Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Control of Hexacopters: A Comparative Study of Adaptive Backstepping and Sliding Mode Control With Control Allocation

Hexacopters are unmanned aerial vehicles that have three pairs of counter-rotating fixed-pitch blades. This paper assesses and compares the robust performance of two control strategies for hexacopters: an adaptive backstepping controller and a sliding mode controller. This paper also introduces a novel control allocation scheme that maintains the rotor speeds within actuator limits. Both controllers perform comparably and prove to be robust against disturbances and uncertainties. Sliding mode control surpasses adaptive backstepping control in tracking altitude, roll, and yaw, at the expense of a more chattered input under uncertainty. Meanwhile, the adaptive backstepping controller is more robust against internal disturbances. The paper concludes by providing an outlook on future work.
Security Task Unloading of Wireless Powered Mobile Edge Computing System

This paper considers a wireless powered mobile edge computing(MEC) system with multiple user nodes and some eavesdropping nodes, studies the physical layer security offloading and resource allocation of wireless powered MEC systems, and proposes a resource allocation scheme for the wireless powered MEC security offloading system suitable for offline state. Under the constraints of ensuring energy harvesting, secure offloading, and computational delay, this scheme achieves the minimization of system's access point(AP) energy consumption by jointly optimizing the covariance matrix of AP energy transmission, local CPU frequency, number of user offloading bits, allocation of user offloading time, and user transmission power. In the process of solving the problem, a difference of convex algorithm(DCA) is first used to convert the original non convex problem into a convex problem, and then the Lagrange duality method is used to solve the optimal solution of the problem in a semi closed form. The simulation experimental results show that the proposed secure partial offloading scheme can effectively reduce AP energy consumption and have good performance gains.
Intranasal Drug Delivery by Nanotechnology: Advances in and Challenges for Alzheimer’s Disease Management

Alzheimer’s disease, a progressive neurodegenerative condition, is characterized by a gradual decline in cognitive functions. Current treatment approaches primarily involve the administration of medications through oral, parenteral, and transdermal routes, aiming to improve cognitive function and alleviate symptoms. However, these treatments face limitations, such as low bioavailability and inadequate permeation. Alternative invasive methods, while explored, often entail discomfort and require specialized assistance. Therefore, the development of a non-invasive and efficient delivery system is crucial. Intranasal delivery has emerged as a potential solution, although it is constrained by the unique conditions of the nasal cavity. An innovative approach involves the use of nano-carriers based on nanotechnology for intranasal delivery. This strategy has the potential to overcome current limitations by providing enhanced bioavailability, improved permeation, effective traversal of the blood–brain barrier, extended retention within the body, and precise targeting of the brain. The comprehensive review focuses on the advancements in designing various types of nano-carriers, including polymeric nanoparticles, metal nanoparticles, lipid nanoparticles, liposomes, nanoemulsions, Quantum dots, and dendrimers. These nano-carriers are specifically tailored for the intranasal delivery of therapeutic agents aimed at combatting Alzheimer’s disease. In summary, the development and utilization of intranasal delivery systems based on nanotechnology show significant potential in surmounting the constraints of current Alzheimer’s disease treatment strategies. Nevertheless, it is essential to acknowledge regulatory as well as toxicity concerns associated with this route; meticulous consideration is required when engineering a carrier. This comprehensive review underscores the potential to revolutionize Alzheimer’s disease management and highlights the importance of addressing regulatory considerations for safe and effective implementations. Embracing this strategy could lead to substantial advancements in the field of Alzheimer’s disease treatment.
Airgapless PMSM with Diamond Like Carbon Coating Technology

This study shows that radial type permanent magnet synchronous motor without air gap using diamond like carbon coating (DLC) technology to maximize the torque. Since existing motor technologies have limitations in improving torque, we introduce the development of motors using new materials. The DLC coating used in automotive combustion engines allows the engine to perform well even at very high temperatures and under strong friction conditions. Also, auto transmission oil, which is used as cooling oil for HEV/EV traction motors, was used to minimize the friction between the rotor and the stator and to cool the rotor and stator. As a result, even if the rotor and stator collide due to vibration generated in the air gap between the bearing cap and ball, the motor can rotate without any problems, and the motor torque greatly increases. After the motor rotates at 5,000 rpm, the DLC coating surface was not damaged and reliability was verified.
Evaluation of Ecosystem for Design Assessment and Verification by BAJA Dynamometer Capstone Team at the University of Nebraska

