bluuebunny/crossref_metadata_2025 huggingface dataset:
A Controllable Suppression Jamming Method against SAR Based on Active Radar Transponder
Authors: Liu Guikun , Li Liang , Ming Feng , Sun Xilong , Hong Jun
Published: August 2022

A chirp-mismatch echo signal can be generated by exchanging the I and Q baseband signals of a received SAR signal. In this paper, the basic generation principles of a chirp-mismatch echo signal are analyzed. Then, a suppression jamming method with controllable jamming position and coverage area is proposed. This method firstly performs chirp-mismatch processing on the received SAR signals, then controls the range jamming coverage and center position through range shift-frequency modulation and time delay, and controls the azimuth jamming coverage and center position through motion modulation and azimuth shift-frequency modulation. Theoretical analysis and simulation results show that this method can effectively control the location and coverage of a jamming result without convolution modulation, and it is easy to implement in engineering. The simulation results verify the correctness of the theoretical model, which can provide a basis for the implementation and application of SAR jamming based on the active radar transponder.
Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples
Authors: Song Di , Feng Qi , Chen Shengyao , Xi Feng , Liu Zhong
Published: August 2022

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm.
A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism
Authors: Zhang Liwen , Wei Wenhao , Qiu Bo , Luo Ali , Zhang Mingru , Li Xiaotong
Published: August 2022

Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural network (CNN) named MA-SegCloud is proposed for segmenting cloud images based on a multibranch asymmetric convolution module (MACM) and an attention mechanism. The MACM is composed of asymmetric convolution, depth-separable convolution, and a squeeze-and-excitation module (SEM). The MACM not only enables the network to capture more contextual information in a larger area but can also adaptively adjust the feature channel weights. The attention mechanisms SEM and convolutional block attention module (CBAM) in the network can strengthen useful features for cloud image segmentation. As a result, MA-SegCloud achieves a 96.9% accuracy, 97.0% precision, 97.0% recall, 97.0% F-score, 3.1% error rate, and 94.0% mean intersection-over-union (MIoU) on the Singapore Whole-sky Nychthemeron Image Segmentation (SWINySEG) dataset. Extensive evaluations demonstrate that MA-SegCloud performs favorably against state-of-the-art cloud image segmentation methods.
Accelerated Deconvolved Imaging Algorithm for 2D Multibeam Synthetic Aperture Sonar
Authors: Wei Bo , He Chuanlin , Xing Siyu , Zheng Yi
Published: August 2022

High-accuracy level underwater acoustical surveying plays an important role in ocean engineering applications, such as subaqueous tunnel construction, oil and gas exploration, and resources prospecting. This novel imaging method is eager to break through the existing theory to achieve a higher accuracy level of surveying. Multibeam Synthetic Aperture Sonar (MBSAS) is a kind of underwater acoustical imaging theory that can achieve 3D high-resolution detecting and overcome the disadvantages of traditional imaging methods, such as Multibeam Echo Sounder (MBES) and Synthetic Aperture Sonar (SAS). However, the resolution in the across-track direction inevitably decreases with increasing range, limited by the beamwidth of the transducer array of MBES. Furthermore, the sidelobe problem is also a significant interference of imaging sonar that introduces image noise and false peaks, which reduces the accuracy of the underwater images. Therefore, we proposed an accelerated deconvolved MBSAS beamforming method that introduces exponential acceleration and vector extrapolation to improve the convergence velocity of the classical Richardson-Lucy (R-L) iteration. The method proposed achieves a narrow beamwidth with a high sidelobe ratio in a few iterations. It can be applied to actual engineering applications, which breaks through the limitation of the actual transducer array scale. Simulations, tank, and field experiments also demonstrate the feasibility and advantages of the method proposed. 3D high-accuracy level underwater acoustical surveying can be achieved through this 2D MBES transducer array system, which can be widely promoted in the field of underwater acoustical remote sensing.
Localization Approach for Underwater Sensors in the Magnetic Silencing Facility Based on Magnetic Field Gradients
Authors: Zhou Guohua , Wang Yufen , Wu Kena , Wang Hanming
Published: August 2022

