Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategy Implementation Practices and Performance of Humanitarian and Development Organisations

Purpose: The field of humanitarian and development groups has grown more significant in recent years in tackling crucial socioeconomic concerns in regions afflicted by crises, catastrophes, and conflicts. This study is a systematic literature review of strategy implementation practices and performance of humanitarian and development organizations (HDOs). The general objective of this study is to examine the effect of strategy implementation practices on performance of HDOs, followed by specific objectives, which are to investigate the effect of organizational culture, structure, leadership and resource allocation on performance of HDOs; to evaluate the moderating effect of donor funding conditions on the relationship between strategic implementation practices on performance of HDOs. The stakeholder theory, resource-based theory, and agency theory all serve as foundations for this research. Methodology: A total of 108 articles were identified and systematically reviewed. The research method was customized for three databases: Emerald, google scholar, and EBSCO. The keywords used were: “Strategy implementation practices” AND, “performance “AND “Humanitarian organization” OR “development organization” These researches were conducted between 2017 and 2023. Only scientific journals and research reports published in English and French were admitted. The study was focused on African, Asian and American countries. At this level, 38 records had been excluded, and 70 articles had been extracted. All duplication was extensively scrutinized, the abstracts of the papers were thoroughly verified for deeper analysis in order to ensure the quality and relevance of academic paper. The exclusion criterion was that papers must be publishable in English and must be from the above cited databases and countries. Following the removal of duplicate records, 29 more articles were eliminated. Finally, we chose 41 relevant articles and the below features were extracted: original journal articles, research reports published, articles related to the relevant field in strategy implementation and performance of humanitarian organizations, published within 2017 and 2023. Unique contribution to theory, practice and policy: The study recommends, techniques for collecting and analyzing data, both qualitative and quantitative, should be used. This provides a more detailed and conclusive study finding
Different Types of Vaccine and Their Properties-taking Inactivated and mRNA Vaccines as Examples

In the background of COVID-19, vaccines have become the central point. Various countries and companies investigated vaccines against the virus in various forms, including inactivated, mRNA vaccines, and so on. However, they always talked independently and never compared with each other. The paper discussed their unique dominance and compared their contributions in the battle with COVID-19. In addition, some accessories for vaccines like carriers and adjuvants were also considered, although they may be special for one type of vaccine. The paper concludes that mRNA as a novel vaccine played a more determined role against COVID-19 than inactivated or other vaccines, but all approved drugs could trigger an immune response. Apart from viruses, mRNA is also a potential warrior against cancer. The investigation of inactivated vaccines is still at its recent pace.
Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events.
Plant Poly(ADP-Ribose) Polymerase 1 Is a Potential Mediator of Cross-Talk between the Cajal Body Protein Coilin and Salicylic Acid-Mediated Antiviral Defence

The nucleolus and Cajal bodies (CBs) are sub-nuclear domains with well-known roles in RNA metabolism and RNA-protein assembly. However, they also participate in other important aspects of cell functioning. This study uncovers a previously unrecognised mechanism by which these bodies and their components regulate host defences against pathogen attack. We show that the CB protein coilin interacts with poly(ADP-ribose) polymerase 1 (PARP1), redistributes it to the nucleolus and modifies its function, and that these events are accompanied by substantial increases in endogenous concentrations of salicylic acid (SA), activation of SA-responsive gene expression and callose deposition leading to the restriction of tobacco rattle virus (TRV) systemic infection. Consistent with this, we also find that treatment with SA subverts the negative effect of the pharmacological PARP inhibitor 3-aminobenzamide (3AB) on plant recovery from TRV infection. Our results suggest that PARP1 could act as a key molecular actuator in the regulatory network which integrates coilin activities as a stress sensor for virus infection and SA-mediated antivirus defence.
ANALYSIS OF THE SUSTAINABILITY OF MAIN COMMODITIES OF NUTMEG IN SOUTH ACEH REGENCY (CASE STUDY OF SAMA DUA DISTRICT)

