Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Modeling of the Tuna-Inspired Robotic Fish Based on the Elastic-Damping Mechanism

Compliant passive mechanism has been widely employed in the development of bioinspired aquatic robots that imitates the variable stiffness of fish muscles. However, the existing compliant passive mechanism cannot produce excellent propulsion performance in a wide frequency range. To solve this problem, this paper proposes a novel modeling method for a tuna-inspired robotic fish with passive compliant joint. The passive compliant joint is developed composed of double torsion springs and damping liquid, attempting to implement on the robotic fish to achieve high swimming performance. In order to analyze the motion of the passive joint, a hydrodynamic-twisting-damping torque (HTDT) model is proposed. A dynamic model based on Kane equation is established and indicates that the compliant joint could affect the torque transmitted to the caudal fin to improve the swimming speed of the robotic fish. The particle swarm optimization (PSO) algorithm is further employed to optimize the joint parameters for an enhanced velocity performance. The obtained simulation results suggest that the proposed robotic fish model can reach to a speed of 2.32 m/s, exhibiting an excellent swimming performance. The dynamic model established by this study can make a guideline for the development of robotic fishes with the compliant passive joint.
Schinopsis brasiliensis Engler—Phytochemical Properties, Biological Activities, and Ethnomedicinal Use: A Scoping Review

Brazil has the most incredible biodiversity globally and has a vast storehouse of molecules to be discovered. However, there are no pharmacological and phytochemical studies on most native plants. Parts of Schinopsis brasiliensis Engler, a tree from the Anacardiaceae family, are used by several traditional communities to treat injuries and health problems. The objective of this scoping review was to summarize the pharmacological information about S. brasiliensis, from ethnobotanical to phytochemical and biological studies. Data collection concerning the geographical distribution of S. brasiliensis specimens was achieved through the Reflora Virtual Herbarium. The study’s protocol was drafted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The search strategy used the keyword “Schinopsis brasiliensis” in the databases: PUBMED, EMBASE, SCOPUS, Science Direct, Web of Science, SciFinder, and SciELO. Rayyan was used for the selection of eligible studies. In total, 35 studies were included in the paper. The most recurrent therapeutic indications were for general pain, flu and inflammation. The bark was the most studied part of the plant. The most used preparation method was decoction and infusion, followed by syrup. Phytochemical investigations indicate the presence of tannins, flavonoids, phenols, and polyphenols. Most of the substances were found in the plant’s leaf and bark. Important biological activities were reported, such as antimicrobial, antioxidant, and anti-inflammatory. S. brasiliensis is used mainly by communities in the semi-arid region of northeastern Brazil to treat several diseases. Pharmacological and phytochemical studies together provide scientific support for the popular knowledge of the medicinal use of S. brasiliensis. In vitro and in vivo analyses reported antimicrobial, antioxidant, anti-inflammatory, antinociceptive, cytotoxic, photoprotective, preservative, molluscicidal, larvicidal, and pupicidal effects. It is essential to highlight the need for future studies that elucidate the mechanisms of action of these phytocompounds.
Multi-UAV Factor Graph Colocation Algorithm

To tackle the difficulty and high cost of UAV positioning in a 5G disaster environment, this study proposes a multi-UAV coordinated positioning algorithm based on a factor graph (MUAV-FGC). A factor graph model is designed for coordinated positioning of the master-slave UAVs. In the positioning process, the influence of the speed and heading errors of the UAV on the positioning accuracy was analyzed, and the expected and variance values of the variables were used as the information transmitted between the factors. The error of the cooperative positioning algorithm was derived based on the factor graph. The coordinated positioning of multiple drones and the positioning accuracy improved. The simulation results show that, compared with the traditional extended Kalman filter (EKF) algorithm, the proposed MUAV-FGC positioning algorithm reduces the mean positioning and root mean square error by approximately 18.86% and 17.54%, respectively, thus proving its effectiveness.
Electrothermal System Design and Evaluation of Low EMI and Thermally Balanced 150 kW T-Type Traction Inverter