Localization of the underwater magnetic sensor arrays plays a pivotal role in the magnetic silencing facility. A localization approach is proposed for underwater sensors based on the optimization of magnetic field gradients in the inverse problem of localization. In the localization system, a solenoid coil carrying direct current serves as the magnetic source. By measuring the magnetic field generated by the magnetic source in different positions, an objective function is established. The position vector of the sensor is determined by a novel multi-swarm particle swarm optimization with dynamic learning strategy. Without the optimization of the magnetic source’s positions, the sensors’ positions, especially in the z-axis direction, struggle to meet the requested localization. A strategy is proposed to optimize the positions of the magnetic source based on magnetic field gradients in the three directions of x, y and z axes. Compared with the former method, the model experiments show that the proposed method could achieve a 10 cm location error for the position type 2 sensor and meet the request of localization.
Decision Feedback Modulation Recognition with Channel Estimation for Amplify and Forward Two-Path Consecutive Relaying Systems
Authors: Marey Mohamed , Esmail Maged Abdullah , Mostafa Hala
Published: August 2022

Automatic modulation recognition (AMR) is an essential component in the design of smart radios that can intelligently communicate with their surroundings in order to make the most efficient use of available resources. Throughout the last few decades, this issue has been subjected to in-depth examination in the published research literature. To the best of the authors’ knowledge, there have only been a few studies that have been specifically devoted to the task of performing AMR across cooperative wireless transmissions. In this contribution, we examine the AMR problem in the context of amplify-and-forward (AAF) two-path consecutive relaying systems (TCRS) for the first time in the literature. We leverage the property of data redundancy associated with AAF-TCRS signals to design a decision feedback iterative modulation recognizer via an expectation-maximization procedure. The proposed recognizer incorporates the soft information produced by the data detection process as a priori knowledge to generate the a posteriori expectations of the information symbols, which are employed as training symbols. The proposed algorithm additionally involves the development of an estimate of the channel coefficients as a secondary activity. The simulation outcomes have validated the feasibility of the proposed design by demonstrating its capacity to achieve an excellent recognition performance under a wide range of running conditions. According to the findings, the suggested technique converges within six rounds, achieving perfect recognition performance at a signal-to-noise ratio of 14 dB. Furthermore, the minimal pilot-to-frame-size ratio necessary to successfully execute the iterative procedure is 0.07. In addition, the proposed method is essentially immune to time offset and performs well throughout a broad range of frequency offset. Lastly, the proposed strategy beats the existing techniques in recognition accuracy while requiring a low level of processing complexity.
Resource Allocation in Multi-Carrier Multiplexed NOMA Cooperative System
Authors: Zeng Jie , Sun Jiaying , Song Yuxin , Mei Jiajia , Lv Tiejun , Zhou Shidong
Published: August 2022

Non-orthogonal multiple access (NOMA) cooperative communication technology can combine the advantages of NOMA and cooperative communication, providing high spectrum efficiency and increasing user coverage for next-generation wireless systems. However, the research on NOMA cooperative communication technology is still in a preliminary stage and has mainly concentrated on the scenario of fewer users. This paper focuses on a user-centered NOMA collaboration system in an ultra-dense network, and it constructs a resource allocation optimization problem to meet the demands of each user. Then, this paper decomposes the optimization problem into two subproblems; one is the grouping match among multiple relays and users, and the other is jointly allocating power and subcarrier resources. Accordingly, a dynamic packet matching algorithm based on Gale–Shapley and an iterative algorithm based on the difference of convex functions programing are proposed. Compared with existing schemes, the proposed algorithms can improve system throughput while ensuring the quality of service of users.
Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization
Authors: Ni Aoxin , Akbarzadeh Sara , Lobarinas Edward , Kehtarnavaz Nasser
Published: August 2022

Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting. The personalization was carried out based on the user preference model within the framework of maximum likelihood inverse reinforcement learning. The testing of ten subjects with hearing loss was conducted, which indicated that the personalized ADRO was preferred over the standard ADRO on average by about 10 times. Furthermore, a word recognition experiment was conducted, which showed that the personalized ADRO had no adverse impact on speech understanding as compared to the standard ADRO.
Automated Real-Time Eddy Current Array Inspection of Nuclear Assets
Authors: Foster Euan Alexander , Bolton Gary , Bernard Robert , McInnes Martin , McKnight Shaun , Nicolson Ewan , Loukas Charalampos , Vasilev Momchil , Lines Dave , Mohseni Ehsan , Gachagan Anthony , Pierce Gareth , Macleod Charles N.
Published: August 2022

