Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Training Effectiveness of One Health Training Programs among Academics

Formal educational education systems do not expose medical and veterinary academics to the One Health approach to infectious disease management. In order to increase their knowledge and exposure on One Health, Malaysian One Health University Network (MyOHUN) has been conducting number of One Health training programs specifically for medical, veterinary, ecosystem, and health stream academics. However, the effectiveness of the One Health training programmes is unfamiliar. Therefore, a qualitative was conducted to explore the training effectiveness by evaluating the academics' reactions, learning experience, and behavior changes among the academics as a result of the One Health training programs based on Kirkpatrick’s four -level evaluation model. All participants were purposively selected which consist of academics who had joined the signature training programs conducted by MyOHUN between 2015 and 2019. The data were collected through in depth semi-structured interviews and supported with participant observation and field notes. The data were further analysed through coding, categorizing and the development of the main theme to answer research questions. The findings broadly suggest that three aspects has emerged which includes reactions to the overall training and the structure, learning eexperiences, as well as behavior changes that have positively influenced the training effectiveness. The findings of this study will help MyOHUN to plan and execute the preparation needed in order to increase the effectiveness of One Health training to the all generation in general.
Climate change and firm productivity: the case of drought

ABSTRACT This paper evaluates the causal effects of climate change on firm productivity from the perspective of drought, which is a complicated climate phenomenon. Based on the panel data of agriculture-related firms in China, we present strong evidence that drought significantly reduces firm’ total factor productivity. Mechanism analysis shows that the negative effect is rooting from higher costs of equity capital, higher operation costs, and lower investment efficiency. Finally, we find that government subsidy and policy stimulating corporate innovation can help firms adapt to climate change.
Tailoring of TiAl6V4 Surface Nanostructure for Enhanced In Vitro Osteoblast Response via Gas/Solid (Non-Line-of-Sight) Oxidation/Reduction Reactions

An aging global population is accelerating the need for better, longer-lasting orthopaedic and dental implants. Additive manufacturing can provide patient-specific, titanium-alloy-based implants with tailored, three-dimensional, bone-like architecture. Studies using two-dimensional substrates have demonstrated that osteoblastic differentiation of bone marrow stromal cells (MSCs) is enhanced on surfaces possessing hierarchical macro/micro/nano-scale roughness that mimics the topography of osteoclast resorption pits on the bone surface. Conventional machined implants with these surfaces exhibit successful osseointegration, but the complex architectures produced by 3D printing make consistent nanoscale surface texturing difficult to achieve, and current line-of-sight methods used to roughen titanium alloy surfaces cannot reach all internal surfaces. Here, we demonstrate a new, non-line-of-sight, gas/solid-reaction-based process capable of generating well-controlled nanotopographies on all open (gas-exposed) surfaces of titanium alloy implants. Dense 3D-printed titanium-aluminum-vanadium (TiAl6V4) substrates were used to evaluate the evolution of surface nanostructure for development of this process. Substrates were either polished to be smooth (for easier evaluation of surface nanostructure evolution) or grit-blasted and acid-etched to present a microrough biomimetic topography. An ultrathin (90 ± 16 nm) conformal, titania-based surface layer was first formed by thermal oxidation (600 °C, 6 h, air). A calciothermic reduction (CaR) reaction (700 °C, 1 h) was then used to convert the surface titania (TiO2) into thin layers of calcia (CaO, 77 ± 16 nm) and titanium (Ti, 51 ± 20 nm). Selective dissolution of the CaO layer (3 M acetic acid, 40 min) then yielded a thin nanoporous/nanorough Ti-based surface layer. The changes in surface nanostructure/chemistry after each step were confirmed by scanning and transmission electron microscopies with energy-dispersive X-ray analysis, X-ray diffraction, selected area electron diffraction, atomic force microscopy, and mass change analyses. In vitro studies indicated that human MSCs on CaR-modified microrough surfaces exhibited increased protein expression associated with osteoblast differentiation and promoted osteogenesis compared to unmodified microrough surfaces (increases of 387% in osteopontin, 210% in osteocalcin, 282% in bone morphogenic protein 2, 150% in bone morphogenic protein 4, 265% in osteoprotegerin, and 191% in vascular endothelial growth factor). This work suggests that this CaR-based technique can provide biomimetic topography on all biologically facing surfaces of complex, porous, additively manufactured TiAl6V4 implants.
What robustly determines FDI in emerging markets and developing countries? A sensitivity analysis

