Miracle-dz/UniFAR-FTUs huggingface dataset:
Bias Learning Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Path tracking error control is an essential functionality in the development of autonomous vehicles to follow a planned trajectory. Significant path tracking errors could lead to a collision or even out of the control of the vehicle. Model-based control strategies have been developed to minimize the vehicle&amp;#39;s path tracking errors. However, the vehicle model may not truly represent the actual vehicle dynamics. Furthermore, the nominal parameters employed in the vehicle dynamic model cannot represent the actual operating conditions of the vehicle under environmental uncertainty. This paper proposes a real-time bias learning method coupling with the model predictive control (MPC) to improve the fidelity of a nominal vehicle model with the aid of a few experiments (or virtual experiments) so that the path tracking error can be reduced in real-time operation. Gaussian process (GP) regression and recurrent neural network (RNN) are employed for bias learning and their effectiveness are compared under different scenarios. Results reveal that the proposed bias-learning model is able to improve the fidelity of a baseline vehicle dynamic model. Consequently, path tracking performance can be greatly improved under environmental uncertainty using the bias learning based MPC.
A survey of techniques for depth extraction in films

The proliferation of 3D-enabled displays in the last decade is increasing the demand for 3D content. There are several approaches to produce 3D content: Live camera capture, Computergenerated content and 2D-to-3D conversion. Creating 3D content directly from cameras requires the use of expensive equipment and facilities, so many content providers prefer to create their material in 2D, and then convert them into 3D. Similarly, these conversion techniques can also be used to convert already existing 2D material (movies, cartoons, etc.) into 3D, so they can take advantage of the new display technologies. The conversion of films from 2D to 3D requires three stages: Assigning depth to each picture element (pixel), generating a stereo pair of images, and filling in missing information. One of the most time consuming parts of this process is assigning a depth value for each pixel of each image, as this process is usually done manually. Two of the open research questions related to converting 2D content are the integration of multiple depth cues and the automation of the conversion process. In this project we present an automatic process that can successfully estimate the depth range of a film scene, as well as the number of depth planes and their relative positions in relation to that range. In addition, we propose a new method to evaluate the quality of the results by comparing them with depth map data generated with the Microsoft Kinect’s depth sensor.
Differentiation between paediatric pneumonia and normal chest x-ray images using convolutional neural networks and pseudo-attention module

The Chest X-Ray imaging as a low resource diagnosing tool that can bring sufficiently information from the thorax, helping to a specialist to find patterns with purpose to diagnose the pneumonia disease. Also, due to the simplicity to obtain these images, Chest X-Ray is the top choice against CT, US, CT, or MRI imaging in paediatric patients. In this work, we propose a novel Pseudo-attention module based on handcraft features. Generating the Region of Interest (ROI) image of the thorax, avoiding the rest of the body and eliminating the labels contained in this type of test. After obtaining the ROI image, it is evaluated with several architectures based on Convolutional Neural Networks such as DenseNET, ResNET and MobileNET. Finally, the designed system employs Grad-Cam algorithm to provide the perceptual image of the relevant features significant in the classification of Pneumonia against Normal class. The system has demonstrated similar or better performance in comparison with the state-of-the-art methods using evaluation metrics such as Accuracy, Precision, Sensibility, and F1 score.
Differential apoptotic gene expression in the male partners of infertile couples with normal and abnormal sperm parameters

