Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Banking Service Quality: The Modified E-Servqual Model & Importance-Performance Analysis (IPA)

Technology is reshaping the digital financial ecosystem and the future of Internet banking. The banking sectors are attempting to emphasize service quality as a way to boost customers’ willingness to adopt digital banking. This study inspects e-banking customer satisfaction by utilizing the underlying dimension of e-service quality and aims to determine the gap between perceived and expected service quality value along with an importance-performance analysis to examine the priority scale of service quality attributes on Generation Z’s satisfaction rate with digital banking. As a result, it promises to help the Internet banking industry in decision-making for creating long-term client relationships and obtaining a competitive advantage in the market. This research obtained a total of 397 Indonesian e-banking clients by using a structured questionnaire. The findings reveal that users’ perception of online banking services is satisfied, i.e., valued at 3.96 out of a possible score of 5.00, or 79% level of satisfaction. Nevertheless, users expected to be very satisfied with the digital banking service, scoring 4.21 or 84% satisfaction. As a result, the gap proved to be −0.25. As a way forward, the importance performance graphic distributes Reliability, Personal Needs, User Friendliness, Efficiency, Site Organization, Responsiveness, and Privacy, which need to be enhanced.
Study of the Car Speed Control System Using the Variable Structure Principle

The development of modern technologies gives rise to new complex scientific and practical tasks, among which the development and implementation of the latest automatic control systems as a component of technological processes are particularly important. To date, various methods of synthesis of automation systems have been developed to control processes in linear objects, which cannot be used in the design of non-linear systems. Since the operation of the vehicle speed control system depends on the selected control law and regulator parameters, systems were modeled using different types of regulators and the influence of their parameters on the initial speed and the determination of the moment of transition from one mode to another was investigated. That is why systems with a variable structure have become widespread, which after a certain period check the value of the regulated value and turn on one or another control algorithm.
Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%.
Methods and Models in Management Decision-Making

The article determines that a solidary information society development, even in the conditions of a hybrid war between Ukraine and Russia, is under the influence of the international conflicts’ aggravation, the globalized social and economic environment instability, and the strengthening of social and ecological problems.Under such conditions, Ukrainian enterprises, especially small and medium-sized business entities, encounter problem situations almost on the daily basis, the solution of which requires the effective functioning of the enterprise management system, taking into account the potential of production and socio-economic processes’ informatization. Therefore, the purpose of this article is to develop scientific and methodical approaches to making informed management decisions based on the use of economic and mathematical methods and models along with modern information systems and computer technologies.The article has defined the role of the external and internal environment factors’ influence on the enterprise management. Also, mathematical models for evaluating management decision-making have been developed. In particular, a mathematical model for assessing the manager’s level of readiness to make a management decision has been built, as well as a model for calculating the coefficients of a manager’s readiness to decision-making. A model of management decision-making based on a non-standard situation in complex social and economic conditions has been built, taking into account the possibilities of using information systems and computer technologies, which creates prerequisites for the successful functioning of the administrative system.
Location Selection of Continuous Multi-logistics Nodes Base on Node Covering Algorithm

In order to relieve ground traffic congestion and reduce logistics costs, this paper studies the location problem of continuous multi-logistics nodes based on graph theory, computer technology, and optimization theory. Based on the data of the 14th China Postgraduate Mathematical Contest in Modeling of “Huawei Cup”, this paper solves the location problem of continuous multi-logistics nodes in three steps. Through a self-designed node coverage algorithm based on the PageRank algorithm and greedy strategy, under the constraints of smooth ground and high node utilization, a total of 18 logistics nodes (including 8 first-level nodes and 10 second-level nodes) are determined for the underground logistics network. The spatial location, coverage, actual freight volume, and transshipment rate of primary nodes) are solved. Finally, a strategy for solving the location problem of continuous multi-logistics nodes is constructed.
Instantaneous Sample Indexed Magnitude-Selective Affine Function Based Digital Predistortion for 2×2 MIMO Transmitters

