Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of Fault Prediction of Anode Effect Based on Elman Neural Network

The purpose of this paper is modeling for the fault of aluminum anode effect by using neural network predictive control. ,predicting whether the aluminum reduction cell occurs of anode effect with the corresponding fault detection strategy.After having analyzed the problems that existed in the production process of aluminum electrolysis,an Elman Neural Network prediction model is established to predict AE through forecasting cell resistance.The Elman neural network has features of simple structure,little error and high accuracy.The establishment of fault prediction system can achieve fault prediction aluminum anode effect fast and accurately when in the unknown production process model circumstances.This method can diagnose the fault of aluminum electrolysis correctly,which not only has stronger adaptability and robustness,but also function approximation and convergence speed.So this model increased the speed and accuracy of fault prediction greatly,avoided the occurrence of faults,improved the efficiency of aluminum production,achieved safety production,and reduced the energy consumption.As a result,the accuracy rate of fault diagnosis is more than 90%.
A complement to the scalar wave theory of light

In this paper, we discuss how the concepts of Hamiltonian optics are internally connected to the scalar wave theory of light rays. It is shown that the solutions of the reduced wave equation are similar to Huygen’s wavelets, and they can be used to understand Snell’s law of refraction. This model can also be used to derive the coefficient of reflection consistent with Fresnel’s equation for s-polarized light.
Deep ensemble-based classifier for transfer learning in rotating machinery fault diagnosis

Nowadays, intelligent models can correctly detect faults by analysing signals from rotating machinery. However, most of the studies are run in controlled environments and the performance in industrial real-world environments is not yet fully validated. Hence, a suitable tool to implement fault diagnosers is transfer learning, this topic is under development and challenges persist. This paper proposes a framework for creating accurate 1D-CNN based fault classifiers that can be transferred between different rotating machines and working conditions. Multiple Bayesian processes select architecture parameters and hyperparameters, which minimize a loss function related to their transferability to other machines and to the same machine under different operating conditions (such as load and engine speed). The resulting model is fitted to heterogeneous fault diagnosis data resulting in a 1D-CNN ensemble that improves the performance of the unitary model. Subsequently, the transfer learning capability of the ensemble is analyzed on two source data sets using function and parameter based transfer. The results are compared with classical fault diagnosis classifiers. Finally, additional transfer operations on five target domain datasets validate our framework on limited labeled samples and allow interpretation of the ensemble results. The ultimate goal is to find an ensemble that can generalize fault diagnosis on rotating machinery for easy implementation and update in industrial settings.
Transformational Impact of COVID-19 on Savings and Spending Patterns of Indian Rural Households

COVID-19 has spread across the globe at a shocking level and significantly affects the world economy. The pandemic has significantly impacted rural households, the primary workforce for industrialized urban areas, in every sector of rural businesses, including agriculture. Furthermore, the dearth of employment in the primary industry has also adversely influenced rural inhabitants’ livelihood and financial decisions. COVID-19 changed the perception of people regarding their income and expenditure. This study is intended to analyse the transformation of savings and spending of rural households during COVID-19. A questionnaire was developed using a Likert scale to elicit study variables, and the collected data were analysed using structural equation modelling. The results showed that all types of savings had a positive and significant relationship with the savings motive of rural households during COVID-19. Further, customary and spontaneous spending had a positive and significant relationship spending pattern of rural households. Rural inhabitants were interested in compromising their spending and other forms of savings to have more emergency savings. Earlier studies have examined either the savings or the spending pattern of rural households, and studies on both savings and spending by rural households are very few. The present study thus adds to the existing literature in the field.
STABILITY EVALUATION OF RIVER LEVEES ON THE BASIS OF ACTUAL LEVEE BREACHINGS

