Miracle-dz/UniFAR-FTUs huggingface dataset:
Section-LIO: A High Accuracy LiDAR-Inertial Odometry Using Undistorted Sectional Point

Simultaneous localization and mapping has become one of the core modules of unmanned platforms. High-precision localization and mapping play an important role in the collision avoidance and planning of robots in complex environments. One of the key technologies for improving the accuracy of LiDAR-Odometry is point cloud distortion removal. In existing research, laser point cloud undistortion is mostly performed on the entire frame of the point cloud, and the lower the frequency of the point cloud, the more difficulty it is to remove distortion. The main innovation of this study is the proposal of a robust point cloud partition boundary division method, Section-LIO, that takes into account the matching and undistortion effects of each point cloud. The accuracy of the overall laser odometer is improved using inertial measurement unit to remove distortion from the segmented point cloud. Section-LIO supports both 360° mechanical rotation LiDAR and light and small solid-state LiDAR. In experiments with public and private datasets, Section-LIO outperforms three existing state-of-the-art algorithms in terms of accuracy. All implementations of our Section-LIO are open-sourced on GitHub.
Toward Optimal Active Distribution Network Planning: A Critical Review of Conductor Size Selection Methods

Conductor size selection (CSS) is critical for the optimal design and operation of distribution networks. CSS, like other aspects of network planning, has become increasingly sophisticated as the prevalence of distributed energy resources (DERs) increases. This complexity is linked to DER operational and planning uncertainties, which often exacerbate network issues. Increased uncertainty and uncontrolled DER penetration can result in increased conductor loading and extreme voltage performance concerns (rise, drop, and unbalance) that lower the quality of supply (QoS). Traditional CSS techniques, which were developed for passive systems, do not take DERs into account. Therefore, continued application of these methodologies in CSS for modern networks is untenable. As a result, the shift towards active network design and planning requires new CSS techniques that can enhance the adoption of DERs and facilitate optimal long-term network planning. This paper presents a review of CSS methodologies, focusing on the advances made to accommodate DERs, particularly the adaptation of modeling processes to cater to DER planning and operational uncertainties, and its impacts on other key aspects of CSS. Key CSS processes, including the choice of the CSS objectives, input (loads, generation, and feeder) modeling, load flow assessment, optimization, as well as the incorporation of risk in DER analysis are discussed. Informed by the review, the paper scopes the requirements for a robust CSS methodology for active networks with high DER penetration. The findings of the paper are relevant to future research in the field of active distribution network design with a focus on optimizing the integration and utilization of DERs and the related technical performance of networks.
Employing Satellite Imagery on Investigation of Convolutional Neural Network Image Processing and Poverty Prediction Using Keras Sequential Model

The primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. This study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. This study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. The broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. The five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. The correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional Neural Networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. This is clear from the complete classification and segmentation outcomes achieved by the analysis of a limited number of map segments. The image is categorized into three groups—low, bright, and high—depending on their specific characteristics obtained and luminosity, and the associated wealth index has been forecasted for each.
In Vitro Targeted Delivery of Simvastatin and Niacin to Macrophages Using Mannan-Grafted Magnetite Nanoparticles

Atherosclerosis, a leading cause of mortality worldwide, involves various subsets of macrophages that contribute to its initiation and progression. Current treatment approaches focus on systemic, long-term administration of cholesterol-lowering antioxidants such as statins and certain vitamins, which unfortunately come with prolonged side effects. To overcome these drawbacks, a mannose-containing magnetic nanoparticle (NP) is introduced as a drug delivery system to specifically target macrophages in vitro using simvastatin or niacin and a combinational therapy approach that reduces local inflammation while avoiding unwanted side effects. The synthesized NPs exhibited superparamagnetic behavior, neutrally charged thin coating with a hydrodynamic size of 77.23 ± 13.90 nm, and a metallic core ranging from 15 to 25 nm. Efficient loading of niacin (87.21%) and simvastatin (75.36%) on the NPs was achieved at respective weights of 20.13 and 5.03 (w/w). In the presence of a mannan hydrolyzing enzyme, 79.51% of simvastatin and 67.23% of niacin were released from the NPs within 90 min, with a leakage rate below 19.22%. Additionally, the coated NPs showed no destructive effect on J774A macrophages up to a concentration of 200 μg/mL. Simvastatin-loaded NPs exhibited a minimal increase in IL-6 expression. The low dosage of simvastatin decreased both IL-6 and ARG1 expressions, while niacin and combined simvastatin/niacin increased the level of ARG1 expression significantly. Toxicity evaluations on human umbilical vein endothelial cells and murine liver cells revealed that free simvastatin administration caused significant toxicity, whereas the encapsulated forms of simvastatin, niacin, and a combination of simvastatin/niacin at equivalent concentrations exhibited no significant toxicity. Hence, the controlled release of the encapsulated form of simvastatin and niacin resulted in the effective modulation of macrophage polarization. The delivery system showed suitability for targeting macrophages to atherosclerotic plaque.
A CNN Bidirectional LSTM framework for predicting monsoon rainfall in India

