Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Adversarial Hybrid Domain-Adaptation Network for Varying Working Conditions Fault Diagnosis of High-Speed Train Bogie

Because of uncertain working conditions and the lack of labels for most conditions, transfer learning has great potential in fault diagnosis for high-speed train bogies. This article proposed a novel domain adaptation model for intelligent diagnosis under varying working conditions, namely, deep adversarial hybrid domain-adaptation network (DAHAN). In the proposed method, to further improve the transfer learning performance at a low signal-to-noise (SNR) ratio, the local domain adaptation by Wasserstein distance is developed to align category-level features which are combined with global domain adaptation to hybrid domain adaptation. In addition, an optimized training procedure is constructed to improve the stability and training speed of the proposed adversarial domain adaptation model to enhance the efficiency and real-time performance of the diagnosis method. The effectiveness and advantage of DAHAN are verified by the Case Western Reserve University (CWRU)-bearing dataset. Finally, trained by train bogie gearbox fault dataset, the proposed method shows a great performance improvement and avoids the mismatch of weak fault features, proving its applicability on train bogie fault diagnosis.
Research on second order complementary filter strapdown inertial navigation algorithm based on information fusion

Inertial navigation is widely used in many fields, such as land, underwater and aviation. Aiming at the shortcoming that the cumulative error of the strapdown inertial navigation system is difficult to be effectively corrected during long-term navigation, it cannot meet the accuracy requirements in the long-term navigation process. In this paper, based on the strapdown inertial navigation, according to the global distribution of the geomagnetic field and the unique characteristics of the magnetic field strength, a second-order complementary filter strapdown inertial navigation algorithm is proposed to overcome the shortcoming that a single inertial navigation system cannot satisfy long-term precise navigation. And the improved algorithm is simulated and analyzed.
Nonlinear Energy-Harvesting for D2D Networks Underlaying UAV With SWIPT Using MADQN

Energy Efficiency (EE) has become an essential metric in Device-to-Device (D2D) communication underlaying Unmanned Aerial Vehicles (UAVs) Among the several technologies that provide significant energy, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve EE. However, it is a challenging task to study the EE under nonlinear energy harvesting (EH) due to the limited sensitivity and the composition of the nonlinear circuit. Moreover, when D2D users transmit information using the EH from UAVs, interferences to cellular users occur and deteriorate the throughput. To tackle these problems, we leverage concepts from artificial intelligence (AI) to optimize EE of UAV-assisted D2D communication. Specifically, multi-agent deep reinforcement learning was proposed to jointly maximize throughput and EE, where the reward function is defined in terms of the introduced goal. Simulation results verify the supremacy of proposed approach over traditional algorithms.
Inertial Sensors for Human Motion Analysis: A Comprehensive Review

Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out on eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies, and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work.
Pourquoi les points périodiques des homéomorphismes du plan tournent-ils autour de certains points fixes ?

Soit. f un homeomorphisme du plan qui preserve l'orientation et qui a un point periodique z* de periode q ≥ 2. Nous montrons qu'il existe un point fixe z tel que le nombre d'enlacement de z* et z ne soit pas nul. En d'autres termes, le nombre de rotation de l'orbite de z* dans l'anneau R 2 \ {z} est un element non nul de R/Z. Ceci donne une reponse positive a une question posee par John Franks.
Antecedents of Social Media Characteristics on users’ Attitudes toward E-Services: Conceptual framework

The present study identified the impact of social media characteristics on the attitude of consumers towards e-services furnished by the Greater Amman Municipality. Quantitative descriptive methodology involving 700 e-service users in the Greater Amman Municipality was used the study population. SPSS (23) statistical software and SEM-designed software AMOS 20 was applied for data analyses. Antecedents were the direct major antecedents in the adoption of Greater Amman municipality e-services (reliability, security, website design, ease of use and usefulness). This study proposed looking into other social media traits such as privacy (among others), to ascertain their impact on how GAM uses electronic services.
All-Fiber 770 nm Laser Stabilized With Bichromatic Absorption Spectroscopy

A high-power, coherent, and frequency-stable 770 nm laser is vital for experiments in atom physics. This letter presents an all-fiber 770 nm laser based on second harmonic generation (SHG) and frequency-stabilized using the R2 absorption line of H13C14N. This stabilization technique utilizes the acoustic effect to generate a dispersion-like error signal with a steep slope. Moreover, a cascaded gas cell configuration is utilized to double the frequency locking range. After frequency stabilization, the frequency offset has been effectively shifted to close zero. The laser provides an output power of 192 mW with a side mode suppression ratio (SMSR) larger than 35 dB, and a significant frequency noise suppression over 30 dB in the range of 10 to 103 Hz. The integral linewidth of the 770 nm laser is 28.1 kHz, which outperforms most semiconductor lasers. The proposed all-fiber configuration offers a new compact laser source potentially useful for quantum measurement.
Transgender Health between Barriers: A Scoping Review and Integrated Strategies

