Miracle-dz/UniFAR-FTUs huggingface dataset:
Adoption of Internet and Web Technology for Hotel Marketing: A Study of Hotels in Thailand

This paper examines the effect of three groups of characteristics: organisational, technological, and environmental on the adoption of Internet and Web based marketing activities (IWMA) in the hotel industry by using data collected from a sample of 152 hotels in Thailand. The adoption of IWMA has been categorised into two levels: 1) non-early adopter hotels, and 2) early adopter hotels. The results indicate that Thai hotels that adopted IWMA at the early adopter stage were larger in size, and more likely to be more ready to adopt IWMA than non-early adopter hotels were. Managers of Thai hotels in the early adopter stage were more likely to perceive the benefits and recognise compatibility when adopting IWMA than were managers in the non-early adopter hotels. Customer power and level of government support were considered to be the push factors for hotels in the use of IWMA at the early adopter stage more than for non-early adopter hotels.
Voluntary adoption of non-local GAAP in the European Union: A study of determinants and consequences

Abstract This study examines the determinants and consequences of voluntary adoption of non-local accounting principles (non-local GAAP) by firms listed and domiciled in the European Union (EU). We restrict ourselves to the two predominant internationally accepted sets of accounting standards: International Accounting Standards (IAS) and United States generally accepted accounting principles (US GAAP). We have used various sources to identify EU firms that use non-local GAAP. We examine the 1999 annual reports of all these firms, because accounting standard choices in more recent years may be affected by the announcement of the proposal by the European Commission in February 2001 to mandate IAS usage from 2005 on. The maintained hypothesis is that firms that voluntarily adopt IAS or US GAAP expect to experience net benefits from adoption. The finding that 133 non-financial firms in the EU voluntarily used non-local GAAP in 1999 suggests that the majority of listed EU firms does not expect to benefit from non-local GAAP adoption. By studying the characteristics of non-local GAAP adopters this study provides insight into the determinants of non-local GAAP adoption. We find that firms voluntarily using non-local GAAP are more likely to be listed on a US exchange, the EASDAQ exchange in Brussels, and have more geographically dispersed operations. Furthermore, they are more likely to be domiciled in a country with lower quality financial reporting and where IAS is explicitly allowed as an alternative to local GAAP. We also study whether non-local GAAP adopters have lower levels of information asymmetry, a much cited benefit of using more transparent financial reporting, than non-adopters. We examine three proxies for information asymmetry: analyst following, cost of equity capital, and uncertainty among analysts and investors (forecast dispersion and stock return volatility). We document a positive effect of non-local GAAP adoption on analyst following, but fail to find evidence of a lower cost of capital for non-local GAAP adopters. Contrary to expectations, uncertainty among analysts and investors appears to be higher for firms using IAS or US GAAP than for firms using local GAAP. However, by comparing ‘early’ and ‘late’ adopters, we find some evidence that suggests that benefits take some time to fully materialise.
Revealed Comparative Advantage and Competitiveness in Hungarian Agri-Food Sectors

We examine the competitiveness of Hungarian agriculture and food processing, in relation to that of the EU, based on four indices of revealed comparative advantage, using highly disaggregate data for the period 1992 to 1998. Consistency tests suggest that the indices are less satisfactory as cardinal measures, but are useful in identifying the demarcation between comparative advantage and comparative disadvantage. Hungary is shown to have a comparative advantage in a range of agri-food products, including animals and meat. This complements the findings of those studies that have used price and cost based approaches in identifying competitiveness in cereals and crops. Results indicate that the RCA indices, when interpreted as a binary measure, have remained surprisingly stable during the period of transition, although there is evidence of a weakening in the level of comparative advantage as revealed in the Balassa index. Copyright Blackwell Publishing Ltd 2003.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS (IJAE)

This paper introduces a method to study the relation among parameters that can cause the origin of M/X solar flares. Solar flares, especially flares of types M and X, make the Earth’s atmosphere more ionized and have an effect on radio signals, which can cause disruptions in wireless communications. This situation points out to the need for better identification of the parameters involved in M/X solar flares. The method is based on four categorical parameters and their relations. Relations are demonstrated by association rules which were extracted by the APRIORI algorithm and the most promising rules were filtered by support and confidence metrics. Results of the most promising rules had been compared by application to different periods of the 23rd and the 24th solar cycles.
Alternative Futures for Changing Landscapes: The Upper San Pedro River Basin In Arizona And Sonora

