bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Oscillation Amplitude Variations on QCM Response to Microspheres of Different Sizes
Authors: Zampetti Emiliano , Mancuso Maria Aurora , Dirri Fabrizio , Palomba Ernesto , Papa Paolo , Capocecera Alessandro , Bearzotti Andrea , Macagnano Antonella , Scaccabarozzi Diego
Published: June 2023

Suspended particulate matter (PMx) is one of the most important environmental pollutants. Miniaturized sensors capable of measuring and analyzing PMx are crucial in environmental research fields. The quartz crystal microbalance (QCM) is one of the most well-known sensors that could be used to monitor PMx. In general, in environmental pollution science, PMx is divided into two main categories correlated to particle diameter (e.g., PM < 2.5 µm and PM < 10 µm). QCM-based systems are capable of measuring this range of particles, but there is an important issue that limits the application. In fact, if particles with different diameters are collected on QCM electrodes, the response will be a result of the total mass of particles; there are no simple methods to discriminate the mass of the two categories without the use of a filter or manipulation during sampling. The QCM response depends on particle dimensions, fundamental resonant frequency, the amplitude of oscillation, and system dissipation properties. In this paper, we study the effects of oscillation amplitude variations and fundamental frequency (10, 5, and 2.5 MHz) values on the response, when particle matter with different sizes (2 µm and 10 µm) is deposited on the electrodes. The results showed that the 10 MHz QCM was not capable of detecting the 10 µm particles, and its response was not influenced by oscillation amplitude. On the other hand, the 2.5 MHz QCM detected the diameters of both particles, but only if a low amplitude value was used.
Quantum Neural Network Based Distinguisher on SPECK-32/64
Authors: Kim Hyunji , Jang Kyungbae , Lim Sejin , Kang Yeajun , Kim Wonwoong , Seo Hwajeong
Published: June 2023

As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr’s work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network’s various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper.
Some Design Considerations in Passive Indoor Positioning Systems
Authors: Engström Jimmy , Jevinger Åse , Olsson Carl Magnus , Persson Jan A.
Published: June 2023

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user’s privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user’s privacy in a busy office environment.
A Circuit-Level Solution for Secure Temperature Sensor
Authors: Kajol Mashrafi Alam , Monjur Mohammad Mezanur Rahman , Yu Qiaoyan
Published: June 2023

Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore. As sensors are typically low-end devices, there is no built-in defense mechanism in sensors. It is common that system-level defense provides protection against security threats on sensors. Unfortunately, high-level countermeasures do not differentiate the root of cause and treat all anomalies with system-level recovery processes, resulting in high-cost overhead on delay and power consumption. In this work, we propose a secure architecture for temperature sensors with a transducer and a signal conditioning unit. The proposed architecture estimates the sensor data with statistical analysis and generates a residual signal for anomaly detection at the signal conditioning unit. Moreover, complementary current–temperature characteristics are exploited to generate a constant current reference for attack detection at the transducer level. Anomaly detection at the signal conditioning unit and attack detection at the transducer unit make the temperature sensor attack resilient to intentional and unintentional attacks. Simulation results show that our sensor is capable of detecting an under-powering attack and analog Trojan from a significant signal vibration in the constant current reference. Furthermore, the anomaly detection unit detects anomalies at the signal conditioning level from the generated residual signal. The proposed detection system is resilient against any intentional and unintentional attacks, with a detection rate of 97.73%.
Research on the Health Assessment Method of the Safety Retaining Wall in a Dump Based on UAV Point-Cloud Data
Authors: Mao Yachun , Zhang Xin , Cao Wang , Fan Shuo , Wang Hui , Yang Zhexi , Ding Bo , Bai Yu
Published: June 2023

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel.
Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications
Authors: Kolaghassi Rania , Marcelli Gianluca , Sirlantzis Konstantinos
Published: June 2023

Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges.
Health Status Recognition Method for Rotating Machinery Based on Multi-Scale Hybrid Features and Improved Convolutional Neural Networks
Authors: Cao Xiangang , Guo Xingyu , Duan Yong , Zhang Fuqiang , Fan Hongwei , Xu Xin
Published: June 2023

Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutional neural networks (MSCCNN) health status identification methods for rotating machinery. Firstly, the rotating machinery vibration signal is decomposed into intrinsic modal components (IMF) using empirical wavelet decomposition, and multi-scale hybrid feature sets are constructed by simultaneously extracting time-domain, frequency-domain and time-frequency-domain features based on the original vibration signal and the intrinsic modal components it decomposes. Secondly, using correlation coefficients to select features sensitive to degradation, construct rotating machinery health indicators based on kernel principal component analysis and complete health state classification. Finally, a convolutional neural network model (MSCCNN) incorporating multi-scale convolution and hybrid attention mechanism modules is developed for health state identification of rotating machinery, and an improved custom loss function is applied to improve the superiority and generalization ability of the model. The bearing degradation data set of Xi’an Jiaotong University is used to verify the effectiveness of the model. The recognition accuracy of the model is 98.22%, which is 5.83%, 3.30%, 2.29%, 1.52%, and 4.31% higher than that of SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features, respectively. The PHM2012 challenge dataset is used to increase the number of samples to validate the model effectiveness, and the model recognition accuracy is 97.67%, which is 5.63%, 1.88%, 1.36%, 1.49%, and 3.69% higher compared to SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features methods, respectively. The MSCCNN model recognition accuracy is 98.67% when validated on the degraded dataset of the reducer platform.
Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems
Authors: Kim Tae-Kyoung , Min Moonsik
Published: June 2023

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.
Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications
Authors: Okita Shusuke , Yakunin Roman , Korrapati Jathin , Ibrahim Mina , Schwerz de Lucena Diogo , Chan Vicky , Reinkensmeyer David J.
Published: June 2023

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call “Hand Activity Recognition through using a Convolutional neural network with Spectrograms” (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R2 = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.
Optimal Pressure Sensor Deployment for Leak Identification in Water Distribution Networks
Authors: Yang Guang , Wang Hai
Published: June 2023

Pipe leakage is an inevitable phenomenon in water distribution networks (WDNs), leading to energy waste and economic damage. Leakage events can be reflected quickly by pressure values, and the deployment of pressure sensors is significant for minimizing the leakage ratio of WDNs. Concerning the restriction of realistic factors, including project budgets, available sensor installation locations, and sensor fault uncertainties, a practical methodology is proposed in this paper to optimize pressure sensor deployment for leak identification in terms of these realistic issues. Two indexes are utilized to evaluate the leak identification ability, that is, detection coverage rate (DCR) and total detection sensitivity (TDS), and the principle is to determine priority to ensure an optimal DCR and retain the largest TDS with an identical DCR. Leakage events are generated by a model simulation and the essential sensors for maintaining the DCR are obtained by subtraction. In the event of a surplus budget, and if we suppose the partial sensors have failed, then we can determine the supplementary sensors that can best complement the lost leak identification ability. Moreover, a typical WDN Net3 is employed to show the specific process, and the result shows that the methodology is largely appropriate for real projects.
Hold My Hand: Development of a Force Controller and System Architecture for Joint Walking with a Companion Robot
Authors: Coronado Enrique , Shinya Toshifumi , Venture Gentiane
Published: June 2023

In recent years, there has been a growing interest in the development of robotic systems for improving the quality of life of individuals of all ages. Specifically, humanoid robots offer advantages in terms of friendliness and ease of use in such applications. This article proposes a novel system architecture that enables a commercial humanoid robot, specifically the Pepper robot, to walk side-by-side while holding hands, and communicating by responding to the surrounding environment. To achieve this control, an observer is required to estimate the force applied to the robot. This was accomplished by comparing joint torques calculated from the dynamics model to actual current measurements. Additionally, object recognition was performed using Pepper’s camera to facilitate communication in response to surrounding objects. By integrating these components, the system has demonstrated its capability to achieve its intended purpose.
Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine
Authors: Qiu Zhongze , Guo Hangfu , Hu Jun , Jiang Hejun , Luo Chaopeng
Published: June 2023

Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine detection. Using the UAV-borne multispectral cruise platform, we establish a multispectral dataset of scatterable mines, with mine-spreading areas of the ground vegetation considered. In order to achieve the robust detection of occluded landmines, first, we employ an active learning strategy to refine the labeling of the multispectral dataset. Then, we propose an image fusion architecture driven by detection, in which we use YOLOv5 for the detection part, to improve the detection performance instructively while enhancing the quality of the fused image. Specifically, a simple and lightweight fusion network is designed to sufficiently aggregate texture details and semantic information of the source images and obtain a higher fusion speed. Moreover, we leverage detection loss as well as a joint-training algorithm to allow the semantic information to dynamically flow back into the fusion network. Extensive qualitative and quantitative experiments demonstrate that the detection-driven fusion (DDF) that we propose can effectively increase the recall rate, especially for occluded landmines, and verify the feasibility of multispectral data through reasonable processing.
Assessing Industrial Communication Protocols to Bridge the Gap between Machine Tools and Software Monitoring
Authors: Tapia Endika , Sastoque-Pinilla Leonardo , Lopez-Novoa Unai , Bediaga Iñigo , López de Lacalle Norberto
Published: June 2023

Industrial communication protocols are protocols used to interconnect systems, interfaces, and machines in industrial environments. With the advent of hyper-connected factories, the role of these protocols is gaining relevance, as they enable the real-time acquisition of machine monitoring data, which can fuel real-time data analysis platforms that conduct tasks such as predictive maintenance. However, the effectiveness of these protocols is largely unknown and there is a lack of empirical evaluation which compares their performance. In this work, we evaluate OPC-UA, Modbus, and Ethernet/IP with three machine tools to assess their performance and their complexity of use from a software perspective. Our results show that Modbus provides the best latency figures and communication has different complexities depending on the used protocol, from the software perspective.
Semiconductor Optical Amplifier (SOA)-Driven Reservoir Computing for Dense Wavelength-Division Multiplexing (DWDM) Signal Compensation
Authors: Yang Yinke , Luo Huiwen , Zhang Rui , Yang Feng , Wu Baojian , Qiu Kun , Wen Feng
Published: June 2023

Optical signal processing (OSP) technology is a crucial part of the optical switching node in the modern optical-fiber communication system, especially when advanced modulation formats, e.g., quadrature amplitude modulation (QAM), are applied. However, the conventional on–off keying (OOK) signal is still widely used in access or metro transmission systems, which leads to the compatibility requirement of OSP for incoherent and coherent signals. In this paper, we propose a reservoir computing (RC)-OSP scheme based on nonlinear mapping behavior through a semiconductor optical amplifier (SOA) to deal with the non-return-to-zero (NRZ) signals and the differential quadrature phase-shift keying (DQPSK) signals in the nonlinear dense wavelength-division multiplexing (DWDM) channel. We optimized the key parameters of SOA-based RC to improve compensation performance. Based on the simulation investigation, we observed a significant improvement in signal quality over 10 dB compared to the distorted signals on each DWDM channel for both the NRZ and DQPSK transmission cases. The compatible OSP achieved by the proposed SOA-based RC could be a potential application of the optical switching node in the complex optical fiber communication system, where incoherent and coherent signals meet.
Diffusion Weighted Imaging Super-Resolution Algorithm for Highly Sparse Raw Data Sequences
Authors: Malczewski Krzysztof
Published: June 2023

The utilization of quick compression-sensed magnetic resonance imaging results in an enhancement of diffusion imaging. Wasserstein Generative Adversarial Networks (WGANs) leverage image-based information. The article presents a novel G-guided generative multilevel network, which leverages diffusion weighted imaging (DWI) input data with constrained sampling. The present study aims to investigate two primary concerns pertaining to MRI image reconstruction, namely, image resolution and reconstruction duration. The implementation of simultaneous k-q space sampling has been found to enhance the performance of Rotating Single-Shot Acquisition (RoSA) without necessitating any hardware modifications. Diffusion weighted imaging (DWI) is capable of decreasing the duration of testing by minimizing the amount of input data required. The synchronization of diffusion directions within PROPELLER blades is achieved through the utilization of compressed k-space synchronization. The grids utilized in DW-MRI are represented by minimal-spanning trees. The utilization of conjugate symmetry in sensing and the Partial Fourier approach has been observed to enhance the efficacy of data acquisition as compared to unaltered k-space sampling systems. The image’s sharpness, edge readings, and contrast have been enhanced. These achievements have been certified by numerous metrics including PSNR and TRE. It is desirable to enhance image quality without necessitating any modifications to the hardware.
Advanced Intelligent Control in Robots
Authors: Vladareanu Luige , Yu Hongnian , Wang Hongbo , Feng Yongfei
Published: June 2023

