bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct Synthesis of Chain-End Toluene Functionalized Hyperbranched Ethylene Oligomers
Authors: Chen Jianhai , Yan Zhengpeng , Li Zhongyuan , Dai Shengyu
Published: July 2022

Chain-end functionalized polymers play an important role in the field of building complex macromolecular structures. In this study, we have synthesized and characterized four dibenzhydryl iminopyridine Ni(II) complexes bearing remote flexible substituents (Et and n-Bu) to provide hyperbranched ethylene oligomers in ethylene oligomerization with moderate to good activities. Most notably, toluene-end-functionalized hyperbranched ethylene oligomers were obtained under elevated temperature conditions and validated by NMR. The tandem catalysis of ethylene oligomerization and the subsequent Friedel–Crafts addition of the resulting unsaturated products to toluene molecules was proposed as the cause of the observed phenomenon.
A Directly Compressible Pregelatinised Sago Starch: A New Excipient in the Pharmaceutical Tablet Formulations
Authors: Widodo Riyanto Teguh , Hassan Aziz , Liew Kai Bin , Ming Long Chiau
Published: July 2022

An excipient intended for direct compression in pharmaceutical tableting must show important features of flowability and compactibility. This study investigated pregelatinised sago starch as an excipient for direct compression tablets. Pregelatinised sago starch was prepared and characterised. Its powder bulk properties and performance in the tablet formulations with paracetamol as a model drug were compared against two commercial, directly compressible excipients, namely Avicel® PH 101 and Spress® B820. The results showed that pregelatinisation did not affect the chemical structure of sago starch, but its degree of crystallinity reduced, and X-ray diffraction pattern changed from C-type to A-type. Powder bulk properties of pregelatinised sago starch and Spress® B820 were comparable, exhibiting better flowability but lower compactibility than Avicel® PH 101. In the formulation of paracetamol tablets, pregelatinised sago starch and Spress® B820 performed equally well, followed by Avicel® PH 101 as indicated in Formulations 3, 2 and 1, respectively.
Numerical Simulation of the Bearing Capacity of Variotropic Short Concrete Beams Reinforced with Polymer Composite Reinforcing Bars
Authors: Beskopylny Alexey , Meskhi Besarion , Stel’makh Sergey , Shcherban’ Evgenii , Mailyan Levon , Veremeenko Andrey , Akopyan Vladimir , Shilov Aleksandr , Chernil’nik Andrei , Beskopylny Nikita
Published: July 2022

One of the disadvantages of reinforced concrete is the large weight of structures due to the steel reinforcement. A way to overcome this issue and develop new types of reinforcing elements is by using polymer composite reinforcement, which can successfully compensate for the shortcomings of steel reinforcement. Additionally, a promising direction is the creation of variotropic (transversely isotropic) building elements. The purpose of this work was to numerically analyze improved short bending concrete elements with a variotropic structure reinforced with polymer composite rods and to determine the prospects for the further extension of the results obtained for long-span structures. Numerical models of beams of a transversally isotropic structure with various types of reinforcement have been developed in a spatially and physically nonlinear formulation in ANSYS software considering cracking and crashing. It is shown that, in combination with a stronger layer of the compressed zone of the beam, carbon composite reinforcement has advantages and provides a greater bearing capacity than glass or basalt composite. It has been proven that the use of the integral characteristics of concrete and the deflections of the elements are greater than those when using the differential characteristics of concrete along the height of the section (up to 5%). The zones of the initiation and propagation of cracks for different polymer composite reinforcements are determined. An assessment of the bearing capacity of the beam is given. A significant (up to 146%) increase in the forces in the reinforcing bars and a decrease in tensile stresses (up to 210–230%) were established during the physically non-linear operation of the concrete material. The effect of a clear redistribution of stresses is in favor of elements with a variotropic cross section in height.
Innovative Methylcellulose-Polyvinyl Pyrrolidone-Based Solid Polymer Electrolytes Impregnated with Potassium Salt: Ion Conduction and Thermal Properties
Authors: Adam Abdullahi Abbas , Ali Mohammed Khalil Mohammed , Dennis John Ojur , Soleimani Hassan , Shukur Muhammad Fadhlullah Bin Abd. , Ibnaouf Khalid Hassan , Aldaghri Osamah A. , Ibrahem Moez A. , Abdel All Naglaa F. M. , Bashir Abdulkadir Abubakar
Published: July 2022