Objective The purpose of this study was to investigate the cellular and molecular levels of apoptosis induction in three groups of male partners of infertile couples, one featuring subjects with normal sperm parameters and unexplained male infertility (UMI), one including men with abnormal sperm parameters, and one with fertile men as controls. Methods Twenty-five infertile men with abnormal sperm parameters and 25 men with UMI and normal sperm parameters were recruited as experimental group I and experimental group II; 25 fertile men were included as controls. The mRNA levels of Fas, Fas ligand, Caspase 8, Bax, and Bcl2 were measured in the three groups. The cellular rates of early and late apoptosis were assessed using annexin V and propidium iodide staining. Results The expression of Bax, Bcl2, and the Bax/Bcl2 ratio in experimental group I was significantly higher than that in experimental group II and controls. However, the Bax/Bcl2 ratio was less than 1 among all groups. No significant difference was found among study groups regarding the gene expression of Fas, Fas ligand, and Caspase 8. No significant difference was seen in early apoptotic rates of sperm among study groups. The highest number of necrotic sperm cells was detected in experimental group I. Conclusions The findings showed that the external pathways of apoptosis were not activated in the absence of external stimuli of sperm apoptosis in ejaculated sperm. Regardless of fertility status, apoptosis gene induction in the internal pathway was associated with abnormalities in sperm motility and/or morphology in men with abnormal parameters.
A new anterograde trans-synaptic tracer based on Sindbis virus

Mapping neural circuits is critical for understanding the structure and function of the nervous system. Engineered viruses are a valuable tool for tracing neural circuits. However, current tracers do not fully meet the needs for this approach because of various drawbacks, such as toxicity and characteristics that are difficult to modify. Therefore, there is an urgent need to develop a new tracer with low toxicity and that allows for long-term studies. In this study, we constructed an engineered Sindbis virus (SINV) expressing enhanced green fluorescent protein (EGFP) reporter gene (SINV-EGFP) and found that it had no significant difference in biological characterization compared with the wild-type Sindbis virus in BHK-21 cells and neurons in vitro. We injected the virus into the visual circuit of mouse brain and found that the virus infected neurons in the local injected site and anterogradely spread in the neural circuits. Although the efficiency of transmission was limited, the findings demonstrate that SINV can be used as a new anterograde tracer to map neural circuits in mouse brain and that it spreads exclusively in the anterograde direction. Further, use of SINV in mouse brain research will provide longer time windows for circuit tracing than is possible with herpes simplex virus and vesicular stomatitis virus tracers.
Spinal cord injury-induced cognitive impairment: a narrative review

Spinal cord injury is a serious damage to the spinal cord that can lead to life-long disability. Based on its etiology, spinal cord injury can be classified as traumatic or non-traumatic spinal cord injury. Furthermore, the pathology of spinal cord injury can be divided into two phases, a primary injury phase, and a secondary injury phase. The primary spinal cord injury phase involves the initial mechanical injury in which the physical force of impact is directly imparted to the spinal cord, disrupting blood vessels, axons, and neural cell membranes. After the primary injury, a cascade of secondary events begins, expanding the zone of neural tissue damage, and exacerbating neurological deficits. Secondary injury is a progressive condition characterized by pro-inflammatory cytokines, reactive oxygen species, oxidative damage, excitatory amino acids such as glutamate, loss of ionic homeostasis, mitochondrial dysfunction, and cell death. This secondary phase lasts for several weeks or months and can be further subdivided into acute, subacute, and chronic. One of the most frequent and devastating complications developed among the spinal cord injury population is cognitive impairment. The risk of cognitive decline after spinal cord injury has been reported to be 13 times higher than in healthy individuals. The exact etiology of this neurological complication remains unclear, however, many factors have been proposed as potential contributors to the development of this disorder, such as concomitant traumatic brain injury, hypoxia, anoxia, autonomic dysfunction, sleep disorders such as obstructive sleep apnea, body temperature dysregulation, alcohol abuse, and certain drugs. This review focuses on a deep understanding of the pathophysiology of spinal cord injury and its relationship to cognitive impairment. We highlight the main mechanisms that lead to the development of this neurological complication in patients with spinal cord injury.
Paulo Freire in Science Education: Trends and Articulations between Scientific Literacy and the STSE Movement

