bluuebunny/crossref_metadata_2025 huggingface dataset:
Facile Synthesis and Characterization of Chitosan Functionalized Silver Nanoparticles for Antibacterial and Anti-Lung Cancer Applications
Authors: Bharathi Devaraj , Thiruvengadam Nandagopal Jaya Ganesh , Lee Jintae , Ranjithkumar Rajamani
Published: June 2023

In the treatment of bacterial contamination, the problem of multi-drug resistance is becoming an increasingly pressing concern. Nanotechnology advancements enable the preparation of metal nanoparticles that can be assembled into complex systems to control bacterial and tumor cell growth. The current work investigates the green production of chitosan functionalized silver nanoparticles (CS/Ag NPs) using Sida acuta and their inhibition efficacy against bacterial pathogens and lung cancer cells (A549). Initially, a brown color formation confirmed the synthesis, and the chemical nature of the synthesized NPs were examined by UV-vis spectroscopy, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). FTIR demonstrated the occurrence of CS and S. acuta functional groups in the synthesized CS/Ag NPs. The electron microscopy study exhibited CS/Ag NPs with a spherical morphology and size ranges of 6–45 nm, while XRD analysis demonstrated the crystallinity of Ag NPs. Further, the bacterial inhibition property of CS/Ag NPs was examined against K. pneumoniae and S. aureus, which showed clear inhibition zones at different concentrations. In addition, the antibacterial properties were further confirmed by a fluorescent AO/EtBr staining technique. Furthermore, prepared CS/Ag NPs exhibited a potential anti-cancer character against a human lung cancer cell line (A549). In conclusion, our findings revealed that the produced CS/Ag NPs could be used as an excellent inhibitory material in industrial and clinical sectors.
Fabrication, Physical–Chemical and Biological Characterization of Retinol-Loaded Poly(vinyl Alcohol) Electrospun Fiber Mats for Wound Healing Applications
Authors: Zamora-Ledezma Camilo , Hernández Ana Belén , López-González Ivan , Elango Jeevithan , Paindépice Janèle , Alexis Frank , González-Sánchez Manuela , Morales-Flórez Víctor , Mowbray Duncan John , Meseguer-Olmo Luis
Published: June 2023

Nowadays, there exists a huge interest in producing innovative, high-performance, biofunctional, and cost-efficient electrospun biomaterials based on the association of biocompatible polymers with bioactive molecules. Such materials are well-known to be promising candidates for three-dimensional biomimetic systems for wound healing applications because they can mimic the native skin microenvironment; however, many open questions such as the interaction mechanism between the skin and the wound dressing material remain unclear. Recently, several biomolecules were intended for use in combination with poly(vinyl alcohol) (PVA) fiber mats to improve their biological response; nevertheless, retinol, an important biomolecule, has not been combined yet with PVA to produce tailored and biofunctional fiber mats. Based on the abovementioned concept, the present work reported the fabrication of retinol-loaded PVA electrospun fiber mats (RPFM) with a variable content of retinol (0 ≤ Ret ≤ 25 wt.%), and their physical–chemical and biological characterization. SEM results showed that fiber mats exhibited diameters distribution ranging from 150 to 225 nm and their mechanical properties were affected with the increasing of retinol concentrations. In addition, fiber mats were able to release up to 87% of the retinol depending on both the time and the initial content of retinol. The cell culture results using primary mesenchymal stem cell cultures proved the biocompatibility of RPFM as confirmed by their effects on cytotoxicity (low level) and proliferation (high rate) in a dose-dependent manner. Moreover, the wound healing assay suggested that the optimal RPFM with retinol content of 6.25 wt.% (RPFM-1) enhanced the cell migratory activity without altering its morphology. Accordingly, it is demonstrated that the fabricated RPFM with retinol content below the threshold 0 ≤ Ret ≤ 6.25 wt.% would be an appropriate system for skin regenerative application.
Reclamation Potential of Onsite Wastewater Post-Treatment with Microalgae: Chemical Elements Perspective
Authors: Valchev Dobril , Ribarova Irina , Uzunov Blagoy , Stoyneva-Gärtner Maya , Lyubomirova Valentina
Published: June 2023