A novel instantaneous sample indexed magnitude-selective affine function model for nonlinear crosstalk (IMSA-NLC) is proposed for digital predistortion of RF power amplifiers with nonlinear crosstalk in 2×2 MIMO system. The core of the model is to use the magnitude-selective affine functions to replace the high-order terms, which is composed of the input signal’s envelope terms, in the 2×2 Parallel Hammerstein (2×2 PH) model. Furthermore, the proposed IMSA-NLC model adds the cross-sectional item of two input signal derived from dual-input memory polynomial (DI-MP) model. The IMSA-NLC can be regarded as using the 2D-IMSA, which classifies signal slices according to magnitude of two inputs, to compensate the nonlinear crosstalk between the transmit circuits. The experimental results show that, compared with some existing classic models, the proposed model has the more superior normalized mean square error and digital predistortion performance when the RF PA is excited by a 20-MHz signal.
Evaluation of the Path Optimization System for Internally Driving Factors in Employment Based on Apriori Algorithm

In our country, the employment problem has become an important issue in our economic construction. EEPOS (Endogenous Employment Path Optimization System) is a tool to analyze the employment situation of the society, which can provide effective policy support for the government. At present, the key to solve the difficult employment problem is to institutionalize the employment support system. Starting from the current situation of China's employment support system, the article gives some reasonable countermeasures against the problems of poor performance, imperfect design, and unclear expression of the current Chinese employment support system in the implementation process. This article discusses the analysis of endogenous employment element path optimization system based on Apriori algorithm, and analyzes the staging problem of endogenous employment element path optimization system based on this algorithm through experiments (e.g., the staging problem of the employment support degree of female doctoral degree), the support degree of female doctoral degree of employment of the tertiary industry is 72.39%, and the degree of certainty is 99.93%, and this system can clearly reflect the law of strong correlation substance).
A P-Band Low-profile 45° Polarized wideband Magnetoelectric Dipole Antenna

In this paper, a P-band low profile wideband 45° polarized magnetoelectric dipole based on tight coupling is provided. By grading the structure of the electric dipole and the vertical metal patch used to work as a magnetic dipole, the low-frequency working bandwidth of the antenna is extended, and the low-frequency cutoff wavelength of the antenna profile height is one-tenth of the low-frequency cut-off wavelength. By opening a series of slits at the end of the dipole arm, the problem of large difference between horizontal and vertical polarization components in large Angle scanning is improved. The simulation results show that the working bandwidth of the antenna unit is 104% for VSWR<2 under the condition of infinite period boundary. Under array conditions, the antenna operating bandwidth is 100%for VSWR<2, and the difference in the polarization component of the ±45° sweep is within 4.5dB.
A High Gain Millimeter Wave Transceiver Array Antenna For Traffic Control Radar

In this paper, a millimeter wave transceiver array antenna operating at 79.5GHz is designed. The basic structure of the antenna is composed of series-fed transmission line, parallel feed network and unequal spoke patch. The layout design of 3T4R antenna is carried out based on the layout principle of virtual array. The receiving and transmitting antenna have impedance bandwidth of 4.2GHz and 4.5GHz (| S11 |<- 10dB) respectively according to the simulation results, with the peak gain of 24.7dBi (79.73GHz) and 26dBi (79.47GHz). In addition, the design also has the characteristics of low sidelobe level and high isolation between ports, achieving good electromagnetic wave reception and reception effect.
MSFF-Net: A Multi-Scale Feature Fusion Network for Hippocampus Segmentation

The hippocampus plays an important role in many brain disorders, such as Alzheimer's disease. Accurate measurement of the hippocampus helps clinical doctors detect lesions and diagnose and treat related brain disorders. Therefore, accurate segmentation of the hippocampus is of great significance for in-depth study of many brain disorders. However, segmentation has been a challenging problem due to the small size, irregular shape, and fuzzy boundaries of the hippocampus with surrounding tissues. This paper focuses on the image features of the hippocampus and proposes a multi-scale feature fusion network to tackle the segmentation challenge of the hippocampus. The network adopts an encoder-decoder structure and replaces conventional convolution operations with residual modules to alleviate the problem of gradient vanishing. In the encoding stage, an adaptive SE Block is used to extract feature information of the image. Additionally, an ASPP module is added at the bottleneck layer to capture feature maps at different scales with various dilation rates, thereby improving the segmentation performance for hippocampi of different shapes and sizes. We also designed a Hybrid Attention Fusion module to selectively aggregate feature information from the encoding and decoding stages. This module generates attention maps using channel attention and spatial attention, allowing us to remove redundant information in the skip connections. Finally, the spatial information is restored through the decoder to generate the final prediction. We conducted extensive quantitative comparisons on a publicly available brain MRI dataset, MICCAI 2012, to evaluate the effectiveness of our algorithm. The experimental results show that our model (MSFF-Net) achieves a Dice coefficient of 92.17%, which is 5.88% higher than the traditional UNet model. These results demonstrate that our proposed model has high potential for hippocampus segmentation.
A Miniaturized High Efficiency Microwave Rectifier Within Wide Power Range

