Miracle-dz/UniFAR-FTUs huggingface dataset:
A microfluidic chip integrated with droplet generation, pairing, trapping, merging, mixing and releasing

Developing a microfluidic chip with multiple functions is highly demanded for practical applications, such as chemical analysis, diagnostics, particles synthesis and drug screening. This work demonstrates a microfluidic chip integrated with a series of functions including droplet generation, pairing, trapping, merging, mixing and releasing, and controlled entirely by liquid flow involving no electrodes, magnets or any other moving parts. This chip design is capable of trapping and merging droplets with different content on demand allowing the precise control of reaction time and eliminates the need for droplet synchronization of frequency, spacing or velocity. A compact model is developed to establish a set of design criterion. Experiments demonstrate that fast mixing in the merged droplets can be realized within several seconds benefiting from the flow fluctuation induced by droplets coming or leaving the trapping region. Additionally, it allows a concentration gradient of a reagent to be established. Finally, this design is applied to screen drug compounds that inhibit the tau-peptide aggregation, a phenomenon related to neurodegenerative disorders.
Survival Status and Determinants of Mortality Among Severely Malnourished Children Aged 0-59 Months Admitted to Jinka Hospital, South Omo Zone, Ethiopia: A Retrospective Cohort Study 

Sever acute malnutrition (SAM) remains a major public health problem contributing to morbidity and mortality among children aged 0-59 months. However, little is known about the survival status and determinants of mortality among children admitted to stabilization centers (SC) in Ethiopia particularly in study setting. The objective: to assess the survival status and determinants of mortality among severely malnourished children aged 0-59 months admitted to SC in Jinka general hospital from September 1, 2014 to August 30, 2018. Hospital based retrospective cohort study design was used among 588 severely malnourished children aged 0-59 months admitted to SC of Jinka general hospital from March 20-30, 2019. Simple random sampling method was used to select medical records of the study participants. Proportional hazard assumption over time was checked by Schoenfeld’s global test. The Kaplan-Mei­er survival curve with log-rank test was used to compare survival across groups. Bivariable and multivariable Cox proportional hazard regression was used to assess the association between each independent variable and the outcome variable. Level of statistical significance was declared at p less than 0.05. Among cohort of children studied, nearly 11% of children were died during follow up. The overall mean survival time was 38 days, 95% CI :( 36.3, 39.7). Urban residence [AHR=2.24, 95% CI: (1.17, 4.30)], presence of dehydration [AHR =3.94, 95% CI:(1.89, 8.22)], presence of shock [AHR=4.15, 95% CI:(2.01, 8.55)], altered body temperature [AHR= 2.01, 95% CI:(1.01, 3.91)], failure to take F100 formula milk [AHR= 4.87, 95%CI:(2.75, 8.63)] and oral antibiotics [AHR=3.57, 95%CI:(1.88,6.77)] were identified to be the independent determinants of mortality The mortality rate for severely undernourished children was higher than global sphere standard and national protocol.Societal and individual level determinants of mortality were identified. Therefore, interventions to reduce mortality should focus on altered clinical conditions and comorbidities/ infections.
Path to Action – Closing the Gap to Make Patient Blood Management the New Norm(al) as Viewed by Implementors in Diverse Countries

