Miracle-dz/UniFAR-FTUs huggingface dataset:
Vibration Control of a Suspension System via a Magnetorheological Fluid Damper

Semi-active control systems are becoming more popular because they offer both the reliability of passive systems and the versatility of active control without imposing heavy power demands. It has been found that magneto-rheological (MR) fluids can be designed to be very effective vibration control actuators. The MR fluid damper is a semi-active control device that uses MR fluids to produce a controllable damping force. The objective of this paper is to study a single-degree-of-freedom suspension system with an MR fluid damper for the purpose of vibration control. A mathematical model for the MR fluid damper is adopted. The model is compared with experimental results for a prototype damper through finding suitable model parameters. In this study, a sliding mode controller is developed by considering loading uncertainty to result in a robust control system. Two kinds of excitations are inputted in order to investigate the performance of the suspension system. The vibration responses are evaluated in both time and frequency domains. Compared to the passive system, the acceleration of the sprung mass is significantly reduced for the system with a controlled MR damper. Under random excitation, the ability of the MR fluid damper to reduce both peak response and root-mean-square response is also shown.
Robot Visual Servo Control System Based on Deep Detection Network and Spatial Pose Estimation

Visual servo control system (VSCS) is an indispensable ability for robots, which has become a hot topic in machine intelligence and attracts extensive attentions. While the enormous success has been witnessed in last years, however, there are still several essential challenges, such as visual detection error caused by unsatisfied accuracy of traditional algorithm, hysteresis of control caused by expensive time-consuming of deep learning. To address above issues, we propose a novel visual servo control framework based on light weight detection network and spatial pose estimation for object following. Specifically, we select stable YOLO network as backbone and introduce Kalman Filter-based pose estimation module into proposed framework to ensure accurate and real-time object detection. In addition, a double loop-based cascade PID algorithm is adopted to drive the robot following the specific objects in real-time. Experimental results demonstrate the effectiveness of proposed framework on mobile object detection and pose estimation.
Valorization Use of Amphipod Meal, Gammarus pulex, as a Fishmeal Substitute on Growth Performance, Feed Utilization, Histological and Histometric Indices of the Gut, and Economic Revenue of Grey Mullet

The future of aquaculture development is directed toward high intensification to overcome the decline in natural fishing and to provide enough protein for the increasing world population. The spread of aquaculture production and intensification requires the search for high-quality, new efficient feed ingredients with low cost and sustainable importance. Therefore, the current study aimed to detect the effects of partial or total replacement of fishmeal with amphipod meal, Gammarus pulex, on growth performance, survival percent, feed utilization, histological alteration of intestine and liver, and economic yield of grey mullet, Mugil cephalus, fry. Five diets were formulated to contain 100% fishmeal (FM), or FM replaced with 25%, 50%, 75%, or 100% amphipod meal (APM) (D0, D25, D50, D75, and D100, respectively). A total of 300 grey mullet fry (0.097 ± 0.001 g), were divided into five groups (three replicates each) at an initial stocking density of 20 fry per aquaria (100 L). The aquarium’s water is renewed at a rate of 30% daily. During a 60-day experimental period, the feeding rate was 20% of body weight, which was introduced as five meals per day. Fish fed D50 achieved the highest significant values of final weight (1.80 g), weight gain (1.70 g), survival (86.67%), final length (4.47 cm), and length gain (2.06 cm). In addition, the feed utilization of diets containing increasing substitution levels of FM showed that the highest protein intake (0.82 g ish−1), protein efficiency ratio (0.83), protein productive value (30.65%), and the lowest significant feed conversion ratio (1.21) were recorded with D50. The dose-response study revealed that the best substitution levels could range between 50% and 75%. Histological observations confirmed that the highest number of goblet cells and intestinal villi were recorded in the group fed D50. No pathological effect was observed in the liver at all substitution levels. In terms of economic efficiency, the best economic conversion ratio was recorded in the group fed D50. This study confirmed that 50% partial substitution of FM with APM is the ideal replacement level for grey mullet fry. In addition, the use of a new renewable alternative, such as APM to substitute FM, could relieve the pressure on the capture of wild fish and reduce the environmental impact of inland aquaculture.
Predicting Accuracy of Early Cost Estimates using Factor Analysis and Multivariate Regression

The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project's ultimate funding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Each of the respondents was asked to assign a one-to-five rating for each of 45 potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and multivariate regression analysis. The factor analysis was used to group the 45 elements into 11 orthogonal factors. Multivariate regression analysis was performed on the 11 factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the estimate score procedure, allows the project team to score an estimate and then predict its accuracy based on the estimate score. In addition, a computer software tool, the Estimate Score Program, was developed to automate the estimate score procedure. The multivariate regression analysis identified 5 of the 11 factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements, and bidding and labor climate.
Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.
A Fire Monitor Terminal for Reliable and Stable Image Acquisition Based on ARM

