Miracle-dz/UniFAR-FTUs huggingface dataset:
Human resource management as an internal antecedent of environmental management: a joint analysis with competitive consequences in the hotel industry

Abstract Firm success depends on economic performance, but also on social and environmental factors. We use abilities, motivations, and opportunities theory and resource-based view to analyse the role of human resources management (HRM) as an antecedent of environmental management and how this relationship contributes to a deeper understanding of the linkage between environmental management and performance. This is because there are no conclusive results of the costs and profits derived from environmental practices and HRM could add more information to this topic. Here, we show that a set of HRM practices, and specially employee participation processes and communication between managers and employees, significantly improves environmental management. Moreover, environmental management explains significantly competitive advantages and performance in the hotel industry. Findings show that hotels should have an HRM structure to train, motivate, develop, and retain employees to develop environmental management aimed at obtaining cost and differentiation advantages and improve their perceptual performance, average daily rate, and revenues per available room. Our findings have implications towards advancing theory because we develop an integrative framework which includes the link between HRM (antecedent) and environmental management, and the relationship between environmental management and competitive advantages and performance (consequences) in the hotel industry.
A Linear Quadratic Regulator With Optimal Reference Tracking for Three-Phase Inverter-Based Islanded Microgrids

This article proposes a power sharing control method based on the linear quadratic regulator with optimal reference tracking (LQR-ORT) for three-phase inverter-based generators using inductor-capacitor-inductor (LCL) filters islanded mode. Compared to single-input single-output (SISO)-based controllers, the LQR-ORT controller increases robustness margins and reduces the quadratic value of the power error and control inputs during transient response. Supplementary loops are used to reduce frequency and voltage deviations in the ac bus without communications. The supplementary loop for voltage regulation is based on the droop controller by reducing direct and quadrature output voltages according to the active and reactive power demand. A model in a synchronous reference frame that integrates power sharing and voltage–current dynamics is also proposed. In addition, a methodology to develop an islanded microgrid model in a synchronous reference frame is proposed. Robustness analysis demonstrates stability of the LQR-ORT controller under variations in the frequency and the LCL filter components. Experimental results demonstrate accuracy of the proposed model and the effectiveness of the LQR-ORT controller on improving transient response and robustness in islanded mode.
Simulation of the Reactivation of Partially Inactivated Biocatalysts in Sequential Batch Reactors

The enzymatic reactivation process enables the recovery of catalytic activity for inactive biocatalysts. However, its effect on the specific productivity of the processes has not been studied. The main objective of this work was to evaluate the specific productivity of the processes with and without reactivation using the program Spyder Python (3.7). Using fixed values for all of the parameters, the global specific productivity was 8 mM/h·gbiocat for the process without reactivation, and 4 mM/h·gbiocat for the process with reactivation. Random numbers were generated to use as different values for parameters, and the results yielded a global specific productivity of 3.79 mM/h·gbiocat for the process with reactivation and 3.68 mM/h·gbiocat for the process without reactivation. ANOVA tests showed that there were significant differences between the specific global productivities of the two processes. Reactivation has great potential for use when the biocatalyst is of high cost.
Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin

Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales; (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash–Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was −6.36% and the correlation coefficient (CC) was 0.93 at the daily scale; (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. −0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world.
Techno-Economic Analysis of a Heat Pump Cycle Including a Three-Media Refrigerant/Phase Change Material/Water Heat Exchanger in the Hot Superheated Section for Efficient Domestic Hot Water Generation

Integration of a three-media refrigerant/phase change material (PCM)/water heat exchanger (RPW-HEX) in the hot superheated section of a heat pump (HP) system is a promising approach to save energy for domestic hot water (DHW) generation in multi-family houses. The RPW-HEX works as a desuperheater and as a latent thermal energy storage in the system. The latent thermal energy storage is charged during heating and cooling operation and discharged for DHW production. For this purpose, the water side of the RPW-HEX is connected to decentralized DHW storage devices. DHW consumption, building standards and climate, energy prices, material costs, and production costs are the constraints for the selection of the optimal storage size and RPW-HEX design. This contribution presents the techno-economic analysis of the RPW-HEX integrated into an R32 air source HP. With the aid of experimentally validated dynamic computer models, the optimal sizing of the RPW-HEX storage is discussed to maximize energy savings and to minimize the investment costs. The results are discussed in the context of a return of investment analysis, practical implementation aspects and energetic potential of the novel technology.
Excitonic effects in ZnO nanowires and hollow nanotubes