Inspection of components with surface discontinuities is an area that volumetric Non-Destructive Testing (NDT) methods, such as ultrasonic and radiographic, struggle in detection and characterisation. This coupled with the industrial desire to detect surface-breaking defects of components at the point of manufacture and/or maintenance, to increase design lifetime and further embed sustainability in their business models, is driving the increased adoption of Eddy Current Testing (ECT). Moreover, as businesses move toward Industry 4.0, demand for robotic delivery of NDT has grown. In this work, the authors present the novel implementation and use of a flexible robotic cell to deliver an eddy current array to inspect stress corrosion cracking on a nuclear canister made from 1.4404 stainless steel. Three 180-degree scans at different heights on one side of the canister were performed, and the acquired impedance data were vertically stitched together to show the full extent of the cracking. Axial and transversal datasets, corresponding to the transmit/receive coil configurations of the array elements, were simultaneously acquired at transmission frequencies 250, 300, 400, and 450 kHz and allowed for the generation of several impedance C-scan images. The variation in the lift-off of the eddy current array was innovatively minimised through the use of a force–torque sensor, a padded flexible ECT array and a PI control system. Through the use of bespoke software, the impedance data were logged in real-time (≤7 ms), displayed to the user, saved to a binary file, and flexibly post-processed via phase-rotation and mixing of the impedance data of different frequency and coil configuration channels. Phase rotation alone demonstrated an average increase in Signal to Noise Ratio (SNR) of 4.53 decibels across all datasets acquired, while a selective sum and average mixing technique was shown to increase the SNR by an average of 1.19 decibels. The results show how robotic delivery of eddy current arrays, and innovative post-processing, can allow for repeatable and flexible surface inspection, suitable for the challenges faced in many quality-focused industries.
Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs
Authors: Rocha Paulo , Souza Alisson , Maia Gilvan , Mattos César , Silva Francisco Airton , Rego Paulo , Nguyen Tuan Anh , Lee Jae-Woo
Published: August 2022

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.
An Improved Circular Fringe Fourier Transform Profilometry
Authors: Chen Qili , Han Mengqi , Wang Ye , Chen Wenjing
Published: August 2022

Circular fringe projection profilometry (CFPP), as a branch of carrier fringe projection profilometry, has attracted research interest in recent years. Circular fringe Fourier transform profilometry (CFFTP) has been used to measure out-of-plane objects quickly because the absolute phase can be obtained by employing fewer fringes. However, the existing CFFTP method needs to solve a quadratic equation to calculate the pixel displacement amount related to the height of the object, in which the root-seeking process may get into trouble due to the phase error and the non-uniform period of reference fringe. In this paper, an improved CFFTP method based on a non-telecentric model is presented. The calculation of displacement amount is performed by solving a linear equation instead of a quadratic equation after introducing an extra projection of circular fringe with circular center translation. In addition, Gerchberg iteration is employed to eliminate phase error of the region close to the circular center, and the plane calibration technique is used to eliminate system error by establishing a displacement-to-height look-up table. The mathematical model and theoretical analysis are presented. Simulations and experiments have demonstrated the effectiveness of the proposed method.
Dual Stream Transmission and Downlink Power Control for Multiple LEO Satellites-Assisted IoT Networks
Authors: Xu Bingyu , Li Xiayu , Ma Yujuan , Xin Xing , Kadoch Michel
Published: August 2022

The multi-satellites cooperative transmission can effectively increase the data rate that can be achieved by internet of things (IoT) terminals. However, the dynamic characteristics brought by low Earth orbit (LEO) satellites will seriously decrease the data rate and make the data rate fluctuate. In this paper, dual-stream transmission and downlink power control for multiple LEO satellites-assisted IoT networks are investigated. To mitigate the effects of the frequency offset caused by different LEO satellites, a multi-satellites synchronization scheme is proposed. Then, different power control schemes are given to resist the data rate fluctuation during the transmission. The simulation results show that the proposed schemes can effectively compensate for the varied frequency offset and keep the data rate stable.
Camera-Derived Photoplethysmography (rPPG) and Speckle Plethysmography (rSPG): Comparing Reflective and Transmissive Mode at Various Integration Times Using LEDs and Lasers
Authors: Herranz Olazábal Jorge , Wieringa Fokko , Hermeling Evelien , Van Hoof Chris
Published: August 2022

Background: Although both speckle plethysmography (SPG) and photoplethysmography (PPG) examine pulsatile changes in the vasculature using opto-electronics, PPG has a long history, whereas SPG is relatively new and less explored. The aim of this study was to compare the effects of integration time and light-source coherence on signal quality and waveform morphology for reflective and transmissive rSPG and rPPG. Methods: (A) Using time-domain multiplexing, we illuminated 10 human index fingers with pulsed lasers versus LEDs (both at 639 and 850 nm), in transmissive versus reflective mode. A synchronized camera (Basler acA2000-340 km, 25 cm distance, 200 fps) captured and demultiplexed four video channels (50 fps/channel) in four stages defined by illumination mode. From all video channels, we derived rPPG and rSPG, and applied a signal quality index (SQI, scale: Good > 0.95; Medium 0.95–0.85; Low 0.85–0.8; Negligible < 0.8); (B) For transmission videos only, we additionally calculated the intensity threshold area (ITA), as the area of the imaging exceeding a certain intensity value and used linear regression analysis to understand unexpected similarities between rPPG and rSPG. Results: All mean SQI-values. Reflective mode: Laser-rSPG > 0.965, LED-rSPG < 0.78, rPPG < 0.845. Transmissive mode: 0.853–0.989 for rSPG and rPPG at all illumination settings. Coherent mode: Reflective rSPG > 0.951, reflective rPPG < 0.740, transmissive rSPG and rPPG 0.990–0.898. Incoherent mode: Reflective all <0.798 and transmissive all 0.92–0.987. Linear regressions revealed similar R2 values of rPPG with rSPG (R2 = 0.99) and ITA (R2 = 0.98); Discussion: Laser-rSPG and LED-rPPG produced different waveforms in reflection, but not in transmission. We created the concept of ITA to investigate this behavior. Conclusions: Reflective Laser-SPG truly originated from coherence. Transmissive Laser-rSPG showed a loss of speckles, accompanied by waveform changes towards rPPG. Diffuse spatial intensity modulation polluted spatial-mode SPG.
A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels
Authors: Ferreira Martha Dais , Spadon Gabriel , Soares Amilcar , Matwin Stan
Published: August 2022

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory.
Tunable Diode Laser Absorption Spectroscopy Based Temperature Measurement with a Single Diode Laser Near 1.4 μm
Authors: Liu Xiaonan , Ma Yufei
Published: August 2022

The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this invited paper, a temperature measurement method based on the TDLAS technique with a single diode laser was demonstrated. A continuous-wave (CW), distributed feedback (DFB) diode laser with an emission wavelength near 1.4 μm was used for temperature measurement, which could cover two water vapor (H2O) absorption lines located at 7153.749 cm−1 and 7154.354 cm−1 simultaneously. The output wavelength of the diode laser was calibrated according to the two absorption peaks in the time domain. Using this strategy, the TDLAS system has the advantageous of immunization to laser wavelength shift, simple system structure, reduced cost, and increased system robustness. The line intensity of the two target absorption lines under room temperature was about one-thousandth of that under high temperature, which avoided the measuring error caused by H2O in the environment. The system was tested on a McKenna flat flame burner and a scramjet model engine, respectively. It was found that, compared to the results measured by CARS technique and theoretical calculation, this TDLAS system had less than 4% temperature error when the McKenna flat flame burner was used. When a scramjet model engine was adopted, the measured results showed that such TDLAS system had an excellent dynamic range and fast response. The TDLAS system reported here could be used in real engine in the future.
A 13 µW Analog Front-End with RRAM-Based Lowpass FIR Filter for EEG Signal Detection
Authors: Ren Qirui , Chen Chengying , Dong Danian , Xu Xiaoxin , Chen Yong , Zhang Feng
Published: August 2022