Introduced the Design and Implementation of fire monitor terminal base on the hardware platform of ARM. The image quality is improved with the use of CCD monitoring camera to capture image, and Pan/Tilt/Zoom are used to focus the fire area, enhance the accuracy of fire image recognition. This paper presents the system architecture and its functional characteristics. Emphasis is put on the hardware design of image capture and Pan/Tilt/Zoom control module. Finally, the software structure and implementation is described and the laboratory result is given. Compared with other similar image monitoring system, this system has the advantage of simple structure, high image quality, stable and reliable, feature-rich, etc.
Bioactive Molecules of Mandarin Seed Oils Diminish Mycotoxin and the Existence of Fungi

Mandarin is a favorite fruit of the citrus family. Mandarin seeds are considered a source of nontraditional oil obtained from byproduct materials. This investigation aimed to assess the biomolecules of mandarin seeds and evaluated their antimycotic and antimycotoxigenic impact on fungi. Moreover, it evaluated the protective role of mandarin oil against aflatoxin toxicity in cell lines. The two types of extracted oil (fixed and volatile) were ecofriendly. The fatty acid composition, tocopherol, sterols, and carotenoids were determined in the fixed oil, whereas volatiles and phenolics were estimated in the essential oil. A mixture of the two oils was prepared and evaluated for its antimicrobial impact. The reduction effect of this mixture was also investigated to reduce mycotoxin secretion using a simulated experiment. The protective effect of the oil was evaluated using healthy strains of cell lines. Fixed oil was distinguished by the omega fatty acid content (76.24%), lutein was the major carotenoid (504.3 mg/100 g) and it had a high β-sitosterol content (294.6 mg/100 g). Essential oil contained limonene (66.05%), α-pinene (6.82%), β-pinene (4.32%), and γ-terpinene (12.31%) in significant amounts, while gallic acid and catechol were recorded as the dominant phenolics. Evaluation of the oil mix for antimicrobial potency reflected a considerable impact against pathogenic bacteria and toxigenic fungi. By its application to the fungal media, this oil mix possessed a capacity for reducing mycotoxin secretion. The oil mix was also shown to have a low cytotoxic effect against healthy strains of cell lines and had potency in reducing the mortality impact of aflatoxin B1 applied to cell lines. These results recommend further study to involve this oil in food safety applications.
Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction

Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues.
A Study of the Coincidence Effect and Critical Frequency of a Dash Panel Using the hybrid FEA–SEA and Experimental Methods

This paper provides a new method to determine critical frequencies of any engineering structures by identification of peak frequencies of their internal damping loss factor curves. The novelty embodies the following aspects: first, the indirect method was applied to measure the critical frequencies of large complex thin-walled panels; second, the hybrid FEA-SEA was applied to analyse the sound insulation characteristics which provide an important approach to solve the middle frequency engineering problems. A hybrid of finite element analysis and statistical energy analysis was applied to calculate the dash sound transmission loss in the middle frequency range of 100∼2,000 Hz. A sound transmission loss experiment was conducted to verify the analysis. The trough of the sound transmission loss curve appeared at 200~250 Hz where the internal damping loss factor curve reached its peak. Therefore, the coincidence of the dash panel is proved to be at the critical frequency of 210 Hz.
Assessment of the GA‐based adaptive array control strategy: The case of stochastic life‐time co‐channel interferences

In this paper, the performances of an adaptive antenna array control strategy, based on an improved genetic algorithm, are evaluated in terms of signal‐to‐interference‐noise ratio (SINR). The working case considered deals with the presence of co‐channel interfering signals characterized by stochastic arrivals, phases, and lifetime. This scenario provides a reliable testing benchmark for such array control methodologies. The simulation results prove the robustness of the learned real‐time genetic algorithm (LRTGA) in the presence of non‐deterministic interferences as well. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 198–201, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10867
Can transfer of learning be enriched by valuing knowledge transformation and co-creation? Exploring new ways to improve training design

This paper argues that a possible cause of issues with management education outcomes is the fact that most training models operate from a limited ‘transfer’ metaphor. This theoretical paper contends that by reconceptualising existing models, specifically Holton’s transfer of learning model, to incorporate multiple processes and acknowledge the importance of educator- or trainer-student interaction in co-creating knowledge, there is potential to improve training design and ultimately achieve more satisfactory training outcomes.
Optimization of the Hole Exit Shaping of Film Holes Without and With Compound Angles for Maximal Film Cooling Effectiveness

