Miracle-dz/UniFAR-FTUs huggingface dataset:
Eﬀect of Maternal Beta-Thalassemia Minor on Obstetrical and Neonatal Outcomes in Kirkuk Province, Iraq

Background: Maternal beta-thalassemia minor may increase the risk of adverse obstetrical outcomes like higher risk of fetal growth restriction, low birth weight, and preterm delivery. Objectives: To determine maternal and neonatal outcomes in pregnant women with beta-thalassemia minor in Kirkuk province, Iraq. Materials and methods: A case-control study was conducted and enrolled 95 pregnant women with beta-thalassemia minor and 100 control pregnant women without beta-thalassemia minor. A comparison between the two groups was performed regarding the general characteristics of the pregnant women, obstetrical complications, and neonatal outcomes. Results: The mean hemoglobin (Hb), gestational age, and birth weight were signiﬁcantly lower in the cases compared to the control group, with a P-value < 0.001, 0.002, and 0.004, respectively. Blood transfusion, emergency caesarean section (CS), postpartum hemorrhage, preterm labor, small for gestational age (SGA), and admission to the neonatal care unit (NCU) in cases were signiﬁcantly higher compared to the control group. Admission to NCU was higher with low maternal Hb, lower gestational age at the time of delivery, pregnancies complicated by diabetes mellitus, oligohydramnios, meconium-stained liquor, preterm labor, pregnant women who delivered by CS, neonates with SGA , and those with a low APGAR (A= appearance, P = pulse rate, G = grimace, A = activity, and R = respiration) score. Conclusion: Beta-thalassemia minor may adversely aﬀect pregnancy outcomes and increase the frequency of neonatal admission to NCU.
COFFEE MICROPROPAGATION IN ETHIOPIA: A REVIEW

: Economically, Coffee is one of the most significant commodities in the world. Coffee is grown in about 80 different species around the world, but only two of them are economically significant: Coffea Arabica L . and Coffea Canephora Pierre . Arabica accounts for 70% of global coffee trade, while Robusta accounts for the remaining 30%. Ethiopia is one of the Arabica coffee's birthplaces. In the third world (such as Ethiopia), population growth combined with climate change has posed a new obstacle to the goal of achieving long-term economic sustainability. Traditional coffee vegetative propagation methods are genetically stable, but the process of producing large coffee plantlets required by farmers and investors is laborious and time consuming. Direct and indirect somatic embryogenesis are the most used strategies for propagating coffee Arabica. One of the promising ways for growing a large number of coffee plantlets in a short period of time is indirect somatic embryogenesis. However, there are other restrictions that occur throughout the somatic embryogenesis of coffee. The most plausible reason is because embryos to plantlet conversions are inefficient. This article presents an overview of the existing stages of micropropagation, their challenges, and recent findings on coffee somatic embryogenesis.
The Amirante ridge/trough complex: response to rotational transform rift/drift between Seychelles and Madagascar

The Amirante ridge/trough complex developed along the Late Cretaceous transform boundary that separated the Seychelles/India and Madagascar/Somali Basin plates. Motion between these plates was complex, comprising sinistral N-S strike-slip movement coupled and coeval with counter-clockwise rotation induced when seafloor spreading developed in only the southern portion of the transform. The overall morphology of the complex comprises a series of arcuate ridge and trough segments. These segments were successively produced by tectonic and volcanic activity within the zone of migrating plate contact adjacent to the rotational pivot where compression was replaced by extension along the transform boundary. In the extensional regime to the south of this contact zone the Mascarene oceanic basin developed, whilst in the compressional zone further north island arcs developed and the ophiolites of Baluchistan were obducted from the Somali Basin onto the leading edge of the Seychelles/India plate.
Effects of Head of School Leadership on Students’ Academic Performance of Form Four National Examinations in Public Secondary Schools in Unguja, Zanzibar, Tanzania