This work aims to articulate Paulo Freire’s studies and the field of science education. We focus on the scientific literacy (SL) perspective and on the mediations on Science, Technology, Society, and Environment (STS/STSE) Education. In his work, the roles of literacy and dialogue are a key to understanding both the common grounds and tensions with scientific literacy and STS/STSE. We recognize that Freire is present in the foundations of STS/STSE studies, however there is still a distance between this body of work and SL in Brazil, especially in its deeper social senses. This is a significant theoretical and practical challenge, as it is necessary to emphasize socio-political assumptions on science education in view of the unfavorable scenario for Brazilian education in its entirety.
A Model-Based Approach for the Energy Monitoring of Handling Machines

An energy-efficient handling system can only be constructed on condition that at the beginning of the development process the energy behavior of handling machines can be evaluated. However, the challenge at the beginning of the development is characterized by the fact that there is no energy data of these machines available. Most of the machine manufacturers do not provide such data or they only offer general data, which is limited to special machine conditions or movements. To solve this challenge, a model-based approach for energy monitoring of handling processes is developed. This approach allows the planning engineers to analyze the energy behavior as well as the kinematic behavior of handling machines at their real conditions. The approach is developed using the multi-domain simulation method, so the detailed components of handling machines can be modeled and simulated. Based on the case study analysis that was performed on a Cartesian robot, the developed approach is conveniently used to predict the energy consumption behavior of handling machines. Thus, using the developed approach, a planning engineer can reduce the energy consumption of handling systems by optimizing the handling sequence.
PENGEMBANGAN APLIKASI PENGENALAN BAHASA ISYARAT ABJAD SIBI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Menyampaikan informasi dibutuhkan bahasa yang mudah dipahami. Penyandang tunarungu memiliki bahasa tersendiri dalam komunikasi yaitu bahasa isyarat. Bahasa isyarat SIBI (Sistem Isyarat Bahasa Indonesia) sering digunakan dalam kondisi formal. Pemahaman mengenai bahasa isyarat perlu ditanamkan sejak dini. Di SDLB Purworejo III Kota Pasuruan telah diajarkan bahasa isyarat. Namun dalam proses pembelajarannya masih menggunakan buku panduan, video pembelajaran atau bahkan peragaan secara langsung oleh guru.Pada penelitian ini dilakukan proses klasifikasi peragaan bahasa isyarat abjad SIBI berdasarkan peragaan secara langsung oleh guru maupun murid di depan kamera secara realtime. Aplikasi dibuat berbasis web serta proses klasifikasi menggunakan metode Convolutional Neural Network (CNN). Dataset berupa citra digital peragaan abjad SIBI diperoleh dari 1 orang guru dan 3 orang murid penyandang tunarungu, diperoleh total dataset 416 citra terdiri dari 384 citra hasil pengambilan gambar dan 32 citra digital hasil konversi video peragaan bahasa isyarat abjad J dan Z karena peragaan berupa gerakan aktif. Keluaran dari sistem ini berupa label kelas abjad dan nilai probalitas hasil klasifikasi yang ditampilkan pada webcam dalam website. Pengujian melibatkan 2 orang relawan dengan melakukan uji coba sebanyak 52 kali munggunakan model klasifikasi hasil training. Berdasarkan pengujian menggunakan rumus confusion matrix, diperoleh nilai accuracy, recall, specificity dan sensitivity sebesar 80,76% .
Co-creation of experiences at dark tourism festivals

This work-in-progress paper presents a novel area of thanatourism research, dark tourism festivals. These alternative festivals are located at the lighter end of the thanatourism spectrum, focusing on fun, culture and entertainment. This paper focuses in particular on the co-creation of festival experience at dark tourism festivals. Preliminary findings are presented, predominantly based on data gathered at the Whitby Goth Weekend, which will later be supplemented by research at other dark tourism festivals. Preliminary findings indicate that aspects such as emotional involvement, authenticity, performance and embodiment are important elements during the co-creation process. Embodied performances during which participants engage in creative practices, such as dressing up in elaborate costumes whilst having their photographs taken, enable them to escape from the routines of their day-to-day lives. Initial findings also indicate links between the festival experience and the concept of liminality. Also, participants engage in meaningful social bonds and essentially take ownership of the festival experience. Whilst engaging in these activities, participants become part of the co-creation of a performance. Festival spaces are specifically managed to enable co-creation, whereby festival participants form part of a sharing economy and actively co-create the festival experience together with the organisers, who enable this process by providing a safe and inclusive festival space.
4-PORT PLANAR MIMO ANTENNA USING OPEN-SLOT RADIATORS FOR 5G NEW RADIO (NR) FREQUENCY BANDS N38 (2570 TO 2620 MHZ) AND N41(2496 MHZ-2690 MHZ) APPLICATIONS

