bluuebunny/crossref_metadata_2025 huggingface dataset:
Research and Evaluation on an Optical Automatic Detection System for the Defects of the Manufactured Paper Cups
Authors: Wang Ping , Lee Yang-Han , Tseng Hsien-Wei , Yang Cheng-Fu
Published: January 2023

In this paper, the paper cups were used as the research objects, and the machine vision detection technology was combined with different image processing techniques to investigate a non-contact optical automatic detection system to identify the defects of the manufactured paper cups. The combined ring light was used as the light source, an infrared (IR) LED matrix panel was used to provide the IR light to constantly highlight the outer edges of the detected objects, and a multi-grid pixel array was used as the image sensor. The image processing techniques, including the Gaussian filter, Sobel operator, Binarization process, and connected component, were used to enhance the inspection and recognition of the defects existing in the produced paper cups. There were three different detection processes for paper cups, which were divided into internal, external, and bottom image acquisition processes. The present study demonstrated that all the detection processes could clearly detect the surface defect features of the manufactured paper cups, such as dirt, burrs, holes, and uneven thickness. Our study also revealed that the average time for the investigated Automatic Optical Detection to detect the defects on the paper cups was only 0.3 s.
Binned Data Provide Better Imputation of Missing Time Series Data from Wearables
Authors: Chakrabarti Shweta , Biswas Nupur , Karnani Khushi , Padul Vijay , Jones Lawrence D. , Kesari Santosh , Ashili Shashaanka
Published: January 2023

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.
Timing in Lower Limb Complex Movement Tests for DanceSport Athletes: Relation between FitLight Trainer and IMU Measurements
Authors: Prelević Marija , Dopsaj Milivoj , Stančin Sara
Published: January 2023

We examine the relation between two devices used in measuring the timing in lower limb complex movement tests for DanceSport athletes, an inertial measurement unit (IMU) and a FitLight Trainer device, with the latter regarded as the gold standard method in the field. Four tests are selected to cover the lower limb movements. The research sample comprises 21 experienced dancers from different dance disciplines, performing the four tests with each of their lower limbs. Compared using concurrent validity, the two devices used show great agreement for estimating the total tests’ run times, with interclass correlation coefficients between 0.967 and 0.994 for all tests. This agreement is additionally confirmed by Bland–Altman plots. As an alternative to other devices, the IMU sensor has proven to be a precise and suitable device for measuring timing and testing in sports. Its mobility, light weight, and size are advantages of this device in addition to measurement accuracy.
Simplified Nonlinear Current-Mode Control of DC-DC Cuk Converter for Low-Cost Industrial Applications
Authors: Al-Baidhani Humam , Kazimierczuk Marian K.
Published: January 2023

This paper presents a robust nonlinear current-mode control approach for a pulse-width modulated DC-DC Cuk converter in a simple analog form. The control scheme is developed based on the reduced-state sliding-mode current control technique, in which a simplified equivalent control equation is derived using an averaged power converter model in continuous conduction mode. The proposed controller does not require an output capacitor current sensor and double proportional-integral compensators as in conventional sliding-mode current controllers; thus, the cost and complexity of the practical implementation is minimized without degrading the control performance. The simplified nonlinear controller rejects large disturbances, provides fast transient response, and maintains a constant switching frequency. The nonlinear control scheme is developed using an analog circuit with minimal added components, which is suitable for low-cost industrial applications. The control law derivation, control circuit design, controller gains selection, and stability analysis are provided. The proposed control methodology is verified via simulating the closed-loop nonlinear power converter model in MATLAB/SIMULINK under abrupt changes in load current and input voltage. The simulation results show that the proposed control scheme provides robust tracking performance, a low percentage overshoot, fast transient response, and a wide operating range. The maximum percentage overshoot and settling time of the closed-loop power converter response during line disturbance are 5.6% and 20 ms, respectively, whereas the percentage overshoot and settling time during load disturbance are 2.8% and 15 ms, respectively.
Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments
Authors: Jayawardana Palihawadana A. D. Nirmal , Obaid Hadeel , Yesilyurt Taylan , Tan Bo , Lohan Elena Simona
Published: January 2023