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges [...]
Correction: O’Brien et al. Correction of Radiometry Data for Temperature Effect on Dark Current, with Application to Radiometers on Profiling Floats. Sensors 2022, 22, 6771
Authors: O’Brien Terence , Boss Emmanuel
Published: June 2023

The authors wish to correct the following errors in the original paper [...]
Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images
Authors: Kim Soon-Young , Muminov Azamjon
Published: June 2023

Wildfire poses a significant threat and is considered a severe natural disaster, which endangers forest resources, wildlife, and human livelihoods. In recent times, there has been an increase in the number of wildfire incidents, and both human involvement with nature and the impacts of global warming play major roles in this. The rapid identification of fire starting from early smoke can be crucial in combating this issue, as it allows firefighters to respond quickly to the fire and prevent it from spreading. As a result, we proposed a refined version of the YOLOv7 model for detecting smoke from forest fires. To begin, we compiled a collection of 6500 UAV pictures of smoke from forest fires. To further enhance YOLOv7’s feature extraction capabilities, we incorporated the CBAM attention mechanism. Then, we added an SPPF+ layer to the network’s backbone to better concentrate smaller wildfire smoke regions. Finally, decoupled heads were introduced into the YOLOv7 model to extract useful information from an array of data. A BiFPN was used to accelerate multi-scale feature fusion and acquire more specific features. Learning weights were introduced in the BiFPN so that the network can prioritize the most significantly affecting characteristic mapping of the result characteristics. The testing findings on our forest fire smoke dataset revealed that the proposed approach successfully detected forest fire smoke with an AP50 of 86.4%, 3.9% higher than previous single- and multiple-stage object detectors.
Enhancing Security in ZigBee Wireless Sensor Networks: A New Approach and Mutual Authentication Scheme for D2D Communication
Authors: Allakany Alaa , Saber Abeer , Mostafa Samih M. , Alsabaan Maazen , Ibrahem Mohamed I. , Elwahsh Haitham
Published: June 2023

The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme’s performance across various factors, including security features, communication, and computational cost.
Isolation and Identification of Flavonoids from Black Cumin (Nigella sativa) by HPLC-MS and In Silico Molecular Interactions of Their Major Compounds with Fusarium oxysporum Trypsin-like Serine Protease
Authors: El-Beltagi Hossam S. , Aziz Seham M. S. Abdel , Aboshady A. I. , Ibrahim Mervat A. R. , Ibrahim Mohamed F. M. , Alenezi Muneefah Abdullah , Darwish Doaa Bahaa Eldin , Al-Qahtani Salem Mesfir , Al-Harbi Nadi Awad , Darwish Hadeer , Srour Hany A. M.
Published: June 2023

Fusarium oxysporum is one of the most harmful soil-borne pathogens that cause root rot, damping-off, and wilt disease in many plant species. Management of Fusarium oxysporum diseases is often by using many harmful and expensive chemical fungicides which have many harmful effects on the environment and human health. The current study was conducted to identify the chemical constituents of black cumin seeds’ methanolic extract and investigate the ability of the major constituents to inhibit the Fusarium oxysporum trypsin-like serine protease, which play an important role in F. oxysporun pathogenicity. The HPLC-MS analysis of black cumin seeds’ methanolic extract revealed the presence of seven major compounds: amentoflavone, Procyanidin C2, Quercetin3-O-sophoroside-7-O-rhamnoside, 5,7-Dihydroxy-3,4-dimethoxyflavone, Borapetoside A, tetrahydroxy-urs-12-en-28-O-[b-D-glucopyranosyl (1-2)-b-D-glucopyranosyl] ester, and kudzusapongenol A-hexA-pen. The results of molecular docking between these compounds and the active site of Fusarium oxysporium trypsin showed that only four compounds were able to bind to the active site of F. oxysporum trypsin. Amentoflavone, 5,7-Dihydroxy-3,4-dimethoxyflavone, and Quercetin3-O-sophoroside-7-O-rhamnoside have the highest binding energy, −6.4, −6.5, and −6.5 Kcal/mol, respectively. In addition, the results clarify that 5,7-Dihydroxy-3,4-dimethoxyflavone was the only compound to form a hydrogen bond with Asp189 (the residue responsible for substrate specificity). The results of the study strongly indicate that flavonoids of black cumin seeds’ methanolic extract could be used as effective inhibitors for the F. oxysporum trypsin-like serine protease.
Phytotoxicity of Rich Oxygenated Terpenes Essential Oil of Prosopis farcta against the Weed Dactyloctenium aegyptium
Authors: Abd-ElGawad Ahmed M. , Assaeed Abdulaziz M. , El Gendy Abd El-Nasser , Pistelli Luisa , Dar Basharat A. , Elshamy Abdelsamed I.
Published: June 2023

