bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonlinearity-Tolerant Probabilistically-Shaped Four-Dimensional Modulation at Spectral Efficiency of 9 Bits/Four-Dimensional Symbol
Authors: Wei Pengpeng , Song Bo , Zhang Yixin , Li Xiang
Published: July 2023

Network traffic between data centers has grown rapidly in recent years. Therefore, an optical modulation format with high spectral efficiency is desired in inter-data center links at data rates of more than 400 Gb/s in each wavelength. In order to further improve the transmission performance in single-span inter-data center links, a nonlinearity-tolerant probabilistically-shaped 4D modulation format with a spectral efficiency of 9 bits/4D symbol is proposed. The proposed modulation format fills the gap between polarization-multiplexed 16- and 32-ary quadrature-amplitude modulation (PM-16QAM and PM-32QAM). In numerical simulations, we compare our proposed modulation format with probability-amplitude-shaped 64QAM (PAS-64QAM) and 512-set-partitioned 32QAM (512SP-32QAM) at a spectral efficiency of 9 bits/4D symbol in a single-span transmission link. The simulation results indicate that the proposed modulation format shows better linear and nonlinear performances than a 512SP-32QAM format. Compared with PAS-64QAM format, the proposed modulation format can provide similar transmission performance with improved fiber nonlinear tolerance.
Casimir Forces between a Dielectric and Metal: Compensation of the Electrostatic Interaction
Authors: Svetovoy Vitaly B.
Published: July 2023

The Casimir forces between metals or good conductors have been checked experimentally. Semiconductors and especially dielectrics have not been investigated because of the surface charges, which generate strong electrostatic forces. Here, it is proposed to study the Casimir interaction of a dielectric and metal using a thin dielectric layer deposited on an optically thick metallic substrate. If the thickness of the layer is a few tens of nanometers, the electrostatic force due to charging can be compensated for by applying an extra voltage between the metallic plates. On the other hand, the contribution of the dielectric layer to the Casimir force is sufficiently large to extract information about the interaction between the bulk dielectric and metal.
Short-Term Impact of Recycling-Derived Fertilizers on Their P Supply for Perennial Ryegrass (Lolium perenne)
Authors: Deinert Lea , Ikoyi Israel , Egeter Bastian , Forrestal Patrick , Schmalenberger Achim
Published: July 2023

Various nutrient recycling technologies are currently under development in order to alleviate the dependency of non-renewable raw material for the production of mineral phosphorus fertilizers commonly used in agriculture. The resulting products, such as struvites and ashes, need to be assessed for their application as so-called recycling-derived fertilizers (RDFs) in the agricultural sector prior to commercialization. Here, we conducted a short-term (54 days) trial to investigate the impact of different phosphorus fertilizers on plant growth and the soil P cycling microbiota. Lolium perenne was grown with application of superphosphate (SP) as inorganic fertilizer, two ashes (poultry litter ash (PLA) and sewage sludge ash (SSA)), and two struvites (municipal wastewater struvite (MWS) and commercial CrystalGreen® (CGS)) applied at 20 and 60 kg P ha−1 in four replicates. A P-free control (SP0) was also included in the trial. Struvite application increased plant dry weights, and available P acid phosphatase activity was significantly improved for struvites at the high P application rate. The ash RDFs showed a liming effect at 60 kg P ha−1, and PLA60 negatively affected acid phosphatase activity, while PLA20 had significantly lower phoD copy numbers. P mobilization from phosphonates and phytates was not affected. TCP solubilization was negatively affected by mineral SP fertilizer application at both P concentrations. The bacterial (16S and phoD) communities were only marginally affected by the tested P fertilizers. Overall, struvites appeared to be a suitable substitute for superphosphate fertilization for Irish L. perenne pastures.
A Review: Systemic Signaling in the Regulation of Plant Responses to Low N, P and Fe
Authors: Geng Zhi , Chen Jun , Lu Bo , Zhang Fuyuan , Chen Ziping , Liu Yujun , Xia Chao , Huang Jing , Zhang Cankui , Zha Manrong , Xu Congshan
Published: July 2023