Algae-based wastewater treatment is a promising technology with various applications for excess biomass such as biofertilizer production or valuable elements extraction. The benefits of the technology have been discussed for larger wastewater treatment plants (WWTPs), but the use of microalgae in decentralized wastewater treatment has been barely reported. The current study screens the possible resource recovery potential of onsite technology, which adds algae-based post-treatment to the conventional biological treatment of domestic wastewater. The effluent from the onsite sequencing batch reactor (SBR) of a household was further processed in laboratory conditions using an SBR technology with two local monocultures of algae—Klebsormidium nitens (Kützing) Lokhorst and Tetradesmus obliquus (Turpin) M. J. Wynne. The decant and the generated algal biomass were analyzed in terms of their element content. The post-treated effluent has a slightly better quality for irrigation purposes than the effluent of the onsite treatment facility—up to 1.6 times increased concentration for macro-elements and up to 1.9 times for micro elements. However, the generated algal biomass shows promising potential for re-use as a fertilizing agent since it contains valuable macro- and micro-elements and the heavy (hazardous) metal content is considerably lower than the limiting values in the current European and national legislations. The K. nitens strain may attract interest since it accumulates valuable metals such as chromium (36 mg/kgDS), nickel (83 mg/kgDS), and silver (0.7 mg/kgDS) that can be derived from the biomass and turn the technology to a circular one.
CFD Simulation on Pressure Profile for Direct Contact Condensation of Steam Jet in a Narrow Pipe
Authors: Chen Xianbing , Fang Liwei , Jiao Shouyi , Zhang Jingzhi , Shi Leitai , Li Bangming , Tang Linghong
Published: June 2023

In the published experimental results, it has been observed that when high-speed steam spurt into the subcooled waterflow, the total pressure along the axial direction at trailing edge of the steam plume shows a pressure-lift. To reveal the mechanism behind this phenomenon, this study utilizes a particle model to investigate the pressure profile of steam jet condensation in subcooled water flow in a narrow pipe. A numerical model based on the Eulerian–Eulerian multiphase model has been developed to accurately simulate the characteristics of pressure profile along the axial direction. The model’s validity is established by comparing the steam plume shape and temperature profiles with the experimental data. By analyzing the total pressure profile of the axis and the contours of gas volume fraction, it is found that there exists a pressure-lift phenomenon at trailing edge of the steam plume. The dynamic pressure of the water also shows a pressure-lift at this position, so it can be inferred that the dynamic pressure of the water is the main factor of the total pressure-lift.
A Novel Energy-Intensity Model Based on Time Scale for Quasi-Continuous Production in Iron and Steel Industry
Authors: Lu Biao , Hao Yongkang , Wang Hao , Chen Demin , Wang Xingyin , Li Ning
Published: June 2023

Energy intensity is an important assessment indicator of energy consumption. Unfortunately, the traditional energy intensity model (TEIM) has obvious limitations when applied to quasi- continuous production process, especially for small time scales (STS). Therefore, a novel energy intensity model (NEIM) has been established in this study. The NEIM includes three main stages. Firstly, the statistical period and time scale have been determined. Secondly, the concept of workpiece valid weight has been proposed for a given time scale. Then the specific calculation method has also been established. Thirdly, a NEIM has been suggested according to the definition of energy intensity. The application results for a reheating furnace show that the NEIM’s effectiveness has been verified via comparison with the TEIM for large time scale (LTS) and critical time scale (CTS), whereas the NEIM still has validity at STS. Additionally, calculation results for the NEIM reflect more trend information at LTS and CTS; whereas, more dynamic information has been reflected at STS. The aim of this research was to expand the NEIM application for different time scales. Meanwhile, NEIM can also be applied to various energy-consuming facilities.
Accelerating SARS-CoV-2 Vaccine Development: Leveraging Novel Hybrid Deep Learning Models and Bioinformatics Analysis for Epitope Selection and Classification
Authors: Ameen Zubaida Said , Mostafa Hala , Ozsahin Dilber Uzun , Mubarak Auwalu Saleh
Published: June 2023