In order to evaluate the stability of river levee, the application of a logit model was attempted. On the basis of actual levee breaches, the probability of collapse for river levee was evaluated with the damage potential function consisting of characteristic factors of river levees. Eleven factors were selected of many possible factors of levee, and four logit models were formulated with them. As a result, the significant characteristic factors which affected the stability evaluation were specified. And it was found that probability of collapse “P” could accurately be represented as index for the stability of river levee. This paper also shows that the predictability of these models are sufficiently effective to evaluate river levee stability in the sense of its simplicity.
NATIVE AMAZONIAN FUNGI TO CONTROL TERMITES Nasutitermes sp. (BLATTODEA: TERMITIDAE)

Termites of the Nasutitermes genus are considered one of the main urban and agroforestry pests in Brazil, where the main method of control is the application of pesticides. The uso of entomopathogenic fungi to reduce the population of this plague in the environment could be use as an alternative. The goal of this study was to evaluate the virulence of isolates native Amazonian fungi belonging to the Tolypocladium endophyticum, Metarhizium anisopliae and Metarhizium marquandii species for the control of Nasutitermes sp. The strains of T. endophyticum (4.439), M. anisopliae (4.443) and M. marquandii (4.472) with their respective isolation codes, were evaluated using suspensions at concentrations of 105, 106, 107 and 108 conidia/mL against the termites. The fungi were characterized to species level by molecular analysis. The greatest virulence was registered with T. endophyticum (4.439), with a mortality of 100 % on the 4th day of treatment for all analyzed concentrations. The M. anisopliae strain (4.443) proved to be efficient, causing a mortality of 100 % on the 7th and 6th days at dilutions of 107 and 108 conidia/mL, respectively. Rates lower than 100 % were registered with M. marquandii (4.472). Therefore, the three fungal strains showed virulence against the termites Nasutitermes sp. In this study, the fungi Tolypocladium endophyticum and Metarhizium marquandii are reported for the first time for the biological control of pests, indicating the potential of native Amazonian fungi for the biological control of thermites Nasutitermes sp.
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques

Machine intelligence can convert raw clinical data into an informational source that helps make decisions and predictions. As a result, cardiovascular diseases are more likely to be addressed as early as possible before affecting the lifespan. Artificial intelligence has taken research on disease diagnosis and identification to another level. Despite several methods and models coming into existence, there is a possibility of improving the classification or forecast accuracy. By selecting the connected combination of models and features, we can improve accuracy. To achieve a better solution, we have proposed a reliable ensemble model in this paper. The proposed model produced results of 96.75% on the cardiovascular disease dataset obtained from the Mendeley Data Center, 93.39% on the comprehensive dataset collected from IEEE DataPort, and 88.24% on data collected from the Cleveland dataset. With this proposed model, we can achieve the safety and health security of an individual.
Effectiveness of Prophylactic Human Cytomegalovirus Hyperimmunoglobulin in Preventing Cytomegalovirus Infection following Transplantation: A Systematic Review and Meta-Analysis

Cytomegalovirus (CMV) is a common infection occurring in patients undergoing solid organ transplantation (SOT) or hematopoietic stem cell transplantation (HSCT). CMV-specific hyperimmunoglobulin (CMVIG) has been used for the past four decades and is typically administered either prophylactically or pre-emptively. The present meta-analysis evaluated CMV infection rates in SOT patients who received prophylactic CMVIG. PubMed and the Cochrane Library were searched for studies published up to October 2021. The primary endpoint was CMV infection rate. Thirty-two SOT studies were identified (n = 1521 CMVIG-treated and n = 1196 controls). Prophylactic CMVIG treatment was often associated with a lower risk of CMV infection in transplant recipients. The average CMV infection rate was 35.8% (95% confidence interval [CI]: 33.4–38.2%) in patients treated prophylactically with CMVIG and 41.4% (95% CI: 38.6–44.2%) in the control group not receiving CMVIG (p = 0.003). Similar results were observed in analyses limited to publications evaluating currently available CMVIG products (Cytotect CP and Cytogam; p < 0.001). In combination with the established safety profile for CMVIG, these results suggest that prophylactic CMVIG treatment in patients undergoing solid organ transplantation may be beneficial, particularly in those at high risk of CMV infection or disease.
Molecular Landscape of Small Bowel Adenocarcinoma