The aim of this study was to investigate the effects of head of school leadership on students’ academic performance of Form Four National Examinations in public secondary schools in Unguja, Zanzibar, Tanzania. The study employed a convergent research design under mixed method approach whereby both quantitative and qualitative data were collected and analyzed simultaneously. The target population consisted of 3,558 respondents, from which a sample size of 266 respondents was selected. The sample included six heads of schools, who were chosen using a total population sampling technique, 24 teachers were selected through stratified sampling technique, 218 students through stratified sampling technique, and 18 parents through convenience sampling technique. Data was gathered using questionnaires and interview guide. The quantitative data was analyzed descriptively using frequencies, percentages, and mean scores, which were presented in a table. The qualitative data was analyzed through techniques such as paraphrasing, quoting, and narration. Findings revealed that head of school leadership has effects on students’ academic performance in public secondary schools. The researcher recommended that heads of schools should participate in training, seminars, and workshops on school leadership. This will enable them to acquire knowledge and skills for effective school leadership, leading to high students’ academic performance.
Testing For Exogeneity In Time-Varying Instrumental Variable Regressions: A Bootstrap Approach

This paper considers bootstrap versions of a Hausman-type test statistic for econo-metric models with endogenous regressors whose coeﬃcients are allowed to vary over time both deterministically or stochastically. I compare the ﬁnite sample performance of the asymptotic and the bootstrap version of the test by means of Monte Carlo simulations. The bootstrap test statistic appears to have proper size and higher power. More importantly, it is shown that the size and the power of the bootstrap test, are invariant to the choice of the bandwidth parameters and the number of instruments.
Ground Contact Force and Moment Estimation for Human–Exoskeleton Systems Using Dynamic Decoupled Coordinate System and Minimum Energy Hypothesis

Estimating the contact forces and moments (CFMs) between exoskeletons’ feet and the ground is a prerequisite for calculating exoskeletons’ joint moments. However, comfortable, portable, and high-precision force sensors for CFM detection are difficult to design and manufacture. In addition, there are many unknown CFM components (six force components and six moment components in the double-support phase). These reasons make it challenging to estimate CFMs precisely. In this paper, we propose a novel method for estimating these CFMs based on a proposed dynamic decoupled coordinate system (DDCS) and the minimum energy hypothesis. By decomposing these CFMs into a DDCS, the number of unknowns can be significantly reduced from twelve to two. Meanwhile, the minimum energy hypothesis provides a relatively reliable target for optimizing the remaining two unknown variables. We verify the accuracy of this method using a public data set about human walking. The validation shows that the proposed method is capable of estimating CFMs. This study provides a practical way to estimate the CFMs under the soles, which contributes to reducing the research and development costs of exoskeletons by avoiding the need for expensive plantar sensors. The sensor-free approach also reduces the dependence on high-precision, portable, and comfortable CFM detection sensors, which are usually difficult to design.
Sandstone Provenance of the Arperos Basin (Sierra de Guanajuato, Central Mexico): Late Jurassic–Early Cretaceous Back-Arc Spreading as the Foundation of the Guerrero Terrane

Three paleogeographic scenarios have been proposed for the Mesozoic volcano-sedimentary successions that compose the Guerrero terrane, western Mexico. In the type 1 scenario, the Guerrero terrane is an exotic Pacific arc accreted to nuclear Mexico by the consumption of a pre-Cretaceous oceanic basin, named Arperos Basin. The type 2 scenario considers the Guerrero terrane as a fringing multiarc system, accreted by the closure of pre-Cretaceous oceanic basin substrates at multiple subduction zones with varying polarities. In the type 3 scenario, the Guerrero terrane is interpreted as a North American west-facing para-autochthonous arc, which was drifted in the paleo-Pacific domain by the opening of the Cretaceous back-arc oceanic Arperos Basin. To test these reconstructions, we present here a combined study that includes geologic mapping, stratigraphy, U-Pb geochronology, and sandstone provenance data from the Arperos Basin in the Sierra de Guanajuato, central Mexico. Our data document that the Arperos Basin developed in a back-arc setting and evolved from continental to open oceanic conditions from the Late Jurassic to the Early Cretaceous. Sandstone provenance analysis shows an asymmetric distribution of the infill sources for the Arperos Basin: continent-recycled sedimentary rocks were deposited along its northeastern side, whereas magmatic arc-recycled clastic rocks developed at its southwestern side. Such asymmetric distribution closely fits with sedimentological models proposed for present-day continent-influenced back-arc basins. On the basis of this evidence, we favor a type 3 scenario for the Guerrero terrane, which is then considered to represent a detached slice of the Mexican leading edge that drifted in the paleo-Pacific domain during back-arc extension and subsequently accreted back to the Mexican craton.
Indexation techniques in decision support systems: Study and Evaluation