Transgender people have garnered attention in recent years. They have different health problems; the fact, however, that they belong to a minority means that this is characterized by complex mechanisms of stigmatization. This paper aims to analyze the current literature on the barriers to health services encountered by transgender people. This scoping review is based on the following research questions: (1) What are the main barriers to health care encountered by transgender people? (2) Is it possible to organize these barriers according to a macro-, meso- and microanalysis approach? (3) What are the main characteristics of the barriers to health care encountered by transgender people? (4) Are there significant relations between the different types of barriers? The review was undertaken following the PRISMA extension for scoping reviews. In total, 32 studies were included from which three types of barriers with different subcategories were identified: health system barriers, social barriers, and individual barriers. In conclusion, due to the complexity of gender issues and barriers to health care, a multidisciplinary approach is necessary. In this regard, some integrated strategies to reduce barriers to health care for transgender people are proposed.
3D Printable Origami-Inspired Pneumatic Soft Actuator with Modularized Design

Pneumatic soft actuators are widely used in various applications. Recent years, pneumatic origami actuators were studied intensively because of the advantages of high energy efficiency, large deformation, and rich deformation patterns. However, the fabrication of pneumatic driven origami-based soft robot is a challenging task, in which air leakage can result in disfunction of the robots. In this paper, we propose an alterative approach to fabricate pneumatic origami structure using an industrial 3D printer which is capable of 3D printing liquid silicone rubber (LSR). We took the Kresling pattern as an example and explored the essential parameters of printer setting. We directly printed the 3D folded structure (origami-inspired structure) instead of 2D folding to simplify the fabrication process. In addition, to maximize the design freedom, we propose modularized design of the origami-inspired actuator. After fabricating the basic modules, robots can be freely assembled according to different applications. Finite element simulations and experiments were conducted to characterize the basic modules in terms of deformation, rotation angle, and generated force. Finally, a robotic gripper were developed using the basic modules and grasping experiments were conducted to prove the concept of modularization.
Unveiling Additional Ambient Degradation Issues of Phosphorene FETs Under Laser Exposure and Positive Gate Bias

Despite many extraordinary properties, phosphorene is not favorable for device applications due to its gradual degradation in ambient environmental conditions. The degradation process is mainly initiated by oxygen molecules attacking the lone pair of phosphorus atoms. However, the same is assisted by additional factors like water molecules, phosphorus vacancy, and negative gate bias. This work extended the journey further and revealed the role of positive gate bias and laser exposure on phosphorene degradation in the ambient environment. The work shows that in the case of phosphorene-based Field Effect Transistors (FETs), the material's degradation due to oxygen molecules can also be influenced by excess electrons due to positive gate bias in the channel. The first-principles molecular dynamics (MD) computations and Raman characterizations show that phosphorene degrades faster under positive gate bias (excess electron) than in pristine conditions (unbiased). The rapid degradation is mainly due to the enhanced oxidation by the excess electrons in the channel. The computational findings are experimentally verified using Raman characterization over phosphorene FETs. Additionally, the work also discusses the role of laser light on phosphorene degradation. The laser light does not play a direct role in pristine phosphorene degradation. However, the same assists the degradation process by etching the residual oxide formed over the phosphorene surface due to its ambient degradation. Our study reveals a unique reliability issue related to phosphorene FETs that gives a broader picture of the limitation of the material in the technology applications.
Free Vibration of Single-Walled Carbon Nanotubes Using Nonlocal Truncated Timoshenko-Ehrenfest Beam Theory

Carbon nanotubes with their outstanding mechanical, physical and electrical properties have stimulated a significant amount of scientific and technological research due to their uniqueness compared to conventional materials. As a result, an extensive study on their mechanical properties has been conducted, and the static and dynamic behavior of single- walled and multi-walled carbon nanotubes has been examined using Euler-Bernoulli and Timoshenko beam models. The main objective of this paper is to study the free vibration behaviour of single-walled carbon nanotubes (SWCNT) using the nonlocal truncated Timoshenko beam theory. According to the Hamilton principle, the equation of motion of Timoshenko single-walled carbon nanotubes is calculated taking into account the truncated theory; and the general corresponding boundary conditions are derived. Finally, some numerical examples are performed to evaluate the effects of the nonlocal coefficient and the length of the nanotube. The obtained results are validated by comparing them with those found in the literature, and they show the accuracy and efficiency of the developed model. Particularly, the results demonstrate that the present formulation is highly efficient and capable of satisfactorily describing the behavior of nanobeams.
Deep Reinforcement Learning for antennas positioning