Carl Steinitz has developed a GIS-based simulation modelling strategy that considers the demographic, economic, physical and environmental processes of an area and projects the consequences to that area of various land-use planning and management decisions. The results of such projections, and the approach itself, are known as "alternative futures". This volume presents a detailed case study of one alternative futures project - an analysis of development and conservation options for the Upper San Pedro River Basin in Arizona and Sonora, Mexico. The book gives a comprehensive overview of how the study was conducted along with descriptions and analysis of the alternative futures that resulted.
Dynamic Composite Multi-Source Bumpless Transfer Control for Mass-Switched Unmanned Marine Vehicles of Dynamic Positioning

This article studies the networked unmanned marine vehicles (UMVs) of dynamic positioning (DP) based on mass-switched with dynamic composite multi-source bumpless transfer (DCMSBT) performance. First, the model of the mass-switched system of networked UMVs based on the quantizer mechanism is established. Second, a kind of DCMSBT control related to the variation of bump value is introduced into the switched UMVs system model. Third, a control strategy is proposed to achieve the DP of UMVs with multiple disturbances based on a joint design of a disturbance observer, the desired controller, event-triggered schemes and a switching rule. Finally, a UMVs of DP example proves the method works.
Experimental Characterization of Rotary Transformer Prototypes for Wireless Power Transfer on Helicopters

Wireless power transfer is an attractive solution for applications which require strong continuity of the service, high reliability, and low maintenance. Rotary transformers represent a practical embodiment at electrical machine level in all those situations where power needs to be transferred across moving parts, in relative rotation (such on a shaft), by inductive coupling of two separate circuits. For this reason, the use of rotary transformers was investigated from first principles, as a direct replacement of conventional "sliprings", in helicopter rotor blade ice protection systems. The idea is to use one transformer to supply the heaters embedded into the blades, and another one to interface with the temperature sensor in bundle with heaters. In this work, the full-scale prototypes of a power and signal transformer are presented. Their characterization in terms of electrical parameters and power transfer efficiency is summarized, when operated within an equivalent circuit at the nominal frequency, but reduced voltage. The experimental results are then compared with the numerical predictions and the discrepancies analyzed to draw a conclusion on the accuracy of the adopted model, the effectiveness of the implemented solution and the feasibility of the final helicopter application.
X-ray dark-field imaging with a single absorption grating

X-ray dark-field imaging is usually performed with a Talbot–Lau interferometer, but the quality of its analyser grating is limited by the fabrication process. This makes it difficult to achieve a large area and high aspect ratio. In this study, the analyser grating is abandoned and dark-field imaging is realized using only one absorption grating. The optimal grating position is derived theoretically in the single absorption grating x-ray dark-field imaging, and the conclusion is partially verified by the experiment. Meanwhile, the optimal object position for dark-field imaging has also been obtained from both the theory and experiment. In the process of comparing x-ray dark-field imaging with x-ray refraction imaging, it is found that both of them can be explained by geometric optics, showing the intrinsic relationship between them.
Integrating activity-based and traffic assignment models: Methodology and case study application

This paper presents a methodology to integrate two existing models: an activity-based model and a traffic assignment model, which are conceived as separately built and calibrated. Such a combination allows us to model the disaggregated interactions between supply and demand, which traditional modelling tools can frame only at the aggregate level (e.g. four-step models). An iterative method is proposed, combined with a novel formulation for a measure of error, which allows for achieving equilibrium in a limited number of iterations. The proposed method is tested using a real case study based on the City of Tallinn, Estonia, showing its effectiveness.
Prevalence of pathogenic E. coli in diarrhoeic cattle calves and antibiotic resistance genes

A total of 190 fecal samples were collected from diarrheic cattle calves aged from 1-6 months from different private farms in Kalubia and Sharkia Governorates, Egypt during the period from November 2019 to September 2020. Samples were carried out to elucidate the prevalence of Escherichia coli (E. coli ). Serological identification of E. coli isolates was done to detect the incriminated serogroups. Detection of Congo red binding activities hemolysis production and enterotoxin assay by infant mouse test as virulence factors. Also, antimicrobial sensitivity test by disc diffusion. Detection of bla TEM and tet A (A) antibiotic resistance genes of 10 isolated E.coli isolates represented by one isolate from each serogroup and the untypable strain was done by PCR. The bacteriological examination revealed that the prevalence of E. coli in diarrheic calves was 37.4%. The serotyping of the isolated E. coli revealed serogroups; O55, O25, O111, O119, O126, O78, O157, O186, and O128, and other untypable 8 strains. Furthermore, the Congo red binding activity revealed that 43 E.coli isolates showed binding activity with an incidence of 60.6%. Out of 71 isolates, 56 showed hemolytic activity with a percentage of (78.9 %) but enterotoxin production was found only in 26 isolates with a percentage of 36.6. The antibiogram of the isolated strains was investigated; the majority of E. coli were resistant to amoxicillin, ampicillin, and tetracycline. On other hand, all serogroups were sensitive to ciprofloxacin and cefotaxime. Polymerase chain reaction (PCR) was used to detect the antibiotic-resistant genes bla TEM and tet A (A) genes and the obtained results showed that all tested isolates contained these genes except the first serogroup O 55 .
Observer-Based SMC for Dynamic Wireless Charging System of Electric Vehicles with Complex Disturbances

