Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovative Detection of Testosterone Esters in Camel Hair: Unravelling the Mysteries of Dromedary Endocrinology

Introduction: Doping and steroid use represent a serious threat to animal health and can even lead to their untimely and painful death. However, doping is an acute problem in today’s animal racing world, particularly in camel racing. Testosterone and its ten esters (benzoate, valerate, isocaproate, hexahydrobenzoate, decanoate, undecanoate, laurate, enanthate, cypionate, and caproate) are of utmost importance, because when they are administered to animals it is difficult to measure them efficiently. The levels of testosterone and its esters in camels and other animals are typically determined using urine and blood tests. The aim of this study was to develop and validate a liquid chromatographic–mass spectrometric (LC-MS/MS) method to determine testosterone esters in camel hair, and to apply the validated method to determine testosterone esters in collected samples. To our knowledge, this is the first report of such research. Results and Discussion: The levels of testosterone and its ten derivatives, along with the cortisol-D4 internal standard, were optimised for LC–MS/MS analysis; however, only testosterone along with its seven esters (namely benzoate, valerate, isocaproate, hexahydrobenzoate, decanoate, undecanoate and laurate) could be validated in camel hair. Only five testosterone esters could be determined in camel hair samples; the concentrations were obtained as 10.5–14.9 pg/mg for valerate (in three camels), 12.5–151.6 pg/mg for hexahydrobenzoate (in six camels), 4.8–32.1 pg/mg for laurate (in five camels), 5.1 pg/mg decanoate (in one camel), and 8.35–169 pg/mg for testosterone (in all 24 camels). Interestingly, the three racing camels displayed high concentrations of testosterone (59.2–169 pg/mg, all three camels), laurate (4.8–14.5 pg/mg, two camels), hexahydrobenzoate (116 pg/mg, one camel), decanoate (5.1 pg/mg, one camel), and valerate (11.7 pg/mg, one camel). Methods: Camel hair samples were collected from 21 non-racing dromedary camels along with three racing camels in Al Ain, UAE; these were decontaminated, pulverised, sonicated, and extracted prior to analysis. An LC–MS/MS method was employed to determine the levels of testosterone esters in the hair samples. Conclusions: This novel camel-hair test procedure is accurate, sensitive, rapid, and robust. The findings reported in this study could be significant to evaluate racing camels for suspected doping offenses. Further controlled testosterone supplementation studies are required to evaluate individual esters’ effects on camel health and diseases and on performance enhancement levels. This new hair test could promote further studies in doping control, toxicology, and pharmacology, as well as having other clinical applications relating to camel health, injury, and disease.
Vision-Based Tactile Sensing for an Omni-Adaptive Soft Finger

Vision-based tactile sensing provides a novel solution to robotic proprioception using visual information to infer physical interaction on the contact surface. In this paper, we leveraged the omni-adaptive capability of a soft finger with differential stiffness by adding a monocular camera at its bottom to track its spatial deformation while interacting with objects. We modeled this soft finger's physical interaction and measured the stiffness distribution through experiments. The camera captured the soft finger's deformation when interacting with probes for different contact forces and positions. Using a neural network modified from AlexNet, we proposed a preliminary estimation model of the contact force and position using the captured images. The results show that the proposed method can achieve an accuracy of 90% for position estimation and a normalized root mean squared error of 3.4% for force estimation, showing the reliability and robustness of the proposed sensing method.
Current Status and Strategies for the Mental Health of Parents of Children with Autism in Sichuan and Chongqing of China

: To understand the mental health of parents of children with autism in Sichuan and Chongqing, this study conducted a questionnaire survey using the SCL-90 scale with 131 parents of children with autism and 298 parents of typically developing young children. The research findings are as follows: (1) Parents of children with autism in Sichuan and Chongqing scored higher than parents of typically developing children in various dimensions, with significant differences in scores on factors such as interpersonal sensitivity, depression, anxiety, and psychotic symptoms, as well as the overall scores. (2) There were no significant differences in the SCL-90 dimensions and overall scores based on the gender of parents. (3) Fathers of male children with autism scored significantly higher than mothers of female children with autism in the dimensions of somatization and anxiety. (4) Parents of children with autism in Sichuan Province scored significantly higher than parents in Chongqing Municipality in dimensions such as hostility, paranoia, psychotic symptoms, and others. (5) The severity of autism symptoms had an impact on the mental health of parents, with parents of children with moderate autism scoring significantly higher than parents of children with mild autism in various dimensions and overall scores, except for hostility and terror. (6) Educational level influenced the mental health of parents of children with autism. Parents holding postgraduate degrees or above get significantly higher score than those with undergraduate degrees in dimensions such as somatization, depression, anxiety, paranoia, and others. (7) Family income had a certain impact on the mental health of parents of children with autism, particularly in the dimension of terror, where parents with incomes below 50,000 per year scored
Social media usage in industrial markets: An examination of technopark companies in Turkey

