With the continuous development of precise measurement and precise tamping (PMPT) technology on Chinese railway conventional speed lines, the efficiency of machinery tamping operation and the quality of the track have been effectively improved. A variety of PMPT modes have been tried in the field operation, however there are some differences in the operation effect. The quality of the tamping operation is affected by multiple factors. In order to identify the key factors affecting the operation quality and to further improve the tamping operation effect, this paper establishes both the database of PMPT operation modes and the selection index system for evaluating the operation effect. Based on mega multi-source heterogeneous data and track geometry inspection data, this paper adopts the Back Propagation Neural Network (BPNN) prognosis model to quantify and sort the main factors affecting the effect of PMPT. The research results show that the initial quality of the track before tamping, whether the stabilizing operation or the tamping modes have great influence weights. It can scientifically guide the field operation to control the key factors and put forward some practical suggestions for promoting the field application of PMPT and the optimization of operation modes on the conventional speed lines. Full article

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process. Full article

An ultra-wideband (UWB), geometrically simple, compact, and high-gain antenna with a WLAN notch band is presented for future wireless devices. The antenna is printed on the top side of the Rogers RT/Duroid 5880 substrate and has a small dimension of 10 mm × 15 mm × 0.254 mm. The primary radiator of the proposed coplanar waveguide-fed monopole antenna is comprised of a rectangular-shaped structure initially modified using a slot, and its bandwidth is further enhanced by loading a Y-shaped radiator. As a result, the antenna offers a –10 dB impedance matching bandwidth of 11.55 GHz ranging from 3–14.55 GHz, covering globally allocated C-, S-, and X-band applications. Afterward, another rectangular stub is loaded in the structure to mitigate the WLAN band from the UWB spectrum, and the final antenna offers a notched band spanning from 4.59 to 5.82 GHz. Moreover, to validate the simulated results, a hardware prototype is built and measured, which exhibits good agreement with the simulated results. Furthermore, the proposed work is compared to state-of-the-art antennas for similar applications to demonstrate its design significance, as it has a compact size, wider bandwidth, and stable gain characteristics. Full article

The relationship between technology and society is an ever-changing dynamic, but one in which education is a key domain. In educational practice, the use of computer technology has increasingly become an inseparable part of teaching students in numerous ways across the world. The COVID-19 global pandemic accelerated this dramatically, with online teaching environments becoming the sole way for students to access education for extended periods of time. This shift to online teaching also required that teachers learn new skills and deal with new challenges. Based on mixed-methods research conducted with 20 teachers from an established content and language integrated learning school in mainland China, this research paper investigates the different challenges and problems that were faced by content and language integrated learning teachers in their experiences of online teaching and, in tandem with wider content and language integrated learning and technology-enhanced language learning literature, develops some potential solutions for future use. Full article

Optimizing the aerodynamic shape of an airfoil is a critical concern in the aviation industry. The introduction of flexible airfoils has allowed the shape of the airfoil to vary, depending on the flight conditions. Therefore, in this study, we propose an algorithm that is capable of robustly optimizing the shape of the airfoil based on variable parameters of the airfoil and flight conditions. The proposed algorithm can be understood as an optimization method, which employs the adjoint method, a powerful tool for estimating the sensitivity of the model output to the input in numerous studies. From an aerodynamic perspective, the development of shape geometry is a crucial step in airfoil development. The study used NACA-4 digit airfoils as input for the initial assumption and the range of shape change. The optimal shape was found using the proposed algorithm by defining one NACA profile as the initial value and another NACA profile as the limit for the optimized shape, considering the aerodynamic coefficients and flight conditions. However, morphing airfoils have certain deformation limitations. As an innovation in the algorithm, bounds were defined for the shape change during optimization so that the result can be constructed within the capabilities of the morphing wing. These bounds can be adjusted (depending on the capabilities of the airfoils). To validate the proposed algorithm, the study compared it with a previous flow solver for the same airfoil. Full article

Strawberries are rich in bioactive compounds that may be of health importance. The technological process often significantly reduces the content of such compounds in the product. The study aimed to compare the effect of enzymatic, ultrasonic and thermal mash treatment on the content of ascorbic acid, anthocyanins, phenolic compounds and the antioxidant activity of strawberry juice. In addition, the effect of increased temperature assisting ultrasonic mash treatment and the use of a vacuum for a short period to remove air from the mash during pectinolysis was investigated. A significant increase in the efficiency of juice pressing was obtained for enzymatic treatment (by 40%), thermal and thermosonication (16%). It was found that the applied methods yield different results depending on the tested compounds. In the case of anthocyanin, the most effective method was thermosonication, which contributed to a 40% increase in their content. The enzymatic and thermal methods resulted in a two-fold increase in the content of phenolic compounds. The antioxidant activity of the juice from the treated mash (regardless of the method used) was significantly higher than samples from the untreated mash. A significant correlation (r = 0.77) was noted between antioxidant capacity and non-anthocyanin phenolic compound content in the tested juices. Full article

