Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bio-Inspired Slip Detection and Reflex-Like Suppression Method for Robotic Manipulators

Robotic manipulators face significant challenges when handling objects of different sizes and shapes. Incorporating a sense of touch into these devices has the potential to improve performance and dexterity. In this paper, a bio-inspired approach is presented for slip detection and suppression during object manipulation. The method was inspired by the behavior of FA-I afferents located in the glabrous skin that encode sliding motion of objects over the skin. The proposed slip detection method encodes object motion captured by a slip sensor into spikes, following principles of neuromorphic sensing. The spikes are used as the feedback signal for an event-based closed-loop control system. The controller behaves in a reflex-like manner and actively engages the robotic fingers to increase grip force and suppress slip. A Dynamic Adaptive Threshold method was designed to improve slip detection for different surface properties of grasped objects. The performance of the method was evaluated following situations of dynamic slip caused by a sudden or gradual increase in object weight. The results demonstrated the feasibility of the proposed method. Slip events were suppressed before complete object slippage in 80% of all experimental trials. The response time ( $\Delta {\text {t}} < 85$ ms) was compatible with the time for grip force adjustments in humans. This paper explored event-based touch applicable to the problem of manipulation, which is less explored than event-based tactile perception and shows promising prospects for both robotics and prosthetics.
A Snake-Like Robot with Envelope Wheels and Obstacle-Aided Gaits

Most of the current snake-like robots can only work in a specific environment, or only have a good movement effect in a certain gait. This paper presents a design for a snake-like robot to improve the adaptability of various environments. Each standard module of the snake-like robot has three degrees of freedom: yawing, rolling, and telescoping. The envelope passive wheels are used to enable the robot to move in complex environments such as a narrow passage. We verified some simple movements such as serpentine movement and rectilinear movement and designed a method for recovering from rollover when the robot is in straight state. In addition, two novel gaits, obstacle-aided concertina gait, and obstacle-aided gait through narrow corner, are proposed in this paper. We demonstrated the feasibility for passing the narrow corner by these gaits in experiments.
INVESTIGATION OF THE EFFECTS OF OCCUPATIONAL HEALTH AND SAFETY BEHAVIOURS ON THE CLIMATE SAFETY OF WORKERS ACCORDING TO PLANNED BEHAVIOUR THEORY

The study was conducted to examine the relationship between safety climate and factors affecting occupational health and safety behaviors of workers. The research was carried out at the Mus Sugar Factory in Mus city center between July 2015 and December 2016. The researchers' universe was formed by 321 workers in the Mus Sugar Factory. To collect data: "Questions about socio-demographic characteristics of workers", ‘The scale used to evaluate workers' behavior’ and ‘Security Climate Scale’ were used. The average points of the scale used to evaluate behavior of the workers was determined to be 122.04±21.840. The average points of workers' safety climate was found to be 53.87 ± 11.430. It was seen that the behavior of the workers was higher than the average of the total points, the gender, the family type did not affect the behavior (p>0.05), the norms of the chefs, the norms of the workmates and the security behavior affected the security climate (p<0.05).
Enhanced Stacked Denoising Autoencoder-Based Feature Learning for Recognition of Wafer Map Defects

In semiconductor manufacturing systems, defects on wafer maps tend to cluster and then these spatial patterns provide important process information for helping operators in finding out root-causes of abnormal processes. Promptly recognizing wafer map defects is an effective way to increase manufacturing process stability and then to improve yields. Deep learning has been widely applied and obtained many successes in image and visual analysis. This paper proposes an effective deep learning method, enhanced stacked denoising autoencoder (ESDAE) with manifold regularization for wafer map pattern recognition (WMPR) in manufacturing processes. This study will concentrate on developing a deep learning model to learn effective discriminative features from wafer maps through a deep network architecture for WMPR improvement. An indication based on ESDAE is developed for detecting map defects online. An ESDAE-based classifier is finally developed to implement recognition of wafer map defects. The most motivation for developing deep learning and manifold regularization techniques is to achieve higher accuracy and applicability than that of some regular recognizers. The effectiveness of the proposed method has been demonstrated by experimental results from a real-world wafer map dataset (WM-811K).
Quantitative values of blood flow through the human forearm, hand, and finger as functions of temperature

