Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing dairy-based livelihoods in India and the United Republic of Tanzania through feed innovation and value chain development approaches: Final project report

The International Livestock Research Institute (ILRI) works to improve food security and reduce poverty in developing countries through research for better and more sustainable use of livestock. ILRI is a member of the CGIAR Consortium, a global research partnership of 15 centres working with many partners for a food-secure future. ILRI has two main campuses in East Africa and other hubs in East, West and Southern Africa and South, Southeast and East Asia. ilri.org
Failing to mitigate COVID-19 severity: the case of Brazil

ABSTRACT Brazilian President Jair Bolsonaro has consistently dismissed the severity of COVID-19, failing to offer guidance and leadership to the governors of federative units responsible for containing the virus and making Brazil an illustrative case of societies most affected by the pandemic due to political negligence and inaction. This article presents a subnational analysis of Brazil’s federative units, examining the effects of policy responsiveness and political capacity on COVID-19 mortality. Specifically, this analysis identifies a conditional relationship between policy effectiveness and governments’ ability to reach and convince their populations to abide by policy recommendations, controlling for other leading explanations of COVID-19 severity (i.e. demographic characteristics, behavioral trends, and preparedness). This work implements subnational measures of political capacity to capture heterogeneity in government responses within the country. Results from random effects regression and a Generalized Additive Model add to recent findings on the role of effective governance in mitigating COVID-19 severity.
STRENGTHENING EFFECTS ON RC BEAMS WITH EXTERNALLY PRESTRESSED CARBON FIBER SHEETS

筆者らはコンクリート構造物の曲げ耐力の補強効果およびひび割れ抑制効果を向上させることを念頭に置き, 連続繊維シートの緊張接着による補強工法を提唱している. 本研究は既報の無筋コンクリート供試体への補強効果を踏まえて, CFRPシートの緊張によるRC曲げ部材への補強効果を検討することを目的として, 一連の実験検討を実施した. その結果, CFRPシートの緊張によりRC供試体においても曲げ耐力の補強効果およびひび割れ抑制効果が十分あることが明らかになった. また, 供試体の補強前のせん断強度によってCFRPシートの緊張接着による補強効果が大きく影響を受けることを実証した. さらに, 通常のRC梁理論の適用性を検討し, 破壊モードごとに補強梁の曲げ耐力の算定および実験値との比較を行った.
Asante in the nineteenth century : the structure and evolution of a political order

suggestions that comparison with Chinese, Vietnamese and Philippine nationalism is helpful. Three particular movements within Korean nationalism are described in some detail, one to found a Korean university, one to improve the Korean language as a weapon of nationalism, and the Korean Production Movement (inspired by Gandhi's Swaraj movement). There are long quotations from the socio-political programme advocated by Yi Kwangsu in the journal Kaebyok (' Creation ') and the Marxist critiques of this in the journal Sinsaenghwal (' New Life'). One might have wished for more in a book of this title—Yi Kwangsu's historical fiction, Ch'oe Namson's publications of traditionalstyle poetry and any literary work of Han Yongun, for instance—but what it does give us is a useful contribution to the understanding outside Korea of that country's recent past.
Experimental Investigation of Shear-Layer/Body Interactions in TSTO at Hypersonic Speeds

§** This paper presents experimental results of TSTO (Two-Stage-To-Orbit) aerodynamic interactions at hypersonic speed. TSTO is a concept as space transportation system. The TSTO vehicle, where a booster is connected to an orbiter, has to fly in a wide range of speed from subsonic to hypersonic. However, a difficult problem of aerodynamic interaction arises between its two bodies, where high pressure and high heat flux occur. In the present study, a two-dimensional TSTO model is considered, where a flat plate is employed as the booster, and a hemi-circular cylinder as the orbiter; hence the flow field can be assumed to be nearly two-dimensional. Specifically the effects of the clearance between the two bodies on the flow field is examined at a hypersonic speed of M∞=8.1, where distributions of pressure and heat flux are measured. Especially, the main focus is on clarifying the mechanism of a feedback loop with regard to shock oscillations. The flow pattern can be classified, depending on the clearance between the two bodies as well as shock/shock interaction pattern in front of the orbiter. In the case of large clearance (h/D > 0.77; D is the diameter of the hemi-circular cylinder) as well as in no clearance case, the flow becomes steady, while in small clearance cases (0.00 < h/D < 0.77) highly unsteady interaction flow fields including Edney’s type IV shock interaction pattern are observed with a frequency of 1.5 kHz; at h/D=0.67, shock/shock interactions from type IV to VI are observed in a period. In addition, it is shown that this unsteadiness can be suppressed by installing a fence on some place of the booster.
The bending control of the soft pneumatic fìnger