This paper focuses on voltage control for a dynamic wireless charging system of electric vehicles based on an extended state observer. Each dynamic wireless charging system will suffer from complex disturbances, leading to the challenge of output voltage control. To address this issue, we propose a disturbance rejection control scheme that utilizes an extended state observer to estimate the unknown disturbances. A dynamic sliding-mode surface is designed to suppress estimation error, and a sliding mode control law based on the estimated information and sliding-mode surface is proposed to accurately track the expected voltage. We prove the stability of the closed-loop system, and simulation results confirm the effectiveness of the proposed disturbance rejection control scheme for dynamic wireless charging systems based on the extended state observer.
Application of Control Scheme for Trajectory Tracking of Hovercraft and Indoor Airship

This paper is concerned with the application of a velocity transformation-based control scheme developed for underpowered marine vehicles to other vehicles with a similar mathematical model to the underwater vehicle. The problem of applicability of known control algorithms to systems that differ from the original is important in that its solution makes it possible to adapt a known control strategy for a similar class of vehicles. It is then not necessary to construct a new algorithm, but it is enough to transform one that is known. The effectiveness of the proposed approach was verified using simulations for a 3 DOF hovercraft model and an 3 DOF indoor airship.
Evaporites: Their Evolution and Economics

Interpreting evaporite textureBrine evolution and mineralogyEvaporite basins and their stratigraphic evolutionIndicators and effects of dissolution: "the evaporite that was"Salt tectonicsMeta-evaporites Evaporites as a mineral resourceEvaporite-metal associations: lower temperature and diageneticEvaporite-metal associations: brines, magma and metamorphism
Thermally Induced Losses on High Speed Interconnects

This paper presents a systematic approach for broadband characterization of thermally induced losses on high-speed interconnects. The characterization relies on S-parameter measurements of striplines fabricated on a commercial board with 20GHz bandwidth. The measurements cover a temperature range from 0 to 100 degrees Celsius $\left({ }^{\circ} \mathrm{C}\right)$, taken at 20-degree steps. For each temperature the high-speed interconnects are characterized from two-line measurements, which yields the complex-valued propagation constant at every temperature. As it is well-known, the real part of the propagation constant contains the loss information about the interconnects. By using the losses associated with the propagation constant at $\mathrm{T}=0{ }^{\circ} \mathrm{C}$ as a reference, the thermally induced losses for all other temperatures are extracted. The thermally induced losses are then analyzed to determine the contribution from both the metal and the dielectric. Our results show that both the metal and the dielectric contribute to the thermally induced losses, but that the main contribution is primarily associated with the dielectric. From that, accurate temperature dependent models for the loss tangent Tanδ(T) and for the metal’s resistivity can be defined from the measured data. It follows that the thermally induced losses due to the dielectric and the metal can be easily modelled in commercial EDA software by simply correcting the loss tangent and resistivity values for the desired temperature.
Comparison of seabed classification using unsupervised and supervised learning on ship noise spectrograms

Applications of supervised machine learning to ocean acoustics is often limited by the lack of labeled measured data. To overcome this, synthetic data can be used for training. This paper explores the potential for unsupervised learning to provide labels for measured data. Specifically, a comparison is made between seabed classification from supervised learning and labels inferred from unsupervised learning. Both networks are trained on synthetic ship noise spectrograms. Six CNN-based supervised learning methods were trained using synthetic data labeled by seabed class. The trained networks were applied to 69 measured spectrograms from the Seabed Characterization Experiment 2017. The results show a distinct preference for seabeds with softer top layer (water-sediment sound speed ratios less than one). The unsupervised ML method, k-means clustering, is applied to same synthetic dataset, and the resulting clusters are evaluated based on the characteristics of the synthetic data samples placed into each cluster. The measured ship noise spectrograms are then passed through the trained clustering model, and the characteristics of the assigned clusters are evaluated. Of the 69 measured data samples, 70% are placed in clusters showing a distinct preference for seabed classes similar to those obtained from the CNN-based classifiers. Other measured data samples are placed in clusters that contain synthetic data samples from short ranges. This work illustrates the potential for using clustering to assign preliminary labels to unlabeled data.
An Autonomous Navigation Approach based on Bird’s-Eye View Semantic Maps

