Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review on the learning outcomes in entrepreneurship education within higher education settings

Abstract Entrepreneurship education is important in creating opportunities and bringing innovation to society. However, learning outcomes of entrepreneurship education remain unclear, causing challenges to curriculum design and structure. The objective of this literature review is to explore what learning outcomes are currently embedded in entrepreneurship education programmes within the context of higher education. Data was drawn from Web of Science, ERIC, ProQuest, Scopus and Science Direct, consisting of 133 peer-reviewed articles published between 2000 and 2020. The literature review contributes to understandings of current learning outcomes for entrepreneurship education that affect persons, institutions and society. It offers a framework that identifies current classifications of learning outcomes in entrepreneurship education, which may be adopted in curricula and other educational settings. Supplemental data for this article is available online at https://doi.org/10.1080/02602938.2021. 2021583 .
SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks

ABSTRACT A consumer in Information Centric Network (ICN) generates an Interest packet by specifying the name of the required content. As the network emphasizes on content retrieval without much bothering about who serves it (a cache location or actual producer), every Content Router (CR) either provides the requested content back to the requester (if exists in its cache) or forwards the Interest packet to the nearest CR. While forwarding an Interest packet, the ICN routing by default does not provide any mechanism to predict the probable location of the content searched. However, having a predictive model before forwarding may significantly improve content retrieval performance. In this paper, a machine learning (ML) algorithm, particularly a Support Vector Machine (SVM) is used to forecast the success of the Interest packet. A CR can then send an Interest packet in the outgoing interface which is forecasted successful. The objective is to maximize the success rate which in turn minimizes content search time and maximizes throughput. The dataset used in is generated from a simulation topology designed in ndnSim comprising 10 K data points having 10 features. The linear, RBF and the polynomial kernel (with degree 3) are used to analyze the dataset. The polynomial kernel shows the best behavior with 98% accuracy. A comparative retrieval time with and without ML demonstrates around 10% improvement with SVM enable forwarding compared to normal ICN forwarding.
Carbon-Coupling Metallic Cobalt as Trifunctional Catalysis for Oxygen Reduction/Evolution and Hydrogen Evolution

The next-generation energy storage focuses on sustainability and renewability, facilitating the process of metal-air batteries and fuel cells. Nevertheless, their performances are suffering from the tardy development of the elaborate design and large-scale synthesis strategy of cost-efficient electrocatalysts. In this work, a convenient strategy for facile synthesizing electroactive material is proposed. As a result, a trifunctional Co/C catalyst is fabricated via a convenient calcination process, utilizing the pyrolysis of cobalt acetate and melamine. The prepared Co/C sample delivers a positive half-wave potential of 0.75 V in oxygen reduction reaction (ORR). Furthermore, the low overpotentials at 10 mA cm −2 are shown in alkaline conditions for oxygen evolution reaction (OER, 388 mV) and hydrogen evolution reaction (HER, 202 mV). The improved activity is mainly due to the interaction between Co and the in situ formed carbon carrier. The promising trifunctional activities endow the Co/C sample with a bright prospect in metal-air batteries and overall water splitting.
Grounding Event of Iceberg D28 and Its Interactions with Seabed Topography

Iceberg D28, a giant tabular iceberg that calved from Amery Ice Shelf in September 2019, grounded off Kemp Coast, East Antarctica, from August to September of 2020. The motion of the iceberg is characterized herein by time-series images captured by synthetic aperture radar (SAR) on Sentinel-1 and the moderate resolution imaging spectroradiometer (MODIS) boarded on Terra from 6 August to 15 September 2020. The thickness of iceberg D28 was estimated by utilizing data from altimeters on Cryosat-2, Sentinel-3, and ICESat-2. By using the iceberg draft and grounding point locations inferred from its motion, the maximum water depths at grounding points were determined, varying from 221.72 ± 21.77 m to 269.42 ± 25.66 m. The largest disagreements in seabed elevation inferred from the grounded iceberg and terrain models from the Bedmap2 and BedMachine datasets were over 570 m and 350 m, respectively, indicating a more complicated submarine topography in the study area than that presented by the existing seabed terrain models. Wind and sea water velocities from reanalysis products imply that the driving force from sea water is a more dominant factor than the wind in propelling iceberg D28 during its grounding, which is consistent with previous findings on iceberg dynamics.
Error-Tolerant Data Sketches Using Approximate Nanoscale Memories and Voltage Scaling

Data sketches (or summaries) are widely used to efficiently process large data sets and obtain relevant information, such as the number of distinct elements, their frequency or the similarity of several sets. Since sketches provide probabilistic estimates (hence accomplishing considerable reductions in storage and computational requirements), it seems that approximate hardware is appropriate for implementation providing an estimate rather than the exact value and enabling further improvements in metrics. In this paper, the use of voltage scaling in an approximate nanoscale FinFET-based memory for implementing sketches is studied and evaluated. To mitigate the errors introduced by the approximate memory, techniques to protect several widely used sketches when implemented in such voltage-scaled FinFET memory, are proposed. The proposed schemes preserve the sketches functionality with a negligible impact on the accuracy of their estimates and tolerate large bit error rates for sketches such as Minhash and HyperLogLog. The analysis and simulation results show that the intrinsic error tolerance of data sketches can be exploited to provide accurate estimates even when the memory suffers many errors due to the voltage scaling of the supply. Voltage scaling enables large power savings for Minhash and HyperLogLog. These initial results suggest that the use of approximate computing for data sketches is an interesting area for further research.
Economic energy management in textile industry using meta-heuristic algorithms incorporating solar distributed generation

Among the various industrial businesses, the textile industry is one of the most energy-intensive. Energy cost reductions in these industries are one of the prime intentions of the industrialists. Energy Management System (EMS) gains importance with the increase in production and higher market space. Distribution Companies (DISCOMS) charge higher tariff while industries use power in the peak loading condition of the distribution system. This paper proposes a load scheduling method among the loads that are uninterrupted loads, fixed loads, and the shiftable loads. Optimization of load scheduling algorithm for minimizing the total energy cost is developed. Load scheduling using optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for lowest energy cost is developed. Analysis by adding the solar based power and finding the break-even point due to the introduction of a capital cost due to Photovoltaic (PV) installation. The proposed algorithm is applied with the energy tariff that is charged by the Bangalore Electricity Supply Company Limited (BESCOM) for different industrial loading levels. MATLAB based implementation of these optimization techniques is compared with the Bat algorithm for performance evaluation. Cost benefits of around 24.4% are evident from the cost analysis while PV installation of 300 kW is incorporated in the textile industry premises. Break even for the industry because the PV installation will be obtained after 3.5 years.
Age of Information in Wireless Networks: Spatiotemporal Analysis and Locally Adaptive Power Control

The boom in Internet of Things has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination and aroused widespread attention from both academia and industry. In this paper, we develop a theoretical framework to evaluate the statistics of AoI, including its average and violation probability, in wireless networks under different types of sources and updating patterns. The analyses account for the randomness that arises from both the spatial deployment and temporal queueing dynamics, and its accuracy is verified through simulations. Based on the analytical results, we design a locally adaptive power control policy to optimize the sum of average AoI of all nodes, which allows each node to assign transmit power according to its local observation. The proposed scheme has low implementation complexity. Numerical results show that the proposed power control policy can significantly improve information freshness. The scheme is well adapted to variants of network environment and heterogeneous source-destination distance. Further, we evaluate the effect of the retransmission mechanism and updating patterns on the AoI performance.
A 2 × 2 planar array of Clover‐leaf shaped printed antennas for weather‐monitoring based applications