A soft finger is derived from the multi-chambers structure of PneuNets actuators. It includes the top extensible layer with ten pleated chambers and the bottom constraint layer. This soft finger is made of the same silicone rubber material, and the thickness of the bottom layer was increased to replace the hard material as the constraint layer. A model between the bending degree of the soft finger and input pneumatic pressure is constructed to describe the bending characteristics. And the finite element method is utilized to analyze the behaviors of the soft finger. It can be seen that the bending angle will fluctuate with the fluctuation of the input pneumatic pressure. The purpose of the bending control is to reduce the pressure fluctuations in the inner cavity of the soft finger through the control strategy. The bending control is realized by PID, PI, nonlinear PID, and PID based on the endocrine system (ES-PID). Compared to other algorithms, the ES-PID controller can improve the positioning accuracy and better adjust the pressure fluctuations in the inner cavity of the soft finger.
Adaptive biohybrid pumping machine with flow loop feedback

Tissue-engineered living machines is an emerging discipline that employs complex interactions between living cells and engineered scaffolds to self-assemble biohybrid systems for diverse scientific research and technological applications. Here, we report an adaptive, autonomous biohybrid pumping machine with flow loop feedback powered by engineered living muscles. The tissue is made from skeletal muscle cells (C2C12) and collagen I/Matrigel matrix, which self-assembles into a ring that compresses a soft hydrogel tube connected at both ends to a rigid fluidic platform. The muscle ring contracts in a repetitive fashion autonomously squeezing the tube, resulting in an impedance pump. The resulting flow is circulated back to the muscle ring forming a feedback loop, which allows the pump to respond to the cues received from the flow it generates and adaptively manage its pumping performances based on the feedback. The developed biohybrid pumping system may have broad utility and impact in health, medicine and bioengineering.
Intelligent Software Service Configuration Technology Based on Association Mining

Association relationship and types between software service configuration parameters determine that the configuration items must meet the corresponding rules and constraints in parameter value and setting order. However, the distribution of software services and the architecture based on the loose coupling of heterogeneous components make the association of configuration parameters more concealed. The correlation and constraints of configuration parameters are crucial to ensure system configuration. When deploying, migrating, and updating systems, violating constraints will lead to configuration errors, resulting in system failures. To minimize the loss caused by configuration errors, association rules between configuration items can be mined to avoid potential incorrect settings. This paper proposes an intelligent software service configuration technology based on association mining by analyzing configuration items. The sample data of the configuration file is extracted from the open source code base, and the configuration file is parsed to obtain the configuration item name and value. Then mine the association rule of each configuration item pair based on the configuration item name, value, and type. According to the mined configuration constraints, configuration item error detection, cascade update and value recommendation are performed. Finally, the proposed method is tested and evaluated on six typical open source software, and the experimental results prove the accuracy and effectiveness of the method in this paper.
An Exploratory Study of Woman Battering in the Grand Forks Flood Disaster: Implications for Community Responses and Policies

This paper presents an exploratory study of woman battering in the Grand Forks, North Dakota flood of April 1997. Based on my qualitative research of women's experiences in this flood, I present two case studies of battered women to enhance understanding of what intimate partner violence means to women in the face of a natural disaster. The case studies illustrate how battered women make sense of their situations and how factors such as class and disability play a role in how women experience domestic violence. The case studies also show why services for battered women, such as emergency shelters and crisis counseling, are crucial during a disaster period. Even though we do not know if domestic violence rates increase in a. disaster, we do have evidence that the demand for domestic violence services increases during disaster times. In light of this, I argue that there is a need to prepare for that situation.
A reinforcement learning algorithm for the 2D-rectangular strip packing problem

The 2D packing problem is categorized as one branch of the cutting and packing problems, which is widely spread in the manufacturing industries. Over the years many metaheuristics have been proposed and applied on the packing problem. Recently, the approach combined with machine learning serves as a novel paradigm for solving the combinatorial optimization problem. However, the machine learning approaches have very limited literature reports on the appliance of the packing problem. We propose a reinforcement learning method for the 2D-rectangular strip packing problem. The solution is represented by the sequence of the items and the layout is constructed piece by piece. We use the lowest centroid placement rule for the piece placement, then a Q-learning based sequence optimization is applied. Three groups of conditions are set for the testing, the computational results show the Q-learning approach has good effect on the compaction of the layout.
Deep Learning Applications in Simultaneous Localization and Mapping

