Miracle-dz/UniFAR-FTUs huggingface dataset:
Three zone learning concepts to improve mathematical proof of probability theory

This study aims to comprehensively analyze the improvement of students ' mathematical development ability through the concept of three zone learning, with the type of research is quantitative and quasi experiment design. Examples of research as many as two classes amounted to 82 pre service teacher. Data analysis by calculating gain normalization. Results: based on the Post Hoc test, the level of the category that has a positive score of 0.012 with the upper category and the middle category means that the average score of students from the upper category is better than the middle category, then also for the level that has a positive score of 0.028 with the upper category and the lower category which means that the average score of students from the upper category is better than the lower category. As for the positive value of 0.016 with the middle category and the lower category means the average of the middle category is better. From the results showed that for levels with upper, middle and lower categories, the effect is greater in improving students.
Online Detection of Fabric Defects Based on Improved CenterNet with Deformable Convolution

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.
Evaluation of the customer requirements for last mile delivery in Brazil

Goal: This paper aims to understand the value of possibility of scheduling, cost of scheduling, app technology and reschedules features that embody the last mile logistics system.Methodology: The method utilized a stated preference technique in a sample of 83 potential consumers in Florianopolis/Brazil.Results: The results show that consumers are interested and willing to pay for services and amenities such as scheduling at the time of purchase and associated technology. This finding corroborates the pricing process for online product delivery and confirms the trend toward customer-customized systems.Limitations of the investigation: This research is limited in the scope of methodology. Practical implications: The presented result can be used by companies to be able to use the right attributes in the last mile process.Originality: The Last Mile is the logistics stage that delivers most value to the end customer. Therefore, there is present in the literature the difficulty of interpreting what the customer most expects in terms of service at this logistical stage. Then, understanding which features are important to the customer is a good way to implement improvements in the last mile logistics.
Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification

Recent research in deep Convolutional Neural Networks (CNN) faces the challenges of vanishing/exploding gradient issues, training instability, and feature redundancy. Orthogonality Regularization (OR), which introduces a penalty function considering the orthogonality of neural networks, could be a remedy to these challenges but is surprisingly not popular in the literature. This work revisits the OR approaches and empirically answer the question: Even when comparing various regularizations like weight decay and spectral norm regularization, which is the most powerful OR technique? We begin by introducing the improvements of various regularization techniques, specifically focusing on OR approaches over a variety of architectures. After that, we disentangle the benefits of OR in the comparison of other regularization approaches with a connection on how they affect norm preservation effects and feature redundancy in the forward and backward propagation. Our investigations show that Kernel Orthogonality Regularization (KOR) approaches, which directly penalize the orthogonality of convolutional kernel matrices, consistently outperform other techniques. We propose a simple KOR method considering both row- and column- orthogonality, of which empirical performance is the most effective in mitigating the aforementioned challenges. We further discuss several circumstances in the recent CNN models on various benchmark datasets, wherein KOR gains more effectiveness.
Fostering Innovation on Digital Platforms: From Two to Multi-Sided Platforms

Two-sided markets and digital platforms are becoming increasingly relevant in the modern scenario. Companies like Airbnb and Uber are inspiring many other firms in different fields that share their basic structure: they match two (or more) groups of customers. This research aims at exploring the innovation strategies the companies such as these rely on to expand their basic structure towards more complex models. Being inspired by previous models in the field and considering the role that big data seems to play in these businesses a first conceptual model is presented. Therefore, 100 companies–using mobile apps as the empirical setting–are explored in this research to understand the common behaviors concerning evolution. In the end, three strategies are presented: Supply (Side) Expansion, Transactional Advertising
Seabird predation effects and population viability analysis indicate the urgent need for rat eradication from Europa Island, western Indian Ocean

