Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed. Full article

In the recent decade, many research efforts in robotic society have considered motion planning for maintaining connectivity in networked robotic system (NRS) by exploiting robotic autonomous mobility. On the other hand, cognitive radio (CR) in the communication society aims at fully exploiting the spectrum in a wireless network, while the motion planning is seldom considered, as a wireless device itself may not decide where to go. In this article, joint communication–motion planning (JCMP) is proposed to boost the capability of NRS by exploiting both the adaptive communications and mobility control of autonomous robots. Specifically, we propose a JCMP framework for NRS, which aims at jointly exploiting the degree-of-freedom in mobility, space, time, frequency and power dimensions from both the motion and communication components. Afterward, we design and evaluate JCMP in a conventional and a CR-relay-assisted robot system, which shows the capability of JCMP in improving the performance of NRS. Finally, we summarize the proposed JCMP-enabled NRS framework and provide a series of future research directions. Full article

Magnetorheological (MR) actuators are semi-active devices controlled by magnetic stimuli. The technology has been commercialized in the automotive industry or high-quality optical finishing applications. It harnesses the rheology of smart fluids to result in the unique application of the material. By a wide margin, the most common example of an MR actuator is a flow-mode single-tube housing with a control valve (electromagnet with a fixed-size air gap filled with the MR fluid) operating in a semi-active vibration control environment. The analysis of the prior art shows that the developed configurations of MR valves vary in size, complexity, the ability to generate adequate levels of pressure, and the interactions with the MR fluid’s rheology resulting in various performance envelopes. Moreover, miscellaneous testing procedures make a direct valve-to-valve comparison difficult. Therefore, in this paper we present a detailed and systematic review of MR control valves, provide classification criteria, highlight the operating principle, and then attempt to categorize the valves into groups sharing similarities in the design and performance envelope(s). Moreover, a simple performance metric based on the shear stress calculation is proposed, too, for evaluating the performance of particular valving prototypes. In the review, we discuss the key configurations, highlight their strengths and weaknesses and explore various opportunities for tuning their performance range. The review provides complementary information for the engineers and researchers with a keen interest in MR applications, in general. It is an organized and and critical study targeted at improvements in the categorization and description of MR devices. Full article

The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there have been several studies that use type-1 fuzzy membership functions to mine spatial fuzzy co-location patterns, there is great uncertainty associated with such membership functions. To address this problem, we propose a spatial fuzzy co-location pattern mining method based on interval type-2 fuzzy sets. First, we collect the interval evaluation values of the interval data of attribute information from experts to form granular data. Next, the original type-1 fuzzy membership function is extended to a granular type-2 fuzzy membership function based on elliptic curves. We use a gradual method to adjust the parameters of the fuzzy membership function so that its footprint of uncertainty satisfies both the connectivity and the given confidence. Based on this granular type-2 fuzzy membership function, we fuzzify the attribute information of instances and define the concepts of fuzzy features and fuzzy co-location patterns. A fuzzy co-location pattern mining algorithm based on spatial cliques is then proposed, termed the FCPM-Clique algorithm. In order to improve the efficiency of the algorithm, we propose two pruning strategies. In addition, we extend two classical spatial pattern mining algorithms, the Join-based algorithm and the Joinless algorithm, to mine fuzzy co-location patterns based on interval type-2 fuzzy sets. Many experiments on synthetic and real-world datasets are conducted, the performance of the three algorithms is compared, and the effectiveness and efficiency of our proposed FCPM-Clique algorithm is demonstrated. Full article

Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time. Full article

The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment. Time-interleaved ADC (TIADC) technology can increase the sampling rate without changing the resolution. However, channel mismatch severely degrades the dynamic performance of the TIADC system. For the channel mismatch problem of TIADC, most of the current solutions have preconditions, such as eliminating only some kind of error or increasing the complexity of the hardware. A few methods can estimate multiple errors without changing the hardware circuit. To improve the dynamic performance of the TIADC system, on the basis of an in-depth study of the channel mismatch error of TIADC, according to the system identification theory, an identification model is designed to characterize the frequency characteristics of TIADC. Using the system observation data, the transfer function parameters of the system are recursively estimated. By constructing and verifying the identification model of the TIADC system, and then through the frequency domain correction method, a digital compensation filter is established to complete the error correction of the system. The test results of the four-channel TIADC high-speed data acquisition system show that the actual input and output characteristics of the test system are consistent with the nature of the identification model. The four channels of the TIADC system are provided by four sub-channels of two AD9653 chips, and the highest sampling rate of a single channel is 125MSPS. For sinusoidal input signals from 20 MHz to 150 MHz, the sampling system can achieve a signal-to-noise ratio (SNR) above 56.8 dB and spurious free dynamic range (SFDR) above 69.7 dB. The dynamic performance of the sampling system is nearly equivalent to that of its sub-ADC; the feasibility of the model identification method and the effectiveness of error correction are verified in simulation and experiment. Full article

