Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Suplementasi Fe dan Vitamin C terhadap Hemoglobin dan Indeks Eritrosit Remaja Putri

Teenager girls are at risk of suffering from iron nutritional anemia. Anemic teenage girls also have less vitamin C intake. Anemia has an impact on learning achievement, ease of infection and impact when pregnant in the future. The study aimed to find out the effect of giving Fe and vitamin C tablets. The research design was a one-group pre-post test, using 41 Health Analyst female students from Tanjungkarang Health Polytechnic, which were determined by random sampling. Blood added tablets containing 60mg Fe, 400mcg folate, and 500mg vitamin C tablets were given once/week for 17 days (3 tablets). Hb levels were measured before and after supplementation using a digital analyzer MINDRAY BC-3600. Data collection was conducted from August-September 2020. The proportion of Hb <12mg/dl decreased from 20 (48,78%) at before to 16 (39,02%) subject after supplementation. The mean Hb level before supplementation was 12,15mg/dl, MCV 80,11fL, MCH 25,85 lpg and MCHC 32,22% and after supplementation, the mean of Hb was 12,05mg/dl, MCV 79,83 MCH 26,45, and MCHC 33,07. The results of statistic test showed that supplementation had a significant effect on Hb levels (p-value=0,000) MCV (p-value=0,000), MCH (p-value=0,000), and MCHC (p-value=0,000). Supplementation can reduce the proportion of anemia and improve the erythrocyte index. Another research with longer duration time and nutritional education combination is needed to confirm the increasing of nutrient intake (Fe, vitamin C) and Hb level.
Model-Agnostic Fast Adaptive Multi-Objective Balancing Algorithm for Multilingual Automatic Speech Recognition Model Training

This paper regards multilingual automatic speech recognition model training as a multi-objective problem because learning different languages may conﬂict, necessitating a trade-off. Most previous works on multilingual ASR model training mainly used data sampling to balance the performance of multiple languages but ignore the conﬂicts between different languages, resulting in an imbalance in multiple languages. The language-speciﬁc parameters of the multilingual ASR model are updated by the single language gradients while the update of the shared parameter is jointly determined by the gradient of every language on its shared parameter, namely shared gradient. Therefore, we propose a model-agnostic fast adaptive (MAFA) multi-objective balancing algorithm to balance multiple languages by avoiding the mutual interferences between their shared gradients. In the algorithm, based on the decrease in the training loss, we dynamically normalize the shared gradient magnitudes representing the speed of learning to balance the learning speed. To evenly learn multiple languages, the language with the worst performance is selected, and a balancing gradient nearest to the normalized gradient of the selected language and positively correlated with other normalized ones is obtained to eliminate the mutual interferences. The model trained by MAFA outperforms the baseline model on the Common Voice corpus.
Insights on Neural Representations for End-to-End Speech Recognition

End-to-end automatic speech recognition (ASR) models aim to learn a generalised speech representation. However, there are limited tools available to understand the internal func-tions and the effect of hierarchical dependencies within the model architecture. It is crucial to understand the correlations between the layer-wise representations, to derive insights on the relationship between neural representations and performance. Previous investigations of network similarities using correlation analysis techniques have not been explored for End-to-End ASR models. This paper analyses and explores the internal dynamics between layers during training with CNN, LSTM and Transformer based approaches using Canonical correlation analysis (CCA) and centered kernel alignment (CKA) for the experiments. It was found that neural representations within CNN layers exhibit hierarchical correlation dependencies as layer depth increases but this is mostly limited to cases where neural representation correlates more closely. This behaviour is not observed in LSTM architecture, however there is a bottom-up pattern observed across the training process, while Transformer encoder layers exhibit irregular coefﬁciency correlation as neural depth increases. Altogether, these results provide new insights into the role that neural architectures have upon speech recognition performance. More speciﬁcally, these techniques can be used as indicators to build better performing speech recognition models.
Peptide KRP Conjugated with Doxorubicin Exerts Anti-Tumor Activity by Regulating RPS6KA2 in Osteosarcoma