Rats are among the worst invasive alien predators. They prey upon eggs, chicks, and sometimes adults of seabirds, leading to the decline of many populations. Rats have invaded 93% of the islands in the western Indian Ocean, which is an avian biodiversity hotspot. Europa Island, Mozambique Channel, is a major seabird breeding site, where at least 1,000,000 pairs bred at the end of 20th century, including the endemic white-tailed tropicbirds ( Phaethon lepturus europae ). This species and the red-tailed tropicbird ( Phaethon rubricauda ) are vulnerable to predation by black rat ( Rattus rattus ), barn owl ( Tyto alba ), and pied-crow ( Corvus albus ) on Europa Island. In this study, we estimate the current population sizes of these tropicbird species and their breeding success. The impact of predators on tropicbird nests was quantified with camera traps. Finally, we model the dynamics of these populations under three management scenarios (no action, complete rat eradication, and targeted control of rats around nests). In 2019, 1,027 pairs of red-tailed and 100 pairs of white-tailed tropicbirds bred on Europa Island, which indicates an annual decline of 5% and 17% since 2009, respectively. Breeding success of red-tailed tropicbirds and white-tailed tropicbirds was extremely low (0.08 ± 0.07 and 0.06 ± 0.03 respectively). Rats were detected at most surveyed nests and were the main predators of eggs and chicks. Pied-crows and barn owls also depredated nests occasionally. Population viability analysis projected a local extinction in 32–85 years for the red-tailed tropicbird, and in 11–50 years for white-tailed tropicbirds without conservation intervention. The complete eradication of rats would result in a rapid recovery of both populations. Rat control around nests only would result in population recovery only if 74% of the nests of white-tailed and 63% of the nests of red-tailed tropicbirds are free of rats.
Investigating Graph Embedding Methods for Cross-Platform Binary Code Similarity Detection

IoT devices are increasingly present, both in the industry and in consumer markets, but their security remains weak, which leads to an unprecedented number of attacks against them. In order to reduce the attack surface, one approach is to analyze the binary code of these devices to early detect whether they contain potential security vulnerabilities. More specifically, knowing some vulnerable function, we can determine whether the firmware of an IoT device contains some security flaw by searching for this function. However, searching for similar vulnerable functions is in general challenging due to the fact that the source code is often not openly available and that it can be compiled for different architectures, using different compilers and compilation settings. In order to handle these varying settings, we can compare the similarity between the graph embeddings derived from the binary functions. In this paper, inspired by the recent advances in deep learning, we propose a new method – GESS (graph embeddings for similarity search) – to derive graph embeddings, and we compare it with various state-of-the-art methods. Our empirical evaluation shows that GESS reaches an AUC of 0.979, thereby outperforming the best known approach. Furthermore, for a fixed low false positive rate, GESS provides a true positive rate (or recall) about 36% higher than the best previous approach. Finally, for a large search space, GESS provides a recall between 50% and 60% higher than the best previous approach.
Four-Level Micro-Via Technology (4LµV) for ASIC Integration in Active Flexible Sensor Arrays

Systems-in-foil with multi-sensor arrays require extensive wiring with large numbers of data lines. This prevents scalability of the arrays and thus limits the applications. To enable multiplexing and thus reducing the external connections down to few digital data links and a power supply, active circuits in the form of ASICs must be integrated into the foils. However, this requires reliable multilayer wiring of the sensors and contacts for chip integration. As an elegant solution to this, a new manufacturing process for multilayer wiring in polyimide-based sensor foils has been developed that also allows ASIC chips to be soldered. The electrical four-level micro-via connections and the contact pads are generated by galvanic copper deposition after all other process steps, including stacking and curing of polyimide layers, are completed. Compared to layer by layer via technology, the processing time is considerably reduced. Because copper plating of vias and solderable copper contact pads happens as the final step, the risk of copper oxidation during polyimide curing is completely eliminated. The entire fabrication process is demonstrated for six strain sensor nodes connected to a surface-mounted ASIC as a detecting unit for sensing spatially resolved bending states. Each sensor node is a full-bridge configuration consisting of four strain gauges distributed across interconnected layers. The sensor foil allows bending of +/−120° without damage. This technology can be used in future for all kinds of complex flexible systems-in-foil, in particular for large arrays of sensors.
Measurement of fNIRS to Estimate Emotion in a VR System

Virtual Reality (VR) had a banner year in 2016. VR has attracted a lot of media attention because many companies have launched Head Mounted Displays (HMDs) and other related products. As a result, the term "immersive feeling" has become more widely known. In this study, we have planned to evaluate immersive feeling of VR content using HMD. For that purpose, we performed an impression survey to find adjectives related to immersive feeling. The results showed that “Thrilling / Not thrilling”, “Complex / Simple”, and “Stereoscopic / Planar" are greatly related to immersive feeling. Then, we created two types of VR content: roller-coaster content expected to generate high immersive feeling (thrilling, complex and stereoscopic), and grassland content to generate low immersive feeling (not thrilling, simple and planar). We used a questionnaire and fNIRS for these evaluations. Performing the experiment to experience two contents stereoscopically using a HMD, we found significant differences between two contents for the immersive and exciting feelings from questionnaire results. In addition, we found the differences of the standard deviations of oxygenated hemoglobin during experiencing two contents. They were larger in the rolling coaster content than those in the grassland content for some participants. We expect that the differences are symptoms of immersion and/or excitement feelings.
Dyspnea Is Attenuated by Auditory Distraction via Music with Headphones during Exercise in Healthy Individuals