Plant signal transduction occurs in response to nutrient element deficiency in plant vascular tissue. Recent works have shown that the vascular tissue is a central regulator in plant growth and development by transporting both essential nutritional and long-distance signaling molecules between different parts of the plant’s tissues. Split-root and grafting studies have deciphered the importance of plants’ shoots in receiving root-derived nutrient starvation signals from the roots. This review assesses recent studies about vascular tissue, integrating local and systemic long-distance signal transduction and the physiological regulation center. A substantial number of studies have shown that the vascular tissue is a key component of root-derived signal transduction networks and is a regulative center involved in plant elementary nutritional deficiency, including nitrogen (N), phosphate (P), and iron (Fe).
Multi-Scale Model for the Aging Performance of Particle-Filled Polymer Composites
Authors: Fang Congli , Wang Huizhen , Zhang Yujiao , Zhang Minghua , Shen Tao , Du Jianke
Published: July 2023

In this study, we developed a novel multi-scale model to predict the aging performance of particle-filled polymer composites (PFPCs) under thermo-oxidative aging conditions. To investigate the aging behavior, high-temperature accelerated aging tests were conducted in combination with macroscopic and microscopic characterization. At the microscopic level, the crosslinking density of the polymer matrix is calculated using the closed-loop chain reaction of polymer oxidation. In addition, the theory of polymer physics was used to determine the relationship between crosslinking density and elastic modulus. Relationships between elastic modulus and dewetting strain were analyzed at the macroscopic level. Based on the observations and analyses, a multi-scale model was developed to predict the aging performance of PFPCs. The predicted results show good agreement with the test results, which verifies the reliability of the model.
Highly Enhanced Mechanical, Thermal, and Crystallization Performance of PLA/PBS Composite by Glass Fiber Coupling Agent Modification
Authors: Fan Zhiqiang , Gao Junchang , Wu Yadong , Yin Dewu , Chen Shunxing , Tu Hua , Wei Tiantian , Zhang Chaoran , Zhu Haoxiang , Jin Huile
Published: July 2023

To improve the toughness and heat resistance of polylactic acid (PLA), polybutylene succinate (PBS) was sufficiently blended with PLA as the base matrix, and the glass fiber (GF) that was modified with 3-aminopropyltriethoxysilane (KF-GF) was added as the reinforcement. The results demonstrated a noteworthy boost in both mechanical and heat resistance properties when employing KH-GF, in comparison to pristine GF. When the content of KH-GF reached 20%, the tensile, flexural, and IZOD impact strength of the composites were 65.53 MPa, 83.43 MPa, and 7.45 kJ/m2, respectively, which were improved by 123%, 107%, and 189% compared to the base matrix, respectively. This enhancement was primarily attributed to the stronger interfacial adhesion between KH-GF and the PLA/PBS matrix. Furthermore, the Vicat softening temperature of the composites reached 128.7 °C, which was a result of increased crystallinity. In summary, the incorporation of KH-GF into PLA/PBS composites resulted in notable enhancements in their mechanical properties, crystallinity, and thermal characteristics. The high performance KH-GF-reinforced PLA/PBS composite showed a broad application potential in the field of biodegradable packaging, biodegradable textiles, and biodegradable plastic bags.
Research and Test on the Device of Downhole Near-Bit Temperature and Pressure Measurement While Drilling
Authors: Lu Ming , Liao Hualin , Wang Huajian , He Yuhang , Liu Jiansheng , Wang Yifan , Niu Wenlong
Published: July 2023

The accurate acquisition of downhole engineering parameters, such as real-time pressure and temperature measurements, plays a crucial role in mitigating drilling risks and preventing accidents. In this study, we present the design of a real-time data acquisition and transmission system for drilling operations. The system utilizes a near-bit measurement method to simultaneously measure downhole parameters, including mud pressure and temperature. By analyzing the pressure and temperature frequencies obtained from a quartz crystal pressure gauge and compensating for temperature effects, accurate pressure values are obtained. The resistance value of a PT1000 sensor is measured, and a second-order fitting is performed using laboratory scale coefficients to determine the temperature values. The data acquisition system employs an advanced microcontroller as the main control chip, along with an A/D conversion chip. Additionally, signal amplification, data storage modules, data transmission modules, and relevant peripheral circuits are designed. The field tests were conducted in the 4605~4620 m well section of well Qing 2-76 in the Yumen Oilfield. The results demonstrate stable transmission signals and accurate decoding, enabling the real-time monitoring of pressure and temperature. The tests yielded favorable outcomes, providing a tangible means to analyze the actual operating conditions of the downhole drill string.
Remote Sensing and Geovisualization of Rock Slopes and Landslides
Authors: Donati Davide , Stead Doug , Onsel Emre , Mysiorek Jesse , Chang Omar
Published: July 2023