A literature search was made to obtain values of human forearm, hand and finger blood flow as functions of environmental temperature. The sources used include both government and laboratory reports and the research presented in the open literature. An attempt was made to review many of the more quantitative noninvasive determinations and to collate the results in such a way as to yield blood flow values for each body segment as continuous functions of temperature. A brief review of the various ways used to measure blood flow is included along with an abstract of each work from which data was taken.
Molecular Anthropology: Tools and Techniques

Molecular Anthropology is a branch of science which uses molecular biology methods to find answers to population genetic problems, for which earlier traditional biological markers were used. Though this field is not new but in India it has been slow to pick up basically because of lack of infrastructure. Molecular Anthropology presents before the young researchers fresh challenges for enquin; and investigation. Molecular Anthropology is a combination of molecular methods and anthropology. Anthropologists are trained to have a holistic view about things, we tend to look at a problem from different angles, socio-cultural to psychological to biological, to find the answers and derive conclusions. This anthropological knowledge combined with the tools of molecular studies enables us to view events, affairs and matters in complete view and interpret results using sound scientific knowledge. This paper simply attempts to introduce to the prospective researchfrs, a field of anthropology hitherto not much explored in India.
Fuzzy Removing Redundancy Restricted Boltzmann Machine: Improving Learning Speed and Classification Accuracy

To improve the feature extraction ability and shorten the learning time, fuzzy removing redundancy restricted Boltzmann machine (F3RBM) is developed. The features extracted by F3RBM with unsupervised learning are imported into support vector machine (SVM) to establish F3RBM-SVM model, which achieves fast and high-precision automatic classification of different samples. To expand the feature extraction capability of restricted Boltzmann machine (RBM), the deterministic parameters of control model are replaced by fuzzy numbers in view of the superiority of fuzzy idea and the redundancy removal mechanism is introduced. Comparing the feature similarity of hidden units with the threshold value, if the similarity is greater than the threshold value, they are considered to be redundant units with the same features. The redundant units are removed to achieve further dimension reduction. Finally, the learning speed, feature extraction ability, and classification accuracy of different models are compared in MINIST handwritten dataset, Fashion MNIST dataset, and Olivetti Face dataset. The experimental results show that the feature extraction capability of FRBM and F3RBM is better than that of RBM. When there are a large number of hidden units, the learning speed of F3RBM is obviously faster than that of FRBM. The features extracted from F3RBM are imported into the SVM to build F3RBM-SVM model, which improves the classification accuracy and learning speed than general classifier. When adding other noises, F3RBM-SVM has better robustness than other models.
Learning Differential Transport Operators for the Joint Super-Resolution of Sea Surface Tracers and Prediction of Subgrid-Scale Features

This work deals with data-driven and learning-based approaches to fill space-time sampling gaps in the observation of sea surface tracers such as Sea Surface Height (SSH), Sea Surface temperature (SST), Ocean Colour,… More precisely, we jointly address field super-resolution and the prediction of subgrid-scale features, which is novel to our knowledge. From a methodological point of view, we consider deep learning architectures with a view to learning geophysically-sound differential operators (i.e. trasnport operators). Based on an Observing System Simulation Experiment representative of SWOT fast sampling phase using NATL60 simulation data, we illustrate the relevance of the proposed methodological framework which reconstructs more than 90% of the variance of the high-resolution SSH anomaly field and above 90% of the subgrid-scale variance of this anomaly. We also illustrate significant gain w.r. t. other baseline neural network architectures and further discuss the relevance of the reported contribution for other tracer fields and case studies.
PENGUKURAN OVITRAP INDEX (OI) SEBAGAI GAMBARAN KEPADATAN NYAMUK DI DAERAH ENDEMIS DEMAM BERDARAH DENGUE (DBD) KABUPATEN BANYUMAS