In this paper we study the position planning of cellular antennas. Informally, we try to find the best positions of antennas by having the maximum coverage and the minimum cost. Several methods were used to solve this problem. Since we have two main objectives, metaheuristics, such as genetic algorithms, simulated annealing, particle swarm optimization, … were used. Actually, artificial intelligence, and more particularly deep learning are compared to other methods in this context. Our work is about applying reinforcement learning on this problem. In this vein, we tried to tune some reinforcement learning methods to solve this problem. We found that the SARSA agent with the EpsGreedy QPolicy is the best option for these kinds of problems.
Ultrasonic scatterer-property images of the eye and prostate

The authors have developed tissue assay procedures employing ultrasonic spectrum analysis of radiofrequency echo signals. They have derived a statistical framework for applying these methods and a theoretical framework that relates measured spectral parameters to physical scatterer properties. The authors have employed their results to generate cross-sectional images that depict estimates of local scatterer properties related to the effective sizes, concentrations, and relative acoustic impedances of sub-resolution tissue constituents. These images are being investigated clinically to detect and localize prostate cancer. Scatter-property images obtained with very high frequency ultrasound are being used for evaluating anterior structures of the eye in order to diagnose ocular diseases, including glaucoma, and to monitor their responses to therapy.
A deep ROSAT survey — V. The extragalactic populations at faint fluxes

In the central regions of five deep (21-49 ks) ROSAT Position Sensitive Proportional Counter (PSPC) pointings we have detected 194 soft X-ray sources down to a limiting flux of '" 3 x 10- 15 erg cm- 2 S-l (0.5-2 keY). Spectroscopic follow-up observations at the Anglo-Australian Telescope (AAT) show that the majority of these sources are active galactic nuclei (AGN) (QSOs and Seyfert 1 galaxies); some X-ray luminous galaxies (Lx'" 1042 erg S-l) are also identified at faint fluxes; these may have active nuclei or they may be star-forming galaxies. The intrinsic spectrum of the total X-ray background, including sources, is well fitted above 1 ke V by a power-law model with a photon index of r", 1.6, flatter than the average AGN spectrum in our fields. The normalization of this component at 1 ke V is some 50 per cent higher than an extrapolation of the X-ray background (XRB) spectrum measured above 3 ke V. This extends the XRB spectral paradox to lower energies than has previously been noted, and suggests that some population other than QSOs and Seyfert 1 galaxies may contribute substantially to the XRB at faint fluxes. This is supported by an analysis of the source number count distribution (logN-IogS): the differential 10gN-logS of AGN and unidentified objects becomes a flat power law (y ~ 1.1 ± 0.9,90 per cent error) at S '" 10- 14 erg cm-2 s-\ yielding a contribution of only 45 per cent at the 1-ke V background when integrated down to 10- 16 erg cm-2 S-l; despite the large 10gN-logS uncertainty at faint fluxes the hypothesis that AGN produce all the soft XRB intensity can be ruled out at the 90 per cent confidence level. Galaxies such as those we have detected will contribute 20 per cent of the 1-keV background if they follow a Euclidean 10gN-logS distribution.
Optimal multi-variable flatness control for a cold rolling mill based on a box-constraint optimisation algorithm

As the flatness control system is a multi-variable control system, the key issue for high-precision flatness is the determination of the optimal adjustments of flatness actuators. In order to determine these the first step is the establishment of a multi-variable control model in the flatness control process, by which the actual flatness control problem has been reduced to a non-linear optimisation problem with box-constraints. Around this optimisation problem, characteristics and applicability of current optimisation algorithms were analysed. Based on the coordinate descent method, a new multi-variable optimisation algorithm with global convergence was proposed. In the algorithm, the problem was transformed into a series of univariate optimisations which could be solved by sequentially searching along coordinate directions. In order to make the objective function decrease rapidly, and ensure that each iteration are carried out within the feasible region, the accelerating step method is adopted to determine the iterative step size. A series of feasible points have been obtained through a series of iterations, which make the objective function decrease gradually until the optimal solution has been obtained. Finally, the algorithm has been tested by numerical experiments with production data of actual flatness control process, and applied to the 1450 mm tandem cold mill. Numerical experiments and application show that the proposed algorithm not only can satisfy the requirements of response speed, but also have a good accuracy, which can provide a reference for the realisation of high-precision flatness control processes of cold rolled strip.
Comparison between Two Methods of Skeletal Growth Evaluation: Cervical Vertebrae Maturations and Middle Phalanx Maturation