Energy levels and wave functions of ground and excited states of an exciton are calculated by the method of imaginary time. Energy levels as functions of radius of single and double wall nanotube are studied. Asymptotic behavior of energy levels at large and small values of the radius using perturbation theory and adiabatic approximation is considered. Spatially indirect exciton in semiconductor nanowire is also investigated. Experimental result from high quality reproducible ZnO nanowires grown by low temperature chemical engineering is presented. State of the art high brightness white light emitting diodes (HB-LEDs) are demonstrated from the grown ZnO nano-wires. The color temperature and color rendering index (CRI) of the HB-LEDs values was found to be (3250 K, 82), and (14000 K, 93), for the best LEDs, which means that the quality of light is superior to one obtained from GaN LEDs available on the market today. The role of VZn and VO on the emission responsible for the white light band as well as the peak position of this important wide band is thoroughly investigated in a systematic way.
Investigation of Finishing Aluminum Alloy A5052 Using the Magnetic Abrasive Finishing Combined with Electrolytic Process

The magnetic abrasive finishing combined with electrolytic (EMAF) process was proposed to improve the finishing efficiency of the traditional magnetic abrasive finishing (MAF) process. Since the EMAF process contains electrolysis reactions, the machining mechanism of processing different metal is different. In this paper, a series of experiments were conducted to explore the feasibility of using the compound processing tool to finish aluminum alloy A5052, and to preliminary explore the machining mechanism. Surface roughness and material removal are used to evaluate the finishing effect and the finishing efficiency, respectively. The EMAF processing current curve is used to evaluate and analyze the EMAF process. The feasibility of the EMAF processing is proved by the analysis of simulations and the experimental results. Finally, through a series of exploration experiments and parameter optimization experiments, the main conclusions are as follows: (1) Compared with the traditional MAF process, when finishing the surface of aluminum alloy A5052 by the same compound processing tool and at the same experimental conditions (except the electrolysis conditions), the EMAF process, which includes electrolysis reactions, can achieve higher finishing efficiency. (2) In this study, when the working gap is 1 mm and the concentration of NaNO3 solution is 15%, the recommended processing voltage is about 3.4 V.
A Review on Voice-based Interface for Human-Robot Interaction

With the recent developments of technology and the advances in artificial intelligence and machine learning techniques, it has become possible for the robot to understand and respond to voice as part of Human-Robot Interaction (HRI). The voice-based interface robot can recognize the speech information from humans so that it will be able to interact more naturally with its human counterpart in different environments. In this work, a review of the voice-based interface for HRI systems has been presented. The review focuses on voice-based perception in HRI systems from three facets, which are: feature extraction, dimensionality reduction, and semantic understanding. For feature extraction, numerous types of features have been reviewed in various domains, such as time, frequency, cepstral (i.e. implementing the inverse Fourier transform for the signal spectrum logarithm), and deep domains. For dimensionality reduction, subspace learning can be used to eliminate the redundancies of high-dimensional features by further processing extracted features to reflect their semantic information better. For semantic understanding, the aim is to infer from the extracted features the objects or human behaviors. Numerous types of semantic understanding have been reviewed, such as speech recognition, speaker recognition, speaker gender detection, speaker gender and age estimation, and speaker localization. Finally, some of the existing voice-based interface issues and recommendations for future works have been outlined.
Hierarchical Anomaly-Based Detection of Distributed DNS Attacks on Enterprise Networks

Domain Name System (DNS) is a critical service for enterprise operations, and is often made openly accessible across firewalls. Malicious actors use this fact to attack organizational DNS servers, or use them as reflectors to attack other victims. Further, attackers can operate with little resources, can hide behind open recursive resolvers, and can amplify their attack volume manifold. The rising frequency and effectiveness of DNS-based DDoS attacks make this a growing concern for organizations. Solutions available today, such as firewalls and intrusion detection systems, use combinations of black-lists of malicious sources and thresholds on DNS traffic volumes to detect and defend against volumetric attacks, which are not robust to attack sources that morph their identity or adapt their rates to evade detection. We propose a method for detecting distributed DNS attacks that uses a hierarchical graph structure to track DNS traffic at three levels of host, subnet, and autonomous system (AS), combined with machine learning that identifies anomalous behaviors at various levels of the hierarchy. Our method can detect distributed attacks even with low rates and stealthy patterns. Our contributions are three-fold: (1) We analyze real DNS traffic over a week (nearly 400M packets) from the edges of two large enterprise networks to highlight various types of incoming DNS queries and the behavior of malicious entities generating query scans and floods; (2) We develop a hierarchical graph structure to monitor DNS activity, identify key attributes, and train/tune/evaluate anomaly detection models for various levels of the hierarchy, yielding more than 99% accuracy at each level; and (3) We apply our scheme to a month’s worth of DNS data from the two enterprises and compare the results against blacklists and firewall logs to demonstrate its ability in detecting distributed attacks that might be missed by legacy methods while maintaining a decent real-time performance.
Lightweight network and parallel computing for fast pedestrian detection

