Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of breast cancer histopathological image with deep residual learning

Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time‐consuming and experience‐dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning‐based convolutional neural network named myResNet‐34 was designed for malignancy‐and‐benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image‐level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2‐3%.
E-module development based on PBL integrated STEM assisted by social media to improve critical thinking skill: A preliminary study

This research aims to develop an electronic module (e-module) based on problem-based learning (PBL) integrated into Science, Technology, Engineering, and Mathematics (STEM) assisted by social media to improve students’ critical thinking skills in a dynamic fluid material. The development of this research used a mixed-method consisting of qualitative and quantitative data. This study involved 308 students and 17 physics teachers in Lampung province. The results of the preliminary research conducted by only 12.5% of teachers used STEM integrated PBL-based e-modules to improve students’ critical thinking skills and 46.6% of students stated that they only used printed books when physics learning. 100% of teachers and 88.2% of students stated that they need e-modules to make learning easier. 50% of teachers stated that they use social media as a learning tool. Based on the results of the preliminary study, the use of e-modules is needed by teachers and students. A need analysis needs to be done for the development of E-Module integrates STEM-assisted PBL-based social media to enhance students’ critical thinking skills.
Fabricate Graphenne-based Textile Sensors and Their Applications

Advanced material science, textile technology and electronic information technology have promoted the development of new functional textiles. Especially the emergence of wearable sensors in textile realizes the monitoring of human vital signs in daily life. Its combination with textile can undoubtedly help people to grasp the individual health situation and even the public health status in a timely, accurate and comprehensive manner. Graphene-based textile sensors and their applications were focused on in this paper. In addition, some technical problems on combining of wearable sensors and textile were summarized in the end.
Spectral characteristics and species identification of rhododendrons using a discriminative restricted Boltzmann machine

ABSTRACT Rhododendrons are an important genus of alpine flowering plant used ornamentally worldwide. The purpose of this study is to improve the application of remote-sensing technology for investigating and monitoring mountain rhododendron germplasm. Research area is the Baili Rhododendron National Forest Park located in the karst region of Guizhou Province, China. Field spectrometry was used to acquire spectral data for 20 samples extracted from eight rhododendron species. A deep-learning algorithm from a discriminative restricted Boltzmann machine was used with the original spectral data from the different rhododendron species to obtain the optimal parameters for the model. Simultaneously, the data processing methodology from the discriminative restricted Boltzmann machine was used to recognize the original spectra, the noise smoothed spectra, and the first- and second-order spectral derivatives with accuracies of 88.54%, 88.54%, 93.75%, and 90.62%, respectively. The results show that the discriminative restricted Boltzmann machine is effective in recognizing spectral information for different rhododendron species. Changes in the first-order derivative gave the most accurate classification, but changes in the second-order derivative significantly reduced the sample training time. Changes in both derivatives therefore proved useful in recognizing and extracting particular features of the plant species. This research may therefore further support the use of hyperspectral remote-sensing imagery for investigating and monitoring germplasm, species classification, and physiological parameter inversions for rhododendrons from various mountain regions of China.
Nanocompósitos de matriz epoxídica com reforços produzidos a partir do grafite natural Epoxy resin nanocomposites with reinforcements produced from natural graphite

RESUMO O grafite e a fonte mais abundante e de baixo custo para obtencao de grafeno. Para sua producao e aplicacao em nanocompositos em escala industrial, o metodo de reducao do oxido de grafite (OG) tem sido o mais utilizado. A oxidacao do grafite promove a introducao de grupos funcionais na sua estrutura lamelar que causam o afastamento dos planos cristalinos do grafite, produzindo assim o OG. Sua reducao pode ser realizada tanto por metodos quimicos quanto termicos, a qual remove parcialmente os grupos funcionais introduzidos na oxidacao, fazendo com que a estrutura grafitica seja tambem parcialmente reestabelecida. Varias matrizes polimericas estao sendo utilizadas na producao de nanocompositos com grafeno, dentre elas as resinas epoxidicas. Um dos desafios e proporcionar a dispersao total do nanoreforco na matriz e promover uma forte adesao interfacial matriz/nanoreforco para se obter melhores propriedades finais. Portanto, o objetivo deste trabalho foi caracterizar as propriedades morfologicas, mecânicas e termicas de nanocompositos de matriz epoxidica a base de eter diglicidilico do bisfenol A (DGEBA) com reforcos produzidos a partir do grafite natural, sendo estes o grafite sonificado (GS), OG e o OG expandido (OGE) na concentracao de 0,1% m/m. O sistema contendo o OGE se mostrou o mais promissor na melhoria das propriedades mecânicas de sistemas com resina epoxidica, uma vez que apresentou um incremento de ~37% na resistencia a tracao. Nao foram observadas alteracoes significativas na estabilidade termica dos nanocompositos, indicando que nao se formam redes de percolacao na concentracao de reforco estudado.
Growth of Self‐Passivating Oxide Layers on Aluminum—Pressure and Temperature Dependence

