Miracle-dz/UniFAR-FTUs huggingface dataset:
Staffing Policy of Delivery Platforms under the Sharing Economy: A Game-Theoretic Analysis

For companies providing grocery and food delivery, some choose to become two-sided platforms and let part-time people deliver for consumers. While some platforms also hire full-time deliverers, some rely solely on part-time deliverers. It is crucial to explain the difference and identify factors to be considered in making the staffing decision. We consider a delivery platform who may hire full-time deliverers or subsidizing part-time ones to induce their participation. While solving the staffing problem, it also solves the pricing problem with respect to consumers by taking cross-side network externality into consideration. We find that hiring both part-time and full-time employees is suboptimal when the wage for full-time deliverers is too high. Interestingly, hiring only full-time employees is never optimal no matter how low the wage is. Our analysis helps us justify the popularity of delivery platforms and may guide platform deliverers to determine their staffing policies.
Survey on Methodologies of Data Mining using Neural Network

: The traditional data mining algorithms are hard to apply on noisy data, redundant information, incomplete data and sparse data in database, or the application effects are not good. But neural network have many virtues such as robustness, parallelism and anti-noise, so it is very effective on data mining in large and real databases. This paper expounds the process of data mining based neural network in detail, discusses the algorithms of classifying and clustering, indicates the problems at present and makes an expectation for the development
Bring Your Own Device (BYOD) Security Policy Compliance Framework

Bring Your Own Device (BYOD) is an environment that allows employees to use their own personal device to access organisation’s resources to perform their work, but it has raised some security concerns as with BYOD, organisations face bigger challenges to safeguard their information assets. Compliance with ISP is a key factor in reducing organisation’s information security risks, as such, understanding employees’ compliance behaviour and other relevant factors that influence compliance with ISP is crucial. Hence, this study aims to explore this phenomenon by investigating the factors influencing employees in complying with BYOD Information Security Policy (ISP) in Malaysian public sector. A mixed method study on five (5) ministries in the public sector is proposed for the study. The understanding of these factors would assist in systematically developing a BYOD compliance framework for the public sector. This is critical as this trend is here to stay or even expand rapidly as employees carry more than one device to the workplace. The proposed framework will help improve ISP compliance to ensure organisational information assets are well protected.
The Impact of Online Shopping Carnival on Consumer Decision-Making: The Moderating Role of Gender

The online shopping carnival has become an annual event for the e-commerce industry in recent years. However, the change of consumers' multi-stage decision-making process during the online shopping carnival remains unknown and has not been investigated before. This study proposes research framework based on stimulus–organism–response (SOR) model, and uses the difference-in-difference (DID) method and regression model to empirically test 50 million shopping log data of more than 420,000 consumers on the Tmall platform. We attempt to identify the impact of online shopping carnival on the relationship between consumers' information processing behavior and purchase decision, and to explore the moderating role of gender. The study expands the research on consumer differences and provides important implications for merchants to formulate targeted marketing strategies based on customer characteristics.
Status of Calcium and Phosphorus of Female Balinese Cattle in Mataram City West Nusa Tenggara

Calcium (Ca) and Phosphorus (P) are closely related to the reproductive abilities of ruminants, especially female cattle; calves, pregnant and dry cattle. Especially in pregnant cattle, these elements are used for fetal growth in addition to maintain the condition of the parent body. The levels of Ca and P in the blood of livestock are strongly influenced by the levels of Ca and P in plants and in the soil where the plants grow. The aim of the study was to determine the status of Ca and P minerals in balinese calves, pregnant and dry balinese cattle which were raised by farmers in the Mataram City, West Nusa Tenggara. Blood samples of 3 ml from 12 balinese calves, 32 pregnant balinese cattles, and 19 dry balinese cattles were accommodated in vacuum tubes. Moreover, 100 g amount of forage were collected by using spot diagonal sampling technique in the feed place of each livestock. And the example of the soil where the forage grew by taking 500 g amount of soil as a composite of representatives of several soil samples from various locations. The results showed that the levels of Ca in the blood of calves were 2.87 ± 0.99 mg / dL; pregnant cattle 2.88 ± 0.85 mg / dL; and dry cattle 2.82 ± 1.2 mg / dL. And the levels of P in the blood of calves were 18.3 ± 2.8 mg / dL; pregnant cattle 21.7 ± 8.2 mg / dL; and dry cattle19.7 ± 3.9 mg / dL. The conclusion of the results is the status of Ca in the blood of female balinese cattle in the city of Mataram is deficient, and the status of P is excessive.
Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.
Do changes in regulation affect job satisfaction of temporary agency workers?

