Miracle-dz/UniFAR-FTUs huggingface dataset:
ADDIS ABABA UNIVERSITY INSTITUTE OF BIOTECHNOLOGY GENETIC DIVERSITY STUDY IN NAPIER GRASS (Cenchrus purpureus) COLLECTIONS AND PROGENIES

Application of currently available advanced molecular tools and characterization of Napier grass forage crop is limited. Furthermore, ILRI genebank held collection of this forage grass from different part of the world, but with little information on the genetic diversity of the collection. Therefore, the objective of this study was to evaluate the extent of the genetic diversity of Napier grass accessions collected from different parts of the world and progeny plants raised from naturally produced seeds. The genomic DNA was extracted from leaves of 347 Napier grass genotypes and genotyped by the DArTseq platform. A total of 96,454 Silico DArT and 96,321 SNP markers were generated, of which highly informative 1001 SNP markers were selected for diversity analysis after screening. The average polymorphic information content (PIC) values of Silico DArT and SNP markers were 0.21 and 0.15 and average heterozygosity of 0.26 and 0.18 respectively. Two major groups and ten sub-clusters were identified by population stratification and diversity analysis using STRUCTURE and hierarchal clustering. Discriminant analysis of principal component (DAPC) further confirmed the sub-clusters. Analysis of molecular variance (AMOVA) showed significant (P<0.00) variation among the populations. The mean values of fixation index (Fst) per cluster ranged from 0.34 in cluster VI, that consisted of progeny plants, to 0.76 in cluster VII, that consisted mostly of the ILRI collections, and the largest divergence (0.38) was also between sub-cluster IV and VII. All these parameters showed the presence of high diversity and genetic differentiation among the assayed Napier grass genotypes.
PENGEMBANGAN PROGRAM ALOKASI AIR (PAA) SEDERHANA BERBASIS EXCEL

The research focuses on the design and implementation of simple water allocation program. The program is used to schemetised water for Irrigation Districs and is applied at Sampean Watershed in East Java Province. Research method include: (1) survey, (2) design software, and (3) imple- mentation on the watersheds. Survey is conducted to collect hydro-meterological data and irrigation water demand at each irrigation district (Dae- rah Irigasi/DI). Microsoft EXCEL is used to design program, calculate water demand, and predict water availibility and schematized water allocation for each irrigation district. In this case, MICROSOFT EXCEL is used to design program since it is simple and easy to use. Water distribution to each irrigation district is scheduled based on ten (10) days interval. Potential Evatranspiration (ETo) is calculted using meteoro- logical data by means of Penman-Monteith Equation. Water availability is predicted from rainfall and observed discharge data. The program is applied to simulate water allocation for nine (9) irrigation districts located at Sampean Watershed. The program perform it capability to predict: water availbility, water demand and recommendation of water allocation for each irrigation district. Program interface is simple and therefore it is easy to use by farmer or irrigation district officer.
PHYSICAL REVIEW MATERIALS 5, 043602 (2021) Asymmetric equilibrium core structures of pyramidal-II 〈c + a〉 dislocations in ten hexagonal-close-packed metals

The structures of pyramidal-II 〈c + a〉 dislocations, one of the most important defects in structural hexagonalclose-packed (HCP) metals, have not been fully characterized for many of the HCP metals in use today. Here, we employ ab initio informed phase-field dislocation dynamics to determine the minimum energy structure of pyramidal {1̄1̄22}〈112̄3〉 dislocations in ten HCP metals, including Be, Co, Mg, Re, Ti, Zn, Cd, Hf, Y, and Zr. As input for the simulations, we calculate, using first-principles density functional theory, the {1̄1̄22} generalized stacking fault energy (GSFE) curves for all ten metals. From these calculations, it is found that magnetism in Co is necessary for achieving a local minimum in the GSFE curve. We observe in simulations that edge and screw character dislocations split into two partials separated by a low-energy intrinsic stacking fault. The splitting distance is shown to scale inversely with the local minimum energy normalized by the product of its shear modulus and Burgers vector. Interestingly, some HCP metals exhibit an asymmetric structure, with either unequal partial Burgers vectors or widths, in contrast to the symmetric configuration expected from linear elastic dislocation theory. We explain these structures by properties of the local maxima in their GSFE curves. Metals with larger degrees of elastic anisotropy result in dislocations with larger splitting distances than would be expected under the commonly used assumption of elastic isotropy. These findings on the sizes and asymmetry in the structures of pyramidal-II 〈c + a〉 dislocations are fundamental to understanding how these dislocations glide and interact or react with other defects when these metals are mechanically strained.
Log Shifting for Enterprise Collaboration Systems: A Supervised Event Abstraction Approach (Extended Abstract)