Power electronics switching devices utilized in traction, propulsion, and power generation applications require advanced thermal management systems and extensive electromagnetic interference filtering components. The traditional indirect cooling methods exhibit limited ability to target switching location hotspots, causing limited cooling capabilities and thermal imbalance. Additionally, they rely on metallic structures directly integrated with module base plates, allowing for an accentuation of common-mode noise. These effects present themselves concurrently, creating reliability concerns while requiring further noise filtering. This work demonstrates the integration of a nonmetallic jet impingement cooler for direct base plate thermal management of a 150 kW silicon-carbide inverter. The use of nonmetallic structures manufactured through additive techniques limits low-induction capacitance pathways by 99%, leading to a 25 dB reduction in common-mode noise. Impingement jets focused on module hotspots reduce maximum device temperatures and increase thermal uniformity. Finally, by attaching individual jet coolers to each power module, hydraulic circuit networks are used to demonstrate thermal balancing between power devices for optimal current sharing.
Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.
Resistance of facial recognition payment service: a mixed method approach

Purpose Sellers view facial recognition mobile payment services (FRMPS) as a convenient and cost-saving way to receive immediate payments from customers. For consumers, however, these biometric identification technologies raise issues of usability as well as privacy, so FRMPS are not always preferable. This study uses the stressor–strain–outcome (S–S–O) framework to illuminate the underlying mechanism of FRMPS resistance, thereby addressing the paucity of research on users' negative attitudes toward FRMPS. Design/methodology/approach Drawing from the stressor–strain–outcome (S–S–O) framework, the purpose of this study is to illuminate the underlying mechanism of FRMPS resistance. To this end, they invited 566 password authentication users who had refused to use FRMPS to complete online survey questionnaires. Findings The findings enrich the understanding of FRMPS resistance and show that stressors (i.e. system feature overload, information overload, technological uncertainty, privacy concern and perceived risk) aggravate the strain (i.e. technostress), which then leads to users’ resistance behaviors and negative word of mouth. Originality/value Advances in payment methods have profoundly changed consumers’ consumption and payment habits. Understanding FRMPS resistance can provide marketers with strategies for dealing with this negative impact. This study theoretically confirms the S–S–O paradigm in the FRMPS setting and advances it by proposing thorough explanations of the major stressors that consumers face. Building on their findings, the authors suggest ways service providers can eliminate the stressors, thereby reducing consumers’ fear and preventing resistance or negative word-of-mouth behaviors. This study has valuable implications for both scholars and practitioners.
Experimental burial diagenesis of aragonitic biocarbonates: from organic matter loss to abiogenic calcite formation

Abstract. Carbonate biological hard tissues are valuable archives of environmental information. However, this information can be blurred or even completely lost as hard tissues undergo diagenetic alteration. This is more likely to occur in aragonitic skeletons because bioaragonite often transforms into calcite during diagenesis. For reliably using aragonitic skeletons as geochemical proxies, it is necessary to understand in depth the diagenetic alteration processes that they undergo. Several works have recently investigated the hydrothermal alteration of aragonitic hard tissues during short-term experiments at high temperatures (T > 160 ∘C). In this study, we conduct long-term (4 and 6 months) hydrothermal alteration experiments at 80 ∘C using burial-like fluids. We document and evaluate the changes undergone by the outer and inner layers of the shell of the bivalve Arctica islandica, the prismatic and nacreous layers of the hard tissue of the gastropod Haliotis ovina, and the skeleton of the coral Porites sp. combining a variety of analytical tools (X-ray diffraction, thermogravimetry analysis, laser confocal microscopy, scanning electron microscopy, electron backscatter diffraction and atomic force microscopy). We demonstrate that this approach is the most adequate to trace subtle, diagenetic-alteration-related changes in aragonitic biocarbonate structural hard materials. Furthermore, we unveil that the diagenetic alteration of aragonitic biological hard tissues is a complex multi-step process where major changes occur even at the low temperature used in this study, well before any aragonite into calcite transformation takes place. Alteration starts with biopolymer decomposition and concomitant generation of secondary porosity. These processes are followed by abiogenic aragonite precipitation that partially or totally obliterates the secondary porosity. Only subsequently does the transformation of the aragonite into calcite occur. The kinetics of the alteration process is highly dependent on primary microstructural features of the aragonitic biomineral. While the skeleton of Porites sp. remains virtually unaltered for the entire duration of the conducted experiments, Haliotis ovina nacre undergoes extensive abiogenic aragonite precipitation. The outer and inner shell layers of Arctica islandica are significantly affected by aragonite transformation into calcite. This transformation is extensive for the prismatic shell layer of Haliotis ovina. Our results suggest that the majority of aragonitic fossil archives are overprinted, even those free of clear diagenetic alteration signs. This finding may have major implications for the use of these archives as geochemical proxies.
Analysis of Profitability Determinants and Effect Value of Automotive Companies in Indonesia