The query optimization process is the big challenge of most of decision support systems around the world, such systems based on data warehouse structure to store all data to be analyzed. Moreover, the volume and variety of data on the data warehouse are very important which involves more effort and investment to minimize the cost of refreshment and data update. Our aim in this paper is to describe the state of the art of research works dealing with the optimization approaches in big data environments especially, the techniques based on indexes selection problems. We will explain the principle of each technique and its particularities approved by our experimental data. In addition, we’ll conclude by the comparison of all these approaches developed in the literature.
Hovering Control of Full-Scaled Helicopter Based on Deep Reinforcement Learning

The control of helicopters, especially full-scaled helicopters, is widely regarded as a challenging problem because of its highly nonlinear dynamics. This paper describes a trial to hover the full-scaled helicopter via the deep reinforcement learning (DRL) method. Unlike previous related works, we train the controller in a model-free way. First, a nonlinear six degrees-of-freedom (DOF) dynamics model of the full-scaled UH-60 Black Hawk helicopter is established with mechanism modeling and flight experimental data. With the dynamics model, we build a numeric simulation environment for training. Then, the interactions between the controller and the environment, including the observation, the reward function, and the action space are designed. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is applied to train the hovering controller, which takes around 6000 episodes to converge. Finally, the trained controller is tested by hovering experiments and further, a trajectory tracking task is carried out to validate the position control performance.
Generating Cancelable Multispectral Palmprint templates based on Cartesian Transformation

Cancelable biometric (CB) has been principally suggested to solve some concerns associated to security and privacy of stored templates in traditional biometric systems. Its basic idea is to design a transformation function that starts from the original template until reaching a pseudo-identities by using an infinite number of secret keys of the same user. The requirements that must be met in an optimal cancelable algorithm are : cancelability, irreversibility, diversity and unlinkablility . In this paper, we propose a cancelable palmprint templates by using Cartesian transformation(CT) of minutiae points. The proposed scheme’s validity is confirmed through verification using the PolyU multispectral palmprint database. The experimental results and security analysis confirms the efficiency of CT to secure palmprint templates.
A QoE aware Clustering MAC protocol for Internet of Vehicles (QoEC-MAC)

An Internet of Vehicles (IoV) network is an evolution of Internet of Things (IoT) and VANET. In IoV, the Medium Access Control (MAC) protocol based on the standard 802.11p remains a big challenge for broadcasting safety messages. As the number of users increases, attention is given to Quality of Experience (QoE) and channel allocation. This paper proposes an efficient QoEC-based MAC scheme for IoV. The goal of this paper is to increase the network's effectiveness and the quality of service it offers. In our scheme, vehicles are organized into clusters. The cluster head is responsible for attributing slots, forwarding messages sent by their cluster members, and scheduling inter-cluster and intra-cluster communications. The efficiency of the QoEC-MAC scheme is evaluated with several simulations. The simulation results show that the proposed QoEC-MAC protocol achieves a lower collision ratio, higher throughput, and shorter delay.
A Discrete-time Robust Adaptive Second-Order Sliding Mode Controller for Grid-connected Power Systems with LCL Filter

