Miracle-dz/UniFAR-FTUs huggingface dataset:
TRACING FISSURE INFORMATION BY SCANNING ELECTRON MICROSCOPY CHARACTERIZATION OF NATURALLY FISSURED SURFACES OF RICE KERNELS

Naturally fissured surfaces of rice kernels contain traceable information on fissure formation. Examination of naturally fissured surfaces of rice kernels can help us better understand the rice fissuring mechanism and find an effective way to prevent fissure formation and preserve head rice yield. Rice samples dried at 60.C were dehulled by hand. The dehulled kernels were examined individually on a light box to detect fissured kernels. The naturally fissured surface inside a kernel was exposed by gently bending the fissured kernels by hand. The microstructures of the naturally fissured surfaces were then examined under a scanning electron microscope. Results showed that the surface of naturally fissured kernels was composed of two regions. One region, near the center of the kernel, exhibited a cleavage between the boundaries of starch granules. The other region, close to the periphery of the fracture surface, showed a breakage between the cell wall interfaces. The breakage patterns were interpreted by (1) the relative strength of cell wall interfaces vs. starch granule interfaces, and (2) the stress concentration at these two interfaces. The breakage patterns observed in this study seem to support the proposition that fissures are initiated from the outer layers of a rice kernel during the drying process.
A Multi-Scale Feature Fusion Network for Facial Expression Recognition

A multi-scale feature fusion network is proposed for facial expression recognition (FER). The three-branch architecture is employed to extract abundant facial features from various depths and scales. Our main contributions are twofold. First, the feature extraction module is designed to effectively extract multi-scale image information and enhance network ability of feature representation. Second, the feature fusion module is presented to adaptively evaluate the importance of different cross-branch features and fully emphasize the role of distinguishable features for expression classiﬁcation. In addition, the basic feature extraction unit cascaded in feature extraction module is proposed with different receptive ﬁeld ﬁlters to extract multi-scale features. Experimental results on two public datasets, FER2013 and CK+, demonstrate that our proposed method outperforms the previous methods with the accuracies of 72.67 % and 96.00 % respectively.
Design of Predictive Alarm System for Artificial Pancreas

Journal of Advanced Computational Intelligent and Intelligent Informatics 1 This paper designs a predictive alarm system 1 for artificial pancreas, which judges whether the 2 closed-loop control of artificial pancreas is in a safe 3 state according to the information transmitted by 4 CGM and insulin pump. It also grades threat 5 degree of different indicators to human safety when 6 various information changes (such as connection 7 information of equipment, blood glucose history, 8 and prediction data). According to the different 9 levels, the system prompts the corresponding 10 warning signal and takes measures to effectively 11 avoid the risk of warning system. Based on the 12 existing human blood glucose metabolism model 13 and Sage-Husa adaptive model, an improved 14 adaptive Kalman filter algorithm is designed to 15 predict the blood glucose data of the model to 16 obtain the blood glucose future blood glucose 17 prediction value and improve the accuracy of 18 multi-step blood glucose prediction firstly. And 19 then simulate the prediction algorithm. Next, the 20 prediction value is used as the information source 21 of the prediction alarm system, and the prediction 22 alarm system for artificial pancreas is established 23 to increase the security control constraints, so as to 24 effectively avoid risks. Finally, experiments are 25 carried out on UVA/Pavoda T1DM hardware in 26 the loop simulation platform to test and verify the 27 effectiveness of the predictive alarm system. 28 29
National Evaluation of the Performance Partnership Pilots for Disconnected Youth (P3): Summary of the Evaluation’s Reports and Findings

Using the established performance partnership model, P3 sought to facilitate improvements in how local youth-serving agencies worked together through supporting changes in their government structures, communication practices, and data-sharing approaches to better meet the needs of disconnected youth. The model, used previously by the National Environmental Performance Partnership System administered by the Environmental Protection Agency, offers state and local agencies the flexibility to integrate separate funding streams and to streamline the administrative requirements of the grants. In exchange, participating agencies face heightened accountability for achieving negotiated performance goals (U.S. Government Accountability Office 2017).
ULTRASONIC DETECTION BY PHOTO-EMF SENSOR AND BY WIDEBAND AIR-COUPLED TRANSDUCER

Two non-contact remote ultrasonic detection systems are compared. The first consists of a continuous wave (CW) Nd:YVO4 laser operating at 532 nm used in conjunction with a photo-induced electromotive-force (EMF) sensor. The second system employs an air-coupled capacitance transducer that has a bandwidth from 100 kHz up to 2 MHz. Both systems exhibit higher sensitivity to out-of-plane than in-plane measurements of stress wave signals. Ultrasound generation is carried out by an Nd:YAG pulsed laser at 532 nm in the thermoelastic regime in a 1.6 mm aluminum plate. A0 Lamb mode is generated at frequency-thickness product with strong out-of-plane displacements. Ultrasonic waveforms acquired are reported and the performance advantages of the two remote non-contact detectors are compared with each other. For lower frequency tests (up to 2 MHz), better results have been achieved with the air-coupled transducer that also has superior overall performance and benefits. The tests enabled direct comparison of air-coupled transducer and photo-EMF detector.
AUTOMATIC DETECTION OF HELMET VIOLATION