Objectives: Osteosarcoma (OS) is the most common primary solid malignant tumor of the bone in adolescents. Conventional treatment of OS by surgery and chemotherapy is not effective and the prognosis is poor. Our previous study demonstrated that a novel cell-penetrating peptide (KRP) that, coupled to doxorubicin (DOX), allowed specific tumor targeting. However, the underlying molecular mechanisms of the KRP-DOX antitumor effect were not completely elucidated. Therefore, the present work aimed to identify key candidate genes by integrated bioinformatics analysis. Methods: Diﬀerentially expressed genes (DEGs) were screened using the Network Analyst. The functions and pathway involvements of the DEGs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. The protein-protein interaction (PPI) network was used to identify hub genes. In addition, quantitative RT-PCR (qRT-PCR) and Western blotting were performed to assess the expression level of candidate biomarkers in OS cells after KRP-DOX treatment. Results: A total of 790 DEGs were identiﬁed. GO functional analysis and KEGG pathway analysis demonstrated that the DEGs were mostly enriched in the ribosome. DEGs were visualized by PPI networks. After treatment of OS cells with KRP-DOX, the downregulated ribosomal protein S6 kinase A2 (RPS6KA2) was found to be closely related to inhibition of OS proliferation. In agreement with the bioinformatics analysis, qRT-PCR and western blot results showed low expression of RPS6KA2 in osteosarcoma cells in the KRP-DOX treatment group.Conclusions: RPS6KA2 is signiﬁcantly associated with the KRP-DOX anti-tumor effect and may serve as a candidate biomarker and therapeutic target for OS.
Nanoparticles based sunscreens provoke adverse effects on marine microalgae Dunaliella tertiolecta

Sunscreens represent one of the main sources of engineered TiO2 and ZnO nanoparticles (NPs) in coastal ecosystems, especially during the summer period. To understand the effective environmental impact of NPs in commercial sunscreens, this study assesses the effect of TiO2 and ZnO NPs extracted from three commercial products (S2; S10; S11) upon marine microalgae Dunaliella tertiolecta by evaluating its growth inhibition, oxidative stress and DNA damage. Algae were exposed to both the sunscreen formulations containing NPs and a sunscreen without NPs. Results showed that algae growth inhibition was affected by the extracted NPs (EC20 S2: 9.2 mg Ti L−1; EC20 S10: 0.49 mg Ti L−1; EC20 S11: 14 mg Ti L−1), and the algae was generally biostimulated by the sunscreen formulations, due to the presence of other components that could enhance algal growth. Among the sunscreens containing NPs, relevant effects, mainly as ROS production and DNA damage, were evidenced when both TiO2 and Zn were present (S10; S11). Moreover, lower effects were always observed for the sunscreens and the corresponding extracts, compared to the effects of industrial NPs. These findings suggest that the product formulation may mitigate toxic effects of NPs either by direct modification of properties or by providing organic and inorganic nutrients that biostimulate microalgae growth. The NPs extracted from sunscreens showed responses that cannot be predicted solely from the response of industrial nanoparticles, suggesting that the aging in sunscreens can influence the final toxicity of nanoparticles. In conclusion, the adverse effects of sunscreens upon microalgae could not be solely attributed to the NPs, but also to the combined action of multiple components.
“Flatten the Curve”: Data-Driven Projections and the Journalistic Brokering of Knowledge during the COVID-19 Crisis