It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on the precise design of epitope regions that can elicit a strong immune response. Choosing epitope regions experimentally for the production of the SARS-CoV-2 vaccine can be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based on machine learning to find these regions, to cut down the number of candidate epitopes that might be tested in experiments, and, as a result, to lessen the time-consuming process of their mapping. However, the tools and approaches involved continue to have low accuracy. In this work, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for the classification of peptides into epitopes or non-epitopes. Numerous transfer learning strategies were utilized, and the fine-tuned method gave the best result, with an AUC of 0.979, an f1 score of 0.902, and 95.1% accuracy, which was far better than the performance of the model trained from scratch. The experimental results obtained show that this model has superior performance when compared to other methods trained on IEDB datasets. Using bioinformatics tools such as ToxinPred, VaxiJen, and AllerTop2.0, the toxicities, antigenicities, and allergenicities, respectively, of the predicted epitopes were determined. In silico cloning and codon optimization were used to successfully express the vaccine in E. coli. This work will help scientists choose the best epitope for the development of the COVID-19 vaccine, reducing cost and labor and thereby accelerating vaccine production.
The Application of Photovoltaic-Electric Spring Technology to Rural Power Grids
Authors: Cui Zhibin , Shi Junsheng , Li Guangpeng , Yuan Zihan , Zang Dehua , Wang Lidi
Published: June 2023

Rural power grids are essential for rural development, impacting the lives of farmers, the agricultural economy, and the overall efficiency of agricultural production. To ensure the reliable operation of these grids, finding ways to provide high-quality power is imperative. In recent years, the penetration rate of distributed photovoltaic (PV) in the distribution network has been increasing. When the output of PV and load are not matched, the voltage fluctuation of the network affects the safe and stable operation of the distribution network. In this study, we propose that the stable operation of rural power grids can be achieved by employing a photovoltaic-electric spring (PV-ES) device. A state space model of PV-ES is established and a single PV-ES voltage control method, based on a PI controller, is proposed, taking a rural user household with a monthly power consumption of about 120 access to distributed power supply as an example. We analyzed the device’s effectiveness in addressing voltage fluctuation issues as well as how light intensity impacts its effectiveness. The implementation of the PV-ES device solves the most significant problem faced by rural power grids, namely, the unstable power supply that occurs during peak electricity consumption periods. In addition, the PV-ES device ensures a high-quality electricity consumption experience for consumers.
Optimal Control of Technological Processes
Authors: Tsirlin Anatoliy M. , Balunov Alexander I.
Published: June 2023

The paper formulates conditions under which the roots closest to the imaginary axis (critical roots) of the characteristic equation of a linearized system are real for the maximum possible degree of stability of the closed-loop control system of a technological process with pure delay. For the parameters of the controllers corresponding to the maximum degree of stability, these roots are multiples. Their multiplicity order is one more than the number of coefficients in the transfer function of the controller. It is demonstrated that for a typical technological control object, these conditions are satisfied for all “serial” control laws. This allowed for obtaining analytical expressions for optimal settings and limiting degrees of stability as functions of object parameters for typical dynamic characteristics of technological processes. The paper considers the problem of robust stability for control systems with an object containing pure delay. It has been proven that in the maximum stability problem, the operations of maximizing over controller parameters and minimizing over the set of possible object parameters can be interchanged. Therefore, selecting robust settings amounts to determining the minimum of the maximum stability over the set of possible object parameter values. Controllers with such settings are suitable, without modification, for a whole class of technological processes.
The Bronze Age Destruction of Jericho, Archaeology, and the Book of Joshua
Authors: Kennedy Titus
Published: June 2023

The ancient city of Jericho, located at the archaeological site of Tell es-Sultan west of the Jordan River and adjacent to the Ein es-Sultan spring on the edge of modern Jericho, has often been associated with the biblical city of Jericho and the story found in the book of Joshua. The identification of Jericho with Tell es-Sultan is not disputed, and numerous excavation teams have affirmed Tell es-Sultan as Jericho. While excavations have also uncovered the fiery destruction of a walled city at Jericho, the date of the fall of Bronze Age Jericho and the association of this destruction with the narrative in the book of Joshua have been a point of disagreement among archaeologists for more than a century. The first excavations at Jericho (Tell es-Sultan) occurred in 1868 under the direction of Charles Warren, followed by soundings conducted by FJ Bliss in 1894, the expeditions of the years 1907–1909 and 1911 by Ernst Sellin and Carl Watzinger, the excavations of 1930–1936 directed by John Garstang, the 1952–1958 project of Kathleen Kenyon, brief excavations by Shimon Riklin in 1992, and the most recent excavations and restorations by the joint Italian–Palestinian team from 1997 to 2000 under Nicolo Marchetti and Lorenzo Nigro, followed by the 2009–2017 seasons directed by Jehad Yasin, Hamdan Taha, and Lorenzo Nigro. Although there is a significant deviation in views over the exact date of the destruction and abandonment, archaeological analyses of Jericho generally agree on the manner in which the city met its end, including a widespread fire, collapsed mudbrick walls, burning of the stored grain, and abandonment. However, assessing all of the archaeological data from Jericho IVc, both new and old, including pottery wares, Egyptian scarabs, a cuneiform tablet, stratigraphic analysis, and radiocarbon samples, allows a more definitive historical reconstruction concerning the chronology of the destruction of Jericho and its connections to the biblical narratives.
The Role of Ritual in Children’s Acquisition of Supernatural Beliefs
Authors: Mathiassen Anna , Nielsen Mark
Published: June 2023