—Event abstraction is a pre–processing method in Process Mining to create an interpretable log and, in turn, interpretable process models. Here, one or more events are aggregated to one event at a higher level of abstraction. The ProM plugin presented in this paper provides such an approach by training a log shifting model based on investigating a known high–level log and the related low–level log. This log shifting model can then be used to convert any low–level log of the same application. First evaluation results show that this approach is applicable. The log shifter presented in this paper is particularly tailored for Enterprise Collaboration Systems (ECS) that come with special low–level log characteristics.
D EALING D ISASTER M ANAGEMENT USING AI

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and disaster management domains.
The Dorsal Bunion Diagnosis, Evaluation and Management: About A Case Report and Review of the Literature

Quick Response Code Abstract: Of all the misalignments of the first ray of the foot, the dorsal bunion is the least known. The dorsal bunion deformity consists of the elevation of first metatarsal head, plantar flexion contracture at the first metatarsophalangeal joint, and dorsiflexion contracture of the tars-metatarsal joint. The deformity presents most commonly after clubfoot treatment but can also be a sequela of various neuromuscular foot conditions, including poliomyelitis and cerebral palsy. We report two case of dorsal bunion. A 30 year-old man and a 14 year-old girl, the both are an iatrogenic pathology secondary to a clubfoot surgery. Treated by different surgery technique and the results were satisfied in the two cases. The dorsal bunion results from a musculo-ligament imbalance at the level of the first ray, which involves 4 muscles: the long fibular, the flexor of the hallux, the anterior tibial and the triceps sural. The flexibility of the navicular-cuneate and cuneo-metatarsal joints also plays an important role. It was Lapidus who coined the term "dorsal bunion" and developed an operative technique that bears his name. Several interventions have been proposed in the literature, the majority of which stem from Lapidus's intervention.
(PCu), in the DMN by applying group independent component analysis (group-ICA) and estimated dFC between component time courses using a sliding window

In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data of 125 schizophrenia (SZ) subjects were analyzed. Based on SZ demographic information and cognitive scores and using an unsupervised clustering method, we identified subgroups of patients and compared DMN dynamic functional connectivity (dFC) between the groups. We captured seven independent subnodes, including anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), in the DMN by applying group independent component analysis (group-ICA) and estimated dFC between component time courses using a sliding window approach. By using k-means clustering, we separated the dFCs into three reoccurring brain states. Using the statistical method, we compared the state-specific DMN connectivity pattern between two SZ subgroups. In addition, we used a transition probability matrix of a hidden Markov model (HMM) and occupancy rate (OCR) of each state between two SZ subgroups. We found SZ subjects with higher positive and negative syndrome scale (PNASS) showed lower within ACC and lower ACC and PCC connectivity (or ACC/PCC). In addition, we found the transition from state1 to same state is significantly different between two groups, while this result was not significant after multiple comparison tests.
Shock-Raindrop Interaction and Potential for Cavitation-Induced Droplet Breakup

Hypersonic vehicles entering the atmosphere have unique challenges including very high loads when interacting with a raindrop. Such loads drive a need to understand droplet breakup mechanisms. In this work, analyses and volume of fluid-based computational fluid dynamics are used to understand breakup mechanisms. At hypersonic speeds, the time scale is very fast with a short distance scale. This leads to conditions where there is not enough thermal energy or time to vaporize the droplet, shear stripping occurs to slow, leaving cavitation as the fastest physics with potential to drive high-speed droplet breakup. The results indicate that studies of rain impact at hypersonic speeds demand and understanding of cavitation and its role in droplet breakup mechanisms.
Analysis on the Temporal and Spatial Heterogeneity of Factors Affecting Urbanization Development Based on the GTWRModel: Evidence from the Yangtze River Economic Belt

Revealing the key influencing factors in urbanization is of great significance to the operability of national urbanization planning and decision-making. Taking 110 cities in the Yangtze River Economic Belt (YREB) as the sample, this paper explores the driving mechanism of urbanization development during 2007–2016 by using Geographically and TemporallyWeighted RegressionModel (GTWR). *e results show that the effects of the four dominant factors of social economy, education, secondary industry, and living environment are in line with expectations, with strong time fluctuation, obvious spatial nonstationarity, and a certain spatial gradient distribution. *e formulation and implementation of the urbanization strategy should be based on the driving mechanism shown by the four models, coordinate the use of relevant policies, and give priority to ensuring the effective play of the primary driving factors.
Fixed frequency forward ZVS-MRC using a saturable core

This paper proposes a fixed frequency controlled forward ZVS-MRC (zero voltage switched-multi-resonant converter) which operates with a fixed duty ratio. It differs from the conventional forward ZVS-MRC by the placement of a saturable core in the secondary side of the transformer. The proposed converter has the following advantages: (1) lower voltage stress, (2) fixed frequency and fixed duty ratio for the main switch, and (3) simple control circuit. <<ETX>>
Terahertz Wireless Communications for 2030 and Beyond: A Cutting-Edge Frontier

For 6G in 2030 and beyond, key performance metrics long for terabit-per-second, one-tenth of millisecond latency with zero jitter, millimeter-preci-sion sensing and positioning, and seamless connectivity, among others. To meet these demands, the terahertz (0.1–10 THz) band comes into the horizon, with ultra-broad bandwidth and sub-millimeter wavelength, which, however, suffers from distance limitation and power consumption concerns. In this article, four interdisciplinary directions to address the above challenges and unleash the full potential of THz communications are investigated, namely, integrated sensing and communication, ultra-mas-sive MIMO and dynamic hybrid beamforming, intelligent surfaces, and machine/deep learning. Furthermore, open problems as well as integration of these solutions are elaborated. Qualitative and quantitative studies demonstrate the benefits and performance of the proposed solutions.
Reusable Device for the Electrical Sensing of Red Blood Cells Rigidity Abnormalities, Based on A Reversible Microfluidic Assembly *

Combining microfluidic with sensors enables the development of smart analysis systems. Microelectrodes can be embedded within the microchannels network for electrical sensing, electrochemical analysis or impedance measurement. However, at the laboratory scale, the assembly between microfluidic network and electrical parts on the substrate remains an issue. This paper first discusses the principles of biosensing, and then proposes an original device integrating microfluidics with microelectrodes for the analysis of red blood cells transit in a structure mimicking micro-vascular flow. Some results concerning red blood cells discrimination of sickle cell disease are discussed with statistical analysis.Clinical relevance— This paper introduces a portable reusable device combining a microfluidic blood vessel mimicking network with microelectrodes for the biosensing of RBC
Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation

Meditation practices are considered mental training and have increasingly received attention from the scientific community due to their potential psychological and physical health benefits. We compared the EEG data recorded from long-term rajayoga practitioners during different meditative and non-meditative periods. Minimum variance modified fuzzy entropy (MVMFE) is computed for each EEG band for all channels of a given lobe. The means across all the channel entropy values were obtained and compared during meditative and non-meditative states. Meditators showed higher frontal entropy in the lower gamma band (25-45Hz) during the meditative states. Independent component analysis was applied to ensure that muscle or eye artifacts did not contribute to the gamma activity. Our results extend previous findings on the changes in entropy observed in long-term meditators during rajayoga practice. Gamma band in EEG is implicated in cognitive processes requiring high-level processing such as attention, learning, memory control, and retrieval. Gamma activity is also suggested as a potential biomarker for therapeutic progress in patients with clinical depression. Based on our findings, there is an excellent possibility to utilize the practice of meditation as a training tool to strengthen the neural circuits, where age-related degeneration is making its pathological impact.
Zeroth-Order Implicit Reinforcement Learning for Sequential Decision Making in Distributed Control Systems