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists.
A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net
Authors: Liu Jiahao , Wang Hong , Zhang Yao , Zhao Xili , Qu Tengfei , Tian Haozhe , Lu Yuting , Su Jingru , Luo Dingsheng , Yang Yalei
Published: July 2023

This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper proposes a deep supervised network RAunet model with multi-scale features that incorporates a dual-attention mechanism with an improved U-Net backbone network. The model mainly consists of a pyramid input layer, a modified U-Net backbone network, and a side output layer. Firstly, the pyramid input layer is used to fuse the feature information of winter wheat at different scales by constructing multiple input paths. Secondly, the Atrous Spatial Pyramid Pooling (ASPP) residual module and the Convolutional Block Attention Module (CBAM) dual-attention mechanism are added to the U-Net model to form the backbone network of the model, which enhances the feature extraction ability of the model for winter wheat information. Finally, the side output layer consists of multiple classifiers to supervise the results of different scale outputs. Using the RAunet model to extract the spatial distribution information of WW from GF-2 imagery, the experimental results showed that the mIou of the recognition results reached 92.48%, an improvement of 2.66%, 4.15%, 1.42%, 2.35%, 3.76%, and 0.47% compared to FCN, U-Net, DeepLabv3, SegNet, ResUNet, and UNet++, respectively. The superiority of the RAunet model in high-resolution images for WW extraction was verified in effectively improving the accuracy of the spatial distribution information extraction of WW.
UAV-Based Quantification of Dynamic Lahar Channel Morphology at Volcán de Fuego, Guatemala
Authors: Mock Jerry C. , Johnson Jeffrey B. , Pineda Armando , Bejar Gustavo , Roca Amilcar
Published: July 2023

This study quantified erosional and depositional processes for secondary lahars in Las Lajas drainage at Volcán de Fuego, Guatemala, during the rainy season from May to October 2021. Abundant pyroclastic material from ongoing eruptive activity is remobilized seasonally during heavy precipitation, which can impact infrastructure and populations living near Fuego. Our region of focus was in an agricultural zone 6 to 10 km from the summit, surveyed with an unoccupied aerial vehicle (UAV) quadcopter at monthly intervals. Imagery was processed into overlapping time-lapse structure from motion digital elevation models (DEMs). DEMs were differenced to find volumetric changes as a function of the channel flow path distance (quantified in 500 m sections) to track channel morphology changes over time. The largest measured volume changes were a 490 m3/day loss in the upper section (~6 km from summit) and a 440 m3/day gain in the lower sections (~10 km from summit). We discussed how the natural channel’s constriction and widening of Las Lajas in more distal sections control the behavior and stability of the stream evolution. Above the constriction, the channel is primarily downcutting and meandering within an old flood plain, which had been filled in by pyroclastic materials deposited by the June 2018 paroxysm.
Could Airborne Geophysical Data Be Used to Improve Predictive Modeling of Agronomic Soil Properties in Tropical Hillslope Area?
Authors: Bastos Blenda P. , Pinheiro Helena S. K. , Ferreira Francisco J. F. , Carvalho Junior Waldir de , dos Anjos Lúcia Helena C.
Published: July 2023

Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (μR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0–30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model’s performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping.
An Anti-Noise Convolutional Neural Network for Bearing Fault Diagnosis Based on Multi-Channel Data
Authors: Zhang Wei-Tao , Liu Lu , Cui Dan , Ma Yu-Ying , Huang Ju
Published: July 2023