This study examines the extent to which companies operating within the technology park, ranked in the top 3 in Turkey according to the performance index, utilise social media marketing. The research explores the level of their engagement, the platforms they leverage, and their relative activity across these platforms. Furthermore, a profile has been delineated specifically for companies in technology-intensive industrial markets. Out of the 1121 companies accessed on their websites, it was observed that 55% of them had added redirects to their social media accounts. Among the 615 companies with accessible social media accounts, it was noted that 102 of them had profiles on Facebook, Instagram, Linked In, YouTube, and X. On the other hand, it has been found that the percentages of companies sharing content on their social media accounts at least one year ago are noteworthy (Among firms using YouTube 33.3%, Facebook 5.9%, and X 33.7%). This study offers perspectives for academic circles and practitioners through analyses of the involvement levels of firms operating in a business-to-business context in social media marketing and the platforms they utilise.
Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) are advanced technologies that assist drivers with vehicle operation and navigation. Recent improvements and brisk expansion in the ADAS market, as well as an increase in the frequency of incidents such as sensor spoofing, communication interruption etc., in autonomous vehicles (AVs), have raised the need to research ADAS security technology. The security issues raised by incorporating these technologies into automobiles must be addressed to protect the privacy and safety of passengers and other road users. As a result, the purpose of this research is to investigate the security issues that arise from the integration of ADAS technologies. Addressing these challenges holds the potential to establish a foundation for enhanced safety and dependability within transportation networks amidst the ongoing advancements in vehicle technology. This paper starts by describing the vulnerabilities, threats, assaults, and defense mechanisms of the ADAS. It then delves into the attacks and countermeasures in three categories, namely VANET, Hardware, and Adversarial attacks. VANET attacks encompass threats targeting Vehicular Ad Hoc Networks, aiming to disrupt communication among vehicles or between vehicles and infrastructure. Hardware attacks focus on vulnerabilities within the physical components of ADAS, including sensors, processors, or communication modules. Adversarial attacks involve deliberate manipulations or perturbations introduced into machine learning models or algorithms utilized within ADAS. These attacks aim to deceive or undermine the functionality of AI-based systems, causing misclassification, compromising system integrity, and posing risks to user safety by exploiting vulnerabilities in the AI decision-making process. Finally, this study highlights potential areas for future research, such as the utilization of artificial intelligence (AI), the necessity of industry-wide standardization, and recommends specific future work tailored to each attack described in the corresponding sections.
Formal Specification and Verification of Architecturally-Defined Attestation Mechanisms in Arm CCA and Intel TDX

Attestation is one of the most critical mechanisms in confidential computing (CC). We present a holistic verification approach enabling comprehensive and rigorous security analysis of architecturally-defined attestation mechanisms in CC. Specifically, we analyze two prominent next-generation hardware-based Trusted Execution Environments (TEEs), namely Arm Confidential Compute Architecture (CCA) and Intel Trust Domain Extensions (TDX). For both of these solutions, we provide a comprehensive specification of all phases of the attestation mechanism, namely provisioning, initialization, and attestation protocol. We demonstrate that including the initialization phase in the formal model leads to a violation of integrity, freshness, and secrecy properties for Intel’s claimed trusted computing base (TCB), which could not be captured by considering the attestation protocol alone in the related work. We open-source our artifacts. Other researchers, including a team from Intel, are adopting our artifacts for further analysis.
Continuous Snow Water Equivalent Measurements with Ultra-Wideband Radar: Towards a New Generation of Real-Time Automated Snow Sensor Arrays

vi LIST OF TABLES . x LIST OF FIGURES . xi LIST OF ABBREVIATIONS . xvii CHAPTER ONE: CONTINUOUS SNOW WATER EQUIVALENT MEASUREMENTS WITH ULTRA-WIDEBAND RADAR: TOWARDS A NEW GENERATION OF REAL-TIME AUTOMATED SNOW SENSOR ARRAYS . 1
SMOG: Accelerating Subgraph Matching on GPUs