This work presents a novel discrete-time robust adaptive super-twisting sliding mode (STSM) controller, which is based on the robust model reference adaptive control theory. The controller is used to regulate the currents of a grid-connected three-phase full-bridge voltage-source static inverter with an LCL filter. The proposed control strategy provides enhanced performance to the grid-tied power system by incorporating robustness to unmodeled dynamics and the ability to handle grid uncertainties. The effectiveness of the proposed controller is validated through reliable simulation results in PSIM, which consider the inverter modulation, synchronization of the controller with the grid voltages using the Kalman filter, and grid inductance variation.
Safe Model Predictive Tracking with an Improved APF-RRT* Path Planning for UGVs

In complex road scenarios, control with excellent robustness and safety is important and critical for unmanned ground vehicles (UGVs). This paper focuses on studying safe tracking control for front-wheel steering UGVs. Specifically, this paper investigates trajectory planning based on the improved Artificial Potential Field (APF) and Rapidly-Exploring Random Trees* (RRT*), as well as trajectory tracking using Model Predictive Control (MPC). To address obstacle avoidance in complex environments, this paper designs a trajectory planning algorithm based on an improved APF-RRT*. In addition, this paper proposes a new evaluation criterion for trajectory planning methods, and compares the improved APF-RRT* algorithm with the existing typical algorithms. Then, an MPC controller is designed to track the planned trajectory. The effectiveness of the safe model predictive tracking is verified finally through simulations.
Unsupervised Anomaly Knowledge Flow: a Digital Signatures Extraction Approach

Various machine learning or clustering techniques are applicable for identifying anomalous activities or particular events in networks by analysing data flows. In this paper we present our networks anomalies detection system which is based on a framework that we named Unsupervised Anomaly Knowledge Flow. Our approach consists of aggregating pre-processed network flows into well-known areas named sectors. For each sector, data describing users activity are aggregated and split into different equal time-periods. After this step, an unsupervised clustering algorithm is employed to extract the digital signatures defining sectors activity. If a specific sector signature for one specific period differs from others, it means that a network anomaly relative to users activity has been detected. A last step is performed to associate highlighted anomalies with their respective events. This framework originality are its generic, cyclic and fractal aspects. Our experiments have been conducted by using a real dataset captured and provided in 2019 by a major French mobile operator. Our proposed knowledge flow is able to detect anomalies related to real crowded events like the Notre-Dame de Paris fire, concerts or soccer matches. For this study, sectors have been computed by using geographic coordinates defining the base transmitting stations, and anomalies are reliant on network activity features.
Millimeter-Wave Channel Modeling In Rural Areas

In this work, the millimeter-wave (mm-wave) channel modeling for rural areas is investigated by using the raytracing method. Firstly, the basics and important sides of the channel are investigated. Then, both statistical and probabilistic models used in channel modeling are briefly explained. Finally, a channel model is simulated using different scenarios on the Matlab program. It has been observed that the distribution approaches probabilistic models such as Rayleigh and concluded that the ray-tracing method provides a good deterministic approximation for the channel while compromising computational cost.
Low-carbon Economic Scheduling of Park Integrated Energy System Considering User-side Shared Energy Storage

Energy storage plays a pivotal role as a flexible resource in the energy system and constitutes an essential component of integrated energy systems. However, the current state of energy storage faces challenges such as exorbitant investment costs and suboptimal utilization rates. Shared energy storage introduces a novel approach to foster scalable development of energy storage. Shared energy storage is introduced on the user side, and a low-carbon economic dispatch model of the park integrated energy system considering shared energy storage is established. Firstly, user-side shared energy storage is introduced, and a framework for the integrated energy system of the park based on shared energy storage is constructed. Then, considering factors such as electricity and gas procurement costs, wind and photovoltaic curtailment costs, investment and construction expenses, shared services, and carbon emissions, a low-carbon economic dispatch model of the park integrated energy system based on user-side shared energy storage is established. Lastly, taking an industrial park in the northern region as an example, four typical application scenarios are set up: no storage configuration, user-self-built independent energy storage, shared power energy storage and shared electricity-heat energy storage. A comparative analysis evaluates each scenario’s low-carbon economic dispatch situation. The results show that shared energy storage has significant low-carbon economic benefits and engineering application value.
Effectiveness and Information Quality Perception of an AI Model Card: A Study Among Non-Experts