With the continuous expansion of the use of mobile robots, providing them with autonomous navigation capabilities for different environments has become a very active research topic. In most cases, navigation systems are built around sensors like LiDAR which are expensive, not only due to the sensor cost but also to the computational power required for the processing of Point-Clouds. To provide an alternative to such systems, we propose a navigation approach that only requires a front-facing RGB Camera. In this system, every image is processed online using a semantic segmentation model to build a Bird’s-Eye view semantic map, from which a local path and the corresponding motion commands can be calculated. Our method is evaluated first in simulation and later on, in a real mobile robot. The results show that our system enables the robot’s successful navigation and collision avoidance through simulated and real indoor and outdoor environments.
A CNN-based generative model for vehicle trajectory reconstruction in mixed traffic flow

With the breakthrough of connected and autonomous vehicles (CAVs) technology, vehicle trajectories can be collected by various sensors installed on CAVs continuously and sent to the traffic control center for operation and management. However, the trajectory data collected by CAVs may contain incomplete part owing to sensor limitation, thus hindering the data availability. To address this issue, we propose a CNN-based generative model for reconstructing multiple vehicle trajectories in multi-lane traffic scenarios using hybrid detection data from both CAVs and fixed sensors. Specifically, we generate missing trajectories using a Generative Adversarial Network (GAN) architecture with spatio-temporal features extracted by Convolutional Neural Networks (CNNs). The performance of the method is examined on a simulated arterial by assessing the mean absolute error (MAE) of the reconstructed data. The results indicate our method is robust even at a low CAV penetration rate.
Node Authentication for Underwater Sensor Networks Based on Time Reversal and LinUCB

Due to the complexity of underwater environment and hash features of underwater acoustic channels, underwater acoustic sensor networks(UASN) has been facing great security challenges. TR process has been generally used for focusing muti-path signals in target detection and communication. In this paper, a node authentication scheme for UASN based on time reversal(TR) process and LinUCB algorithm is presented. This paper proposed, with the hope of authenticating network nodes effectively. Based on network link channel impulse response(CIR), the TR process is used to extract time reversal resonance strength(TRRS) between links. The node authentication problem for UASN is modeled as a contextual multi-armed bandit problem in this paper, where the LinUCB algorithm is utilized to iteratively calculate the decision threshold of TRRS for node authentication. The effectiveness of the proposed online learning node authentication algorithm is verfied through simulation results.
Sensitivity of Climate Changes to CO2 Emissions in China

Abstract In this study, the authors demonstrate that the Coupled Model Intercomparison Project Phase 5 (CMIP5) models project a robust response in changes of mean and climate extremes to warming in China. Under a scenario of a 1% CO2 increase per year, surface temperature in China is projected to increase more rapidly than the global average, and the model ensemble projects more precipitation (2.2%/°C). Responses in changes of climate extremes are generally much stronger than that of climate means. The majority of models project a consistent response, with more warm events but fewer cold events in China due to CO2 warming. For example, the ensemble mean indicates a high positive sensitivity for increasing summer days (12.4%/°C) and tropical nights (26.0%/°C), but a negative sensitivity for decreasing frost days (-4.7%/°C) and ice days (-7.0%/°C). Further analyses indicate that precipitation in China is likely to become more extreme, featuring a high positive sensitivity. The sensitivity is high (2.4%/°C) for heavy precipitation days (> 10 mm d-1) and increases dramatically (5.3%/°C) for very heavy precipitation days (> 20 mm d-1), as well as for precipitation amounts on very wet days (10.8%/°C) and extremely wet days (22.0%/°C). Thus, it is concluded that the more extreme precipitation events generally show higher sensitivity to CO2 warming. Additionally, southern China is projected to experience an increased risk of drought and flood occurrence, while an increased risk of flood but a decreased risk of drought is likely in other regions of China.
Simulations of an off-shore WEC: analysis of the potential energy producibility in the Mediterranean Sea

Among the renewable energy sources that, by definition, are not subject to depletion, marine energy is one of the most widely available forms in the world. The exploitation of sea wave represents an important frontier for the energy sustainability, in addition to the main and well-known forms of renewable energy such as wind energy, solar energy or hydroelectric energy. The ever-increasing demand for energy means that interest in wave motion has been growing all over the world for many years now. In order to reduce the energy dependence from fossil fuels, destined to exhaustion, and to avoid the emission of several polluting substances and greenhouse gases (GHG) it is necessary to invest in new technologies and improve the efficiency of energy production plants in general. In this context, the paper propones the energy assessment of a new technology for the wave harvesting in offshore WEC, for a potential application in Mediterranean Sea.
Agrochemicals Desk Reference

The Second Edition of the Agrochemicals Desk Reference is a revised, up-to-date volume inspired by the growing number of research publications and continued interest in the fate, transport, and remediation of hazardous substances. Much new data has been added to this edition, reflecting the wealth of literature in the field. Featured are environmental and physical/chemical data on more than 200 compounds, including pesticides, herbicides, and fungicides.