Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic DNA extraction and amplification of Leishmania donovani using polymerase chain reaction (PCR) from archived, Giemsa- stained slides

In Sri Lanka, diagnosis of cutaneous leishmaniasis (CL) is usually based on clinical features and direct microscopy examinations. Polymerase chain reaction (PCR) for Leishmania is usually performed on tissue samples. In this study, we extracted DNA from archived Giemsa-stained slides which were prepared from cutaneous lesions. A total number of 85 Giemsa-stained slides fixed between 2008-2017 were selected. All the slides were examined using light microscopy and the number of amastigotes in positive smears was recorded. A nested PCR was carried out to amplify the 385 bp fragment of L. donovani kinetoplast mini-circle sequence. All 40 positive slides had only 1-2 amastigotes per slide. Of these, only 20% were PCR positive. Of the 45 negative slides, only one gave positive PCR result. Further studies are required to confirm the efficacy of PCR on Giemsa-stained smears in our setting.
Application of Attribute Analysis and Probabilistic Neural Network to predict Missing Log data

Petro-Physical parameters obtained from the well logging data is crucial in decision making for reserve estimation. Tool response is an important criteria in the acquisition of good quality data. Malfunctioning of logging tool leads to data loss while recording the data. To overcome this problem, a methodology has been developed to generate the missing log from the other nearby wells log data. In one well of CBM block located in Bokaro field the density log data could not be recorded due to tool malfunctioning. The developed methodology is followed to predict the missing data using seven nearby well data. The density data is selected as the target log with the training of external attributes. Single attributes analysis is carried out and number of regression equations are generated. The attribute with minimum error and maximum correlation relationship is selected as input to initiate the multi-attribute analysis. Multiattribute analysis are carried out by creating a list of transform by stepwise regression between a subset of the attributes and the target log values from single attribute. Proper gain and lag are applied by Probabilistic Neural Networks (PNNs) using the concept of distance in attribute space from known to unknown point. Attribute with minimum value in training and validation error is selected to obtain the relationship between the predicted and observed density data to generate the final data. The predicted density data is validated with nearby well data. The result obtained using multi attribute and PNN has correlation 149.2 and error 0.91 compared to single attribute correlation 0.80 and error 214.5. This methodology of artificial intelligence helps to predict the missing log with accuracy and to obtain sub-surface information.
Practical suggestions for addressing common deficiencies

Four issues that can affect statistical conclusions from mediation analysis are presented here: The implications of omitting mediators; not conducting reverse mediation analysis; using inappropriate measures; and not considering a wider array of experiment-based methods. Suggestions for addressing each of these are advanced. Previous issues of AMJ, JMR and JCR are then examined to gauge the extent to which these suggestions were used. Less than half of the published papers inspected (44.4% of the total) endeavoured to address at least three of the four issues raised above. AMJ authors will realize higher statistical as well as theoretical rigor if they consider these suggestions.
Paper Title (use style: paper title)

This paper presents the mixed-mode effect on motor common mode current (CMC) in an adjustable speed drive (ASD). It is expected that the motor CMC is lower than inverter output CMC. However, the measurement result of the ASD shows that the AC motor input CMC is higher than the inverter output CMC in a frequency range. By using a mixed-mode (MM) 6-port networks, it is proven that the AC motor input CMC is higher due to an MM CMC which is generated from differential mode current.
Sliding Mode Based Virtual Sensor for Feedback Linearization in Delta Robots with Unknown Dynamic Model

Delta robots are of nonlinear dynamics, therefore applying linear controllers to satisfy the required trajectory tracking is a challenge which becomes more significant when the robot model is unknown. This paper proposes a stable virtual sensor to be used for feedback linearization for delta robots with unknown dynamic model. The sensor design is based on the second order sliding mode observer. This sensor considers that only the inertia of the robot upper links with the motor inertia are known. As a measurement, only the active joints position angles are measured. The stability is proven using Lyapunov theory and explicit relations for the design parameters are obtained. To overcome the effect of the transient response of this sensor, an adaptive tuning gain with maximum value of unity is introduced in the feedback to improve the compensation performance. The results validate this sensor and prove its dramatic performance improvement in trajectory tracking when combined with the PD controller. Index Term-Delta robot, sliding mode observer, disturbance estimation, adaptive control, virtual sensor.
Effect of Corruption on Economic Growth in Eastern Africa: Disaggregated Approach