Abstract The Coronavirus has prompted an urgent need for guidance and practical intervention. Newsmakers responded to this demand by providing outlooks that plotted the contagion’s contours against a host of parameters. The article looks back at this acute area of sensemaking where journalistic forecasts, epidemiological modelling, and policy measures intertwined. It examines how possible courses of the pandemic were displayed and discussed in the multimodal infographics and reports of data journalistic news products. The estimations predominantly choose to take the form of bell-shaped curves which conceived of the disease as a kind of wave that should, after reaching its peak, flatten out again. Confronted with an immense degree of uncertainty around the illness and an ambiguous environment of conflicting meanings and explanations, we argue that this predictive newswork fulfilled some of the journalistic functions of brokering knowledge. By giving cogent visual form to the incoherent prognoses, it raised awareness of the available models and rendered COVID-19’s potential developments accessible to policy makers and the public. By comparing sources, the data-driven forecasts fostered engagement with the spectrum of outlooks and the uncertainty they entailed. Furthermore, the news pieces connected consonant sources from science and public health institutions.
Basic Regularities of Vertical Turbulent Exchange in the Mixed and Stratified Layers of the Black Sea

Purpose. The purpose of the study is to assess the coefficient of vertical turbulent exchange for different layers of the Black Sea basin based on the experimental data on microstructure of the physical fields obtained for the period 2004–2019 in the Black Sea and using the semi-empirical models. Methods and Results. For the upper mixed layer, the turbulent energy dissipation rate ɛ and the exchange coefficient were calculated using the velocity fluctuation spectra based on the Kolmogorov hypotheses on the turbulence spectrum inertial range. In the stratified layers, the turbulence coefficient and the dissipation rate were experimentally determined both from the spectra of the velocity horizontal fluctuations’ gradients and the vertical spectra of temperature fluctuations using the concept of the effective scale of turbulent spots. Depending on the features of the hydrological regime and the prevailing energy contributors to turbulence generation, five layers were identified and described (including their characteristic power dependences of the vertical turbulent diffusion coefficients K on the buoyancy frequency N) using the 1.5D-model of vertical turbulent exchange for the basin under study. For the stratified layers, the 1.5D-model results were comparatively analyzed with those of the other semi-empirical and theoretical models describing the most probable hydrophysical processes in each specific layer; the relations for the vertical turbulent exchange coefficient were obtained depending on the buoyancy frequency. Conclusions. Comparison of the experimental data collected under different hydrometeorological conditions with the simulations resulted from the known turbulence models for the sea upper layer showed that the best agreement between the simulation and measurement data was provided by a multiscale model taking into account three basic mechanisms of turbulence generation: current velocity shear, instability of wave motions, and wave breaking. The turbulent exchange coefficient dependencies on depth are conditioned by the affect of the turbulence dominant source at a given level. In the stratified layers, the exchange coefficient dependence on buoyancy frequency is determined by the hydrophysical processes in each layer; the relations obtained for individual layers indicate intensity of the contributions of vertical advection, internal wave breakings, turbulence diffusion and geothermal flux.
Modelling and optimal strategy to control coffee berry borer

Coffee berry borer Hypothenemus hampei (Coleoptera: Scolytidae), denoted CBB, is the most important insect pest of coffee worldwide, with a high impact on the economy of coffee producing countries. The insect spends a great part of its life cycle inside the coffee berry and causes severe crop losses. Biological control based on the use of an entomopathogenic fungus is a major alternative to chemical pesticides in order to control CBB. The fungus is sprayed on the coffee berries to kill CBB when the insects drill holes to penetrate inside the berries. Our aim in this work is to optimise the fungus application, using a modelling approach. We first formulate a mathematical model describing the infestation dynamics of coffee berries by CBB. We analyse the model and show that the stability of the pest‐free and coexistence equilibria depends on the basic reproduction number. To introduce a control variable corresponding to the application of an entomopathogenic fungus, we then extend the model and include the fungus dynamics. We formulate an optimal problem which consists in maximising the coffee yield, while minimising the control cost, as well as the CBB population for the next cropping season. The existence of the optimal control and the necessary optimality condition are established using Pontryagin's Maximum Principle. The optimal control problem is solved numerically using the BOCOP software and simulations are provided, showing that the use of entomopathogenic fungus effectively controls CBB.
50 m freestyle in 21, 22 and 23 s: What differentiates the speed curve of world-class and elite male swimmers?