In this research, innovative green and sustainable solid polymer electrolytes (SPEs) based on plasticized methylcellulose/polyvinyl pyrrolidone/potassium carbonate (MC/PVP/K2CO3) were examined. The MC/PVP/K2CO3 SPE system with five distinct ethylene carbonate (EC) concentrations as a plasticizer was successfully designed. Frequency-dependent conductivity plots were used to investigate the conduction mechanism of the SPEs. Electrochemical potential window stability and the cation transfer number of the SPEs were studied via linear sweep voltammetry (LSV) and transference number measurement (TNM), respectively. Additionally, the structural behavior of the SPEs was analyzed using Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), X-ray diffractometry (XRD), and differential scanning calorimetry (DSC) techniques. The SPE film complexed with 15 wt.% EC measured a maximum conductivity of 3.88 × 10−4 Scm−1. According to the results of the transference number examination, cations that record a transference number of 0.949 are the primary charge carriers. An EDLC was fabricated based on the highest conducting sample that recorded a specific capacitance of 54.936 Fg−1 at 5 mVs−1.
Assessment of a Coated Mitomycin-Releasing Biodegradable Ureteral Stent as an Adjuvant Therapy in Upper Urothelial Carcinoma: A Comparative In Vitro Study
Authors: Soria Federico , Aznar-Cervantes Salvador David , de la Cruz Julia E. , Budia Alberto , Aranda Javier , Caballero Juan Pablo , Serrano Álvaro , Sánchez Margallo Francisco Miguel
Published: July 2022

A major limitation of the treatment of low-grade upper tract urothelial carcinoma is the difficulty of intracavitary instillation of adjuvant therapy. Therefore, the aim of this in vitro study was to develop and to assess a new design of biodegradable ureteral stent coated with a silk fibroin matrix for the controlled release of mitomycin C as a chemotherapeutic drug. For this purpose, we assessed the coating of a biodegradable ureteral stent, BraidStent®, with silk fibroin and subsequently loaded the polymeric matrix with two formulations of mitomycin to evaluate its degradation rate, the concentration of mitomycin released, and changes in the pH and the weight of the stent. Our results confirm that the silk fibroin matrix is able to coat the biodegradable stent and release mitomycin for between 6 and 12 h in the urinary environment. There was a significant delay in the degradation rate of silk fibroin and mitomycin-coated stents compared to bare biodegradable stents, from 6–7 weeks to 13–14 weeks. The present study has shown the feasibility of using mitomycin C-loaded silk fibroin for the coating of biodegradable urinary stents. The addition of mitomycin C to the coating of silk fibroin biodegradable stents could be an attractive approach for intracavitary instillation in the upper urinary tract.
Thermal Expansion of Plastics Used for 3D Printing
Authors: Rădulescu Bruno , Mihalache Andrei Marius , Hrițuc Adelina , Rădulescu Mara , Slătineanu Laurențiu , Munteanu Adriana , Dodun Oana , Nagîț Gheorghe
Published: July 2022

The thermal properties of parts obtained by 3D printing from polymeric materials may be interesting in certain practical situations. One of these thermal properties is the ability of a material to expand as the temperature rises or shrink when the temperature drops. A test experiment device was designed based on the thermal expansion or negative thermal expansion of spiral test samples, made by 3D printing of polymeric materials to investigate the behavior of some polymeric materials in terms of thermal expansion or contraction. A spiral test sample was placed on an aluminum alloy plate in a spiral groove. A finite element modeling highlighted the possibility that areas of the plate and the spiral test sample have different temperatures, which means thermal expansions or contractions have different values in the spiral areas. A global experimental evaluation of four spiral test samples was made by 3D printing four distinct polymeric materials: styrene-butadiene acrylonitrile, polyethylene terephthalate, thermoplastic polyurethane, and polylactic acid, has been proposed. The mathematical processing of the experimental results using specialized software led to establishing empirical mathematical models valid for heating the test samples from −9 °C to 13 °C and cooling the test samples in temperature ranges between 70 °C and 30 °C, respectively. It was found that the negative thermal expansion has the highest values in the case of polyethylene terephthalate and the lowest in the case of thermoplastic polyurethane.
Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms
Authors: Khan Kaffayatullah , Ahmad Waqas , Amin Muhammad Nasir , Ahmad Ayaz , Nazar Sohaib , Alabdullah Anas Abdulalim
Published: July 2022

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R2 value with fewer errors, producing higher precision than other models with lesser R2 values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.
Surface-Passivated Single-Crystal Micro-Plates for Efficient Perovskite Solar Cells
Authors: Wu Tzu-Hsueh , Sharma Ganesh D. , Chen Fang-Chung
Published: July 2022

