Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of river water quality assessment methods using the tree model and the nearest neighbor method (A case study: AhvazHydrometric Station) (preprint)

Ahwaz Hydrometric Station is responsible for controlling surface water resources and the Karoon River near Ahwaz city in southwestern Iran. And the present study aimed to determine the parameters affecting water quality, especially TH and SAR parameters. For this purpose, 39-year old statistical data were collected with 463 samples. To determine the water quality, first the correlation matrix method and statistical analysis were conducted, and then the correlation between them and the accuracy of these methods were checked using the tree model and the K-Nearest Neighbor (K-NN) method. The K-NN method and multivariate regression were compared for water quality characteristics, including SAR. The results indicated that K-NN methods were better than the regression method. In addition, the K-NN method using the effective anion and cation combinations yielded better results of estimating Sodium Absorption Ratio (SAR) and Total hardness (TH). Furthermore, the accuracy of the tree model after estimating TH using SO42- was more than that of Ca2+. Moreover, the accuracy of the tree model using the Cl- data for SAR estimation was higher than that of the Na+ data. In general, according to the APHA standard (2005), river water is in the high-risk and low-alkaline group.
About kernel-based estimation of conditional Kendall's tau: finite-distance bounds and asymptotic behavior

We study nonparametric estimators of conditional Kendall's tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic bounds with explicit constants, that hold with high probabilities. We provide "direct proofs" of the consistency and the asymptotic law of conditional Kendall's tau. A simulation study evaluates the numerical performance of such nonparametric estimators.
SERVITIZED BUSINESS MODELS IN PROJECT-BASED FIRMS: THE CASE OF ENERGY EFFICIENT ARCHITECTURE

Business models of project-based firms have traditionally been based on delivering value through the product. In the building sector, the customer perceives value only when it receives the finished building, at which point it terminates its relationship with the firm. New research strands on value in use and the employment of new business models lead us to propose the creation of new solutions that can include services as part of the firm’s offering for the consumer. This proposal involves increasing both the value perceived by the customer and the benefits to be obtained by integrating all phases of the business, thereby achieving a lasting relationship with the customer. In the context of energy-efficient buildings, technological innovation and the particularities of design, installations, maintenance and use are presented as an ideal framework for studying the introduction of the phenomenon of servitization into the business models of project-based firms (PBFs). In this article, we attempt to evaluate how servitization gains a leading role in existing business models in this area. We will propose a structure for new business models in this sector and, through case study, examine what kinds of models are emerging. We will then analyse these models and propose improvements for integrating services into all phases of the business.
Face Recognition Techniques: A Survey

Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.
Encoding: A Prospective Multicenter Study Imaging with Compressed Sensitivity Acceleration of Brain Susceptibility-Weighted

BACKGROUND AND PURPOSE: While three-dimensional susceptibility-weighted imaging has been widely suggested for intracranial vessel imaging, hemorrhage detection, and other neuro-diseases, its relatively long scan time has necessitated the clinical veri ﬁ cation of recent progresses of fast imaging techniques. Our aim was to evaluate the effectiveness of brain SWI accelerated by compressed sensitivity encoding to identify the optimal acceleration factors for clinical practice. MATERIALS AND METHODS: Ninety-nine subjects, prospectively enrolled from 5 centers, underwent 8 brain SWI sequences: 5 different folds of compressed sensitivity encoding acceleration (CS2, CS4, CS6, CS8, and CS10), 2 different folds of sensitivity encoding acceleration (SF2 and SF4), and 1 without acceleration. Images were assessed quantitatively on both the SNR of the red nucleus and its contrast ratio to the CSF and, subjectively, with scoring on overall image quality; visibility of the substantia nigra – red nucleus, basilar artery, and internal cerebral vein; and diagnostic con ﬁ dence of the cerebral microbleeds and other intracranial diseases. RESULTS: Compressed sensitivity encoding showed a promising ability to reduce the acquisition time (from 202 to 41 seconds) of SWI while increasing the acceleration factor from 2 to 10, though at the cost of decreasing the SNR, contrast ratio, and the scores of visual assessments. The visibility of the substantia nigra – red nucleus and internal cerebral vein became unacceptable in CS6 to CS10. The basilar artery was well-distinguished, and diseases including cerebral microbleeds, cavernous angiomas, intracranial gliomas, venous malformations, and subacute hemorrhage were well-diagnosed in all compressed sensitivity encoding sequences. CONCLUSIONS: Compressed sensitivity encoding factor 4 is recommended in routine practice. Compressed sensitivity encoding factor 10 is potentially a fast surrogate for distinguishing the basilar artery and detecting susceptibility-related abnormalities (eg, cerebral microbleeds, cavernous angiomas, gliomas, and venous malformation) at the sacri ﬁ ce of visualization of the substantia nigra – red nucleus and internal cerebral vein.
A Comprehensive Analysis Of Object Detection And Classification Techniques For The Purpose Of Computer Vision Applications