This study aimed to evaluate prognostic factors associated with nidus obliteration following stereotactic radiosurgery (SRS) for cerebral arteriovenous malformations. From January 2001 to January 2018, 119 patients who underwent SRS with AVM were studied to analyze major prognostic factors (age, prescription dose (Gy), volume (mm³), nidus size (cm), and Spetzler–Martin (SM) grade) for nidus obliteration. A random forest and tree explainer was used to construct a predictive model of nidus obliteration. The prognostic factors affecting nidus obliteration from most to least important were age, nidus size, volume, total prescription dose, and SM grade, using a predictive model. In a specific case for nidus size (1.5 cm), total dose (23 Gy), and SM grade (2), the result showed a high obliteration score of 0.75 with the actual obliteration period of 6 months spent; the mean AUC was 0.90 in K-fold cross validation. The predictive model identified the main contributing factors associated with a prognostic of nidus obliteration from linear accelerator-based SRS for cerebral AVM. It was confirmed that the results, including the prognostic factors, are potentially useful for outcome prediction for patient and treatment. Full article

The low-efficiency rate of urban wastewater (UWW) treatment generates tons of discharged water with a high concentration of pollutants, pathogens and antibiotic-resistance genes (ARGs). Microalgal systems may be a green alternative to be implemented as a UWW polishing treatment. This study assessed the ability of Chlorella vulgaris and UWW autochthonous microalgal species (AMS) to simultaneously remove PO₄–P, and reduce the proliferation of coliforms and ARGs. AMS seems to be more promising due to: (i) the higher specific growth rate, μ_max (0.687 ± 0.065 d⁻¹); (ii) efficient PO₄–P removal (92.62 ± 0.10%); (iii) faster reduction of coliforms proliferation achieving concentrations below the limits of quantification (6 d); (iv) the reduction of intl1 and the ARGs sul1 and bla_TEM abundance in ca. of 70.4%, 69.2%, and 75.7%, respectively (9 d); and (v) the additional reduction of these genes in ca. of 97.1%, 94.2%, and 99.9%, respectively, after 5 d storage in the dark and at room temperature. Results also revealed that the high pH values in both microalgal systems (due to microalgal growth) were highly correlated with a reduction in the proliferation of coliforms, including Escherichia coli. In conclusion, using AMS as a final polishing treatment of UWW seems to be very promising. Full article

Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command. Full article

This paper takes the gas cabin in the utility tunnel in the Xuwei District of Lianyungang as the study object. Based on the computational fluid dynamics (CFD) theory, a simulation model of the gas cabin in the utility tunnel is established. The propagation law of methane leakage and diffusion and the characteristics of methane explosion shock wave propagation were simulated under different conditions of the gas cabin. These conditions are the presence or absence, spacing and height of the air baffle. The results show that: (1) the gas baffle can limit the propagation of methane at the top of the gas cabin and slow down the velocity of diffusion so as to increase the concentration of methane near the baffle and speed up the time for the monitor to reach the alarm concentration; (2) the first peak pressure and the second peak pressure generated in the middle of the gas cabin are smaller than that when the gas baffle is installed. The gas baffle has the function of blocking the propagation of shock waves. However, due to the installation of the gas baffle, the superposition of the shock wave will make the pressure surge at the gas baffle; and (3) combined with the simulation results, it is recommended that the gas baffle spacing is not less than 50 m and the height setting is not greater than 0.5 m. Full article

The academic community has extensively explored, over the years, heterocyclic compounds of the carbazolic motif [...] Full article

There is an increasing interest in applying artificial intelligence techniques to forecast epileptic seizures. In particular, machine learning algorithms could extract nonlinear statistical regularities from electroencephalographic (EEG) time series that can anticipate abnormal brain activity. The recent literature reports promising results in seizure detection and prediction tasks using machine and deep learning methods. However, performance evaluation is often based on questionable randomized cross-validation schemes, which can introduce correlated signals (e.g., EEG data recorded from the same patient during nearby periods of the day) into the partitioning of training and test sets. The present study demonstrates that the use of more stringent evaluation strategies, such as those based on leave-one-patient-out partitioning, leads to a drop in accuracy from about 80% to 50% for a standard eXtreme Gradient Boosting (XGBoost) classifier on two different data sets. Our findings suggest that the definition of rigorous evaluation protocols is crucial to ensure the generalizability of predictive models before proceeding to clinical trials. Full article

Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the “main speaker voice” is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications. Full article

Human–computer interaction (HCI) research involves the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. As HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant. This Special Issue, “Human Computer Interactions 2.0”, presents 11 excellent papers about topics related to human–computer interactions. It aims to provide a broad international forum for world researchers, engineers and professionals in human–computer interaction research for the discussion and exchange of various scientific, technical and management discoveries across the world. Full article

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset. Full article