Optical transmission spectroscopy is used to monitor the in situ oxidation of granular aluminum films over a wide range of temperatures and pressures. By fitting the time evolution of the transmission to adequate geometric models, the rising transparency of the sample is converted into oxide growth during reaction. The observed oxidation rates increase steeply with temperature, but depend on pressure only below 100 mbar O2 and become pressure‐independent above. The Al oxidation proceeds in two regimes. The initial fast one is compatible with the Cabrera–Mott mechanism of an electric‐field‐driven process and self‐terminates at 1–2 nm oxide thickness depending on oxidation conditions. The subsequent slow regime exhibits a two times larger activation energy and is relevant only for high‐temperature oxidation. It is assigned to the thermally activated transport of Al ions through the emerging oxide layer. In contrast to the initial Cabrera–Mott process, it shows no self‐passivation and enables oxide thickening beyond 2 nm.
A study of clinical profile and factors affecting mortality in patients with acute on chronic liver failure in a tertiary hospital in north east India

Background: Data regarding Acute on Chronic Liver Failure (ACLF) patients from North East India is scarce and presentation to hospital is often late. We aim to study their clinical profile, aetiology of underlying chronic liver disease, precipitating factors, predictors of mortality and their short term outcome (3 months). Methods: Among 1000 consecutive patients of any form of acute decompensation, 245 patients diagnosed as ACLF were prospectively studied. Comparison was done between survivors versus non survivors of ACLF and between ACLF and Non ACLF patients. Results: Mean age of ACLF patients was 44.2±10.3 years and male:female ratio was 13.4:1. Common causes of underlying cirrhosis in ACLF was alcoholic liver disease, 210 (85.7%), Hepatitis B virus related cirrhosis, 20 (8.2%), Hepatitis C virus related cirrhosis, 6 (2.4%) and cryptogenic in 8 (3.3%). Precipitating causes were alcoholic hepatitis in 98 (46.6%) among alcoholic cirrhosis, acute flare of Hepatitis B infection in 12 patients (60%) among Hepatitis B related cirrhosis, recent use of drugs in 110 (44.8%), sepsis in 71 (28.9%), spontaneous bacterial peritonitis (SBP) in 36 (14.7%), urinary tract infection in 36 (14.7%), acute hepatitis A in 5 (2%) and acute hepatitis E in 3 (1.2%). hepatic encephalopathy, low sodium, high International Normalised Ratio (INR) were found to be significantly associated with high mortality. Increasing number of organ failures is associated with increasing risk of death. Conclusions: ACLF is characterized by rapid deterioration especially when multiorgan failure sets in due to certain precipitating factors in a previously diagnosed or undiagnosed chronic liver disease.
Two approaches to car sequencing in the paint shop

The problem of car sequencing has been considered in the literature many times, but very often with the assumption of many simplifications, which meant that the discussed issue was far from the problems occurring in reality. The article presents an attempt to capture the real problem of sequencing, in particular in paint shop, because from the point of view of economic indicators, painting process is today a complex, multistage, extremely energy consuming and cost intensive operation. The main goal is therefore to minimize the number of costly changeovers of painting guns, resulting from color changes and synchronize those with periodic cleanings, forced by technological requirements. For this purpose, a buffer located in the paint shop is applied. In the paper a game-theoretic framework is presented to analyze the considered problem. Two games: Buffer Slot Assignment Game and Buffer-OutShuttle are compared with algorithms based on priority rules. Based on the performed simulations the effectivity of presented algorithms is verified using several datasets.
PHYTOCHEMICAL COMPOSITION AND ANTIINFLAMMATORY ACTIVITY OF NELUMBO NUCIFERA GAERTN

