Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Factors of Full Immunization of Children among 12-23 Months Old in Rural Ethiopia

Immunization is a confirmed instrument for controlling and eliminating life-threatening infectious diseases. It is one of the most cost-effective health investments, with recognized strategies that make it accessible to even the most hard-to-reach and exposed populations. Globally, immunization coverage has increased during the past decade, but in developing countries including Ethiopia, the coverage is still very low. The general objective of this study was to assess and identify factors associated with full immunization among children aged 12-23 months in rural Ethiopia. The study used the 2016 Ethiopian Demographic and Health Survey children’s data. A study was conducted on total of 5,063 children aged between 12- 23 months born to 5,063 women aged 15-49 years. The result revealed that only about 34.9% of the children in rural Ethiopia received full immunization. Binary logistic regression model was used to identify the critical determinants of full child immunization status and the result confirmed that region, mother’s education, antenatal care, religion, household wealth, delivery place as well as mother’s age significantly influenced full immunization of children. In conclusion, Full child immunization coverage is low but it is increasing in rural Ethiopia. The children in households with low income, and those born to mothers without formal education were less likely to be fully immunized. These groups need to be further targeted for a more comprehensive immunization for children.
To Evaluate the Outcome of Use of Inhalers in Patient with Chronic Obstructive Pulmonary Disease (COPD) Following Training

Background: Chronic obstructive pulmonary disease (COPD) is a common respiratory condition associated with high morbidity and mortality. A correct diagnosis of COPD can decrease symptoms, reduce frequency and severity of exacerbations, improve health status, improve exercise capacity, and prolong survival. Inhaler devices are mainstay in management of chronic obstructive pulmonary disease and their proper usage requires continuous training. A thorough literature search has shown limited data on the outcome of various techniques and devices used in COPD; hence this study is being done to study the outcome of use of inhalers in patients with COPD following training. Methods: This prospective, observational study was conducted on patients suffering from COPD as per Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria. The patients were given instructions and training correctly all patients were called after one month and spirometry parameters were assessed after one month. Spirometry and St. George Respiratory Questionnaire was administered to all participants. Results: A total of 60 patients were enrolled in study with the mean age of 49.37±17.65 years. 19 patient suffering from COPD also had concurrent diseases associated. Revolizer was used by 60% of patients. There was a significant (p<0.05) improvement in FEV1 and PEF after 1 month of proper use of inhalation technique. All patients showed a statistically significant (p<0.05) improvement in symptoms score in SGRQ. Conclusion: A significant improvement in FEV1, Ratio of FEV1/FVC and PEF was observed after 1 month, there was also improvement in Quality of life of patients.
Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

The hybrid architecture still be the most attractive solution for the analog‐digital beamforming in millimeter wave (mmWave) large‐scale multiple input multiple output (LS‐MIMO) systems, since it provides the best trade‐off between the achievable rate and the power consumption. When maximizing performance metrics, channel knowledge is required to optimally design the hybrid beamforming. The channel estimation (CE) is essential for wideband mmWave LS‐MIMO to substantially enhance data rates, however, is a challenging task. Practically, the antenna number and the bandwidth for such communications are very large. Therefore, the CE scheme should account for the frequency‐dependence of the array response vectors and thereby the beam squint effect. In the first part, this article addresses a structured analysis compressive sensing (CS) for CE in a frequency selective mmWave LS‐MIMO system without neglecting the beam squint effect. Compared to sparse mmWave CE approach that mostly uses on‐grid dictionaries, our proposed approach considers continually distributed angles of arrival and departure. In the second part, we show that the frequency‐selective mmWave MIMO channels share a common sparsity structure within the same subcarrier‐group. Then, we propose a block‐sparsity‐based CS approach that uses the simultaneous orthogonal matching pursuit method to estimate the mmWave channel. Moreover, we prove the partial sparsity structure shared between all the subcarrier‐groups. Hence, we develop our proposed joint subcarrier‐block‐based CE scheme exploiting these two block‐sparsity structures. Numerical results evaluate the performances in terms of normalized mean‐squared error and spectral efficiency to select the most accurate channel estimator with lower overhead.
Modeling Pupil Dilation as Online Input for Estimation of Cognitive Load in non-laboratory Attention-Aware Systems

—Dynamic changes of pupil dilation represent an established indicator of cognitive load in cognitive sciences Exploitation of these insights regarding pupil dilation as an indicator of cognitive load for attention-aware Information and Communication (ICT) systems has been impeded due to restrictions of pupil analysis to a posteriori processing and exclusion of disturbing environmental factors. To overcome these issues, this paper proposes an algorithm based on Hoeks’s pupil response model, enabling online analysis of pupil dilation for the dynamic interpretation of cognitive load as an input for interactive, attention-aware systems, which outperforms state-of-the-art approaches regarding complexity, accuracy, ﬂexibility and computation time. Beyond mathematical pupil modeling, this paper identiﬁes Environment Illumination compensation (IC), Blink Compensation (BC), Reference Baseline computation (RB) and Onset/Offset detection (OO) as crucial ﬁelds of research for the transfer of pupillometry from the laboratory into real-life application scenarios.
Deletion of voiced plosives in Chilean Spanish

