Miracle-dz/UniFAR-FTUs huggingface dataset:
Indirect Field Orientation Control of Induction Machine With Detuning Effect

Induction motors (IM) arc characterized by complex, highly nonlinear time varying dynamics and inaccessibility of some states and outputs for measurements and hence may be considered as a challenging problem. Field orientation control (FOC ) methods of an induction machine achieve decoupled torque and flux dynamics leading to independent control of torque and flux as for separately excited DC motor, but there are sensitive to motor parameter variations. The present work select the indirect field orientation control (IFOC) as an effective method for eliminating the coupling effect. The results showed how well the drive performance has been improved by this control strategy. However, to what extent the control strategy can perform the decoupling relies on the accuracy of the slip frequency calculation. Unfortunately, the slip frequency depends on the rotor time constant that varies continuously according to the operational conditions and then the coupling effect may again arise. This paper investigates the improvement in the performance of the induction machine dynamics as the IFOC technique is utilized. Also, it investigates the degradation in dynamic performance when the rotor resistance is deviated from its nominal value.
A research on radiation calibration of high dynamic range based on the dual channel CMOS

The dual channel complementary metal-oxide semiconductor (CMOS) can get high dynamic range (HDR) image through extending the gray level of the image by using image fusion with high gain channel image and low gain channel image in a same frame. In the process of image fusion with dual channel, it adopts the coefficients of radiation response of a pixel from dual channel in a same frame, and then calculates the gray level of the pixel in the HDR image. For the coefficients of radiation response play a crucial role in image fusion, it has to find an effective method to acquire these parameters. In this article, it makes a research on radiation calibration of high dynamic range based on the dual channel CMOS, and designs an experiment to calibrate the coefficients of radiation response for the sensor it used. In the end, it applies these response parameters in the dual channel CMOS which calibrates, and verifies the correctness and feasibility of the method mentioned in this paper.
Glued laminated timber and steel beams

Purpose This paper aims to compare glued laminated timber and steel beams with respect to structural design, manufacturing and assembly costs and the amount of greenhouse gas emissions. Design/methodology/approach This paper presents structural design requirements in conformance with EN 1993: Eurocode 5 and Eurocode 3. With the help of these standards, expressions are derived to evaluate the design criteria of the beams. Based on the results of life-cycle analysis, the economic properties and environmental impact of the two types of beam are investigated. In this paper, the effect of beam span on the design values, costs and carbon dioxide emissions is analysed when investigating aspects of the structural design, economy and environmental impact. Different cross-sections are chosen for this purpose. Findings The study shows that the glued laminated (abbreviated as “glulam”) beams have a smaller tendency to lateral torsional buckling than the steel beams, and that they can be cheaper. From an environmental point of view, glulam beams are the more environmentally friendly option of the two beam materials. Furthermore, glulam beams may have a direct positive effect on the environment, considering the carbon storage capacity of the wood. The disadvantage of glued wood is that larger dimensions are sometimes required. Research limitations/implications Wind load and the effect of second-order effects have not been considered when analysing the static design. Only straight beams have been studied. Furthermore, the dynamic design of the beams has not been investigated, and the bearing pressure capacity of the supports has not been analyzed. We have investigated timber beams with a rectangular cross-section, and steel beams of rolled I-sections, known as “HEA profiles”. The cost analysis is based mainly on the manufacturing and assembly costs prevalent on the Swedish market. The only environmental impact investigated has been the emission of greenhouse gases. The design calculations are based on the European standards Eurocode 5 and Eurocode 3. Practical implications To achieve sustainability in construction engineering, it is important to study the environmental and economic consequences of the building elements. By combining these two effects with the technical design of buildings made of steel and/or timber, the concept of sustainable development can be achieved in the long run. Social implications The study concerns sustainability of building structures, which is an important of the sustainable development of the society. Originality/value The paper contains new information and will be useful to researchers and civil engineers.
Adaptive Macro Spatial Modulation for mmWave Dense Networks

In this letter, an adaptive macro spatial modulation scheme is proposed for the uplink of a millimeter-wave dense network. In the proposed scheme, a user equipment (UE) adaptively establishes communication links with multiple small base stations (SBSs) and switches its beam direction between these SBSs according to part of its transmitting information. We first model the optimal signal transmission scheme that minimizes system symbol-error rate (SER) as an optimization problem and propose an iterative method with relative high computational complexity to solve it. To reduce the complexity, an approximation scheme is also provided, by ignoring the side lobe of the UE. Numerical results show that both proposed schemes can effectively reduce the system SER.
DEVELOPMENT OF METHOD FOR IDENTIFICATION THE COMPUTER SYSTEM STATE BASED ON THE DECISION TREE WITH MULTI-DIMENSIONAL NODES

Context. The problem of identifying the state of a computer system is considered. The object of the research is the process of computer system state identification. The subject of the research is the methods of constructing solutions for computer system state identification. Objective. The purpose of the work is to develop a method for decision trees learning for computer system state identification. Method. A new method for constructing a decision tree is proposed, combining the classical model for constructing a decision tree and the density-based spatial clustering method (DBSCAN). The simulation results showed that the proposed method makes it possible to reduce the number of branches in the decision tree, which will increase the efficiency of identifying the state of the computer system. Belonging to hyperspheres is used as a criterion for decision-making, which enables to increase the identification accuracy due to the nonlinearity of the partition plane and to perform a more optimal adjustment of the classifier. The method is especially effective in the presence of initial data with high correlation coefficients, since it combines them into one or more multivariate criteria. An assessment of the accuracy and efficiency of the developed method for identifying the state of a computer system is carried out. Results. The developed method is implemented in software and researched in solving the problem of identifying the state of the functioning of a computer system. Conclusions. The carried out experiments have confirmed the efficiency of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of a computer system. Prospects for further research may consist in the development of an ensemble of decision trees.
Improving the thermal performance of the transparent building envelope: finite element analysis of possible techniques to reduce the U-value of the glassblocks

U-value of glazed elements is often a critical issue because these components, due to their small thickness and to the poor resistance of the glass and frame materials, cause very relevant heat fluxes. This paper presents an investigation on the thermal properties of a particular glazed component: the glassblock. Generally standard glassblocks have high Uvalues in comparison to the maximum values allowed by energy efficiency standards for glazed surfaces. This paper reports a summary of possible solutions that could improve the performances of the glassblock. A set of new configurations of the glassblock has been defined by schematic models and their overall thermal resistance has been assessed by the means of Finite Element software. The resulting performances are presented in terms of the global thermal transmittance of the modified glassblocks, also considering the effects of sealing and mortar. The paper also shows some significant potential improvements to address new production lines.
CeO2-calcein nanoconjugate protective action against H2O2-induced oxidative stress in vitro

A BSTRACT We studied cerium oxide-calcein nanoconjugate, which is capable of providing intracellular detection and simultaneous inactivation of reactive oxygen species (ROS). The synthesized nanoconjugate is easily uptaken by human mesenchymal stem cells (MSCs) and demonstrates antioxidant properties, protecting cells from H 2 O 2 -induced oxidative stress in vitro . Cerium oxide-calcein nanoconjugate neutralizes hydrogen peroxide, meanwhile releasing brightly ﬂuorescent calcein from its surface, which is easily detected by ﬂuorimeter or ﬂuorescent microscope. This nanoconjugate is biocompatible and non-toxic to MSCs in concentrations below 2 mM. Such a theranostic agent can be considered as a promising tool for tracking the redox status of human MSCs in vivo. nanoconjugate protective action -induced . Nanosystems: Phys. Chem. Math. , 2022, 13 (3),
Leaf Disease Detection and Automatic Pesticide Suggestion Using Deep Learning

Diseases that are caused by fungus are developed through soil-borne, above-ground infections. Pest and insect feeding causes the transmission of fungus. However, the existing research lacks an accurate and fast detector of leaf diseases for ensuring the healthy development of the agricultural industry. This project proposes a novel approach for developing an effective method for identifying the plant leaf diseases. Based on the identification of diseases, suggestion forthe pesticide is also given. A deep learning approach which is based on Multilayer Deep convolutional neural networks (CNNs) for the real-time detection of leaf diseases is used in the work. It also detects the types of leaf diseases with high accuracy. In addition, the proposed approach can handle the images of the diseased leaves. The results showed good improvement in identifying the plant leaf diseases.
Abundance of zooplankton from different zones (pelagic and littoral) and time periods (morning and night) in two Amazonian meandering lakes