High corruption phenomenon is a common problem faced by several developing countries. Weak bureaucracy, weak law and justice enforcement are assumed to be the causes of the corruption. However, the available studies majority are of single countries that has focused mainly on the relationship between overall corruption and economic growth and failing to provide the specific relationship between corruption and economic growth in Eastern African countries. The main objective of this study was to investigate the effect of corruption on economic growth in Eastern Africa. This study was modeled using Public choice theory and corruption. Hausman Specification tests was conducted to assess whether to use the fixed effects or random effect panel estimation. The Random effect model results showed that corruption had a negative and statistically significant relationship with economic growth, fixed effect model results on public resource equity had a negative and a statistically insignificant relationship with economic growth whereas the random effect model results on public sector management had a negative and statistically significant relationship with economic growth. The study concluded that Corruption decreases the growth rate of per capita income directly by decreasing the productivity of existing resources and indirectly through reduced investments and also discourages investment savings and culture of hard work among the Eastern African countries. The study recommended that there should be formulation of policies in Eastern Africa that aims to help and improve the quality of the public sector management and minimize corruption activities in the public sectors.
Enhancement of sorghum (Sorghum bicolor (L.) Moench) growth performance by plant-growth promoting endophytes

A study was conducted to evaluate the endophytes effect on sorghum growth promotion under drought condition (PEG imposed). Sorghum root associated endophytes were isolated and characterized through partial 16S rRNA gene sequencing and validating the genera of isolates by BLAST analysis. Bacterial strains namely, Acinetobacter pittii, Bacillus sp. and Pseudacidovorax intermedius were inoculated with sorghum seeds TNS 661 and TKSV 1311. Results exhibited that endophytes had many positive effects on sorghum seedling in laboratory level experiments. Moreover, selected endophytes have the ability to enhance the growth potential such as germination percentage and germination rate index (GI) when plants were subjected to drought stress. Sorghum seeds treated with Bacillus sp. SR2 recorded an increased root length by 13% and Acinetobacter pittii VR2 and Bacillus sp. SR2 treatment significantly increased the shoot length of TNS 661 seedling as compared to uninoculated under drought condition. Similarly, TKSV 1311 seeds treated with Pseudacidovorax intermedius SR3 significantly recorded increased root: shoot ratio and biomass in terms of shoot fresh weight and total dry matter production of TNS 661. The results of this study concluded that when root endophytic bacteria interact with sorghum seedlings can subsidize to plant growth promotion as well as induced stress tolerance in sorghum.
Habituation of character values in junior high school students

The purpose of this study was to analyse the habituation of character education in students by paying attention to factors influencing them to deviate from character values. The study employed a mixed-method approach. The qualitative approach involved 28 participants (teachers and students). While in the quantitative study, 65 students were purposively selected as respondents. Qualitative data were analysed through some stages, including data presentation, data reduction, conclusion and verification, while the quantitative data was processed using linear regression statistics assisted by Statistical Package for the Social Sciences and STATA software to analyse the influences of variables identified from qualitative data collection and to measure how influential independent variables on dependent variables are. The findings show that the factor leading to the deviation of character values at school is the diversity of cultural backgrounds applied where students come from. Findings of the qualitative investigation show seven aspects influencing the character value deviation, including parents’ attention, socialisation, media, consumerism behaviour, self-actualisation, economic status and family harmony. While the quantitative stage found that among seven investigated variables, four of them were significantly influential (parents’ attention, socialisation, media and self-actualisation) and three others were not significantly influential (consumerism, economic status and family harmony) on the deviation of character values performed by students Keywords: Character value deviation, educational value, habituation.
Accessibility of Financial Inclusion among Women in Akoko Areas of Ondo State, Nigeria