Perovskite solar cells (PeSCs) prepared with single crystals (SCs) ideally exhibit higher power conversion efficiencies (PCEs) because they possess a lower density of structural imperfection and superior charge transport. However, the density of the surface defects on the SCs is still very high, thereby inevitably affecting the device performance. Herein, perovskite single-crystal micro-plates were grown on a hole-transporting material, poly[bis(4-phenyl)(2,4,6-trimethylphenyl) amine], through a space-limited inverse temperature crystallization method. The surfaces of the as-prepared SCs were passivated using trioctylphosphine oxide (TOPO) during the device fabrication to alleviate the impact of surface defects. The PCE values are averagely improved from 11.90 ± 0.30% to 14.76 ± 0.65% after the surface passivation; the champion device even exhibits a PCE of 15.65%. The results from photoluminescence and hole-only devices reveal that TOPO treatments effectively reduce the number of surface defects on the single crystals, thereby improving the photovoltaic performance. The surface passivation also inhibits the hysteresis behavior due to the lower defect density. Finally, the TOPO treatment also improves the stability of the single-crystal PeSCs, presumably due to the hydrophobic long alkyl chains. Thus, this work provides an effective approach to achieving high efficiencies of single-crystal PeSCs.
Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
Authors: Yin Hu , Cui Hongwei , Gao Jiajia
Published: July 2022

The detection of the formation of pore pressure while drilling is of great importance to ensure safe drilling operations. At present, the dc-exponent concept is mainly used to detect pore pressure while drilling. The dc-exponent concept is based on the theory of shale compaction, which is limited when used in carbonate rocks. A mechanical specific energy (MSE)-based method is proposed to detect pore pressure in deep, complex intervals. The method is based on the theory that the energy consumed by the bit to break and remove a unit volume of rock can reflect the effective stress and pore pressure of the rock in situ. In this paper, a torque and weight on bit (WOB) transfer model is proposed for estimating the downhole torque and WOB using drill string mechanics. Meanwhile, the rotary speed and torque of the positive displacement motors under compound drilling are considered, and the model of total MSE under compound drilling is modified. The MSE-based method was used to estimate the pore pressure in a region in western Sichuan, and there is a good agreement between the detected and measured pore pressure. The results demonstrate that the accurate computed MSE-based method is useful in detecting pore pressure in deep complex intervals.
Identification of Four Chicken Breeds by Hyperspectral Imaging Combined with Chemometrics
Authors: Cheng Tiande , Li Peng , Ma Junchao , Tian Xingguo , Zhong Nan
Published: July 2022

The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400–900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13 important wavelengths, and the SVM model based on SPA (SPA-SVM) achieved 90% CCR. This study can provide a theoretical reference for the discriminant analysis of chicken breeds.
Effect of Shear Flow on Drag Reducer Performance and Its Microscopic Working Mechanism
Authors: Liu Zhiyu , Tian Zaifu , Yuan Haoren , Li Yuan , Ge Hongkui , Zhou Fujian
Published: July 2022

As the development of unconventional oil and gas resources goes deeper, the stimulation of reservoirs goes deeper year by year. Flow in longer wellbores poses a challenge to the stability of drag-reduction performance of fracturing fluid. However, at present we have limited understanding of the mechanism of drag-reduction damage caused by shear flow, especially the microscopic mechanism. Therefore, in this work, the variation pattern of drag reducer solution performance with shear rate has been analyzed by using a high precision loop flow drag test system. The test results show that there is a critical shear rate for the performance damage of the drag reducer solution, and high strength shear flow and cumulative shear flow time are the main factors leading to the performance degeneration of the drag reducer. Based on the nanometer granularity distributions, rheological properties and microscopic structures observed with a transmission electron microscope of drag reducer solutions subjected to shear flows of different velocities, it is confirmed that the damage to the microscopic structure of the solution is the main reason leading to its performance degeneration. The destruction of the microscopic structure causes the drag reducer solution to degrade in non-Newtonian characteristics, so it becomes poorer in its capability of reducing turbulent dissipation and drops in drag-reduction capability. This research can provide a reference for improving and optimizing drag-reduction capability of fracturing fluid.
Interpenetration Logic: Pauline Spirituality and Union with Christ
Authors: Wood Shane J.
Published: July 2022