Background: Patient Blood Management (PBM) is an evidence-based approach in surgery and emergency care which aims to minimize the risk for blood loss and the need for blood replacement for each patient through a coordinated multidisciplinary care process before, during, and after surgery. In combination with blood loss, anemia is the main driver for transfusion and an independent risk factor for adverse outcomes including morbidity and mortality. Hence, identifying and correcting anemia as well as minimizing blood loss are important pillars of PBM. Evidence demonstrates that PBM significantly improves outcomes and safety while reducing cost by macroeconomic magnitudes. Despite its huge potential to improve healthcare systems, PBM is not yet adopted broadly. The aim of this study is to analyze the collective experiences of a diverse group of PBM implementors across countries reflecting different healthcare contexts and to use these experiences to develop a guidance for initiating and orchestrating PBM implementation for stakeholders from diverse professional backgrounds.Methods: Semi-structured interviews were conducted with 1-4 PBM implementors from 12 countries in Asia, Latin America, Australia, Central and Eastern Europe, the Middle East, and Africa. Responses reflecting the drivers, barriers, measures, and stakeholders regarding the implementation of PBM were summarized per country, and key observations extracted. By clustering the levels of intervention for PBM implementation, a PBM implementation framework was created and populated.Results: A set of PBM implementation measures were extracted from the interviews with the implementors. Most of these measures relate to one of six levels of implementation including government, healthcare providers, funding, research, training/education, and patients/public. Essential cross-level measures are multi-stakeholder communication and collaboration.Conclusion: This implementation framework helps to decompose the complexity of PBM implementation into concrete measures on each implementation level. It provides guidance for diverse stakeholders to independently initiate and develop strategies to make PBM a national standard of care, thus closing current practice gaps and matching this unmet public health need.
Research Progress of Green Immunoassay for Mycotoxins

Mycotoxin pollution not only causes huge economic losses, but also seriously threatens the health of humans and animals. Immunoassay methods are widely used in the rapid detection of mycotoxins, but the method requires the use of toxin standards and artificial antigens.The artificial antigen synthesis is not only difficult to prepare, but also requires toxin standards as raw materials. Toxin standards are expensive and harmful to the health of producers and operators, which restricts the application and popularization of immunoanalytic methods in mycotoxin detection. If the toxic antigens and standards involving in the immune reaction is substituted with the non-toxic antigen, a green immunology test method can be established to overcome the above deficiencies. Establishing a green, fast, simple and highly sensitive immunoassay method to detect these toxins has become a hot topic in recent years. This paper reviews the application and prospect of green immunoanalysis methods based on anti-unique antibody and phage display techniques inmycotoxins.
Non-swellable F127-DA hydrogel with concave microwells for formation of uniform-sized vascular spheroids

Hydrogels with concave microwells are one of the simplest means to obtain uniform-sized cellular spheroids. However, the inherent swelling of hydrogels leads to reduced mechanical strength and thus deforms the structure of the microwells. In this study, we developed a hydrogel with microwells for formation of vascular spheroids via non-swellable di-acrylated Pluronic F127 (F127-DA), which showed higher mechanical strength than a conventional di-acrylated polyethylene glycol (PEG-DA) hydrogel. The uniform-sized vascular spheroids were spontaneously generated by human umbilical vein endothelial cells (HUVECs) and fibroblasts in the microwells. The endothelial functions of vascular spheroids were about 1-fold higher than those in two-dimensional (2D) culture, as indicated by secretion of nitric oxide (NO), prostacyclin (PGI2) and tissue factor pathway inhibitor (TFPI). Interestingly, the vascular spheroids with large diameter showed higher sensitivity to ethanol toxicity than those with small diameter, possibly due to the higher endothelial functions of large spheroids. Hence, F127-DA hydrogel with concave microwells provides a convenient way of forming uniform-sized spheroids that are useful for high throughput screening of drug/food toxicity.
Online Learning and Pricing for Service Systems with Reusable Resources

Revenue Management of Service Systems under Incomplete Information Revenue management with reusable resources finds many important applications in today's economy, such as cloud computing services, car/bicycle rental services, ride-hailing services, hotel management, project team management, and call center services. The existing literature predominantly assumes that the stochastic demand and service processes are given as an input to the models, and the pricing decisions are made with full knowledge of the distributional information. However, in practice, the decision maker may not know how demand or service rates react to price changes. Thus, the decision maker needs to learn the underlying mapping between prices and rates from past observations, while maximizing the total expected revenue on the fly. In “Online Learning and Pricing for Service Systems with Reusable Resources”, H. Jia, C. Shi, and S. Shen developed a series of online learning algorithms for revenue management problems with reusable resources and showed that they admit an optimal regret bound.
Naturally occurring phytochemical as inhibitors from Catharanthus roseus: An In-silico approaches for drug development against COVID-19

