Miracle-dz/UniFAR-FTUs huggingface dataset:
DETERMINANTS OF A TODDLER'S VISIT TO POSYANDU DURING THE COVID-19 PANDEMIC IN JAMBI CITY

Introduction, the Covid-19 pandemic and the Large-Scale Social Control Policy (PSBB) have a very significant effect on health services, especially the visit of mothers under five to posyandu. The purpose of this study was to determine the factors that influence the visit of toddler mothers to posyandu at the Kebun Kopi Health Centre, Jambi City. Materials and Methods, this study is descriptive analytic with Cross Sectional approach with accidental sampling technique. The respondents in this study were mothers who had toddlers aged 12-59 months, the working area of the Kebun Kopi Health Centre, Jambi City was 101 people. The analysis in this study used univariate analysis and bivariate analysis using Chi Square. The results/findings, this study showed that the variables related to visits by mothers of toddlers to posyandu during the Covid-19 pandemic were the role of cadres (p = 0.000), family support (p = 0.003) and anxiety (p = 0.034), while unrelated factors were knowledge (p = 0.158), sources of information (p = 0.561), and work (p = 0.290). In conclusion, the factors that influence the visit of mothers of toddlers to posyandu during the Covid-19 pandemic are the role of cadres, family support and anxiety. The need for education about Covid-19 transmission to mothers under five and posyandu strictly implements health protocols.
Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images

Optical and synthetic aperture radar (SAR) images provide complementary information to each other. However, the heterogeneity of same-ground objects brings a large difficulty to change detection (CD). Correspondingly, transformation-based methods are developed with two independent tasks of image translation and CD. Most methods only utilize deep learning for image translation, and the simple cluster and threshold segmentation leads to poor CD results. Recently, a deep translation-based CD network (DTCDN) was proposed to apply deep learning for image translation and CD to improve the results. However, DTCDN requires the sequential training of the two independent subnetwork structures with a high computational cost. Toward this end, a concatenated deep-learning framework, multitask change detection network (MTCDN), of optical and SAR images is proposed by integrating the CD network into a complete generative adversarial network. This framework contains two generators and discriminators for optical and SAR image domains. Multitask refers to the combination of image identification by discriminators and CD based on an improved UNet++. The generators are responsible for image translation to unify the two images into the same feature domain. In the training and prediction stages, an end-to-end framework is realized to reduce cost. The experimental results on four optical and SAR datasets prove the effectiveness and robustness of the proposed framework over eight baselines.
Taxonomic appraisal of Fagraea ceilanica (Gentianaceae), and description of a new tree species from the Bird's Head Peninsula, western New Guinea

Abstract Low, Y.W. , J.F. Wanma, B. Ibrahim, C.D. Heatubun & V.A. Albert (2023). Taxonomic appraisal of Fagraea ceilanica (Gentianaceae), and description of a new tree species from the Bird's Head Peninsula, western New Guinea. Candollea 78: 167–175. In English, English abstract. DOI: http://dx.doi. org/10.15553/c2023v782a8 A new rainforest understorey tree, Fagraea christinae Y.W. Low & V.A. Albert (Gentianaceae), is described here based on three collections from the Bird's Head Peninsula, western New Guinea. The new species was previously included in the species complex F. ceilanica Thunb. s.l. Fagraea christinae is superficially similar to F. annulata Hiern and F. berteroana A. Gray ex Benth. in having stamens inserted upon a fleshy ring on the inner surface of the corolla tube, but differs from those two taxa in having crenate bracteoles and calyx lobes. The new species is provided with line drawings, field photographs and a distribution map. Received on April 5, 2022. Accepted on October 5, 2023. First published online on November 20, 2023.
Predicting Acute Kidney Injury in Trauma Patients: Biomarkers as Early Indicators

