Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedagogical Competence: Effective Communication of Students of Primary School Teacher Education of Universitas Terbuka in Palembang

Teacher is the party who is responsible for continuity of effective communication in learning so the teacher is urged to have a good communicative competence with students in order to obtain effective learning process. In this study teacher is students of primary school teacher education who has worked as a teacher (in-service training) or educator. This study is aimed to know how the competence of teacher in communicating effectively to the students. The research method used is mix research method (Survey and Thematic Qualitative). As a result, the level of effective communicative competence of students of primary school teacher education of UT who are also as teacher of primary school in Palembang is under the standard determined by The Ministry of Education and Culture (Kemendikbud). Hence, some ideas are formulated in Forum Group Discussion (FGD) namely the need of comprehensive training program and are continuously from the related institution, need of teacher’s supplementary book, and need of more action research on effective communication of teacher. Some indicators agreed for effective communication for teacher are (1) the importance of communication with students. (2) Competence and performance of teacher in communicating with the students. (3) Communication with the students in education. (4) Effective communication with the students. (5) Effective communication with students. (6) Obstacles in communication among the students in education. (7) Learning strategy that can build communication and cooperation among the students. (8) Communication with students in learning as educative interaction. The indicator will further be a reference in developing module for improvement of learning program in Universitas Terbuka.
Value for all? Dynamics of partnership formation in occupational healthcare collaboration

Interorganizational relationships take advantages of surrounding networks to create value. However, there is little processual understanding of how cooperative partnerships ‘work’ in healthcare collaboration. From the value creation perspective, their mobilization, management and maintenance are challenging. To understand the value of cooperative partnerships, we explore the dynamics of partnership formation in occupational healthcare collaboration. The empirical data is based on a two-year qualitative case study examining e-value co-creation in healthcare. The research data was obtained through a participatory action research method. We facilitated and followed up a developmental process of the partnership between an occupational health service company and its customer organization. This partnership aimed to add strategic value through the co-creation method to improve the well-being of employees and to promote eHealth solutions. In analyzing the data, we adopted a process orientation that allowed us to explore dynamics in partnership formation and its e-value co-creation. We used Ring and Van de Ven’s [1] framework to examine how cooperative interorganizational relationship develops through the stages of negotiation, commitment and execution. Our longitudinal case study analysis reveals how interaction, mutual sensemaking and institutional logics affect partnership and its value creation. The results show that the formation of a cooperative partnership is a challenging inter-organizational learning process. Our study demonstrates three tensions characterizing the dynamics of partnership: asymmetrical roles and positions between partners (customer and service provider) in co-creation, exploitation of institutionalized practices versus the exploration of new methods for collaboration, and tradeoffs between the operational logic and the co-creation logic. To create value for all in cooperative partnership, we emphasize the necessity of dialogue, mutual trust, interorganizational learning and processual feedback of accomplishments. At its best, cooperative partnership in healthcare collaboration can challenge existing practices of service provision and develop new concepts, roles and tools to promote health and well-being at workplaces through co-creation as a working method in occupational health collaboration.
Analysis of the Impact of Coulomb Stress Changes of Tehoru Earthquake, Central Maluku Regency, Maluku Province

The Tehoru earthquake occurred due to the release of stress in rocks. There is a release of energy as an earthquake as a result of the rock elasticity limit has been exceeded because the rock is no longer able to withstand the stress. One method to determine the distribution of earthquake stress is the Coulomb stress change method. The study aimed to determine the DCS of the Tehoru earthquake, Seram Island, and the effect of the main earthquake stress release on aftershocks. The research results show that the DCS distribution of the Tehoru June 16, 2021 earthquake is shown with negative lobes and positive lobes. The negative lobe is found in an area that is perpendicular to the fault plane and has been identified as having experienced relaxation, so there may be still aftershocks with stress values ranging from (0.0 – 0.3) bar. The dominant positive lobe occurs next to the southern end of the fault plane, too much located in the area of increasing Coulomb stress with values ranging from (0.2 - 0.6) bar
Emotions During the COVID-19 Crisis: A Health Versus Economy Analysis of Public Responses