This brief presents an analog front-end (AFE) for the detection of electroencephalogram (EEG) signals. The AFE is composed of four sections, chopper-stabilized amplifiers, ripple suppression circuit, RRAM-based lowpass FIR filter, and 8-bit SAR ADC. This is the first time that an RRAM-based lowpass FIR filter has been introduced in an EEG AFE, where the bio-plausible characteristics of RRAM are utilized to analyze signals in the analog domain with high efficiency. The preamp uses the symmetrical OTA structure, reducing power consumption while meeting gain requirements. The ripple suppression circuit greatly improves noise characteristics and offset voltage. The RRAM-based low-pass filter achieves a 40 Hz cutoff frequency, which is suitable for the analysis of EEG signals. The SAR ADC adopts a segmented capacitor structure, effectively reducing the capacitor switching power consumption. The chip prototype is designed in 40 nm CMOS technology. The overall power consumption is approximately 13 µW, achieving ultra-low-power operation.
New Conceptional Study of a Portable Highly Sensitive Photometric Raman Sensor
Authors: Manser Steffen , Kommert Sandy , Keck Shaun , Spoor Erik , Rädle Matthias
Published: August 2022

Quality control and reaction monitoring are necessary to ensure the consistency of any kind of mixing or reaction process. In this context, a novel portable high-sensitivity sensor prototype based on the Raman effect is presented in this study. The elongated probe head is designed for (but not limited to) monitoring high temperature batch processes for quality assurance. Thanks to the highly sensitive special detectors, concentration differences of up to 50 mmol/L can currently be detected, which facilitates compliance with high product quality standards. In addition, seamless reaction tracking is possible. Small individual adjustments through simple, intuitive mechanical components provide a high degree of flexibility in reaction selection by the end user. Specially developed software automates the evaluation process and gives the user visual signals about the current status and progress of the batch as well as an emergency stop if temperature limits could damage individual components. By using all the individual components developed, the problem of the limited integration times of previous spectrometric measuring instruments could be reduced. The prototype was validated using concentration measurements of various substances that occur as standard in batch processes. In addition, this article provides an outlook on the fact that Raman measurements can also be carried out successfully and reliably in explosive environments in the future with the prototype presented.
Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model
Authors: Xie Jia , Wang Zhu , Yu Zhiwen , Guo Bin
Published: August 2022

Modern healthcare practice, especially in intensive care units, produces a vast amount of multivariate time series of health-related data, e.g., multi-lead electrocardiogram (ECG), pulse waveform, blood pressure waveform and so on. As a result, timely and accurate prediction of medical intervention (e.g., intravenous injection) becomes possible, by exploring such semantic-rich time series. Existing works mainly focused on onset prediction at the granularity of hours that was not suitable for medication intervention in emergency medicine. This research proposes a Multi-Variable Hybrid Attentive Model (MVHA) to predict the impending need of medical intervention, by jointly mining multiple time series. Specifically, a two-level attention mechanism is designed to capture the pattern of fluctuations and trends of different time series. This work applied MVHA to the prediction of the impending intravenous injection need of critical patients at the intensive care units. Experiments on the MIMIC Waveform Database demonstrated that the proposed model achieves a prediction accuracy of 0.8475 and an ROC-AUC of 0.8318, which significantly outperforms baseline models.
Uncovering Streptomyces-Derived Compounds as Cosmeceuticals for the Development of Improved Skin Photoprotection Products: An In Silico Approach to Explore Multi-Targeted Agents
Authors: Sánchez-Suárez Jeysson , Villamil Luisa , Díaz Luis , Coy-Barrera Ericsson
Published: August 2022