Background Acute kidney injury (AKI) is a common cause of organ failure in patients after major trauma and is associated with increased morbidity and mortality. Early identification of patients at risk enables the implementation of a bundle of supportive care, which reduces the incidence of AKI. The primary objective of our study was to investigate whether the levels of biomarkers on admission predicted the onset of early AKI in patients with serious injuries. Material/Methods This prospective observational study included 98 adult patients of both sexes with a serious injury (injury severity score >16). At admission, blood samples were taken, and creatinine, neutrophil gelatinase-associated lipocalin (NGAL), high mobility group box 1 (HMGB-1), and markers of rhabdomyolysis (creatine kinase, myoglobin) were evaluated. The patients were provided with standard resuscitation care, and the occurrence of AKI was monitored during the first 7 days after admission to the Intensive Care Unit, according to the Kidney Disease Improving Global Outcomes diagnostic criteria. Results AKI occurred in 25 (25.5%) patients, in whom the admission levels of HMGB-1, NGAL, creatinine, and myoglobin were significantly higher than in non-AKI patients (48.3±98.4 vs 113.0±209.4 μg/L, P=0.006; 150.2±349.9 vs 181.4±152.2 μg/L, P=0.004; 83.1±20.8 vs 118.8±32.2 μmol/L, P<0.005; 2734.4±2214.5 vs 4182.3±2477.1 μg/L, P=0.008, respectively). Creatine kinase was 14.5±9.2 μkat/L in non-AKI patients and 13.7±7.9 μkat/L in AKI patients (P=0.916). Conclusions Admission levels of HMGB-1, NGAL, creatinine, and myoglobin predicted the risk of AKI in severely injured patients.
Estimation of genetic parameters related to morpho-agronomic traits of Durum Wheat (Triticum turgidum var. durum)

Grain yield is a complex trait and is greatly influenced by various environmental conditions. A 2-year field investigation was carried out to estimate genetic parameters and relationship among morpho-agronomic traits studied on 12 advanced durum wheat and two checks under rainfed conditions. The investigated variables included the grain yield (YLD), plant height (PH), number of tiller per plant (NT), peduncle length (PL), flag length (FL), leaf dry weight (LDW), stem dry weight (STW), spike dry weight (SPW), spike height (SH), leaf area index (LAI), crop growth rate (CGR), relative growth rate (RGR), leaf area ratio (LAR) and net assimilation rate (NAR). Combined variance analysis showed a significant variation among genotypes for the traits PH, NT, PL, FL, LDW, STW, SPW, SH, LAR and NAR. High correlations were found among the PL, LDW, STW, SPW, LAR and NAR. Heritability was high for PH, PL, LDW, STW and NAR. High genetic gains were observed for YLD, NT, PL, LDW, STW, SPW, LAR and NAR. The effects of year and year × genotype showed non-significant effects.
A Profiling Float’s Sense of Ice

Abstract The Argo project intends to continuously monitor temperature and salinity of the upper 2000 m of the global ocean by use of autonomous, vertically profiling floats. They are currently generating the largest oceanographic dataset that ever existed, covering most of the world’s oceans. However, the use of these instruments in the polar oceans is seriously impeded by the presence of sea ice, as floats are hindered from transmitting their profile data or, even more seriously, potentially damaged when ascending to, or being at, the ice-covered sea surface. The authors present a cost neutral ice sensing algorithm (ISA), which alerts for the likely presence of sea ice. In this event, the profile is aborted and no surfacing attempted. To retrospectively track floats that actively remained under the sea ice because of ISA, acoustic tracking via the RAFOS technique was tested in the Weddell Sea. Last but not least, the most recent version of floats features the option of interim storage of profiles that co.
Characterization and Test of Intermittent Over RESET in RRAMs