Despite being reliable and well-established, convex optimization models, once built, do not adapt to the changing real-world conditions or fast evolving data streams. Such rigidness may lead to higher costs and even unsafe scenarios, rendering traditional model-based approaches inadequate for modern distributed control systems (DCSs). Recently, learning-based techniques have achieved remarkable success in operating systems with unknown dynamics; nevertheless, it is difficult to include safety and assurance during the online process. We pro-pose a novel zeroth-order implicit reinforcement learning framework for adaptive decision making in DCSs. We solve the sequential decision making problem with a policy class based on convex optimization. The reinforcement learning (RL) agent aims to adapt the parameters within the optimization model iteratively to the dynamically changing conditions. This approach enables us to include general constraints within the RL framework. We also improve the convergence of the proposed zeroth-order method by introducing a guidance mechanism. Our theoretical analysis of the non-asymptotic global convergence rate reveals the benefits of the guidance factor. The effectiveness of our proposed method is validated on two real-world applications, including a recent RL competition, showing a significant improvement over existing algorithms.
A More Interpretable Classifier For Multiple Sclerosis

Over the past years, deep learning proved its effectiveness in medical imaging for diagnosis or segmentation. Nevertheless, to be fully integrated in clinics, these methods must both reach good performances and convince area practitioners about their interpretability. Thus, an interpretable model should make its decision on clinical relevant information as a domain expert would. With this purpose, we propose a more interpretable classifier focusing on the most widespread autoimmune neuroinflammatory disease: multiple sclerosis. This disease is characterized by brain lesions visible on MRI (Magnetic Resonance Images) on which diagnosis is based. Using Integrated Gradients attributions, we show that the utilization of brain tissue probability maps instead of raw MR images as deep network input reaches a more accurate and interpretable classifier with decision highly based on lesions.
Exploring Big Historical Data: The Historian's Macroscope

The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information. Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities. Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings. Readership: Researchers, graduate and undergraduate students in the field of Digital Humanities, and people looking to digitize historical archives.
Integration of an Explainable Predictive Process Monitoring System into IBM Process Mining Suite (Extended Abstract)

Predictive process monitoring aims to forecast the running process instances with the purpose of timely signalling those that require special attention (those that may take too long, cost too much, etc.). Within the field of Process Mining, this problem has received significant attention in the last years, yielding a large body of techniques capable to give accurate predictions [1]. However, an industrial application of predictive monitoring requires to build trust among process participants, who otherwise would be reluctant to believe the forecasts and adopt this type of predictive systems [2], [3]. An important step towards building trust is to provide users with explanations of the reasons why a given process execution is predicted to behave in a certain way [3], [4]. This paper reports on the introduction of explainable predictive process monitoring functionalities into the IBM Process Mining Suite.1 After training the prediction model, the IBM Process Mining Suite performs a prediction of the completion time and cost for each running case. The prediction returned for each running case is also accompanied by a set of explanations of the attributes that influence these predictions and how. Each explanation is of form a=x to indicate that the prediction is driven by the fact that the case attribute a took on value x. Explanations for all running cases are also grouped and shown through a bar chart to highlight the most predominant.
Modelling the Electrification of Bus Depots using Real Data: Consequences for the Distribution Grid and Operational Requirements

The city of Hamburg will fully electrify the public bus service in the coming years, starting from 2018. The transformation process from diesel to electric mobility will have a significant impact on the electrical infrastructure. Due to this, it is of high interest for both distribution system operators and public transport service companies to quantify the electric power demands and the impacts on the local grid infrastructure. In the following study, bus depots of the Hamburg Hochbahn AG (HOCHBAHN) were investigated. Real data from the field was used to model the bus service trips of a whole week, calculate load profiles, predict yearly energy consumption and identify additional parameters for future bus operation, such as required charging power, battery capacity and effects on the buses’ standing time. The results show that the bus depots’ peak power demand of up to 15 MW might be critical for a 10 kV medium voltage grid that has a power limitation of about 14 MW. Furthermore it was found that the chosen charging algorithm has a major impact on the peak power demand. Mostly, the charging power of 150 kW per bus is sufficient, however in some cases, articulated buses find it hard to get enough standing and recharging time before starting their next service. The results initially quantify the impacts on the distribution grid when whole conventional bus fleets are transformed into fully electric bus fleets. The broad data basis allows to estimate the actual power demands of future bus depots. Hence, this allows improving long-term development of local distribution grid structures.