Nutmeg, which is native to Indonesia, is also a superior plantation crop in South Aceh District. As a superior crop that concerns the lives of many farmers, the sustainability of the nutmeg crop needs attention. Sustainability research conducted in Samadua District was conducted to determine the condition of the sustainability of nutmeg plants based on the economic, social, environmental and post-harvest cultivation dimensions. The results showed that in multi-dimensional, social, and environmental dimensions, the nutmeg plant was in a fairly sustainable condition. But the nutmeg crop is less sustainable in the economic dimension, post-harvest cultivation. Therefore, the development of nutmeg in South Aceh District, especially in Samadua District, needs to consider this condition of sustainability status.
Diaspora, curriculum, community: A transnational approach to community-based learning

Introduction The 2020 Black Lives Matter protests sparked by George Floyd’s murder were characterized by countless marches through America’s cities and towns, as well as renewed calls for the removal of controversial memorial statues—sometimes resulting in their immediate defacement or even toppling by protesters. These acts of resistance played out differently in different parts of the USA, targeting most directly memorials to Confederate soldiers and leaders in the American South. But the summer also featured renewed calls for a reconsideration of the polarizing figure of Christopher Columbus, revered by some Italian American ethnics who see the Genoese explorer as inscribed into their own hybrid identity, and simultaneously reviled by other Americans who view him, instead, as a symbol of colonialism and genocide. In Pittsburgh, PA, where a memorial statue of Columbus has stood on prime city property since its October 1958 dedication, new but familiar graffiti soon appeared on the memorial’s granite base, and a petition calling for its removal from city property garnered over 15,000 signatures in just a few weeks’ time before the summer was out. In his formal response to the petition and to the City Art Commission hearings that ensued, then-Mayor Bill Peduto wrote that he was of two minds. The letter announcing his decision to remove the statue and “return it to the Italian American community” called out Columbus’ divisiveness in today’s America, underscoring the “reckoning” that our society has begun to make with his legacy of cruelty to and oppression of indigenous groups. On the other hand, Peduto wrote, a shared Italian American heritage had shaped his ancestors’ vision of Columbus as a navigator, a discoverer, a figure that legitimized Italian immigrants’ “right to be here”; further, the former mayor seemed to be moved by the narrative of “generations of Italian-Americans raising nickels and dimes,
Altered Levels of Blood Glucose and Serum Lipids in Sudanese Patients with Ovarian Cancer

Background: The etiology of ovarian cancer is not well-understood; numerous metabolomics profiling, epidemiological, and hospital-based case control studies have associated abnormal levels of blood glucose and serum lipids with the risk and the prognosis of various types of cancers including ovarian cancer. The association between the risk of the incidence of ovarian cancer and the alterations in the levels of blood glucose and serum lipids is not well defined. Objective: In this study we aimed to compare the levels of blood glucose, triacylglycerols, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol in patients with different stages of ovarian cancer and healthy controls to determine how they relate to the risk and prognosis of ovarian cancer. Methodology: In a case-control cross sectional study, we enrolled ninety-nine Sudanese women, diagnosed with ovarian cancer but had not received any kind of treatment as the study group, and a control group of forty-one age-matched, apparently healthy women. The patients were classified according to the International Federation of Obstetricians and Gynecologists
Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users’ emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).
The Effect of V-Thread and Square Thread Dental Implants on Bone Stresses

The relationship between implant thread design and dental bone arguably has an influence on the distribution of bone stresses. However, the existing data on the influence of the thread profiles on bone stresses is considerably conflicting. For example, some studies concluded that thread shape has a substantial effect on the intensity of bone stresses, while others revealed that thread shape has no effect on the intensity of bone stresses. Accordingly, this study aims to computationally investigate and compare the effect of dental implant thread design on bone stresses under axial loading using a finite element analysis (FEA) approach. A geometrical model of V-thread and square thread implants, with a fixed thread pitch of 0.8 mm and a depth of 42 mm, and the surrounding bone was developed to assess the stresses generated within the implant components and bone structure under a 114 N axial load. The simulation is primarily concerned with the von Mises stresses within the implant components and the surrounding bone. The results demonstrate that the V-thread implant causes extremely high stress on the cortical and cancellous bones compared to the square thread implant. For example, the maximum stresses induced in the cortical bone are 195.3 MPa and 68.8 MPa, while the maximum stresses created in the cancellous bone are 19.7 Mpa and 2.2 Mpa in both designs, respectively. In addition, the cortical bone stresses substantially exceed the implant body stresses in both designs, with maximum stresses of 93.18 Mpa and 41 Mpa for V-thread and square-thread implants, respectively. However, the implant thread shape doesn’t affect the stress distribution in the abutment and screw. In general, the results show that implant thread design can result in featured mechanical stresses in the implant body and bone structure.
Numerical Modeling of the Capsular Ligament Failure for the C2-C3 Segment in the Case of a Frontal Impact