What determines Foreign Direct Investment (FDI) inflows remains a primary concern of economists and policy makers; yet the uncertainty surrounding FDI theories and empirical approaches has created ambiguity regarding the determinants of FDI. This paper applies Leamer’s (1983, 1985) traditional Extreme Bounds Analysis (EBA) as well as the Sala-I-Martin modified form of EBA to identify the robust determinants of FDI using cross-sectional data (averaged for the period of 1985-2016) covering 103 emerging and developing countries. We consider 33 potential economic, political and institutional determinants. To address concerns about multicollinearity, the VIF restriction specifies that only estimates with VIF below five are reported. The results show that all variables of interest are fragile determinants of FDI when using traditional EBA. Yet, when applying the Sala-i-Martin EBA, nine robust variables are revealed. Some policy implications are discussed. Highlights What determines Foreign Direct Investment (FDI) inflows remains a primary concern of economists and policy makers; yet the uncertainty surrounding FDI theories and empirical approaches has created ambiguity regarding the determinants of FDI. It is a common practice in the fields of economics and finance to report the most ‘appealing’ or convenient regression or regressions after an extensive search and data mining, given that the ‘true’ model is unknown. This paper applies traditional Extreme Bounds Analysis (EBA) as well as the Sala-i-Martin modified form of EBA to identify the robust determinants of FDI. We demonstrate that what is important is not the significance or otherwise according to one regression equation but rather the robustness or fragility of the variables in the sense that the sign and significance do not change over a large number of regressions selected according to a predetermined procedure. Policy makers would be able to understand the importance of the major determinants of FDI mentioned in the paper and take steps to formulate policies that encourage and attract more FDI. Such determinants could include market size, making regulations more international trade friendly and investing in the nation’s human capital. Further, research and development facilities could be developed to provide a basis for technological advancements which would attract more FDI inflows.
Real-Time Nondestructive Evaluation of Additive Manufacturing using a Laser Vibrometer and Shock Tube

Additive manufacturing (AM) parts retain a certain degree of individuality and could suffer from a combination of different defect types, and is therefore the non-destructive evaluation on AM parts remains a challenging task. Engineering non-contact and non-destructive real-time inspection and in-situ quality assurance of AM parts would be a net improvement compared to current quality control methods that are conducted post-production. Here, the authors propose to combine the use of a laser vibrometer with a compression-driven shock tube to assess the quality of AM parts through the evaluation of the vibration spectra of the part. An AM of a cylindrical part was selected for the study, along with different defect types and sizes. These defects include internal voids of different sizes at different locations, local changes in thickness (infill), and local changes in melting temperatures. A numerical model was created and validated using experimental data to conduct model assisted probability of detection (MAPOD). Results were analyzed by evaluating correlation matrices between different models. Results showed that vibration spectra induced by a shock wave were sensitive to different types and sizes of defects under the studied geometry. The defect index yielded an approximately linear relationship with respect to defect void severity. MAPOD curve studies revealed a minimum detectable void defect 0.039% of the AM parts volume.
On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, $k$ -fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.
A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal’s angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low.
Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts

Abstract Employing both the mean-variance framework and the common portfolio risk-optimization, this study adds to the investment research by examining how ideal holdings for emerging and frontier markets (EFM) of the four global regions (Asian, Europe, and Commonwealth of Independent States (Eastern + Central), Africa, and Latin America and the Caribbean) differ from the benchmark (MSCI EFM (World) index) weights. MSCI previously stood for Morgan Stanley Capital International. The optimal weights were computed for the MSCI Asia, MSCI Europe, MSCI Africa, MSCI Latin America, and MSCI Caribbean for four unique schemes: historical variance (HV), global-minimum variance (GMV), μ-fixed minimum variance (MV), and market timing (MT). The portfolio study shows that the market timing (MT) portfolio performs well, with only the fixed minimum variance (MV) portfolio outperforming it overall. In terms of steady and positive returns in 2019 and beyond, the MT portfolio emerges as the best. Also, the volatility forecasts generated from multivariate time series models can be successfully converted into higher portfolio returns using quantitative investment approaches if the right balance of volatility modelling and portfolio strategy is determined. Given the well-functioning MT portfolio, this study offers some implications for scholars and funding managers based on the risk-return trade-off.
Cold Hardening in Citrus Seedlings as Related to Artificial Hardening Conditions1