The study analyzes the financial performance of automotive companies' firm value on the Indonesia Stock Exchange. The financial performance is DER, CR, and company size. The secondary data was a company report, which is eight companies. The panel data regression using Eviews 11.0 to analyze hypotheses. The finding of the t-test of the DER, CR and Firm size negatively and significantly affect profitability; therefore, profitability on firm value has no significant impact. Partially DER positively affects firm value; CR and size have no significant effect on firm value. Profitability as a mediating increases the impact of DER, CR, and firm size on firm value. CR, and firm size, have a positive and significant effect on firm value. The Company should pay attention to the liquidity ratio because the most influential in reducing profitability in the future. Therefore, the investors must check the company's cashflow in order to understand company's liquidity level.
Research on Silicone Rubber Sheds of Decay-Like Fractured Composite Insulators Based on Hardness, Hydrophobicity, NMR, and FTIR

The safety and stability of power systems are seriously threatened by the decay-like fracture of composite insulators. This paper analyzes the aging characteristics (physical properties, NMR, and FTIR) of the silicone rubber sheds of the decay-like fractured insulator. The same V-string insulator and a new insulator are used for comparison. The study shows that the sheds’ degradation is concentrated on the side with heavy pollution. The physical properties (appearance, pollution, hardness, and hydrophobicity) of the high voltage end decrease significantly compared to other positions, but there is no direct connection between the physical properties of sheds and the decay-like fracture of the core rod. The severity of aging increases with a decrease in the equivalent transverse relaxation time T2. The main chain of the PDMS material was severely damaged at the location of the insulator fracture. NMR and FTIR can well judge the aging degree of silicone rubber housings. However, no definite characteristic quantity can characterize the decay-like fracture. It is challenging to evaluate the decay-like fracture of the silicone rubber shed only by its aging degree.
Three-dimensional feature extraction using local reference frame for detecting human–object interaction

Abstract. To understand the visual world, a device knows not only the instances but also how they interact. Humans are at the center of such interactions. Detection of human–object interaction (HOI) is one of the growing research fields in computer vision. However, identifying HOIs due to the large label space of verbs and their interaction with various object types still needs much research. We focus on HOIs in images, which is necessary for a deeper understanding of the scene. In addition to two-dimensional (2D) information, such as the appearance of humans and objects and their spatial location, three-dimensional (3D) status, especially in the configuration of the human body and object as well as their location and spatial, can play an important role in learning HOI. The mapping of 2D to 3D world adds depth information to the problem. These issues led us to collect 3D information along with the 2D features of the images to provide more accurate results. We show 3D attributes, such as face transformation, the viewing angle, the position of an object, and its related location to the human face, can improve HOI learning. The results of experiments on large-scale data show that our method has been able to improve the outcome of interactions.
Environmental strategy and firm performance: A new methodological proposal

Environmental strategies and their effects on firm performance are receiving increased attention in the literature, but the results are inconclusive. To fill this gap, we propose to evaluate the effect of environmental strategies on firm performance, thereby making two significant contributions. The first is the use of Bayesian techniques to estimate a stochastic frontier model with random coefficients to evaluate the relationship between environmental strategies and performance at the individual firm level, thus adequately incorporating heterogeneity; the second is the adoption of profit efficiency as a measure of firm performance. To test this idea, we studied the effect of a set of pollutants on profit efficiency in a sample of livestock firms in Spain. The results reveal that i) the success of environmental strategies depends on the properties and internal characteristics of each firm and the environment in which it operates and ii) the mean efficiency is 55.80%, which implies that these firms are losing on average 44.20% of their maximum potential profit. These results have significant strategic implications for firms' ability to achieve a competitive advantage.
Data Augmentation of Semantic Embeddings for Skeleton based Zero-Shot Gesture Recognition

Interaction with computer systems is one of the most important topics of the digital age. Interfacing with a system through body movements rather than tactile controls can provide significant advantages. To make that possible, the system needs to reliably detect the performed gestures. Systems using conventional deep learning methods are therefore trained on all possible gestures beforehand. Zero-Shot learning models, on the other hand, aim to also recognize gestures not seen during training when given their labels. The model thus needs to extract information about an un-seen gesture’s visual appearance from its label. Using typical text embedding modules like BERT, that information will be focused on the semantics of the label rather than its visual characteristics. In this work, we present several forms of data augmentation that can be applied to the semantic embeddings of the class labels in order to increase their visual information content. This approach achieves a significant performance increase for a Zero-Shot gesture recognition model.
A Model of Environment Representation Architecture for Intelligent Robot

