Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Modeling and Simulation of Vehicle Active Aerodynamic Safety

The aim of this study was to extend the safety limits of fast moving cars by the application, in a controlled way, of aerodynamic forces which increase as the square of a car’s velocity and, if left uncontrolled, dramatically reduce car safety. This paper presents the methods, assumptions, and results of numerical and experimental investigations by modeling and simulation of the aerodynamic characteristics and dynamics of a small sports car equipped with movable aerodynamic elements operated by an electronic subsystem for data acquisition and aerodynamics active automatic control.
Planar Optofluidic Integration of Ring Resonator and Microfluidic Channels

We report an optofluidic hybrid silicon-polymer planar ring resonator with integrated microfluidic channels for efficient liquid delivery. The device features a planar architecture of intersecting liquid-core waveguides and microfluidic channels. A low-loss integration of microfluidic channels is accomplished by exploiting the interference pattern created by the self-imaging effect in the multimode interference-based coupler waveguides. Numerical simulations have been performed in order to minimize the propagation losses along the ring loop caused by the integration of microfluidic channels. The device has been fabricated and optically characterized by measuring the quality factor, obtaining a value of 4 × 103. This result is comparable with the quality factor of an optofluidic ring with the same optical layout but without integrated microfluidic channels, thus, confirming the suitability of the proposed approach for microfluidics integration in planar optofluidic design.
IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.
Direct Yaw-Moment Control Integrated With Wheel Slip Regulation for Heavy Commercial Road Vehicles

When a road vehicle is subjected to combined cornering and emergency braking, it potentially has a greater risk of wheel lock followed by loss of steerability and/or undesired yaw motion. While an Anti-lock Braking System (ABS) is mandatory in many countries to avoid wheel lock, more attention is required to its combined cornering and braking performance. This paper aims to design a Direct Yaw-Moment Controller (DYC) integrated with ABS to achieve vehicle directional stability for Heavy Commercial Road Vehicles (HCRVs). This study implements a robust reaching law-based sliding mode controller for DYC and ABS. The developed algorithm was evaluated in a Hardware-in-Loop (HiL) setup. The experimental results are compared for the integrated algorithm and standalone ABS algorithms. The proposed algorithm improved the Directional Performance Index (DPI) in the range of 29% to 84% over the open-loop behavior while maintaining vehicle stability. Moreover, it also improved the DPI in the range of 5% to 53% over standalone ABS in various emergency test cases.
Segmentation and graph generation of muzzle images for cattle identification

The current methods for the organizing the records (i.e., cataloguing) of cattle are known to be archaic and inefficient, and often harmful to the animal. Such methods include the use of metal tags attached to the animal's ears like earrings and of branding irons on their necks. Previous research on new methods of livestock branding based on computer vision techniques utilized a mixture of texture features such as Gabor Filters and Local Binary Pattern as a means of extracting identifying features for each animal. The presented approach proposes a new technique using the muzzle image as an individual identifier as a novel technique, assuming that the muzzle RoI taken as input for the model pipeline is already extracted and cropped. This task is performed in three steps. First, the muzzle image is segmented via a convolutional neural network, resulting in a bitmap from which a graph structure is extracted in the second phase. The final phase consists of matching the resulting graph with the ones previously extracted and stored in the database for an optimal match. The results for the segmentation quality show a fidelity of around seventy percent, while the extracted graph perfectly represents the extracted bitmap. The matching algorithm is currently in progress.
Single-Molecule Surface-Enhanced Raman Spectroscopy

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to detect single molecules in a non-invasive, label-free manner with high-throughput. SM-SERS can detect chemical information of single molecules without statistical averaging and has wide application in chemical analysis, nanoelectronics, biochemical sensing, etc. Recently, a series of unprecedented advances have been realized in science and application by SM-SERS, which has attracted the interest of various fields. In this review, we first elucidate the key concepts of SM-SERS, including enhancement factor (EF), spectral fluctuation, and experimental evidence of single-molecule events. Next, we systematically discuss advanced implementations of SM-SERS, including substrates with ultra-high EF and reproducibility, strategies to improve the probability of molecules being localized in hotspots, and nonmetallic and hybrid substrates. Then, several examples for the application of SM-SERS are proposed, including catalysis, nanoelectronics, and sensing. Finally, we summarize the challenges and future of SM-SERS. We hope this literature review will inspire the interest of researchers in more fields.
Erfahrungen zur Schmerzausschaltung bei der Ferkelkastration in der Schweiz. Teil 1: Inhalationsanästhesie

The aim of this study was to describe the practical implementation of the painless castration under injection anaesthesia in Switzerland. 30 swine farms were visited and 60 farmers answered a questionnaire. 34 % of the piglets showed movements during castration under injection anaesthesia and 17 % had excitations during recovery from anaesthesia. After 48 minutes half of the piglets were in sternal position and after 112 minutes half of them showed coordinated movements. The body temperature decreased by 3.1 °C until 60 minutes after castration, especially small piglets reached critical temperature levels. 38 % of the piglets showed strong bleeding after castration. The healing of the wound was good according to 82 % of the farmers. 83 % of the farmers reported piglet losses, especially at the beginning of the anaesthesia period. The anaesthesia may be improved by using butorphanol in addition to the combination of ketamine and azaperone. The recovery of the piglets should be in a warm place without any risk of injury by obstacles or the sow. Increased bleeding can be controlled by using an emasculator.
The adaptive role of contemporary journalism and methods for its implementation: Based on the material of urban-oriented network media