Shaped film holes can achieve higher film cooling effectiveness compared with the simple cylindrical film holes. According to former studies, the geometry of the shaped film holes has significant influence on the cooling performance. In order to maximize the film cooling effectiveness of the shaped holes, a two-level design optimization methodology of the hole exit shaping is developed in the present study.The optimization methodology consists of a parametric design and CFD mesh generation tool called Coolmesh, a RANS CFD solver, a database of film cooling effectiveness distributions, a metamodel, and a genetic algorithm (GA) for evolutionary optimization. A binary parametric representation of the 2D hole exit shaping is initiated based on the B-spline methods. The metamodel can efficiently predict the detailed distribution of film cooling effectiveness using the CFD results in the database, which is continuously updated for higher accuracy. In each first-level iteration, a second-level GA optimization search is carried out coupled with the metamodel, and then the optimal geometry is evaluated using CFD methods and added to the database. An anisotropic turbulence model is applied to the CFD solver for higher accuracy according to a detailed experimental validation using PSP measurements.In the present study, three design optimizations of the shaped holes without and with compound angles are carried out on a flat plate. The optimization methodology can efficiently find the optimal geometries of shaped holes using only hundreds of CFD runs. For the shaped holes with compound angle, the optimized geometry can generate a back flow vortex which interacts with the shear vortex and weakens the mixing of coolant and hot gas, resulting in a higher film cooling effectiveness on the plate.Copyright © 2014 by ASME
A Review of Emotion Recognition Methods from Keystroke, Mouse, and Touchscreen Dynamics

Emotion can be defined as a subject’s organismic response to an external or internal stimulus event. The responses could be reflected in pattern changes of the subject’s facial expression, gesture, gait, eye-movement, physiological signals, speech and voice, keystroke, and mouse dynamics, etc. This suggests that on the one hand emotions can be measured/recognized from the responses, and on the other hand they can be facilitated/regulated by external stimulus events, situation changes or internal motivation changes. It is well-known that emotion has a close relationship with both physical and mental health, usually affecting an individual’s and a team’s work performance, thus emotion recognition is an important prerequisite for emotion regulation towards better emotional states and work performance. The primary problem in emotion recognition is how to recognize a subject’s emotional states easily and accurately. Currently, there are a body of good research on emotion recognition from facial expression, gesture, gait, eye-tracking, and other physiological signals such as speech and voice, but they are all intrusive and obtrusive to some extent. In contrast, keystroke, mouse and touchscreen (KMT) dynamics data can be collected non-intrusively and unobtrusively as secondary data responding to primary physical actions, thus, this paper aims to review the state-of-the-art research on emotion recognition from KMT dynamics and to identify key research challenges, opportunities and a future research roadmap for referencing. In addition, this paper answers the following six research questions (RQs): (1) what are the commonly used emotion elicitation methods and databases for emotion recognition? (2) which emotions could be recognized from KMT dynamics? (3) what key features are most appropriate for recognizing different specific emotions? (4) which classification methods are most effective for specific emotions? (5) what are the application trends of emotion recognition from KMT dynamics? (6) which application contexts are of greatest concern?
Existence of optimum cold FET intrinsic reference plane for active FET small signal modeling

Cold FET methods have been widely used for active FET modeling assuming the bias independence of parasitic elements. However, the assumption has been merely justified by the resulting modeling accuracy. This paper investigates the consistency of cold FET conditions with active FET through the exact and the error minimizing solutions of cold FET intrinsic reference plane constrained by the feedback condition of active FET model. Additionally, drain bias dependence of parasitic resistances will be presented.
Innovations to Manage Disputes: DRB and NEC

How do we reduce conflict and litigation in the construction process? On one hand, some industry leaders focus their efforts on improving alternative dispute resolution mechanisms to resolve disputes. For instance, the American Society of Civil Engineers has introduced the Dispute Review Board (DRB) as a complementary provision to standard U.S. construction contracts and practices. On the other hand, other industry leaders focus their efforts toward making fundamental changes to the way construction contracts are written. The Council of the Institution of Civil Engineers in the United Kingdom has introduced the New Engineering Contract (NEC) to the construction industry as an alternative to presently used contracts. Both of these efforts have proved to be effective in improving communication, relationships, and the management and reduction of disputes. This paper explores the philosophy of the DRB and NEC and compares their success stories as approaches to combating adversarial relationships and rising litigation costs in the construction industry.
Predicting cerebral infarction in patients with atrial fibrillation using machine learning: The Fushimi AF registry

The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66–0.79); CHADS2, 0.61 (95%CI, 0.53–0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54–0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF.
Morphological fluctuations of individual mitochondria in living cells

Uncovering the link between mitochondrial morphology, dynamics, positioning and function is challenging. Mitochondria are very flexible organelles that are subject to tension and compression within cells. Recent findings highlighted the importance of these mechanical aspects in the regulation of mitochondria dynamics, arising the question on which are the processes and mechanisms involved in their shape remodeling. In this work we explored in detail the morphological changes and spatio-temporal fluctuations of these organelles in living Xenopus laevis melanophores, a well-characterized cellular model. We developed an automatic method for the classification of mitochondria shapes based on the analysis of the curvature of the contour shape from confocal microscopy images. A persistence length of 2.1 μm was measured, quantifying, for the first time, the bending plasticity of mitochondria in their cellular environment. The shape evolution at the single organelle level was followed during a few minutes revealing that mitochondria can bend and unbend in the seconds timescale. Furthermore, the inspection of confocal movies simultaneously registering fluorescent mitochondria and microtubules suggests that the cytoskeleton network architecture and dynamics play a significant role in mitochondria shape remodeling and fluctuations. For instance changes from sinuous to elongated organelles related to transitions from confined behavior to fast directed motion along microtubule tracks were observed.