ABSTRACT We examined the association between 50 m freestyle performance (50FS) and average speed (AS), peak speed (PS), minimum speed (MS) and intracyclic speed variation (ISV) and compared the speed curves from swimmers with different performance levels using functional analysis of variance (FANOVA). Fourteen male swimmers (50FS: 22.50 ± 0.58 s) performed a maximal sprint with a speedometer and AS, PS, MS, and IVV were assessed for correlational analysis. 50FS were obtained in official competitions. Swimmers were assigned to three groups based on actual 50FS: G21 (n = 2, 21.99 ± 0.04 s), G22 (n = 6, 22.82 ± 0.10 s) or G23 (n = 6, 23.55 ± 0.18 s). FANOVA compared the average curves. 50FS correlated to AS (r = −0.781, p = 0.001) and PS (ρ = −0.766, p = 0.001), but not to MS (r = −0.185, p = 0.527) or IVV (r = −0.323, p = 0.259). FANOVA showed that faster swimmers achieved higher PS and stayed longer at the upper part of the curve. 50FS performance is related to average and peak speed assessed with the speedometer. Swimmers should seek techniques to maintain the speed at the upper part of the curve as long as possible.
Molecular Prevalence and Epidemiological Characteristics of Diarrheagenic E. coli in Children under 5 Years Old in the City of Koula-Moutou, East-Central Gabon

Background and Purpose: Diarrhoeagenic E. coli (DEC) is one of the germs responsible for childhood diarrhea in developing countries. This study aims at determining the prevalence of the five main pathotypes of DEC isolated from faeces of children under five years old with diarrhea or not, living in the city of Koula-Moutou. Methodology: Isolates of E. coli were phenotypically screened on chromIDTM agar and molecularly by multiplex PCR to detect the presence of enteroaggregative E. coli (EAEC), enteropathogenic E. coli (EPEC), enterotoxigenic E. coli (ETEC), enterohemorragic E. coli (EHEC) and enteroinvasive E. coli (EIEC). The evaluation of their sensitivity to 12 β-lactam antibiotic molecules was carried out by Kirby Bauer method. This method has also made it possible to characterize phenotypically the different β-lactamases produced. Results and Conclusion: Overall, at least one DEC pathovar was detected in the 63 E. coli strains with phenotypic and molecular frequencies of 63.5% and 68.5% respectively. Thus, ETEC (28.3%) and EHEC (28.3%) were the most frequent DEC in diarrheal isolates. ETEC/EHEC hybrid was recorded in both groups with rates of 7.5% in diarrheal cases and 10.0% for controls. The results showed produced carbapenemase type β-lactamases (31.7%), followed by ESBL (24.4%) and few produced high level penicillinases (4.9%). The DEC, in particular ETEC and EHEC are most likely the epidemiological agents responsible for childhood diarrhea in this study.
On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub.
Health‐related quality of life and treatment effects in patients with well‐differentiated gastroenteropancreatic neuroendocrine neoplasms: A systematic review and meta‐analysis