This study investigated how observing the ritualisation of objects can influence children’s encoding and defence of supernatural beliefs. Specifically, we investigated if ritualising objects leads children to believe those objects might be magical, buffering against favouring contrary evidence. Seventy-nine children, aged between 3 and 6 years, were presented with two identical objects (e.g., two colour-changing stress balls) and tasked with identifying which was magical after being informed that one had special properties (e.g., could make wishes come true). In a Ritual condition, an adult acted on one of the objects using causally irrelevant actions and on the other using functional actions. In an Instrumental condition, both objects were acted on with functional actions. The children were given a normative rule relating to the use of the objects and an opportunity to imitate the actions performed on them. A second adult then challenged their magical belief. Ritualistic actions increased the likelihood of children attributing magical powers to the associated object but did not affect resistance to change or adherence to normative rules. However, children who engaged in ritual actions protested more when the magical belief was challenged. Our findings suggest that rituals can play an important role in shaping children’s perception and defence of supernatural beliefs.
Crisis as Opportunity: The Politics of ‘Seva’ and the Hindu Nationalist Response to the COVID-19 Pandemic in Kerala, South India
Authors: Paleri Dayal
Published: June 2023

The paper examines how Hindu nationalist social service organizations, specifically the Deseeya Seva Bharathi (DSB), reconfigured the religious conception of ‘Seva’ to advance the project of constructing a Hindu social identity during the COVID-19 pandemic in the state of Kerala. The southern Indian state of Kerala has remained an exception in the story of the rise of the Hindu nationalist movement in contemporary India, which has repeatedly failed to make any considerable political inroads in the state. However, the disastrous economic consequences and livelihood challenges during the COVID-19 pandemic in the state, which was heavily dependent on foreign remittance and service industries, have opened up new spaces of engagement for Hindu nationalists. Drawing on the fieldwork conducted in central Kerala during the pandemic, this paper will elaborate on how the DSB used the crisis moment of the pandemic to reach out to economically and socially disadvantaged communities using the language of ‘Seva’ to build a Hindu social identity, which imbues the influence of majoritarian Hindu nationalist politics. The paper argues that the DSB’s articulation of ‘Seva’ as a distinct and superior form of social service that is ‘self-less’, ‘non-instrumental’ and ‘non-reciprocal’ is significant in understanding the growing appeal of Hindu nationalist social service in the contested political sphere of Kerala, which is marked by competing social provisions by the state as well as other secular and religious groups. The paper notes that the reconfiguration of ‘Seva’ as a continuous religious concept enables Hindu nationalists to attain greater acceptance and legitimacy that even the secular state welfare could not achieve, while also concealing the inherent instrumental nature of its social service towards the construction of a Hindu social identity in the region.
A Preliminary Genealogy of Yoga in Italy: Between Religion and Contemporary Spirituality
Authors: Di Placido Matteo , Palmisano Stefania
Published: June 2023