In real world industrial applications, the working environment of a bearing varies with time, and some unexpected vibration noises from other equipment are inevitable. In order to improve the anti-noise performance of neural networks, a new prediction model and a multi-channel sample generation method are proposed to address the above problem. First, we proposed a multi-channel sample representation method based on the envelope time–frequency spectrum of a different channel and subsequent three-dimensional filtering to extract the fault features of samples. Second, we proposed a multi-channel data fusion neural network (MCFNN) for bearing fault discrimination, where the dropout technique is used in the training process based on a dataset with a wide rotation speed and various loads. In a noise-free environment, our experimental results demonstrated that the proposed method can reach a higher fault classification of 99.00%. In a noisy environment, the experimental results show that for the signal-to-noise ratio (SNR) of 0 dB, the fault classification averaged 11.80% higher than other methods and 32.89% higher under a SNR of −4 dB.
Thermal Time Constant CNN-Based Spectrometry for Biomedical Applications
Authors: Strąkowska Maria , Strzelecki Michał
Published: July 2023

This paper presents a novel method based on a convolutional neural network to recover thermal time constants from a temperature–time curve after thermal excitation. The thermal time constants are then used to detect the pathological states of the skin. The thermal system is modeled as a Foster Network consisting of R-C thermal elements. Each component is represented by a time constant and an amplitude that can be retrieved using the deep learning system. The presented method was verified on artificially generated training data and then tested on real, measured thermographic signals from a patient suffering from psoriasis. The results show proper estimation both in time constants and in temperature evaluation over time. The error of the recovered time constants is below 1% for noiseless input data, and it does not exceed 5% for noisy signals.
Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the Fritchman–Markov Model
Authors: Iyiola Akintunde O. , Familua Ayokunle D. , Swart Theo G. , Shongwe Thokozani
Published: July 2023

Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman–Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system’s statistical error process. The PLC system’s fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman–Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman–Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time.
Construction of Data-Driven Performance Digital Twin for a Real-World Gas Turbine Anomaly Detection Considering Uncertainty
Authors: Ma Yangfeifei , Zhu Xinyun , Lu Jilong , Yang Pan , Sun Jianzhong
Published: July 2023

Anomaly detection and failure prediction of gas turbines is of great importance for ensuring reliable operation. This work presents a novel approach for anomaly detection based on a data-driven performance digital twin of gas turbine engines. The developed digital twin consists of two parts: uncertain performance digital twin (UPDT) and fault detection capability. UPDT is a probabilistic digital representation of the expected performance behavior of real-world gas turbine engines operating under various conditions. Fault detection capability is developed based on detecting UPDT outputs that have low probability under the training distribution. A novel anomaly measure based on the first Wasserstein distance is proposed to characterize the entire flight data, and a threshold can be applied to this measure to detect anomaly flights. The proposed UPDT with uncertainty quantification is trained with the sensor data from an individual physical reality and the outcome of the UPDT is intended to deliver the health assessment and fault detection results to support operation and maintenance decision-making. The proposed method is demonstrated on a real-world dataset from a typical type of commercial turbofan engine and the result shows that the F1 score reaches a maximum of 0.99 with a threshold of 0.45. The case study demonstrated that the proposed novel anomaly detection method can effectively identify the abnormal samples, and it is also possible to isolate anomalous behavior in a single performance signal, which is helpful for further fault diagnosis once an anomaly is detected.
An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery
Authors: Zhang Bo , Zhao Dehao
Published: July 2023

Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R2 of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.
Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity
Authors: Almadhor Ahmad , Sampedro Gabriel Avelino , Abisado Mideth , Abbas Sidra
Published: July 2023

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.
Climate Change during Cretaceous/Paleogene as a Driving Force for the Evolutionary Radiation of Physical Dormancy in Fabaceae
Authors: Jaganathan Ganesh K. , Berry Keith
Published: July 2023