Materials such as silicon, copper, gold, and aluminum exhibit strong absorption and scattering characterization under short-pulsed laser irradiation. Due to the photoelastic effect and thermoelastic relaxation, the focal area may induce a local modulation in the refractive index, which can be detected with the intensity reflection coefficient perturbation. Normally, the thermal effect causes a weak refractive index change and is negligible, compared with the pressure-induced effect in most photoacoustic analytical systems. In this study, we present a theoretical model with the whole process of absorbed energy conversion analysis for the refractive index perturbation induced by both thermal effect and photoacoustic pressure. In this model, data analysis was carried out on the transformation of the energy absorbed by the sample into heat and stress. To prove the feasibility of this model, numerical simulation was performed for the photothermal and photoacoustic effects under different incident intensities using the finite element method. Experiment results on silicon and carbon fiber verified that the refractive index change induced by the photothermal effect can be detected and be incorporated with pressure-induced refractive index change. The simulation results showed very good agreement with the results of the experiments. The main aim of this study was to further understand the absorption and conversion process of short-pulsed light energy and the resulting photothermal and photoacoustic effects. Full article

The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics. Full article

Codonopsis ussuriensis (Rupr. and Maxim.) Hemsl is a medicinal herb commonly found in Korea, Japan, and the Russian Far East. However, its chemical composition and bioactivity have not been extensively studied. Thus, in this study, we aimed to examine and characterize the major components of the plant’s roots. Liquid chromatography with high-resolution mass spectrometry (LC-HRMS) was used to identify the components of the crude extracts. The compounds that were identified were named ussurienoside I (1) and tangshenoside I (2). The identified compounds were tested in vitro for inhibitory action against bacterial neuraminidase, and the mechanisms of inhibition were revealed. Compound 1 significantly inhibited bacterial neuraminidase activity in a dose-dependent manner (IC₅₀ = 56.0 μM). The neuraminidase inhibitor (compound 1) exhibited mixed type-I kinetic characteristics. The LC-HRMS data showed that the root extracts contained eight compounds. The findings of this study may aid in the development of bacterial neuraminidase inhibitors with medicinal potential. Full article

The aim of this study is to assess the surface alterations of four reciprocating instruments before and after the shaping of four resin-simulated root canals. The following four different reciprocating instruments are selected: 10 Reciproc Blue (RB25), 10 WaveOne Gold (WOG), 10 EdgeOne Fire (EOF) and 10 recently introduced instruments OneRECI (OR), for a total of 40 new instruments. Before root canal shaping, each instrument is mounted on a stub in a standardized position and observed using a scanning electron microscope (SEM) to detect any surface alteration, such as microfractures, metal defects, deformations, blunt and disruption of cutting edges, debris, pitting and tip flattening. Micrographs are acquired at the level of the tip, 4 mm, 8 mm and 12 mm from it. After that, each instrument is used in four simulated resin root canals. SEM observation was repeated after the simulated clinical use to assess the wear resistance of the instruments. Surface alterations are registered before and after instrumentation and statistical analysis is performed using a Chi-Square test to verify homogeneity of defects distribution and GLM to evaluate the differences of RMS at baseline and after use for both groups (α level 0.05). Before simulated clinical uses, no alterations are found except for three cases of EOF with metal strips in correspondence to the tip, disruption of the cutting edge of WOG and oily spotting on two different OR. After simulated clinical use, EOF shows a statistically significant difference in terms of spiral distortion and flattening of the cutting edges. The OR shows the highest presence of debris despite the ultrasonic cleaning procedures. No instrument fractures are observed. EOF should be discarded after four clinical uses and carefully inspected after each insertion into root canals. Moreover, tough attention should be paid during disinfection and cleaning procedures after instrumentation considering the copious debris detected in each instrument, particularly in OR. Full article

