Miracle-dz/UniFAR-FTUs huggingface dataset:
DISAGGREGATED EVALUATION OF ENERGY CONSUMPTION AND GHG EMISSION IN SORGHUM PRODUCTION IN NIGER STATE OF NIGERIA

This research empirically modeled energy consumption and GHG emission reduction in sorghum production in Niger State of Nigeria, using cross sectional data collected from 90 farmers’ viz. multi-stage sampling techniques. Efficiency of energy inputs used in sorghum production was studied; degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined; wasteful uses of energy by inefficient units were assessed; energy saving of different sources was computed; effect of energy optimization on greenhouse gas (GHG) emission was investigated; and, the total amount of GHG emission of actual energy used was compared with optimum energy required using data envelopment analysis (DEA). Based on findings, it was found that 20% and 80% of sorghum producers based on BCC model were efficient and inefficient, respectively. However, the average technical, pure technical and scale efficiency scores of producers were 0.599, 0.778 and 0.77, respectively. Furthermore, it was observed that 34.15% (1009.35MJha -1 ) of total energy input can be saved if the performance of inefficient farms rose to a high level while still maintaining the same level of yield currently achieved. The improved energy use efficiency, energy productivity, net energy, total energy output and productivity of efficient units were found to be higher than that of inefficient units’ by 39.47%, 38.46%, 20.95%, 9.88% and 9.88%, respectively, using energy optimization technique. Furthermore, the greenhouse gas emission in efficient units was less than that of inefficient units by about 20.01% which translate into the value of 11.83KgCO2eqha -1 . However, nitrogen fertilizer had the highest difference of greenhouse gas emissions between efficient and inefficient unit in sorghum production. Based on these, it is recommended that policies should emphasize on development of new technologies to substitute agrochemical inputs with renewable energy sources aiming efficient use of energy and lowering the environmental footprints. Also, the use of stereotype fertilizer should be discouraged, and if possible policy banning it should be enacted.
Multi-Relational Latent Lexicology-Semantic “LEXICOLSEM” Analysis Model for Extracting Qura’nic Concept

Al Quran is a divine text which represents the purest and most authentic form of the classical Arabic language. In order to understand the meaning of each verse, a deep knowledge of Arabic linguistic is essential. Therefore, our scholars have made their efforts by engaging themselves in the works of explaining al Quran’s words, interpreting its meanings into Arabic and other languages. Currently, more people are interested in knowing the content of al-Quran, especially for non-Muslim, after 9/11 tragedy. Thus, a flexible model that can represent Qur’anic concept is required for people to understand the content of the Quran. In this research, we propose a Multi-Relational Latent Lexicology –Semantic Analysis Model (LEXICOLSEM) based on a combination of Arabic Semantic and six multiple relations between words, which are synonym, antonym, hypernym, hyponym, holonym and meronym, to precisely extract Qur’anic concept. The existing literatures focus only on very limited relationships between words which could not extract the in-depth concept of Qur’anic without considering the importance Arabic Semantic. Therefore, the objectives of this research are:(1) to analyse and categorize Quranic words according to Arabic Semantic patterns,(2) to propose a new model for extracting Quranic concept using LEXICOLSEM,(3) to investigate semantic relationships between Qura’nic words, and (4) to validate the proposed model with Arabic linguistic, and Qura’nic experts. This research will be conducted qualitatively through content analysis approach a new innovative technological technique. It is expected that the model will come out with a precise analysis for extracting Qur’anic concept. This will be very significant in enhancing the overall Quran’s understanding among the society in Malaysia and Muslim’s world for sustainable society.
PHOSPHOLIPID BASED VESICULAR SYSTEM FOR TRANSDERMAL AND DERMAL DELIVERY

Phospholipid based vesicular appears to be most promising transdermal and dermal delivery system due to their merits over other delivery systems. This article reviews the types of phopholipid based vesicular system such as liposomes, transferosomes and ethosomes and their mechanism of skin permeation . It also provides an information on the method of preparation and application of the system.
Inequality of Opportunity and Beliefs About Success and Failure

This paper looks at the links between inequality of opportunity and views about the underlying processes that support economic success or failure. Unlike income inequality, inequality of opportunity isolates the extent to which inequality in circumstances beyond an individual's control influences inequality of outcomes. Disparity in outcomes due to exogenous circumstances may be bad for growth because when socio-economic background determines economic outcomes, rather than hard work and creativity, there is less reason to invest in human capital or innovation. Inequality of opportunity may also contribute to civil unrest, thus reducing sustainability of any growth. One channel through which inequality of opportunity can influence individual under-investment and discontent is through perceptions and beliefs. If people do not believe they can get rewarded for hard work and ability, there may be little incentive for people to "buy into the system'", for example by building their skills or building a business. Blaming failure on an unfair system can lead to dramatically different choices than if one believes effort is largely rewarded. The working paper shows that while inequality of opportunity is strongly correlated with beliefs about failure, it is not correlated with perceptions about success. These results suggest that while people take credit for their own success, they acknowledge when economic failure may be due to unfair processes. This has implications for how we think about the channels through which inequality of opportunity may impact individual behaviour and, in turn, growth.
OmniDRL: Robust Pedestrian Detection using Omnidirectional Cameras and Deep Reinforcement Learning*

Pedestrian detection is one of the most explored topics in computer vision and pattern recognition. The increase use of deep learning methods in the computer vision community allowed the development of new and highly competitive algorithms for object detection and classification. One class of such methods is based on deep Reinforcement Learning. However, for omnidirectional camera systems the literature is still scarce, due to the complexities associated with their high distortions. Nonetheless, these systems, namely catadioptric imaging devices are strongly beneficial for various applications (ranging from video surveillance to perception in robotics). In this paper, we present a novel efficient technique for robust pedestrian detection, using omnidirectional catadioptric camera systems with deep Reinforcement Learning. Our method is tested and compared with current related approaches that do not consider the underlying distortions. Beyond the novel solution, our method improves significantly the results when compared with state-ofthe-art methodologies.
Vascular ultrasound and cardiovascular risk assessment

Vascular ultrasound is able to detect endothelial dysfunction, arterial structural remodeling and increased arterial stiffness. These alterations have been shown to be associated with established and emerging cardiovascular risk factors and with incident cardiovascular events. Therefore, vascular ultrasound has been proposed to evaluate the role of different risk factors in the initiation and progression of atherosclerotic process, to study vascular aging and the relationship between arterial stiffness and atherosclerosis, to assess the efficacy of life-style and therapeutic interventions, and to improve the estimation of individual cardiovascular risk. The present paper provides a critical overview of the clinical evidence appraising the association of flow-mediated dilation, carotid and femoral intima-media thickness and plaque presence as well as local arterial stiffness with cardiovascular risk factors and cardiovascular events.
A critical evaluation of mathematical molecular weight distribution models proposed for real polymer distributions. I. Effects of a low molecular weight cut‐off value

It is of considerable practical importance to be able to describe the distribution of molecular sizes of a polymeric material by a convenient mathematical expression. Since the various molecular averages of any distribution are obtained from a summation process, the general models require an analytical expression of the differential distribution and a corresponding definite integral generally having the integration limits of zero and infinity. In general, real polymer distributions do not fit these integration limits and an error, which can be very large, is therefore introduced. If the error in the various molecular weight averages due to changes in integration limits is of the order of or less than the error generally encountered in the experimental evaluation of the average, the distribution function can be considered to be applicable from a practical point of view. In addition, the selected model should fulfill the requirements of fitting known experimental facts such as: the polymerization kinetics, the effects of random degradation and crosslinking, fractionation data, and the effects of polymerization and processing on the differential distribution maxima. It should also conform to the rheological behavior based on current theoretical ideas. The integration limit criteria are applied to logarithmic normal type distributions, e.g., Wesslau, and generalized exponential type distributions, e.g., Schulz-Zimm and Tung. The results show that logarithmic normal type distributions cannot be considered as useful models, while the generalized exponential type distributions can be valid and useful representations.
Special Section on the GAF: Selection of Outcome Assessment Instruments for Inpatients With Severe and Persistent Mental Illness

The purpose of this article is to describe a procedure to assist in selecting outcome measures for inpatients treated at a state psychiatric hospital. The procedure combines evidence-based criteria from the literature, instruments shown to be sensitive to change in clinical trials, and the perspectives of a multidisciplinary team of researchers, administrators, providers, and patient advocates. Recent efficacy and effectiveness studies were used to identify recurrently used outcome instruments. A computerized search of more than 30 bibliographic databases, such as PsycINFO, MEDLINE, Social SciSearch, and ERIC, was conducted for articles published between 1990 and 2002. Comparisons of the most frequently used instruments were made on seven criteria proposed as best-practice indicators, including sensitivity to change and robust psychometrics. The sample produced 110 measures. Rater-completed instruments were represented more often than patient-completed ones. However, considerable variability across both m.
Management and Treatment of Paediatric Neurovascular Diseases in an Interdisciplinary Setting

Introduction: Paediatric neurovascular diseases include different patterns, such as cerebral aneurysms, arteriovenous or vein of Galen and cerebral cavernous malformations, as well as ischemic stroke and other infrequent clinical entities like Moyamoya disease. Management and treatment of this topic requires well-coordinated multidisciplinary care of pediatric neurosurgeons, interventional neuro-radiologists and paediatricians. Materials and methods: We present six cases of neurovascular pathologies in paediatric patients and highlight the multimodal treatment and management strategy. This case-series included patients with Vein of Galen malformation, Arteriovenous-malformation-bleeding, cerebellar ischaemic stroke, dissecting PICA-aneurysm and one patient with a vascular compression of the vestibular nerve inside the internal auditory meatus. Results: All patients show a good outcome because of a fast and interdisciplinary work and a close collaboration of the neurosurgeon, the neuroradiologist and the neuropadiatrician. Discussion: A multimodal therapy-management with an effective interdisciplinary setting including the pediatric neurosurgeon, the interventional neuroradiologist and the neuropaediatricians is necessary to reach the best outcome of the patient. Paediatric neurovascular diseases should be treated in an experienced centre with good infrastructure and good interdisciplinary work.
Bis[1,3-bis(2-methyltetrazol-5-yl-κn4) triazenido-κn2]nickel(II)

The title compound, [Ni(BMTT) 2 ], where BMTT is 1,3-bis(2-methyltetrazol-5-yl)triazenide (C 4 H 6 N 11 ), presents a molecular complex with tridentate ligands. The tridentate mode of the ligand is realised through the central N atom of the triazene group and two N atoms of the two tetrazole rings. The [Ni(BMTT) 2 ) molecule is the meridional isomer, with crystallographic 4 symmetry in space group P4 2 /n. The nickel centre has a distorted octahedral environment, with two axial Ni-N bonds of 2.041(2) A and four equatorial Ni-N bonds of 2.0739 (14) A. The molecules are linked together by van der Waals interactions only.
Management of subclinical mastitis with mastilep gel and herbal spray (AV/AMS/15)

A clinical trial was conducted to evaluate therapeutic efficacy of Mastilep gel and topical herbal spray (AV/AMS/15) against treatment of sub clinical mastitis. The mastitis suspected bovine cases presented to Teaching Veterinary Clinical Complex (TVCC) and the instructional livestock farm, College of Veterinary and Animal Sciences, Parbhani, Maharashtra state, India were incorporated in study. The cows were screened by performing Mastrip test and Modified California Mastitis Test. All animals were grouped in 3 groups of 10 animals in each. Group A (Control Group) was given no treatment, Group B – treated with herbal spray (AV/AMS/15) sprayed on udder/affected quarters post milking BID for 5 days, Group CMastilep gel (applied gently by massaging the udder), post milking BID for 5 days. Somatic cell count (SCC), milk fat content, milk yield was evaluated along with microbiological investigations (isolation and identification). SCC in AV/AMS/15 sprayed and Mastilep gel treated group was reduced significantly on the 5 th day of treatment to 2.89±0.83 and 4.01±2.06 respectively. After treatment milk yield in cows suffering from sub clinical mastitis increased significantly with unchanged Milk fat %. Milk samples of all the affected quarters revealed predominance of Staphylococcus, Streptococcus and Escherichia coli as the causative agents of bovine sub clinical mastitis. On the basis of microbial investigation on the 5 th day, 60% cure rate was recorded in Mastilep gel treated cows and 70% after treatment with AV/AMS/15 herbal formulation. Affected animals were negative for subclinical mastitis at the end of the experiment. As far as comparison between therapeutic efficacy of AV/AMS/15 herbal spray (Group B) and Mastilep gel (Group C) is concerned with both at par. However, the efficacy of AV/AMS/15 herbal spray, in reducing the SCC in milk was higher as compared to Mastilep gel and the significant difference (P<0.01) was observed between them.
Scenario optimization for optimal training of Echo State Networks

Echo State Networks (ESNs) are widely-used Recurrent Neural Networks. They are dynamical systems including, in state-space form, a nonlinear state equation and a linear output transformation. The common procedure to train ESNs is to randomly select the parameters of the state equation, and then to estimate those of the output equation via a standard least squares problem. Such a procedure is repeated for different instances of the random parameters characterizing the state equation, until satisfactory results are achieved. However, this trial-and-error procedure is not systematic and does not provide any guarantee about the optimality of the identification results. To solve this problem, we propose to complement the identification procedure of ESNs by applying results in scenario optimization. The resulting training procedure is theoretically sound and allows one to link precisely the number of identification instances to a guaranteed optimality bound on relevant performance indexes, such as the Root Mean Square error and the FIT index of the estimated model evaluated over a validation data-set. The proposed procedure is finally applied to the simulated model of a pH neutralization process: the obtained results confirm the validity of the approach.
WIKIR: A Python Toolkit for Building a Large-scale Wikipedia-based English Information Retrieval Dataset

Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated data to be effective. Since most standard ad-hoc information retrieval datasets publicly available for academic research (e.g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets. These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collected from commercial search engines not publicly available for academic research which is a problem for reproducibility and the advancement of research. In this paper, we propose WIKIR: an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia. WIKIR is publicly available on GitHub. We also provide wikIR59k: a large-scale publicly available dataset that contains 59,252 queries and 2,617,003 (query, relevant documents) pairs.
Integrating Whole Context to Sequence-to-sequence Speech Recognition

Because an attention based sequence-to-sequence speech (Seq2Seq) recognition model decodes a token sequence in a left-to-right manner, it is non-trivial for the decoder to leverage the whole context of the target sequence. In this paper, we propose a self-attention mechanism based language model called casual cloze completer (COR), which models the left context and the right context simultaneously. Then, we utilize our previously proposed "Learn Spelling from Teachers" approach to integrate the whole context knowledge from COR to the Seq2Seq model. We conduct the experiments on public Chinese dataset AISHELL-1. The experimental results show that leveraging whole context can improve the performance of the Seq2Seq model.
X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers

Extreme multi-label text classification (XMC) concerns tagging input text with the most relevant labels from an extremely large set. Recently, pre-trained language representation models such as BERT (Bidirectional Encoder Representations from Transformers) have been shown to achieve outstanding performance on many NLP tasks including sentence classification with small label sets (typically fewer than thousands). However, there are several challenges in extending BERT to the XMC problem, such as (i) the difficulty of capturing dependencies or correlations among labels, whose features may come from heterogeneous sources, and (ii) the tractability to scale to the extreme label setting because of the Softmax bottleneck scaling linearly with the output space. To overcome these challenges, we propose X-BERT, the first scalable solution to finetune BERT models on the XMC problem. Specifically, X-BERT leverages both the label and input text to build label representations, which induces semantic label clusters to better model label dependencies. At the heart of X-BERT is a procedure to finetune BERT models to capture the contextual relations between input text and the induced label clusters. Finally, an ensemble of the different BERT models trained on heterogeneous label clusters leads to our best final model, which leads to a state-of-the-art XMC method. In particular, on a Wiki dataset with around 0.5 million labels, the precision@1 of X-BERT is 67:87%, a substantial improvement over the neural baseline fastText and a state-of-the-art XMC approach Parabel, which achieves 32:58% and 60:91% precision@1, respectively.
Pitchnet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network

Singing voice conversion is to convert a singer’s voice to another one’s voice without changing singing content. Recent work shows that unsupervised singing voice conversion can be achieved with an autoencoder-based approach [1]. However, the converted singing voice can be easily out of key, showing that the existing approach cannot model the pitch information precisely. In this paper, we propose to advance the existing unsupervised singing voice conversion method proposed in [1] to achieve more accurate pitch translation and flexible pitch manipulation. Specifically, the proposed Pitch-Net added an adversarially trained pitch regression network to enforce the encoder network to learn pitch invariant phoneme representation, and a separate module to feed pitch extracted from the source audio to the decoder network. Our evaluation shows that the proposed method can greatly improve the quality of the converted singing voice (2.92 vs 3.75 in MOS). We also demonstrate that the pitch of converted singing can be easily controlled during generation by changing the levels of the extracted pitch before passing it to the decoder network.
A Variational Perturbative Approach to Planning in Graph-based Markov Decision Processes

Coordinating multiple interacting agents to achieve a common goal is a difficult task with huge applicability. This problem remains hard to solve, even when limiting interactions to be mediated via a static interaction-graph. We present a novel approximate solution method for multi-agent Markov decision problems on graphs, based on variational perturbation theory. We adopt the strategy of planning via inference, which has been explored in various prior works. We employ a non-trivial extension of a novel high-order variational method that allows for approximate inference in large networks and has been shown to surpass the accuracy of existing variational methods. To compare our method to two state-of-the-art methods for multi-agent planning on graphs, we apply the method different standard GMDP problems. We show that in cases, where the goal is encoded as a non-local cost function, our method performs well, while state-of-the-art methods approach the performance of random guess. In a final experiment, we demonstrate that our method brings significant improvement for synchronization tasks.
BGK model of the multi-species Uehling-Uhlenbeck equation

We propose a BGK model of the quantum Boltzmann equation for gas mixtures. We also provide a sufficient condition that guarantees the existence of equilibrium coefficients so that the model shares the same conservation laws and $H$-theorem with the quantum Boltzmann equation. Unlike the classical BGK for gas mixtures, the equilibrium coefficinets of the local equilibiriums for quantum multi-species gases are defined through highly nonlinear relations that are not explicitly solvable. We verify in a unified way that such nonlinear relations uniquely determine the equilibrium coefficients under the condition, leading to the well-definedness of our model.