— Recently Computer Vision System Based Applications Have Grown Rapidly. In The Field Of Computer Vision There Are Various Application Present Which Are Used In Real-World Application Scenarios Such As Medical Imaging, Security, Visual Surveillance Etc. Among These Application Visual Surveillance Is Also An Important Application. In Order To Obtain Better Surveillance System, Object Detection And Recognition Techniques Need To Be Addressed. In This Field, Object Recognition Have Gained Lots Of Attraction For Real Time Application Various Techniques Have Been Proposed For Object Recognition. In This Article We Study About The Stat-Of-Art Techniques Presented For Object Recognition. Later, A Study Is Presented Regarding Learning Based Scheme For Object Detection System. Finally We Have Concluded That Learning Based Approaches Are Robust To Obtain The Object Detection But Still The Complexity Remains A Challenging Issue For Learning Based Schemes.
Enhanced Ct-image for Covid-19 Classification Using Resnet 50

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pre-train Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score. © 2022 Little Lion Scientific. All rights reserved.
Online Planning for Stochastic Collaborative Privacy Preserving Planning

Collaborative multi-agent privacy preserving planning (CPPP) models problems where agents must work together to achieve joint goals, while keeping some information private. Recently, CPPP was extended to the stochastic case, where actions may fail, producing different effects than intended. Stochastic CPPP (SCPPP) problems can be solved using offline algorithms, such as RTDP. However, in many cases, we are not interested in computing a complete policy offline, and prefer to use an online approach, where one decides online on the next action only, without exploring the complete state space. This can allow us to scale to much larger problems. In this paper we thus explore online approaches for SCPPP. We suggest using a variant of the well known FF-Replan approach, adapted to CPPP, and a plan repair approach, where we try to locally return to the plan if an undesirable effect has occurred. We provide an empirical evaluation, comparing our approaches to an offline solver, showing that we can scale to much larger problems, and analyzing the strengths and weaknesses of our methods.
Analyzing the Relationship Between Gene Expression and Phenotype in Space-Flown Mice Using a Causal Inference Machine Learning Ensemble

Spaceflight has several detrimental effects on human and rodent health. For example, liver dysfunction is a common phenotype observed in space-flown rodents, and this dysfunction is partially reflected in transcriptomic changes. Studies linking transcriptomics with liver dysfunction rely on tools which exploit correlation, but these tools make no attempt to disambiguate true correlations from spurious ones. In this work, we use a machine learning ensemble of causal inference methods called the Causal Research and Inference Search Platform (CRISP). It was specifically developed to predict causal features of a binary response variable in high-dimensional input. We used CRISP to search gene expression data for genes truly correlated with a lipid density phenotype using transcriptomic and histological data from the NASA Open Science Data Repositories (OSDR). Our approach identified genes and molecular targets not predicted by previous traditional system biology approaches. These genes are likely to play a pivotal role in the liver dysfunction observed in space-flown rodents, and this work opens the door to identifying novel countermeasures for space travel.
FACIAL EXPRESSION RECOGNITION USING CNN FOR HEALTHCARE

Human emotion is the way how human express anything from themselves. We can extract many informations from a human emotion alone like temper and health condition. Due to in Indonesia, people usually don’t visit doctor just to seeing their own face condition. We can use a computer vision task to do so. This paper is aim to train a computer vision model that can recognize a human face emotion. Inspired by Convolutional Neural Networks (CNNs) that capable of doing image classification, we proposed our own designed multiple CNN classification model with different layer settings to do facial expression recognition. Next we applied the model on the FER2013 dataset. The best training accuracy achieved by the second model in 92.40% accuracy and the best validation accuracy achieved by third model in 67.43% accuracy.
Choroidal laser Doppler ﬂowmeter with enhanced sensitivity based on a scattering plate

A portable choroidal laser Doppler ﬂowmeter (LDF) with enhanced sensitivity based on a scattering plate is developed. The portable LDF is weighted 2 kg operated at center wavelength of 780 nm, leading to a better penetration into the eye fundus in contrast to the previous LDF operated at center wavelength of 670 nm. Enhancement of number of detected photons that undergo Doppler scattering and improved measured speed of choroidal blood ﬂow are achieved with the use of a scattering plate positioned in front of the eye. The mechanism of detection and sensitivity enhancement is theoretically analyzed. Evaluation of system performance is done by in vivo measurements on ten volunteers. The results demonstrate that an increased percentage of backscattering light at high Doppler shift frequency is collected due to utilization of the scattering plate. However, this kind of light detection inﬂuences spatial resolution of the system and decreases the total signal measured. The proposed method for detection and sensitivity enhancement might be useful in a case where the perception of very slight alternation of blood ﬂow is pursued and the spatial resolution is not as critical as that in a choroidal vascular bed.
IMPROVING ACCURACY IN SENTIMENT ANALYSIS FOR FORMAL AND INFORMAL MALAY LANGUAGE USING SEMANTIC INFORMATION