In this article, we design a preliminary genealogy of yoga in Italy, showing its positioning within the growing field of “contemporary spiritualities”, their premodern, esoteric and theosophical roots and Catholicism. Our main claim is that yoga and contemporary spiritualities as practiced in XXI-century Italy are neither entirely new nor are they clearly an alternative to more established religions. We rely on the methods and tools of a “discursive study of religion” approach to unpack the intricacies, genealogical roots and definitional boundaries that yoga, contemporary spiritualities and religion in Italy share. More specifically, we question the novelty of contemporary spiritualities in Italy, unveiling some of their esoteric, theosophical and anthroposophical roots, presenting, in turn, a preliminary genealogy of yoga in Italy, discussing its positioning amid Catholicism and contemporary spiritualities. We conclude by reflecting on the creation, use and limits of sociocultural theorizing about interpreting and understanding the spiritual and religious field, with a specific emphasis on the overlapping and porous boundaries between the concepts of religion, contemporary spiritualities, Western esotericism and modern yoga.
A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data
Authors: Chen Junyu , Sang Jizhang , Li Zhenwei , Liu Chengzhi
Published: June 2023

Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness.
Assessment of GRAS Ionospheric Measurements for Ionospheric Model Assimilation
Authors: Hoque M. M. , Prol F. S. , Hernandez-Pajares M. , Notarpietro R. , Yuan L. , Olivares-Pulido G. , Graffigna V. , Von Engeln A. , Marquardt C.
Published: June 2023

We conducted a study to assess the GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding (GRAS) ionospheric data quality by processing Radio Occultation (RO) observations of ionospheric products. The main objective of the study is to validate ionospheric data generated at EUMETSAT, such as ionospheric bending angle profiles, amplitude and phase scintillations, topside Total Electron Content (TEC) from MetOp-A GRAS instrument as well as generating and validating new ionospheric products derived from GRAS RO observations such as the TEC, rate of TEC and vertical electron density profiles. The assessment is conducted by comparing and evaluating the systematic differences between similar products from other Low Earth Orbit (LEO) satellite missions or from ground-based ionospheric measurements. The study confirms that the GNSS topside and RO observations recorded by the GRAS instrument onboard MetOp satellites are of good quality and are a valuable source of data for ionospheric research.
Identifying Ecological Security Patterns Meeting Future Urban Expansion in Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China
Authors: Wang Weilin , Li Bin , Su Fei , Jiang Zhenfeng , Chen Shulu
Published: June 2023

Ecological security patterns (ESPs) provide a solution for balancing the conflicts between ecological conservation and socio-economic development and maintaining sustainable regional development. Identifying sustainable ESPs should be a matter given attention to in order to meet future urban expansion needs. Based on multi-source eco-environmental datasets and remote sensing (RS) containing the history of land-use patterns, our study incorporated future land-use patterns into the ESP identification framework to reduce the potential impact of future urban expansion on the identified ESPs (i.e., ecological corridors and nodes) and to reserve space for future urban development from an urban development perspective. Considering the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration, the results show that 57 corridors are extracted, within which 36 key corridors are mainly distributed in the Lukou and Wangcheng districts and Xiangtan and Changsha counties; an additional 21 potential corridors are mainly distributed in the urban built-up area and these corridors connect to Xiangjiang River or its tributary. In addition, 18 pinch points were identified, mainly located adjacent to the Xiangjiang River, Liuyang River, and Laodao River; 9 barrier points are distributed at the junctions of the main roads. A comparison of ESP identification with and without simulated land-use patterns showed the incorporation of future land-use patterns favoured sustainable ESP construction. Furthermore, we discuss the rationality and application of ESP identification from an urban development perspective and emphasize the trade-off between future land-use patterns and ecological conservation, better reflecting the forward-looking thinking of spatial planning.
Retrieving the Kinematic Process of Repeated-Mining-Induced Landslides by Fusing SAR/InSAR Displacement, Logistic Model, and Probability Integral Method
Authors: Chen Hengyi , Zhao Chaoying , Tomás Roberto , Chen Liquan , Yang Chengsheng , Zhang Yuning
Published: June 2023