Rainfall prediction has evolved as a paramount research significance in recent times due to its complexities and ongoing demand such as water resource planning and management. Agriculture is a major source of employment in India, as well as a substantial contributor to gross domestic product, and crop output is dependent on the monsoon season. Rainfall prediction is useful to authorities for water storage and timely release to increase crop productivity. The current study proposes a Deep Neural Network (DNN) based hybrid model using a combination of convolutional neural network bi-directional long short-term memory (CNN BiLSTM) to predict monthly rain fall during monsoon seasons. The DNN models were used to analyze the average monthly rainfall data collected across the country from 1871 to 2019 during the monsoon seasons. Furthermore, the hybrid model's results were compared to the Bidirectional LSTM (BiLSTM) architecture. In predicting rainfall in India, the proposed hybrid model framework has been found to be more accurate than the BiLSTM. The findings of the study suggest that a DNN frame work can be successfully adopted for time series analysis in water resource management and related domains to reduce the associated risks.
Local application of Opuntia ficus-indica / Punica granatum oils on cutaneous wound healing: a histochemical study

Background: The healing process involves the restoration of the body’s structural integrity. The extracellular matrix, blood cells, cytokines, and growth factors are all involved in this dynamic, intricate, multicellular process. Hemostasis, the inflammatory phase, the proliferative phase, and the maturation phase are all included. Opuntia ficus-indica oil (OFI) and Punica grantum (PGS) oil are extensively used natural treatments that are regarded as advantageous for their sedative, spasmolytic, and anti-inflammatory properties, as well as for angiogenesis promotion, fibroblast increase, collagen production and deposition, and extracellular-matrix remodeling. Materials and methods: Twenty-four New Zealand rabbits were used. Four circular wounds were induced on their dorsum skin with a sterile biopsy punch (8 mm in diameter). A wound on the upper right side left with no treatment healed spontaneously. A wound on the upper left side was treated with OFI. A wound on the lower right side was treated with PGS oil. Finally, the lower left-side wound was treated with a combination of OFI and PGS oils. After each healing period (days 3, 7, and 14), animals were sacrificed to collect specimens by cutting the skin at the edges of the wound by about 5 mm. Results: Collagen formation was faster in the experimental groups than in the control group. Conclusion: Among the experimental groups, the joint local application of OFI and PGS oils was the most effective in improving wound healing by promoting the synthesis of matrix collagen compared with controls.
Efficient Use of Photoelectric Potential for a Next-Generation Student Building. A Case Study

The efficient use of photoelectric potential is becoming an increasingly important issue in the context of EU policies to reduce greenhouse gas emissions. The demand for sustainable and energy-efficient buildings is growing, and this has led to an increased focus on the use of renewable energy sources, especially solar for the residential sector. The paper describes the methodology to identify and quantify the photoelectric potential for a student building by discussing the design and implementation of the photovoltaic-based generation system, including the selection of photovoltaic module technology. To derive the specifications of the generation system, the information layer is based on high-reporting rate measurements defining the power profile for the selected location. In this paper we explore the feasibility of a building transformed into a prosumer including PV generation. The low uncertainty and good time resolution for the power profiles of the analyzed buildings were achieved using extensive local measurements set on 1 or 0.5 frames/second reporting rates.
Implementation of Potential Fields in a Differential Mobile Robot (TurtleBot2)