Objective: Inflammation is a process of injuries caused by physical, chemical, and biological factors. Nitric oxide (NO) plays an important role in theregulation of various pathological and pathophysiological processes. Overproduction of NO induces tissue damage associated with acute and chronicinflammations. This study was conducted to determine the phytochemical composition and the NO inhibitory properties of Nelumbo nucifera extractsin lipopolysaccharide(LPS)-stimulated macrophage cell line.Methods: The dried leaf, stalk, and flower materials of roseum plenum and album plenum (AP) were extracted with 95% ethanol solvents. Thephytochemical compounds of the extraction were analysed by gas chromatography-mass spectrometry. The cytotoxic assay of extracts againstmacrophage cells was conducted using resazurin. The NO was determined using LPS-induced RAW264.7 cells to measure inhibitory activity of extracton the production of NO.Results: The extracts from Lotus, which exhibited the non-cytotoxic to the RAW264.7 cells. The AP-stalk extracts were capable to reduce the NO levelin LPS-activated RAW264.7 cells. GC-MS analysis of AP-stalk extraction revealed pharmacologically active compounds.Conclusion: The results conduct that the AP-stalk extract effectively inhibited the NO production and may be useful in preventing inflammatorydiseases mediated by excessive production of NO. Bio-active phytoconstituents from AP stalk extract could potentially be used for anti-inflammation.These data also suggest that AP-stalk extract may serve as a good indicator of the pharmacological activities of medicinal plants.
Stock Price Prediction Based on an Energy-Efficient Spiking-LSTM Hardware Accelerator

Inspired by the way the human brain thinks, the neuromorphic system applies the principles of biological brains to computer architecture, providing low-energy, distributed, and massively parallel advantages for brain-inspired systems. This work presents an energy-efficient spiking long short-term memory (sLSTM) neural network hardware accelerator for sequence prediction applications, containing 256 neurons and 64k synapses in 0.96 mm 2 area. The sLSTM model can process time-dependent data and realize long-term and short-term memory to forget, memorize selectively. A leaky integrate and fire (LIF) neuron model is proposed to characterize the stimulation of neuronal membrane potentials using simple digital logic circuit without any multipliers, which extremely reduces the power consumption of the hardware system. Accordingly, the chip achieved an energy efficiency of 10.3 uj@50 MHz per sample and a predicting accuracy of about 93.2% in sLSTM neural network model using the stock price of Google from Yahoo finance, based on the modified LIF neuron.
Immobilization of Copper Complex by Phosphotungstate, Their Characterization and Application in Catalysis

The synthesized ternary metal complex was immobilized onto ammonium salt of phosphotungstate (NH4)3(PW12O40) under solvothermal condition to synthesize a new organic-inorganic hybrid (NH4)3[(CuComplex)(PW12O40)]. The synthesized metal complex and the hybrid (NH4)3[(CuComplex)(PW12O40)] were characterized by FT-IR, CHNO elemental analysis, SEM, powder-XRD, DR UV-Vis. Hybrid was further characterized by EDX. The metal complex as well as organic-inorganic hybrid were studied for their catalytic activity in oxidative conversion of alkyl aromatic, cycloalkane and cycloalcohols. The products formed during catalysis were analyzed by gas chromatography (GC) –mass spectroscopy (MS). The heterogeneous catalyst was recycled and reused multiple times with no substantial loss of their catalytic activity. A green procedure was followed and no toxic chemicals were released during solvothermal synthesis of heterogeneous catalyst. The resulting catalytic oxidation products are valuable intermediates and are employed for various medicinal and pharmaceutical industrial synthesis.
Predicting the potency of anti-Alzheimer drug combinations using machine learning

BACKGROUND Clinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially potent combinations. OBJECTIVE To use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. METHODS Three-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken. RESULTS The predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-lowering, and antihypertensive drugs, and female hormones. CONCLUSION The results suggest that AD, as a multifactorial disorder, could be effectively treated using a combination of repurposed drugs.
Superhydrophobic Sands for the Preservation and Purification of Water