People all over the world were under severe stress and were concerned about their health after a devastating pandemic struck the world in the form of a novel coronavirus disease (COVID-19) in late December 2019. Many nations imposed strict lockdowns and quarantines, causing citizens to maintain social isolation, throwing many companies to a halt. Thousands of people took to Twitter during these challenging circumstances to express their feelings about being caught in the middle of a storm. Twitter witnessed an outpouring of emotions ranging from fear, anger, and sadness associated with the spread of a novel virus that has no known cure, to voices of support and trust for nations’ official response to the pandemic. In studying the emotional response (anger, fear, and sadness) on Twitter about the COVID-19 crisis, we thus see a tale of two crises unfold—choosing health or economy. We capture collective emotions on social media and investigate the patterns and impact of these negative emotions during various stages of the disease outbreak. It also provides crucial insights to health officials and government agencies on communicating crisis information to the public via social media.
A Framework for Generating Evasion Attacks for Machine Learning Based Network Intrusion Detection Systems

Intrusion Detection System (IDS) plays a vital role in detecting anomalies and cyber-attacks in networked systems. However, sophisticated attackers can manipulate the IDS’ attacks samples to evade possible detection. In this paper, we present a network-based IDS and investigate the viability of generating interpretable evasion attacks against the IDS through the application of a machine learning technique and an evolutionary algorithm. We employ a genetic algorithm to generate optimal attack features for certain attack categories, which are evaluated against a decision tree-based IDS in terms of their fitness measurements. To demonstrate the feasibility of our approach, we perform experiments based on the NSL-KDD dataset and analyze the algorithm performance.
Hazardous Loss of Genetic Diversity through Selective Sweeps in Asexual Populations

With the twofold cost of sex, derived asexual organisms have an immediate reproductive advantage over their sexual sisters. Yet the “twiggy” phylogenetic distribution of asexual lineages implies that they become extinct relatively quickly over evolutionary time. Meanwhile, bacteria and archaea have persisted for billions of years without requiring sexual reproduction. A simple explanation for this difference is that prokaryotes have very large population sizes that are not subject to the accumulation of deleterious mutations, but this implies that drift and mutational meltdown dominate derived asexual populations. Here, we explore a different hazard, quantifying the degree to which genetic variation is lost in asexual populations experiencing selective sweeps. Even though large populations generate diversity by mutation during sweeps, we find that populations that are safe from mutational meltdown may still be reduced to dangerous effective population sizes by sweeps. Thus, ironically, adaptation itself reduces further adaptive potential and may predispose asexual populations to extinction. We derive a simple approximation for the effective population size after a hard sweep and explore the impact of recent sweeps on evolutionary rescue. These factors may help to explain the phylogenetic twigginess of asexuals, the maintenance of sex and recombination, and the evolutionary persistence of prokaryotes.
Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies

Modeling communication networks to predict performance such as delay and jitter is important for evaluating and optimizing them. In recent years, neural networks have been used to do this, which may have advantages over existing models, for example from queueing theory. One of these neural networks is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions. One key simplification is the restriction to a single scheduling policy, which describes how packets of different flows are prioritized for transmission. In this paper we propose a solution that supports multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can handle mixed scheduling policies in a single communication network. Our solution is based on the RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute percentage error under 1% with our extended model on the evaluation data set from the challenge. This takes neural-network-based delay estimation one step closer to practical use.
Type S Non‐Ribosomal Peptide Synthetases for the Rapid Generation of Tailormade Peptide Libraries

Bacterial natural products in general, and non-ribosomally synthesized peptides in particular, are structurally diverse and provide us with a broad range of pharmaceutically relevant bioactivities. Yet, traditional natural product research suffers from rediscovering the same scaffolds and has been stigmatized as inefficient, time-, labour- and cost-intensive. Combinatorial chemistry, on the other hand, can produce new molecules in greater numbers, cheaper and in less time than traditional natural product discovery, but also fails to meet current medical needs due to the limited biologically relevant chemical space that can be addressed. Consequently, methods for the high throughput generation of new-to-nature natural products would offer a new approach to identifying novel bioactive chemical entities for the hit to lead phase of drug discovery programms. As a follow-up to our previously published proof-of-principle study on generating bipartite type S non-ribosomal peptide synthetases (NRPSs), we now envisaged the de novo generation of non-ribosomal peptides (NRPs) on an unreached scale. Using synthetic zippers, we split NRPS in up to three subunits and rapidly generated different bi- and tripartite NRPS libraries to produce 49 peptides, peptide derivatives, and de novo peptides at good titres up to 145 mgL-1. A further advantage of type S NRPSs not only is the possibility to easily expand the created libraries by re-using previously created type S NRPS, but that functions of individual domains as well as domain-domain interactions can be studied and assigned rapidly.
Superposed Epoch Analyses of Electron‐Driven and Proton‐Driven Magnetic Dips