Simple Summary Small bowel adenocarcinoma (SBA) is a rare malignancy with worse prognosis compared to other cancers of the gastrointestinal tract. Over 90% of SBA tumors harbor targetable genetic alterations. Molecular analysis to identify these alterations, using tissue- or blood-based next generation sequencing, is critical and may impact treatment decisions. The aim of our review is to highlight molecular drivers of SBA tumorigenesis. We highlight key mutational and transcriptomic differences between SBA and colorectal cancer, from which much of the clinical management of SBA is currently extrapolated. We provide evidence that SBA is a molecularly unique intestinal malignancy, with distinct genomic alterations predictive of response to targeted therapy and immunotherapy. Abstract Small bowel adenocarcinoma (SBA) is a rare malignancy, with lower incidence, later stage at diagnosis, and poor overall prognosis compared to other cancers of the gastrointestinal tract. Owing to the rarity of the disease along with the paucity of high-quality tissue samples and preclinical models, little is known about the molecular alterations characteristic of SBA. This is reflected by the fact that the clinical management of SBA is primarily extrapolated from colorectal cancer (CRC). Recent advances in genomic profiling have highlighted key differences between these tumors, establishing SBA as a molecularly unique intestinal cancer. Moreover, comprehensive molecular analysis has identified a relatively high incidence of potentially targetable genomic alterations in SBA, predictive of response to targeted and immunotherapies. Further advances in our knowledge of the mutational and transcriptomic landscape of SBA, guided by an increased understanding of the molecular drivers of SBA, will provide opportunities to develop novel diagnostic tools and personalized therapeutic strategies.
Transformations of structure and programme in three Slovene coastal cities

There is an intense need to conceive residential houses for diff erent kinds of reconstruction; and for the revitalisation of devastated parts of cities, the so-called brown fi elds. An integration with civic services saves on public transport and the environment; construction plots; investment funds; the workforce; expensive equipment and maintenance. It also provides new possibilities of architectural composition, and an increased attractiveness of poly-functional facilities. It goes without saying that the removal of architectural barriers is necessary for the improved quality of life of the handicapped, mothers with prams, children, and the elderly.
Adaptive Self-Tuning Flight Control System for ATLANTE UAS

AbstractThis paper presents an adaptive self-tuning flight control system (AST-FCS) for a fixed-wing aircraft, applied to a high-fidelity dynamic model of the ATLANTE unmanned aircraft system (UAS). This new adaptive control system is based on adaptive predictive (AP) control methodology and a new guidance system. AP control has been implemented in many complex industrial applications with excellent results, but it has not been applied in the aerospace sector yet. Experimental results obtained in simulation by flying a high-fidelity model of the ATLANTE UAS illustrate excellent control system performance and show the robustness of the AST-FCS, which has been designed without using accurate knowledge of the aircraft dynamics embedded within the model. It is expected that the application of AP control in this field could introduce significant advantages in aircraft control, improving performance in a wide variety of flight conditions and reducing design costs.
Continuous lightweight authentication according group priority and key agreement for Internet of Things

In the IoT, authentication is challenged by the limited resources of devices. Most existing continuous authentications require plenty of memory and computing. Also, the time interval between static authentications is fixed, and no attention is paid to the importance of their traffic. Therefore, in this article, we propose a lightweight protocol for mutual authentication between nodes and servers in IoT. To this aim, the nodes have been divided into three priority groups, and for the high priority group, a longer time interval is considered. Each group node at the beginning of the time interval performs static authentication and generates tokens. Continuous authentication is performed until the end of the time interval using this token. High‐priority nodes also perform more continuous authentication instead of static authentication. The proposed method provides privacy‐preserving through node anonymity, forward secrecy without using asynchronous encryption, key agreement. It is resistant to eavesdropping, replay, server spoofing, and impersonation attacks. Also, the proposed method has been verified using BAN logic and AVISPA tools. The computation time of the node and server in authentication has been decreased by 16.8% and 8.7%, respectively, compared with reviewing protocols, and the communication cost is 1902 bits.
Optically assisted mm-wave-based multi-Gbps RoFSO transmission link under channel fading models