|This article presents a compact, low pro(cid:12)le, four ports multiple-input-multiple-output (MIMO) antenna operating at the n38 and n41 5G frequency bands. The antenna has a measured (cid:0) 10 dB bandwidth of 2.5{2.9 GHz with isolation less than (cid:0) 11 dB. The designed antenna system employs open slot radiators etched on a single-sided rectangular PCB substrate with a total size of 40 (cid:2) 100 mm 2 , including a ground plane. The open slot radiators are symmetrically printed at the four corners of the rectangular substrate. The radiators are excited by 50-Ω strip lines. Rectangle-shaped slits are used as decoupling structures. MIMO parameters such as the envelope correlation coeﬃcient (ECC), channel capacity loss (CCL), and mean eﬀective gain (MEG) are calculated using the measured results. The ECC is less than 0.1 over the entire operating band despite the antenna’s small size. The proposed antenna shows good performance in two sub-6-GHz frequency bands for 5G NR applications: n38 (2570 to 2620 MHz) and n41 (2496 MHz{2690 MHz).
Analgesic efficacy of preoperative ultrasound transversal abdominal plane block for open hysterectomy

Abstract Transversus abdominis plane (TAP) block is a promising technique in post-operative pain relief, following various types of surgeries with an incision on the anterio-lateral abdominal wall. Our randomized, prospective, control trial evaluates its peri-operative analgesic effectiveness for total abdominal hysterectomy (AH) via Pfannenstiel approach. Forty-six women, admitted for intervention, were randomized. A group with an application of bilateral, preincisional TAP block with ropivacaine (n = 23) versus a control group (n = 23) were formed. The post-operative pain relief for all the participants consisted of a mono-component, patient-controlled morphine analgesia. The amount of total 24 h morphine consumption and intraoperative fentanyl use was calculated. Pain at rest and in motion was assessed on a Verbal Numeric Scale (VNS). The incidence of post-operative nausea and vomiting was registered. TAP block with ropivacaine reduced the 24 h post-operative cumulative morphine requirements. The morphine consumption of the TAP block group (14.5 mg ± 4 mg) was statistically significantly lower than the control one (21.7 mg ± 6.8 mg). TAP block decreased the intraoperative use of fentanyl and the post-operative VNS scores at rest and in motion. The groups did not demonstrate any difference in the incidence of nausea and vomiting after surgery. TAP block was not associated with any complications. Our study showed that TAP block effectively leads to superior analgesia in patients undergoing total AH.
Neural-Driven Multi-criteria Tree Search for Paraphrase Generation

A good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity. To deal with this trade-off, we propose to cast the paraphrase generation task as a multi-objectives search problem on the lattice of text transformations. We use BERT and GPT2 to measure respectively the semantic distance and the correctness of the candidates. We study two search algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS) that we use to explore the huge sets of candidates generated by applying the PPDB-2.0 edition rules. We evaluate this approach on 5 datasets and show that it performs reasonably well and that it outperforms a state-of-the-art edition-based text generation method.
A divide‐and‐conquer algorithm for core‐periphery identification in large networks

Core‐periphery structure is an important network feature where the network is broken into two components: a densely connected core and a loosely connected periphery. In this work, we propose a divide‐and‐conquer algorithm to identify the core‐periphery structure in large networks. By finding this structure on much smaller sub‐samples of the network and then combining the results across sub‐samples, this method yields fast and accurate core‐periphery labels. Additionally, the method provides a measure of the statistical significance of the structure. We apply our approach to synthetic data to find the algorithm's detection limit and on a real‐world network with more than 35,000 nodes.
Biogeochemical Distinctiveness of Peatland Ponds, Thermokarst Waterbodies, and Lakes

