency leakage current and grid interface system. An improved H7 common‐mode voltage (CMV) clamped TLI with discontinuous pulse width modulation (DPWM) is designed using a conventional neural network (CNN)‐based deep learning approach. In this, a completely minimized leakage current is obtained to avoid CMV transients. The proposed PV‐connected improved H7‐TLI provides low‐loss DC‐side decoupling, which further reduces leakage current and isolation of the PV system during off‐grid. In addition, the effects of several factors on CNN deep learning performance are explored, including training data size, image resolution, and network configuration. The proposed technique has the potential to be used in a test instrument for intelligent signal analysis or used in an artificial intelligence system. Switching loss is analyzed using proposed and existing H7 inverters under different load conditions. To verify the theoretical explanation, existing H7 inverters is analyzed by MATLAB/Simulink, and the outcomes are tested experimentally. The total harmonic distortion (THD) analysis of proposed and existing topology is analyzed and compared. The THD values of the existing and proposed topology are 3.74% and 3.23%, respectively.
Fatigue behaviour of rock asphalt concrete considering moisture, high-temperature, and stress level

ABSTRACT Rock asphalt has been widely used to improve the performance of asphalt concrete, owing to its cost efficiency and beneficial modification effect. However, the moisture and high-temperature effects interdependently determine the degradation of fatigue resistance of rock asphalt concrete, owing to the unique climatic summer conditions in most high-temperature regions of China (e.g. southeastern coastal and south regions). Accordingly, the water absorption characteristics of rock asphalt concrete at different temperatures were analysed. Based on this analysis, 75 groups of modified three-point bending fatigue tests were implemented to investigate the combined effect of moisture, high-temperature, and stress level on the fatigue life of rock asphalt concrete. The moisture, temperature, and stress level conditions significantly influence the fatigue resistance of rock asphalt concrete. Finally, fatigue equations considering moisture and stress level were established to predict the fatigue life of rock asphalt concrete at different temperatures.
Accurate and Lightweight RailNet for Real-Time Rail Line Detection

Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.
Design of trisection filter with reconfigurable center frequency and transmission zeros using evanescent‐mode cavity resonators

In this paper, a trisection filter with reconfigurable transmission zeros based on the evanescent‐mode cavity (EVA) resonators is proposed. The design incorporates three EVA resonators. The varactors are set between two EVA resonators. By controlling the varactor capacitance value, couplings can be switched between electrical coupling and magnetic coupling. Based on this technique, the resulting two zeros can be designed to the desired position. Furthermore, a novel method by analyzing the magnitude and phase of trisection filters is proposed. Using this method, the transmission zeros position can be determined quickly. Measurement shows that the filter covers a frequency range of 2.11–2.83 GHz. Two transmission zeros can be arbitrarily designed to the left or right sides of the passband. Good agreement is obtained between simulated and measured results.
Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.