The following paper is a quantitative analysis of the influence of linguistic and social factors over the deletion of the voiced plosive series /bdg/ in Chilean Spanish, in order to determine whether the deletion of the series follows the same conditioning that has been described for its weakening. The study is based on a sample of ten informants native to Santiago in sociolinguistic interviews using four speech styles. Additionally, a careful procedure of spectrographic inspections and identification of non-deleted and deleted variants of the series was defined. After the coding and analysis, it is concluded that the voiced plosive series does not present a regular behaviour in the deletion of these segments in intervocalic contexts. Deletion of voiced plosives /b/ and /d/ was found mainly in reduced speech contexts, but style of speech was not a factor for /g/ deletion. Voiced plosive /d/ showed 65.6% of deletion, in addition to showing a stronger tendency to deletion in more linguistic contexts and across genders. Voiced plosive /b/ showed 37.6% of deletion, in similar linguistic contexts as for /d/, but more predominant in male speakers. Voiced plosive /g/ showed 5.8% of deletion, and its conditioning for deletion differed from /b/ and /d/ with regards to linguistic factors, and neither gender nor speech style were seen as factors involved in its deletion.
Perspectives of Education in Diabetes

Diabetes Mellitus (DM) is a chronic disease that is a public health problem worldwide due to its prevalence, and mortality and complications that arise and consequently the high costs for both the patient, his family, the society and therefore for the state [1]. In accordance with the statement by Joslin E [2-4] when he said “Education is not a part of the treatment of diabetes, it is the treatment” and “The diabetic who knows the most is the one who lives the most” and given that Diabetes education is one of the fundamental pillars to achieve the short and medium term objectives and in virtue that any member of the health team can fulfill this role, it is necessary to give relevance to education in DM as a therapeutic strategy of high value.
Cylindrical neutrosophic single-valued number and its application in networking problem, multi-criterion group decision-making problem and graph theory

In this study, the authors envisage the neutrosophic number from various distinct rational perspectives and viewpoints to give it a look of a conundrum. They focused and analysed neutrosophic fuzzy numbers when indeterminacy and falsity functions are dependent on each other, which serves an indispensable role for the uncertainty concept. Additionally, the idea of cylindrical neutrosophic single-valued number is focused here, when the indeterminacy and falsity functions are dependent to each other using an influx of different logical and innovative graphical representation. They also developed the score and accuracy function for this particular cylindrical neutrosophic single-valued number and analysed some real-life problems like networking critical path model problem and minimal spanning tree problem of operation research field when the numbers are in cylindrical neutrosophic ambiance. They also introduced a multi-criterion group decision-making problem in this cylindrical neutrosophic domain. This noble thought will help us to solve a plethora of daily life problems in the neutrosophic arena.
Covert Communication and Secure Transmission Over Untrusted Relaying Networks in the Presence of Multiple Wardens

In this paper, we address the problem of joint covert communication and secure transmission in untrusted relaying networks when multiple wardens exist in the network. We first consider a system model consisting of one source, one untrusted amplify-and-forward relay, one destination, and one warden. For covert communications, our aim is to prevent the warden from detecting the presence of communications via the source-relay-destination link. For secure transmission, our aim is to prevent the untrusted relay from decoding the source signal. To satisfy these requirements, we propose that the destination and the source inject jamming signals during the source-to-relay and relay-to-destination transmission phases, respectively. For the considered system model, we propose a power allocation strategy to maximize the secrecy rate and satisfy the covert requirements in both of the phases. Given that the proposed optimization problem is non-convex, we employ the successive convex approximation (SCA) approach to derive a tractable solution. To obtain further insights, we generalize our analytical results to consider multiple untrusted relays and multiple wardens. We focus on two scenarios of non-colluding and colluding wardens. Our results highlight that as the number of relays increases, the achievable secrecy rate increases while the average detection error probability decreases. This is a novel observation of a fundamental trade-off between covert requirement and secrecy performance.
Deep Learning in Aquaculture: A Review

Aquaculture is a complex, multicomponent, interactive process that is dependent on water resource, animal, human as well as capital investment. The measured data affected by complicated environmental factors are usually nonlinear and various, which make it difficult to accurately control system. Traditional machine learning methods have not satisfied the actual requirements because these models can’t extract intrinsic features of data. As an important branch of machine learning, deep learning has been emphasized in both academia and industry due to the specialty of automatic feature extraction from raw data. Accordingly, application of deep learning in aquaculture is expected to produce promising results. Quite a number of researches have highlighted its potential for detection, classification, counting and prediction tasks in aquaculture. Here, we review application of deep learning in aquaculture, and categorize research by aquatic products (i.e. fish, shrimp, scallop, coral, jellyfish, aquatic macroinvertebrates, phytoplankton and water quality), presenting examples of current research in each object. The studies involve fish classification, fish counting, fish behavior monitoring, fish fillets defect detection, shrimp disease research, shrimp freshness detection, pearl classification, scallops counting, coral species classification, activity monitoring of cold water coral polyps, jellyfish detection, aquatic macroinvertebrates classification, phytoplankton classification, trend prediction of red tide biomass
Increasing sample efficiency in deep reinforcement learning using generative environment modelling