Physical dormancy (PY) due to a water-impermeable seed/fruit coat is one of the characteristic features of many species of Fabaceae; however, the timing and context of the evolution of this trait are poorly understood. In this investigation, fossil and molecular data are used to constrain the timing of the evolution of PY. The phylogenetic reconstruction programs GB-to-TNT and BEAUTi/BEAST are used to create chloroplast gene-based (rbcL and matK) phylogenies of taxa with well-represented fossil records. PY and non-dormancy are mapped to the terminals of the phylogeny, and ancestral states are reconstructed using parsimony. The initial evolution of PY in Fabaceae is reconstructed to have occurred sometime in the interval between divergence from Polygalaceae (late Campanian) to the diversification of crown-group Fabaceae (late Paleocene) when Fabaceae is known to have undergone multiple whole genome duplication (WGD) events across the Cretaceous/Paleogene (K/Pg) boundary. As in Nelumbo, another taxon with PY, Fabaceae may have developed PY in association with climatic change and WGD across the K/Pg boundary. The evolution of PY in association with WGD at the K/Pg boundary is an intriguing hypothesis that requires further investigation.
Does Activism Mean Being Active? Considering the Health Correlates of Activist Purpose
Authors: Hill Patrick L. , Rule Payton D. , Wilson Megan E.
Published: July 2023

Individuals with a purpose in life tend to experience better health outcomes, in part because they engage in healthier lifestyle behaviors. However, it is unclear whether these associations are due to the sense of purpose, or if the form of purpose may also correlate with health indicators. The current study examined this claim, with regard to activist purpose, or a commitment to engaging in social activism and causes, given that this type of purpose may be especially vulnerable to health risks. In a cross-sectional study of 307 US adults (mean age: 38.1 years), participants completed surveys regarding their activist purpose, sense of purpose, health, and health behaviors. In addition, we asked participants about their other purpose orientations: prosocial, occupational, personal recognition, and creative purposes in life. The results suggested consistent evidence of positive associations between a sense of purpose, self-rated health, and health behaviors. The activist purpose levels were positively associated with a higher health behavior engagement, but not with the indices of self-rated health. Other purpose orientations exhibited similarly weak-to-null associations with health. The findings are discussed with regard to whether activist purpose should be viewed as health-promoting, and what future research should be conducted to evaluate this claim.
Mechanical Properties of Polyamide Fiber-Reinforced Lime–Cement Concrete
Authors: Jafari Mohammad Mostafa , Jahandari Soheil , Ozbakkaloglu Togay , Rasekh Haleh , Jahed Armaghani Danial , Rahmani Aida
Published: July 2023

Lime–cement concrete (LCC) is a type of lime-based concrete in which lime and cement are utilized as the main binding agents. This type of concrete has been extensively used to construct support layers for shallow footings and road backfills in some warm regions. So far, there has been no systematic research conducted to investigate the mechanical characteristics of polyamide fiber-reinforced LCC. To address this gap, LCC specimens were prepared with 0%, 0.5%, 1%, and 2% of polyamide fibers (a synthetic textile made of petroleum-based plastic polymers). Specimens were then cured for 3, 7, and 28 days at room and oven temperatures. Then, the effects of the fibers’ contents, curing conditions, and curing periods on the mechanical characteristics of LCC, such as secant modulus, deformability index, bulk modulus, shear modulus, stiffness ratio, strain energy, failure strain, strength ratio, and failure patterns, was investigated. The results of the unconfined compressive strength (UCS) tests showed that specimens with 1% fiber had the highest UCS values. The curing condition and curing period had significant effects on the strength of the LCC specimens, and oven-cured specimens developed higher UCS values. The aforementioned mechanical properties of the LCC specimens and the ability of the material to absorb energy significantly improved when the curing period under the oven-curing condition was increased, as well as through the application of fibers in the mix design. Based on the test results, a simple mathematical model was also established to forecast the mechanical properties of fiber-reinforced LCC. It is concluded that the use of polyamide fibers in the mix design of LCC can both improve mechanical properties and perhaps address the environmental issues associated with waste polyamide fibers.
Proactive Decentralized Historian-Improving Legacy System in the Water Industry 4.0 Context
Authors: Korodi Adrian , Nicolae Andrei , Drăghici Ionel Aurel
Published: July 2023