The current outbreak of the novel 2019 Coronavirus disease (COVID-19) is caused by SARS-CoV-2, has developed a threat to the world's human population. There are no effective therapies or vaccines yet, urging the serious efforts to tackle this pandemic situation.SARS-CoV-2 spike protein, papain like protease protein (PLPRO), main protease (3CLpro) and RNA dependent DNA polymerase are key factors in the virus infectious process and have been identified as potential targets for therapeutic formulation. Most people in India depend on conventional Indian medicine (phytochemical compounds) to treat diseases due to lower cost, easier accessibility and no adverse effects. A lot of studies have recently shown that phytochemicals contain an effective anti-viral activity. This study aims to investigate phytochemicals metabolites from the IMPPAT database (Indian Medicinal Plants Database) in order to identify potential COVID-19 inhibitors using in silico approaches. Certain phytochemical compounds with structure analogs like hydroxychloroquine and chloroquine from the IMPPAT database were taken for interaction with SARS-CoV-2 proteins. The Apparicine, 12-Chlorotabersonine, AC1NSULH and Vindolininol identified from Catharanthus roseus were further checked the ADMET property as well as ‘Lipinski’s rule and resulted in a strong binding affinity of (-7.6,-7.5) and (-7.6 -7.5) kcal/mol respectively for spikes and papain like protease protein of SARS-CoV-2. Our results indicate that these specific compounds can be used as effective inhibitors and help to pace up the drug development against SARS-CoV-2. Further investigation and testing of these inhibitors against SARS‐CoV‐2 are however required to check their clinical trial candidacy.
An acoustic eigenvalue problem and its application to electrochemistry

The fundamental process that lies at the foundation of batteries, capacitors, and solar cells is the electron transfer process. This takes place at an interface or boundary in each device and is governed by its corresponding chemical reaction. Making these devices more efficient can help decrease our negative impact on the environment. Recent experiments in the field of Electrochemistry demonstrate that sound waves act as a catalyst for these electron transfer reactions. A model is developed using an Euler Equation (1), Conservation of Mass Equation (2), boundary motion equation (3), and surface tension equation (4). ut + u · ∇u+ (1 ρ ) ∇P = 0 (1) ρt +∇ · (ρu) = 0 (2) Ψt + u · ∇Ψ = 0 (3)
Technological innovations in the cultivation of Haematococcus pluvialis microalgae: review

The cultivation of microalgae presents a great biotechnological potential, mainly to produce natural bioactive substances, which can be used in the pharmaceutical industry and especially in the development of functional foods, thanks to its nutritional properties. Among the commercially important microalgae, Haematococcus pluvialis is considered the main source of natural astaxanthin, a carotenoid of high antioxidant action and with wide applications in the nutraceuticals, cosmetics, food and aquaculture industries. This review aimed to cover the most important aspects of biology, biochemical composition, biosynthesis and astaxanthin accumulation in the cells of H. pluvialis, in addition to its broad application to humans and animals. The methodology used in this work was a systematic review of the literature, presenting the gaps and opportunities for research. This work provided a broader view of the technologies and methodologies used to produce H. pluvialis, providing a direction for future work to be undertaken. During the bibliographic survey, it was observed that information regarding the cultivation of H. pluvialis, aiming at the production of astaxanthin, is still very incipient in Brazil, with results observed only on a laboratory scale, making it difficult to really understand the implementation costs for a possible commercial production. This work has started a larger research and will serve as a basis for future activities, mainly to solve possible doubts.
Alocação de Recursos para Redes Virtuais com Seleção de Método de Resolução via Aprendizado de Máquina