Simultaneous Location and Mapping (SLAM) is a research hotspot in the field of intelligent robots in recent years. Its processing object is the visual image. Deep learning has achieved great success in the field of computer vision, which makes the combination of deep learning and slam technology a feasible scheme. This paper summarizes some applications of deep learning in SLAM technology and introduces its latest research results. The advantages and disadvantages of deep-learning-based-SLAM technology are compared with those of traditional SLAM. Finally, the future development direction of SLAM plus deep learning technology is prospected.
A Simulation Method for Assembly Process of Branch Cables Based on the Minimal Energy Principle

To solve the simulation problem of branch cables in virtual assembly process, a cable simulation method based on the minimal energy principle is proposed. Firstly, to describe the postures and deformationss of the branch cables, a discrete Cosserat rod model is established. Next, according to the description of the minimal energy principle, each system is in the most stable state when its energy takes the minimum, so the postures of the cables could be solved by calculating the extremum of energy optimization model. After that, on the circumstance of ignoring low-speed movement of the cables, the energy could be obtained by the summation of the tensile, bending and torsional deformations potential energy. Then, the Levenberg-Marquardt algorithm is applied to solve the energy optimization model, and the postures of cables is calculated through Hermite interpolation. Finally, the verification example was established to simulate the deformations of the branch cables, which verifying the high calculating speed of algorithm and the authenticity of simulation.
Angle Steel Tower Bolt Defect Detection Based on Transformer

The bolts of the angle steel tower will rust, loose, and fall off under natural conditions. Traditional manual bolt defect detection is inefficient and dangerous. This paper proposes CViT-FRCNN based on ViT-FRCNN, which uses a convolutional neural network as the backbone model and the output features are input to the Transformer encoder. Compared with the direct patch embedding of ViT-FRCNN, this can improve the richness of input features and detection accuracy. A series of experiments show that our proposed model achieves the best performance in angle steel tower bolt defect detection and meets the needs of power inspection scenarios.
Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines

Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can increase the accuracy of insulator defect detection by 12.58%, which effectively improves the performance of the detection model.
Metamaterials Design Method based on Deep learning Database

In recent years, deep learning has risen to the forefront of many fields, overcoming challenges previously considered difficult to solve by traditional methods. In the field of metamaterials, there are significant challenges in the design and optimization of metamaterials, including the need for a large number of labeled data sets and one-to-many mapping when solving inverse problems. Here, we will use deep learning methods to build a metamaterial database to achieve rapid design and analysis methods of metamaterials. These technologies have significantly improved the feasibility of more complex metamaterial designs and provided new metamaterial design and analysis ideas.
Target Detection Based on Deep Learning

As an important research direction of computer vision, target detection has been widely used in face recognition, intelligent driving, robot navigation and other fields. In recent years, with the deepening research on deep learning, great progress has been made in the field of computer vision, such as image acquisition, image processing and target detection. Compared with the traditional target detection algorithm based on candidate regions, it has the problems of poor timeliness and slow detection speed. Recently, the popular target detection algorithm based on regression realizes the real sense of end-to-end detection and greatly improves the detection efficiency. However, the accuracy of small target detection and dense target detection has not been solved. In the future, we still need to improve the efficiency and accuracy of recognition on the existing basis, and solve the problem of small target and dense target detection to make it more widely used in practical application scenarios. In this paper, the principle, advantages and disadvantages, accuracy and other aspects of the above algorithms are introduced in detail, the problems existing in the target detection algorithm are summarized, and the future development direction has been prospected. In short, both algorithms have advantages and disadvantages, but the regression-based target detection algorithm has better practicality and development prospects.
Heat Supply Optimization Based On Machine Learning And Knowledge Graph

At present, heating companies mostly use a set-point temperature control curve. The operator adjusts the temperature control curve according to watching the outdoor weather conditions, which usually results in economic conditions. Given the above condition, we analyze the heating system’s mechanism first, then compile and analyze much historical data. According to the analysis results, we establish the heat exchange station-user room temperature joint model based on the machine learning method. Using the above model, the system is deeply explored, and the optimized secondary heating curve is designed and simulated. The results show that the user’s room temperature stability has an obvious control effect, which is of great significance to meet the room temperature compliance rate. At the same time, the knowledge graph of the heating field and some applications based on the knowledge graph are also built to provide more convenient and friendly operating advice for operators.
Chronic Disease and Workforce Participation Among Medicaid Enrollees Over 50: The Potential Impact of Medicaid Work Requirements Post-COVID-19