The industry is in continuous evolution in the context of the Industrial Internet of Things (IIoT) and Industry 4.0 requirements and expected benefits. Some sectors allow higher reconfiguration dynamics considering the interference capabilities and process/equipment renewals, but others have considerable inertia that is many times justified. In most encountered situations, the reality confirms that the industry is struggling with new demands such as interoperation and efficiency improvements. The water industry makes no difference, being a sector with critical infrastructures and highly varied subsystems, where invasive interference in legacy solutions tends to be avoided. Following previous successful footsteps in researching a proactive decentralized historian, the current work focuses on a case study that refers to a water treatment and distribution facility that has been operated for several years, and the current operating regime was established by local operators following accumulated observations, restrictions, and response strategies. The proactive historian was tailored for the current case study, and it was applied and tested in the suboptimal functioning scenario where the water sources configuration was manually selected and used for water availability and energy efficiency, but without assuming current/future failures or different water demands. The proposed low-cost historian targeted to improve the functioning and operation of the water facility considering energy efficiency and other impacting outcomes of the current strategy and to establish an automatic functioning regime in a completely non-invasive manner towards the local legacy solution. The results were satisfactory, proving that the historian is able to adapt to a particular and suboptimal functioning real industrial scenario, establish recipes in a process-aware manner, and interoperate with the local legacy solution in order to apply improving actions.
The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant
Authors: Amoussou Isaac , Tanyi Emmanuel , Fatma Lajmi , Agajie Takele Ferede , Boulkaibet Ilyes , Khezami Nadhira , Ali Ahmed , Khan Baseem
Published: July 2023

Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios.
Endogeic Earthworms Avoid Soil Mimicking Metal Pollution Levels in Urban Parks
Authors: Chatelain Marion
Published: July 2023

In response to long-lasting high levels of metallic trace elements (MTEs) in urban soils, we expect soil invertebrates inhabiting urban environments to have evolved detection and avoidance and/or tolerance mechanisms to MTE pollution. In this study, I used artificial soils with concentrations of lead, zinc, copper, chromium and nickel that reflect pollution levels in the soils of Parisian parks. Using choice experiments, I compared habitat preference (i.e., the occurrence of individuals in the polluted vs. unpolluted soil) and health status (i.e., body mass maintenance, mobility, mortality) between three species of endogeic earthworms—Aporrectodea caliginosa, Aporrectodea icterica and Allolobophora chlorotica—originating either from urban or rural grasslands. This study highlights a clear avoidance of MTE-polluted soils in all three species, as well as MTE-induced health impairments, especially in A. chlorotica. Interestingly, earthworm response to MTE exposure only slightly differed between earthworms of urban and rural origin, suggesting the absence of widespread acclimatization or adaptation mechanisms to MTE pollution in cities. As a consequence, MTE pollution is expected to significantly shape earthworm spatial distribution in both urban and rural environments and, as a consequence, affect ecosystem functioning.
Mapping Ecotourism Potential in Bangladesh: The Integration of an Analytical Hierarchy Algorithm and Geospatial Data
Authors: Abtahee Muhallil , Islam Afra Anika , Haque Md. Nazmul , Zonaed Hasan , Ritu Samiha Mahzabin , Islam Sk Md Imdadul , Zaman Atiq
Published: July 2023

The significance of ecotourism has been increasing due to its potential for biodiversity preservation, economic advancement, and the promotion of sustainability awareness. In this research, geospatial analysis and the Analytical Hierarchy Process (AHP) was employed to identify feasible ecotourism sites in Bangladesh. The study applied Geographical Information System–Remote Sensing (GIS-RS) parameters and weighted overlay techniques for selected ecotourism characteristics, such as natural attractiveness, topographic features, accessibility, proximity to facilities, and community characteristics. The study found that a significant proportion (around 44%) of Bangladesh’s land exhibits high potential for ecotourism. Cox’s Bazar, Chittagong, and Rangamati are particularly favorable ecotourism locations. However, some difficulties emerge in regions that are not easily reachable, such as mangrove forests, and in densely inhabited localities like Dhaka. The research also identified the ecological costs linked with ecotourism, such as the exhaustion of resources, the fragmentation of habitats, contamination, and the disruption of wildlife. The primary recommendations to address the adverse effects include educating the local populace, enforcing regulatory measures, implementing efficient waste management systems, enforcing a stringent code of conduct, providing economic incentives to the local communities, and addressing the issue of food security. The cartographically delineated potential zones have the potential to function as a navigational instrument for global travelers and facilitate the decision-making process of policymakers in the realm of sustainable land resource management in Bangladesh. This study enhances the understanding of the potential of ecotourism and offers valuable insights for advancing responsible and sustainable tourism practices within the nation.
Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction
Authors: Li Xianzhong , Hao Shigang , Wu Tao , Zhou Weilong , Zhang Jinhao
Published: July 2023