Resistive Random Access Memories (RRAMs) are being commercialized with significant investment from several semiconductor companies. In order to provide efficient and high-quality test solutions to push high-volume production, a comprehensive understanding of manufacturing defects is significantly required. This paper identifies and characterizes the over-RESET phenomenon based on silicon measurements. In our case study, 30% cycles suffered from intermittent extremely high resistance state exceeding the high resistance state criteria. The paper shows the limitations of conventional defect modeling based on linear resistors. To address this challenge, the Device-Aware (DA) defect modeling method is applied; a model of the defective RRAM device is developed and calibrated using measurements to accurately describe the impact of the defect on the electrical behavior of the memory device. Afterward, fault analysis is performed based on the DA defect model, and appropriate fault models are introduced; they show that the DA defect model will sensitize deep (extremely high resistance) state faults. Finally, dedicated test solutions for over-RESET devices are proposed.
Polymeric reinforcements for cellularized collagen-based vascular wall models: influence of the scaffold architecture on the mechanical and biological properties

A previously developed cellularized collagen-based vascular wall model showed promising results in mimicking the biological properties of a native vessel but lacked appropriate mechanical properties. In this work, we aim to improve this collagen-based model by reinforcing it using a tubular polymeric (reinforcement) scaffold. The polymeric reinforcements were fabricated exploiting commercial poly (ε-caprolactone) (PCL), a polymer already used to fabricate other FDA-approved and commercially available devices serving medical applications, through 1) solution electrospinning (SES), 2) 3D printing (3DP) and 3) melt electrowriting (MEW). The non-reinforced cellularized collagen-based model was used as a reference (COL). The effect of the scaffold’s architecture on the resulting mechanical and biological properties of the reinforced collagen-based model were evaluated. SEM imaging showed the differences in scaffolds’ architecture (fiber alignment, fiber diameter and pore size) at both the micro- and the macrolevel. The polymeric scaffold led to significantly improved mechanical properties for the reinforced collagen-based model (initial elastic moduli of 382.05 ± 132.01 kPa, 100.59 ± 31.15 kPa and 245.78 ± 33.54 kPa, respectively for SES, 3DP and MEW at day 7 of maturation) compared to the non-reinforced collagen-based model (16.63 ± 5.69 kPa). Moreover, on day 7, the developed collagen gels showed stresses (for strains between 20% and 55%) in the range of [5–15] kPa for COL, [80–350] kPa for SES, [20–70] kPa for 3DP and [100–190] kPa for MEW. In addition to the effect on the resulting mechanical properties, the polymeric tubes’ architecture influenced cell behavior, in terms of proliferation and attachment, along with collagen gel compaction and extracellular matrix protein expression. The MEW reinforcement resulted in a collagen gel compaction similar to the COL reference, whereas 3DP and SES led to thinner and longer collagen gels. Overall, it can be concluded that 1) the selected processing technique influences the scaffolds’ architecture, which in turn influences the resulting mechanical and biological properties, and 2) the incorporation of a polymeric reinforcement leads to mechanical properties closely matching those of native arteries.
Information and health-related beliefs as determinants of fish consumption

Consumers’ cognitive mechanisms and their perception of product properties are markedly affected by information. This chapter focuses on consumers’ information needs and interests related to fish. The objective is to explore consumers’ use of internal and external information sources and their use of information cues with regard to fish. Qualitative exploratory research was performed in May 2004 through focus group discussions in two European countries: Belgium and Spain. Personal sources are found as the most important information sources with regard to fish. Although a majority of consumers use mandatory information cues on fish labels, they express doubts whether information provided on the labels can be trusted. People who are more experienced and have higher familiarity with fish, seem to be more efficient in searching and using information. Instead of providing one message for the consumers, segmentation and targeted information provision is recommended. This chapter is compiled and adapted from: Pieniak, Z. , Verbeke, W. , Fruensgard, L. , Brunso, K. & Olsen, S.O. (2004). Determinants of fish consumption: Role and importance of information. Polish Journal of Human Nutrition and Metabolism, 31 (Suppl.2), 409-414. Pieniak, Z. , Verbeke, W. , Brunso, K. & Olsen, S.O. (2007). Consumer interest in fish information, traceability and labelling: exploratory insights. Journal of International Food and Agribusiness Marketing, 19 (2&3), 117-141. Chapter II Consumer interest in fish information and labeling: exploratory insights
Benchmark Study of the Electronic States of the LiRb Molecule: Ab Initio Calculations with the Fock Space Coupled Cluster Approach