This paper evaluates the impact of a reform, which considerably relaxed regulations covering the temporary help service sector on temporary agency workers’ job satisfaction. We isolate the causal effect of this reform introduced in Germany in 2003 using rich survey data and combining a difference-in-difference and matching approach. We find that the change of the law substantially decreased job satisfaction of agency workers while job satisfaction of regular workers remained unchanged. Further analysis reveals that the negative effect on agency workers’ jobsatisfaction can be attributed to a decrease in wages and an increase in perceived job insecurity after the reform. These results are also robust to the use of different specifications and placebo tests.
Long-term survival following cardiac transplantation.

The article by Sareyyupo¤lu et al. (1), published in the current issue of the Anadolu Kardiyoloji Dergisi, presents a retrospective study aiming to analyze the early and long-term outcomes of patients undergoing cardiac transplantation performed between 1989 and 2006 in their centre. There are only few studies addressing post transplantation survival in Turkey and the authors should be commented for the number of successful cardiac transplants performed within the study period. The article emphasizes higher preoperative creatinine level, acute rejection after transplantation; early postoperative functional status and unsatisfactory rejection surveillance protocol in the first 6 months after transplantation affect long-term survival. However, recipients with complex congenital heart disease, diabetes-related complications, or who require mechanical circulatory support while on waiting list, donor left ventricular hypertrophy, presence of preformed antibodies against the donor heart could have been included in multivariate analysis (2, 3). Re-transplantation is another important risk factor for overall survival. According to the International Society for Heart and Lung Transplantation (ISHLT) database, re-transplantation comprises approximately 3% of cardiac transplants (4). The investigators preferred the bicaval orthotopic heart transplantation technique after 2002 (1). Future studies are encouraged to compare biatrial and bicaval techniques concerning not only survival but also right ventricular function, atrial geometry, sinus node dysfunction, tricuspid and mitral regurgitation. Patients’ education is also another critical issue in Turkey, which requires dedication. The authors should be congratulated for keeping some of their patients at hospital and arranging accommodation and job facilities in order to provide better care. Four decades after the first heart transplants performed in Turkey, which was also the beginning of solid organ transplantation in our region, heart transplantation has become the “gold standard” for the treatment of end-stage heart failure in Anatolia. With the increasing presence of cardiovascular surgeons in the treatment of heart failure (5), we can look forward to continued advances in organ, tissue, and cell transplantation for cardiovascular disease.
Towards a Unified Understanding of Digital Nudging by Addressing its Analog Roots

Nudging has become a well-renowned concept in policy making throughout the previous decade. Based on insights from behavioral economics (BE) and psychology, nudges are defined as intentional and targeted interventions to modify the choice architecture and alter individuals’ behavior to a desired direction. As digital choice environments increasingly influence individuals’ lives, a discourse on digital nudging has begun within the information systems (IS) community. With a transdisciplinary bridge-building approach, we address the current state of digital nudging and argue that a more thorough foundation and definition of digital nudging can render itself useful for integrating the extensive research on choice architectures and nudging present in BE. In this article, we summarize, transfer and discuss the current state of digital nudging’s conceptualization in BE and IS. Furthermore, we propose an extended definition of digital nudging and introduce Blended Environments Framework as integrated views of analog and digital realms.
Applying Soft Computing Approach to Uncertainty Analysis in Oil and Gas Economic Evaluations

Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques such as Artificial Neural Network, Support Vector Machines, Fuzzy Logic, Evolutionary algorithm and Swarm Intelligence. Oil and gas investment project is comprehensive and capital intensive engineering system characterized by uncertainty and high risk. The decision to invest is usually based on the evaluation of the project profitability which in most cases is not accomplished with high level of certainty. These economic evaluations are carried out with estimated set of parameters such as project costs, oil and gas prices, production profiles and inflation rate. The paper considers an approach to apply the concept of soft computing to uncertainty analysis in the economic analysis of oil and gas investment. A description of methods and models used for economic evaluation are provided. The application of soft computing such as support Vector Machines and Fuzzy Logic models are also provided. The soft computing approach is compared with basic evaluation without consideration for uncertainty. A case study is presented to illustrate that the data from which the evaluation is drawn are limited leading to an estimate that are inherently uncertain. The motivation of the proposed method is the fact that that the hybrid Support Vector Machines and Fuzzy Logic models have been used to predict oil prices, cost, net present value and internal rate of return with reasonable level of certainty. It also enables adequate risk assessment inherent in an investment to facilitate appropriate decision making.
Predicting the Effects of Product Description on Purchase Intentions in Cross-border E-commerce: An Integration of Involvement Theory and Commitment-involvement Theory