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.
Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La
Authors: Xu Li , Lai Hongyan , Yu Jinge , Luo Shaolong , Guo Chaosheng , Gao Yingqun , Zhou Wenwu , Wang Shuwei , Shu Qingtai
Published: July 2023

The assessment of forest carbon storage plays a crucial role in forest management and ecosystem exploration, enabling the evaluation of forest quality, resources, carbon cycle and management. The Global Ecosystem Dynamics Investigation (GEDI) satellite provides a means to accurately measure these various forest vertical structure parameters by penetrating the forest canopy. However, the distribution of the footprint along the orbit track is heterogeneous and discontinuous, preventing the acquisition of spatially distributed carbon storage formation at the county level. Consequently, this study integrated GEDI and Landsat 9 data to estimate Quercus aquifolioides carbon storage in Shangri-La. By applying the Kriging interpolation to previously pretreated footprints, surface information from the GEDI L2B footprints was obtained. At the same time, Landsat 9 vegetation indices and band reflectance were extracted to analyze the correlation with the carbon storage of Quercus aquifolioides samples. Then, three methods (support vector machine, bagging, and random forest) were used to create a carbon storage estimation model for Shangri-La. The research results showed that (1) among the models for the selection of GEDI footprint parameters based on semi-variance, the optimal model of the digital_elevation_model was the spherical model, while the best model of percentage tree cover from the MODIS data (modis_treecover) and the foliage height diversity index (fhd_normal) was the exponential model. (2) Analyzing the thirty-three extracted independent variable factors correlated with the carbon storage of Quercus aquifolioides showed that the top five variables with the highest correlation were digital_elevation_model, modis_treecover, fhd_normal, DEM, and band 1 (B1). (3) After variable selection, the R2 = 0.82 and RMSE = 11.92 t/hm2 values of the Quercus aquifolioides carbon storage estimation model established via random forest were obtained, and its evaluation precision was superior to that of the support vector machine method and bagging regression. The carbon storage of Quercus aquifolioides was primarily in the range of 8.22~94.63 t/hm2, and the mean value was 42.44 t/hm2, while the total carbon storage was about 5,374,137.62 t. The findings from this paper illustrated the feasibility of obtaining carbon storage data on a county scale by combining GEDI LiDAR data with Landsat 9 optical data. The results also suggested a new perspective for combining GEDI L2B data with other remote sensing images to estimate other forest structure parameters.
Embed-Solitons in the Context of Functions of Symmetric Hyperbolic Fibonacci
Authors: Youssif Mokhtar. Y. , Helal Khadeeja A. A. , Juma Manal Yagoub Ahmed , Elhag Amna E. , Elamin Abd Elmotaleb A. M. A. , Aiyashi Mohammed A. , Abo-Dahab Sayed M.
Published: July 2023

In this article, we discuss the findings of new developments in a class of new triangular functions that blend the quantity functions of the traditional triangular. Considering the significant role played by the triangular functions in applied mathematics, physics, and engineering, it is conceivable to predict that the theory of new triangular functions will provide us with additional interpretations and discoveries in mathematics and physics. The solutions which consider variable separation based on arbitrary functions are constructed to the (3+1)-dimensional Burgers model by presenting the Fibonacci Riccati technique and the linearly independent variable separation approach. This technique’s fundamental concept is to describe the solution of the Burgers model as a polynomial in the Riccati Equation solution that satisfies the symmetrical hyperbolic and triangular Fibonacci functions. Depending on the choice of suitable functions for variable separation, an abundance of new localized solutions were obtained. Moreover, examples such as embedded solitons, rectangle-solitons, plateau-type ring solitons, taper-like solitons, and their interactions with each other, following the symmetrical hyperbolic and triangular Fibonacci functions, as well as the golden mean, could be explored.
Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm
Authors: Guan Yihong , Lv Mou , Li Shuyan , Su Yanbo , Dong Shen
Published: July 2023

The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes.