Miracle-dz/UniFAR-FTUs huggingface dataset:
GENDER EQUALITY FOR ACHIEVING SUSTAINABLE DEVELOPMENT GOAL ONE (NO POVERTY) IN SOUTH AFRICAN MUNICIPALITIES

Sustainable Development Goal One demands the reduction of poverty, hence holds significance in the South African context where a high unemployment rate still prevails, hampering socio-economic development of the country as a whole. In addition, poverty is linked to gender inequality with female counterparts occupying fewer jobs in the South African labour market than men. This situation is even more dire at grassroots levels, where a lack of education and civic awareness, inadequate gender-based poverty alleviation policies, and inappropriate gender-based participation in pro-poor growth strategies, contribute towards the weakening of women’s empowerment. This article hypothesises that gender equality in poverty reduction strategies could have a positive impact in the realisation of Sustainable Development Goal One. In order to explore the gender equality measures in poverty reduction initiatives, the article utilizes a qualitative research approach with an exploratory design. This involved distributing a survey questionnaire to residents of Emfuleni Local Municipality as part of the Mainstreaming Gender Equality (MGE) approach to promote equal opportunities in poverty reduction programmes at grassroots level in South Africa. The responses showed that community members are changing their perspectives on gender equality, the role of gender in achieving Development Goal One, and the significance of gender-based contributions towards social-economic development. The article recommends that future research should focus on gender-disaggregated needs, gender-sensitive roles, gender-based demographic data, and gendermainstreaming, as policy options for socio-economic development at local government level in South Africa.
Two-step estimation of simultaneous equation panel data models with censored endogenous variables

This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the reduced form residuals. The panel nature of the data allows adjustment, and testing, for two forms of endogeneity and/or sample selection bias. Furthermore, it incorporates roles for dynamics and state dependence in the reduced form. Finally, we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions.
overcomes gefitinib resistance in non-small cell lung cancer cells

The epidermal growth factor receptor (EGFR) is known to play a critical role in non-small cell lung cancer(NSCLC). Several EGFR tyrosine kinase inhibitors(TKIs), such as gefitinib, have been used as effective clinical therapies for patients with NSCLC. Unfortunately, acquired resistance to gefitinib commonly occurs after 6–12 months of treatment. The resistance is associated with the appearance of the L858R/T790M double mutation of the EGFR. In our present study, we discovered a compound,referred to as 244-MPT, which could suppress either gefitinib-sensitive or -resistant lung cancer cell growth and colony formation, and also suppressed the kinase activity of both wildtype and double mutant (L858R/T790M) EGFR. The underlying mechanism reveals that 244-MPT could interact with either the wildtype or double-mutant EGFR in an ATP-competitive manner and inhibit activity. Treatment with 244-MPT could substantially reduce the phosphorylation of EGFR and its downstream signaling pathways, including Akt and ERK1/2 in gefitinib-sensitive and -resistant cell lines. It was equally effective in suppressing EGFR phosphorylation and downstream signaling in NL20 cells transfected with wildtype, single-mutant (L858R) or mutant (L858R/ T790M) EGFR. 244-MPT could also induce apoptosis in a gefitinib-resistant cell line and strongly suppress gefitinib-resistant NSCLC tumor growth in a xenograft mouse model. In addition, 244-MPT could effectively reduce the size of tumors in a gefitinibresistant NSCLC patient-derived xenograft (PDX) SCID mouse model. Overall, 244-MPT could overcome gefitinib-resistance by directly targeting the EGFR.
Deep Residual Networks with Auditory Inspired Features for Robust Speech Recognition