ABSTRACT Listening to music while exercising can alter afferent feedback from breathing sounds; however, it is currently unknown whether auditory distraction with music mitigates exertional dyspnea in healthy individuals. Purpose This study aimed to determine whether listening to music through headphones (a) affects the sensory (breathing intensity [BI]) and/or affective (breathing unpleasantness [BU]) components of dyspnea during exercise at different intensities and (b) affects exercise performance. Methods Twenty-two recreationally active individuals (24 ± 3 yr, 10 women) performed two 5-min constant loads (10% below/above gas exchange threshold [GET]) and an 8-km cycling time trial with ambient laboratory noise or self-selected music in a randomized crossover design. BI, BU, and ventilation (V̇E) were measured at each minute of the constant loads and every 2 km of the time trial. Ratios of BU/V̇E and BI/V̇E were used to examine the gain in dyspnea during the time trial. Results In the 10% below GET trial, BU was reduced in the first (P = 0.03) and final (P = 0.04) minutes. In the 10% above GET trial, BU and BI were reduced with music (P < 0.05). During the time trial with music, BU/V̇E was significantly attenuated by 9%–13% (P < 0.05) despite a greater heart rate and self-selected power output (P < 0.05). Conclusions Music through headphones mitigated the sensation of dyspnea and changed the accretion of dyspnea per unit increase in V̇E leading to a higher self-selected workload during self-paced exercise. The dyspnea-reducing intervention of self-selected music may improve exercise tolerance and performance and promote adherence to regular aerobic exercise.
Purification and partial characterization of a novel lectin from Ruta montana roots

Lectins are proteins or glycoproteins, which participate in various biological processes. The purpose of this study was to purify new lectin from Ruta montana roots. Lectin of Ruta montana has been purified from roots using ultrafiltration and precipitation with ammonium sulfate followed by gel filtration chromatography whereas protein estimation was done by Bradford’s method. Within sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) analysis, the extract exhibited three bands and one band after purification. The molecular weight of lectin was determined by SDS–PAGE and gel filtration chromatography, which was found to be a monomeric protein of approximately 28.8 kDa. The agglutination activity of Ruta montana lectins was stable within a temperature range from 4 to 50° C for 30 min and the pH range from 4.2 to 9. This study presents a natural source of lectins that can be used in several other studies due to its different biological activities.
Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots

This letter presents an approach for auto-tuning feedback controllers and online trajectory planners to achieve robust locomotion of a legged robot. The auto-tuning approach uses an Unscented Kalman Filter (UKF) formulation, which adapts/calibrates control parameters online using a recursive implementation. In particular, this letter shows how to use the auto-tuning approach to calibrate cost function weights of a Model Predictive Control (MPC) stance controller and feedback gains of a swing controller for a quadruped robot. Furthermore, this letter extends the auto-tuning approach to calibrating parameters of an online trajectory planner, where the height of a swing leg and the robot’s walking speed are optimized, while minimizing its energy consumption and foot slippage. This allows us to generate stable reference trajectories online and in real time. Results using a high-fidelity Unitree A1 robot simulator in Gazebo provided by the robot manufacturer show the advantages of using auto-tuning for calibrating feedback controllers and for computing reference trajectories online for reduced development time and improved tracking performance.
Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

Multi-modal medical image fusion can reduce information redundancy, increase the understandability of images and provide medical staff with more detailed pathological information. However, most of traditional methods usually treat the channels of multi-modal medical images as three independent grayscale images which ignore the correlation between the color channels and lead to color distortion, attenuation and other bad effects in the reconstructed image. In this paper, we propose a multi-modal medical image fusion algorithm with geometric algebra based sparse representation (GA-SR). Firstly, the multi-modal medical image is represented as a multi-vector, and the GA-SR model is introduced for multi-modal medical image fusion to avoid losing the correlation of channels. Secondly, the orthogonal matching pursuit algorithm based on geometric algebra (GAOMP) is introduced to obtain the sparse coefficient matrix. The K-means clustering singular value decomposition algorithm based on geometric algebra (K-GASVD) is introduced to obtain the geometric algebra dictionary, and update the sparse coefficient matrix and dictionary. Finally, we obtain the fused image by linear combination of the geometric algebra dictionary and the coefficient matrix. The experimental results demonstrate that the proposed algorithm outperforms existing methods in subjective and objective quality evaluation, and shows its effectiveness for multi-modal medical image fusion.
Performance Analysis of Current Control Strategies for Hybrid Stepper Motors

