Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology transformation and changing demographic patterns: Perspectives for Africa’s future elections

ABSTRACT The last decade of the 21st century has so far seen many important elections on the African continent. These elections offer windows for the development of democracy and freedom throughout the continent. The same period has been characterised by a burgeoning population, estimated to be over one billion. The rapid growth in population has fuelled a quick growth in the number of eligible voters. Around the same period, another increase has been under way: evolving new technology penetration in electoral management systems. The introduction of innovative technologies into the electoral management systems (prior, during and post-election) has raised both interest and concerns. This review article provides insights into, and a critique of the role of emerging technologies in Africa’s electoral managements systems. The article taps on some of the best practices of modern technology applications in the electoral process. Some of the areas of focus in this article include constituency delimitation, political party registration, voter registration, voting operations and stakeholder engagements. The discussion denotes a rising recognition and use of new technologies in these areas to improve efficiency, ensure credibility of democratic processes and reliability of election results. The literature engages with a mixture of successes and failures, improvements and challenges, innovations and obstacles in the context of countryspecific electoral systems technological applications.
Kernel Partial Least Squares for the Identification of Mixture Content from TeraHertz Spectra

This paper introduces kernel partial least squares (K-PLS) for the identification of mixture content from terahertz spectra. Kernel partial least squares is a nonlinear extension of the partial least squares (PLS) method, commonly used in chemometrics. K-PLS and PLS are considered superior to peak matching methods for mixture spectra of multiple compounds because it avoids having to address the problem of overlapping peaks explicitly. Terahertz (THz) radiation is capable of transmitting easily through most dielectric materials and is used as a new tool to collect the original spectral readings from transmission, diffusion and reflection. A multi-output kernel partial least squares method is presented to model mixture composition based on pure substance training patterns, under the assumption of linear spectral mixture behavior. Preprocessing consists of a wavelet transform of the THz spectra and an independent component analysis (ICA) transform. Preliminary results show that the ICA+K-PLS approach is able to classify pure spectra accurately and allows for an accurate estimate of the composition from THz mixed spectra even where there are severe overlapped peaks in these spectra.
GABA A Receptor-Mediated Excitation in the Hippocampus of Adult and Newborn rats

Academic Dissertation To be presented for public criticism, with the permission of the This thesis is based on the following original articles, which are referred to in the text by their Roman numerals, and on unpublished results presented in the text. (1999) Synaptic activation of GABA A receptors induces neuronal uptake of Ca 2+ in adult rat hippocampal slices. Lamsa K. & Taira T. (2000) Use-dependent shift from inhibitory to excitatory GABA A action in interneurons induces 20-40 Hz oscillations in the hippocampus (manuscript). (IV) Lamsa K. & Kaila K. (1997) Ionic mechanisms of spontaneous GABAergic events in rat hippocampal slices exposed to 4-aminopyridine. (2000) Synaptic GABA A activation inhibits AMPA/kainate receptor-mediated bursting in the newborn (P0-P2) rat hippocampus.
Proactive small cell networks

Proactive scheduling in mobile networks is known as a way of using network resources efficiently. In this work, we investigate proactive Small Cell Networks (SCNs) from a caching perspective. We first assume that these small base stations are deployed with high capacity storage units but have limited capacity backhaul links. We then describe the model and define a Quality of Experience (QoE) metric in order to satisfy a given file request. The optimization problem is formulated in order to maximize this QoE metric for all requests under the capacity constraints. We solve this problem by introducing an algorithm, called PropCaching (proactive popularity caching), which relies on the popularity statistics of the requested files. Since not all requested files can be cached due to storage constraints, the algorithm selects the files with the highest popularities until the total storage capacity is achieved. Consecutively, the proposed caching algorithm is compared with random caching. Given caching and sufficient capacity of the wireless links, numerical results illustrate that the number of satisfied requests increases. Moreover, we show that PropCaching performs better than random caching in most cases. For example, for R = 192 number of requests and a storage ratio γ = 0.25 (storage capacity over sum of length of all requested files), the satisfaction in PropCaching is 85% higher than random caching and the backhaul usage is reduced by 10%.
A FINE-GRAIN METHOD FOR SOLVING THE PARTITIONING PROBLEM IN DISTRIBUTED VIRTUAL ENVIRONMENT SYSTEMS ∗