Weeds are considered the main reason for crop yield loss in the world. Weed control and management include various treatments such as cultural, physical, chemical, and biological methods. Chemical control of weeds is the most common method; however, the application of commercial synthetic herbicides caused several dangerous hazards in the environment including the appearance of resistant weed biotypes. Prosopis farcta (Banks & Sol.) J.F.Macbr. (Family: Fabaceae), is a common weed plant in the Middle East, where it is hard to eliminate due to its deep and overlapped roots. On the other side, it has many traditional uses around the world. Herein, the essential oil (EO) of P. farcta above-ground parts was extracted via hydrodistillation techniques and then analyzed using gas chromatography-mass spectroscopy (GC-MS). From the GC-MS analysis, 47 compounds were identified with a relative concentration of 98.02%, including terpenes as the main components (95.08%). From overall identified compounds, cubenol (19.07%), trans-chrysanthenyl acetate (17.69%), torreyol (8.28%), davana ether (3.50%), camphor (3.35%), and farnesyl acetone (3.13%) represented the abundant constituents. Furthermore, the phytotoxic activity of the P. farcta EO was assessed against the weed Dactyloctenium aegyptium (L.) Willd. The EO of P. farcta, at a concentration of 100 µL L−1, significantly inhibited the germination, seedling shoot growth, and seedling root growth by 64.1, 64.0, and 73.4%, respectively. The results exhibited that the seedling root growth is the most affected followed by the seed germination and seedling shoot growth with respective IC50 at 64.5, 80.5, and 92.9 µL L−1. It can be concluded that weeds are not absolutely harmful, but they may have beneficial uses, such as, for example as a source of phytochemicals with application in weed control practices (bioherbicides). It is advised to conduct additional research to characterize the allelopathic action of the major chemicals in their pure form, either alone or in combination, against a variety of weeds.
Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation
Authors: Routray Abhinandan , Reddy Yiza Srikanth , Hur Sung-ho
Published: June 2023

Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.
Evaluation of Construction and Demolition Waste and Other Alternative Fills for Strip-Reinforced Soil Walls
Authors: Corrales Luis Alonso González , Pierozan Rodrigo Cesar , Araújo Gregório Luís Silva , Palmeira Ennio Marques
Published: June 2023

This article assesses the pullout performance of ribbed metallic strips embedded in fill soils that do not conform to conventional design criteria for mechanically stabilized earth (MSE) walls. These alternative fill soils include gravelly and sandy recycled aggregates from construction and demolition waste, artificial and natural sands, and fine-grained lateritic soil. The research included soil characterization tests and large-scale pullout tests, conducted as part of this study. The results showed that the reinforcement pullout behavior was similar for recycled, artificial, and natural sands, indicating that soil particle size played a crucial role in mobilizing the interface pullout resistance. However, in the case of recycled sand, stress concentration at the reinforcement level led to particle crushing during pullout conditions, causing this material to exhibit less efficient performance compared to other sands. The fine-grained lateritic soil demonstrated inferior behavior compared to sandy soils, despite the interparticle bonding provided by the sesquioxide coating characteristic of intensely weathered tropical soils. Finally, an analytical prediction tool based on experimental results was developed, providing an alternative method to make conjectures about the performance of different soils during the pre-design stages, particularly based on particle size attributes.
Effects of Biofermented Feed on Zophobas morio: Growth Ability, Fatty Acid Profile, and Bioactive Properties
Authors: Čaloudová Jana , Křištofová Kateřina , Pospiech Matej , Klempová Tatiana , Slaný Ondrej , Čertík Milan , Marcinčák Slavomír , Makiš Andrej , Javůrková Zdeňka , Pečová Martina , Zlámalová Michaela , Vrbíčková Lucie , Tremlová Bohuslava
Published: June 2023