Abstract Introduction Gastroenteropancreatic neuroendocrine neoplasms (GEPNENs) are often diagnosed in an advanced stage. As the optimal sequence of therapy remains largely unclear, all treatment‐related outcomes, including health‐related quality of life (HRQoL) prospects, should be assessed according to patients' preferences. Methods A targeted search was performed in PubMed and EMBASE to identify studies on treatment effect and HRQoL, measured using the EORTC QLQ‐C30 tool, in patients with advanced, well‐differentiated GEPNENs. Study quality was assessed, and meta‐analyses were performed for global health status/QOL and tumour response. Results The search yielded 1,322 records, and 20 studies were included, examining somatostatin analogues (SSA), peptide receptor radionuclide therapies (PRRT), chemotherapy, SSA‐based combination therapies, and targeted therapies. Global HRQoL was stable, and rates for disease stabilisation were moderate to high across all treatments. Meta‐analyses for global health status/QOL after SSA treatment were not significant (mean difference: –0.3 [95% CI: −1.3 to 0.7]). The highest pooled overall tumour response rate was 33% (95% CI: 24–45%) for PRRT. The highest pooled clinical benefit rate was 94% (95% CI: 65–99%) for chemotherapy. Conclusion All treatments appeared beneficial for disease stabilisation while maintaining stable global health status/QOL. High‐quality HRQoL reporting was lacking. HRQoL should be a central outcome next to well‐established outcomes.
Academic Planning Functions as Predictors of Goals Achievement in Colleges of Education, Kwara State, Nigeria

Academic planning is an essential function of Higher Education Institutes (HEIs). The extent towhich academic planning functions are performed determines to a large degree the attainment of the goals of HEIs in Nigeria. This research was conducted toexamine if academic planning functions as a predictor of goal achievement in the Colleges of Education in Kwara State, Nigeria. Survey research method was used to establish the relationship between academicplanning functions and goal achievement in/for the above mentioned colleges. A sample size of 421 out of 1,668 staff was determined using research advisors. The sample size of 421 participants comprised 147 academic staff and 274 non-teaching staff selected using a stratified and proportionate samplingtechnique. Two instruments were designed and used to collect the relevant data for this study. These instruments were Academic Planning Functions Questionnaire (APFQ) and Goals AchievementQuestionnaire (GAQ). The data collected were analyzed using Smart PLS 3.1. 4. The findings showed that program accreditation, information processing, and monitoring functionspredicted goal achievement in the said colleges, while program initiation, internal quality assurance, resourceprojection, and financial planning functions did not predict goal achievement. Based on the findings, recommendations weremade to improve the academic planning unit for increased efficiency, effectiveness, and productivity in these colleges.
Learning Mutual Correlation in Multimodal Transformer for Speech Emotion Recognition

Various studies have conﬁrmed the necessity and beneﬁts of leveraging multimodal features for SER, and the latest research results show that the temporal information captured by the transformer is very useful for improving multimodal speech emotion recognition. However, the dependency between different modalities and high-level temporal-feature learning using a deeper transformer is yet to be investigated. Thus, we pro-pose a multimodal transformer with sharing weights for speech emotion recognition. The proposed network shares the weights across the modalities in each transformer layer to learn the correlation among multiple modalities. In addition, since the emotion contained in a speech generally include audio and text features, both of which have not only internal dependence but also mutual dependence, we design a deep multimodal attention mechanism to capture these two kinds of emotional dependence. We evaluated our model on the publicly available IEMOCAP dataset. The experimental results demonstrate that the proposed model yielded a promising result.
Aromachology and Customer Behavior in Retail Stores: A Systematic Review

Interest in the use of scents in retail environments for creating better customer experiences is growing. Yet, knowledge of the effectiveness of aromachology to affect actual customer behavior and ultimately increase turnover is incomplete, as published results present inconsistencies and are often based on highly controlled environments rather than actual store environments. Situated within this context, this paper offers a systematic review on research in aromachology with a focus on effects on actual customer behavior in actual retail environments. As expected, the available research on actual environments (relative to laboratory-based studies) is limited, with only 20 articles meeting the inclusion criteria. While reported results are, overall, indicative of the positive effects of scent on customers’ emotional states and on their in-store behavior (dwell time, product choices) and attitudes (purchase intention, intention to revisit), several critical issues with the available literature emerged. These pertain primarily to a lack of sufficient methodological details (specifically on the scents, their compositions, intensity and methods of delivery), a narrow focus on scents without considering interactions with other atmospherics factors, and a general disregard of individual differences in olfaction. The review provides suggestions for addressing these shortcomings and improving the quality and actionability of this line of research.