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$. And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.
A Comparative Study of Generational Preferences for Trip-Planning Resources: A Case Study of International Tourists to Shanghai

Although many previous studies have investigated the information search behavior in some specific age groups of tourists, few of them comprehensively examined the relationships between cross-generational tourists and their preferences for various types of trip-planning resources. This study aimed to fill this research gap. The authors employed correspondence analysis to assess and visually display the correlations between four generational groups—the Silent Generation (aged over 65), the Baby Boomers (aged 50–64 years), Generation X (aged 30–49 years), and Y (aged 18–29 years)—and 10 primary travel information sources for the Shanghai international tourist market. The results indicated that tourists in the Baby Boomers and Silent Generation groups rely most on travel agencies and tour operators. The Generation X tourists are inclined to search websites for their travel information. Generation Y tourists are more likely to consult friends and/or relatives and travel guidebooks. The study has both theoretical and practical contributions. The core values of generations are used to explain their preference of information sources from a theoretical perspective. This study assists destination marketers to design effective promotional strategies suited for different generational segments.
A Comparative Analysis of Machine Learning Algorithms for Credit Risk Scoring using Chi-Square Feature Selection

Credit payment, which involves making predetermined installment amounts starting from the receipt of the purchased product, has become increasingly popular. Lending institutions often employ various risk assessment strategies, commonly known as credit risk scoring. Manual credit risk analysis often requires a longer processing time and may result in a decrease in accuracy. By leveraging the advancements in artificial intelligence, the current landscape offers promising opportunities for conducting credit risk analysis in a more efficient and accurate manner. The primary objective of this paper is to utilize the Chi-Square feature selection method in order to improve the speed and efficiency of these algorithms. Four machine learning algorithms are used to assess the outcomes. The 22 features have been carefully selected for further use and thoroughly evaluated. The final results indicate that the Naive Bayes Classifier technique, with a score of only 0.65, is less accurate when compared to other models. In the realm of predicting whether a client will default or not, the Random Forest Classifier model emerges as the most effective model. This model boasts the highest accuracy value while maintaining the evaluation value at 0.68, making it the top choice among all the models. The deployment of the model reveals that there has been a decrease of 10.22% in the percentage of LGD.
A numerical study on thermal control of batteries by phase change materials with liquid cooling

The thermal control of the battery system concerns about reduction in heat generation during the operation of battery and the limitations of the vehicles due to the external climatic factors. This study focuses on the attempt to control the battery from external climatic factors and temperature peaks during its operation by the principle of passive thermal management system using phase-change material. A numerical investigation of a three-dimensional model for a cylindrical lithium-polymer battery as a single module is done. The thermal control is realized by a phase-change material for a battery placed inside a parallelopiped and cooled by convective tubes placed at constant temperature and the surface of the module is considered as adiabatic. The governing equations are solved using the Finite Volume method using Ansys-Fluent. The investigation involves 3 different C-rates for two different phase change materials. The results analyse the maximum temperature, temperature distribution and liquid fraction fields for the different cases. The obtained results indicated that for the two different PCM materials namely RT25HC and RT35HC used for the cooling of the batteries the maximum temperature reached by battery and the average temperature of the PCM materials used was observed to be higher in RT35HC.
Effect Analysis of Random-weighted Equipoise Multi-channel Adaptive Matched Filtering on Regular Interference Removal

