Miracle-dz/UniFAR-FTUs huggingface dataset:
The intrinsic motivational factors of small and medium business growth : a study on the furniture manufacturing sector in the Ethekwini Metropolitan area

Small and Medium Enterprises (SMEs) in South Africa (SA) contribute significantly to job creation, wealth, social stability and economic growth. Growth is stimulated endogenously, through motivation, while employment is a natural consequence of a larger number of SMEs as well as the expansion of small businesses into larger enterprises. Although it has been noted that SMEs are viable avenues through which jobs can be created, relatively few studies have explicitly investigated the link between intrinsic motivation and small business growth; this study filled the gap by exploring the impact of intrinsic motivation on SMEs’ growth. This is particularly important when one considers that the failure rate of SMEs in SA is very high and it is of great importance to investigate the factors that can impact on the growth of SMEs. Why some individuals are more successful in business than others is a major question in SME research. The common view is that there are a numbers of factors involved related to the entrepreneur or business owners and the business and the business environment. Building on the concept of human capital and psychological theories, this study investigated the influence of intrinsic motivational factors in SME business growth in the eThekwini Metropolitan Area, in South Africa. The target population for this study was SMEs in the furniture manufacturing sector. Based on census study, the sample size of the study was 68 owners of SMEs in the eThekwini Metropolitan Area. Both primary and secondary data sources were used in the study. A quantitative research design was adopted to conduct this research. The survey method, by way of a self-administered questionnaire, was used to collect primary data. Various techniques such as descriptive statistics, Analysis of Variances (ANOVA), factor analysis, Chi-square, and regression analysis were used to analyse the data. The findings of this study revealed that intrinsic motivational factors of SME managers positively impact on the growth of SMEs in SA. The findings further revealed that creativity and necessary entrepreneurial skills influence implementation of sustainable growth of SMEs, and the entrepreneur’s success depends on the identification of crucial entrepreneurial skills for starting a business as well as for helping the business to survive and grow in the early years. Therefore, capacity building for SME owners to sustain business growth is recommended.
Finite Element Simulation of Solid–Liquid Interdiffusion Bonding Process: Understanding Process-Dependent Thermomechanical Stress

Solid–liquid interdiffusion (SLID) bonding finds a wide variety of potential applications toward die-attach, hermetic encapsulation of microelectromechanical systems (MEMS) devices and 3-D heterogeneous integration. Unlike soft soldering technique, the solidification of intermetallic compound (IMC) formation in SLID bonding occurs during the process isothermally, making it difficult to predict and mitigate the sources of process-dependent thermomechanical stresses. Literature reports two dominant factors for the built-in stress in SLID bonds: volume shrinkage (due to IMC formation) and coefficient of thermal expansion (CTE) mismatch. This work provides a detailed investigation of the Cu–Sn SLID bonding process by finite element (FE) simulations. Specifically, the FE simulation of the SLID bonding process is divided into three steps: ramp-up, hold-time, and ramp-down stages to understand the stresses formed due to each individual step. Plastic material properties for Cu as well as temperature-dependent material parameters for different entities are assigned. Process-dependent thermomechanical stresses formed during the ramp-up and hold-time steps (IMC formation) were found not to be significant. The hold-time step is governed by the reaction and diffusion kinetics, which determines the bond line quality including defects, such as voids. The ramp-down step is the dominant phase influencing the final stress formations in the bonds. The results show an average of >30% decrease in the stress levels in Cu3Sn layer (IMC) when the bonding temperature is brought down from 320 °C to 200 °C, thus demonstrating the importance of low-temperature SLID process.
The effect of adsorbing fine particulate matter( PM_(2.5)) by garden plants: A review.

Garden plants have a significant ability to remove air particulate matters( PM) and can effectively improve the urban environmental quality. The study of dust-retention effect of garden plants in China and other countries have made considerable achievements so far. However,most researches on the dust-retention effect of garden plants had been primarily concentrated on the total suspended particulates( TSP) and coarse particles( PM10),while few studies have focused on the effects of plants' adsorbing air fine particulate matter( PM2. 5). In order to provide references for further study in the PM2. 5adsorption capacity and adsorption mechanism of garden plants,we have concluded that the effects of garden plants' adsorption of PM2. 5according to different perspectives,i. e. ,the way of plant leaves absorbing air particles,the transfer process of PM2. 5upon leaves and the PM2. 5adsorption periodicity of plant leaves,the characteristics of adsorption of plant leaves to different diameter particles,the garden plants' ability and mechanism of adsorbing PM2. 5. Additionally,we propose that the key points and trends about garden plants absorbing PM2. 5based on three aspects: the quantitative analysis method of PM2. 5removal efficiency of garden plants,the measurement and evaluation of garden plants' adsorption abilities to PM2. 5,selection of plants with a higher ability to adsorb PM2. 5,the mechanism and influencing factors of garden plants' adsorption to PM2. 5. The present paper provides a basis for deepening the research on the mechanism of plant adsorbing PM2. 5and selection of garden plants with a high ability of adsorption to PM2. 5.
Exact Convergence Rates of the Neural Tangent Kernel in the Large Depth Limit

Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent in parameter space is related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Lee et al. (2019) built on this result by establishing that the output of a neural network trained using gradient descent can be approximated by a linear model when the network width is large. Indeed, under regularity conditions, the NTK converges to a time-independent kernel in the inﬁnite-width limit. This regime is often called the NTK regime. In parallel, recent works on signal propagation (Poole et al., 2016; Schoenholz et al., 2017; Hayou et al., 2019a) studied the impact of the initialization and the activation function on signal propagation in deep neural networks. In this paper, we connect these two theories by quantifying the impact of the initialization and the activation function on the NTK when the network depth becomes large. In particular, we provide a comprehensive analysis of the convergence rates of the NTK regime to the inﬁnite depth regime.
Vinogradov three prime theorem with Piatetski-Shapiro primes

We prove that for any c 1 , c 2 , c 3 ∈ (1 , 1 . 17), every suﬃciently large odd n can be represented as the sum of three primes n = p 1 + p 2 + p 3 with p i is of the form ⌊ n c i ⌋ for 1 ≤ i ≤ 3.
The computational complexity of knot genus in a fixed 3‐manifold

We show that the problem of deciding whether a knot in a fixed closed orientable 3‐dimensional manifold bounds a surface of genus at most g$g$ is in co‐NP. This answers a question of Agol, Hass and Thurston in 2002. Previously, this was known for rational homology 3‐spheres, by the work of the first author.
DO WE STILL NEED IMAGENET PRE-TRAINING IN REMOTE SENSING SCENE CLASSIFICATION?

Abstract. Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a de facto standard in remote sensing scene classification. Recently, the availability of larger high resolution remote sensing (HRRS) image datasets and progress in self-supervised learning have brought up the questions of whether supervised ImageNet pre-training is still necessary for remote sensing scene classification and would supervised pre-training on HRRS image datasets or self-supervised pre-training on ImageNet achieve better results on target remote sensing scene classification tasks. To answer these questions, in this paper we both train models from scratch and fine-tune supervised and self-supervised ImageNet models on several HRRS image datasets. We also evaluate the transferability of learned representations to HRRS scene classification tasks and show that self-supervised pre-training outperforms the supervised one, while the performance of HRRS pre-training is similar to self-supervised pre-training or slightly lower. Finally, we propose using an ImageNet pre-trained model combined with a second round of pre-training using in-domain HRRS images, i.e. domain-adaptive pre-training. The experimental results show that domain-adaptive pre-training results in models that achieve state-of-the-art results on HRRS scene classification benchmarks. The source code and pre-trained models are available at https://github.com/risojevicv/RSSC-transfer.
Vision-based driver behaviour analysis

With the ever-growing traffic density, the number of road accidents is anticipated to further increase. Finding solutions to reduce road accidents and to improve traffic safety has become a top-priority for many government agencies and automobile manufactures alike. It has become imperative to the development of Advance Driver Assistance Systems (ADAS) which is able to continuously monitor, not just the surrounding environment and vehicle state, but also driver behaviours. Dangerous driver behaviour including distraction and fatigue, has long been recognized as the main contributing factor in traffic accidents. This thesis mainly presents contributing research on vision based driver distraction and fatigue analysis and pedestrian gait identification, which can be summarised in four parts as follows. First, the driver distraction activities including operating the shift lever, talking on a cell phone, eating, and smoking, are explored to be recognised under the framework of human action recognition. Computer vision technologies including motion history image and the pyramid histogram of oriented gradients, are applied to extracting discriminate feature for recognition. Moreover, A hierarchal classification system which considers different sets of features at different levels, is designed to improve the performance than conventional "flat" classification. Second, to solve the effectiveness problem in poor illuminations and realistic road conditions and to improve the performance, a posture based driver distraction recognition system is extended, which applies convolutional neural network (CNN) to automatically learn and predict pre-defined driving postures. The main idea is to monitor driver arm patterns with discriminative information extracted to predict distracting driver postures. Third, supposing to analysis driver fatigue and distraction through driver’s eye, mouth and ear, a commercial deep learning facial landmark locating toolbox (Face++ Research Toolkit) is evaluated in localizing the region of driver’s eye, mouth and ear and is demonstrated robust performance under the effect of illumination variation and occlusion in real driving condition. Then, semantic features for recognising different statuses of eye, mouth and ear on image patches, are learned via CNNs, which requires minimal domain knowledge of the problem.
Formation of Low-Resistivity Germanosilicide Contacts to Phosporous Doped Silicon-Germanium Alloy Source/Drain Junctions for Nanoscale CMOS

Mo, Hongxiang. Formation of Low-Resistivity Germanosilicide Contacts to Phosphorus Doped Silicon-Germanium Alloy Source/Drain Junctions for Nanoscale CMOS. (Under the direction of Mehmet C. Öztűrk) Conventional source/drain junction and contact formation processes can not meet the stringent requirements of future nanoscale complimentary metal oxide silicon (CMOS) technologies. The selective Si1-xGex source/drain technology was proposed in this laboratory as an alternative to conventional junction and contact schemes. The technology is based on selective chemical vapor deposition of in-situ boron or phosphorus doped Si1-xGex alloys in source/drain areas. The fact that the dopant atoms occupy substitutional sites during growth make the high temperature activation anneals unnecessary virtually eliminating dopant diffusion to yield abrupt doping profiles. Furthermore, the smaller band gap of Si1-xGex results in a smaller Schottky barrier height, which can translate into significant reductions in contact resistivity due to the exponential dependence of contact resistivity on barrier height. This study is focused on formation of self-aligned germanosilicide contacts to phosphorous-doped Si1-xGex alloys. The experimental results obtained in this study indicate that self-aligned nickel germanosilicide (NiSi1-xGex) contacts can be formed on Si1-xGex layers at temperatures as low as 350oC. Contacts can yield a contact resistivity of 10 ohm-cm with no sign of germanosilicide induced leakage. However, above a threshold temperature determined by the Ge concentration in the alloy, the NiSi1-xGex/Si1-xGex interface begins to roughen, which affects the junction leakage. For phosphorus doped layers considered in this study, the threshold temperature was around 500oC, which is roughly 100oC higher than the threshold temperature for NiSi1-xGex contacts formed on boron doped Si1-xGex layers with a Ge percentage of ~ 50%. Nickel and zirconium germanosilicides were also considered as contact candidates but they were found to result in a contact resistivity near 10 ohmcm. FORMATION OF LOW-RESISTIVITY GERMANOSILICIDE CONTACTS TO PHOSPHORUS DOPED SILICON-GERMANIUM ALLOY SOURCE/DRAIN JUNCTIONS FOR NANOSCALE CMOS
The impact of pollution abatement investments on production technology: new insights from frontier analysis

This paper estimates the impact of pollution abatement investments on the production technology of firms by pursuing two new directions. First, we take advantage of recent econometric developments in productivity and efficiency analysis and compare the results obtained with two complementary approaches: parametric stochastic frontier analysis and conditional nonparametric frontier analysis. Second, we focus not only on the average effect but also on its heterogeneity across ﬁrms and over time and search for potential nonlinearities. We provide new results suggesting that such an effect is heterogeneous both within firms and over time and indicating that the effect of pollution abatement investments on the production process is not monotonic. These results have relevant implications both for modeling and for the purposes of advice on environmentally friendly policy.
Association of Frailty, Organ Support, and Long-Term Survival in Critically Ill Patients With COVID-19

OBJECTIVES: Few studies have explored the effect of frailty on the long-term survival of COVID-19 patients after ICU admission. Furthermore, the Clinical Frailty Scale (CFS) validity in critical care patients remains debated. We investigated the association between frailty and 6-month survival in critically ill COVID-19 patients. We also explored whether ICU resource utilization varied according to frailty status and examined the concurrent validity of the CFS in this setting. DESIGN: Ancillary study of a longitudinal prospective cohort. SETTING: University hospital in São Paulo. PATIENTS: Patients with severe COVID-19 admitted to ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We assessed baseline frailty using the CFS (1–9; frail ≥ 5) and used validated procedures to compute a Frailty Index (0–1; frail > 0.25). We used Cox models to estimate associations of frailty status with 6-month survival after ICU admission and area under the receiver operating characteristic curves (AUCs) to estimate CFS’s accuracy in identifying frailty according to Frailty Index. We included 1,028 patients (mean age, 66 yr; male, 61%). Overall, 224 (22%) patients were frail (CFS ≥ 5), and 608 (59%) died over the 6-month follow-up. Frailty was independently associated with lower 6-month survival and further stratified mortality in patients with similar age and Sequential Organ Failure Assessment scores. We additionally verified that the CFS was highly accurate in identifying frailty as defined by the Frailty Index (AUC, 0.91; 95% CI, 0.89–0.93). Although treatment modalities did not diverge according to frailty status, higher CFS scores were associated with withholding organ support due to refractory organ failure. CONCLUSIONS: One in five COVID-19 patients admitted to the ICU was frail. CFS scores greater than or equal to 5 were associated with lower long-term survival and decisions on withholding further escalation of invasive support for multiple organ failure in the ICU. Clinicians should consider frailty alongside sociodemographic and clinical measures to have a fuller picture of COVID-19 prognosis in critical care.
Constructing Multilingual CCG Treebanks from Universal Dependencies

This paper introduces an algorithm to con-001 vert Universal Dependencies (UD) treebanks 002 to Combinatory Categorial Grammar (CCG) 003 treebanks. As CCG encodes almost all gram-004 matical information into the lexicon, obtaining 005 a high quality CCG derivation from a depen-006 dency tree is a challenging task. Our algo-007 rithm contains four main steps: binarization 008 of dependency trees, functor/argument iden-009 tiﬁcation, category assignment through hand-010 crafted rules, and category inference for unas-011 signed constituents. To evaluate our converted 012 treebanks, we perform lexical, sentential, and 013 syntactic rule coverage analysis, as well as 014 CCG parsing experiments. We achieve over 015 80% conversion rate on 68 treebanks of 44 lan-016 guages, and over 90% lexical coverage on 81 017 treebanks of 52 languages. 018
Investment expectations by vulnerable European firms A difference-in-difference approach

The effect of the COVID shock on European economies has been severe and also unequal, with some firms being affected much more strongly than others. To improve the effectiveness of policy interventions, policymakers need to understand which types of vulnerable firms have been suddenly pushed into dire circumstances. We seek to fill this important gap in our knowledge by providing evidence from the EIBIS (European Investment Bank Investment Survey, 2016-2020) on how the COVID shock has affected the investment activity and investment-related framework conditions of vulnerable firms. While data on actual investment activity post-COVID is not yet available to us, we focus on investment expectations. We exploit the fact that the same questions relating to investment expectations have been asked in several previous survey waves, which enables a difference-in-differences approach to investigate how investment expectations might have suddenly changed, for vulnerable groups of firms, immediately after the onset of the COVID crisis. We focus on 4 groups of vulnerable firms: High-Growth Enterprises (HGEs), young and small firms, R&D
Does board gender diversity play significant role in determining firm performance?

The aim of this research is to analyse whether the board gender diversity plays vital part in explaining firm performance. The research is performed using static panel analysis using the sample of Croatian largest manufacturers that operated in the 2015 – 2019 period. In order to conduct such analysis several variables relating to board characteristics are employed in the research including proportion of women in the boardroom, dummy variable whether a female is present on the board, Blau index and size of the board. Furthermore, a set of firm-specific, industry oriented and macroeconomic variables are encompassed by the analysis as well comprising of size, liquidity, leverage, inventory management, capital intensity, market structure expressed with concentration ratio and GDP real growth rate. In order to obtain more robust results, three performance measures are introduced ROA, ROS and NPM. The findings reveal that size, liquidity, leverage, inventory management, capital intensity and concentration of the industry play an important role in determining firm profitability whereas we have not found support for greater gender diversity in the boardroom in terms of superior performance. Similar results are also provided with robustness check. The paper contributes to the scientific thought in a way that it adds to the scarce empirical evidence on gender diversity in manufacturing industry in general and particularly in Croatian context.
Linear Stability of Magneto Viscoelastic Walter ’ s B ′ Type with Heat and Mass Transfer

In this paper, the couple in ﬂ uence of axial magnetic ﬁ eld and the heat and mass transfer on two rotating cylinders of Walter ’ s B ′ viscoelastic ﬂ uids by using the potential ﬂ ow theory of viscoelastic ﬂ uid has been investigated. The normal mode method is used to compute the growth rate of disturbance. The in ﬂ uence of gravity is ignored at the interface, while the e ﬀ ect of surface tension is present in the analysis. The e ﬀ ect of the parameters on the stability is studied. It is found that the rotation of the inner cylinder induces stability. Furthermore, the viscoelastic ratio of ﬂ uids has a stabilizing in ﬂ uence. The heat and mass transfer, magnetic permeability ratio, and Ohnesorge number have a stabilized in ﬂ uence, while viscosity ratio of ﬂ uids, density ratio of ﬂ uids, and axial magnetic ﬁ eld have destabilizing in ﬂ uence on the considered system.
The Dark Side of Status at Work: Perceived Status Importance, Envy, and Interpersonal Deviance

Organizations differ in the extent to which they emphasize the importance of status, yet most extant research on the role of status at work has utilized a limited view of status as merely a matter of a person’s status rank. In contrast, we examine people’s perceptions of the extent to which having status matters in their work context and explore the behavioral implications of such perceptions. We offer a new construct, perceived status importance, defined as employees’ subjective assessment of the degree to which people within their organization are preoccupied with status. Relying on social comparison theory, we propose that higher perceived status importance triggers envy, which leads to interpersonal deviance. Across three studies, using multiwave survey and experimental designs, we find support for these relationships. We also find support for the mitigating influence of core self-evaluations on the perceived status importance—envy relationship. Implications are discussed.