Accurate potential energy curves (PECs) are determined for the twenty-two electronic states of LiRb. In contrast to previous studies, the applied approach relies on the first principle calculations involving correlation among all electrons. The current methodology is founded on the multireference coupled cluster (CC) scheme constructed within the Fock space (FS) formalism, specifically for the (2,0) sector. The FS methodology is established within the framework of the intermediate Hamiltonian formalism and offers an intruder-free, efficient computational scheme. This method has a distinctive feature that, when applied to the doubly ionized system, provides the characteristics of the neutral case. This proves especially beneficial when investigating PECs in situations where a closed-shell molecule dissociates into open-shell fragments, yet its double positive ion forms closed-shell species. In every instance, we successfully computed continuous PECs spanning the entire range of interatomic distances, from the equilibrium to the dissociation limit. Moreover, the spectroscopic characteristic of various electronic states is presented, including relativistic effects. Relativistic corrections included at the third-order Douglas-Kroll level have a non-negligible effect on the accuracy of the determined spectroscopic constants.
Structure-Aware Dialogue Modeling Methods for Conversational Semantic Role Labeling

Conversational semantic role labeling (CSRL) is believed to be a crucial step toward dialogue understanding. By incorporating the CSRL information into the conversational models, previous work (Xu et al., 2021) has confirmed the usefulness of CSRL to downstream conversation-based tasks, including multi-turn dialogue rewriting and multi-turn dialogue response generation. However, (Xu et al., 2021) found that the quality of the extracted CSRL structures would consequently affect the performance of downstream dialogue tasks while the performance of existing CSRL models is still unsatisfactory. There are two major problems in existing CSRL models to handle predicate-aware and conversational structural information. First, they ignore the fact that explicitly correlating the predicate and the context utterances could help the model better identify the arguments. Secondly, these models do not encode some vital conversational structural information, such as the speaker information which is necessary for modeling inter-speaker dependency. In this paper, we model the conversational structure-aware features based on three components: 1) the predicate-aware module which aims to capture rich correlations between the predicate and utterances; 2) a speaker-aware graph network which explicitly encodes the speaker-dependent information; 3) a novel structure-aware dialogue modeling method for the model warm-up. Experimental results on benchmark datasets show that our model significantly outperforms the baselines. We also examine the efficiency of our model and its effectiveness in low-resource scenarios. We find that our model can achieve better performance with less training time and training data than the existing models. In addition, further improvements are observed when applying the CSRL information extracted by our model into downstream dialogue tasks, which consistently indicates the superiority of our model.
Incentive Mechanism for Federated Learning With Random Client Selection

Federated learning (FL) is a distributed machine learning framework allowing edge devices (a.k.a clients) to participate in training while protecting their privacy. While much research in this field focuses on improving training performance and reducing communication costs, how to incentivize clients to participate in FL still remains a challenge. Most existing FL algorithms assume that clients voluntarily participate in the training process, which is unrealistic. This paper proposes an incentive mechanism for FL servers to motivate clients to contribute their data and computing power to local training. The mechanism consists of two steps. First, a subset of clients is selected randomly under an importance sampling scheme. Then, the interaction between the server and the subset of sampled clients is modeled as a Stackelberg game, where the server releases offers to the clients based on their potential contributions. The clients then decide how much data and computation to contribute. We prove that the client-level subgame of the Stackelberg game has a subgame equilibrium that can be written in a semi-closed form. We also propose an approximation algorithm for computing the subgame equilibrium of the server's level subgame. Our simulation results verify the analysis and demonstrate the effectiveness of the proposed mechanism.
A feature fusion-based attention graph convolutional network for 3D classification and segmentation