Equipoise multi-channel adaptive matched filtering is an effective method to remove regular interference signals from the original seismic records. In order to weaken the dependence of data on orthogonality restrictions, some channels around the target are usually selected as equipoise channels. In order to accurately extract the wavelet shape, the selected equipoise channels need to be as few as possible. The fewer equipoise channels, however, will lead to unsatisfactory filtering effect because of data non-orthogonality. In view of this defect, we develop conventional equipoise multi-channel adaptive matching via randomly selecting channels away from the target as the equipoise channels and assigning different weight factors to different equipoise channels simultaneously. This method may weaken the data non-orthogonality and reduce the influence related to the number of equipoise channels. In order to verify the effectiveness of this method, numerical experiments and analysis of different models have been carried out. The results show that in the case of a small number of matching channels, the effects of the random or random-weighted equipoise multi-channel adaptive matched filtering are better than the conventional one. The random-weighted has the best effect.
Limestone calcination optimization of vertical container oven

Modern science has no basis of positivist epistemological and methodological foundations sufficient for building a comprehensive understanding of the world of man and society. A study of contrasts to the paradigms of science, technology and modern rationality expressed the thoughts of Jürgen Habermas, Herbert Marcuse, Martin Heidegger, Theodor Adorno, Max Horkheimer, and Boaventura Santos Souza was conducted. We used a dialectic approach to highlight the contradictions and limitations of this paradigm and highlight other paradigms of positivist epistemological basis not carry that enabled the overcoming of knowledge as regulation by knowledge empowerment. It was evident, based on the theories of these authors and arguments of researchers such as Albert Einstein, Ilya Prigogine, Michel Foucault, among others, that modern science has methodological limitations Cartesian basis and fragile epistemological basis when trying to conceive man, world and society while dynamically articulated.
Relating big data to local natural hazards: lessons learned from data mining the Twitter API for volunteered geographic information on earthquakes, wildfires, and prescribed fires in the contiguous United States

New media are increasingly used to capture ambient and volunteered geographic information in multiple contexts, from mapping the evolution of the social movements to tracking infectious disease. The social media platform Twitter is popular for these applications; it boasts over 500 million messages (‘tweets’) generated every day from as many total users at an average rate of 5,700 messages per second. In the United States, Japan, and Chile to name a few, Twitter is officially and unofficially used as an emergency notification and response system in the event of earthquakes, wildfires, and prescribed fires. A prototype for operational emergency detections from social media, specifically Twitter, was created using natural language processing and information retrieval techniques. The intent is to identify and locate emergency situations in the contiguous United States, namely prescribed fires, wildfires, and earthquakes, that are often missed by satellite detections. The authors present their methodologies and an evaluation of performance in collecting relevant tweets, extracting metrics such as area affected and geo-locating the events. Lessons learned from data mining Twitter for spatiotemporally-explicit information are included to inform future data mining research and applications.
Numerical study on the mixed convection with nanofluids in vertical channel asymmetrically heated

Mixed convection heat transfer in cavities is of great interest in many industrial applications, such as the development of crystals, the thermal control of electronic components, the nuclear reactors and various manufacturing processes. Components are generally located on the wall of a channel, that can be also tilted and their thermal control is carried out by the fluid flow. They can be schematized as thermal sources on vertical boards and the heat flux is removed by natural or mixed convection. As to the mixed convection, the interaction between the buoyancy forces and the external flow plays a significant role in the thermal design of systems. When natural convection is not strong enough to remove heat flux, mixed convection is adoptedIn particular, mixed convection due to moving surfaces is very important is these applications. Mixed convection because of buoyancy and motion of one of the channel walls has received little research attention and few guidelines are available for choosing the best performing channel configuration. Moreover, to optimize the transfer through natural convection, it is possible to modify the geometric parameters of the component, thus maximizing the convective heat transfer coefficient. If this is not possible or if one chooses to keep the geometry of the component unchanged for design choices, a possible alternative is the use of nanofluids. The properties of nanofluids exhibit a behaviour ranging between the properties of the base fluid and the properties of the nanoparticles. However, the addition of nanoparticles in the working fluid is considered to enhance the thermal behaviour of the base fluid. In this study a numerical investigation of mixed convection in nanofluids due to the interaction between a buoyancy flow and a moving plate induced flow in a vertical channel is carried out. The moving plate has a constant velocity and moves in the same direction with respect to the buoyancy force. The channel principal walls are heated with uniform heat flux. The numerical analysis is obtained by means of the commercial code Fluent. The effects of the different wall heat fluxes and moving plate velocities are investigated and results in terms of the channel wall and moving plate temperatures and Nusselt numbers are given.