While definitions of spirituality vary, each contains elements of union, a coming together of humanity and divinity. Scholars agree on the centrality of “union with Christ” in Pauline thought, yet not on a definition of union. Ephesians 5:31–32, however, provides important insight through a quotation—“…the two will become one flesh” (Genesis 2:24)—and an explanation—“I say this with reference to Christ and the church”. While scholars highlight the preservation of distinct identities, Paul’s emphasis in the marriage metaphor, both here and elsewhere, is union, an interpenetration logic where two become one. Indeed, interpenetration logic is present in our union with Christ (Galatians 2:19–20) and our union with sin (Romans 7:9–25). Both unions harness the same two-become-one logic with drastically different ends: sin intends to obliterate (Rom. 3:23); Christ intends to resurrect (1 Corinthians 15:22). The crux of Pauline spirituality, then, is not ecstatic experiences or ethical imitations but union—reciprocal residence, where we are in Christ and Christ is in us; a mutual indwelling consummated by “the Spirit of life” (Rom. 8:2). Thus, Eph. 5:31–32 provides insight into interpenetration logic, where two become one without the obliteration of either. This is an insight that profits our understanding of Pauline spirituality and the “profound mystery” of union with Christ.
An Efficient Method for Pricing Analysis Based on Neural Networks
Authors: Arabyat Yaser Ahmad , AlZubi Ahmad Ali , Aldebei Dyala M. , Al-oqaily Samerra’a Ziad
Published: July 2022

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data.
Analyzing the Spatiotemporal Vegetation Dynamics and Their Responses to Climate Change along the Ya’an–Linzhi Section of the Sichuan–Tibet Railway
Authors: Xu Binni , Li Jingji , Luo Zhengyu , Wu Jianhui , Liu Yanguo , Yang Hailong , Pei Xiangjun
Published: July 2022

Vegetation dynamics and their responses to climate change are of significant spatial and temporal heterogeneity. The Sichuan–Tibet Railway (STR) is a major construction project of the 14th Five-Year Plan for Economic and Social Development of the People’s Republic of China that is of great significance to promoting the social and economic development of Sichuan–Tibet areas. The planned railway line crosses areas with a complex geological condition and fragile ecological environment, where the regional vegetation dynamics are sensitive to climate change, topographic conditions and human activities. So, analyzing the vegetation variations in the complex vertical ecosystem and exploring their responses to hydrothermal factors are critical for providing technical support for the ecological program’s implementation along the route of the planned railway line. Based on MOD13Q1 Normalized Difference Vegetation Index (NDVI) data for the growing season (May to October) during 2001–2020, a Theil-Sen trend analysis, Mann–Kendall test, Hurst exponent analysis and partial correlation analysis were used to detect the vegetation dynamics, predict the vegetation sustainability, examine the relationship between vegetation change and hydrothermal factors, regionalize the driving forces for vegetation growth and explore the interannual variation pattern of driving factors. The growing season NDVI along the Ya’an–Linzhi section of the STR showed a marked rate of increase (0.0009/year) during the past 20 years, and the vegetation’s slight improvement areas accounted for the largest proportion (47.53%). Among the three hydrothermal parameters (temperature, precipitation and radiation), the correlation between vegetation growth and the temperature was the most significant, and the vegetation response to precipitation was the most immediate. The vegetation changes were affected by the combined impact of climatic and non-climatic factors, and the proportion of hydrothermal factors’ combined driving force slightly increased during the study period. Based on the Hurst exponent, the future vegetation sustainability of the area along the Ya’an–Linzhi section of the STR faces a risk of degradation, and more effective conservations should be implemented during the railway construction period to protect the regional ecological environment.
Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression
Authors: Bian Wanchao , Hou Hao , Chen Jiang , Zhou Bin , Xia Jianhong , Xie Shanjuan , Liu Ting
Published: July 2022

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.
KASiam: Keypoints-Aligned Siamese Network for the Completion of Partial TLS Point Clouds
Authors: Liu Xinpu , Ma Yanxin , Xu Ke , Wang Ling , Wan Jianwei
Published: July 2022