The introduction of Deep Neural Networks (DNN) based acoustic models has become the new state of the art of speech recognition systems. The main reason for this is their lower recognition error rates in comparison with the traditional GMM-based systems. However, the problem of robustness in noisy environments still exits. Deep Residual networks (ResNet), a special type of DNNs, are popular in computer vision due to their increasing number of convolutional layers and ease of optimization, achieving a better performance in almost all the standard image recognition datasets. In this paper, a Deep Residual Network architecture is proposed, allowing ResNets to be used in speech recognition tasks where the network input is small in comparison with the image dimensions for which they were initially designed. Although the proposed model improves robustness against noisy conditions itself we also introduce a modification of the well-known Power Normalized Cepstral Coefficients (PNCC) as input to the ResNet with the aim of creating a noise invariant representation of the acoustic space. Experiments show that deep residual learning in conjunction with these features provides substantial improvements in recognition accuracy in both, mismatched and matched conditions, in comparison to a conventional baseline in the Aurora-4 dataset.
Intramuscular Injection of Adipose Tissue Derived Stromal Vascular Fraction in Subjects with Poliomyelitis: Case Reports

Poliomyelitis is a disabling condition that cause muscular weakness, and may develop further deterioration known as post-polio syndrome a few decades later. Adipose tissue derived stromal vascular fraction is a major source of mesenchymal stem cells and its clinical application for neuromuscular disease has been advocated. Four male participants with lower limb weakness and gait disturbance secondary to poliomyelitis underwent liposuction procedure. Stromal cells freshly isolated from the adipose tissue extract were injected into affected muscles. Liposuction and intramuscular injection procedures were done without any significant adverse event in all participants. Stromal cells were successfully isolated and showed sufficient cell counts and colony forming units. Participants reported improved walking ability, increased muscle strength, decreased fatigue and general well-being sense. We found possibility of improving muscle function in poliomyelitis subjects by intramuscular injection of autologous adipose tissue derived mesenchymal stromal cells, and call for further research.
BEAM STOP DESIGN AND CONSTRUCTION FOR THE FRONT END TEST STAND AT ISIS

A Front End Test Stand is being built at the Rutherford Appleton Laboratory in the UK to demonstrate a chopped H beam of sufficiently high beam quality for future highpower proton accelerators (HPPA). The test stand consists of a negative hydrogen ion source, a 3 solenoid LEBT, a 324 MHz four vane RFQ, a MEBT composed of rebunching cavities and choppers and a set of diagnostics ending with a beam stop. The beam stop, which has to accept a 3 MeV, 60 mA, 2 ms, 50 Hz (10% duty factor) H beam, consists of a coaxial double cone configuration where the inner cone's inner surface is hit by the beam and the inter-cone gap is cooled by high-speed water. In order to minimize both prompt and induced radiation pure aluminium is used, but the poor mechanical properties of pure aluminium are overcome by employing a metal spinning process that increases the yield strength to several times the original value of the non-deformed material.
Road Sector Development and Economic Growth in Ethiopia

The study attempts to see the trends, stock of achievements, and impact of road network on economic growth in Ethiopia. To do so, descriptive and econometric analyses are utilized. From the descriptive analysis, the findings indicate that the stock of road network is by now growing at an encouraging pace. The government? s spending has reached tenfold relative to what it was a decade ago. It also reveals that donors are not following the footsteps of the government in financing road projects. The issue of rural accessibility still remains far from the desired target level that the country needs to have. Regarding community roads, both the management and accountancy is weak, even to analyze its impact. Thus, the country needs to do a lot to graduate to middle income country status in terms of road network expansion, community road management and administration, and improved accessibility. The econometric analysis is based on time series data extending from 1971-2009. Augmented Cobb-Douglas production function is used to investigate the impact of roads on economic growth. The model is estimated using a two-step efficient GMM estimator. The findings reveal that the total road network has significant growthspurring impact. When the network is disaggregated, asphalt road also has a positive sectoral impact, but gravel roads fail to significantly affect both overall and sectoral GDP growth, including agricultural GDP. By way of recommendation, donors need to strengthen their support on road financing, the government needs to expand the road network with the aim of increasing the current rural accessibility, and more attention has to be given for community road management and accountancy. Lastly, gravel road expansion has to be made to meet the target level of the road network and simultaneously ascertain rural accessibility, thereby improving agricultural productivity and market access of the poor rural population.
Acute reSpoNSeS of cArDIoVASculAr VArIAbleS IN hyperteNSIVe