The study is an examination of how accessible financial services are, especially the microfinance for women in Akoko areas of Ondo State. The target population was 400 women in the study area, which includes all the four local government areas of Akoko area. The researchers used both the descriptive and logit regression techniques. The descriptive statistics revealed that majority of the respondents (31%) received lower monthly income below N 20,000. Also, 36% of the respondents had access to deposit money bank, microfinances (41%), ATMs (13%), POSs (7%), mobile agent (2%) and 1% for other forms of financial services. The logit regression showed that 33.6% of the respondents were likely to experience decline in poverty level compared to people that are financially excluded in the areas, 14.9% women are likely to be poorer than their male counterparts when they are financially excluded and empowerment of women reduced poverty level by 25.0%. The researchers concluded that women in the four local government areas of Akoko in Ondo state have access to financial services through microfinance, which has reduced the severity of poverty among them. And it was, therefore, recommended that Central Bank of Nigeria should deepen financial inclusion through the introduction of capital encouragement funds with little interest rate that microfinance banks would be the ones to introduce it to their customers. Also, microfinance banks should embrace the aspect of non-financial services.
Use of Tilapia Green Water to Eliminate Vibrio parahaemolyticus and Vibrio cholerae in Cultured Green Mussel, Perna viridis (Linnaeus 1758)

Vibrio parahaemolyticus and Vibrio cholerae are two widely known pathogens associated with bivalves that cause food-borne diseases including gastroenteritis and cholera. Aquaculture of the green mussel Perna viridis (Linnaeus 1758) is an important source of income for coastal and island communities in the Philippines, but issues regarding Vibrio bacterial load in this bivalve limits its market potential. Various depuration methods have been tested with ultraviolet light (UVL) method found to be highly efficient, widely practised but reported to be ineffective in removing pathogenic Vibrio species. The present study evaluates the potential of a biological approach using tilapia green water (TGW) in eliminating the pathogenic Vibrio species associated with the green mussel. Results show that the TGW can significantly decrease the level of V. parahaemolyticus and V. cholerae down to the safe level at 10 CFU.g tissue after 76 h of exposure. Depuration of pathogenic Vibrios with TGW was found comparable to the treatment using the standard UVL depuration method. Overall, the present findings provide evidence that depuration using TGW can effectively decrease bacterial load of V. parahaemolyticus and V. cholerae in the green shell P. viridis.
DESI Mock Challenge: Constructing DESI galaxy catalogues based on FastPM simulations

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a Halo Occupation Distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 Emission Line Galaxy sample down to non-linear scales, i.e. k < 0.5 h/Mpc or s > 10 Mpc/h. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate [e.g. power spectrum] on the covariance matrix of the Fourier counterpart [e.g. correlation function]. We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between two to ten per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.
Emotional Data Mining and Machine Learning in College Students’ Psychological Cognitive Education

Emotion analysis method in AI technology can quickly and effectively analyze the emotions in text, and it is an important way to identify the emotional information of students’ feedback text. In this paper, the author analyzes the application of emotional data mining and machine learning in college students’ psychological cognitive education. Result shows that there is a relationship between Academic Emotion and students’ learning achievement. Using artiﬁcial intelligence technology to discover Academic Emotion in educational texts can more easily understand students’ learning behavior and solve students’ psychological problems. Through the use of computer access and mobile terminal access to the two access methods, psychological test system provides a variety of choices for teachers and students. Therefore, applying the mobile terminal to the psychological testing work has exerted the portability advantage of the mobile terminal, so that the psychological testing work can be performed anytime and anywhere, which not only facilitates the teachers and students, but also improves the work efﬁciency.
Finite Element Analysis of Titanium Foam in Mechanical Response for Dental Application

Metals with certain porosity are a new class of materials with extremely low density and a unique combination of excellent mechanical, thermal, electrical, and biocompatible properties. Absorption of impact and shock energy, dust and fluid filtration, construction materials, and most importantly, biocompatible implants are all potential applications for metallic foams. An orthopaedic implant made of metallic foam can provide an open-cell structure that allows for the ingrowth of new bone tissue and the transport of body fluids. Due to its strong biocompatibility and stable fixation between the implant and human bone, titanium foam has recently received much attention as an implant material. Finite element modelling is a suitable method to obtain an efficiently designed implant. Accurate finite element analyses depend on the precision before implementation as well as the functionality of the material properties employed. Since the mechanical performances of titanium foam and solid titanium are different, a constitutive model for porous metal is required. The model of Deshpande and Fleck in the finite element analysis software ABAQUS is used to describe the compressive and flexural deformation properties of titanium foam with 63.5% porosity. The finite element simulation results were compared with the practical mechanical properties obtained by compression testing of the foam. Finally, the material modelling was used to investigate the stress distributions on the dental implant system.
Information sampling for contingency planning