In this study, we statistically investigate the features of magnetic dips by constructing superposed epoch analysis on Van Allen Probe data. Based on the values of electron and proton plasma betas, we categorize dips into two types: electron‐dominant and proton‐dominant. The global distributions of dips are obtained. Superposed epoch analysis on two types reveals a correlation between the magnetic fluctuations and plasma betas. Moreover, the occurrences of butterfly pitch angle distributions of relativistic electrons driven by the magnetic dips are confirmed on a statistical basis. Our results reveal the statistical characteristics of magnetic dips and establish the relationship among the magnetic fluctuations and background plasma parameters, indicating the potentially important role of magnetic dips in the inner magnetosphere dynamics.
Examining the Direction of Effects Between COVID-19 Experiences, General Well-Being, Social Media Engagement, and Insomnia Symptoms Among University Students

The present longitudinal study assessed the direction of effects between COVID-19 experiences, general well-being, socio-emotional motives for online communication, problematic social media use, and insomnia symptoms, among a sample of emerging adults at university. Participants (N = 619; 64% female; 66% White) completed an online survey 5 months apart during the COVID-19 pandemic. Results from an autoregressive cross-lagged model showed that poorer general well-being at baseline predicted greater endorsement of socio-emotional motives for online communication and higher insomnia symptoms over time. Negative COVID-19 experiences predicted higher social (but not emotional) motives for online communication. Lastly, insomnia symptoms and more problematic social media use predicted more negative COVID-19 experiences 5 months later (but not vice versa). Understanding the mechanisms of these associations will provide a better understanding of the factors that promote positive psychosocial functioning among emerging adults during the current COVID-19 pandemic and may inform psychosocial adjustment during future pandemics.
Neural basis of learning to perceive speech through touch using an acoustic-to-vibrotactile speech sensory substitution

The goal of sensory substitution is to convey the information transduced by one sensory system through a novel sensory modality. One example is vibrotactile (VT) speech, for which acoustic speech is transformed into vibrotactile patterns. Despite an almost century-long history of studying vibrotactile speech, there has been no study of the neural bases of VT speech learning. We here trained hearing adult participants to recognize VT speech syllables. Using fMRI, we showed that both somatosensory (left post-central gyrus) and auditory (right temporal lobe) regions acquire selectivity for VT speech stimuli following training. The right planum temporale in particular was selective for both VT and auditory speech. EEG source-estimated activity revealed temporal dynamics consistent with direct, low-latency engagement of right temporal lobe following activation of the left post-central gyrus. Our results suggest that VT speech learning achieves integration with the auditory speech system by “piggybacking” onto corresponding auditory speech representations. Significance statement In sensory substitution, the information conveyed by one sensory system is used to replace the function of another. Blind individuals, for example, can learn to use a visual-to-acoustic sensory substitution device to navigate the world. We tested the hypothesis that sensory substitution can more generally occur in typical individuals, by exploiting the existence of multi-sensory convergence areas in the brain. We trained hearing participants to recognize speech syllables presented as vibrotactile stimulation patterns. Using fMRI and EEG, we show that vibrotactile speech learning integrates with the auditory speech system by “piggybacking” onto corresponding auditory speech representations. These results suggest that the human brain can bootstrap new senses by leveraging one sensory system to process information normally processed by another.
Aqueous humor proteomic analysis of acute angle-closure glaucoma with visual field loss

Background Acute angle-closure glaucoma (AACG) is an ophthalmic emergency that occurs over the course of hours or days and may cause irreversible blindness if not treated immediately. In most cases, optic nerve damage is the cause of visual field (VF) loss in AACG. There has been no reliable biomarker found to evaluate optic nerve damage to date. Aqueous humor (AH) proteome analysis might reveal the proteomic alterations in AACG and provide helpful clues in the search for an AH biomarker of optic nerve damage and VF loss. Methods In this study, we used the AH proteome to explore the functions of differentially expressed proteins (DEPs) during disease progression. The AH proteins from the early-stage group and late-stage group were extracted and analyzed by the data-independent acquisition (DIA) method. The DEPs functions were annotated, and parallel reaction monitoring (PRM) was used to validate the key DEPs. Results A total of 87 DEPs were found. Gene Ontology analysis showed that most DEPs were enriched in immunology, hemodynamics, and apoptosis. Ingenuity pathway analysis found that vascular endothelial growth factor (VEGF) signaling, the production of reactive oxygen species (ROS) in macrophages, and the nuclear factor erythroid 2-related factor 2 (NRF2)-mediated oxidative stress response were active pathways in the late stage of AACG. The mechanism of retinal ganglion cell (RGC) death was hypothesized on the basis of DEP functional analysis. A total of 20 DEPs were validated by using PRM, and prostaglandin-H2 D-isomerase was found to have the potential to evaluate optic nerve damage. Conclusions This study showed that AH proteomic analysis could reveal the proteomic alterations in the pathogenesis of VF loss in AACG and help to provide objective protein biomarkers to evaluate VF loss. These findings will benefit the application of the AH proteome to clinical research.
AUTOMATED RECOGNITION OF DEPRESSION FROM FEWER-SHOT LEANING IN RESTING-STATE fMRI WITH ReHo USING DEEP CONVOLUTIONAL NEURAL NETWORK