Distributed Virtual Environment (DVE) systems have experienced a spectacular growth last years. The partitionin g problem has been proven as the most critical issue in order to design scalable and efficient DVE systems. It consists of efficiently assigning clients (3-D avatars) to the server s in the system, and some methods have been proposed for solving it. However, only two of these methods take into account the non-linear behavior of DVE servers with the number of avatars attached to them. In this paper, we propose a fine-grain load balancing technique for solving the partitioning problem in DVE systems. Unlike a previously proposed technique, this proposal takes into account the estimated state of the target server before re-assignin g avatars. The exceeding workload that causes the saturation of a given server is proportionally distributed among several servers, if necessary. This method avoids the cascadin g effect, and it allows to increase system throughput with few re-assignments of avatars. Evaluation results show that th e proposed method can improve DVE system performance, regardless of both the movement pattern and also the initial distribution of avatars in the virtual world.
A meta-analysis of the association between intimate partner violence and age disparity in sub-Saharan Africa

A Meta-analysis of the Association between Intimate Partner Violence and Age Disparity in sub-Saharan Africa Fanuel Omondi Otieno Department of Statistics & Actuarial Science, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MSc. (Mathematical Statistics)
Coordination dynamics in a socially situated nervous system

Traditional theories of cognitive science have typically accounted for the organization of human behavior by detailing requisite computational/representational functions and identifying neurological mechanisms that might perform these functions. Put simply, such approaches hold that neural activity causes behavior. This same general framework has been extended to accounts of human social behavior via concepts such as “common-coding” and “co-representation” and much recent neurological research has been devoted to brain structures that might execute these social-cognitive functions. Although these neural processes are unquestionably involved in the organization and control of human social interactions, there is good reason to question whether they should be accorded explanatory primacy. Alternatively, we propose that a full appreciation of the role of neural processes in social interactions requires appropriately situating them in their context of embodied-embedded constraints. To this end, we introduce concepts from dynamical systems theory and review research demonstrating that the organization of human behavior, including social behavior, can be accounted for in terms of self-organizing processes and lawful dynamics of animal-environment systems. Ultimately, we hope that these alternative concepts can complement the recent advances in cognitive neuroscience and thereby provide opportunities to develop a complete and coherent account of human social interaction.
Heteromeric and homomeric interactions correlate with signaling activity and functional cooperativity of Smad3 and Smad4/DPC4

Homologs of Drosophila Mad function as downstream mediators of the receptors for transforming growth factor beta (TGF-beta)-related factors. Two homologs, the receptor-associated Smad3 and the tumor suppressor Smad4/DPC4, synergize to induce ligand-independent TGF-beta activities and are essential mediators of the natural TGF-beta response. We now show that Smad3 and Smad4 associate in homomeric and heteromeric interactions, as assessed by yeast two-hybrid and coimmunoprecipitation analyses. Heteromeric interactions are mediated through the conserved C-terminal domains of Smad3 and Smad4. In Smad3, the homomeric interaction is mediated by the same domain. In contrast, the homomeric association of Smad4 requires both the N-terminal domain and the C-terminal domain, which by itself does not homomerize. Mutations that have been associated with impaired Mad activity in Drosophila or decreased tumor suppressor activity of Smad4/DPC4 in pancreas cancer, including a short C-terminal truncation and two point mutations in the conserved C-terminal domains, impair the ability of Smad3 and Smad4 to undergo homo- and heteromeric associations. Analyses of the biological activity of Smad3 and Smad4 and their mutants show that full signaling activity correlates with their ability to undergo efficient homo- and heteromeric interactions. Mutations that interfere with these interactions result in decreased signaling activity. Finally, we evaluated the ability of Smad3 or Smad4 to induce transcriptional activation in yeast. These results correlate the ability of individual Smads to homomerize with transcriptional activation and additionally with their biological activity in mammalian cells.
Pacific university graduates in New Zealand: what helps and hinders completion

Increasing the numbers of Pacific Island students who graduate from New Zealand universities is important for the students’ futures, their families, communities and society in general. In this study, Pacific graduates (N = 365) who are participants in the Graduate Longitudinal Study New Zealand were asked to describe what factors helped or hindered their qualification completion. Graduates from all eight New Zealand universities were surveyed between July and December 2011. Pacific graduates identified external (e.g. family), institutional (e.g. university staff) and student or personal (e.g. motivation) factors that both helped and hindered the successful completion of their studies. Understanding the hindrance factors faced by Pacific students and the factors that have contributed to their educational success can help to inform policy and practice to achieve national priorities of equity and successful outcomes for Pacific learners.
Articulated pose estimation with parts connectivity using discriminative local oriented contours

This paper proposes contour-based features for articulated pose estimation. Most of recent methods are designed using tree-structured models with appearance evaluation only within the region of each part. While these models allow us to speed up global optimization in localizing the whole parts, useful appearance cues between neighboring parts are missing. Our work focuses on how to evaluate parts connectivity using contour cues. Unlike previous works, we locally evaluate parts connectivity only along the orientation between neighboring parts within where they overlap. This adaptive localization of the features is required for suppressing bad effects due to nuisance edges such as those of background clutter and clothing textures, as well as for reducing computational cost. Discriminative training of the contour features improves estimation accuracy more. Experimental results verify the effectiveness of our contour-based features.
An Adaptively Iterative Method of Document Image Binarization