In order to solve the problems of low rectification efficiency, narrow wide-power and high-efficiency rectification range and large device size, this design adopts a dual-branch dual-load band-stop circuit structure, and improves the traditional impedance matching and pass-through filter structure. The traditional rectifier circuit is matched by multiple branches, which increases the insertion loss and circuit size. In this design, a microstrip line is added to the front end of the two branches to adjust the real part resistance value, and the band-stop structure is used to reduce the imaginary part resistance value and suppress the high-order harmonics. The effect of wide range matching can be achieved by only one matching in the front end, and the λ/4 microstrip line is added to the high-power branch to change the branch resistance change trend, and the power is reasonably distributed by the resistance change. In addition, the straight-through filter structure is simplified, and a miniaturized rectifier circuit structure is realized, which achieves a wide power range, high efficiency rectification and miniaturization design purpose. At a frequency of 5.8 GHz and an input power of 12.9 dBm, the rectification efficiency of the rectifier reaches 68.6%. Furthermore, over the input power range of -0.4 dBm to 25.2 dBm, the rectification efficiency remains above 60%. The S11 simulation curve of the rectifier also indicates that the reflection coefficient stays below -10 dB from 0 dBm to -30 dBm. Overall, the goal of achieving high-efficiency rectification in a wide power range is successfully achieved through this proposed design.
Research on Pose Measurement and Modelling Technology of Malfunctioning Spacecraft Based on SLAM

In order to realize the on-orbit servicing of malfunctioning spacecraft and space debris removal, it is necessary to relative pose measurement and establish the fault model accurately. Aiming at the problem of pose measurement and modeling for non-cooperative malfunctioning spacecraft, a method based on monocular visual Simultaneous Localization and Mapping (SLAM) is proposed in this paper. The algorithm is mainly used to extract feature points, match and calculate the pose relation between the camera and the malfunctioning spacecraft, measure the pose change of the malfunctioning spacecraft, and use the relative pose and image sequence to build a three-dimensional model of the malfunctioning spacecraft. The results of ground experiments show that the accuracy of pose measurement and modeling is high, which verifies the feasibility of SLAM technology in the field of on-orbit servicing for non-cooperative target spacecraft.
Design and Qualification of a High-Speed Low-Power Comparator in 40 nm CMOS Technology

This paper presents the design methodology, test setup and experimental qualification results of a high-speed low-power threshold comparator in 40 nm CMOS technology intended for the registry of particles landing on a PIN-detector surface in particle detector readout electronics. The operation of the designed comparator is experimentally qualified for ideal digital pulses and analog signals generated by the preceding stages in a targeted potential application.
Evaluation of the Carbon Footprint of Greenhouse Vegetable Production Based on Questionnaire Survey from Nanjing, China

A questionnaire survey of material and management inputs in production of five typical kinds of greenhouse vegetables from Nanjing was conducted. Carbon cost and C emission intensity involved in these vegetable production were evaluated using life cycle analysis methodology. Results showed that the total C inputs in a single growing season of the studied 5 types of greenhouse vegetables ranged from (867.1±240.6)kgCE·hm-2 to(2 039.4±1 163.3)kgCE·hm-2. While the material input possessed a poredominant proportion in the total C cost, fertilizers and agricultural films were found in a proportion of 58%~82% and of 12%~29%, respectively. Furthermore, there were significant differences in the C intensity either per hectare or per ton of production between the vegetable types. Above all, reducing C input in the growing of vegetables and better application techniques to improve nutrient uptake and choosing suitable greenhouse vegetable varieties would be an effective measure to increasing carbon sequestration and mitigation in soil.
A 60 GHz High Cross-Polarization Discrimination 45° Linearly Polarized Cavity-Backed 2 × 2-Slot Subarray Based on Gap Waveguide Technology