Mosquito density could predict the risk of Dengue Virus (DENV) transmission in certain area. Ovitrap Index (OI) is one of the methods to measure the mosquito density beside other methods such as Stegomyia Indices and free larvae index. Ovitrap index is cheaper, more appliacable and sensitive to detect Aedes sp activity to lay their eggs on the wall of container. The aims of this research were to measure ovitrap index in endemic DENV areas, to find out the preferences of female Aedes sp to lay their eggs, and find out the correlation between OI and dengue cases in endemic DENV areas. Type of this study was decriptive with cross sectional design. The area of study was in Community Health center Purwokerto Timur II in Kranji, Sokanegara and Purwokerto Lor. Ovitrap were installed at 50 houses of each vilagges (one indoor, one outdoor), with total ovitrap 100 in each villages. After 6 days of installation, ovistrips were collected and the eggs were counted. The Ovitrap index in three villages were observed at percentage more than 50%, with the highest OI detected in Kranji Village (97.56%). Female Aedes sp were monitored to lay their eggs outdoor than indoor. The OI is not always relevant to predict the dengue cases but could inform the risk of DENV tranmission in certain area. The community should do efforts to minimize potential breeding sites, mainly unused container outdoor to reduce the mosquito density.
Veterinary Ocular Pathology: A Comparative Review

Ch 01 The Principles and Practice of Ocular Pathology Ch 02 Pathologic Mechanisms in Ocular Disease Ch 03 Congenital, Developmental, or Hereditary Abnormalities in Animals Ch 04 Surgical Trauma and Iatrogenic Lesions Ch 05 Non-surgical Trauma Ch 06 Diseases of the Orbit Ch 07 Diseases of the Eyelids and Conjunctiva Ch 08 Diseases of the Cornea and Sclera Ch 09 The Uvea Ch 10 Diseases of the Lens Ch 11 The Retina Ch 12 Optic Nerve Ch 13 The Glaucomas
Overcoming Contractual Incompleteness: The Role of Guiding Principles

Transactions of any complexity between buyers and sellers are supported by long-term contracts and these contracts are inevitably incomplete. We propose an approach for overcoming contractual incompleteness based on the idea that most people are inclined to follow widely accepted social norms, such as being fair-minded and acting with integrity. We suggest that this tendency can be reinforced if these social norms are incorporated into a formal contract in the form of guiding principles. We develop a model in which guiding principles reduce shading behavior and discuss cases where the approach has been successfully applied in practice (JEL D23, D86, K12).
SENSITIVITY OF EVAPOTRANSPIRATION ESTIMATED BY ORBITAL IMAGES UNDER INFLUENCE OF SURFACE TEMPERATURE

Surface temperature (Ts) is a determining factor to obtain energy balance parameters, being relevant to understand the influence of this variable on the estimation of evapotranspiration. Thus, the objective of this study was to simulate errors in Ts estimation to verify the consequences of actual evapotranspiration (ETa) estimated by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model. For this, an image of the Landsat-8 satellite was used to induce errors from 0.2K to 10K in the variable Ts, allowing verifying the consequences in the ETa data. After the estimations of Ts and ETa, the quantitative consequences and dynamics of Ts impact on the ETa data were verified along the different land uses in the study area. The results showed that the precise estimation of Ts is essential to obtain ETa accurately. The image of ETa errors presented the highest relative errors on the surface with exposed soils and with high Ts values. However, the highest residuals of ETa images occurred on the surfaces with milder Ts and higher evapotranspiration rates (irrigated surfaces).
Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks

The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting. The main research objective was to assess the applicability and functionality of intraday traffic volume forecasting, based on toll station measurements. The proposed methodology was implemented and evaluated upon a custom developed forecasting software toolbox, based on the software Mathworks MatLab, by using real data from Iasmos-Greece toll station. Experimental results demonstrated a superior ex post forecasting accuracy of the proposed hybrid forecasting methodology against conventional ANN, when compared to performance of usual statistical criteria (Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Coefficient of Determination R2, Theil's inequality coefficient). The obtained results revealed that the hybrid model could advance forecasting accuracy of a conventional ANN model in intraday traffic volume forecasting, while embedding hybrid forecasting algorithm in an Intelligent Transport System could provide an advanced decision support module for transportation system maintenance, operation and management.
A Dual-Stage Boost Converter Using Two- Dimensional Adaptive Input-Sampling MPPT for Thermoelectric Energy Harvesting

Herein, we present a dual-stage boost converter (DSBC) for ultra-low power (ULP) thermoelectric energy harvesting applications. The proposed DSBC provides improved efficiency over a wide input range by effectively combining inductor-based boost converter (IBC) and transformer-based boost converter (TBC) modes. The converter supports self-startup by using a transformer-based oscillating mode (TOM) and reuses otherwise wasted leakage through energy regenerative recovery. To provide efficient power extraction, we propose a two-dimensional (2D) adaptive input-sampling maximum power point tracking (MPPT) technique. Input sampling is performed adaptively when a relatively large change in the input is detected. Otherwise, the input is finely tracked by 2D MPPT, which controls the on-time of a power switch. The proposed approach provides two main advantages: 1) adaptive sampling prevents unnecessary periodic detachment of the thermoelectric generator (TEG) from the converter and 2) in addition to enhanced tracking resolution, the 2D MPPT controller achieves both MPPT and output regulation simultaneously. To support the DSBC, we present a synchronized zero current switching (S-ZCS) controller. The S-ZCS controller achieves fine tracking and optimization of the on-times of the two power switches for TBC and IBC. The converter was fabricated via a 180-nm CMOS process with a thick top-metal option. Measurements show that the converter achieves energy harvesting with an efficiency of 81.5%. Over a wide input range, from 70 to 450 mV, the efficiency remains higher than 75%, thereby demonstrating the effectiveness of the proposed DSBC with the S-ZCS and 2D MPPT techniques. Self-startup is achieved at a low input voltage of 38 mV.
Dual‐band circularly polarized multiple‐input multiple‐output antenna for GSM and lower LTE applications

A circularly polarized multiple‐input multiple‐output (MIMO) antenna is presented for global system for mobile (GSM) (710 MHz) and lower long term evolution (LTE) (900 MHz) frequency bands. The antenna consists of four ports with four impedance transformers on the bottom substrate and elliptical rings on the upper substrates. Impedance transformers include open stub and irregular microstrip lines in order to control impedance matching and resonant frequencies. Two upper substrates that contain orthogonal elliptical rings cause the circular polarization property of the proposed antenna. The results of measurement for the presented antenna show its performance with S‐parameters of less than −10 dB in the frequency ranges of 699‐750 MHz for GSM and 880‐1115 MHz for lower LTE applications. Also, the gain and radiation efficiency are higher than 5dBi and 70%, respectively.
Rotor Stress Analysis for High-Speed Permanent Magnet Machines Considering Assembly Gap and Temperature Gradient

For predesigned high speed permanent magnet machines (HSPMMs), the accurate evaluation of rotor strength is extremely important to ensure the reliability of the rotor at high speeds. This paper mainly addresses a comprehensive study on the rotor stress of one predesigned HSPMM with predetermined dimensions, by considering the effect of the assembly gaps between the segmented PMs and between the PMs and the pole filler, and temperature gradient in the rotor. First, the influence of the assembly gaps for different rotor structures, different PM segments, different pole fillers, different material properties on the rotor stress are summarized by Ansys Workbench. Then, the full investigation on the rotor stress distribution is performed under the influence of the rotor temperature gradient, which is obtained by Ansys-Cfx. And then, by considering the non-isothermal distribution of rotor temperature, the 3D temperature-stress coupling analysis is performed to obtain the optimal sleeve thickness. After fabricating the prototype, continuous operation test is carried out, which validates the effectiveness of aforementioned theoretical analysis.
Therapeutic Applications of Prostaglandins