This thesis presents a model of Environment Representation Architecture for Intelligent Robot. The model consists of a vehicle, an environment and landmarks. The proposed method is based on using Simultaneous Localization and Mapping based on Artificial Neural Network, back propagation algorithm, to be trained on predefined datasets on some environment. The Finding in this thesis dun Using Artificial Neural on the Simultaneous Localization and Mapping enhances the obtained maps of the robot. Limitation of this thesis to test the proposed system, different maps with different datasets are required, however, these datasets, need expensive sensors, vehicles and GPS receivers to be built. The Originality he contribution of this thesis, is the use of Artificial Neural Network Back Propagation algorithm on the Simultaneous Localization and Mapping algorithm to obtain enhanced results, enhanced maps and datasets obtained by the robot. Using several methods of preparing data sets for the landmarks of an environment, SLAM was able to navigate and build its own map of landmarks based on them. In this work, the benefitted from previous approaches to design an enhanced SLAM system, which is based on Artificial Neural Network. However, by training this network on real dataset of environments, the proposed system was able to navigate an environment and build its own map accurately and without prerequisites.
WHY DO PEOPLE AVOID INFORMATION ABOUT PRIVACY?

Why do people keep their head in the sand when making data sharing decisions? There is a widespread intuition, supported by copious research, that people are inconsistent in their behavior around internet privacy. Anger about privacy scandals dominates newspaper headlines, but most people don’t change their default privacy settings, even when it’s easy to do so. New evidence confirms that this inconsistency is real, and that information avoidance helps drive the inconsistency. This raises a new question: how does information avoidance work? This paper presents a new experimental design to start unpacking how information avoidance operates. There are two main results. First, the experiment replicates existing information avoidance experiments: people who value privacy are willing to deal away their data for small money amounts if given a chance to avoid seeing the privacy consequences of their actions. Second, the experiment shows that while people are comfortable avoiding information about privacy in a passive way, they are not comfortable actively hiding it. These results show that people’s ability to keep their head in the sand is fragile: it is a preference people are not willing to exercise conspicuously.
Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications

This paper presents an energy management integrated circuit for multiple energy harvesters in wireless body area network applications. The electrical power acquired from a single energy harvester around a human body is limited to micro watts, which is insufficient to drive a wearable electronic device. To increase this small amount, the energy from a number of harvesters has to be combined. By combining the energy from multiple distributed harvesters, each one producing negligible energy, significant energy for wearable devices can be obtained. In designing an energy management circuit for a wearable device, there are two issues to be resolved. The first is related to the power consumption of the circuit, and the second issue is related to the methods needed to manage the wide range of power that occurs as the energy input changes during harvesting. In this paper, an energy management circuit that resolves the two issues above is described. The circuit was integrated using 0.13 µm Taiwan Semiconductor Manufacturing Company complementary metal-oxide-semiconductor technology. The energy management circuit is designed to combine up to three sources of harvested energy with more than 90% operating efficiency over the entire power range of the energy harvested.
Towards Analysis of the Performance of IDSs in Software-Defined Networks

As a promising technique for the design of 5G wireless networks, software-defined networks (SDNs) have been proposed. However, SDNs are vulnerable to most of the attacks that traditional networks are vulnerable to. Various techniques have been developed and designed to help in the detection as well as the prevention of various attacks. An intrusion detection system (IDS) is one of the common techniques used to detect malicious activity in a network. Intrusion detection systems have strengths and weaknesses when it comes to detecting intrusions. It becomes a challenging task for IDS to process any mixture of traffic that results in packet drop and delay. In this study, we scrutinized two open-source IDS, including Snort IDS and Zeek IDS, to assess the IDS performance in terms of various parameters such as detection rate, dropping rate, and latency. The method of detection was one of the main differences between Snort and Zeek. Zeek IDS uses an anomaly-based detection method as opposed to Snort IDS, which uses a signature-based detection method. Differences between them had an impact on the way network traffic was handled. Such a thought analysis is expected to be of great value in selection and further enhancement of IDS in SDN.