The search for novel photoprotective substances has become a challenge in cosmeceutical research. Streptomyces-derived compounds can serve as a promising source of photoprotective agents to formulate skin photoprotection products, such as sunscreens. This study aimed to identify specialized metabolites with the potential to modulate UV-induced cellular damage in the skin by identifying potential multi-target-directed ligands. Using a combination of ligand- and target-based virtual screening approaches, a public compound library comprising 6524 Streptomyces-derived specialized metabolites was studied for their photoprotective capability. The compounds were initially filtered by safety features and then examined for their ability to interact with key targets in the photodamage pathway by molecular docking. A set of 50 commercially available UV filters was used as the benchmark. The protein–ligand stability of selected Streptomyces-derived compounds was also studied by molecular dynamics (MD) simulations. From the compound library, 1981 compounds were found to meet the safety criteria for topically applied products, such as low skin permeability and low or non-toxicity-alerting substructures. A total of 34 compounds had promising binding scores against crucial targets involved in UV-induced photodamage, such as serotonin-receptor subtype 5-HT2A, platelet-activating factor receptor, IL-1 receptor type 1, epidermal growth factor receptor, and cyclooxygenase-2. Among these compounds, aspergilazine A and phaeochromycin F showed the highest ranked interactions with four of the five targets and triggered complex stabilization over time. Additionally, the predicted UV-absorbing profiles also suggest a UV-filtering effect. Streptomyces is an encouraging biological source of compounds for developing topical products. After in silico protein–ligand interactions, binding mode and stabilization of aspergilazine A and phaeochromycin F led to the discovery of potential candidates as photodamage multi-target inhibitors. Therefore, they can be further explored for the formulation of skin photoprotection products.
Serial vs. Integrated Outdoor Combined Training Programs for Health Promotion in Middle-Aged Males
Authors: Grivas Gerasimos V. , Karatrantou Konstantina , Chasialis Athanasios , Batatolis Christos , Ioakimidis Panagiotis , Gerodimos Vassilis
Published: August 2022

The purpose of this study was to examine and compare the training and detraining effects of outdoor serial and integrated combined exercise programs on health, functional capacity, and physical fitness indices. Fifty-one untrained overweight/obese males (47 ± 4 years) were divided into a serial combined (SCG), an integrated combined (ICG), or a control (CG) group. The SCG and ICG implemented a 3-month training (3 sessions/week) consisting of walking and body weight exercises. The only difference between SCG and ICG was the sequence of aerobic and strength training. In SCG, the strength training was performed before aerobic training, while in ICG the aerobic and the strength training were alternated repeatedly in a predetermined order. Health, functional capacity, and physical fitness indices were measured before the training, following the termination of programs, and 1-month after training cessation. Following the training, both the SCG and ICG groups showed reduced blood pressure, heart rate, body fat, and waist-to-hip ratio (3–11%; p < 0.001), with improved respiratory function, muscle strength, aerobic capacity, flexibility, and balance (14–61%; p < 0.001). After 1-month of training cessation, significant reductions (p < 0.05) were observed in health indices and physical fitness without returning to baseline levels. However, there were no differences between SCG and ICG after training and training cessation (p > 0.05). In CG, all the above variables did not change. Furthermore, a great percentage of participants in both exercise groups (90%) reported high levels of enjoyment. In conclusion, both serial and integrated outdoor combined walking and body weight strength training programs are enjoyable and equally effective for improving health, functional capacity, and physical fitness indices in overweight/obese middle-aged males.
How the Agricultural Press Addresses the Slurry–Water Nexus: A Text Mining Analysis
Authors: Artner-Nehls Astrid , Uthes Sandra , Zscheischler Jana , Feindt Peter H.
Published: August 2022

Water pollution from intensive livestock husbandry is a persistent social-ecological problem. Since remedies require attention to the slurry–water nexus among practitioners, the agricultural press is a strategic entry point for agenda setting. Systematic content analysis can provide insights into how farming practices and sustainability issues are communicated, which may influence farmers’ attention to the issue and to potential solutions. To address this question, we present a semantic network analysis of three specialized farming magazines in Germany and analyze their coverage of the slurry–water nexus, in particular relationships of actors and issues and co-occurrence with political events. We used text mining methods in order to analyze a text corpus consisting of 4227 online articles published between 2010 and 2020. Results show that one fifth of all slurry-themed articles contained water-related content. We found a shift over time from dominantly management-oriented content towards a politicized debate with more actors and stronger semantic relationships with water protection constructed as an insulated stand-alone issue. This is accompanied by a shift from thematic reporting to episodic reporting focused on environmental legislation and compliance management. This implies less attention to innovations for water-conserving slurry management. Despite its shortcomings, episodic coverage may open up windows of opportunity to improve communication by experts and policy makers.
Fatigue Durability Analysis for Suspenders of Arch Bridge Subjected to Moving Vehicles in Southwest China
Authors: Zhang Zimo , Wang Hua , Yang Tao , Wang Longlin , Wang Xirui
Published: August 2022

An analysis of the effect of vehicle loads on the fatigue life of local arch bridge suspenders in Southwest China was conducted by generating vehicle statistics, creating a fatigue vehicle model, and developing a fatigue life prediction method: First, the Midas model of an arch bridge was established, a random vehicle flow simulation was conducted in the Monte Carlo method, and the stress spectrum of the suspenders was calculated. Secondly, ANSYS software was used to estimate the fatigue life of the suspenders, and the accuracy of the proposed method was verified. Finally, the method was applied to the practical project of the Dafeng River Bridge in Southwest China. The results show that this method can predict the fatigue life of suspenders. It can be applied in the bridge health monitoring software system to realize real-time fatigue damage monitoring of the fatigue damage of suspenders, which could be used to predict the fatigue lives of bridges in Southwest China and provide references for the replacement of suspenders.
Similarities and Proximity Symmetries for Decisions of Complex Valuation of Mining Resources in Anthropically Affected Areas
Authors: Gâf-Deac Ioan I. , Jaradat Mohammad , Bran Florina , Crețu Raluca Florentina , Moise Daniel , Platagea Gombos Svetlana , Breaz Teodora Odett
Published: August 2022

After 1990, when the economic system changed in Romania, the mining industry was the most controversial field from a productive-economic point of view and subject to reforms and transformations for efficiency. Currently (2022), there are nine main mining perimeters in which the production of useful, energetic, and nonenergetic mineral substances is operational, and in others it has decreased or stopped. Still active mining areas need economic and ecological assessments to identify similarities and proximity symmetries for informed exploitation decisions and feasible complex resource utilization. The main objective of our study is to define a framework for the theoretical and practical contribution to the substantiation of decisions and expressions of interest regarding future investments in mining projects for useful and energetic and non-energetic mineral substances in Romania. Investments in the mining industry are expensive, with major risks and subunit success rates for specific geotechnological conditions. The purpose of the research is to provide the methodology for using some variables of similarities from proximity mining deposits in the stage of exploitation or post-exploitation affected by anthropogenic activity in the national geological territory through a case study of Romania. With the help of statistical scales, the research results highlight that in the exploitation and post-mining perimeters in Romania, the states of “affect” and “post-affect” anthropic, respectively, of eco-economic damage are in a maximum proportion of approximately 36% in relation to the ideal situations of no affect. For a mining investment project, knowing similar or symmetrical exploitation and post-exploitation properties and situations, and from the geological deposits in the vicinity, premises are created for optimized strategic and tactical decisions, based on reality and, above all, for the provision of expressions of interest for new investments that have a programmed, expected success rate.
An Empirical Research of Students’ Perceptions Regarding M-Commerce Acquisitions during the COVID-19 Pandemic
Authors: Grădinaru Cătălin , Catană Ștefan-Alexandru , Toma Sorin George , Barbu Andreea
Published: August 2022

The emergence and spread of the COVID-19 pandemic have significantly changed the way commerce processes have been carried out over the last two years. Considering the development of the Internet and the increasing use of digitalization in recent years, electronic commerce has become an important part of the global retail framework. Accordingly, mobile commerce has emerged and developed through various applications as a modern alternative for buying and selling products and/or services using only mobile devices. This paper aims to identify and analyze several key factors that influence students’ perceptions regarding m-commerce acquisitions. It also attempts to illustrate some of the main advantages and disadvantages of m-commerce acquisition and to investigate its influence on students’ perceptions regarding m-commerce purchases. In order to achieve these objectives, the authors gathered data through a quantitative research method by using a questionnaire. The data were analyzed and interpreted through a factorial analysis that uses the presentation of the main components as an extraction method, with the varimax rotation method adopting Kaiser normalization, and processed with SPSS statistical software. The results of this research show that mobile-commerce acquisitions are influenced by five factors (social, political-legislative, technological, financial, and economic). In this respect, social and political-legislative factors influence, at a moderate level, the general frequency of m-commerce acquisition, while the economic factor does not influence the general frequency of m-commerce purchases. The study provides a theoretical model that takes into account the factors that influence m-commerce acquisition, including the influence of the perceived advantages and disadvantages on m-commerce purchase. The paper also displays the way in which these items influence students’ perception on m-commerce acquisitions.
Advantages and Challenges of Composting Reactors for Household Use: Smart Reactor Concept
Authors: Stipniece Alise Anna , Vladinovskis Vlads , Daugulis Pauls , Zemite Marta , Vitola Laura , Mezule Linda
Published: August 2022