Up to now, there is still the absence of research about depression recognition using resting-state functional magnetic resonance imaging (rest_fMRI) and deep learning. Previous studies have shown that regional homogeneity (ReHo) of rest_fMRI (rest_ReHo_fMRI) is a characterization of the functional synchronization of adjacent voxels in brain regions, and the mental and behavioral abnormalities in depression are due to an imbalance of ReHo synchronization in some brain functional areas. Accordingly, this paper presents a method for depression recognition using rest_ReHo_fMRI. First, the rest_ReHo_fMRI is extracted from the preprocessed rest-fMRI by calculation. Then, deep convolutional networks (such as VGG16) pretrained on ImageNet are used to automatically complete extracting the classification features from rest_ReHo_fMRI. Finally, the Kernel Extreme Learning Machine (KELM) was used to classify the depression. The results of the test set show that the proposed method achieves 89.07% in sensitivity and 89.74% in specificity. This study suggests that features of rest_ReHo_fMRI can be used as biomarkers to distinguish depression from normal people.
Stability analysis and feedback design of a direct current–direct current power converter by vector Lyapunov function

To prove the stability of hybrid systems, where, several subsystems are in series–parallel arrangement, Vector Lyapunov functions are appropriate substitutes for the scalar Lyapunov ones. By assigning a scalar Lyapunov function to each subsystem, the stability of the whole hybrid system can be analyzed. In this paper, having reviewed the preliminaries, a direct current (DC) –DC converter working as an incremental voltage source is selected. We show that the conditions of the vector Lyapunov stability are satisfied by deriving the variation interval for the feedback coefficients and obtaining the appropriate feedback factor given the desired output. Simplicity and less restrictedness of vector Lyapunov method is shown comparatively. All the derived mathematical relations are validated by numerical computations.
Dietary Resin Acid Concentrate Improved Performance of Broiler Chickens and Litter Quality in Three Experiments

Simple Summary Resin acids are antimicrobial and anti-inflammatory compounds derived from spruce and pine trees. In a previous study, in which a mixture of resin acids and fatty acids from pine and spruce trees was added to the feeds of broiler chickens, they grew heavier, and their footpads stayed in better condition. Here, three separate trials were conducted to study whether dietary resin acids without the fatty acids have the same effects on broiler chickens. Broiler chicken diets were amended with different dosages of a resin acid concentrate product. Bird weight gain increased, and the efficiency of broiler production was improved when the resin acid concentrate was added to the diet at 125–250 g/ton. The litter material of pens stayed drier in resin acid concentrate groups, which may indicate better intestinal condition of the birds. However, the footpad condition was unaffected by the experimental diets. The results suggest that the resin acid concentrate improves bird weight gain and the efficiency of broiler production. Therefore, the resin acid concentrate may be a promising feed additive for broiler chickens. Abstract Dietary coniferous resin acids have previously been suggested to support the intestinal integrity of broiler chickens by reducing mucosal collagen degradation. The present study examined the effects of resin acid concentrate (RAC) on broiler performance and litter quality. In trial 1, RAC was added to diets at 0, 125, 250, or 1250 g/ton, while in trials 2 and 3, RAC dosing was 0 or 175 g/ton. Bird weight, feed consumption, mortality, feed conversion ratio (FCR), European Efficiency Index (EEI), litter moisture, and footpad dermatitis (FPD) lesions were measured. In trial 1, RAC at 125 and 250 g/ton improved weight gain and EEI, while RAC at 1250 g/ton group did not differ from control. Feed consumption, FCR, FPD scores and mortality were similar in all treatments, but litter quality was improved by all doses of RAC. In trials 2 and 3, RAC increased the final weight of birds, improved FCR, EEI, and litter quality, but had no effects in other parameters. In summary, RAC at 125–250 g/ton improved bird performance and thus shows promise as a feed additive. The dryer litter in RAC treatments may suggest improved intestinal condition as a response to in-feed resin acids.