Completing point clouds from partial terrestrial laser scannings (TLS) is a fundamental step for many 3D visual applications, such as remote sensing, digital city and autonomous driving. However, existing methods mainly followed an ordinary auto-encoder architecture with only partial point clouds as inputs, and adopted K-Nearest Neighbors (KNN) operations to extract local geometric features, which takes insufficient advantage of input point clouds and has limited ability to extract features from long-range geometric relationships, respectively. In this paper, we propose a keypoints-aligned siamese (KASiam) network for the completion of partial TLS point clouds. The network follows a novel siamese auto-encoder architecture, to learn prior geometric information of complete shapes by aligning keypoints of complete-partial pairs during the stage of training. Moreover, we propose two essential blocks cross-attention perception (CAP) and self-attention augment (SAA), which replace KNN operations with attention mechanisms and are able to establish long-range geometric relationships among points by selecting neighborhoods adaptively at the global level. Experiments are conducted on widely used benchmarks and several TLS data, which demonstrate that our method outperforms other state-of-the-art methods by a 4.72% reduction of the average Chamfer Distance of categories in PCN dataset at least, and can generate finer shapes of point clouds on partial TLS data.
A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR
Authors: Wang Cangjiao , Elmore Andrew J. , Numata Izaya , Cochrane Mark A. , Lei Shaogang , Hakkenberg Christopher R. , Li Yuanyuan , Zhao Yibo , Tian Yu
Published: July 2022

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.
Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking
Authors: Liang Shuang , Zhu Yun , Li Hao , Yan Junkun
Published: July 2022

In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods.
Definition of Optimized Indicators from Sensors Data for Damage Detection of Instrumented Roadways
Authors: Souriou David , Simonin Jean-Michel , Schmidt Franziska
Published: July 2022

Pavement instrumentation sensors have become a common practice for structural monitoring. Data processing often consists of extracting different values generally representative of variations in the whole pavement, but this makes it more difficult to follow a specific characteristic of the structure. To overcome this limitation, this paper aimed to transpose an original method, labeled the Optimized Indicators Method (OIM), which consists of finding some weighting functions of a signal to evaluate some indicators linked to a physical characteristic of the structure. The main advantage of this method is that the inferred indicators are particularly sensitive to a specific characteristic of the structure without being sensitive to the others. This research work consisted of analyzing, conventionally and with the OIM, the signals of strain gauges, which were recorded during an experimental campaign carried out on a circular instrumented pavement and submitted to an accelerated fatigue testing. The OIM, calibrated through an experimental reference signal, makes it possible to evaluate independently, through weighting functions, some specific physical characteristics of the pavement. It showed a two-stepped degradation, starting with a damaging of the bituminous layer, followed by an alteration of the base layer, which could not be easily deduced from a conventional processing method.
A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments
Authors: Reynolds James , Williams Evan , Martin Devon , Readling Caleb , Ahmmed Parvez , Huseth Anders , Bozkurt Alper
Published: July 2022

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.
Jitter-Robust Phase Retrieval Wavefront Sensing Algorithms
Authors: Guo Liang , Ju Guohao , Xu Boqian , Bai Xiaoquan , Meng Qingyu , Jiang Fengyi , Xu Shuyan
Published: July 2022

Phase retrieval wavefront sensing methods are now of importance for imaging quality maintenance of space telescopes. However, their accuracy is susceptible to line-of-sight jitter due to the micro-vibration of the platform, which changes the intensity distribution of the image. The effect of the jitter shows some stochastic properties and it is hard to present an analytic solution to this problem. This paper establishes a framework for jitter-robust image-based wavefront sensing algorithm, which utilizes two-dimensional Gaussian convolution to describe the effect of jitter on an image. On this basis, two classes of jitter-robust phase retrieval algorithms are proposed, which can be categorized into iterative-transform algorithms and parametric algorithms, respectively. Further discussions are presented for the cases where the magnitude of jitter is unknown to us. Detailed simulations and a real experiment are performed to demonstrate the effectiveness and practicality of the proposed approaches. This work improves the accuracy and practicality of the phase retrieval wavefront sensing methods in the space condition with non-ignorable micro-vibration.
Lightweight Single Image Super-Resolution with Selective Channel Processing Network
Authors: Zhu Hongyu , Tang Hao , Hu Yaocong , Tao Huanjie , Xie Chao
Published: July 2022