This paper presents a lexicon-based sentiment analysis model that is specifically designed for analyzing sentiments in formal and informal Malay language texts. The main challenge when dealing with Malay language is handling noisy texts and sarcasm. To overcome this hurdle, we propose a method to enhance the lexicon by incorporating semantic information and gloss information from Kamus Dewan and synonym chains from WordNet Bahasa to obtain sentiment terms. The goal is to utilize these semantic information and sentiment terms to enhance the accuracy of sentiment analysis in both formal and informal Malay language. The proposed model generates a semi-supervised Malay sentiment lexicon for both formal and informal Malay language and utilizes semantic information to further enhance its performance accuracy. We manually annotated two evaluation datasets, one in formal Malay and one in informal Malay, with sentiment values. We then conducted experiments on two models corresponding to formal Malay language and informal Malay language using these datasets. The results demonstrated that the proposed approach achieved an average accuracy of 90.0% and 88.4% for formal and informal Malay language, respectively. This confirmed that semantic information can effectively boost the performance accuracy of sentiment analysis model (in comparing with existing models) for Malay language.
In silico evaluation of anti SARS-CoV-2 antibodies neutralization power: A blueprint with monoclonal antibody Sotrovimab (preprint)

Immune escape caused by genetic variations of SARS-CoV-2 S protein immunogenic epitopes affects the efficiency of monoclonal antibody-based therapy of COVID-19. Therefore, predicting the effects of these variations on immune escape is important to adapt rapidly anti SARS-CoV-2 Mab therapy. We herein describe a computational method to evaluate the neutralizing power a monoclonal antibody specific of a given SARS-CoV-2 variant and to compare it to its potential neutralizing power of others and emergent variants. The method’s calls for building in silico complex between the spike protein of a SARS-CoV-2 variant and a neutralizing antibody, analyzing the molecular interactions pattern and calculating the binding energy. This data is assigned a neutralizing value of 100% to which can be compared the neutralization value of any SARS-CoV-2 variant determined after molecular replacement in the complex of the RBD sequence with the RBD of this variant. Application of this method to the class 3 neutralizing antibody Sotrovimab and 24 variants and subvariants showed that the affinity binding and neutralizing power, decreased gradually with new variants. This method is of interest to adapt the use of therapeutic antibodies to the treatment of emerging variants. It could be applied to antibody-based treatment of other viral infections.
CLUSTER TRACE ANALYSIS FOR PERFORMANCE ENHANCEMENT IN CLOUD COMPUTING ENVIRONMENTS

Cloud computing has received considerable interest from research institutions, developers, and individuals in the last years. A trace’s cluster of approximately 12,500 machines, referred to as the "Google cluster trace” had been initiated by Google. This paper examines the characteristics, download process and tools, and analysis of this trace dataset in an attempt to provide insights into a type of trace date similar to the date which is in the cloud environment. We analyzed trace dataset by using the K-means clustering algorithm executed over SQL Server to use the implemented methodology for enhancing cloud environment performance by allocating the data into clusters. This allocation was aimed to be used in distributing the upcoming tasks to the most suitable cluster, then to the most suitable machine which covers its need for resources. The clustering process generates some clusters depending on CUP rate for each task, these clusters represent the machines suitable to each range (Average) of CPU rate which is required from the upcoming task to be allocated. Depending on the relationship between tasks and machine data, machines could be selected of each produced cluster for calculating the availability of CPU usage. This calculation will be the millstone of future tasks allocation over cloud cluster machines depending on its resources availability and its suitability to future task resource requirements.
High-Speed and Low-Power Recursive Rounding Based Approximate Multipliers for Error-Resilience Applications

: Approximate multipliers play a requisite role in error-resilience applications having a trade-off between error and design metrics. Consequently, in this paper, recursive rounding-based approximate multipliers are proposed to enhance both error and design metrics. First, in the n/2 - bit, the most significant and least significant bits are chosen for every n-bit input operand. Next, these n/2 -bits are rounded to the closest power of two in the proposed multipliers architecture. Succeedingly, the final multiplication product is achieved using rounding, shifters, and adders. On top of that, all the proposed and existing multipliers are developed using Verilog Code with inputs widths ranging from 8-bit to 32-bit, synthesized using Cadence RTL compiler, and simulated using Vivado and MATLAB for Image Smoothing Filter. Further, the error and design metrics of the newly developed and existing multipliers are analyzed. It is observed through the simulation results that reductions in delay and power consumption are achieved through the proposed multipliers on an average of 21.35 % and 40 %, respectively, compared to that of state-of-the-art multipliers. Furthermore, the proposed multipliers also provide high PSNR and SSIM over the existing multipliers after incorporating the proposed multipliers in the image smoothing filter, which is suitable for Error-Resilience applications.
Performance Estimating And Optimizing Neural Network For Botnet Detection In Machine Learning