A alocação de recursos é um problema NP-difícil, presente no Mapeamento de Redes Virtuais (VNE) e na Virtualização de Funções de Rede (NFV). Nesse contexto, um ponto pouco explorado na literatura é relacionado a quando se deve aplicar um método de resolução heurístico ou exato. Tanto para o VNE, quanto para o NFV, normalmente a literatura indica um tratamento heurístico devido à complexidade do problema e à elevada dimensão dos dados. Entretanto, em experimentos preliminares, percebe-se que em alguns momentos um tratamento exato pode ser aplicado em um tempo praticável. Foi notado que esses eventos não se tratam de casualidades, mas decorrem de várias condições relacionadas ao Substrato de Rede (SN) residual e às demandas de rede requisitadas. A abordagem proposta neste artigo é baseada na aplicação de técnicas de Aprendizado de Máquina (AM) para alocação de recursos em rede, e tem como objetivo prever em quais situações se pode preterir uma abordagem heurística em prol de uma exata. Para tal, foi adotado o VNE e duas abordagens da literatura. As simulações realizadas mostram que o tratamento com AM melhora a taxa de aceitação e a receita de mapeamento em relação à heurística, e reduz o tempo de processamento em relação à abordagem exata.
Impact of severe cracked germanium (111) substrate on aluminum indium gallium phosphate light-emitting-diode’s electro-optical performance

Abstract Cracked die is a serious failure mode in the Light Emitting Diode (LED) industry – affecting LED quality and long-term reliability performance. In this paper an investigation has been carried out to find the correlation between severe cracked germanium (Ge) substrate of an aluminum indium gallium phosphate (AlInGaP) LED and its electro-optical performance after the Temperature Cycle (TC) test. The LED dice were indented at several bond forces using a die bonder. The indented dice were analysed using a Scanning Electron Microscope (SEM). The result showed that severe cracks were observed at 180 gF onward. As the force of indentation increases, crack formation also becomes more severe thus resulting in the chipping of the substrate. The cracked dies were packaged and the TC test was performed. The results did not show any electro-optical failure or degradation, even after a 1000 cycle TC test. Several mechanically cross-sectioned cracked die LEDs, were analysed using SEM and found that no crack reached the active layer. This shows that severely cracked Ge substrate are able to withstand a −40°C/+100°C TC test up to 1000 cycles and LED optical performance is not affected. A small leakage current was observed in all of the cracked die LEDs in comparison to the reference unit. However, this value is smaller than the product specification and is of no concern.
Reformas administrativas do Estado: o caso da reestruturação promovida na Secretaria de Desenvolvimento Social de Minas Gerais

The study describes the process of the Administrative Reform occurred in the state of Minas Gerais in 2019, from the administrative restructuring promoted by the State Secretariat of Social Development (Sedese). The objective of the research is analyze the impact of the absorption of some public policies in the scope of the institution, considering the changes promoted. In addition to the theoretical framework related to administrative reforms, it was also sought to appropriate the research of existing relationships on the theme of organizational change, considering aspects related to change management and resistance to change. The study positively evaluates the changes made from the managerial capacity presented in the absorption of different public policies, in addressing the legal, strategic and managerial dimensions. However, there are some negative impacts on the processes, especially the difficulty of organic adaptation and the absence of transition mechanisms to promote a better change management.
Multi-dialect Neural Machine Translation for 48 Low-resource Japanese Dialects

We present a multi-dialect neural machine translation (NMT) model tailored to Japanese. Although the surface forms of Japanese dialects diﬀer from those of standard Japanese, most of the dialects have common fundamental properties, such as word order, and some also use numerous same phonetic correspondence rules. To take advantage of these properties, we integrate multilingual, syllable-level
Introduzindo a qualidade de imagem como uma nova condição de particionamento de DNN na borda