Free space optic (FSO) communication offers a supplement to optical fibre for satisfying the demand of next-generation wireless networks. This paper signifies photonic generation of a double sideband-suppressed carrier (DSB-SC)-based 60 GHz mm-wave by operating a dual-drive Mach–Zehnder modulator (DD-MZM) in the null point region. The radiofrequency (RF) carrying signal is investigated for dual-hop FSO/RF links of 500 m/50 m by taking optical links in the turbulent atmosphere.At the data rates of 1.25, 2.5, and 5 Gb/s, overall operation is evaluated in terms of bit error rate (BER) as a function of the received optical power. In our analysis, system performance under the Gamma-Gamma (ΓΓ) matches with the Lognormal (LN) model in moderate fluctuation using aperture averaging effect. The results are obtained with a maximum difference in the receiver sensitivity of ∼1 dB at BER = 10−6 between ΓΓ and LN models.
Approximation of Caputo‐Prabhakar derivative with application in solving time fractional advection‐diffusion equation

This work aims to numerically approximate the Caputo‐Prabhakar derivative and use this approximation for solving the time‐fractional advection‐diffusion equation defined in Caputo‐Prabhakar sense which is widely used in fluid dynamics. In this approach, we approximate the time‐fractional derivative of the mentioned equation by two schemes, namely NS1 and NS2 , using linear and quadratic interpolation functions, respectively. The convergence order of the two schemes is 2−α , 3−α , respectively, for 0
Chemical Synthesis, Pharmacokinetic Properties and Biological Effects of JM-00266, a Putative Non-Brain Penetrant Cannabinoid Receptor 1 Inverse Agonist

Targeting cannabinoid 1 receptors (CB1R) with peripherally restricted antagonists (or inverse agonists) shows promise to improve metabolic disorders associated with obesity. In this context, we designed and synthetized JM-00266, a new CB1R blocker with limited blood–brain barrier (BBB) permeability. Pharmacokinetics were tested with SwissADME and in vivo in rodents after oral and intraperitoneal administration of JM-00266 in comparison with Rimonabant. In silico predictions indicated JM-00266 is a non-brain penetrant compound and this was confirmed by brain/plasma ratios and brain uptake index values. JM-00266 had no impact on food intake, anxiety-related behavior and body temperature suggesting an absence of central activity. cAMP assays performed in CB1R-transfected HEK293T/17 cells showed that the drug exhibited inverse agonist activity on CB1R. In addition, JM-00266 counteracted anandamide-induced gastroparesis indicating substantial peripheral activity. Acute administration of JM-00266 also improved glucose tolerance and insulin sensitivity in wild-type mice, but not in CB1R−/− mice. Furthermore, the accumulation of JM-00266 in adipose tissue was associated with an increase in lipolysis. In conclusion, JM-00266 or derivatives can be predicted as a new candidate for modulating peripheral endocannabinoid activity and improving obesity-related metabolic disorders.
Celastrol and Resveratrol Modulate SIRT Genes Expression and Exert Anticancer Activity in Colon Cancer Cells and Cancer Stem-like Cells