The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.
Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
Authors: Chen Tongwaner , Xia Menghua , Huang Yi , Jiao Jing , Wang Yuanyuan
Published: January 2023

The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric.
RDASNet: Image Denoising via a Residual Dense Attention Similarity Network
Authors: Tao Haowu , Guo Wenhua , Han Rui , Yang Qi , Zhao Jiyuan
Published: January 2023

In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.
Field Modeling of the Influence of Defects Caused by Bending of Conductive Textronic Layers on Their Electrical Conductivity
Authors: Pawłowski Stanisław , Plewako Jolanta , Korzeniewska Ewa
Published: January 2023

One of the critical parameters of thin-film electrically conductive structures in wearable electronics systems is their conductivity. In the process of using such structures, especially during bending, defects and microcracks appear that affect their electrical parameters. Understanding these phenomena in the case of thin layers made on flexible substrates, including textile ones, which are incorporated in sensors that monitor vital functions, is a key aspect when applying such solutions. Cracks and defects in such structures appearing during their use may be critical for the correct operation of such systems. In this study, the influence of defects resulting from the repeated bending of the conductive layer on its conductivity is analyzed. The anisotropic and partly stochastic characteristics of the defects are also taken into account. The defects are modeled in the form of broken lines, whose segments are generated in successive iterative steps, thus simulating the gradual wear of the layer material. The lengths and inclinations of these sections are determined randomly, which makes it possible to consider the irregularity of real defects of this type. It was found that near the percolation threshold, defects with a more irregular shape have a dominant effect on the reduction of conductivity due to the greater probability of their connection. The simulation results were compared with the experimental data. It was found that the dependence of the conductivity of the conductive layer on the number of bends is logarithmic. This allowed for the derivation of a formula linking the iteration number of the simulation procedure with the number of bends. Improving the strength of such layers is a technological challenge for researchers.
Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data
Authors: Vakil Asad , Blasch Erik , Ewing Robert , Li Jia
Published: January 2023

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input.
Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
Authors: Pato Matilde , Eleutério Ricardo , Conceição Raquel C. , Godinho Daniela M.
Published: January 2023

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to 3.07 dB, a Signal-to-Mean Ratio of up to 20.78 dB and a Location Error of 1.58 mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.
Microwave Cavity Sensor for Measurements of Air Humidity under Reduced Pressure
Authors: Galka Alexander Georgievich , Kostrov Alexander Vladimirovich , Priver Stanislav Eduardovich , Strikovskiy Askold Vitalievich , Parshin Vladimir Vladimirovich , Serov Evgeny Alexandrovich , Nikolenko Andrey Sergeevich , Korobkov Sergey Vladimirovich , Gushchin Mikhail Evgenievich
Published: January 2023

A high-sensitivity sensor for measuring moisture content in the air or air humidity under low pressure was designed on the basis of a half-wave coaxial microwave cavity. The method of measuring small variations in the signal phase at a cavity excitation frequency of 1.63 GHz was applied to detect low densities of water vapor. It allows the detection of variations in dielectric air permittivity in the seventh decimal place. A prototype of the sensor was tested in a vacuum chamber. It was calibrated by comparing the test results with the readings of a commercial pressure gauge and using the independent method of measuring the moisture content in rarefied air on the basis of the absorption of millimeter waves in the local line of water vapor at 183 GHz. The developed sensor can be used in laboratory experiments and full-scale geophysical research in the atmosphere onboard various aircrafts.
SLAM and 3D Semantic Reconstruction Based on the Fusion of Lidar and Monocular Vision
Authors: Lou Lu , Li Yitian , Zhang Qi , Wei Hanbing
Published: January 2023