Seedlings of 18 citrus types were exposed to artificial hardening conditions. ‘Nagami’ kumquat, false hybrid satsuma and ‘Cleopatra’ mandarins were the most cold hardy and ‘Lisbon’ lemon, ‘Mexican’ lime, Rangpur’ lime, and ‘Calamondin’ hybrid kumquat the least cold hardy. Three mandarins, 1 tangelo, 3 oranges, and 4 grapefruit types were intermediate in hardiness. Generally, the most hardy types hardened some at 70° day and 50°F night temperatures, and 60°/40° as well as at lower temperatures while the least hardy types hardened primarily at 50°/30° and 45°/26°. Sugar accumulation was associated with hardening.
Nanostructuring Effect of Nano-CeO2 Particles Reinforcing Cobalt Matrix during Electrocodeposition Process

The electrodeposition method was used to obtain nanostructured layers of Co/nano-CeO2 on 304L stainless steel, from a cobalt electrolyte in which different concentrations of CeO2 nanoparticles (0, 10, 20, and 30 g/L) were dispersed. The electrodeposition was performed at room temperature using three current densities (23, 48, and 72 mA cm−2), and the time was kept constant at 90 min. The influence of current densities and nanoparticle concentrations on the characteristics of the obtained nanostructured layers is also discussed. An X-ray diffractometer (XRD) was used to investigate the phase structure and cobalt crystallite size of the nanostructured layers, and a contact angle (sessile drop method) was used to assess the wettability of the electrodeposited layers. The roughness of the surfaces was also studied. The results show that the nanostructured layers became more hydrophilic with increasing nanoparticle concentration and increasing current density. In the case of pure cobalt deposits, an increase in the current density led to an increase in the size of the cobalt crystallites in the electrodeposited layer, while for the Co/nano-CeO2 nanostructured layers, the size of the crystallites decreased with increasing current density. This confirms the nanostructuring effect of nano-CeO2 electrocodeposited with cobalt.
Robust Sparse Weighted Classification for Crowdsourcing

Data collected from nature is usually unlabeled, and it is difficult to be used directly. This issue is well addressed by crowdsourcing, which provides a reasonable way for effectively using these unlabeled data. Generally, workers in crowdsourcing tasks are not professionals, so it is hard to obtain high-quality labels. To address this issue, a robust sparse weighted classification algorithm is proposed, which try to adjust the samples that are not correctly classified in the original lables as much as possible. Specifically, we evalute the ability of different workers(indicator weight matrix) to accurately label different samples by fitting the real data matrix to its weighted reconstruction matrix. And then, <inline-formula><tex-math notation="LaTeX">$ l_{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>l</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq1-3201955.gif"/></alternatives></inline-formula>-norm and worker labeling ability similarity matrix are added, and negative effects of some bad workers are eliminated through the row sparsity property of <inline-formula><tex-math notation="LaTeX">$ l_{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>l</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq2-3201955.gif"/></alternatives></inline-formula>-norm. Finally, the optimal indicator weight matrix is obtained by optimizing the two matrices in the objective function simultaneously. Therefore, the obtained optimal indicator weight matrix takes the similarity of worker labeling ability into consideration, and infers all the predicted labels. The results on synthetic and real data sets demonstrate that our algorithm is superior to other state-of-the-art methods.
DREAMS: status update and assembly/alignment challenges

The Research School of Astronomy and Astrophysics at the Australian National University is currently building the Dynamic REd All-sky Monitoring Survey (DREAMS). DREAMS is a 0.5m wide-field near-infrared survey telescope that will be located at Siding Spring Observatory, Australia. DREAMS will utilise Indium Gallium Arsenide (InGaAs) detectors and a 3.71 sq. degree field-of-view to survey the visible sky to MAB=17.8 in the J-band every 4-7 days. Due to the noise properties of the InGaAs detectors, DREAMS is required to convert a F/6 telescope beam to an F/2 detector beam. Combining this with the wide-field nature of the telescope, DREAMS requires a large number of additional optical and mechanical elements with relatively tight tolerances to meet the performance requirements. This paper discusses the current status of the assembly and alignment of DREAMS, along with the on-going alignment procedures, techniques, and methods used to meet these survey requirements.
Development and Modeling of a Biomimetic Punting Unmanned Underwater Vehicle