- Riding motorcycles without wearing a helmet is the violation of an important traffic rule – ‘Mandatory Wearing of Helmet by motorcyclists’. The mortality rate is higher in two-wheeler accidents as compared to that of others. The current systems include conventional methods that involve police officers observing violators by patrolling the roads or via traffic surveillance cameras. This requires a lot of time and manpower. It is impossible to catch and impose fines on every violator using these methods. This paper proposes a method for helmet detection and license plate recognition to detect and identify the two-wheeler riders without helmet and thereby penalizing them. Object detection using YOLOV4 is the main concept involved. Object detection is performed at different levels to identify the helmet law violator and their license plate number. Using a License Plate Recognition API, the license plate number is then extracted. A database is maintained that consists of details of two-wheeler owners. An email consisting of violation details and a link to pay the penalty is sent to the helmet law violators. An interface is developed using Tkinter that can be used by the administrative officer to check for violators in the videos provided as input and also to monitor the entire process.
Automatic Attendance System Based On Face Recognition and Face Detection

: In the traditional system, it is hard to be handle the attendance of huge students in a classroom. As it is time-consuming and has a high probability of error during the process of inputting data into the computer. Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling a bulk of student’s attendance. Face Recognition is a process of recognizing the students face for taking attendance by using face biometrics. In this project, a computer system will be able to find and recognize human faces fast that are being captured through a surveillance camera. Numerous algorithms and techniques have been developed for improving the performance of face recognition but our proposed system uses Haar cascade classifier to find the positive and negative of the face and LBPH (Local binary pattern histogram) algorithm for face recognition by using python programming and OpenCV library. Here we use the tkinter GUI interface for user interface purpose.
Scaling Political Positions from Text

In this chapter we review the different automated techniques for scaling text most commonly used in political science. We start by relating text scaling to the broader field of measurement models aimed at estimating latent positions. Through this comparison, we outline the assumptions underlying the scaling of political text. We proceed to show how the most commonly used scaling techniques build statistical models of text through these assumptions. In doing so, we also show the utility of the various techniques as well as their vulnerable spots. We then focus on two assumptions that are common across all techniques: that the texts are sufficiently a) long and b) similar in the way meaning is ascribed to words. Through simulations, we investigate how sensitive various techniques are to violations of these assumptions. We conclude by discussing how the need for awareness about model assumptions illustrates how automated scaling should not replace human judgement. We also discuss how a) techniques for estimating word and text embeddedness may improve our scaling techniques by incorporating context, and b) the need for conceptualizing measurement error arising
Perceived Social Support as a Predictor of Teacher Candidates' Smartphone Cyberloafing

This study investigated whether teacher candidates' perceived social support (PSS) predicts smartphone use during lectures. To achieve the aim of the study, a relational survey model was used. The study sample composed of students studying at the College of Education in Elazığ Province, Turkey, during the 2019-2020 academic semester. The correlation analysis was conducted to determine the relationships among the variables, and hierarchical linear regression analysis was used to determine the predictive effects of demographic characteristics (gender and class) and PSS on smartphone cyberloafing. The results of the study revealed a significant, negative, and low relationship between PSS and smartphone cyberloafing. Social support from family negatively predicted smartphone cyberloafing during lectures, while social support from significant others positively predicted it. Finally, the family and significant other subscales significantly predicted interactive, browsing, and entertainment cyberloafing. In contrast, the friends subscale predicted only interactive cyberloafing. The belief that a sense of trust in family and needed family support in times of need could decrease smartphone cyberloafing and lead teacher candidates to focus more on teaching. Educational institutions should develop policies that involve families at every stage of education, and decisions should be made with the support of experts, institutions, or organizations on problematic issues.
PHYTOCHEMICAL QUANTIFICATION AND IN VITRO ANTIOXIDANT ACTIVITY OF THE LEAVES, STEM-BARK AND ROOT OF NauclealatifoliaSMITH

The phytochemical composition and anti-oxidant activities using Di-phenyl-1-picryl-hydrazyl (DPPH) Radical Scavenging assay, Ferric Reducing and Antioxidant Power(FRAP) and the Total Phenolic contents (TPC) of the extracts from the leaves, stem-bark and root of Nauclealatifolia.The phytochemical analysis revealed the presence of bioactive compounds in the entire plant samples. The leaves of Nauclealatifoliacontained tannins 0.17 g/100g, alkaloid 2.29 g/100g, 0.83 g/100g flavonoid and 0.56% saponin. The stem bark also contained 0.53, 2.58, 0.08 g/100g and 0.84% while the composition of the root are 0.13, 0.88, 0.32 g/100g and 0.72% tannins, alkaloids, flavonoids and saponin, respectively. The stem bark had the highest antioxidant potential; DPPH (30.60 mg/mL), FRAP (184.64 mg/GAE/g) and TPC (30.16 g/100g). Our findings provide evidence that the extract of Nauclealatifolia could be a potential source of natural antioxidants that may be used to combat stress related conditions.The phytochemical analysis supports the extensive use of the leaves bark and root of Nauclealatifoliain ethno-medicine in many parts of Africa. Overall, the result suggests that Nauclealatifolia could be a potential source of pharmacologically active natural product and/or for development of neutraceuticals.
CONTRASTIVE LEARNING FOR IMAGE-TO-IMAGE TRANSLATION

Image-to-image translation is an important task of computer vision with many interesting applications. Recently, very interesting results have been achieved with convolutional models based on cyclic adversarial loss. A recent improvement of this approach learns from image crops and uses negative samples from the input image. In order to complete the thesis, the candidate has to choose a framework for automatic differentiation and get acquainted with libraries for handling matrices and images. Study and briefly describe existing approaches to image classification. Select a freely available dataset and partition it into train, validation and test subsets. Propose a suitable deep architecture for image-to-image translation with patchwise contrastive loss. Establish procedures for training models and validating hyperparameters. Evaluate the trained models and present the achieved accuracy. The thesis should be accompanied with the source code of the developed solution, complementary test sequences and results, as well as with necessary explanations and documentation. Please cite the used literature and document the received help.
The PC Main Board Static Stress Analysis Using ABAQUS

This article took the PC main board as a research object and simulated how to optimize the position of the desktop main board pads. The static stress analysis was executed using ABAQ -US/Standard. The objective was to reduce the local deformation of computer main board occurs when the connector was inserted and extracted during the assembly process, preventing the chips and capacitances on the main board from becoming invalid. The simulation results showed that the FEA method with Abaqus can be used to predict the main board performance and structure optimization.
VOLTAGE REGULATION IN NETWORKED ELECTRICAL POWER SYSTEMS FOR DISTRIBUTED GENERATION: A CONSTRAINED SUPERVISORY APPROACH

Abstract In this paper a constrained control strategy based on predictive control ideas is used to regulate the voltage in electrical Medium Voltage (MV) power networks. The aim is to regulate the voltage at certain nodes of the network in the presence of Distributed Generation (DG) units and despite of various changing loads acting as exogenous disturbances. The voltage regulation problem is rephrased as a constrained tracking control problem by imposing that the voltages at certain loads have minimum deviation from their nominal values. Simulations studies show the effectiveness of the proposed approach.
Study on Temperature, Force and Specific Energy of AISI 1020 under MQL Grinding Process

Grinding is one of the most difficult processes in machining operations. Normally, the flood coolant method was used as a cooling agent in the grinding process. The most common defects using flood coolant are higher grinding friction, higher heat generation, and thermal damage. Therefore, the minimum quality lubricant (MQL) was introduced to minimize the defects. The main objective of this project is to compare the performance of MQL and flood coolant techniques in terms of grinding temperature, grinding force and specific energy. Three levels of cutting speeds, three levels of feed rate and depth of cut are adopted in the evaluation. The experiments were conducted on a thin plate of mild steel AISI 1020. The result shows that the MQL technique was effectively supplied to the grinding contact zone. This research revealed that the MQL technique exhibited an advantages on the surface temperature compared to the flood coolant.
AJA2000077 825..837

Purpose: The two primary purposes of this report are (a) to compare the results of three brief cognitive screens in older adults and (b) to examine associations between performance on each of the screens and auditory function measured either concurrently or 9 years earlier. Method: This was a prospective longitudinal study of 98 adults (66 women) with baseline ages ranging from 40 to 85 years. The mean interval between T1 baseline and T2 follow-up measurements was 8.8 years with a range from 7 to 11 years. Measures of hearing threshold, gap detection, and auditory temporal-order identification were completed at T1 and T2. The Mini-Mental State Examination was completed at T1 and T2, whereas the Montreal Cognitive Assessment (MoCA) and A Quick Test were completed at T2 only. Results: Higher scores and pass rates were obtained for the Mini-Mental State Examination than for the MoCA
Modelling and assessment of the long- term leachate generation potential in deposits of ferro-chromiun slags

The environmental impact of landfill deposits is closely associated with the potential of the stored waste material to generate a hazardous leachate. While the problem has long been recognised in the context of municipal waste disposal and the associated co-disposal of certain industrial wastes, environmental concerns regarding wastes originating from mining and metallurgical operations have been voiced only in more recent times1,2. The emergence of a hazardous leachate in ground or surface waters in the vicinity of a waste deposit is usually delayed over many years due to the slow progression of leachate generation and migration in the deposit and the surrounding soil. Consequently there is considerable reluctance by environmental authorities to issue closure certificates to the operators of those disposal sites for which the long-term environmental stability cannot be assured3. The environmental hazard potential of a particular waste material is commonly assessed with simple laboratory leach tests, such as the Toxicity Characteristic Leaching Procedure (TCLP), devised by the US Environmental Protection Agency (US EPA)4. Based on the outcome of such tests the waste is classified in terms of its hazardousness and must be disposed of correspondingly. In South Africa guidelines for the classification and disposal of hazardous wastes are detailed in the Minimum Requirements document by the Department of Water Affairs and Forestry (DWAF)5. Waste assessment tests, which have by now found widespread application in the minerals industry, are, however, limited to broad waste classification, often in terms of a ‘worst case’ scenario6. As such they are only useful in assessing the short to medium term environmental risk of a particular waste material, but they are far too simplistic to allow meaningful prediction of the long-term leachate generation behaviour7. To be able to make such predictions, an in-depth understanding of the underlying processes is required. These are discussed in this paper and a modelling framework is formulated on the basis of this understanding. Combined with suitably designed laboratory test procedures this provides a powerful tool for the prediction of leachate generation in waste deposits typical of the mining and metallurgical industry. The application of this approach is demonstrated on studies involving a ferro-chromium slag material. Modelling and assessment of the longterm leachate generation potential in deposits of ferro-chromiun slags
Joint Military Exercises

Joint military exercises (JMEs) are an indicator of military cooperation that has the potential to reveal much about the nature of the international system’s underlying relationships. States participate in JMEs for a variety of reasons and receive a host of benefits, ranging from improved military coordination during natural disasters to disguising troop buildups in advance of military operations. Quantifying this interaction is valuable because it provides an insight into latent relationships in the international system that are not revealed when examining higher-cost options such as formal alliances or arms transfers. However, quantifying them also presents a host of challenges due primarily to their high frequency of occurrence and the fact that JMEs are not always newsworthy events. This article of record presents the first systematic attempt at collecting data on all JMEs from 1970 through 2010. During this time period, 1,479 JMEs have been recorded. Generally, JMEs appear to be proliferating in both time and space, although the data themselves do contain certain biases. Importantly, these data do not express the same underlying relationships represented by formal alliances and arms transfers, two other indicators of military cooperation. Thus, JMEs are shown to be a distinctly important indicator for measuring the nature of cooperative relationships in the international system.
The Behavior Design of Swarm Robots based on a Simplified Gene Regulatory Network in Communication-free Environments

Aiming at the problem of frequent failure of swarm robots encirclement strategy in the battlefield environment without communication and unknown environment, this paper proposes a model named S-GRN (Simplified Gene Regulation Network) based on individual cognition, which make swarm robots track and entrap targets in communication-free environments. FSM (Finite-State Machine) model is designed to guide the behavior mode of robot and achieve effective control of swarm robot. Based on the above ideas, the behavior mode of swarm robots in communication-free environments and the emergence method of swarm aggregation form are designed. This method is a distributed control method. Swarm robots recognize the approximate orientation of targets through their own visual sensors, and measure the orientation of obstacles with laser sensors. These information are used as the inputs of S-GRN, then output is target entrapping pattern. Through the control algorithm based on FSM, each robot moves to the target and reaches the pattern point around the target. Finally, as the robot gathers towards the target, cluster tracking and encirclement forms emerge. The effectiveness of this method is verified by experiments.
Changes in audio-spatial working memory abilities during childhood: The role of spatial and phonological development

Working memory is a cognitive system devoted to storage and retrieval processing of information. Numerous studies on the development of working memory have investigated the processing of visuo-spatial and verbal non-spatialized information; however, little is known regarding the refinement of acoustic spatial and memory abilities across development. Here, we hypothesize that audio-spatial memory skills improve over development, due to strengthening spatial and cognitive skills such as semantic elaboration. We asked children aged 6 to 11 years old (n = 55) to pair spatialized animal calls with the corresponding animal spoken name. Spatialized sounds were emitted from an audio-haptic device, haptically explored by children with the dominant hand’s index finger. Children younger than 8 anchored their exploration strategy on previously discovered sounds instead of holding this information in working memory and performed worse than older peers when asked to pair the spoken word with the corresponding animal call. In line with our hypothesis, these findings demonstrate that age-related improvements in spatial exploration and verbal coding memorization strategies affect how children learn and memorize items belonging to a complex acoustic spatial layout. Similar to vision, audio-spatial memory abilities strongly depend on cognitive development in early years of life.