Monocular camera and Lidar are the two most commonly used sensors in unmanned vehicles. Combining the advantages of the two is the current research focus of SLAM and semantic analysis. In this paper, we propose an improved SLAM and semantic reconstruction method based on the fusion of Lidar and monocular vision. We fuse the semantic image with the low-resolution 3D Lidar point clouds and generate dense semantic depth maps. Through visual odometry, ORB feature points with depth information are selected to improve positioning accuracy. Our method uses parallel threads to aggregate 3D semantic point clouds while positioning the unmanned vehicle. Experiments are conducted on the public CityScapes and KITTI Visual Odometry datasets, and the results show that compared with the ORB-SLAM2 and DynaSLAM, our positioning error is approximately reduced by 87%; compared with the DEMO and DVL-SLAM, our positioning accuracy improves in most sequences. Our 3D reconstruction quality is better than DynSLAM and contains semantic information. The proposed method has engineering application value in the unmanned vehicles field.
Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey
Authors: Uddin Md Kamal , Bhuiyan Amran , Bappee Fateha Khanam , Islam Md Matiqul , Hasan Mahmudul
Published: January 2023

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.
Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods
Authors: Pereira Teresa M. C. , Conceição Raquel C. , Sencadas Vitor , Sebastião Raquel
Published: January 2023

In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.
Inducing Performance of Commercial Surgical Robots in Space
Authors: Sands Timothy
Published: January 2023

Pre-existing surgical robotic systems are sold with electronics (sensors and controllers) that can prove difficult to retroactively improve when newly developed methods are proposed. Improvements must be somehow “imposed” upon the original robotic systems. What options are available for imposing performance from pre-existing, common systems and how do the options compare? Optimization often assumes idealized systems leading to open-loop results (lacking feedback from sensors), and this manuscript investigates utility of prefiltering, such other modern methods applied to non-idealized systems, including fusion of noisy sensors and so-called “fictional forces” associated with measurement of displacements in rotating reference frames. A dozen modern approaches are compared as the main contribution of this work. Four methods are idealized cases establishing a valid theoretical comparative benchmark. Subsequently, eight modern methods are compared against the theoretical benchmark and against the pre-existing robotic systems. The two best performing methods included one modern application of a classical approach (velocity control) and one modern approach derived using Pontryagin’s methods of systems theory, including Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. The key novelty presented is the best performing method called prefiltered open-loop optimal + transport decoupling, achieving 1–3 percent attitude tracking performance of the robotic instrument with a two percent reduced computational burden and without increased costs (effort).
Prehensile and Non-Prehensile Robotic Pick-and-Place of Objects in Clutter Using Deep Reinforcement Learning
Authors: Imtiaz Muhammad Babar , Qiao Yuansong , Lee Brian
Published: January 2023

In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases.
User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions
Authors: Shoitan Rasha , Moussa Mona M. , Gharghory Sawsan Morkos , Elnemr Heba A. , Cho Young-Im , Abdallah Mohamed S.
Published: January 2023

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.
The Explanation of Photopic Luminous Efficiency Curve by Using Both of the Cones’ Optical Fiber Coupling Effects and the Absorption of L Cones
Authors: Liang Anhui , Yu Kexin , Min Xiaolin , Li Jing , Li Jianyu , Zuo Xiaoling , Yao Youli
Published: January 2023