Nowadays, cross-border e-commerce (CBEC) has become an important channel for promoting international trade. There are many factors that influence consumer participation in CBEC, including product description. An excellent product description can not only attract more consumers, but it can also increase product sales. However, little research has addressed product description in a CBEC setting. From the perspective of psychology, this study proposes a research model of purchase intention in CBEC based on the involvement theory and commitmentinvolvement theory. The research model will be tested using the covariance-based Structural Equation Modeling. Data will be collected from users of a popular CBEC platform in China. Results will have important theoretical and practical implications for CBEC researchers and practitioners.
Report from the Great Lakes Acoustic Workshop III Translation of acoustic data to fish abundance (and standardization of acoustic methods for the Great Lakes Region)

Day 1 of the workshop was devoted to several lectures and shorter presentations by the participants. The lectures included three presentations on theory and techniques and four case studies (Great Lakes, Rivers, Lakes, Marine): Guy Fleischer, USGS-BRD, Ann Arbor, MI: Target strength of Great Lakes fishes John Horne: NOAA Great Lakes Laboratory, Ann Arbor, MI: Theoretical scattering models for predicting target strength and the choice of operating frequency: applications to alewife and smelt in the Great Lakes Patrick Sullivan, Cornell University, Ithaca NY: Survey design, introduction to the theory Doran Mason, Purdue University. Acoustics in Lake Superior. Kyle Hartman, West Virgnia University: Acoustics in riversthe Hudson experience Paul Walline, Lake Kinneret Laboratory, Israel: Acoustics in lakes the Lake Kinneret experience Fredrik Arrhenius, Marine Laboratory, Sweden. Acoustics in oceans -Atlantic herring in the Norwegian Sea.
Holistic Versus Component-Based Strategies

In this study, we investigated the mental representations used in a fraction comparison task. Adults were asked to quickly and accurately pick the larger of two fractions presented on a computer screen and provide trial-by-trial reports of the types of strategies they used. We found that adults used a variety of strategies to compare fractions, ranging among just knowing the answer, using holistic knowledge of fractions to determine the answer, and using component-based procedures such as cross multiplication. Across all strategy types, regression analyses identified that reaction times were significantly predicted by numerical distance between fractions, indicating that the participants used a magnitude-based representation to compare the fraction magnitudes. In addition, a variant of the problem-size effect (e.g., Ashcraft, 1992) appeared, whereby reaction times were significantly predicted by the average cross product of the two fractions. This effect was primarily found for component-based strategies, indicating a role for strategy choice in the formation of mental representations of fractions.
DECOMPOSING MULTISPECTRAL FOREST SIGNATURES OF SATEL- LITE IMAGERIES BY MODELLING RADIATIVE TRANSFERS BASED ON STRUCTURAL DATA FROM TERRESTRIAL LASER SCANNING

The mapping of variability in vegetation from space contributes valuable information for ecosystem research such as the timing and spacing of the phenological cycle. The objective of this study is to map the phenology of the European larch (Larix decidua), i.e. its leaf's growing characteristics from leaf unfolding to leaf fall off. The study is set up at an Austrian Alpine site (Pinnistal, Tyrol) analysing SPOT-5 (Satellite Pour l'Observation de la Terre) scenes. In order to retrieve the Larix decidua signal from remote sensed imagery, it is crucial to understand its contribution to the entire forest-canopy-understorey signal composition, which ultimately results in a single pixel value of the image. Decomposition of the mixed signal is deduced by reconstructing architectural vector models of a Larix decidua forest from terrestrial laser scanning (TLS) under leaf-off conditions combined with a leaf growing algorithm simulating different stages of leaf growth. The derived tree architectural data is used as input for radiative transfer modelling to retrace the multispectral scattering that takes place when sun radiation propagates through the atmosphere, the vegetation canopy, and back to the satellite sensor. The Discrete Anisotropic Radiative Transfer (DART) model is employed simulating SPOT-5 images at different phenological phases. For the radiative transfer modelling branch architectures are used as mesh geometries whereas the foliation is considered as "turbid voxel cells" in order to reach an optimal trade-off between accuracy and computation time. Subsequently, sensitivity analysis are conducted by testing the effect of changes between vegetation optical properties and vegetation related indices such as the normalized difference vegetation index (NDVI) and the leaf-area-index (LAI) under different parameter settings. Ultimately, time series of these indices are deduced from the SPOT-5 scenes and compared to the simulated ones.
The Relationship Between Epicardial Adipose Tissue Thickness and Presence of Left Atrial Thrombus in Mitral Stenosis Patients