With the development of deep learning, considerable progress has been made in image restoration. Notably, many state-of-the-art single image super-resolution (SR) methods have been proposed. However, most of them contain many parameters, which leads to a significant amount of calculation consumption in the inference phase. To make current SR networks more lightweight and resource-friendly, we present a convolution neural network with the proposed selective channel processing strategy (SCPN). Specifically, the selective channel processing module (SCPM) is first designed to dynamically learn the significance of each channel in the feature map using a channel selection matrix in the training phase. Correspondingly, in the inference phase, only the essential channels indicated by the channel selection matrixes need to be further processed. By doing so, we can significantly reduce the parameters and the calculation consumption. Moreover, the differential channel attention (DCA) block is proposed, which takes into consideration the data distribution of the channels in feature maps to restore more high-frequency information. Extensive experiments are performed on the natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, Manga109) and remote-sensing benchmarks (i.e., UCTest and RESISCTest), and our method achieves superior results to other state-of-the-art methods. Furthermore, our method keeps a slim size with fewer than 1 M parameters, which proves the superiority of our method. Owing to the proposed SCPM and DCA, our SCPN model achieves a better trade-off between calculation cost and performance in both general and remote-sensing SR applications, and our proposed method can be extended to other computer vision tasks for further research.
A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types
Authors: Tavakoli Yashar , Peña-Castillo Lourdes , Soares Amilcar
Published: July 2022

The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship’s trajectory, or kinematic, which capture the motion properties of a ship’s movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric–kinematic taxonomy could be further developed as a tool for more accessible feature selection.
Estimation of Head Accelerations in Crashes Using Neural Networks and Sensors Embedded in the Protective Helmet
Authors: Bracali Andrea , Baldanzini Niccolò
Published: July 2022

Traumatic Brain Injuries (TBIs) are one of the most frequent and severe outcomes of a Powered Two-Wheeler (PTW) crash. Early diagnosis and treatment can greatly reduce permanent consequences. Despite the fact that devices to track head kinematics have been developed for sports applications, they all have limitations, which hamper their use in everyday road applications. In this study, a new technical solution based on accelerometers integrated in a motorcycle helmet is presented, and the related methodology to estimate linear and rotational acceleration of the head with deep Artificial Neural Networks (dANNs) is developed. A finite element model of helmet coupled with a Hybrid III head model was used to generate data needed for the neural network training. Input data to the dANN model were time signals of (virtual) accelerometers placed on the inner surface of the helmet shell, while the output data were the components of linear and rotational head accelerations. The network was capable of estimating, with good accuracy, time patterns of the acceleration components in all impact conditions that require medical treatment. The correlation between the reference and estimated values was high for all parameters and for both linear and rotational acceleration, with coefficients of determination (R2) ranging from 0.91 to 0.97.
Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior
Authors: Gao Xianjie , Zhang Mingliang , Luo Jinming
Published: July 2022

Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.
Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation
Authors: Chen Jie , Liu Chang , Xie Jiawu , An Jie , Huang Nan
Published: July 2022

Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidirectional long short-term memory (Bi-LSTM) approach to explore the features of a time–frequency (T-F) mask, and propose a T-F-mask-aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the T-F image, the designed Bi-LSTM network is able to extract the discriminative features for separation, which further improves the separation performance. In particular, this method breaks through the limitations of the existing methods and not only achieves good results in multivariate separation but also effectively separates signals when they are mixed with 40 dB Gaussian noise signals. The experimental results show that this method can achieve a 97% guarantee ratio (PSR), and the average similarity coefficient of the multivariate signal separation is stable above 0.8 under high noise conditions. It should be noted that our model can only handle known signals such as test signals for calibration.
Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method
Authors: Gu Chen , Qi Jifeng , Zhao Yizhi , Yin Wenming , Zhu Shanliang
Published: July 2022

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.
Investigation of the Rearrangement of Reactive–Inert Particulate Structures in a Single Channel of a Wall-Flow Filter
Authors: Thieringer Julia R. D. , Hafen Nicolas , Meyer Jörg , Krause Mathias J. , Dittler Achim
Published: July 2022

Wall-flow filters are a standard component in exhaust gas aftertreatment and have become indispensable in vehicles. Ash and soot particles generated during engine combustion are deposited in diesel or gasoline particulate filters. During regeneration, the soot particles are oxidized. The remaining ash particles can form different deposition patterns: a homogenous layer or plug-end filling. It has not yet been clarified whether the plug-end filling is first formed by rearrangements of agglomerates before and during the regeneration of the reactive particles. In this study, experiments are carried out with a single channel of a wall-flow filter. For the investigations, a layer of inert and reactive particles is formed. The rearrangement of agglomerates is achieved by flowing through the model filter channel and observed with a high-speed camera. The particulate structures detach at the channel inlet, are transported along the channel and deposited at the plug. The velocity of the detached agglomerates depends on their size, shape, track and the gas velocity in the channel. If the agglomerate is near the walls of the model filter channel, the gas velocity deviates from the gas velocity in the core flow. The higher the gas velocity, the higher the agglomerate velocity achieved and the larger the detached agglomerates.