Document image binarization is difficult when the image is affected by background noise or nonuniformly illuminated. In this paper, an adaptively method is proposed to address the above problems and thus get expected binarization results. This method begins by defining a filter window length with initialized stroke width, and then transforms the document image into a feature space with Gaussian kerneled Bhattacharyya distance and sets up a threshold with Ostu’s method to temporarily binarize the image, and in the end, it uses the stroke width extracted from the newly obtained image to update the filter window length until the iteration convergent. The proposed method has been tested on the DIBCO(2009-2012) databases and get acceptable results on most images in these databases. In addition, expected results have been achieved when we tested this method on several other seriously noised and shadowed document images, even with various resolutions, which indicates our method is effective. Key–Words: adaptive binarization, document image, Bhattacharyya distance, stroke width, run-length histogram
Silencing of tryptamine biosynthesis for production of nonnatural alkaloids in plant culture

Natural products have long served as both a source and inspiration for pharmaceuticals. Modifying the structure of a natural product often improves the biological activity of the compound. Metabolic engineering strategies to ferment “unnatural” products have been enormously successful in microbial organisms. However, despite the importance of plant derived natural products, metabolic engineering strategies to yield unnatural products from complex, lengthy plant pathways have not been widely explored. Here, we show that RNA mediated suppression of tryptamine biosynthesis in Catharanthus roseus hairy root culture eliminates all production of monoterpene indole alkaloids, a class of natural products derived from two starting substrates, tryptamine and secologanin. To exploit this chemically silent background, we introduced an unnatural tryptamine analog to the production media and demonstrated that the silenced plant culture could produce a variety of novel products derived from this unnatural starting substrate. The novel alkaloids were not contaminated by the presence of the natural alkaloids normally present in C. roseus. Suppression of tryptamine biosynthesis therefore did not appear to adversely affect expression of downstream biosynthetic enzymes. Targeted suppression of substrate biosynthesis therefore appears to be a viable strategy for programming a plant alkaloid pathway to more effectively produce desirable unnatural products. Moreover, although tryptamine is widely found among plants, this silenced line demonstrates that tryptamine does not play an essential role in growth or development in C. roseus root culture. Silencing the biosynthesis of an early starting substrate enhances our ability to harness the rich diversity of plant based natural products.
Using Easily Accessible Resources to Teach Acids and Bases: A Namibian Case Study

ABSTRACT The aim of the study was to explore context based teaching and learning approaches in Namibia using the local improvised instructional materials to teach acid and bases concepts in grade 9. The study is located within an interpretive paradigm as an in-depth study of exploration using easily accessible resources as instructional materials with a grade 9 (27 learners). While the findings revealed that learners did not know that acids and bases exist in their surroundings, it emerged that after the scaffolding by the researcher, learners managed to mention quite a number of local acidic and basic materials. The finding further showed that, conducting practical activities with the improvised instructional materials resulted in the same level of performance as the standardized instructional materials. Therefore, probing of learners’ prior everyday knowledge during instruction and usage of improvised instructional materials was a good indicator in promoting conceptual understanding and learner engagement.
JI.FI: Visual test and debug queries for hard real‐time

Hard real‐time systems have stringent timing and resource requirements. As such, debugging and tracing such systems often requires low‐level hardware support, and online debugging is usually precluded entirely. In other areas, however, visual debugging has greatly improved program understanding and late cycle development times for nonreal‐time applications. In this paper, we introduce a visual test and debug framework for hard real‐time Java applications built around the JIVE platform and realized in the Fiji virtual machine.
Contention-based forwarding for street scenarios

In this paper, we propose to apply Contention-Based Forwarding (CBF) to Vehicular Ad Hoc Networks (VANETs). CBF is a greedy position-based forwarding algorithm that does not require proactive transmission of beacon messages. CBF performance is analyzed using realistic movement patterns of vehicles on a highway. We show by means of simulation that CBF as well as traditional position-based routing (PBR) achieve a delivery rate of almost 100 given that connectivity exists. However, CBF has a much lower forwarding overhead than PBR since PBR can achieve high delivery ratios only by implicitly using a trial-and-error next-hop selection strategy. With CBF, a better total throughput can be achieved. We further discuss several optimizations of CBF for its use in VANETs, in particular a new position-encoding scheme that naturally allows for communication paradigms such as `street geocast` and `street flooding`. The discussions show that CBF can be viewed as a concept for convergence of intelligent flooding, geocast, and multi-hop forwarding in the area of inter-vehicle communication.
Financial Forecasting Using Genetic Algorithms