This paper summarizes the outcome of an evaluation by capstone design teams from the Department of Mechanical and Materials Engineering at the University of Nebraska-Lincoln, of the Ecosystem for Design Assessment and Verification. The Ecosystem is a design decision support tool whose main goal is to identify design oversights, defined in terms of deviations from the design process or unfulfilled design requirements, early in the design process, guide designers through the design process, and teach proper design techniques. It is capable of automatically assessing students’ design work against ABET compliant learning outcomes. The Ecosystem offers many additional features found useful by capstone design teams, such as automatic generation of formatted project reports as well as interfaces to tools for team communications (Google Drive, Dropbox or OneDrive) or development (e.g., SolidWorks, CATIA, NX Unigraphics or AutoCAD). The Ecosystem was recently evaluated by a capstone team working on an automated straw flattening machine and again during a following semester by a team designing a dynamometer used for measuring the engine power of a BAJA race car. The paper draws upon the improvements of the Ecosystem software completed during the aforementioned time period, identifies the features the capstone teams found most useful, and compares the design experience (productivity) of the BAJA Dynamometer team to that of the a team not using the Ecosystem, but with the same faculty adviser. Through comparison of two such teams, it was found that the team using the Ecosystem managed to stay on schedule a little better.
Coherency limits and synchronisation of a netted radar system using USRPs as nodes

This paper intends to further evaluate the synchronisation capabilities of multiple networked USRPs. We obtained a priori knowledge of the USRP’s coherency limits between daughterboards within a single device and presented such results in our previous work [1]. The final aim of this work is to build a versatile networked radar system which may take advantage of the transceiver capabilities of its different nodes to form a multistatic radar. At the expense of high complexity, multistatic radar systems exploit spatial diversity to increase the probability of detection and tracking of targets. To properly make use of these advantages, the involved nodes must agree on a common time base $({t}_{0})$. A succession of precisely timed events must occur, as to maximise the resulting SNR on the measurement side. In this paper, a customized software network time protocol (NTP) server is added between the control computers and it is combined with crontab as to timely start the transmitting and sampling sequences on the different nodes. Up-chirp and sinusoid waveforms were used for measuring the offset with ${t}_{0}$ and for evaluating the achievable coherency limits. Thus, by evaluating different synchronisation methods (analogue and digital), fractional frequency of $2.8{\cdot }{10}^{-13}$ and phase noise of −118 dBc/Hz at 10 Hz frequency offset were measured.
Adaptive Bipartite Consensus of Multi-agent Systems with Communication Link Uncertainty under Cooperative and Competitive Interactions

This paper investigates bipartite consensus problem of multi-agent systems (MASs) with communication link uncertainty under a signed digraph. The existence of communication link uncertainty have affected information interaction between agents. Therefore, this paper designs a distributed observer so that the followers agents can estimate the leader's state information. Afterwards, a controller with adaptive coupling gain is designed under a signed digraph to reduce the impact of unknown time-varying communication link uncertainty. Then, based on Lyapunov stability analysis, it is proved that the MASs with communication link uncertainty can realize bipartite consensus. Finally, the validity of the proposed control scheme is verified through simulation examples.
Attack Detection for Distributed Photovoltaic Generation Systems Leveraging Cyber and Power Side Channel Data

With the growing focus on reducing carbon emissions, there has been a significant increase in the deployment of distributed photovoltaic generation systems. However, their distributed nature makes them vulnerable to adversarial threats. Moreover, these systems can serve as potential entry points or attack surfaces for targeting the power grid. Existing methods are either designed for specific attacks or intrusive to power terminals (e.g., intelligent terminal, communication device). To tackle this issue, this paper introduces a method for detecting and classifying attacks on distributed PV systems leveraging cyber and power side channel data. The proposed method is capable of detecting various attacks rather than specific ones and is non-intrusive for collecting information from power terminals.
UMA HISTÓRIA AMBIENTAL DA FLORESTA NACIONAL DE PASSO FUNDO: A AQUISIÇÃO DAS TERRAS