With the rising popularity of artificial intelligence (AI) applications, the use of the underlying models has spread to the general public. These AI models have limitations and biases, and knowing about their characteristics could promote their safe use. Although there is some information about AI models available, in the form of AI Model Cards, there is little research on how useful this information is for non-expert users. In this paper, we conduct an experiment to evaluate the effectiveness and perception of information quality of the Model Card of a currently available AI and compare it with shorter versions. The results show that participants can use the Model Card to answer questions about the AI, but they are less confident about their answers compared to shorter versions. In addition, the full Model Card is considered less understandable and interpretable compared with a short version. On the other hand, a short version had a negative effect on perceived trustworthiness of the AI, but in all cases the participants had a positive attitude towards seeking information about the AI.
Tobacco Leaf Disease Segmentation based on TDSSNet

In recent years, many researchers have utilized deep learning methods to detect and identify plant diseases to help farmers improve the quality of their crops. Tobacco is one of the most important crops. There are few researches on disease identification using deep learning. Most of the existing methods are based on image classification, which can not identify and locate the specific type and location of the lesion. Since the correlation of tobacco diseases is less and there is no relevant dataset, this paper also created tobacco leaf disease dataset on the basis of Tobacco Disease Semantic Segmentation Network(TDSSNet) to study the classification and location of tobacco leaf disease. TDSSNet incorporates Convolutional Block Attention Module(CBAM) and skip connections to enhance feature extraction and fusion. The proposed network achieves a mean Intersection over Union (mIoU) of 64.99% for tobacco leaf disease segmentation.
Interacting effects of soil nitrogen supply and light availability on understory sapling growth and foliar attributes

Light availability in forest understories is a well recognized constraint on sapling growth, but limitations in soil nitrogen (N) availability, and the link to foliar photosynthetic capacity, typically receive less consideration in describing stand dynamics. My primary hypothesis is that light and soil N availability have speciesspecific effects on photosynthetic activity and growth, and that together these resources will better define understory development in complex forests. To test these relationships, I examined 1) soil N indices and the tradeoffs between soil fertility and light attenuation in old-growth forest understories; 2) the effects of light and N constraints on understory sapling foliar N concentration (N%), N per unit area (Na), and natural abundance of C; 3) the effects of light and soil N supply on species growth and photosynthetic activity in a factorial field experiment; and 4) the mechanisms responsible for the stagnation of understory saplings. Soil N indices incorporating dissolved inorganic N and organic N were useful in characterizing differences in N supply among contrasting sites. Understory light availability declined with increasing soil N supply, while understory Abies lasiocarpa had strong correlations between foliar N% and soil N availability, despite shading effects. In partial-cut forests, understory Tsuga heterophylla and Picea glauca x sitchensis had consistent foliar N% across gradients of light availability; in contrast, foliar N% of Betula papyrifera and Thuja plicata declined with increasing shade, which would distort assessments of soil fertility and perhaps contribute to increased mortality of these species in deep shade. Strong correlations between foliar Na and C or growth increment suggest foliar N per unit area is the simplest integration of light availability and N nutrition on leaf photosynthetic activity. Ontogenic interactions that occur among foliar attributes and tree size in forest understories, especially for saplings < 1 m in height, contribute to time effects on growth patterns and emphasize the need for long-term studies of species autecology and stand dynamics. My experimental manipulation of light and N supply on saplings was ineffective, and future research using natural gradients in site productivity may be more fruitful in defining species response to light and N interactions.