In the European Union, 88 Mt of food waste is generated annually, accounting for 6% of total EU greenhouse gas emissions. To reduce the amount of bio-waste going into the landfills, the composting of bio-waste at a household level must be facilitated. Traditional composting devices for garden and household biological waste solely rely on natural processes and do not hold online process control features or require energy input. This study describes a design and construction of a smart composting reactor for improved composting process control and compares the developed system with other laboratory-scale reactors and commercial devices available for this purpose. The Alternative Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis method were used to assess the importance of various parameters and devices. The results showed good thermal insulation by reducing thermal transmittance from 1.87 W/m²K to 1.27 W/m²K, the effective sensor system performance of the constructed system, providing continuous data logging of temperature, moisture, and gas concentration levels. The system demonstrated 58% proximity to the ideal solution.
Study on the Digital Transformation Capability of Cost Consultation Enterprises Based on Maturity Model
Authors: Han Xiao , Zhang Menghan , Hu Yixuan , Huang Yuan
Published: August 2022

The digital maturity model (DMM) can help enterprises scientifically evaluate their digital maturity level, identify their weaknesses, and then further formulate improvement strategies. Most of the existing relevant studies focus on the Internet industry, new retail industry, and manufacturing industry, while discussion on other industries is rare. The cost consultation industry is a typical data-intensive industry. The study on its digital transformation capacity is a response to the digital cost trend in the current engineering field. The purpose of this paper is to build a DMM to evaluate the digital maturity level of cost consultation enterprises. Based on capability maturity theory and industry characteristics, the key dimensions and specific evaluation index system of the digital transformation of cost consultation enterprises are determined by using literature analysis, expert interview, and other methods, and a DMM of cost consultation enterprises is built. The model covers six dimensions, namely, top-level design, infrastructure, cost consultation business process, professional management, comprehensive integration, and digital cost performance, which are divided into 18 categories and 61 domains. On this basis, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation are comprehensively used to evaluate the digital maturity. Finally, a benchmark central enterprise in China’s high-speed rail industry, the Institute of Industry and Economics, China Railway Siyuan Survey and Design Group Co., Ltd. is selected for the case study. Results show that this enterprise, as a whole, is at the digital pioneer level. However, the effects of professional management and comprehensive integration are general, which should be the future improvement direction of digital transformation. This paper can provide the theoretical reference for the digital transformation of the cost consultation industry.
Multimodal Evaluation of Changes in National Potential Passenger and Freight Accessibility during the EU-Driven Big Push to Transport Infrastructure
Authors: Rosik Piotr , Komornicki Tomasz , Goliszek Sławomir , Pomianowski Wojciech , Stępniak Marcin
Published: August 2022

The main purpose of the paper is to present a methodical approach to differences in changes in intra-national potential accessibility. Research concerns level of accessibility and its dispersion, both for freight and passenger transport regarding four transport modes. The main added value of the paper is an indication of how to monitor changes in the accessibility of many modes of transport at the municipal level, as a result of investment processes, while taking into account the available data sources, both on the land use data and travel times, with the support of regional and central offices. We focus on the intensive development of transport infrastructure after Poland’s accession to the EU. We conclude that outcomes of reducing territorial differences in accessibility are diverse depending on the transport mode: from a generally positive effect in passenger transport to varied effects in freight transport, including particularly highly polarising effects in rail transport. The research method provides the possibility of analysing multimodal changes in accessibility concerning numerous transport modes for any large country or group of countries. Certain development opportunities of the model to be implemented in the future are discussed.