From navigation in unfamiliar environments to career planning, people typically ﬁrst sample information before committing to a plan. However, most studies ﬁnd that people adopt myopic strategies when sampling information. Here we challenge those ﬁndings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path ﬁnding problem under uncertainty of bridge closures. Participants ( n = 109) were allowed to sample information on bridge statuses prior to committing to a path. We developed a computational model in which the agent samples information based on the cost of switching to a contingency plan. We ﬁnd that this model ﬁts human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling.
THE INFLUENCE OF DISRUPTIVE INNOVATION, ORGANISATIONAL CAPABILITIES AND PEOPLE ON ORGANISATIONAL PERFORMANCE AMONG MANUFACTURING BASED COMPANIES IN MALAYSIA

E-mail: rajen789@usm.my Received March 2021; accepted May 2021 Abstract Organisational performance has become the main issue as organisations are struggling for business sustainability. Organisations have not achieved the expected organisational performance in the dynamic environment. This research aims to examine the influence of disruptive innovation, organisational capabilities, and people on organisational performance. A quantitative study was designed, and one hundred and twenty-one (121) data among organisations involved in the manufacturing of electrical and electronics (E&E) products in Malaysia were gathered through a structured questionnaire using stratified sampling techniques. Data gathered were analysed with SPSS software version 24 and Smart Partial Least Squares-Structural Equation Modeling (PLS-SEM) version 3.2. 7. The result demonstrated that disruptive innovation, organisational capabilities, and people significantly influence organisational performance. From the academic aspect, the conceptual framework articulated based on the research gap, the I-TOP Strategic Agility model and dynamic capabilities theory provides more insights into optimising organisational performance. In addition, this study measured organisational performance from the perspective of happiness that comprises of employee happiness, customer happiness, and shareholder happiness. Practitioners could utilise this study's findings to formulate a more effective strategy to optimise their organisational performance that is critical for business sustainability.
Interaction of Amino Acids and Single-Wall Carbon Nanotubes

In this article, we investigated the interactions between oxidized single-wall carbon nanotubes and three amino acids. A simple and environmental benign method to realize solubility of oxidized single-wall carbon nanotubes (OSWNT) in water was described. The amino acids used in this study include l-glycine (Gly), l-lysine (Lys), and l-phenylalanine (Phe). The OSWNT became soluble in water under ambient conditions and formed a stable suspension when amino acids (AA) were adsorbed on it. The interactions between OSWNT and three AA were characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), Raman spectroscopy, Fourier transform infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA). The results indicate that there is an increasing in the diameter of OSWNT after AA adsorption. The OSWNT with different diameters were separated as a result of AA adsorption. The smaller the diameter of OSWNT, the more the AA adsorption amount is. The adsorbed amount of different.
CASA: A Bridge Between Gradient of Policy Improvement and Policy Evaluation

This paper introduces a novel design of model-free reinforcement learning, CASA, C ritic AS an A ctor. CASA follows the actor-critic framework that estimates state-value, state-action-value and policy simultaneously. We prove that CASA integrates a consistent path for the policy evaluation and the policy improvement, which completely eliminates the gradient conﬂict between the policy improvement and the policy evaluation. The policy evaluation is equivalent to a compensational policy improvement, which alleviates the function approximation error, and is also equivalent to an entropy-regularized policy improvement, which prevents the policy from being trapped into a suboptimal solution. Building on this design, an expectation-correct Doubly Robust Trace is introduced to learn state-value and state-action-value, and the convergence is guaranteed. Our experiments show that the design achieves State-Of-The-Art on Arcade Learning Environment.
Optimal Algorithms for Geometric Centers and Depth

$\renewcommand{\Re}{\mathbb{R}}$ We develop a general randomized technique for solving"implic it"linear programming problems, where the collection of constraints are defined implicitly by an underlying ground set of elements. In many cases, the structure of the implicitly defined constraints can be exploited in order to obtain efficient linear program solvers. We apply this technique to obtain near-optimal algorithms for a variety of fundamental problems in geometry. For a given point set $P$ of size $n$ in $\Re^d$, we develop algorithms for computing geometric centers of a point set, including the centerpoint and the Tukey median, and several other more involved measures of centrality. For $d=2$, the new algorithms run in $O(n\log n)$ expected time, which is optimal, and for higher constant $d>2$, the expected time bound is within one logarithmic factor of $O(n^{d-1})$, which is also likely near optimal for some of the problems.