Objective To examine the relationship between the left atrial (LA) thrombus presence and the epicardial adipose tissue (EAT) thickness. Methods Three hundred and twelve consecutive rheumatic mitral valve stenosis (RMVS) patients with mitral valve area (MVA) < 2 cm2 were included in this cross-sectional study. Patients were divided into two groups, those with and those without LA thrombus. Routine biochemical analysis and electrocardiographic examinations were carried out. EAT was measured using transthoracic echocardiography. Results LA thrombus was determined in 84 (26.9%) RMVS patients. In echocardiographic examinations, higher mean gradient and LA diameter as well as lower MVA were found in the group with LA thrombus (P<0.001). In this group, higher C-reactive protein (CRP) and EAT values were also determined (P<0.001). There was significant correlation between EAT and MVA, CRP, LA appendage peak flow velocity, LA anteroposterior diameter, and mean gradient (P<0.001). Higher EAT values were identified as independently associated with the presence of LA thrombus (odds ratio 59.5; 95% confidence interval 12.1-290.10; P<0.001). Conclusion Transthoracic echocardiography, routinely used in patients with RMVS, can measure EAT to determine patients who are under risk for thrombus.
Participation and Power Dynamics Between International Non-Governmental Organisations and Local Partners: A Rural Water Case Study in Indonesia

Community-Based Management (CBM) is an important part of Indonesia’s goal of universal access to water. However, approaches to CBM tend to neglect the impact of power relationships between community-based organisations (CBOs) and their external donor partners on CBO management capacity. This paper explores the power dynamics between a CBO and their donor partner, the international NGO Engineers Without Borders Australia (EWB), in a rural water supply project in Tenganan, Indonesia. A diffracted power frame was used to analyse the response of CBO power to EWB’s participatory approach. The approach was sensitised to power, gave primacy to the CBO’s vision, used local assets, and had a flexible timeline. The CBO’s power was evident in the strength of its vision, its resistance to government involvement, the occasional rejection of technical advice from EWB, and its increased confidence in its capacity to manage Tenganan’s water supply. The findings reinforce the political nature of participation, with implications for approaches to establishing CBM in Indonesia and elsewhere. Strengthened outcomes in rural water supply are likely to result from greater self-reflection by external partners regarding their own positionality, coupled with a focus on strategies for maintaining and enhancing the power of CBOs.
Bayesian Manifold Learning: Locally Linear Latent Variable Models (LL-LVM)

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variational optimisation of the posterior distribution on coordinates and locally linear maps from the latent space to the observation space given the data. Thus, the LL-LVM encapsulates the local-geometry preserving intuitions that underlie non-probabilistic methods such as locally linear embedding (LLE). Its probabilistic semantics make it easy to evaluate the quality of hypothesised neighbourhood relationships, select the intrinsic dimensionality of the manifold, construct out-of-sample extensions and to combine the manifold model with additional probabilistic models that capture the structure of coordinates within the manifold.
Sound Risk Culture in Banks

In this paper, we examine the effect of different risk cultures on a banks performance, risk-taking incentives and corporate governance. We extend beyond banks' risk culture evaluation and classification using interviews and surveys, and apply textual analysis and cluster analysis to banks' annual 10-K reports. Benefiting from the sentiment dictionary built by Loughran and McDonald (2011) and risk culture framework proposed by McConnell (2013), we develop a twodimensional dictionary and extract paragraph level features reflecting sentiment and risk culture topics from 10-K reports. We use legal expense data in a regression analysis based supervised learning for feature reduction and implement a k-means clustering to categorize the 10-K reports into three risk culture classes: high, moderate and low risk cultures. For our sample of U.S. bank holding companies, we find that banks in the high and moderate risk culture classes have better performance, lower total compensation for executives and fewer institutional ownership, than banks in the low risk culture cluster. We also find that, the boards of banks in the high and moderate risk are more diverse, have more meetings in a year and decrease the number of options received by non-employee directors. Our results suggest that promoting a sound risk culture can improve financial performance and risk governance within the banking industry.