The global population’s increasing demand for sustainable and nutritious food sources has led to the exploration of alternative approaches in livestock production. Edible insects have emerged as a promising solution due to their nutritional composition, including high protein content, balanced fats, minerals, vitamins, and bioactive peptides. Biofermentation offers a viable method to enhance the nutritional value of insect feed. This study aimed to investigate the effects of feeding biofermented feeds derived from less valuable raw materials on the yield and nutritional composition of Zophobas morio larvae. The focus was on assessing fat quality, omega-3 and omega-6 fatty acids, and bioactive compounds and monitoring the larvae’s weight and length increases. Three feed types were tested: wheat bran (control), fermented wheat bran, and a mixture of fermented corn and flaxseed in a five-week period. The findings demonstrated a noteworthy (p < 0.05) elevation in polyunsaturated fatty acids, such as gamma-linolenic acid, alpha-linolenic acid, and eicosapentaenoic acid in Zophobas morio larvae fed with biofermented corn and flaxseed pomace, both pre- and postculinary treatment, as compared to the control group. The study also showed increased chelation of Cu2+ (p < 0.05) and Fe2+ (p < 0.05) between native and roasted insects in the samples without in vitro digestion, as well as increased Cyclooxygenase-1 activity (p < 0.05), indicating improved bioavailability. Additionally, culinary processing led to a reduction in polyphenol content (p < 0.05), antioxidant activity (p > 0.05) except DPPH, and peptide concentration (p < 0.05) in the samples without in vitro digestion.
Efficiency of Transport Infrastructure in Asian Russia, China, Mongolia, and Kazakhstan in the Context of Creating New Trans-Eurasian Transport Corridors
Authors: Rygzynov Tumun Sh. , Batomunkuev Valentin S. , Gomboev Bair O. , Dong Suocheng , Sharaldaev Bayanzhargal B. , Ayusheeva Valentina G. , Badmaev Aldar G. , Motoshkina Marina A. , Zangeeva Natalya R. , Tsybikova Aryuna B. , Tsydypov Vitaly E. , Zhamyanov Daba Ts.-D. , Banzaraktcaev Zorikto E. , Alekseev Aleksei V. , Popov Dmitry V. , Bazarzhapov Tcogto Zh.
Published: June 2023

This article discusses the efficiency of transport infrastructure and cooperation of neighboring regions located in Asian Russia, China, Mongolia, and Kazakhstan in the context of creating new international economic corridors from the Silk Road and trans-Eurasian transport corridors. This study aims to highlight the possible ways of strengthening cross-border cooperation in the field of transport infrastructure. We evaluated the current state of the transport infrastructure, the dynamics of its development, and its influence on the territorial–production complex. Using quantitative data and the unified indicator for the efficiency of transport infrastructure, we also characterized the territorial differentiation, its causes, and prerequisites for further economic and trade cooperation between these countries. The main results are as follows: (1) The lowest levels of the efficiency of transport infrastructure are typical for the northeast of Asian Russia, as well as for the border regions of China, Mongolia, and Kazakhstan. (2) For Asian Russia, Kazakhstan, and Mongolia, the highest levels of the unified indicator are typical for regions located along the main transport routes and for regions with a developed mining industry. This is due to the strong unevenness of the socio-economic development of the territories. (3) The largest industrial and economic centers have been developing along the main transport corridors primarily due to the accumulated potential of equivalent freight turnover and export potential. This study can be useful for authorities and business, as well as for other users of transport infrastructure to improve its regulation and efficiency.
Pastoralism and Tourism in Eastern Africa—Quantitative Analysis from 2004 to 2018
Authors: Bacsi Zsuzsanna , Gebbisa Mesfin Bekele , Dávid Lóránt Dénes , Hollósy Zsolt
Published: June 2023