A new genetic algorithm based system is presented and applied to the task of predicting the future performances of individual stocks. The system in its most general form can be applied to any inductive machine learning problem given a database of examples the system will return a general description applicable to examples both within and outside the database. This differs from traditional genetic algorithms which perform optimization The genetic algorithm system is compared to an established neural network system in the domain of financial fore casting using the results from over 1600 stocks and roughly 5000 experiments. Synergy between the two systems is also examined.
The Development of Single Crystal Superalloy Turbine Blades

Single crystal superalloy turbine blades have recently entered production for JTSD commercial engine applications. This significant technical advance was made possible by the development of an alloy with improved properties and the development of a production casting process. The absence of grain boundary strengthening elements provided considerable alloying and heat treatment flexibility that resulted in single crystal Alloy 454 with an outstanding balance of properties. Major improvements in temperature gradients, the use of helical grain selectors, and the incorporation of these advances into existing vacuum furnaces have led to the rapid development of a production casting capability.
Association between the Number of Unfamiliar Vegetables and Dietary Factors of Elementary School Children

Despite well established health benefits, today’s children do not eat enough vegetables. The purpose of this study was to determine the association between the number of unfamiliar foods in the vegetable food group and the preference for these foods and dietary factors. Subjects were 1,072 children in 5th and 6th grades from elementary schools located in 4 cities in Gyeonggi-do, Korea. A two-page questionnaire consisted of questions asking about the subjects’ preference and familiarity for 64 foods in the vegetable group, preferences for three fast foods, four types of vegetable dishes, and six tastes. Also included in the questionnaire were questions assessing subjects’ adherence to national dietary guidelines. The subjects were divided into quintiles according to the number of unfamiliar vegetables; the 1st quintile (N=226) was children who had less than 14 unfamiliar vegetables and the 5th quintile (N=229) was more than 29. Compared to the children in the other quintiles, the children in the 5th quintile had a significantly lower adherence to national dietary guidelines regarding consumptions of vegetable and protein sources, regular exercise, awareness of desirable height and weight, and reading food labels, as well as total (P<0.05). The preferences for bland tastes (P<0.05), salads (saengchae, P<0.01), stir-fried vegetables (P<0.01), and several vegetable items (P<0.05) were significantly lower in the 5th quintile compared to the other quintiles. This study proposes the potential benefits of teaching children names of a variety of vegetables to improve their dietary factors such as food preferences and dietary habits.
Exploring key-level analytics for computational modeling of typing behavior

Typing is a human activity that can be affected by a number of situational and task-specific factors. Changes in typing behavior resulting from the manipulation of such factors can be predictably observed through key-level input analytics. Here we present a study designed to explore these relationships. Participants play a typing game in which letter composition, word length and number of words appearing together are varied across levels. Inter-keystroke timings and other higher order statistics (such as bursts and pauses), as well as typing strategies, are analyzed from game logs to find the best set of metrics that quantify the effect that different experimental factors have on observable metrics. Beyond task-specific factors, we also study the effects of habituation by recording changes in performance with practice. Currently a work in progress, this research aims at developing a predictive model of human typing. We believe this insight can lead to the development of novel security proofs for interactive systems that can be deployed on existing infrastructure with minimal overhead. Possible applications of such predictive capabilities include anomalous behavior detection, authentication using typing signatures, bot detection using word challenges etc.
Decreased regional cortical gray matter volume in schizophrenia.

OBJECTIVE The authors hypothesized that cortical gray matter volume reduction in schizophrenia is greatest in the heteromodal association cortex. This area comprises a highly integrated, reciprocally interconnected system that coordinates higher order cortical functions. METHOD Total brain and regional gray matter volumes were calculated in 46 schizophrenic patients and 60 age and sex-matched comparison subjects by using magnetic resonance images. Disease specificity was examined by assessing 27 patients with bipolar disorder. Approximations to the dorsolateral prefrontal cortex, inferior parietal lobule, and superior temporal gyrus were selected as regions of interest for the heteromodal association cortex. Occipital and sensorimotor areas were used as comparison regions to test the hypothesis for regional specificity. RESULTS Gray matter volume was reduced in schizophrenic patients in index regions even after covariance for overall brain volume, sex, and age. Bipolar disorder patients did not exhibit heteromodal gray matter reduction. Comparison regions did not differ among the three groups. Global gray matter volume was not different among groups after covariance for global brain volume. Comprehensive individual region post hoc analysis found no additional gray matter differences. CONCLUSIONS These findings support the theory of disproportionate reduction of gray matter volume in the heteromodal association cortex specific to schizophrenia.