Abstract Aim Growth measurement has always been essential to identify the best time to employ orthopedic or orthodontic appliances. Optimal timing for orthodontic treatment is strictly linked to the identification of periods of craniofacial growth when treatment is more effective. The aim of this study was to compare two different methods, middle phalanx maturation (MPM) and cervical vertebrae maturation (CVM), used to evaluate the stage of facial growth. Materials and methods The research data was collected from July 2018 to April 2019 at the Dental Clinic of the San Gerardo Hospital in Monza. The study included a sample of 98 patients—46 males and 52 females. For each patient, a latero-lateral teleradiography of the skull and an X-ray on the middle finger of the right hand were obtained. The statistical analysis of the comparison of the stages of skeletal maturation obtained by the MPM and CVM methods was performed using the correlation coefficient for ranks of Spearman. Results A descriptive statistical analysis of the entire sample of 98 patients was performed (mean age of 12.2 years and median of 12.2 years). The average age of females in every single stage of MPM was significantly lower than the average age of males. Of the total sample, 87 patients (88.8%) showed complete agreement between the two methods. Conclusion The results obtained from the statistical analysis of this study allowed us to confirm a satisfactory agreement between the two methods. The intermediate phalanx method is a valid and alternative indicator to CVM for the identification of the puberty growth peak. We can, therefore, consider the MPM method a valid indicator of skeletal maturity. How to cite this article Mirabelli L, Bianco E, Pigato G, et al. Comparison between Two Methods of Skeletal Growth Evaluation: Cervical Vertebrae Maturations and Middle Phalanx Maturation. Int J Clin Pediatr Dent 2023;16(2):327-332.
An Optical Shape Sensor for Integration in Soft Grippers

Soft grippers are very popular for complex gripping tasks, as they can easily grip objects of different shapes. Also, usually they cannot damage gripped objects because of their inherent softness. Additionally, in contrast to rigid grippers no or only very little control effort is needed for the gripping process. However, also for soft grippers sensor feedback can help to improve the gripping process and thus expand the range of applications. Thereby, besides gripping force measurements, especially curvature measurements are of interest to reconstruct the deformation of the gripper. In this contribution, a soft three-finger-gripper with integrated optical shape sensor, based on curvature sensors, is presented. The shape sensor allows to control the gripping process and check if an object is gripped correctly.
Study of artificial immune based schema theorem

Artificial immune is another research focus in artificial intelligence after neural networks, fuzzy logic and evolutionary computation. However, the complete theoretical system in artificial immune is not proposed yet, which hinders its further development. In this paper, Holland’s schema theorem is applied to artificial immune, and the impacts of operators in artificial immune optimization algorithms on schema survival and population diversity are analyzed. Therefore, the schema theorem based on artificial immune is proposed and proved, and is validated in the experiments, which provides the theoretical basis for the designs and improvements of operators in artificial immune algorithms.
A Self-Tuning Controller Based on PI Algorithm

A simple self-tuner algorithm based on the tuning of the proportional band of a conventional Proportional Integral mode action is proposed. The algorithm, based on intuitive reasoning as adopted by process experts is simpler than many of its type specially from the viewpoint of implementation on a 8 or 16-bit microprocessor. It has been tested in a stable second order process model as also in an actual second order thermal process. Test results show that the proposed algorithm has an edge over its conventional discrete counterpart even when the latter is perfectly tuned.
Trailer allocation and truck routing using bipartite graph assignment and deep reinforcement learning

Trailer allocation and truck routing are critical components of truck transportation management. However, in real‐world applications, inter‐influence between selecting the best trailers and trucks, strict fulfillment of Pickup or Delivery (PD) orders, and the size of the fleet are some of the challenges that need to be dealt with in a large truck company. In addition, trailer allocation and truck routing problems are considered to be NP‐hard combinatorial optimization (CO) problems. Therefore, we use deep reinforcement learning (DRL), which has the capability of solving routing problems with a single set of hyperparameters. This is significant progress toward finding strong heuristics for a special case of the trailer allocation to customers and truck routing problem presented in this article. Given a set of trailers, trucks, customers, and orders we propose a novel two‐phase framework based on Bipartite Graph Assignment (BGA) and attention‐based DRL to minimize the total traveling distance traveled from trucks to trailers and then to customers. The BGA heuristic finds the minimum traveling distance from the trailers to the customers based on the edge information and the encoder‐decoder helps DRL to get useful node and graph feature representations and trains the model to find the proper solutions for the trailer allocation and truck routing problem. Our experiments on three different problem sizes showcase the effectiveness of ARTT‐DRL. The results indicate that ARTT‐DRL produces desirable outcomes and has strong generalization capabilities.