The cervical spine is a complex anatomical structure that mainly stabilizes the head and protects the spinal cord. Injuries of the cervical spine often occur during falls or road accidents and are particularly serious since they generate strong threats of paralysis and death. It should be noted that the ligaments provide cervical stability but their stabilization in case of injury is not yet well investigated. In this context, the objective of the present work is to study the failure of the ligaments by developing a bio-faithful numerical model while using a more realistic geometry of the spinal components and behavior laws that take into account the effect of strain rate and motion amplitudes. In order to validate the results of the study, we conducted a comparison with previous literature studies. It has been found that damage is often supported by intervertebral discs, anterior longitudinal ligaments (ALL) and capsular ligaments (CL) in the case of frontal impact. Indeed, the highest stresses are concentrated in the annulus fibrosus and the capsular ligaments. In this study, we tested the effect of ligament tears on disc behavior, where it was found that the stress rate increased by approximately 6%. The effect of capsular ligament tear orientation was also examined. The obtained results show that the most dangerous inclination was downward at an angle of 45°.
ORCA: Outage Root Cause Analysis in DER-Rich Power Distribution System Using Data Fusion, Hierarchical Clustering and FP-Growth Rule Mining

Power outages have been an inevitable part of power delivery systems. In case of large scale outages with conflicting information in the distribution network with distributed energy resources (DER), recognition of the root causes becomes increasingly complex. Root cause detection is very important for routing the crew and for faster restoration. Number of studies have been conducted on analysis of the outages, planning predictive actions, and improving the restoration process for minimization of the load outages using various statistical and data processing. A comprehensive methodology with the ability to find root causes in quick and efficient manner is still missing. This work proposes an Outage Root Cause Analysis (ORCA) tool, with various internal modules. The proposed ORCA utilizes the available data from diverse set of sensors (Distribution Phasor Measurement Units, metering and relay) within the distribution system (DS) to: a) aggregate using data fusion algorithm: an Ensemble Extended Kalman Filter (EEKF) approach, b) classify events at broader level (fault, breaker operation or cyber-attack with/without load outage) using hierarchical agglomerative clustering in online and offline manners, and c) narrow down the outage root cause to the possible nuanced identified causes (vegetation, animal, weather, wildfire, protection, equipment failure, planned) using the available historical data and the Frequent Pattern-Growth data mining approach. Simulation results demonstrate the superiority of the proposed approach compared to the other existing key approaches using two different test systems and multiple outage with different locations case scenarios.
Effect of Uncertainty in Nasal Airway Deposition of Radioactive Particles on Effective Dose

In the current ICRP human respiratory tract (RT) model (ICRP Publication 66), the deposition of particles in various regions of the RT during natural breathing is modelled by considering the RT as a series of filters, resulting in deposition probabilities for distal portions of the RT being dependent on those of the proximal segments. Thus, uncertainties in regional deposition in proximal segments of the RT are reflected or propagated in uncertainties in deposition in the distal segments of the lung. Experimental data on aerosol particle deposition have demonstrated significant variability in nasal airway (NA) deposition for different individuals studied. This report summarises the impact of introducing variability in NA deposition efficiency on the calculation of effective doses using the ICRP 66 model for selected radionuclides. The computer software LUDEP, modified for this purpose, was used to customise deposition patterns, and effective doses were calculated for several radionuclides ( 111 In, 106 Ru, 60 Co, 210 Po, 238 U and 239 Pu) chosen to represent isotopes with various decay schemes and half-lives. The results indicated significant but particle-size-specific effects of assumed NA deposition efficiencies on the calculated effective doses, which varied typically by factors of five to six. The majority of the variability was associated with direct effects on deposition patterns, but in some cases, alterations of radiation dose distribution within the various target organs also contributed to the variability. These results provide a basis for evaluating uncertainties due to inter-individual differences in deposition patterns for radiation protection and risk analysis.
Mining Salt Tolerance SNP Loci and Prediction of Candidate Genes in the Rice Bud Stage by Genome-Wide Association Analysis