Este artigo narra e interpreta o processo de aquisicao das terras do Parque Florestal Jose Segadas Viana, atual Floresta Nacional de Passo Fundo (FLONA), que, de acordo com a imprensa, foi marcado por conflitos envolvendo os proprietarios das terras, os intermediarios da negociacao, o Instituto Nacional do Pinho (INP) e a imprensa local. A principal fonte utilizada e o jornal O Nacional , que veiculou muitos textos e noticias sobre o tema no periodo de setembro a outubro de 1946. Emprega a abordagem da Historia Ambiental e afirma que a criacao do Parque, com formato irregular e descontinuo, prejudica a funcao atual da FLONA como unidade de conservacao.
The Weaponisation of the Internet – effect models

Policy makers and governments are increasingly fearful of growing efforts by malicious parties to weaponise the Internet. There are currently few tools to detect or measure the threat level, and few methods to either prevent or protect against these threats. One major aspect of the weaponisation of the Internet is to spread misinformation, disinformation and harmful online content through social media and the web. In order to understand how this is done, to be able to assess its effectiveness and most importantly to assist in designing mitigation techniques, it is necessary to be able to model threats. In particular to understand the origin of threats, their spread, virality, and their effects on both their targets and others. This paper presents a framework to model the weaponisation of the internet in a large scale statistical sense. It considers threat actors who create a range of threats which are propagated to a susceptible target population, and caters to the varied effectiveness of those threat actors, the varied susceptibility of the population to different threats, and the varied responses, including turning or radicalisation. This model is developed, discussed and explored through a time-based simulation to understand and visualise the propagation and effects of such threats.
Distributed Differential Protection for Distribution Automation Based on 5G Edge Computing

In this paper, firstly, the business characteristics of the distribution network are sorted out, and the demand of power business for 5G communication is analyzed. Thus, a 5G distribution scheme is established. On this basis, a distributed distribution differential protection scheme based on 5G edge calculation is proposed for the monitoring ability of distributed power supply and differential protection of the distribution network. In this scheme, the multi-access edge computing server and distributed power supply monitoring are used. The monitoring information unloading model of differential protection with joint communication and calculation is designed. The delay optimization problem of differential protection under the constraints of computing power and power is established, and a solution is proposed by combining the alternating optimization algorithm. The time delay of differential protection tasks under different conditions is simulated and analyzed. The results show that the scheme can effectively reduce the time delay of differential protection.
: SHAPING THE FUTURE OF PROCESS IMPROVEMENT IN HIGHLY AUTOMATED PRODUCTION WITH DIGITAL INNOVATION

: Companies must continually evolve to meet dynamic conditions and sustain competitiveness, necessitating fundamental shifts in organizational structure, employee mindset, and ongoing improvement of internal processes across direct and indirect areas. This complexity is particularly pronounced in highly automated production settings, challenging further enhancements to existing processes. This paper explores the evolving role of process improvement in highly automated production and the relevance of emerging digital technologies in boosting productivity. To address this research inquiry, virtual focus groups were selected as a qualitative approach, with seven executives from German large-scale enterprises evaluating the future role of process improvements in highly automated manufacturing. The findings indicate a significant potential for process improvements in manufacturing. While the integration of continuous process improvement aims for incremental enhancements, new technologies offer avenues to address existing inefficiencies, unscheduled downtimes, and excessive rework by tackling previously unresolved issues.
Data Mining Application Forecast of Business Trends of Electronic Products

— Sales forecasting is a pressing concern for companies amid rising consumer demand and intensifying competition, compounded by declining sales due to growing socio-economic challenges. Currently, many companies are having difficulty selling products due to a lack of management systems. To assist that, data mining techniques are introduced but it is difficult to evaluate the data and it is practically impossible to accurately forecast large amounts of data. However, data mining remains an important management tool that supports early decisions to increase profits, innovate business trends and improve sales by generating intelligence from the company's data resources. In this article, the research object chosen is the data of a nationwide electronics company. Their sales volume data for consumer electronics was used and applied to this study. The study used a "clustering" algorithm to group data based on the unique characteristics of each product, region, season, and time to estimate the amount of goods sold in the past, thereby predicting the amount of goods that will be exported. Password is sold in the following years and look for market trends. For each group, the results obtained with k = 3 show that the number of elements in each cluster is 771422, 11874, and 312, respectively. Combined with the “regression tree” algorithm for cluster partitioning and using the protocol Evaluate MSE and RMSE to evaluate the accuracy of the model, a result of 43065.66 Sales forecasting results show that the model's accuracy is close to realistic accuracy and depends on seasonal factors that are really important to some people. Based on the above results, the business's marketing campaigns and strategies will be deployed and achieve high results.
A Review of Applications of Artiﬁcial Intelligence in Heavy Duty Trucks

: Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efﬁcient, and provide safety for drivers. Artiﬁcial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artiﬁcial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity proﬁles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artiﬁcial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artiﬁcial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artiﬁcial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed.
Robust Interactive Learning

In this paper we propose and study a generalization of the standard active-learning model where a more general type of query, class conditional query, is allowed. Such queries have been quite useful in applications, but have been lacking theoretical understanding. In this work, we characterize the power of such queries under two well-known noise models. We give nearly tight upper and lower bounds on the number of queries needed to learn both for the general agnostic setting and for the bounded noise model. We further show that our methods can be made adaptive to the (unknown) noise rate, with only negligible loss in query complexity.
Asthma , chronic obstructive pulmonary disease , or both ? Diagnostic labeling and spirometry in primary care patients aged 40 years or more

Correspondence: Hasse Melbye General Practice Research Unit, Department of Community Medicine, University of Tromsø, 9037 Tromsø, Norway Tel +47 776 44816 Fax +47 776 44831 Email hasse.melbye@uit.no Aims: To describe symptoms and lung function in patients registered with asthma or chronic obstructive pulmonary disease (COPD) in primary care and to examine how spirometry findings fit with general practitioners’ (GPs) diagnoses. Methods: Patients aged $40 years with a diagnosis of asthma or COPD registered in the electronic medical record during the previous 5 years were recruited at seven GP offices in Norway in 2009–2010. Registered diagnosis, spirometry results, comorbidity, and reported symptoms were compared. Results: Among 376 patients, 62% were women. Based on Global Initiative for Chronic Obstructive Lung Diseases criteria, a spirometry diagnosis of COPD could be made in 68.1% of the patients with a previous COPD diagnosis and in 17.1% of those diagnosed with asthma only (P < 0.001). The κ agreement between last clinical diagnosis of COPD and COPD based on spirometry was 0.50. A restrictive spirometry pattern was found in 19.4% and more frequently in patients diagnosed with both asthma and COPD (23.9%) than in patients diagnosed with COPD only (6.8%, P = 0.003). Conclusion: The ability of GPs to differentiate between asthma and COPD seems to have considerably improved during the last decade, probably due to the dissemination of spirometry and guidelines for COPD diagnosis. A diagnosis of COPD that cannot be confirmed by spirometry represents a challenge in clinical practice, in particular when a restrictive pattern on spirometry is found.
Comprehensive Analysis of Topic Models for Short and Long Text Data

— The digital age has brought significant information to the Internet through long text articles, webpages, and short text messages on social media platforms. As the information sources continue to grow, Machine Learning and Natural Language Processing techniques, including topic modeling, are employed to analyze and demystify this data. The performance of topic modeling algorithms varies significantly depending on the text data's characteristics, such as text length. This comprehensive analysis aims to compare the performance of the state-of-the-art topic models: Nonnegative Matrix Factorization (NMF), Latent Dirichlet Allocation using Variational Bayes modeling (LDA-VB), and Latent Dirichlet Allocation using Collapsed Gibbs-Sampling (LDA-CGS), over short and long text datasets. This work utilizes four datasets: Conceptual Captions and Wider Captions, image captions for short text data, and 20 Newsgroups news articles and Web of Science containing science articles for long text data. The topic models are evaluated for each dataset using internal and external evaluation metrics and are compared against a known value of topic 'K. ' The internal and external evaluation metrics are the statistical metrics that assess the model's performance on classification, significance, coherence, diversity, similarity, and clustering aspects. Through comprehensive analysis and rigorous evaluation, this work illustrates the impact of text length on the choice of topic model and suggests a topic model that works for varied text length data. The experiment shows that LDA-CGS performed better than other topic models over the internal and external evaluation metrics for short and long text data.
Testing Low-Degree Polynomials over

We describe an efficient randomized algorithm to test if a given binary function "! $# % &(') *! $#+% is a low-degree polynomial (that is, a sum of low-degree monomials). For a given integer ,. -/# and a given real 0213 , the algorithm queries at 425 687 ,"9;:=< points. If is a polynomial of degree at most , , the algorithm always accepts, and if the value of has to be modified on at least an 0 fraction of all inputs in order to transform it to such a polynomial, then the algorithm rejects with probability at least >=? @ . Our result is essentially tight: Any algorithm for testing degree, polynomials over ACB25D> < must perform E 5 6 7 >=:F< queries.