Essential fatty acids and prostaglandins - an introductory overview, Erik Anggard / Rod Flower. Prostanoid receptors: their function and classification Robert A Coleman / Patrick P A Humphrey. Metabolism and toxicology of the prostaglandins, Jacqueline G Hanss & Graham W Taylor. Prostaglandins - analogues and mimetics, Bernd Buchman & Ulrich Klar & Hartmut Rehwinkell & Helmut Vorbrueggen. Dietry manipulation of prostaglandin synthesis in man, Steen Dalby Krisensen. & Jorn Dyerberg. Prostaglandins for termination of pregnancy, Marc Bygdentan. Prostaglandins for cervical ripening induction labour, A A Calder/ M G Elder. Prostaglandins in erectile dysfunction, Otto L. Linet. Postaglandins in management of gastroduodenal ulceration, C J. Hawkey & B.J.R. Whittle. Postaglandins in the treatment of heart and lung diseas in infants, Elliot A Shinebourne & Andrew Bush. Postaglandins in myocardial infraction and angina, Ake Wennmalam. Prostaglandins in percutaneous transluminal coronary angioplasty, Donald M. Demuke. Prostaglanins in congestive heart failure, Christy L. Cooper & James W. Crow, William S. Wheeler and Walker A. Long. Prostaglandins and pulmonary hypertension, A. Yazdani Butt and T. W Higenbottam and J. Wallwork. Prostaglandins in ischaemic peripheral vascular disease, Helmut Sinzinger. Prostaglandins in Raynaud's phenomenon J. Belch. Proseaglandins in cerebral ischaemia and stroke, Richard J. Gryglewski. Prostaglandins in liver disease, organ transplantation and extracorporeal circulation, A.E. S. Gimson, M. Greaves and J. F Martin. Future therapeutic opportunities H. Graff, G. M. Rubanyi and G Stock.
Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

ABSTRACT In architectural planning and initial designing process, it is critical for architects to recognise users’ emotional responses toward design alternatives. Since Building Information Modelling and related technologies focuses on physical elements of the building, a model which suggests decision-makers’ subjective affection is strongly required. In this regard, this paper proposes an electroencephalography (EEG)-based hybrid deep-learning model to recognise the emotional responses of users towards given architectural design. The hybrid model consists of generative adversarial networks (GANs) for EEG data augmentation and an EEG-based deep-learning classification model for EEG classification. In the field of architecture, a previous study has developed an EEG-based deep-learning classification model that can recognise the emotional responses of subjects towards design alternatives. This approach seems to suggest a possible method of evaluating design alternatives in a quantitative manner. However, because of the limitations of EEG data, it is difficult to train the model, which leads to the limited utilisation of the model. In this regard, this study constructs GANs, which consists of a generator and discriminator, for EEG data augmentation. The proposed hybrid model may provide a method of developing supportive and evaluative environments in planning, design, and post-occupancy evaluation for decision-makers.
Measuring School Effectiveness in Memphis--Year 2. Final Report.

ii ACKNOWLEDGMENTS The authors would like to thank the many people who contributed to this report, especially at Memphis City Public schools for their hard work in putting together the data used in this report and responding to our questions about the data. Several staff at Mathematica Policy Research assisted in this effort, including Brian Gill and Steve Glazerman. Tara Anderson helped with the programming. Molly and Jim Cameron edited this report. Alfreda Holmes provided production support. We are grateful for the assistance of all of these people. Only the authors bear responsibility for the judgments and conclusions in this report.