Nowadays, in Europe, several infrastructures, such as bridges, viaducts, and maritime structures, are in an advanced state of degradation. Therefore, novel repair/rehabilitation techniques are sought. Recent advances in ultra-high-performance fibre-reinforced cement-based composites (UHPFRC) represent a significant step towards resilient structures. In addition to their remarkable mechanical properties (compressive strength > 150 MPa), they present extremely low permeability and, as a premise, very high durability. Despite their relatively high cost, UHPFRC can be a competitive solution for rehabilitation/strengthening applications where smaller volumes are needed. UHPFRC applied in thin layers (with or without reinforcement) can replace carbonated and/or cracked concrete acting as a protective watertight and/or strengthening layer. The structural capacity increases (stiffness, ultimate strength), and the durability is expected to improve significantly while keeping cross-sectional dimensions. Additional advantages are expected, such as reduced intervention time, fewer maintenance routines, reduced life-cycle cost, and longer service life. Although much of the focus on UHPFRC has centred on mechanical and/or structural performance, durability is inevitably linked with mechanical properties. The current work evaluated the durability of non-property and greener UHPC concerning expansive reactions, alkali-silica reactions and expansion due to external sulphates, by macro and micro-scale integrative study. Linear expansion tests were performed in UHPC specimens according to ASTM C 1260 and LNEC E−364. At the macro level, no deleterious expansion due to ASR or external sulphate occured. Expansion due to ASR was 0.0018% after 14 days of immersion in an alkali-rich environment, and no expansion was recorded regarding sulphate attack. However, SEM analysis reveals reactive products of ASR and sulphate attack, namely, ASR gel and ettringite, respectively, in UHPC specimens. Full article

Remanufacturing is considered as the most preferable recovery option. The objective of this paper was to identify the environmental inventory of a remanufacturing initiative. A window-mount air conditioner (AC) was selected as a case study to verify the environmental benefits of remanufacturing a product characterized by high energy consumption during its life cycle phases. A life cycle approach was followed. The research aims to provide a reference case in the remanufacturing of products characterized by energy intensive use, of which an AC is one of them. The assessment was conducted with the guidance of the ISO 14044-2006 life cycle assessment (LCA). The significance of this study lies in the fact that it provides an authentic example wherein the environmental inventory of the remanufacturing process is identified and documented. It can be used in further studies to compare the environmental burden of remanufacturing processes to the process of manufacturing a new AC. The developed life cycle analysis can be utilized to help AC manufacturers make decisions about the overall environmental performance of their products if it goes through the remanufacturing cycle. Full article

Tunable metamaterials belonging to the class of different reconfigurable optical devices have proved to be an excellent candidate for dynamic and efficient light control. However, due to the consistent optical response of metals, there are some limitations aiming to directly engineer electromagnetic resonances of widespread metal-based composites. The former is accomplished by altering the features or structures of substrates around the resonant unit cells only. In this regard, the adjusting of metallic composites has considerably weak performance. Herein, we make a step forward by providing deep insight into a direct tuning approach for semiconductor-based composites. The resonance behavior of their properties can be dramatically affected by manipulating the distribution of free carriers in unit cells under an applied voltage. The mentioned approach has been demonstrated in the case of semiconductor metamaterials by comparing the enhanced propagation of surface plasmon polaritons with a conventional semiconductor/air case. Theoretically, the presented approach provides a fertile ground to simplify the configuration of engineerable composites and provides a fertile ground for applications in ultrathin, linearly tunable, and on-chip integrated optical components. These include reconfigurable ultrathin lenses, nanoscale spatial light modulators, and optical cavities with switchable resonance modes. Full article

Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures. Full article

The duration of dust days (DDD) is one of the most important parameters that may worsen the effects of the presence of dust in the affected areas. Persistent pollution over consecutive dusty days can have particularly negative effects on the human respiratory system. The present analysis was conducted in Central Iran, where the phenomenon of dust is one of the most important problems. In this study, using dust codes recorded at 35 synoptic stations, the homogeneity of DDD across the region was investigated using the L-moments method. Then, characteristics of DDD over the period 1999–2018 were calculated. The results showed that the region is statistically homogeneous. Furthermore, Zabol and Zahdan are the stations worst affected, with the longest durations of 22 and 21 days in 2014. Additionally, the values of DDD with return periods of 5, 10, 25, and 50 years were calculated using fitted statistical distributions and kriging and mapped. Finally, using the K nearest neighbor method the most important factor affecting DDD of the spatial characteristics, including longitude, latitude, elevation, average daily temperature (t_m), dew point (t_d), wind altitude (u), maximum wind speed (ff_max), and direction of the fastest wind (dd_max), was determined. It was found that the southeastern parts of the study area are affected by the longest dust storm duration in all return periods; over longer return periods, long dust storms are also found in the central parts, especially the central desert of Iran. Therefore, these areas should be given priority in fighting and controlling wind erosion. Furthermore, the results showed that the maximum wind speed has the greatest effect on DDD. Full article