In recent years, researchers have made great efforts in computer vision task (e.g., object detection) with the widely use of convolutional neural networks (CNNs). However, object detection algorithms based on CNNs suffer from high computation cost even on the high‐performance computers. In addition, with the development of high‐resolution videos, the deployment of object detection algorithms becomes more and more difficult because of the large amount of data, let alone the portable platforms, such as unmanned aerial vehicles (UAVs). In this paper, we research a lightweight network on portable platform for outdoor tiny pedestrian detection. Concretely, we first set up a training dataset manually for lack of tiny pedestrian samples in common datasets. We provide a lightweight network, and then, parallel computing is introduced to make the most of the advantage of GPU. Finally, our method can achieve real‐time performance on Jetson TX2. Experimental results verify that the proposed model has promising performance in tiny pedestrian detection designed for portable GPU platforms.
Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.
Developing activities of green design, green purchasing, and green transportation as the part of green supply chain management in construction sector

Green Supply Chain Management (GSCM) is a concept that integrates environmental factors into the traditional SCM started from the process of design, material procurement, production, distribution and end-of life. The benefits of implementing GSCM does not only improve the company’s environmental performance, but also improve economic performance such as reducing operational costs and organizational performance such as improving the company’s image. In manufacture and automotive industry, GSCM is able to improve company performance, therefore construction industry can consider of adopting this approach. Some GSCM practices in construction sector include green design, green purchasing, green transportation, green construction, green warehousing, green recycling and facilitating green practices. The aim of this study is to identify relevant green design, green purchasing and green transportation practices as part of GSCM if applied in the construction sector. The research variables were obtained through a literature study on GSCM both in construction sector and other sectors, then the variables were validated by respondents, practitioners in construction sector. Based on three respondents, there are ten green design practices, six green purchasing practices and five green transportation practices that are relevant to the construction sector, especially building projects.
Special Collection: The Road to Sociality: Integrated Concepts of Social Behavior in Insects Movement, Encounter Rate, and Collective Behavior in Ant Colonies

Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.
Special Section: Multispecies Co-existence in Inner Asia

In this special section our focus is on human relations with animals in the domestic sphere (or domus) in Inner Asia. In the existing academic literature, there has been greater attention paid to human–nonhuman relations in North Asia (or Siberia), often between hunter and prey animal. The intention of this special section is to ask what we can learn about relations between humans and domestic animals when we shift the focus to Inner Asia, a region that has long been characterised by multispecies pastoralism. The various contributors to this issue have conducted research across a broad swathe of Inner Asia, from Buryatia in the southeast of Siberia (Oehler), Mongolia (Bumochir et al., Fijn, Hutchins, Swancutt), Inner Mongolia (White), Qinghai (Bumochir) to the southwest of China (Swancutt). Within anthropology, a multitude of terms have emerged for a focus beyond the human, now superseding older literature within ecological or environmental anthropology to become ‘multispecies ethnography’ (Kirksey & Helmreich 2010), ‘anthropology beyond humanity’ (Ingold 2013), or an ‘anthropology of life’ (Kohn 2013). This terminological proliferation has occurred in the context of the broader ‘animal turn’ in the humanities and social sciences and the ‘ontological turn’ within anthropology. These diverse bodies of literature share a concern with thinking beyond the anthropocentrism which has historically dominated the humanities and social sciences, previously confining attributes such as subjectivity and agency to humans alone. This concern has gained a
No Clear Response in the Stormwater Phytoplankton Community to Biocide Contamination

This study investigated the responses of natural phytoplankton communities of an urban stormwater pond to biocide contamination. The biocides carbendazim, terbutryn, diuron, and irgarol 1051, and their mixture, were used in two laboratory microcosm and one outdoor mesocosm studies at concentrations of 10, 100, and 1000 ng L−1. The water samples were collected in a pond receiving significant biocide contamination. The mesocosm study was carried out in the same pond. The phytoplankton community response was evaluated after 10–15 days of exposure, with respect to its taxonomic composition, abundance and biovolume. No significant changes were observed in any of the experiments. Only at the highest applied terbutryn concentration were lower numbers of taxa identified in both microcosm and mesocosm experiments. Additionally, increases in Chlorophyta abundance and biovolume, along with an increase in irgarol concentration, were observed throughout the three experiments. Nevertheless, the statistical analysis did not confirm any significant differences among the biocide treatments. These results suggest that the biocide concentrations probably were below the harmful or toxic threshold to the stormwater pond phytoplankton. On the other hand, as the investigated pond phytoplankton taxa face biocide inputs throughout the year, they could have already adapted to the tested biocide contamination.