Small lentic freshwater ecosystems play a disproportionate role in global biogeochemical cycles by processing large amounts of carbon (C), nitrogen (N), and phosphorus (P), but it is unlikely that they behave as one homogenous group for the purpose of extrapolation. Here, we synthesize biogeochemical data from >12,000 geographically distinct freshwater systems: lakes, peatland ponds, and thermokarst waterbodies. We show that peatland ponds are biogeochemically distinct from the more widely studied lake systems, while thermokarst waterbodies share characteristics with peatland ponds, lakes, or both. For any given size or depth, peatland ponds tend to have dissolved organic carbon concentrations several‐fold higher and are 100‐fold more acidic than lakes because of the organic matter‐rich settings in which they develop. The biogeochemical distinctiveness of freshwater ecosystems highlights the need to account for the fundamental differences in sources and processing of organic matter to understand and predict their role in global biogeochemical cycles.
The effect of quantization at different resolutions on the classification of lesion texture in dermoscopic images using support vector machine

Dermoscopic images have been recently used for automated diagnosis of skin lesions with successful results and a potential for widescale applications. Cooccurrence matrix (COM) is a statistical method of texture feature extraction that remains possibly the most interpretable and expandable method in its category. COM is investigated on four groups of dermoscopic images (high resolution ROI: HRR, medium resolution ROI: MRR, low resolution ROI: LRR, and mixed resolution: MixR), using three different gray level quantization values (b = 25, 15, and 8). Three skin regions were outlined: melanoma, common nevi, and normal skin. Texture features were extracted then Support Vector Machine (SVM) was used as a machine learning classification algorithm. Sensitivity, specificity, and the area under the ROC curve (AUC), were calculated and compared. In the classification processes that involved either melanoma or common nevi against normal skin, MRR at quantization value b = 15 showed an effective and a high‐performance choice. On the other hand, when classification involved the two lesions, melanoma, and common nevi, quantization value b = 25 showed best results at both HRR and MRR. A high quantization value at intermediate resolution shows to be an effective choice that balances performance with calculation cost. In all cases, quantization at b = 8 deteriorated the classification output even at high resolution. MixR showed good results when normal skin was classified against either type of lesion. In conclusion, selecting the appropriate quantization is a crucial factor that outweighs resolution effect by itself, and can significantly alter the SVM‐COM model performance in automated skin cancer diagnosis.
Study on Soldering Alloy Structure Influence on Whiskers Germination and Grow Process in Joints Executed Using Tin Base Soldering Alloys

Electronic components soldered with tin base alloys are subject to whiskers formations after an operation period, which produced failures by short circuits. The main factors which determine formation of whiskers are soldering alloy characteristics, substrate alloys which is placed on the surface of the PCB boards and not the least the working environment. Soldering alloy has a major influence on germination and growing whiskers process, by its nature and grain form, dimensions and orientation. Given the preliminary research carried out to diminish the formation of these defects, was proposed to realize these soldered joints using new soldering alloys obtained by melt-spinning method, which have a nanocrystalline, quasi-crystalline or even amorphous structure. In this paper are presented some results obtained on following the influence of soldering alloy structure over whiskers germination and growing process, by simulating the operation conditions according to applicable standards. Were obtained soldered joints on copper plates using a commercial soldering alloy as well as soldering alloys base on Sn-Cu-Ga, Sn-Cu-Ni, Sn-Cu-CO families, obtained by melt-spinning method. Soldering was performed using a soldering iron and after this process, the soldered joints were placed in an incubator under controlled temperature and humidity conditions. Performing SEM analyzes on soldered joints subject to the above treatment, was observed that formation and growing of whiskers diminished with decrease of the grain size.