Methods of realizing the adaptive role of contemporary journalism are considered on the example of urban-oriented network media that emerged as an independent segment of the Russian media in the 2010s. An accommodative role is one of the main institutional functions of journalism and is to provide “useful news”. The implementation of this role is closely related to the formation of service, or consumer, journalism (M. Eide, G. Knight), and such spheres as lifestyle journalism, entertainment journalism, and brand journalism. The relevance of the subject is confirmed by the findings of the international study “Worlds of Journalism: Journalism Cultures Around the Globe” (T. Hanitzsch, F. Hanusch, J. Ramaprasad, A. S. de Beer, 2019), which recorded the increased activity of this role in Russia. The information agenda of urban-oriented network media, which focuses on urban everyday life and creative urban practices, organically includes extensive service content. The study has shown that the strengthening of the accommodation function in contemporary journalism is closely related to the phenomenon of participation. Convenience, including compactness and reading speed, interactivity and narrativization, which manifests itself both in the orientation towards personal stories and in a special personalized manner of communication between the author of the material and the reader, become the guiding principles in the design of “useful news”. The genres of informational instructions or advice characteristic of traditional consumer journalism in the spirit of lifestyle publications are transformed under the influence of the new communicative attitude into interactive (cards, games, tests, a cartographic application) and narrative (self-narrative, annotated announcement) formats. Combining the principles of service and participatory journalism, urban-oriented network media represent a completely successful experience in implementing an adaptive role aimed not only at shaping a culture of consumption, but also creating actual urban identity and a socially active community.
Using two feed-forward layers for fault detection of hole

Automated assembly technology is a major component of modern aerospace manufacturing. Hole-Making Robot (HMR)s can significantly improve the efficiency of the aircraft manufacturing and can also ensure reliability of processing. The machining accuracy of the HMR depends on the precision of the mechanism, the accuracy of hole-positioning, and the vertical degree of the hole. Evaluating the precision of the hole system requires a complete set of inspection systems to measure the relevant hole parameters. Visual detection technology can provide more information on the object, which in theory is better suited to simulate real world applications. Due to the rapid development of current visual technology and the actual demand of aviation holes, this paper attempts to apply Convolution Neural Network (CNN) technology to the concrete task of visual inspection and improve the performance of visual inspection systems. Hole classification and hole flaw detection are realized via two feed-forward layers. We created a dataset of 1300 images of holes for testing. These were collected from various sources. The highest accuracy was 98.15% and was achieved using the two feed-forward neural network algorithm.
Long-Term Postoperative Pain Prediction Using Higher-Order Singular Value Decomposition of Intraoperative Physiological Responses: Prospective Cohort Study

Background Long-term postoperative pain (POP) and patient responses to pain relief medications are not yet fully understood. Although recent studies have developed an index for the nociception level of patients under general anesthesia based on multiple physiological parameters, it remains unclear whether these parameters correlate with long-term POP outcomes. Objective This study aims to extract unbiased and interpretable descriptions of how the dynamics of physiological parameters change over time and across patients in response to surgical procedures and intraoperative medications using a multivariate-temporal analysis. We demonstrated that there is an association (correlation) between the main features of intraoperative physiological responses and long-term POP, which has a predictive value, even without claiming causality. Methods We proposed a complex higher-order singular value decomposition method to accurately decompose patients’ physiological responses into multivariate structures evolving over time. We used intraoperative vital signs of 175 patients from a mixed surgical cohort to extract three interconnected, low-dimensional, complex-valued descriptions of patients’ physiological responses: multivariate factors, reflecting subphysiological parameters; temporal factors, reflecting common intrasurgery temporal dynamics; and patients’ factors, describing interpatient changes in physiological responses. Results Adoption of the complex higher-order singular value decomposition method allowed us to clarify the dynamic correlation structure included in the intraoperative physiological responses. Instantaneous phases of the complex-valued physiological responses of 242 patients within the subspace of principal descriptors enabled us to discriminate between mild and not-mild (moderate-severe) levels of pain at postoperative days 30 and 90. Following rotation of physiological responses before projection to align with the common multivariate-temporal dynamic, the method achieved an area under curve for postoperative day 30 and 90 outcomes of 0.81 and 0.89 for thoracic surgery, 0.87 and 0.83 for orthopedic surgery, 0.87 and 0.88 for urological surgery, 0.86 and 1 for colorectal surgery, 1 and 1 for transplant surgery, and 0.83 and 0.92 for pancreatic surgery, respectively. Conclusions By categorizing patients into different surgical groups, we identified significant surgery-related principal descriptors. Each of them potentially encodes different surgical stimulation. The dynamics of patients’ physiological responses to these surgical events were linked to long-term POP development.
What Influences Fashion Consumers’ Loyalty On Social Media in Indonesia?