Mining salt tolerance genes is significant for breeding high-quality salt-tolerant rice varieties in order to improve the utilization of saline–alkaline land. In this study, 173 rice accessions were measured for their germination potential (GP), germination rate (GR), seedling length (SL), root length (RL), germination potential relative to salt damage rate (GPR), germination rate relative to salt damage rate (GRR), seedling length relative to salt damage rate (SLR), relative salt damage rate at the germination stage (RSD) and comprehensive relative salt damage rate in the early seedling stage (CRS) under normal and salt stress conditions. Genome-wide association analysis was performed with 1,322,884 high-quality SNPs obtained by resequencing. Eight quantitative trait loci (QTLs) related to salt tolerance traits at the germination stage were detected in 2020 and 2021. They were related to the GPR (qGPR2) and SLR (qSLR9), which were newly discovered in this study. Three genes were predicted as salt tolerance candidate genes: LOC_Os02g40664, LOC_Os02g40810, and LOC_Os09g28310. At present, marker-assisted selection (MAS) and gene-edited breeding are becoming more widespread. Our discovery of candidate genes provides a reference for research in this field. The elite alleles identified in this study may provide a molecular basis for cultivating salt-tolerant rice varieties.
Finite element approximation and iterative solution of a class of mildly non-linear elliptic equations

We describe in this report the numerical analysis of a particular class of nonlinear Dirichlet problems. We consider an equivalent variational inequality formulation on which the problems of existence, uniqueness and approximation are easier to discuss. We prove in particular the convergence of an approximation by piecewise linear finite elements. Finally, we describe and compare several iterative methods for solving the approximate problems and particularly some new algorithms of augmented lagrangian type, which contain as special case some well-known alternating direction methods. Numerical results are presented.
3D MODELING OF DAMAGE GROWTH AND CRACK INITIATION USING ADAPTIVE FINITE ELEMENT TECHNIQUE

In this paper, the continuum damage mechanics model originally proposed by Lemaitre (Journal of Engineering Materials and Technology. 1985; 107: 83-89) is presented through an adaptive nite element method for three-dimensional ductile materials. The macro-crack initiation-propagation criterion is used based on the distribution of damage variable in the continuum damage model. The microcrack closure e ect is incorporated to simulate the damage evolution more realistic. The Zienkiewicz-Zhu posteriori error estimator is employed in conjunction with a weighted Superconvergence Patch Recovery (SPR) technique at each patch to improve the accuracy of error estimation and data transfer process. Finally, the robustness and accuracy of proposed computational algorithm is demonstrated by several 3D numerical examples.
Recurrent Neural Networks for Artifact Correction in HRV Data During Physical Exercise

In this paper, we propose the use of recurrent neural networks (RNNs) for artifact correction and analysis of heart rate variability (HRV) data. HRV can be a valuable metric for determining the function of the heart and the autonomic nervous system. When measured during exercise, motion artifacts present a significant challenge. Several methods for artifact correction have previously been proposed, none of them applying machine learning, and each presenting some limitations regarding an accurate representation of HRV metrics. RNNs offer the ability to capture patterns that might otherwise not be detected, yielding predictions where no prior physiological assumptions are needed. A hyperparameter search has been carried out to determine the best network configuration and the most important hyperparameters. The approach was tested on two extensive multi-subject data sets, one from a recreational bicycle race and the other from a laboratory experiment. The results demonstrate that RNNs outperform by order of magnitude existing methods with respect to the calculation of derived HRV metrics. However, they are not able to accurately fill in individual missing RR intervals in sequence. Future research should pursue improvements in the prediction of RR interval lengths and reduction in necessary training data.