Objective: To evaluate the effects after treatment and the benefits of participation in hypertensive patients in a supervised program of therapeutic exercises. Method: Patients were submitted to a kinetic-functional evaluation; measurements of height and weight; analysis of resting heart rate (FCr) and measurement of blood pressure (BP). The intensity of training was done by calculating the heart rate training (FCT), between 60% and 75% heart rate reserve (FCr) and indirect value of Vo2 demarcated by pulse frequency meters. results: There was a reduction of the values of resting heart rate, systolic blood pressure and systolic maximum pressure with significant increase in Vo2 after the period of indirect intervention. conclusions: The program supervised therapeutic index obtained significant acute change in cardiovascular variables.
Statistical Arbitrage Pairs Trading with High-frequency Data

In recent years, more sophisticated techniques for analyzing data and exponential increase in computing power allow high-frequency trading. This paper provides a detailed overview on pairs trading in the context of intraday data and applies different strategies to minute-by-minute prices of the S&P 500 constituents from 1998 to 2015. In the back-testing study, the best performing pairs trading approach produces statistically and economically significant returns of 50.50 percent p.a. and an annualized Sharpe ratio of 8.14 after transaction costs. Although most algorithms show declining returns over time, there still exist pairs trading strategies with favorable results in the recent past.
Genetic Variability and Associations of Yield and Yield Related Traits of Lentil (Lens culinaris Medik.) Genotypes at Dire Dawa, Ethiopia

Lentil (Lens culinaris Medikus) is a self-pollinated diploid (2n = 14) annual cool season legume crop that is produced throughout the world and valued as high protein food. Genetic diversity is essential for genetic improvement of crops to make selection and improvement for a given trait. The objective of the were study the genetic variation for different agronomic traits of lentil, the phenotypic and genotypic correlation coefficien0ts among these traits and the direct and indirect effects of these traits on seed yield. In the present study, 30 lentil genotypes were used for the study and evaluated for genetic variability and associations of yield and yield related traits at Dire Dawa, Tony farm, from July 6, 2017 to October 10, 2017 in a completely randomized block design (CRBD). The genotypes were obtained from Institute of Biodiversity Conservation (IBC), Addis Ababa, Ethiopia. The collected data were analyzed to determine significant varietal difference by employing analysis of variance for each trait. High significant differences among genotypes for all traits were recorded indicating the presence of genetic variability. The broad sense heritability estimates were high for all traits except for pod length and biological yield which showed moderate heritability and seed yield which showed low heritability, thus, indicating little environmental influence on the traits. Correlation analysis indicated that the values of genotypic correlation coefficients were slightly higher than their corresponding phenotypic correlation coefficients for all except for 100 seed weight, signifying that the association among various traits is of genetic cause rather than environmental effect. The path analysis revealed that days to 95% maturity (1.41633), plant height (1.01723), number of pods per plant (0.98701), number of seeds per pod (0.29773), pod length (0.07098), 100 seed weight (0.29655) and days to emergence (0.11418) contributed positive direct effects on seed yield at genotypic level; so direct selection for these traits may be given more emphasis while selecting high yielding lentil genotypes at the genotypic level whereas at phenotypic level days to 50% flowering (0.17214), plant height (2.4178), number of pods per plant (2.47851), number of seeds per pod (0.09475) and pod length (1.42147) contributed positive direct effect along with their positive significant phenotypic association with seed yield, signifying that direct selection for these traits may be desirable for yield improvement in lentil at the phenotypic level. Keywords: correlation analysis, heritability, path analysis, seed yield, trait
A Novel Component Carrier Selection Algorithm for LTE-Advanced Heterogeneous Networks

—Carrier aggregation has been proposed in LTE-advanced to support a wider bandwidth up to 100 MHz. The basic aggregated unit is called component carrier (CC). CCs are shared among different devices. Therefore it may cause performance degradation due to severe interference. A good CC assignment mechanism is desired to alleviate the interference problem. In this article, we propose a CC selection algorithm called Interference Management based Component Carrier (IMCC) scheduling to tackle the problem in heterogeneous networking environments of Femto Access Points (FAPs) and Macro-cell base stations. IMCC assigns CCs according to the entire system information, such as, location of FAPs, location of UEs (User Equipments), and the channel quality based on an evolutionary approach. In this way, IMCC mitigates the interference, and improves the system throughput. We construct a simulation environment with some stripes of apartments, which is often used to evaluate the performance of FAPs in prior works. The simulation results indicate the proposed approach outperforms other algorithms and show the effectiveness of IMCC.
Mindfulness-Based Cognitive Therapy on Symptom of Complicated Grief on People with Symptoms of Complicated Grief

Author Address 1. Associate Professor, Counseling Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran; 2. PhD. Student, Counseling Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran; 3. Professor, Counseling Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran; 4. Associate Professor, Counseling Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran. *Corresponding Author E-mail: hajiheydarimahnaz@yahoo.com
Multilingual and Multi-Domain Adaptation for Neural Machine Translation

Neural machine translation (NMT) (Bahdanau et al., 2015) allows one to train an end-to-end system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine translation (SMT) systems. A number of studies have shown that vanilla NMT works better than SMT only when there is an abundance of parallel corpora (Zoph et al., 2016). In a low resource situation, it is important to apply various domain adaptation techniques for NMT to beat SMT (Chu et al., 2017). Domain adaptation is the process of developing high quality domain specific NLP models by leveraging out-of-domain data or models in order to improve the in-domain performance. In the context of NMT, several domain adaptation approaches have been proposed and shown to be effective in a low resource scenario (Chu et al., 2017). Most domain adaptation approaches focus on using a single resource rich out-of-domain data source to improve the low resource in-domain translations. There are also studies that use multiple out-ofdomain data for adaptation (Sajjad et al., 2017). It may not always be possible to use an out-ofdomain parallel corpus in the same language pair and thus it is important to use data from other languages (Johnson et al., 2016). This approach is known as cross-lingual transfer learning, which transfers NMT model parameters among multiple languages. It is well known that a multilingual model, which relies on parameter sharing, helps in improving the translation quality for low resource languages especially when the target language is the same (Zoph et al., 2016). In this paper, we propose to simultaneously use both, multilingual and multi-domain data for domain adaptation of NMT, which might outperform the methods that use them independently. To the best of our knowledge, this is the first study that uses both multilingual and multi-domain data for domain adaptation. To verify the effective methods in this multilingual and multi-domain adaptation scenario, we compare the different methods in the empirical study of single language pair domain adaptation for NMT (Chu et al., 2017). In particular, we compare fine tuning, multi-domain and mixed fine tuning (Chu et al., 2017). We study how multilingualism impacts the indomain translation performance and how transfer learning can be performed by fine tuning multilingual out-of-domain models to learn multilingual in-domain models.
Bioprospective potentials of endophytic fungi penicillium SPP isolated from leaves of azadirachta indica (A. JUSS)

The focus of this research work was to isolates and identify endophytic fungi from young leaves and stems (twig) of Azadirachta indica, family (Meliaceae) for the production and assay of the produced secondary metabolites. Altogether 126 segments were used, of which 63 segments each from leaves and stems (twig) tissues were screened, using modified surface sterilization techniques. A total of 12 species of endophytic fungi were purely isolated, Penicillium spp was randomly selected for the extraction and evaluation of its secondary metabolites. The eluent collected from column chromatography mixture of ethylacetate and n- hexane (50:50v/v) was phytochemically screened, and the results showed the presence of saponins, flavonoids and phenols. Based on disc diffusion method of sensitivity, the eluent possessed some degree of antibacterial and antifungal activities. In addition, in-vitro antioxidant potentiality of the eluent was also evaluated using 2, 2- Diphenyl-1-picrylhydrazyl radical (DPPH, Sigma Aldrich). Statistical package for social science (SPSS 3/93) software was used to analyze the results using one way Analysis of Variance (ANOVA), which revealed no significant difference, on the effect of concentrations of eluent on test organisms but the eluent showed significant difference on scavenging free radicals at a critical value (p >0.05).
Surviving gas expulsion with substructure