This article presents a 2 × 2 printed planar array and its single element for weather‐monitoring (8.175–8.125 GHz) related applications. The proposed array's single antenna prototype is a nature‐inspired‐based design and is derived from natural Clover‐leaves. The single proposed element gives an appreciably decent gain and heavily suppresses cross‐polarization (x‐pol.) along its main beam direction. The array is designed using the Wilkinson divider‐based corporate feed network to achieve a low loss level and better bandwidth response. The sole fabricated element resonates at 8.19 GHz and offers 7.85 dBi peak gain and 336 MHz impedance bandwidth, respectively. Similarly, 12.30 dBi gain and 263 MHz impedance bandwidth have been achieved from the array geometry at the resonating frequency of 8.195 GHz. The prototypes' computed correction factor is 40.64 and 36.20 dB/m, respectively. Both the prototypes provide 100% bandwidth coverage for weather‐monitoring applications under X‐band. Both the prototypes provide a below −35 dB x‐pol. Along its main beam direction. They offer reasonably good radiation efficiency of 92.77% and 89.16%, respectively. An excellent agreement has been entrenched in between measured and simulated results of the prototypes.
An Approach of Rhetorical Status Recognition for Judgments in Court Documents using Deep Learning Models

In a court document, the rhetorical status of a sentence conveys the intention of the sentence, whether is is a claim or contains supporting evidences, thus, is beneficial to court document processing systems, for example, court document retrieval systems. Besides, rhetorical structure analysis has high-impact applications in natural language processing, for instances, text summarization, sentiment analysis, question answering. The output structures of the analysis contain high-level relationship between clauses and so provides valuable information. Despite of a wide range of applications and the necessity for automatic court document processing, automatic rhetorical structure analysis has not been well noticed in the legal domain. We propose to use deep learning models for tackling the task of recognizing the rhetorical status of each sentence in a court document. Deep learning has been shown effective towards natural language processing tasks including discourse analysis. We have achieved promising results for the task, which suggests the applicability of artificial neural module embedding rhetorical information for other tasks, for example, summarization and information retrieval.
Mg/Al-CH, Ni/Al-CH and Zn/Al-CH, as adsorbents for Congo Red removal in aqueous solution

In this study, chitosan was extracted from shrimp shells by demineralization and deproteination processes. The extracted chitosan was used to modify the layered double hydroxide and used as an adsorbent for the removal of congo red from aqueous solutions. Composites were successfully synthesized using M2+/Al (M2+ = Zn, Mg, Ni) and chitosan (CH) and the samples obtained were characterized using XRD and FTIR. The X-ray diffraction (XRD) pattern appeared at the layered double hydroxide peak of 2? = 11.63°; 23.00°; 35.16°; and 61.59° and chitosan at 2? = 7.93° and 19.35. The composite appearing in the layered double hydroxide and chitosan indicated that the composite material has been successfully synthesized. The XRD diffraction patterns of Zn/Al-CH, Ni/Al-CH, and Mg/Al-CH showed low crystallinity. The Fourier Transform Infrared (FTIR) spectra verifying absorption spectrum showed the presence of two bands at 3448 cm-1, 1382cm-1 characteristic to both chitosan and LDH. Adsorption of Congo Red (CR) followed the pseudo-second-order and Langmuir isotherm models. The adsorption capacities of Zn/Al-CH, Ni/Al-CH, and Mg/Al-CH were 181.818 mg/g, 227.273 mg/g, and 344.828 mg/g, respectively. The layered double hydroxide-chitosan composite adsorption was endothermically characterized by positive enthalpy and entropy values. On the other hand, the adsorption spontaneously was characterized by a negative Gibbs free energy value. The composites in this study were formed from LDH modified from chitosan extracted from shrimp shells to form Zn/Al-CH, Ni/Al-CH, and Mg/Al-CH. The results of the characterization showed a number of characteristics that resembled the constituent materials in the form of LDH and chitosan. After being applied as an adsorbent to absorb Congo red dye, it then showed the most effective results using Mg/Al-CH adsorbent with an adsorption capacity of 344.828 mg/g.
The effects of protein crowders on small molecule drug diffusion