Eastern Africa is a relatively dry area, with a considerable pastoralist population, which is among the poorest segments of society. Pastoralism is a form of subsistence lifestyle, and while pastoralists produce a large proportion of the region’s livestock products, they are not covered well by statistical recording. Pastoralists are experts in keeping livestock in arid rangelands, but they often suffer from land alienation, environmental degradation, and conflict with other land use intentions. The semiarid rangelands in Eastern Africa are home to spectacular savanna wildlife populations, attracting substantial conservation and tourism revenues. Estimations indicate that pastoralism generates significant economic values in the national income due to livestock production and maintenance of tourism attractions. To assess this contribution, the concept of total economic valuation (TEV) is applied. The main aim of the paper is to analyze the contribution of pastoralism to the tourism-related GDP of Ethiopia, Kenya, Uganda, and Tanzania, where considerable numbers of pastoralists live. Because of the lack of statistical data on pastoralism, the second objective is to construct a database of indicators that measure the extent of pastoralism for these countries for 2004, 2014, and 2018. The methodology includes the construction of the above database using secondary sources, and then to apply correlation and regression analysis on this database and the economic and tourism performance data series of the studied four countries. The results of the analysis showed that the extent of pastoralism is positively related to GDP and to value added by tourism and agriculture, and international tourism receipts are positively related to pastoralism’s contribution to GDP. The tourism competitiveness index (TTCI) was found to be negatively related to the size of the pastoralism sector. The policy implications of our findings are that pastoralist societies are increasingly important not only for their marketed economic output, but for their services provided to tourism and to the environment; therefore, instead of neglecting them, they should be more in the focus of development.
More Than Half of Emitted Black Carbon Is Missing in Marine Sediments
Authors: Chen Bing , Tiwari Shani , Liu Kun , Zou Jianjun
Published: June 2023

Marine sediments are the ultimate reservoir for black carbon (BC) preservation, and BC burial in sediment/soils is an efficient method for carbon sequestration to mitigate CO2 emissions. A portion of soil charcoal and atmospheric BC is dissolved in inland and oceanic water, but the amount of BC in the ocean remains unclear. We analyzed multi-sediment cores from the northwestern Pacific Ocean and lakes in China and reconstructed the timeline of BC deposition from 1860 to ~2012. The lacustrine sediment cores showed an increase in BC deposition by a factor of 4–7 during the industrialization period in China compared to the years 1860–1950 (reference level). Such increasing trends in BC have also been reproduced by ten global climate model simulations. However, the marine sediment cores did not retain these significant increases in BC deposition. Meanwhile, the model simulations predicted increased trends compared to the observed flat trends of BC deposition in marine sediments. The discrepancy suggests a large amount of BC, i.e., 65 (±11)%, is missing in marine sediment sinks. Thus, since more than half of emitted BC has dissolved into oceanic water, the dissolved BC and carbon cycle should be reconsidered in the global carbon budget.
GHG Emission Accounting and Reduction Strategies in the Academic Sector: A Case Study in Mexico
Authors: Cardoza Cedillo Leslie , Montoya Michelle , Jaldón Mónica , Paredes Ma Guadalupe
Published: June 2023

The carbon footprint (CF) quantifies the greenhouse gas (GHG) emissions generated by human activities, expressed in carbon dioxide equivalent (CO2e) units. It is an instrument for monitoring and mitigating the effects of climate change, which particularly affects low- and middle-income countries such as Mexico. The Mexican government has established a goal of reducing GHG emissions by 22% from the levels in 2000 by 2030. Although most efforts to reduce GHG emissions have been focused on the energy and agriculture sectors, the academic sector is also important since it can advise changes in public policy. In this study, the 2019 CF of the Centro Roberto Garza Sada (CRGS), a design school at the Universidad de Monterrey, was estimated in an effort to develop measures for reducing GHG emissions. The GHG Protocol was employed to calculate the total CF of the CRGS and identify the greatest contributors, including commuting (50.2%), energy purchase (28.5%), business travel (19.6%), and energy generation, use of paper, refrigerants, and shipments (1.7%). Three progressive mitigation scenarios were developed to reduce the GHG emissions from commuting, energy consumption, collaborators and student mobility, and material resources. These strategies could reduce the GHG emissions of the CRGS by 63.5% of the baseline assessed.