In Deep Neural Network (DNN) applications with edge computing, it is possible to process the first DNN layers at the edge and the other layers at the cloud. For image classification, DNNs can infer a high-quality image with high confidence in the first DNN layers. Thus, depending on the image quality, we can avoid sending data to the cloud, reducing the inference time and network utilization. This paper evaluates, experimentally, how image quality impacts the inference time in edge computing infrastructures. In the employed scenario, this work shows that considering image quality reduces inference time up to 35.33%. Resumo. Em aplicações de redes neurais profundas (DNNs) com computação na borda, é possı́vel processar as primeiras camadas da DNN na borda e as demais camadas na nuvem. Quando DNNs são utilizadas para classificação de objetos em imagens de alta qualidade, o processo de inferência pode apresentar um nı́vel de confiança satisfatório já nas primeiras camadas da DNN. Assim, dependendo da qualidade da imagem, é possı́vel evitar o uso da nuvem, reduzindo o tempo de inferência e o uso da rede. Este trabalho avalia, de forma experimental, o impacto da qualidade de imagem no tempo de inferência em infraestruturas de computação na borda. No cenário utilizado, mostra-se que considerar a qualidade da imagem reduz o tempo de inferência em até 35,33%.
A STUDY ON PRESENTATION AND BEHAVIOR OF COLO-RECTAL CARCINOMA IN JHARKHAND

Introduction: Colo-rectal carcinoma is most frequent neoplasm of Western countries. It is 3rd most common in men and 2nd most common in women. The Aim of this study is to know different presentations and age variations and its method of diagnosis and management. Materials and methods: This is a observational study including 25 patients presented in the Emergency and OPD of RIMS, Ranchi with complaints of bleeding PR, alteration of bowel habits, rectal pain and discharge, incontinence, abdominal pain and distention and loss of weight. Results: Most common age of presentation is 40-50 years, most commonly involving men. Most common presentation is alteration of bowel habits followed by bleeding PR. Diagnosis was mostly made by CECT whole abdomen and colonoscopic /Rectal biopsy. Follow-up was done by CEA levels. Conclusion: There is increased incidence of colo-rectal carcinoma in young patients and there is tendency of late presentation of colo-rectal cancers.
MFA-RNN: Uma Rede Neural Recorrente para Predição de Próximo Local de Visita com Base em Dados Esparsos

Predicting the mobility of a user is an important task to enhance the effectiveness of mobile applications. In this work, we present the MFA-RNN (Multi-Factor Attention Recurrent Neural Network), a neural network that uses the Multi-Head Self-Attention technique to extract correlations under several features of the sequence of the visited places. The proposed model is able to predict the next place of visit considering multiple factors (user, location, time and type of the day) of each record of the sequence. Moreover, we propose a method to fill sparse data to enhance the performance of the solution. The obtained results indicate the effectiveness of the MFA-RNN model in relation to four known solutions of the literature. Resumo. Prever a mobilidade de um usuário é uma tarefa importante para se elevar a efetividade de aplicações móveis. Neste trabalho, é apresentada a MFA-RNN (Multi-Factor Attention Recurrent Neural Network), uma rede neural recorrente que utiliza a técnica Multi-Head Self-Attention para extrair correlações sob diversos aspectos da sequência de locais visitados. O modelo é capaz de prever o próximo local de visita considerando múltiplos fatores (usuário, localização, tempo e tipo do dia) de cada registro da sequência. Além disso, é proposto um método para o preenchimento de dados esparsos para melhorar o desempenho da solução. Os resultados obtidos indicam a eficácia do modelo MFA-RNN em relação a quatro soluções conhecidas na literatura.
Learning-based Green Workload Placement for Energy Internet in Smart Cities

The Energy Internet is a fundamental infrastructure for deploying green city applications, where energy savingand job acceleration are two critical issues to address. In contrast to existing approaches that focus on static metrics withthe assumption of complete prior knowledge of resource information, both application-level properties and energy-level requirements are realized in this paper by jointly considering energy saving and job acceleration during job runtime. Considering the online environment of smart city applications, the mainobjective is transferred as an optimization problem with a model partition and function assignment. To minimize the energycost and job completion time together, a green workload placement approach is proposed by using the multiaction deep rein-forcement learning method. Evaluations with real-world applications demonstrate the superiority of this method over state-of-the-art methods.