Crowded environments affect the pharmacokinetics of drug molecules. Here, we investigate how three macromolecular protein crowders, bovine serum albumin, hen egg-white lysozyme and myoglobin, influence the translational diffusion rates and interactions of four low molecular-weight drugs, fluorescein, doxorubicin, glycogen synthase kinase-3 inhibitor SB216763 and quinacrine. Using Fluorescence Recovery After Photo-bleaching in Line mode (Line FRAP), Brownian dynamics simulations and molecular docking, we find that the diffusive behavior of the small molecules is highly affected by self-aggregation, interactions with the proteins, and surface adhesion. Fluorescein diffusion is decreased by protein crowders due to their interactions. On the other hand, for doxorubicin, the presence of protein crowders increases diffusion by reducing surface interactions. SB216763 shows a third scenario, where BSA, but not myoglobin or lysozyme, reduces self-aggregation, resulting in faster diffusion. Quinacrine was the only small molecule whose diffusion was not affected by the presence of protein crowders. The mechanistic insights gained here into the effects of interactions of small molecules with proteins and surfaces on the translational diffusion of small molecules can assist in optimizing the design of compounds for higher mobility and lower occlusion in complex macromolecular environments.
Sensing and feedback of everyday activities to promote environmental behaviors

With population increases, global economic growth, and shifts in climate, the world is facing an unprecedented demand for resources that are becomingly increasingly scarce. Although often overlooked, our everyday activities such as commuting to work, showering, and clothes washing can have significant impact on the environment. The central problem addressed in this dissertation is not that humans negatively impact the environment—indeed, some amount of impact is unavoidable—but rather that we have insufficient means to monitor and understand this impact and to help change our behavior if we so desire. This dissertation focuses on creating new types of sensors to monitor and infer everyday human activity such as driving to work or taking a shower and taking this sensed information and feeding it back to the user in novel, engaging, and informative ways with the goal of increasing awareness and promoting environmentally responsible behavior. We refer to these sensing and feedback systems as eco-feedback technology. Our research takes advantage of a number of technology trends including the increasingly low cost of fast computation, advances in machine learning, and the prevalence and affordability of new types of display mediums (e.g., mobile phones) to design systems never before possible. This dissertation provides a theoretical perspective with which to guide the design of new eco-feedback systems as well as specific formative and technical contributions for eco-feedback in the domains of personal transportation any water usage. Key contributions include the invention of new low-cost sensing systems for monitoring and inferring transit routines and disaggregated water usage in the home along with eco-feedback visualizations that take advantage of this unprecedented data. The approaches, empirical findings and a design space for eco-feedback should be of interest to researchers working on eco-feedback in HCI, Ubicomp and environmental psychology, as well as to practitioners and designers tasked with constructing new types of ecofeedback systems and/or utility bills. More broadly, this dissertation also has implications for the construction of sensing and feedback technology in general, including domains such as persuasive technology, personal informatics, and health behavior change.
Efficiency of banks in Croatia

Croatian banking sector amounts to the majority of its financial sector. Therefore, it is necessary that Croatian banks operate efficiently. In the past two decades, the Croatian banking sector went through a consolidation process that steadily decreased the number of banks and allocated the majority of assets and market share to a few large banks. A simple definition of efficiency is cost minimization and profit maximization. Therefore, a bank is efficient when it strives to minimize its costs while maximizing its profits. This paper aims to estimate efficiency of Croatian banks using the DEA methodology within the period 2014-2019. In addition, the performance indicators (return on assets, return on equity) calculated for the same period aim at comparing performance indicators to efficiency results. The results indicate that larger banks are generally more efficient in operating on the frontier. And, in comparison to performance indicators, they achieve higher levels of returns on assets and equity. Furthermore, some small banks tend to be efficient, while the benefits of being a medium bank are inconclusive since the results reveal that some medium banks have below average efficiency. Overall, average efficiency improved in the observed period, which means that the consolidation process of financial institutions creates large and efficient banks.
A Deformation Method for Simulating Coating Degradation While Taking Mechanical Behavior into Account