Sand, a cheap and naturally abundant particulate material, was modified with photocatalytic and hydrophobic coatings to reduce evaporation loss and facilitate the purification of water. The first-level photocatalytic coatings (TiO2 or ZnO nanocrystals) rendered nanoscale roughness on the surface of the sand. The additional second-level hydrophobic coating of a self-assembled monolayer of octyltrimethoxysilane (OTS) made the sand particles superhydrophobic because of the nanoscale roughness imposed by the nanocrystals. The superhydrophobic sand particles, floating on the free surface of water due to their superhydrophobicity, significantly reduced the evaporation loss of water by 60%–90% in comparison to an uncovered water surface. When the outer hydrophobic coatings are weathered or disengaged, the inner photocatalytic coatings become exposed to water. Then, the sand particles act as photocatalysts to degrade the contaminants in water under solar radiation.
Opinion mining from student text review for choosing better online courses

The process of online assessment has utilized get increased gradually as an evaluating tool in order to measure the lectures performance over Institution of Higher Learning (IHLs). However, these approaches generally have questionnaires sets consists of both quantitative and qualitative whereas the teaching assessment systems for several online lecture have concentrated on quantitative questions part due to its quick analysis. Contrarily, the part of qualitative has needed opinion from students that frequently ignored or misplaced and the opinion results level are omitted. This is due to student’s opinion which is generally in term of unstructured text that made hard to analyze the feedback manually. Moreover, the enormous data amount has produced on daily basis which can be utilized to opinion mining for extracting the student’s opinion in this exact online education area that has not widely applied in all educational system. Therefore, this paper has designed a framework by applying opinion mining concept to the student of coursera online machine learning course as the dataset whereas the comparison is accomplished using opinion mining framework to extract. The unstructured text gets processed and classification results based on polarity to obtain better outcome. Hence, this framework maintain several steps namely opinion extraction, unstructured text processing and polarity classification using NLP. Moreover, the performance of the outcome from the Opinion Mining (OM) gets evaluated using machine learning algorithms.
Recent Advances in Wearable Devices for Non-Invasive Sensing

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.
O3-R-CP-ABE: An Efficient and Revocable Attribute-Based Encryption Scheme in the Cloud-Assisted IoMT System

With the processes of collecting, analyzing, and transmitting the data in the Internet of Things (IoT), the Internet of Medical Things (IoMT) comprises the medical equipment and applications connected to the healthcare system and offers an entity with real time, remote measurement, and analysis of healthcare data. However, the IoMT ecosystem deals with some great challenges in terms of security, such as privacy leaking, eavesdropping, unauthorized access, delayed detection of life-threatening episodes, and so forth. All these negative effects seriously impede the implementation of the IoMT ecosystem. To overcome these obstacles, this article presents an efficient, outsourced online/offline revocable ciphertext policy attribute-based encryption scheme with the aid of cloud servers and blockchains in the IoMT ecosystem. Our proposal achieves the characteristics of fine-grained access control, fast encryption, outsourced decryption, user revocation, and ciphertext verification. It is noteworthy that based on the chameleon hash function, we construct the private key of the data user with collision resistance, semantically secure, and key-exposure free to achieve revocation. To the best of our knowledge, this is the first protocol for a revocation mechanism by means of the chameleon hash function. Through formal analysis, it is proven to be secure in a selectively replayable chosen-ciphertext attack (RCCA) game. Finally, this scheme is implemented with the Java pairing-based cryptography library, and the simulation results demonstrate that it enables high efficiency and practicality, as well as strong reliability for the IoMT ecosystem.
Efficient single round attribute-based authenticated key exchange protocol

ABSTRACT Attribute-Based Authenticated Key Exchange (ABAKE) protocols allow two or more users to establish a shared key and achieve mutual authentication over an insecure network, while providing fine-grained access control over transmitted data. The existing ABAKE protocols are very inefficient, requiring a large number (polynomial in the size of the access policies) of pairing and exponentiation operations. This presents a major hindrance in the real-world deployment of these protocols. In this work, we present a construction of ABAKE protocol where the number of pairing operations is constant (to be precise only 7) and the number of exponentiation operations is linear to the number of clauses in the disjunctive normal form representing the general access policies. To this end, we construct an Attribute-Based Signcryption (ABSC) scheme with constant number of pairings, and use it as the main building block in our ABAKE construction. This also gives the first construction of ABSC schemes with constant number of pairings for general purpose access policies in the standard model. Another important and desirable feature of our ABAKE construction is that it is round-optimal, i.e. it is a single round protocol. We analyse the security of our ABAKE construction in the Attribute-Based extended Canetti-Krawzyck (ABeCK) model, and prove its security assuming the hardness of a variant of the Bilinear Diffie-Hellman Exponent problem in the random oracle model.