This paper presents a V-band 45° linearly polarized 2 × 2-slot subarray antenna based on gap waveguide (GW) technology. It can be a good candidate subarray for larger planar array antennas. The proposed subarray is composed of two unconnected metal plates: radiating layer and cavity layer. The radiating layer contains 45° tilted radiating slots, upper exciting slots, 45° twist slots and lower exciting slots. The cavity layer contains cavities (formed by pins) and a coupling slot. With the benefit of GW technology, the electromagnetic (EM) leakage among the metallic blocks can be cancelled. Besides, by introducing the novel 45° twist slots to the radiating layer, the grating lobe suppression and XPD improvement over a wide band are achieved without increasing additional metal structures. The simulated results of the proposed antenna over the operating frequency range from 57 GHz to 66 GHz are as follows: The input reflection coefficient is better than -18.47 dB. The minimum XPD can reach a considerable level of 39.8 dB.
Real-time Detection of Aerial Refueling Drogue Based on Convolutional Neural Network

A real-time detection method of aerial refueling drogue based on convolutional neural network is proposed to address the problem of insufficient accuracy and robustness of vision-based aerial refueling drogue detection in the autonomous aerial refueling docking by drogue-probe method. Firstly, a lightweight backbone feature extraction network is designed based on the Stem module and ELAN module. Secondly, the feature focus unit is designed to enhance the deep feature space information and improve the localization accuracy of the model. Then, the attention concatenation module is proposed to correct the performance impairment of large objects caused by FPN. Finally, an anchor-free detection head is used to decompose the classification and regression tasks in the detection task and output the classification information in advance to avoid the spatial misalignment problem. Experiments demonstrate that the proposed method is more accurate than the current mainstream object detectors on the publicly available real air drogue dataset, with frame rates up to 117 FPS, which proves the effectiveness, robustness and real-time performance of the proposed method.
A Low Sidelobe Wideband Slotted Antenna Array Fed by High-Order Modes Resonant Cavity

A low sidelobe wideband waveguide slotted cavity antenna is presented for K-band wireless communication. By exciting the radiation slots in high-order modes, the antenna abandons the back cavity structure, thereby reducing the profile height. And due to the combination of multiple high-order modes, it can obtain a wide impedance bandwidth of 21.9% (17.9 to 22.3 GHz). Then, an 16×16 array antenna with a peak gain of 29.1 dBi at at the center frequency of 20 GHz was designed. In the feed network, the Dolph-Chebyshev power distribution was implemented by multistage T-junction. The power distribution between the subarrays is suitably adjusted to achieve a sidelobe level below -20 dB.
Text Label Construction Based on BERT Word Vector Fusion Self-attention Mechanism and Deep Convolution Neural Network

Since CNNs cannot obtain long-distance relational dependencies of text through convolution, deep convolutional neural networks can extract long-distance textual dependencies by deepening the network layer. In addition, in order to highlight the importance of each word in text semantics, the self-attention mechanism is introduced to solve the problem that keyword features cannot be focused in the classification process. In order to further optimize the word vector of the input text and take into account the accuracy of text label construction, the pre-trained model BERT based on Transformer architecture is used as the embedding layer of the entire model. The B-SADS model proposed in this paper has efficient text label construction capabilities, and achieves an accuracy rate of 94.19% on the public THUCNews dataset, which fully reflects the superiority of the model.
Predicting Over the Top Services Movies and Shows Success Using Machine Learning

Product innovation plays a crucial role in the film business, especially in the highly competitive and uncertain landscape of the entertainment sector. To stay ahead of the competition, filmmakers, investors, and OTT services need to continuously innovate and create successful movies and shows. The film business is a multi-billion-dollar entertainment sector with significant hazards. The excessive costs of production still do not guarantee the success of a movie or show. To overcome this uncertainty, machine learning technology can be used to predict the success of a film. Currently, people are starting to watch movies and TV shows on the over-the-top (OTT) services. Netflix is an OTT service that has a lot of subscribers. With such a large business opportunity in this industry, it would be better if the stakeholders could predict the success of a film earlier, so filmmakers, investors, and OTT services will benefit from it, such as by saving costs. Therefore, this study's purpose is to find the best model for predicting the success of Netflix movies and shows using various attributes, namely type, release year, age certification, runtime, genre, production country, IMDB vote, TMDB popularity, TMDB score, and Top Movie (The class label generated from IMDB score, which classifies a movie to be successful (“Yes”) if the score is ≥ 8 and unsuccessful (“No”) if the score is <8). This paper applied Random Forest, Naive Bayes, and k-Nearest Neighbors machine learning algorithms for predicting the success of a movie or show. This study shows the comparison of the measurements of the three algorithms without and with SMOTE. The best algorithm in this case is Random Forest with 81.59% Accuracy, 87.41% Precision, 73.82% Recall, 91.30% AUC, and 79.48% F1-Score.