In this paper, we build four-part cone models to explore the coupling effect of seven cone fiber couplers. Moreover, this is the first study of the coupling effect of four layers of biological couplers in animals and other biological lives. We simulate the four layers cone couplers by using the beam propagation method, and we assume the input beam is located at the outer fiber of the central cone. Our simulation results showed that there are two wavelength regions (short and long wavelength regions) with the strongest coupling, where the most power of input optical powers of the central cones will transfer to the six surrounding cones after transmitting through the four layers of cone couplers. However, within a wavelength region of ±75 nm near to the peak wavelengths, located in the yellow–green wavelength range, the splitting ratios at the output of the outer segment of the central cone are always greater than the sum of the splitting ratios of the six surrounding cones. These cone couplers may play an important role in color preprocessing (e.g., doing opponent color processing partially). The cone fiber coupler effect and light absorption of cones are considered separately in our models. By taking account of both the cone fiber coupling effect and absorption of outer segment of L cone, we find the multiplication of the relative optical power of cone couplers, the spectral sensitivity data of the L cone, and a normalized coefficient that matches with the photopic luminous efficiency of the human eye well. This is the attempt to use both the cone fiber coupling effect and the absorption of L cones to explain the photopic luminous efficiency. The splitting ratios of the central cones are greater than 80% at peak wavelengths located in the yellow-green wavelength range, and this can help to explain why the human eye is more sensitive to green light.
Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
Authors: Guo Shirong , Yao Jielin , Wu Pingfan , Yang Jianjie , Wu Wenhao , Lin Zhijian
Published: January 2023

With the development of wireless technology, signals propagating in space are easy to mix, so blind detection of communication signals has become a very practical and challenging problem. In this paper, we propose a blind detection method for broadband signals based on a weighted bi-directional feature pyramid network (BiFPN). The method can quickly perform detection and automatic modulation identification (AMC) on time-domain aliased signals in broadband data. Firstly, the method performs a time-frequency analysis on the received signals and extracts the normalized time-frequency images and the corresponding labels by short-time Fourier transform (STFT). Secondly, we build a target detection model based on YOLOv5 for time-domain mixed signals in broadband data and learn the features of the time-frequency distribution image dataset of broadband signals, which achieves the purpose of training the model. The main improvements of the algorithm are as follows: (1) a weighted bi-directional feature pyramid network is used to achieve a simple and fast multi-scale feature fusion approach to improve the detection probability; (2) the Efficient-Intersection over Union (EIOU) loss function is introduced to achieve high accuracy signal detection in a low Signal-Noise Ratio (SNR) environment. Finally, the time-frequency images are detected by an improved deep network model to complete the blind detection of time-domain mixed signals. The simulation results show that the method can effectively detect the continuous and burst signals in the broadband communication signal data and identify their modulation types.
Acknowledgment to the Reviewers of Scientia Pharmaceutica in 2022
Authors:   Scientia Pharmaceutica Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Simultaneous Quantification of Avermectins in Six Aquatic Foods by UHPLC/FLD with Precolumn Derivatization
Authors: Sun Ruyu , Liu Yongtao , Ai Xiaohui , Du Xiangxuan , Zhang Xiaoyi
Published: January 2023

In this study, a fast, concise and reliable ultra-high performance liquid chromatography-fluorescence (UHPLC/FLD) detection method for simultaneous quantification of avermectins (AVMs), including avermectin (AVM), ivermectin (IVM), emamectin (EMM), moxidectin (MOX) and doramectin (DOR) in six aquatic foods was established. Based on the QuEChERS pretreatment method, the samples were extracted with 0.2% (v/v) ammonia acetonitrile. N-methyl imidazole mixed with acetonitrile (1:1, v/v) and trifluoroacetic anhydride with acetonitrile (1:2, v/v) were used as derivatization reagents. The mobile phase consists of acetonitrile and water with a flow rate of 1.0 mL/min. An Infinity Lab Poroshell 120 EC-C18 column was used for optimum chromatographic separation of target analytes at 40 °C; the excitation and emission wavelengths were set at 365 nm and 465 nm, respectively. In six kinds of aquatic foods, the limits of detection (LODs) of AVM, IVM, EMM, MOX, and DOR were 2.7 μg/kg, 1.8 μg/kg, 2.1 μg/kg, 1.2 μg/kg, and 2.7 μg/kg, respectively, and the limits of quantification (LOQs) of AVM, IVM, EMM, MOX, and DOR were 5 μg/kg, 4.5 μg/kg, 4.5 μg/kg, 3.5 μg/kg and 5.0 μg/kg, respectively. The recoveries were all above 85.38% when the samples were spiked with the target compounds at the concentration level of 5, 10, 50, and 100 μg/kg. The intra-day and inter-day relative standard deviations (RSDs) were all less than 15%. This method considers the requirements of sensitivity, accuracy, and economics of the instrument.
Functionalization of Sodium Magnesium Silicate Hydroxide/Sodium Magnesium Silicate Hydrate Nanostructures Using 2,3-Dihydroxybenzaldehyde as a Novel Nanocomposite for the Efficient Removal of Cd(II) and Cu(II) Ions from Aqueous Media
Authors: Al-Wasidi Asma S. , Basha Maram T. , Alghanmi Reem M. , Al-Farraj Eida S. , Abdelrahman Ehab A.
Published: January 2023

Cd(II) and Cu(II) ions cause many diseases in humans. Therefore, they should be removed from water sources using simple and cost-effective adsorbents. Consequently, sodium magnesium silicate hydroxide/sodium magnesium silicate hydrate nanostructures were synthesized and functionalized using 2,3-dihydroxybenzaldehyde as a novel nanocomposite. Several instruments were used to characterize the synthetic products, such as an X-ray diffractometer (XRD), a Fourier-transform infrared spectrophotometer (FT-IR), an N2 adsorption/desorption analyzer, a CHN elemental analyzer, an energy-dispersive X-ray spectrophotometer (EDS), and a field emission scanning electron microscope (FE-SEM). The functionalization of the nanostructures with 2,3-dihydroxybenzaldehyde led to the disappearance of the XRD peaks of the nanostructures and the presence of a broad XRD peak at 2θ = 32°. In addition, the FE-SEM images revealed that the nanostructures consisted of spheres, cubes, and irregular shapes with an average grain size of 115 nm, and the nanocomposite consisted of spherical conglomerates consisting of needle-like shapes. The anticipated morphology following the functionalization of the nanostructures with 2,3-dihydroxybenzaldehyde resulted from the presence of 2,3-dihydroxybenzaldehyde on the backbones of the nanostructures. The EDS results showed that the nanostructures were composed of O, Na, Mg, and Si with weight percentages equal to 38.59%, 5.95%, 16.60%, and 38.86%, respectively. Additionally, the nanocomposite was composed of C, N, O, Na, Mg, and Si with weight percentages equal to 55.31%, 2.23%, 30.09%, 6.56%, 2.98%, and 12.83%, respectively. The synthesized nanostructures and nanocomposite samples were utilized for the efficient removal of cadmium and copper ions from aqueous media using the ion exchange and chelation adsorption procedures, respectively. Optimum conditions for removing the cadmium and copper ions were achieved at a pH, time, and temperature equal to 7.5, 80 min, and 298 K, respectively. The maximum uptake capacities of the synthesized nanostructures and nanocomposite samples toward cadmium ions were 89.44 mg/g and 155.04 mg/g, respectively, and the maximum uptake capacities of the synthesized nanostructures and nanocomposite samples toward copper ions were 103.73 mg/g and 177.94 mg/g, respectively. Moreover, the adsorption processes were exothermic, chemical, and followed the pseudo-second-order model and Langmuir equilibrium isotherm model.
A Comparative Study on Extraction and Physicochemical Properties of Soluble Dietary Fiber from Glutinous Rice Bran Using Different Methods
Authors: Chen Haoshuang , He Shudong , Sun Hanju , Li Qiuyang , Gao Kuan , Miao Xinya , Xiang Jie , Wu Xiongjie , Gao Longmei , Zhang Yi
Published: January 2023