Among all usual formats of representing 3D objects, including depth image, mesh and volumetric grid, point cloud is the most commonly used and preferred format, because it preserves the original geometric information in 3D space without any discretization and can provide a comprehensive understanding of the target objects. However, due to their unordered and unstructured nature, conventional deep learning methods such as convolutional neural networks cannot be directly applied to point clouds, which poses a challenge for extracting semantic features from them. This paper proposes a feature fusion algorithm based on attention graph convolution and error feedback, which considers global features, local features and the problem of the features loss during the learning process. Comparison experiments are conducted on the ModelNet40 and ShapeNet datasets to verify the performance of the proposed algorithm, and experimental results show that the proposed method achieves a classification accuracy of 93.1% and a part segmentation mIoU (mean Intersection over Union) of 85.4%. Our algorithm outperforms state-of-the-art algorithms, and effectively improves the accuracy of point cloud classification and segmentation with faster convergence speed.
Ingestion of triglycerides containing medium- and long-chain fatty acids can increase metabolism of ingested long-chain triglycerides in overweight persons

Introduction Medium-chain fatty acids (MCFAs) have attracted considerable attention for preventing or improving obesity, which is a recognized risk factor for lifestyle-related diseases. Medium- and long-chain triglycerides (MLCTs) are expected to improve the metabolism of ingested long-chain triglycerides (LCTs). However, previous studies have reported mixed results. In this study, the effect of ingestion of MLCTs was evaluated on the metabolism of LCTs and compared to the ingestion of rapeseed oil (control oil). Methods A randomized, double-blind, placebo-controlled crossover study was performed among sedentary participants with BMIs ranging from 25 below 30 kg/m2. Thirty participants were asked to ingest either 14 g of MLCTs or a control oil for 4 weeks. The metabolism of ingested LCTs was evaluated by measuring isotopically labeled carbon dioxide released by the degradation of carbon-13 (13C)-labeled LCTs. Results Ingestion of MLCTs markedly enhanced the metabolism of ingested LCTs by comparison to the control oil. Conclusion The findings of this study suggest that ingestion of MLCTs may enhance the metabolism of dietary LCTs through activation of β-oxidation in liver mitochondria, which may increase the metabolic kinetics of ingested long-chain fatty acid (LCFAs). Clinical trial registration https://center6.umin. ac.jp/cgi-open-bin/ctr/ctr_view. cgi? recptno=R000053101, identifier: UMIN000046604.
Role of colour Doppler in predicting foetal outcome in maternal anaemia

Context: Anaemia is the most common nutritional deficiency in Indian pregnant females. It ensues foetal hypoxia resulting in different compensatory mechanisms in the foetus which may result in adverse perinatal outcomes. Colour Doppler can be used to measure these hemodynamic changes of the foetus ahead of the clinical manifestations, guiding the obstetrician for the appropriate management and circumventing any dire complication. Aims: The aim of this study is to detect foetal haemodynamic changes associated with maternal anaemia and assess the parameters which predict these changes accurately. Settings and Design: Prospective cohort study. Materials and Methods: Two hundred and forty pregnant females in the third trimester, divided into four groups based on their haemoglobin levels in the non-anaemic, mild, moderate and severe anaemic groups, were included in the study. These patients were followed up for foetal outcome in terms of effective foetal weight, APGAR score and neonatal intensive care unit admission. Statistical Analysis Used: Analysis of Variance Appearance, Pulse, Grimace, Activity and Respiration (ANOVA) test was used to compare the quantitative variables. SPSS software was used. Results: The middle cerebral artery (MCA) Doppler indices and cerebroplacental ratio (CPR) values were increasing while umbilical Doppler indices were decreasing with the increasing severity of anaemia. CPR was found to be the most sensitive predictor for foetal outcome. Conclusions: Maternal anaemia results in foetal hypoxia which can be measured in terms of foetal Doppler indices. CPR was found to be more sensitive than the umbilical or MCA in predicting foetal hypoxia and in turn the perinatal outcome of foetuses of anaemic pregnant females. The foetuses with low CPR values will require urgent intervention to improve the outcomes.