This research examines the determining factors which can affect consumer loyalty for fashion products on social media mediated by purchase intention. This research focuses on both locally produced and imported fashion products sought after by Batam City residents. Collected data is pooled from 352 respondents using Google Forms. The questionnaires were taken from the works of Abou Ali et al., (2021) dan Prasad et al., (2019). The causal-comparative quantitative approach is used in this research, while linear regression is used to prove the relationship between each hypothesis. This research shows that the relationship between country of origin, customer engagement, influencer marketing, purchase intention, and trust can explain about 53% of customer loyalty. Meanwhile, perceived value does not significantly affect customer loyalty directly. To examine the mediating relationship of purchase intention, this research uses the aforementioned variables to be compared. The result shows that there is a mediating relationship between purchase intention and independent perceived value and trust. Altogether this research is conducted in a relatively short time. Additionally, the lack of supporting journals and sources that can reinforce the mediating relationship of purchase intention toward perceived value and trust makes this study not thoroughly discussed.
How commercializing academic patents promote economic growth: mediating effect and spatial spill over

ABSTRACT Governmental investment in commercializing academic patents has spurred economic growth. This study explores the mechanism underlying the impact of academic patent commercialization on regional economic development based on holistic view of the integration of local and neighbourhood hierarchies. The empirical analysis of a sample of 31 Chinese provinces from 2010 to 2019 shows that fiscal expenditures on science and technology mediate the relationship between academic patent commercialization and economic development. Moreover, there is a positive spatial spill over effect between developing economies of neighbouring provinces. In the knowledge economy era, academic patent commercialization, supported by fiscal expenditure, plays an increasingly important role in regional economic growth.
The asexual morph and molecular phylogeny of endemic Phyllactinia verruculosa on Indigofera tinctoria

Phyllactinia verruculosa is a powdery mildew species (Erysiphaceae, tribe Phyllactinieae) so far only known from its type material collected in China on Indigofera scabrida in 1992, which only comprised the sexual morph. Two asexual morph samples were observed, one was collected on I. tinctoria on the campus of Guizhou University, Guiyang, China, and another one, on I. scabrida, was borrowed from Kunming Institute of Botany, Chinese Academy of Sciences. The anamorphic characters were observed, described and illustrated. The phylogenetic analysis of the combination of internal transcribed spacer regions (ITS) and 28S rDNA sequences showed that Ph. verruculosa is phylogenetically distantly related from other Phyllactinia species. To our knowledge, this is the first record of the asexual morph and first ITS+28S sequences for Ph. verruculosa, and I. tinctoria is a new host record for this species.
Exploring the Effects of the Human–Animal Relationship on Care-Farms in the Context of Trauma Histories

ABSTRACT This qualitative study explored the effects of human–animal relationships on care-farms, with specific attention to the context of trauma histories. We questioned how the interpretative act and belief in identifying shared narratives of prior suffering could change how people relate to their own narratives of trauma and grief and to animals. Drawing on a study of grieving individuals’ experiences on a care-farm providing support and psychoeducation to individuals who have experienced traumatic grief, we present the results of an in-depth qualitative survey. As part of the study, participants were asked to reflect on whether it was important that the service-provider’s model included helping rescue animals: 91% answered affirmatively. Participants were invited to expand discursively why, or why not, this had been meaningful to them. Our results show that participants assigned benefits from personally identifying a “shared narrative” of trauma with the animals, that witnessing a level of rehabilitation and resilience in animals with trauma histories was meaningful for participants for their own integration of grief, and that being able to contribute “care” for animals provided a mechanism for compassionate practice. Our findings suggest that animals with loss and trauma biographies may provide unique and unexpected psychological benefits to humans facing grief and trauma. We are not suggesting that animals who have a traumatic past have an inherent capacity for providing salutary benefit or that such animals should be engaged to provide therapeutic opportunities. Rather, we emphasize the importance of narrative and how such narratives change how participants relate to, and interact with, animals. Our research serves as an important reminder that “therapy animals” are living beings with their own life histories and experiences. Careful thought needs to be given when working with animals in a therapeutic context in order to protect both vulnerable humans and animals.
Elementary School Teachers’ Competence Development in Dealing with The COVID-19 Conditions: An Interpretative Phenomenological Study

The Covid-19 pandemic has had an impact on the education system, leading to school closures and switching toonline learning with very short notice. In order to support the effectiveness of online learning, it is necessary todevelop teachers’ competence in the utilization and use of online media. The study aimed to investigate educators’teaching experience during online learning during the Covid-19 pandemic. The three teachers who participated inthis study were recruited from the Gubeng 1 Public Elementary School in Surabaya. Interview transcripts wereanalyzed qualitatively by using an interpretative phenomenological analysis approach. Data analysis raises threemain themes: 1) an overview of online learning implementation in elementary schools, 2) challenges in the teachercompetence development process, 3) support for teacher competence development. The results of this studyindicate the importance of developing teacher digital competence to increase the effectiveness of online learning.Support from related parties is also needed to encourage the success of online learning.
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge IoT

Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(1/k) + \mathcal {O}(1/M)$ </tex-math></inline-formula> convergence rate, where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments.