Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Aspilia africana on conception rates of rabbit does

The study was conducted to investigate the conception rates of rabbit does fed Aspilia africana as forage using thirty (30) Dutch breed rabbit does of average age of 6 months which were randomly divided into three treatment groups with ten does per treatment. The treatment consisted of T1 - mixed forages (Ipomea batatas leaves, Centrosema pubescens, Musa sapientum leaves, Panicum maximum) without A. africana (control), T2 - fresh A. africana and T3 - wilted A. africana. Each treatment was replicated five times with two does per replicate in a Completely Randomized Design (CRD). The rabbits in all the treatment groups were fed 300 g of the same concentrate diet which contained 18.0% crude protein (CP) and 2620 kcal ME/kg throughout the study and 500 g of mixed forages which consisted of Ipomea batatas leaves, Centrosema pubescens, Musa sapientum leaves, and Panicum maximum from the commencement of the experiment until parturition. The test forage was introduced in treatments 2 and 3 following parturition. Does in the control group (T1) continued on the mixed forages fed during acclimatization. After weaning at four weeks, the does were remated two weeks later. Two weeks after remating, the does were palpated in the lower abdomen to confirm pregnancy. When no pregnancy was found, A. africana was suspended for three weeks. Three weeks later, the does were mated again. The study revealed no significant (P > 0.05) differences in daily feed intake of does among the various treatment groups throughout the study. Gestation length (29.10, 29.40, 29.20 for T1, T2 and T3 respectively), receptivity (3, 3, 2 for T1, T2 and T3 respectively) conception rates (96, 96, 99 for T1, T2 and T3 respectively) and ovarian weights (0.20, 0.22 and 0.20 for T1, T2 and T3 respectively) of the does in the various treatment groups showed no significant differences (P > 0.05) before the introduction of the test plants (A. africana). During the period of administration of the test plant, the treated groups T2 and T3 had significantly lower (P < 0.05) mean values for receptivity (T2 and T3 = 1) compared to T1 (3), conception rates for T2 and T3 were 0% and T1 was 100%. The gestation length for the control was 30.5 days while gestation was not recorded for T2 and T3 since they did not conceive at all. The ovarian weight of the control T1 (0.20 g) was significantly higher (P < 0.05) than T2 and T3 both of which recorded 0.13 g for their ovarian weights. The study showed that the use of A. africana was deleterious to fertility in rabbits.
Biochemical and functional characterization of the RNA binding protein SAF-A/hnRNP U and investigation of its potential role in mammalian X chromosome inactivation

X chromosome inactivation (XCI) is an important mechanism for gene dosage compensation between XY males and XX females during early embryonic development of eutherian mammals. The major regulator of XCI is the long non-coding RNA (lncRNA) X inactive specific transcript (Xist). Xist RNA is believed to recruit epigenetic modifiers and initiate a silencing cascade that alters the chromatin structure and culminates in the transcriptional repression of most genes at the inactive X chromosome (Xi). Though extensively studied, the protein factors involved in the recruitment, spreading and maintenance of Xist RNA at the Xi are largely unknown. Recently published studies provide evidence that the DNA and RNA binding nuclear matrix protein scaffold attachment factor A/heterogeneous ribonucleoprotein U (SAF-A/hnRNP U) is required for Xist localization to the Xi. These studies, however, are incomplete in that they neither provide information on the RNA binding consensus sequence for SAF-A/hnRNP U on Xist, nor on its functional role in XCI. The present study was designed to re-investigate the role of SAF-A/hnRNP U in XCI using state-of-the-art techniques, notably fluorescence recovery after photo-bleaching (FRAP), 3D-structured illumination microscopy (3D-SIM), and in vivo crosslinking and RNA immunoprecipitation (CLIP). The results indicate that SAF-A/hnRNP U exists in two forms; a highly dynamic pan-nuclear protein and a stably Xi-associated form. 3D-SIM reveals that the latter closely associates with Xist RNA, and lines out the interchromatin channels within the Barr body. This finding suggests that SAF-A/hnRNP U takes part in the structural organization of the Xi by creating a secluded chromatin environment that retains XCI factors in their dedicated nuclear location. In contrast to observations made in microscopy imaging, biochemical experiments did not confirm a direct binding of SAF-A/hnRNP U to Xist RNA. This observation led to the conclusion that the protein plays an indirect, structural role in XCI.
On the convergence of exact distributed generalisation and acceleration algorithm for convex optimisation

In this paper, we study distributed multiagent optimisation over undirected graphs. The optimisation problem is to minimise a global objective function, which is composed of the sum of a set of local objective functions. Recent researches on this problem have made significant progress by using primal-dual methods. However, the inner link among different algorithms is unclear. This paper shows that some state-of-the-art algorithms differ in that they incorporate the slightly different last dual gradient terms based on the augmented Lagrangian analysis. Then, we propose a distributed Nesterov accelerated optimisation algorithm, where a doubly stochastic matrix is allowed to use, and nonidentical local step-sizes are employed. We analyse the convergence of the proposed algorithm by using the generalised small gain theorem under the assumption that each local objective function is strongly convex and has Lipschitz continuous gradient. We prove that the sequence generated by the proposed algorithm linearly converge to an optimal solution if the largest step-size is positive and less than an explicitly estimated upper bound, and the largest momentum parameter is nonnegative and less than an upper bound determined by the largest step-size. Simulation results further illustrate the efficacy of the proposed algorithm.
Reproducible Extraction of Cross-lingual Topics (rectr)

ABSTRACT With global media content databases and online content being available, analyzing topical structures in different languages simultaneously has become an urgent computational task. Some previous studies have analyzed topics in a multilingual corpus by translating all items into a single language using a machine translation service, such as Google Translate. We argue that this method is not reproducible in the long run and proposes a new method – Reproducible Extraction of Cross-lingual Topics Using R (rectr). Our method utilizes open-source-aligned word embeddings to understand the cross-lingual meanings of words and has a mechanism to normalize residual influence from language differences. We present a benchmark that compares the topics extracted from a corpus of English, German, and French news using our method with methods used in the literature. We show that our method is not only reproducible but can also generate high-quality cross-lingual topics. We demonstrate how our method can be applied in tracking news topics across time and languages.
Deservingness, Conditionality and Public Perceptions of Work Disability: The Influence of Economic Inequality

This article contributes to classical debates about the role of self-interest and social norms in shaping the moral economy of work and welfare by incorporating economic inequalities in the analysis of opinions about welfare deservingness. The relationship between inequality and perceptions of work conditionality has received little attention in previous studies. This article addresses this issue by investigating the association between economic inequalities and perceived work limitations of disabled people experiencing various conditions related to health using vignettes from the English Longitudinal Study of Ageing. The results show that people living in areas with higher levels of wealth inequality, but not income inequality, were more likely to rate the vignettes as limited in the amount of work that individuals can do due to health problems. This finding casts doubts on the crucial role attributed to self-interest as the central mechanism linking economic inequality and solidaristic, pro-welfare attitudes.
Influence of axial tension on the shear strength of transversally reinforced concrete beams

This contribution is addressing the investigation on the effect of axial tension on the shear strength of transversally reinforced concrete beams. It presents the results of a test program of 27 beams with different amounts of shear reinforcement subjected to varying levels of axial tension. After a general discussion about the truss model, the experimental shear strength results from the present study along with the results from some previous studies are analyzed, mainly with respect to the ultimate angle of the concrete struts. Then, several design codes are discussed in order to assess their practical validity, and finally a new method is proposed for the design of the longitudinal and transverse reinforcements, valid irrespective of the levels of the axial tensile force.
Predictable spatiotemporal dynamics of a dense cuttlefish spawning aggregation increases its vulnerability to exploitation

Predictable spatiotemporal dynamics of a dense cuttlefish spawning aggregation increases its vulnerability to exploitation Karina C. Hall,* Anthony J. Fowler, Michael C. Geddes, and Julian D. Taylor South Australian Research and Development Institute, PO Box 120, Henley Beach, SA 5024, Australia Department of Ecology and Environmental Science, School of Biological Sciences, The University of Adelaide, SA 5005, Australia School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia *Corresponding author: tel: þ61 (0)2 66483900; e-mail: karina.hall@dpi.nsw. gov.au. Present address: New South Wales Department of Primary Industries, National Marine Science Centre, PO Box 4321, Coffs Harbour, NSW 2450, Australia.
Structural Properties of Solid Energetic Materials: a Van Der Waals Density Functional Study

In the present work we have focused our attention towards the complete description of structural properties of energetic solids, namely inorganic azides, secondary explosives and oxidizers through density functional theory-based calculations. We find large deviations in structural parameters calculated with the standard exchange-correlation functional such as local density approximation and generalized gradient approximation (GGA). On the other hand, dispersioncorrected density functional of (GGA + G06) describes the crystal structure of the energetic solids with good accuracy. This leads to the fact that the dispersioncorrected density functionals are essential to describe the crystal structure and thereby the related physical and chemical properties of the energetic solids.
A new augmented support vector regression‐based behavioral model for multi‐device power amplifiers

A new behavioral model based on augmented time‐delay support vector regression (SVR) for multi‐device radio frequency (RF) nonlinear power amplifiers (PAs), is proposed in this paper. The theory of the proposed modeling technique is provided, along with a detailed modeling procedure. Experimental results show that the proposed new augmented SVR (NASVR) model leads to significant performance improvements when compared with basic SVR models and existing Augmented SVR (ASVR) models. When applied to a multi‐device Doherty PA, a normalized mean square error (NMSE) between model prediction and measurement data of −44.4 dB is obtained, representing a more than 9 and 4.5 dB improvement compared with the basic SVR model and the existing ASVR model, respectively.
FIViz: Forensics Investigation through Visualization for Malware in Internet of Things

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.
HEART: Unrelated parallel machines problem with precedence constraints for task scheduling in cloud computing using heuristic and meta‐heuristic algorithms

Cloud computing is becoming a profitable technology because of it offers cost‐effective IT solutions globally. A well‐designed task scheduling algorithm ensures the optimal utilization of clouds resources and reducing execution time dynamically. This research article deals with the task scheduling of inter‐dependent subtasks on unrelated parallel computing machines in a cloud computing environment. This article considers two variants of the problem‐based on two different objective function values. The first variant considers the minimization of the total completion time objective function while the second variant considers the minimization of the makespan objective function. Heuristic and meta‐heuristic (HEART) based algorithms are proposed to solve the task scheduling problems. These algorithms utilize the property of list scheduling algorithm of unrelated parallel machine scheduling problem. A mixed integer linear programming (MILP) formulation has been provided for the two variants of the problem. The optimal solution is obtained by solving MILP formulation using A Mathematical Programming Language (AMPL) software. Extensive numerical experiments have been performed to evaluate the performance of proposed algorithms. The solutions obtained by the proposed algorithms are found to out‐perform the existing algorithms. The proposed algorithms can be used by cloud computing service providers (CCSPs) for enhancing their resources utilization to reduce their operating cost.
A Practical Application for Text-Based Sentiment Analysis Based on Bayes-LSTM Model

Text-based sentiment analysis algorithms have now become one of the active research areas in emotional analysis which has gained much attention nowadays. Text emotion classification can be widely used in social public opinion analysis, product use feedback, harmful information filtering, etc. In this paper, we first developed a robotic crawler to gather data about comment on Huawei cellphone from Sina weibo microblog sites (Chinese twitter). Then we generate the data text to be trained according to the input requirements of the Keras module, and perform formal training and learning on the model after data preprocessing. Subsequently, the classifier was constructed based on the Bayes-LSTM model in which TF-IDF model was used for feature selection. The LSTM model can be characterized by the ability to self-evaluate the usefulness of the information obtained, which makes up for the shortcoming of naive Bayes formula that only applies to two independent events. We finally have a practical application that generates a word cloud from text, showing frequently used words in larger font sizes, effectiveness of the algorithm was also verified by experiment.
Effect of Haptic Assistance Strategy on Mental Engagement in Fine Motor Tasks

This study investigates the effect of haptic control strategies on a subject’s mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA), which are tested on both dominant and nondominant hand. A noninvasive brain–computer interface is used to monitor the electroencephalogram (EEG) activities of the subjects and evaluate the subject’s mental engagement using the power of multiple frequency bands (theta, alpha, and beta). Statistical analysis of the effect of the control strategy on mental engagement revealed that the choice of the haptic control strategy has a significant effect ([Formula: see text]) on mental engagement depending on the type of hand (dominant or nondominant). Among the evaluated strategies, EA is shown to be more mentally engaging when compared with the ER under the nondominant hand.
An in silico Analysis of Upstream Regulatory Modules (URMs) of Tapetum Specific Genes to Identify Regulatory cis -Elements and Transcription Factors

The present work presents an iin silicoi analysis of Upstream Regulatory Modules (URMs) of genes expressed in tapetum specific manner in dicotyledon and monocotyledon plants. In the current analysis, we identified several motifs conserved in these URMs of which ten were observed to be part of known icisi-elements using tools and databases like MEME, PLACE, MAST and TFSEARCH. We also identified that binding sites for two transcription factors, DOF and WRKY71 were found to be present in majority of the URMs.
Street-level bureaucrats under new managerialism: a comparative study of agency cultures and caseworker role identities in two welfare state bureaucracies

Officials in welfare state bureaucracies face the challenge of negotiating their role identities in the context of changeable organizational priorities and managerial styles. Previous studies have found that the professional values may mediate top-down demands and enable the preservation of professional autonomy also under public management reforms. But how do street-level bureaucrats who lack a common professional or occupational training respond to shifting organizational demands? Based on comparative ethnography, the present article investigates how caseworkers’ role identities are conceived and practised in two of the largest state bureaucracies in Sweden, the Social Insurance Agency (SIA) and the Public Employment Service (PES). The article identifies two radically different agency cultures, resulting in rather opposite caseworker role identities. These role identities affect how front-line staff respond to organizational demands, either by focusing externally on client-related outcomes (PES) or internally on organizational output (SIA). The analysis suggests that agency culture may shape caseworker responses to governance in patterned ways, also in the absence of joint professional training or strong occupational communities.
Action Selection in the Creative Systems Framework

The Creative Systems Framework (CSF) formalises creativity as search through a space of concepts. As a formal account of Margaret Boden’s descriptive hierarchy of creativity, it is at the basis of multiple studies dealing with diverse aspects of Computational Creativity (CC) systems. However, the CSF at present neither formalises action nor action selection during search, limiting its use in analysing creative processes. We extend the CSF by explicitly modelling these missing components in the search space traversal function. We furthermore integrate the distinction between a concept and an artefact, and provide stopping criteria for creative search. Our extension, the Creative Action Selection Framework (CASF), is informed by previous studies in CC and draws on concepts from Markov Decision Processes (MDPs). It allows us to describe a creative system as an agent selecting actions based on the value, validity and novelty of concepts and artefacts. The CASF brings more analytical depth for creative systems that can be modelled as utilising an action selection procedure.
Plant-microbial interactions are strong determinants of plant population and community dynamics

Plant-microbial interactions are ubiquitous and yet the consequences of these interactions on plant population and community dynamics are relatively unknown. Here, we used two different classes of plant-microbial interactions to examine their effects on key plant population and community characteristics such as commonness and rarity, competition and coexistence, as well as community stability. Vertically-transmitted endophytes had stage-dependent effects on the population growth of two grass species Poa sylvestris and Poa alsodes, and generally increased host population growth rates. However, it was the intrinsic demographic advantage of P. sylvestris that allowed its population to grow at a much faster rate compared to P. alsodes rather than endophyte benefits. In a greenhouse experiment, we showed that plant-soil microbial feedbacks were important in regulating the strength of self-limitation, or negative frequency dependence, of a strong competitor Bouteloua gracilis. These negative feedbacks increased the potential for its coexistence with Bouteloua eriopoda.
Anticipatory witnessing: military bases and the politics of pre-empting access

ABSTRACT As part of their newsgathering process, journalists often seek access to places owned or controlled by the government, such as military bases. In the United States, the military has rigorously limited journalists’ access to military bases, operations, and personnel, but the logics invoked by the government actors who decide whether journalists should be allowed, for the sake of a story, to see what happens on military bases often remain unknown. Drawing on archival research, I introduce what I call anticipatory witnessing, the use of analytical techniques to identify a priori certain people, such as media practitioners, whom state actors foresee will draw on specific elements of professionalism, credentialism, and objectivity in conveying what they have seen to their audiences. The notion of anticipatory witnessing encompasses a form of organizational vetting that is attuned to the specific social, cultural, political, and legal dimensions of those who see and subsequently speak. To engage in anticipatory witnessing is therefore to attempt to limit not just what is seen but who sees it. By analyzing practices adopted by military servicemembers at Guantánamo Bay, I demonstrate the ways in which cross-institutional communications preceding journalists’ arrival reveal elements of anticipatory witnessing, including efforts to imagine how specific rhetoric, technologies, modes of address, and partnerships might damage the reputation of the US security state. Anticipatory witnessing thus considers the logics invoked and constellations of stakeholders responsible for limiting access to tightly controlled spaces, such as military bases.