This paper focuses on the implementation of a method based on the physical principle of “Potential Fields. ” A differential robot, known as TurtleBot2 (Kobuki), was utilized to address the problem of autonomous navigation. To perceive the environment, a laser-based object detection and measurement system was used, enabling TurtleBot2 to navigate complex environments by detecting and avoiding obstacles. Additionally, an NVIDIA Jetson Nano ARM architecture computer was utilized, equipped with a streamlined set of instructions for essential processing, enabling it to control the robot's angular and linear speed. The wireless connection between the computer and the TurtleBot2 enabled efficient information transfer and data processing. This wireless connection extends robot's working range based on Wi-Fi network coverage, ensuring safe navigation in restricted and challenging spaces. Tests were conducted in two distinct environments, presenting results from both simulation and experimentation, demonstrating the efficacy of the technique.
Fusion of UWB and Magnetic Ranging Systems for Robust Positioning

Measuring the position of robots or other devices in challenging environments, with tight accuracy requirements and insufficient coverage from satellite navigation systems, is crucial for improving existing applications and enabling novel solutions. In this article, two of the most commonly used positioning technologies are ultrawide band (UWB) ranging systems (RSs) and magnetic RS. These technologies present limitations that can be overcome by combining them and exploiting their complementary features. Specifically, the UWB RSs provide long-range accuracy and the standard deviation in the line-of-sight (LOS) condition is distance invariant, but the accuracy degrades in short-range and non-LOS (NLOS) conditions. Contrastingly, a magnetic RS exhibits robustness to NLOS scenarios and provides high accuracy over short distances, yet does not perform as well as UWB RSs over long distances. In this article, the compatibility and complementary characteristics of these two RSs are experimentally proven for the first time. The robustness in NLOS conditions of the magnetic RS and the low dispersion with increasing distance of the UWB RS represent the complementary characteristics. This complementarity is leveraged by the proposed fusion method: the adaptive tightly coupled extended Kalman filter (ATCEKF). The proposed fusion method is experimentally characterized in mixed LOS and NLOS conditions, resulting in 6.9 cm of error in an outdoor environment. The positioning error decreased by 55% with respect to the UWB standalone positioning system and 32% with respect to the magnetic standalone positioning system. Therefore, the proposed fusion method improves the robustness and accuracy of position estimation. These results are also confirmed by an additional experiment. In it, the new small-sized and battery-powered mobile node are implemented using a Raspberry Pi 4 Model B.
Machine Learning Based Indo-Western Music Identification, Classification and Disease Healing

Progress in availability of musical data over the last several years has given renewed importance to the task of categorizing musical genres. For easier access, we need an accurate index of them. Automatic music genre categorization is vital whenever dealing with large music library. Most recent efforts to categorize musical styles have used machine learning approaches. In this study, we utilized two data sets that included a wide range of genres. Deep Learning is used to train and categorize the system. A convolutional neural network is used for both classification and training. The Mel Frequency Cepstral Coefficient (MFCC) is now the most used tool for extracting audio features. Using the extracted feature vector, the proposed technique classifies songs into indo-western genres. In this paper, we offer a system for classifying and identifying Indo-Western musical styles. We thought about the GTZAN data collection for western music. One hundred samples, each lasting 30 seconds, have been compiled to represent each genre. We only included the top five genres from GTZAN dataset since there are five distinct types of Indian music. The research uses a neural network to identify indo-western hybrids and Indian classical compositions by feeding them tracks from each genre. In addition, if the classical raga is identified, the categorization of classical ragas associated with illness treatment is provided. It tells us which raga is effective against particular illness. We're demonstrating the physiological effects of each raga. In this paper, we implement machine learning algorithm for raga identification and assess its performance, contrasting the accuracy of a system that only uses MFCC as a feature with that of one that also makes use of pitch and Chroma information. Raga identification for therapeutic use is suggested using an algorithm based on machine learning. This technique extracts features using MFCC features, together with Pitch and Chroma data. Ragas was sorted using a K-Nearest Neighbors approach. The study is expanded to use an open-set method to determine the identities of many ragas (including Asvari, Bageshree, Bhairavi, Darbari, and Yaman). Diseases like heart disease, depression, autism, and addiction may all be treated by playing the appropriate raga, therefore it's important to know which one to utilize. Detailed explanation of the positive outcomes associated with raga.
Study on Massive-Scale Slow-Hash Recovery Using Unified Probabilistic Context-Free Grammar and Symmetrical Collaborative Prioritization with Parallel Machines