The abundance of zooplankton in two lakes of Southwest Amazonia was studied for 10 months in different regions and at different periods of the day. The lakes were Lago Amapa, located at 10°02'36''S, 67°50'24''W, and Lago Pirapora, at 9°27'21"S, 67°31'39". Both lakes are characterized as oxbow lakes. The aim of this study was to compare the pelagic and littoral regions, as well as to determine differences in the distribution of zooplankton in the water column in the morning and at night. Collections were made by filtering water through a 55µm zooplankton net into a 5L Van Dorn bottle, collecting 4L from the top and 5L from the middle and bottom layers, totaling 14L of water for each sampling location. In addition, physical and chemical parameters were measured, including transparency, temperature, pH, dissolved oxygen, electrical conductivity and turbidity. Anova (analysis of variance) and Tukey's test were used. There was no statistically significant difference between the regions studied, nor between the two time periods examined. The results of the Pearson correlation (p<0.05) demonstrated that the physical and chemical characteristics of the water correlated with the cladocerans Moina spp. (represented by M. minuta and M. reticulata) and Ceriodaphnia cornuta, and that Daphnia gessneri was associated with Chaoboridae.
ECF (electro-conjugate fluid) finger with bidirectional motion and its application to a flexible hand

In this paper, we propose and develop an electro-conjugate fluid (ECF) finger with bidirectional motion and its application to a flexible hand. The finger is composed of a finger tube (5 mm in diameter) and a unit of ECF jet generators (5 × 7 × 8 mm3). The ECF is a kind of functional and dielectric fluid. Its jet flow is generated between two electrodes surrounded by the ECF under the high DC voltage. The ECF jet generator can be utilized as a hydraulic pressure source for the flexible finger tube and embedded in the finger. The flexible finger tube consists of two silicone rubber chambers whose cross-section is the shape of semi-circle along its axis. In the unit, there are two ECF jet generators, each of which has two pairs of needle-ring electrodes in parallel and which are connected to each other through the common line and placed to the opposite direction for the bidirectional motion of the ECF finger. One chamber of the finger expands due to the inner pressure generated by the ECF jet and the other one as an ECF container shrinks, causing the ECF finger to bend. The suitable parameters of the flexible finger tube are obtained by optimizing the cross-sectional shapes, the thickness of the partition and the fiber interval of the finger tube. Besides, the characteristics of the ECF finger including the displacement and self-generating force are investigated by experiments. The experimental results indicate that the proposed ECF finger can realize bidirectional motions with 9.2 mm in left-displacement and 6.4 mm in right-displacement and generate the maximum force: 6.6 mN. The final application of the ECF finger to a flexible hand also clearly shows the fingers are capable of grasping and releasing the object.
The Potential of Novel Lipid Agents for the Treatment of Chemotherapy-Resistant Human Epithelial Ovarian Cancer

Simple Summary Disease recurrence and chemotherapy resistance are the major causes of mortality for the majority of epithelial ovarian cancer (EOC) patients. Standard of care relies on cytotoxic drugs that induce a form of cell death called apoptosis. EOC cells can evolve to resist apoptosis. We developed drugs called glycosylated antitumor ether lipids (GAELs) that kill EOC cells by a mechanism that does not involve apoptosis. GAELs most likely induce cell death through a process called methuosis. Importantly, we showed that GAELs are effective at killing chemotherapy-resistant EOC cells in vitro and in vivo. Our work shows that the EOC community should begin to investigate methuosis-inducing agents as a novel therapeutic platform to treat chemotherapy-resistant EOC. Abstract Recurrent epithelial ovarian cancer (EOC) coincident with chemotherapy resistance remains the main contributor to patient mortality. There is an ongoing investigation to enhance patient progression-free and overall survival with novel chemotherapeutic delivery, such as the utilization of antiangiogenic medications, PARP inhibitors, or immune modulators. Our preclinical studies highlight a novel tool to combat chemotherapy-resistant human EOC. Glycosylated antitumor ether lipids (GAELs) are synthetic glycerolipids capable of killing established human epithelial cell lines from a wide variety of human cancers, including EOC cell lines representative of different EOC histotypes. Importantly, GAELs kill high-grade serous ovarian cancer (HGSOC) cells isolated from the ascites of chemotherapy-sensitive and chemotherapy-resistant patients grown as monolayers of spheroid cultures. In addition, GAELs were well tolerated by experimental animals (mice) and were capable of reducing tumor burden and blocking ascites formation in an OVCAR-3 xenograft model. Overall, GAELs show great promise as adjuvant therapy for EOC patients with or without chemotherapy resistance.
Product quality management based on CNC machine fault prognostics and diagnosis

This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor's equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.
Short-Term Drift Prediction of Multi-Functional Buoys in Inland Rivers Based on Deep Learning

The multi-functional buoy is an important facility for assisting the navigation of inland waterway ships. Therefore, real-time tracking of its position is an essential process to ensure the safety of ship navigation. Aiming at the problem of the low accuracy of multi-functional buoy drift prediction, an integrated deep learning model incorporating the attention mechanism and ResNet-GRU (RGA) to predict short-term drift values of buoys is proposed. The model has the strong feature expression capability of ResNet and the temporal memory capability of GRU, and the attention mechanism can capture important information adaptively, which can solve the nonlinear time series drift prediction problem well. In this paper, the data collected from multi-functional buoy #4 at Nantong anchorage No. 2 in the Yangtze River waters in China were studied as an example, and first linear interpolation was used for filling in missing values; then, input variables were selected based on Pearson correlation analysis, and finally, the model structure was designed for training and testing. The experimental results show that the mean square error, mean absolute error, root mean square error and mean percentage error of the RGA model on the test set are 5.113036, 1.609969, 2.261202 and 15.575886, respectively, which are significantly better than other models. This study provides a new idea for predicting the short-term drift of multi-functional buoys, which is helpful for their tracking and management.
Design and implementation of an intrusion detection system (IDS)for in-vehicle networks

The Controller Area Network (CAN) was specified with no regards to security mechanisms at all. This fact in combination with the widespread adoption of the CAN standard for connecting more than a hundred Electrical Control Units (ECUs), which control almost every aspect of modern cars, makes the CAN bus a valuable target for adversaries. As vehicles are safety-critical systems and the physical integrity of the driver has the highest priority, it is necessary to invent suitable countermeasures to limit CAN’s security risks. As a matter of fact, the close resemblances of in-vehicle networks to traditional computer networks, enables the use of conventional countermeasures, e.g. Intrusion Detection Systems (IDS). We propose a software-based light-weight IDS relying on properties extracted from the signal database of a CAN domain. Further, we suggest two anomaly-based algorithms based on message cycle time analysis and plausibility analysis of messages (e.g. speed messages). We evaluate our IDS on a simulated setup, as well as a real in-vehicle network, by performing attacks on different parts of the network. Our evaluation shows that the proposed IDS successfully detects malicious events such as injection of malformed CAN frames, unauthorized CAN frames, speedometer plausibility detection and Denial of Service (DoS) attacks. Based on our experience of implementing an in-vehicle IDS, we discuss potential challenges and constraints that engineers might face during the process of implementing an IDS system for in-vehicle networks. We believe that the results of this work can contribute to more advanced research in the field of intrusion detection systems for in-vehicle networks and thereby add to a safer driving experience.
Athletic Knowledge Management and Its Role in Achieving Competitive Advantage in Sports Work Environment

Background. Knowledge management in successful sports organizations is associated with their sustainability, continuity, and achievement of long-lasting competitive sports results. Objectives. This study aims to identify athletic knowledge management and its role in achieving a competitive advantage in the sports work environment in Jordan. Methods. The study sample consists of (60) male and female students studying the Sports Organization and Management Course at the Faculty of Physical Education and Sport Sciences at Hashemite University. Due to the nature of the study, the quantitative approach has been used. Data has been collected by distributing a 2-domain questionnaire: the role of the athletic knowledge management in achieving the competitive advantage in the sports work environment and the contributions of the athletic knowledge management in improving the sports work environment. Results. The results show that 98% of the participants believed that knowledge management plays a crucial role in achieving a competitive advantage in the sports work environment and improving the sports work environment. Also, the results indicate that knowledge management plays a vital role in contributing to knowledge management operations and developing the sports work environment. Regarding gender, the results show no significant differences between males and females. Conclusion. Several significant recommendations have been made, i.e., providing physical and moral infrastructure as a priority by senior management to be taken into account to implement this concept and offering safe, stable, systematic, and programmed conditions for all courses to improve and develop the level of sports performance among workers.