Rust on the surface of metal is one of the most common examples of deterioration with various appearances. Because metal is usually coated with rust–preventive paint, rust may mostly appear after the coat is peeled off due to various factors. In this poster paper, we propose a visual simulation method for peeling off coating on the surface of metal. We allow for the mechanical behavior of the deteriorated coating to express realistically mottled patterns and paint bulges.
The Determinants of Turkey Household Catastrophic Health Expenditures: A Different Approach by Data Mining

This study aims to determine socioeconomic, demographic, and household characteristics that affect Turkish household catastrophic health expenditure (CHE). Data gathered by TurkStat belonging to 40,033 households for the years 2009-2012 were used in the analysis. In the study, CHE was defined as household health expenditures that were 40% (or above) greater than the capacity pay of the household. CHAID analysis was used to determine characteristics affecting Turkish household CHE. According to the CHAID analysis; income, presence of a sick/disabled person, residential area, household size, age, education level and gender of the household head, presence of individuals aged 65+, presence of people between the ages of zero and five, and access to health institutions have been observed to affect CHE, while the marital status, age, and employment status of the head of the household, or the household type do not affect CHE. The proportion of households exposed to CHE was 0.62%, and the proportion of households making out-of-pocket health expenditure (OOPHE) was 62.71%. It was especially observed that households with low income, with sick/disabled individuals, and those with difficult access to healthcare facilities are more likely to be exposed to CHE. None of the households exposed to CHE has supplementary health insurance.
The moderating effect of coworkers’ training participation on the influence of peer support in the transfer process

Purpose The impact of the number of coworkers participating in training on transfer outcomes has largely been overlooked. This paper aims to examine whether the number of coworkers participating in training interacts with peer support (PS) to influence training motivation and transfer. Design/methodology/approach Data were collected using a cross-sectional survey from a sample of 688 employees working in 14 midsize and large companies. All participants were recent trainees in various open skill (e.g. leadership) training programs. Moderated mediation was used to test the hypotheses. Findings Motivation to transfer (MTT) mediated the relationship between PS and perceived training transfer. When more coworkers participated in the training, PS had a stronger influence on trainee MTT. Practical implications Organizations should consider training coworker cohorts at the same time to influence MTT and training transfer. Generally, whole-team training programs could be used to boost training transfer outcomes, although it could potentially have a negative impact on transfer if PS is low. Originality/value To the best of the authors’ knowledge, this was the first study to demonstrate that the number of coworkers participating in training can moderate the effect of PS on MTT and training transfer.
CAN TOUR GUIDES PROTECT THE MARINE AND COASTAL ENVIRONMENT FROM MASS TOURISM IMPACTS: SITUATION OF ANDAMAN OCEAN, THAILAND, PRE-COVID-19

This article studies the role of Thai guides in protecting marine and coastal environment against impacts from international tourism, in the Andaman Ocean, Thailand. This is a qualitative study with in-depth interviews of 26 licensed guides that were selected by using purposive and snowball sampling. Quantitative data were analyzed with descriptive statistics, and qualitative data from in-depth interviews and field notes were analyzed with qualitative data analysis. The results show that behaviors affecting marine and coastal environment mostly involve chasing and catching beautiful fish and/or marine animals (76.9%), feeding fish (73.1%), and collecting beautiful stones, rare shells, and/or coral reefs (73.1%). Guides act in the roles of educator, psychologist, entertainer, ambassador, and servant, and protect against impacts to the extent that they can. While some impacts can be avoided, others remain out of scope and control of the guides.