The methods of hot water extraction and ultrasound-assisted enzymatic treatment were applied for extracting the soluble dietary fiber from the glutinous rice bran in the study. Based on the single factor experiment for the hot water method, the optimum parameters of the extraction time of 120 min, solid-liquid ratio 1:20 (w/v), and pH 8.0, as well as the extraction temperature 80 °C, were obtained, while the yield and purity of SDF reached 31.83 ± 0.06% and 93.28 ± 0.27%, respectively. Furthermore, the SDF yield was improved to 34.87 ± 0.55% by using the ultrasound-assisted enzymatic treatment under the optimum conditions of cellulase dosage 9 × 103 U/g and ultrasonic temperature of 50 °C. Similar polysaccharide compositions were detected based on the infrared spectroscopic analysis. Compared with the SDF obtained from hot water extraction, the whiteness, solubility, water holding capacity, and swelling properties of SDF extracted by ultrasound-assisted enzymatic method improved significantly. These results demonstrated that both two strategies could be applied to SDF extraction in practical production, and the ultrasound-assisted enzymatic method might be an effective tool to improve the functional properties of SDF.
Intrasexual Competition in Women’s Likelihood of Self-Enhancement and Perceptions of Breast Morphology: A Hispanic Sample
Authors: Garza Ray , Pazhoohi Farid
Published: January 2023

Women’s breasts are considered sexually attractive because they may infer a woman’s residual reproductive value. Given that men find women’s breasts attractive, women may compete with other women to enhance their physical attractiveness when primed with an intrasexual competitive cue. The current study investigated women’s intrasexual competition when viewing variations in breast morphology. Women (N = 189) were randomly assigned to a partner threat condition and shown images of women’s breasts that included variations in breast size, ptosis (i.e., sagginess), and intermammary distance (i.e., cleavage). Women were more likely to report an increase in enhancing their appearance, wearing revealing clothing, dieting and exercising, and perceiving the breasts as a sexual threat as a function of larger breast sizes with low ptosis and intermediate distances. The partner threat prime did not play a role in ratings. Interestingly, there was a moderating role for women’s dispositional levels in intrasexual competition. Women with higher levels of intrasexual competition were more likely to enhance their appearance when viewing large breast sizes. The study points to the role that breast morphology indicative of residual reproductive value has on increasing enhancement strategies.
A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
Authors: Isaia Constantina , Michaelides Michalis P.
Published: January 2023

In recent years, tremendous advances have been made in the design and applications of wireless networks and embedded sensors. The combination of sophisticated sensors with wireless communication has introduced new applications, which can simplify humans’ daily activities, increase independence, and improve quality of life. Although numerous positioning techniques and wireless technologies have been introduced over the last few decades, there is still a need for improvements, in terms of efficiency, accuracy, and performance for the various applications. Localization importance increased even more recently, due to the coronavirus pandemic, which made people spend more time indoors. Improvements can be achieved by integrating sensor fusion and combining various wireless technologies for taking advantage of their individual strengths. Integrated sensing is also envisaged in the coming technologies, such as 6G. The primary aim of this review article is to discuss and evaluate the different wireless positioning techniques and technologies available for both indoor and outdoor localization. This, in combination with the analysis of the various discussed methods, including active and passive positioning, SLAM, PDR, integrated sensing, and sensor fusion, will pave the way for designing the future wireless positioning systems.
Assessing the Energy and Climate Sustainability of European Union Member States: An MCDM-Based Approach
Authors: Brodny Jarosław , Tutak Magdalena
Published: January 2023

Topics related to sustainable economic development are currently important issues in the modern world. However, the implementation of this concept and related operational strategies raises many controversies. On the one hand, it offers hope for ecological, safe, and independent economic development, while on the other hand, it raises public concerns about the costs of such changes. These problems are widely appreciated in the EU, which is the undoubted leader in implementing the concept of sustainable economic development. With regard to this issue, this paper presents the developed methodology for assessing the sustainable energy and climate development of the EU-27 countries. The basis of this assessment is 17 selected indicators characterizing the most important areas related to this development. Their selection was conditioned by the assumptions of the Europe 2020 Strategy and the goals (7 and 13) of the UN Agenda for Sustainable Development 2030. Five widely used methods for multi-criteria analysis supporting management processes (CODAS, EDAS, TOPSIS, VIKOR, and WASPAS) were used for the study. In order to carry out an unambiguous assessment and determine the final ranking of countries in terms of energy and climate sustainability, a methodology was developed to specify the normalized value of the Final Assessment Score (Asfinal). Based on it, the sustainability of individual EU-27 countries in 2010, 2015, and 2020 was assessed, and this assessment formed the basis for dividing these countries into four classes (levels) in terms of sustainability. The results confirmed the high differentiation of the EU-27 countries in terms of sustainability, indicating leaders as well as countries with low levels of sustainability. The countries with the highest and most stable levels of sustainable development of the economy are Sweden and Denmark. The results provide opportunities for their interpretation, both in terms of analyzing changes in individual indicators and in terms of the global assessment of sustainable development in individual countries. These results should be used when developing an energy and climate strategy for the next few years for the EU as a whole and for individual countries.
Rethinking Sense of Place Interpretations in Declining Neighborhoods: The Case of Ami-dong Tombstone Cultural Village, Busan, South Korea
Authors: Konduri Sreenidhi , Lee In-Hee
Published: January 2023

In recent years, urban regeneration strategic plans have been implemented across South Korea to curb the negative impacts of depopulation, physical deterioration and economic decline. By adopting a people-centered regeneration process, context-sensitive plans are formulated by integrating local people’s perceptions and expectations. This paper examines urban regeneration plans implemented in Ami-dong Tombstone Cultural Village, a declining hillside village in Busan, using “people–place–process framework of place attachment” to analyze the process of articulating “sense of place” through community-driven approaches. Based on archival research, site investigations, secondary data and semi-structured interviews, the paper explores the revival of social capital, integration of place-sensitivity and improvement of everyday landscapes through affective, behavioral and cognitive responses of urban professionals and community members involved in the project. Findings of the study show that place attachment, sense of community and community participation in regeneration can help in re-creating meaningful places. Lessons from Ami-dong offer insights on ways to strengthen people–people and people–place relationships through design and planning processes in a declining context with an aging population.
Assessing the Effectiveness and Justice of Protected Areas Governance: Issues and Situated Pathways to Environmental Policies in Río Negro National Park, Paraguay
Authors: Bonatti Michelle , Bayer Sabeth , Pope Kamila , Eufemia Luca , Turetta Ana Paula Dias , Tremblay Crystal , Sieber Stefan
Published: January 2023

Protected areas are a fundamental element for the protection of ecological integrity and, in some cases, the livelihood of local communities worldwide. They are also embedded in socio-ecological systems, and their management is subject to various political, economic, and social influences. Good governance of protected areas is recognized as a decisive aspect of ecological conservation, which is at risk in institutional contexts where there is a weak scope of action alongside issues with misrecognition of key actors and their representation in procedures. In this context, the present study case aims to assess the performance of the Río Negro National Park governance system in terms of effectiveness and justice to enable the identification of strategies to improve this protected area governance system for the achievement of its desired outcomes. Using the social-ecological systems approach, this paper proposes an analytical framework for the performance assessment, including both the effectiveness and justice of the governance of socio-ecological systems, stemming from the socio-ecological justice framework. It uses mixed methods based on semi-structured in-depth interviews supplemented by a focus group discussion, participant observation, and secondary data analysis. Results show that the governance of Río Negro National Park is negatively impacted by low-capacity, a lack of human, financial, and technical resources, as well as the lack of recognition of the indigenous community of the Yshiro and the rural community as key actors, leading to a lack of representation of their interests, values, and knowledge in norm-making and decision-making processes. The findings unveil some windows for improvement through better-designed environmental policies specifically based on collective action and social learning. The results demonstrate that effectiveness and justice influence each other and, therefore, are deeply intertwined. From the assessment conducted, the paper highlights the components of the governance system that should be improved to achieve good governance of the protected area as a socio-ecological system, promoting the ecological integrity and the dignity of life (socio-ecological justice) of the individuals and communities that are part of this system.