: These days, botnets are being considered as the most vital security dangers in the web and it is vital to discover new routes for their recognition. Shared (P2P) botnets are the most imperative sorts of botnets that utilization P2P correspondence conventions to control their bots remotely. Along these lines, their recognition is more troublesome than different botnets. In this paper, we propose another way to deal with distinguish P2P botnets in the direction and control (C&C) period of life cycle dependent on the examination of activity conduct. The proposed methodology can distinguish C&C activity of P2P botnets by utilizing stream based highlights and order techniques. The execution of the proposed approach is assessed dependent on various parameters. The aftereffects of the assessment demonstrate that the proposed approach can recognize P2P botnet from ordinary system activity with high identification rate.
Isolation, puri�cation, and identi�cation of antifungal protein produced by Bacillus subtilis SL-44 and anti-fungal resistance in apple

Apple anthracnose is a fruit fungal disease that is currently recognized as one of the most serious threats to apples worldwide. In this study, antifungal protein from B. subtilis SL-44 was isolated, puri�ed, identi�ed, and applied for apple anthracnose control. The antagonistic experiment showed that SL-44 had an excellent broad spectrum against plant pathogenic fungi. Initial pH, inoculum volume, and rotation speed were evaluated by single-factor experiments and Box-Behnken Design and which were optimized to be 7, 2%, and 180 rpm, respectively. Under these conditions, the yield of antifungal protein increased by 45.83% compared to that before optimization. The crude protein from SL-44 was isolated and puri�ed by (NH 4 ) 2 SO 4 precipitation, DEAE-Sepharose fast �ow column, and Sephadex G-100 column. TEM revealed that antifungal protein could severely disrupt the mycelial of C. gloeosporioides, resulting in the loss of its intracellular contents. LC-MS analyzed that antifungal protein was likely to be an uncharacterized protein YefB with a molecular weight of 35 kDa. Additionally, antifungal protein signi�cantly alleviated the size of the lesion to more than 70% in the apple infection protection test. In conclusion, B. subtilis SL-44 and its main antimicrobial components have remarkable potential in the development of fungicides for biological control of apple anthracnose.
(cid:1) A-and (cid:1) C-activin, follistatin, activin receptor mRNA and (cid:1) C-activin peptide expression during rat liver regeneration

The mRNA expression of two activin growth factor subunits ( (cid:1) A-and (cid:1) C-activin), activin receptor subunits (ActRIIA, ActRIIB) and the activin-binding protein follistatin, and peptide expression of (cid:1) A-activin and (cid:1) C-activin subunits, were examined in regenerating rat liver after partial hepatectomy (PHx). Liver samples were collected from adult, male Sprague–Dawley rats, 12–240 h ( n =3–5 rats per time point) after PHx or from sham-operated controls at the same time points. Hepatocyte mitosis and apoptosis were assessed histologically and by in situ cell death detection. RT and PCR were used to assess relative gene expression. (cid:1) A-and (cid:1) C-activin peptide immunoreactivity was assessed in liver and serum samples by western blotting, whereas cellular expression was investigated by immunohistochemistry, using speciﬁc monoclonal antibodies. (cid:1) A-and (cid:1) C-activin mRNA dropped to , 50% of sham control values 12 h after PHx and remained at this level until 168 h post-PHx, when (cid:1) A-activin expression increased to three times sham control values and (cid:1) C-activin mRNA returned to pre-PHx levels. A peak in follistatin expression was observed 24–48 h post-PHx, coincident with an increase in hepatocyte mitosis. No changes were observed in ActRIIA mRNA, whereas ActRIIB expression paralleled that of (cid:1) A-activin mRNA. (cid:1) C-activin immunoreactive homo-and heterodimers were observed in regenerating liver and serum. Mitotic hepatocytes frequently contained (cid:1) C-activin immunoreactivity, whereas apoptotic hepatocytes were often immunoreactive for (cid:1) A-activin. We conclude that (cid:1) A-and (cid:1) C-activin subunit proteins are autocrine growth regulators in regenerating liver and when expressed independently lead to hepatocyte apoptosis or mitosis in a subset of hepatocytes.