This thesis details the development of a novel propulsion system for unmanned underwater vehicles (UUV’s) inspired by the punting locomotion of the ‘Little Skate’. The hybrid legged, gliding and punting gait is a potential enabling technology for UUV access to complex littoral environments. Design, development and initial trials with the prototype vehicle are presented. A combination of video motion capture and on-board inertial sensing is validated and used for preliminary analysis of a limited set of punting gaits. The results indicate the importance of matching vehicle morphology and kinematics to achieve effective locomotion and that the prototype is viable to be used as a validation tool for a general punting dynamic model that is in development. A practical method for modeling the vehicle is also presented with results comparing the model to experimental trials.
Functional Characterization of MaZIP4, a Gene Regulating Copper Stress Tolerance in Mulberry (Morus atropurpurea R.)

ZIP4 (zinc transporter 4) plays important roles in transporting Cu2+ ions in plants, which may contribute to the maintenance of plant metal homeostasis in growth, plant development and normal physiological metabolism. However, ZIP4 transporters have not been described in mulberry and the exact function of ZIP4 transporters in regulating the homeostasis of Cu in mulberry remains unclear. In this study, a new ZIP4 gene (MaZIP4) was isolated and cloned from Morus atropurpurea R. Phylogenetic analysis of amino sequences suggested that the amino-acid sequence of the MaZIP4 protein shows high homology with other ZIP4 proteins of Morus notabilis, Trema orientale, Ziziphus jujube and Cannabis sativa. In addition, a MaZIP4 silenced line was successfully constructed using virus-induced gene silencing (VIGS). The analysis of MaZIP4 expression by quantitative real-time PCR in mulberry showed that the level of MaZIP4 expression increased with increasing Cu concentration until the Cu concentration reached 800 ppm. Relative to the blank (WT) and the negative controls, malondialdehyde (MDA) levels increased significantly and rose with increasing Cu concentration in the MaZIP4 silenced line, whereas the soluble protein and proline content, superoxide dismutase (SOD) and peroxidase (POD) activities of these transgenic plants were lower. These results indicated that MaZIP4 may play an important role in the resistance of mulberry to Cu stress.
EFL Teachers’ Lived Experiences on Using Grading System

The Grading System, first used by Yale University in the 18th century for the purpose of descriptive evaluation of the students’ achievement, has now become popular worldwide. This paper explores and analyzes the Grade nine and ten English teachers’ lived experiences of using Grading System (GS) in English language teaching and testing in the institutional schools of Kathmandu. For this, we collected the required information taking phenomenological interview with the five purposively selected teachers of English from five respective institutional schools of Nagarjun Municipality in Kathmandu. The collected information was thematically analyzed using ATLAS.ti 9. The lived experiences of the concerned teachers showed the praxis of arbitrary and systematic use of GS in the researched context. The results reveal that the arbitrary use of GS has negative impact on the students’ classroom participation and teaching learning environment while the systematic use of GS has motivated the students towards learning, and thereby enhanced the teachers’ professional development. The study concludes that there is need for using systematic GS making it even more appropriate and scientific for the betterment of overall teaching and learning of English in particular, and thereby the system of secondary level education as a whole.
IPCRF: An End-to-End Indian Paper Currency Recognition Framework for Blind and Visually Impaired People

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). The problem is more severe in developing countries such as India, where there is still a lack of robust currency recognition systems. BVIP primarily relies on size variations and patterns such as intaglio printings for recognizing the underlying currency denominations. Most of the current Indian legal tenders resemble in size, thus making the identification process more strenuous. Also, the engraved patterns are not as distinctive as BVIP standards, and they fade over time. For an automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This paper ventures to present an end-to-end and robust framework for assisting BVIP in recognizing the Indian paper currency denomination. This paper presents a lightweight network, IPCRNet, useful in a resource-constrained environment such as low/medium level smartphones. The proposed network is based on Dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Additionally, we congregated one of the most diversified Indian paper currency image dataset with more than 50,000 images belonging to almost all denominations in circulation. A customized and publically available android application, “Roshni-Currency recognizer”, has also been introduced. The experimental results on multiple datasets demonstrate the superiority of the proposed model. IPCRNet improves the classification accuracy by more than 2% on the proposed dataset compared to the state-of-the-art networks.