As the COVID-19 pandemic wanes, states may reintroduce Medicaid work requirements to reduce enrollment. Using the Health and Retirement Study, we evaluated chronic disease burden among beneficiaries aged >50 (n=1460) who might be impacted by work requirements (i.e. working <20 hours per week). Seven of eight chronic conditions evaluated were associated with reduced workforce participation, including history of stroke (OR: 7.35; 95% CI: 2.98-18.14) and lung disease (OR: 4.39; 95% CI: 2.97-7.47). Those with more severe disease were also more likely to work fewer hours. Medicaid work requirements would likely have great impact on older beneficiaries with significant disease burden.
Factors associated with higher levels of grief and support needs among people bereaved during the pandemic: Results from a national online survey

Background: The COVID-19 pandemic has affected millions of people's experiences of bereavement. We aimed to identify risk factors for grief and support needs. Methods: Online survey of people bereaved in the UK (deaths 16 March 2020-2 January 2021), recruited via media, social media, national associations/organisations. Grief was assessed using the Adult Attitude to Grief (AAG) scale, which calculates an overall index of vulnerability (IOV) (range 0-36). Practical and emotional support needs were assessed in 13 domains. Results: 711 participants, mean age 49.5 (SD 12.9, range 18-90). 628 (88.6%) were female. Mean age of the deceased 72.2 (SD 16.1). 311 (43.8%) deaths were from confirmed/suspected COVID-19. Mean IOV was 20.41 (95% CI = 20.06 to 20.77). 28.2% exhibited severe vulnerability (IOV [≥] 24). In six support domains relating to psycho-emotional support, 50% to 60% of respondents reported high/fairly high levels of need. Grief and support needs increased strongly for close relationships with the deceased (versus more distant) and with reported social isolation and loneliness (P < 0.001), whereas they reduced with age of the deceased above 40 to 50. Other risk factors were place of death and reduced support from health professionals after death (P < 0.05). Conclusions: High overall levels of vulnerability in grief and support needs were observed. Relationship with the deceased, age of the deceased, and social isolation and loneliness are potential indicators of those at risk of even higher vulnerability in grief and support needs. Healthcare professional support after death is associated with more positive bereavement outcomes.
Long‐term nitrogen enrichment mediates the effects of nitrogen supply and co‐inoculation on a viral pathogen

Abstract Host nutrient supply can mediate host–pathogen and pathogen–pathogen interactions. In terrestrial systems, plant nutrient supply is mediated by soil microbes, suggesting a potential role of soil microbes in plant diseases beyond soil‐borne pathogens and induced plant defenses. Long‐term nitrogen (N) enrichment can shift pathogenic and nonpathogenic soil microbial community composition and function, but it is unclear if these shifts affect plant–pathogen and pathogen–pathogen interactions. In a growth chamber experiment, we tested the effect of long‐term N enrichment on infection by Barley Yellow Dwarf Virus (BYDV‐PAV) and Cereal Yellow Dwarf Virus (CYDV‐RPV), aphid‐vectored RNA viruses, in a grass host. We inoculated sterilized growing medium with soil collected from a long‐term N enrichment experiment (ambient, low, and high N soil treatments) to isolate effects mediated by the soil microbial community. We crossed soil treatments with a N supply treatment (low, high) and virus inoculation treatment (mock‐, singly‐, and co‐inoculated) to evaluate the effects of long‐term N enrichment on plant–pathogen and pathogen–pathogen interactions, as mediated by N availability. We measured the proportion of plants infected (i.e., incidence), plant biomass, and leaf chlorophyll content. BYDV‐PAV incidence (0.96) declined with low N soil (to 0.46), high N supply (to 0.61), and co‐inoculation (to 0.32). Low N soil mediated the effect of N supply on BYDV‐PAV: instead of N supply reducing BYDV‐PAV incidence, the incidence increased. Additionally, ambient and low N soil ameliorated the negative effect of co‐inoculation on BYDV‐PAV incidence. BYDV‐PAV infection only reduced chlorophyll when plants were grown with low N supply and ambient N soil. There were no significant effects of long‐term N soil on CYDV‐RPV incidence. Soil inoculant with different levels of long‐term N enrichment had different effects on host–pathogen and pathogen–pathogen interactions, suggesting that shifts in soil microbial communities with long‐term N enrichment may mediate disease dynamics.