Reinforcement learning is a broad scheme of learning algorithms that, in recent times, has shown astonishing performance in controlling agents in environments presented as Markov decision processes. There are several unsolved problems in current state‐of‐the‐art that causes algorithms to learn suboptimal policies, or even diverge and collapse completely. Parts of the solution to address these issues may be related to short‐ and long‐term planning, memory management and exploration for reinforcement learning algorithms. Games are frequently used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible and easy to control environments. Regardless, few games feature the ability to perceive how the algorithm performs exploration, memorization and planning. This article presents The Dreaming Variational Autoencoder with Stochastic Weight Averaging and Generative Adversarial Networks (DVAE‐SWAGAN), a neural network‐based generative modelling architecture for exploration in environments with sparse feedback. We present deep maze, a novel and flexible maze game‐engine that challenges DVAE‐SWAGAN in partial and fully observable state‐spaces, long‐horizon tasks and deterministic and stochastic problems. We show results between different variants of the algorithm and encourage future study in reinforcement learning driven by generative exploration.
Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.
On the saddlepoint approximation of the dependence testing bound in memoryless channels

This paper introduces an upper-bound on the absolute difference between: (a) the cumulative distribution function (c.d. f.) of the sum of a finite number of independent and identically distributed (i.i.d) random variables; and (b) a saddlepoint approximation of such c.d. f. This upperbound is general and particularly precise in the regime of large deviations. This result is used to study the dependence testing (DT) bound on the minimum decoding error probability (DEP) in memoryless channels. Within this context, the main results include new lower and upper bounds on the DT bound. As a byproduct, an upper bound on the absolute difference between the exact value of the DT bound and its saddlepoint approximation is obtained. Numerical analysis of these bounds are presented for the case of the binary symmetric channel and the additive white Gaussian noise channel, in which the new bounds are observed to be tight.
End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features

In this article, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.
Use of differentiated THP. 1 cells to mimic nurse-like cells for its pro-survival effect on chronic lymphocytic leukaemia cells

Introduction: Chronic lymphocytic leukaemia (CLL), the most common adult leukaemia in the West, is characterised by an accumulation of monoclonal CD5 mature B-cells in the peripheral blood and lymphoid tissues including lymph nodes and bone marrow. Despite the recent advance in treatment options, the disease is still incurable. The persistence of the leukemic cells is likely due to the presence of a protective microenvironment which provides pro-survival signals to the malignant cells. One of the major components of the CLL microenvironment is the monocyte-derived nurse-like cells (NLCs) which predominantly display an M2 macrophage phenotype and express CD68 and CD163.
Homogeneous and heterogeneous multiple representations in equation-solving problems: An eye-tracking study

Multiple external representations (MERs) play an important role in the learning field of mathematics. Whereas the cognitive theory of multimedia learning and the integrative text and picture comprehension model assume that the heterogeneous combination of symbolic and analogous representations fosters learning; the design, functions, and tasks framework holds that learning benefits depend on the specific functions of MERs. The current paper describes a conceptual replication study of one of the few studies comparing single representations, heterogeneous, and homogeneous MERs in the context of mathematics learning. In a balanced incomplete block design, the participants were provided single representations (a graphic, text, or formula) or a heterogeneous (e.g., text + graphic) or homogeneous (text + formula) combination of these to solve linear system of equations problems. In accordance with previous research, performance was superior in conditions providing MERs compared to single-representation conditions. Moreover, heterogeneous MERs led to time savings over homogeneous MERs which triggered an increase in cognitive load. Contrary to previous research, text was the least fixated representation whereas the graphical representation proved to be most beneficial. With regard to practical implications, experts should be fostered through more challenging homogeneous MERs whereas novices should be supported through the accessible graphic contained in heterogeneous MERs.
Assistance Systems in Manual Assembly

Due to the continuing trend towards more complexity of products with an increasing number of variants and smaller lot sizes, the assembly often takes place -despite relatively high labor costs in Western industrialized nations -manually or partially automated. An outsourcing or relocation of assembly function abroad is not suitable in most cases.Therefore, it is increasingly important to reduce process variations and waste in manual assembly processes. Assistance systems have the potential, depending on the situation, to assist the worker in his work, to reduce error rate and to increase productivity. In a first part of the paper an overview will be given to different types of assembly assistance systems. Then a morphological chart is developed, which can provide assistance in selecting or comparing assembly assistance systems. With the help of this chart an assembly assistant system is presented. Finally a quick look is taken at further research being done in this area