Hybrid stepper motors are widespread in industrial automation due to their robustness and high torque performance in low speed range, e.g. 3D printers, pick and place, and generally in many low power positioning applications. In order to increase the efficiency and dynamic performance, current/speed/position closed loop controls are implemented for high performance sensored stepper drives. The main challenge comes from the high number of magnetic poles which these motors feature, increasing the ratio between the fundamental and switching frequency. This paper critically evaluates four current control structures based field oriented control: classic PI regulators, sliding mode control, deadbeat predictive current control and model predictive current control. Simulations and experimental results aim to evaluate the dynamic performance, phase current amplitude and distortion in order to support the critical comparison.
The Impact of Adolescent Resilience on Mobile Phone Addiction During COVID-19 Normalization and Flooding in China: A Chain Mediating

Natural disasters cause long-term psychological problems and increase substance use in some adults. However, it is unclear whether disasters also lead to these problems in adolescents. We hypothesized the influence of adolescent resilience on mobile phone addiction during the normalization of COVID-19 and flooding. We tested the mediating role of coping style and depression, anxiety, and stress (DASS) on phone addiction among 1,751 adolescents in the Henan Province in China. The adolescents were surveyed via an online questionnaire, and we used structural equation modeling to examine the correlations and moderation effects. The results show that coping style and DASS could mediate the relationship between adolescent resilience and mobile phone addiction among Chinese adolescents. A chain of coping styles and DASS mediated the relationship between adolescent resilience and mobile phone addiction in Chinese adolescents.
BIROn - Birkbeck Institutional Research Online

is triggered upon receptor activation. Abstract Clathrin-mediated endocytosis (CME) is the main endocytic pathway supporting housekeeping functions in cells. However, CME may be too slow to internalize proteins from the cell surface during certain physiological processes such as reaction to stress hormones (‘fight-or-flight’ reaction), chemotaxis or compensatory endocytosis following exocytosis of synaptic vesicles or hormone-containing vesicles. These processes take place on a millisecond to second timescale and thus require very rapid cellular reaction to prevent overstimulation or exhaustion of the response. There are several fast endocytic processes identified so far: macropinocytosis, activity-dependent bulk endocytosis (ABDE), fast-endophilin-mediated endocytosis (FEME), kiss-and-run and ultrafast endocytosis. All are clathrin-independent and are not constitutively active but may use different molecular mechanisms to rapidly remove receptors and proteins from the cell surface. Here, we review our current understanding of fast and ultrafast endocytosis, paper established a role for macropinocytosis in providing ammino acids to Ras-transformed from endosomes. Nature 2014; 515:228-233. This study revealed that clathrin coats act on endosomes generated by ultrafast endocytosis to reform synaptic vesicles, but at central synapses. Neuron 2014; 82:981-988. This study shows that AP-2 and clathrin-independent endocytosis is dominant upon high frequency stimulation. Clathrin coats act on endosomes generated by
Solving Exact Cover Instances with Molecular-Motor-Powered Network-Based Biocomputation

Information processing by traditional, serial electronic processors consumes an ever-increasing part of the global electricity supply. An alternative, highly energy efficient, parallel computing paradigm is network-based biocomputation (NBC). In NBC a given combinatorial problem is encoded into a nanofabricated, modular network. Parallel exploration of the network by a very large number of independent molecular-motor-propelled protein filaments solves the encoded problem. Here we demonstrate a significant scale-up of this technology by solving four instances of Exact Cover, a nondeterministic polynomial time (NP) complete problem with applications in resource scheduling. The difficulty of the largest instances solved here is 128 times greater in comparison to the current state of the art for NBC.
“Their Color is a Diabolic Die,” Colonialism and the State of Environmental Justice in Africa

Environmental justice is crucial to the discourse on the African environment since Africa bears a significant share of the world’s resources, and conversely, it is home to a large number of globally disadvantaged people, whose access to the wealth of their native lands is beyond their reach. Therefore, it is necessary to examine how colonialism and the structures it has laid down in Africa are impeding environmental justice. Content analysis was used in the data collection. In this article, drawing from the slum arrangements in the cities of four African countries colonized by the British and French as case studies, it was revealed how colonialism has caused the degeneration of the African environment. Furthermore, colonialism created a class system that has fed social and economic inequality and has resulted in an intra-racial system of oppression that has made Africa’s poorest neighborhoods more vulnerable.