We investigate the reaction of clumpy stellar distributions to gas expulsion. We show that regions containing highly unbound substructures/subclusters after gas expulsion can produce a significant final bound cluster. The key quantity in determining if a region is able to form a bound cluster is the global virial ratio, and so regions must be looked at as a whole rather than by individual substructure/subclusters when determining if they might survive as a bound cluster.
Preliminary studies on the in vivo performance of various kinds of nanocellulose for biomedical applications

Nanocellulose has recently attracted a great deal of attention for numerous biomedical applications due to its superior mechanical properties, high surface area, tailorable surface chemistry, good biocompatibility, renewable nature, and cost-effectiveness. The objective of this study was to investigate the long-term in vivo biocompatibility of three kinds of nanocellulose, namely, cellulose nanocrystal (CNC), cellulose nanofiber (CNF), and TEMPO-oxidized cellulose nanofiber (TOCNF). The morphological features of these nanocellulose materials were examined by transmission electron microscopy. In vivo biocompatibility of all nanocellulose materials was investigated using a subcutaneous rat model for 1, 2, 4, and 12 weeks of implantation. The average diameters of prepared homogenous CNC, CNF, and TOCNF were ∼6 nm, ∼8 nm, and ∼5 nm respectively, as depicted by micrographs obtained by using transmission electron microscopy. TOCNF showed highest in vitro biocompatibility with respect to CNC and CNF in response to L929 fibroblast cells. In vivo studies revealed that all nanocellulose materials exhibited no foreign body reaction up to two-week post-injection. Subcutaneous implantation at 12 weeks showed lowest inflammation and better tissue repair for TOCNF compared to CNC and CNF. The results suggest that the long-term biocompatibility profile follows the sequence: TOCNF > CNC > CNF.
Comparison of Zn accumulation and speciation in kernels of sweetcorn and maize differing in maturity.

BACKGROUND AND AIMS Understanding the speciation of Zn in edible portions of crops helps identify the most effective biofortification strategies to increase the supply of nutrients for improving the health and nutrition of consumers. METHODS Kernels of twelve sweetcorn and three maize (Zea mays) varieties were analysed for Zn concentration and content. The speciation of the Zn in the embryos, endosperms and whole kernels at 21, 28 and 56 d after pollination (DAP) was then examined for one maize and one sweetcorn variety using synchrotron-based X-ray absorption spectroscopy (XAS). KEY RESULTS Averaged across all sweetcorn and maize varieties at 21 DAP, the embryo contributed 27-29% of the whole kernel Zn whilst the endosperm contributed 71-73%. While sweetcorn embryos contributed a lower proportion to the total kernel Zn than in maize, the proportion of total Zn in the embryo increased as kernels aged for both varieties, reaching 33% for sweetcorn and 49% for maize at 28 DAP. Using XAS, it was predicted that an average of 90% of the Zn in the embryos was present as Zn-phytate, while in the endosperm the Zn was primarily complexed with an N-containing ligand such as histidine and to a lesser extent with phytate. However, in maize endosperms, it was also observed that the proportion of Zn present as Zn-phytate increased as the kernel matured, thereby also likely decreasing its bioavailability in these mature maize kernels. CONCLUSIONS The apparent low bioavailability of Zn supplied in maize at its consumption stage (i.e. mature kernels) likely undermines the effectiveness of biofortification of this crop. Conversely, successful biofortification of Zn in sweetcorn and green maize consumed as immature kernels could potentially provide a good source of bioavailable Zn in human diets.