Slow-hash algorithms are proposed to defend against traditional offline password recovery by making the hash function very slow to compute. In this paper, we study the problem of slow-hash recovery on a large scale. We attack the problem by proposing a novel concurrent model that guesses the target password hash by leveraging known passwords from a largest-ever password corpus. Previously proposed password-reused learning models are specifically designed for targeted online guessing for a single hash and thus cannot be efficiently parallelized for massive-scale offline recovery, which is demanded by modern hash-cracking tasks. In particular, because the size of a probabilistic context-free grammar (PCFG for short) model is non-trivial and keeping track of the next most probable password to guess across all global accounts is difficult, we choose clever data structures and only expand transformations as needed to make the attack computationally tractable. Our adoption of max-min heap, which globally ranks weak accounts for both expanding and guessing according to unified PCFGs and allows for concurrent global ranking, significantly increases the hashes can be recovered within limited time. For example, 59.1% accounts in one of our target password list can be found in our source corpus, allowing our solution to recover 20.1% accounts within one week at an average speed of 7200 non-identical passwords cracked per hour, compared to previous solutions such as oclHashcat (using default configuration), which cracks at an average speed of 28 and needs months to recover the same number of accounts with equal computing resources (thus are infeasible for a real-world attacker who would maximize the gain against the cracking cost). This implies an underestimated threat to slow-hash protected password dumps. Our method provides organizations with a better model of offline attackers and helps them better decide the hashing costs of slow-hash algorithms and detect potential vulnerable credentials before hackers do.
GRD for R: An intuitive tool for generating random data in R

Statistics courses prove to be a common diﬃculty to many social sciences students. To address this problem, we developed a tool in the R programming language (R Core Team, 2018) that can be used to easily and quickly generate data to be analysed. The Generator of Random Data or GRD, allows the user to build datasets according to any design type, both within and between, with or without statistical effects and/or correlations. By default, GRD creates normally distributed data, but any type of distribution deﬁned in R can be speciﬁed. With GRD, it is possible to generate samples of any size and see the beneﬁt of larger sample sizes on the precision of statistical measures. The students of statistics can acquire better skills in analyzing custom-made datasets, skipping long data-acquisition processes. They can also experience ﬁrst-hand concepts such as statistical power and type-I and type-II errors. Each sample being different, students can appreciate randomness at the tips of their ﬁngers.
A Case Report of Atraumatic Tooth Extraction Followed by Ridge Preservation for Implant-Supported Prosthetic Rehabilitation Using an Alloplastic Bone Graft

Exodontia is a painful treatment that frequently causes the alveolar bone and surrounding soft tissues to be immediately destroyed and lost. With regard to the amount of resorption taking place after extraction, various treatment protocols aimed at preventing or decreasing alveolar ridge collapse have been presented over the past three decades. Ridge preservation is a clinical technique used to prevent the socket walls' bone resorption after tooth extraction. A 43‑year‑old female patient with a non-significant medical history visited the Department of Periodontology and Implantology with a chief complaint of a decayed tooth in the upper left back region (26) for three years and wants to get it replaced. The treatment option that was given to the patient was atraumatic extraction, followed by ridge preservation. A cautious and conservative treatment strategy is necessary to preserve the oral structures as they currently exist and are intact for a successful outcome; careful case selection and thorough treatment planning are crucial. Atraumatic tooth extraction is a procedure used to delicately remove a tooth while upholding the fundamental principles of preserving the surrounding bone and gingival structure. This will ultimately maximize the success of implant placement in terms of appearance and functionality.
Analytical and Neural Processing of Multispectral Images for Identification of Water Pollution

The article is devoted to the development of algorithms for the analysis of water pollution based on visual information obtained using a multispectral camera mounted on the UAV. The structure of the algorithmic complex for the analysis of multispectral aerial images is proposed. In the developed approach, each of the analyzed images undergoes a preprocessing procedure that aligns and combines its spectral channels into a single multidimensional raster. The developed analytical algorithm allows processing and convolution of multispectral image channels using three mathematical operators - value filtering, contrast change and brightness change. At the same time, the choice of parameters for identifying pollution on the surface of water bodies is based on a preliminary stage, associated with maximizing the contrast ratio for the reference area. At the same time the proposed neural pollution analysis algorithm is based on the application of the sliding window method in combination with the convolutional architecture of the neural network classifier for image fragments located on a rectangular grid. Experimental studies have shown that the neural network algorithm wins in accuracy, and the analytical approach is easier to interpret from the point of view of an expert.