Simple Summary The recovery rate in patients with metastatic colorectal cancer (CRC) remains low and declines with successive lines of treatment. This phenomenon is caused by the development of drug resistance and the presence of colorectal cancer stem cells (CSCs). Phytochemicals, like -celastrol and resveratrol, are very promising for colon cancer therapy, owing to their low or no toxicity and their pleiotropic activity, enabling them to interact with various biological targets. In the present study, the potential anticancer mechanisms of both compounds against metastatic colon cancer cells and the capacity to eradicate CSCs were investigated. Abstract Metastatic colorectal cancer (CRC) remains a hard-to-cure neoplasm worldwide. Its curability declines with successive lines of treatment due to the development of various cancer resistance mechanisms and the presence of colorectal cancer stem cells (CSCs). Celastrol and resveratrol are very promising phytochemicals for colon cancer therapy, owing to their pleiotropic activity that enables them to interact with various biological targets. In the present study, the anticancer activities of both compounds were investigated in metastatic colon cancer cells (LoVo cells) and cancer stem-like cells (LoVo/DX). We showed that celastrol is a very potent anti-tumor compound against metastatic colon cancer, capable of attenuating CSC-like cells at the molecular and cellular levels. In contrast, resveratrol has a much greater effect on colon cancer cells that are expressing standard sensitivity to anticancer drugs, than on CSC-like cells. In addition, both polyphenols have different influences on the expression of SIRT genes, which seems to be at least partly related to their anti-tumor activity.
Modelling to Engineering Data: Using a New Two-Parameter Lifetime Model

A novel two-parameter continuous lifespan model is developed, based on a truncated Fréchet produced family of distributions known as the truncated Fréchet inverted Lindley distribution. It includes a thorough discussion of statistical features such as the quantile function, moments, order statistics, incomplete moments, and Lorenz and Bonferroni curves. The greatest likelihood approach for estimating population parameters is described. Finally, one real-world data set to application is utilized to demonstrate the new distribution’s utility. The data represent the tensile strength, measured in GPa, of 69 carbon fibers tested under tension at gauge lengths of 20 mm.
Encapsulation of Lactobacillus gasseri: Characterization, Probiotic Survival, In Vitro Evaluation and Viability in Apple Juice

The development of functional foods containing probiotic bacteria has become increasingly relevant to improve and maintain health. However, this is often limited to dairy food matrices given the complexity involved in maintaining a stable system together with high microbial viability in matrices such as juices. The objective of this study was to develop and characterize sodium alginate capsules loaded with Lactobacillus gasseri ATCC® 19992 ™ (LG). Cell viability under in vitro gastrointestinal conditions and during storage in apple juice were evaluated. The capsules were prepared by ionic gelation and an emulsification process was performed as pretreatment using two homogenization methods: magnetic stirring (AM) and Ultraturrax® rotor-stator homogenizer (UT). Cell viability after encapsulation was similar in the two processes: 65%. At the end of the in vitro gastrointestinal evaluation, the non-encapsulated probiotic cells did not show any viability, while the AM system was able to retain 100% of its viability and the UT retained 79.14%. The morphology of the capsules consisted of a continuous and homogeneous surface. Cell viability of LG encapsulated in apple juice stored at 4 °C for 21 days was 77% for AM, 55.43% for UT, and 63.10% for free LG.
DETERMINATION OF GROUNDWATER POTENTIAL AREAS IN KADUNA METROPOLIS, KADUNA, NIGERIA

Groundwater is one of the major resources important for sustainable growth and development. Proper management of the resources is therefore important to meet all the water requirements for human existence to continue. In this study, an investigation is made to explore for groundwater potential areas of Kaduna Metropolis, Kaduna State using remote sensing and GIS technique. The data used for the study are as follows; Landsat 8 (OLI) 30m, Aster data (30m), Geologic map, Soil map and Topographic map was used to generate the factors such as; Land use/cover, Slope, Geomorphology, Geologic rock types, soil textures and drainage density. All the factors and its attributes were weighted and rated respectively and were classified according to their importance to groundwater occurrence using the modified DRASTIC model and the weighted overlay technique was used to create a groundwater potential map of the metropolis. Groundwater potential map were classified into four categories that best describes the potential of each area. These classes are; Poor (21%), Moderate (23%), Good (26%) and Very Good (30.2%) groundwater potential which was found to be concentrated in the central part of the metropolis. This result was verified against existing borehole data and field observations to validate the accuracy and was found to be 76% accurate. The study demonstrated the effectiveness of Remote Sensing and GIS as an effective tool in delineation and identification of groundwater potential areas.