The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the traditional InSAR technique, making it difficult to explore the relationship between landslides and subsurface excavations in both temporal and spatial domains. In this study, the Tengqing landslide in Shuicheng, Guizhou, China, was chosen as the study area. Firstly, the large-gradient surface displacement in the line of sight was obtained by the fusion of SAR offset tracking and interferometric phase. Subsequently, a multi-segment logistic model was proposed to simulate the temporal effect induced by repeated mining activities. Next, a simplified probability integral method (SPIM) was utilized to invert the geometry of the mining tunnel and separate the displacement of the mining subsidence and landslide. Finally, the subsurface mining parameters and in situ investigation were carried out to assess the impact of mining and precipitation on the kinematic process of Tengqing landslides. Results showed that the repeated mining activities in Tengqing can not only cause land subsidence and rock avalanches at the top of the mountain, but also accelerate the landslide displacement. The technical approach presented in this study can provide new insights for monitoring and modeling the effects of repeated mining-induced landslides in mountainous areas.
Method of Development of a New Regional Ionosphere Model (RIM) to Improve Static Single-Frequency Precise Point Positioning (SF-PPP) for Egypt Using Bernese GNSS Software
Authors: Abdallah Ashraf , Agag Tarek , Schwieger Volker
Published: June 2023

Due to the lack of coverage of IGS in Africa, especially over North Africa, and the construction revolution of infrastructure in Egypt, a geodetic CORS stations network was established in 2012. These CORS stations are operated by the Egyptian Surveying Authority (Egy. SA) and cover the whole of Egypt. The paper presents a fully developed regional ionosphere model (RIM) depending on the Egyptian CORS stations. The new model and the PPP solution were obtained using Bernese GNSS V. 5.2 software. An observation data series of eight days (DOY 201–208)/2019 was used in this study. Eighteen stations were used to develop the RIM model for each day; fifteen stations were used to validate the new RIM model. A static SF-PPP solution was obtained using the CODE-GIM and RIM models. Comparing the outcomes to the reference network solution, based on the recently developed RIM model, the solution showed a mean error of 0.06 m in the East direction, 0.13 m in the North direction, and 0.21 m in the height direction. In the East, North, and height directions, this solution improves the SF-PPP result achieved by the Global Ionosphere Maps (CODE-GIM) model by 60%, 68%, and 77%, respectively.
UAV-Based Hyperspectral Imaging for River Algae Pigment Estimation
Authors: Logan Riley D. , Torrey Madison A. , Feijó-Lima Rafael , Colman Benjamin P. , Valett H. Maurice , Shaw Joseph A.
Published: June 2023

Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates.
Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging
Authors: Janoušek Jiří , Marcoň Petr , Dohnal Přemysl , Jambor Václav , Synková Hana , Raichl Petr
Published: June 2023

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.
Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing
Authors: Dong Kaitai , Lotfipoor Ashkan
Published: June 2023

Finding relevant features that can represent different types of faults under a noisy environment is the key to practical applications of intelligent fault diagnosis. However, high classification accuracy cannot be achieved with only a few simple empirical features, and advanced feature engineering and modelling necessitate extensive specialised knowledge, resulting in restricted widespread use. This paper has proposed a novel and efficient fusion method, named MD-1d-DCNN, that combines statistical features from multiple domains and adaptive features retrieved using a one-dimensional dilated convolutional neural network. Moreover, signal processing techniques are utilised to uncover statistical features and realise the general fault information. To offset the negative influence of noise in signals and achieve high accuracy of fault diagnosis in noisy settings, 1d-DCNN is adopted to extract more dispersed and intrinsic fault-associated features, while also preventing the model from overfitting. In the end, fault classification based on fusion features is accomplished by the usage of fully connected layers. Two bearing datasets containing varying amounts of noise are used to verify the effectiveness and robustness of the suggested approach. The experimental results demonstrate MD-1d-DCNN’s superior anti-noise capability. When compared to other benchmark models, the proposed method performs better at all noise levels.
SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm
Authors: Wan Pengfei , Liao Guisheng , Xu Jingwei , Fu Xiaolong
Published: June 2023

In the electronic warfare environment, the performance of ground-based radar target search is seriously degraded due to the existence of smeared spectrum (SMSP) jamming. SMSP jamming is generated by the self-defense jammer on the platform, playing an important role in electronic warfare, making traditional radars based on linear frequency modulation (LFM) waveforms face great challenges in searching for targets. To solve this problem, an SMSP mainlobe jamming suppression method based on a frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar is proposed. The proposed method first uses the maximum entropy algorithm to estimate the target angle and eliminate the interference signals from the sidelobe. Then, the range-angle dependence of the FDA-MIMO radar signal is utilized, and the blind source separation (BSS) algorithm is used to separate the mainlobe interference signal and the target signal, avoiding the impact of mainlobe interference on target search. The simulation verifies that the target echo signal can be effectively separated, the similarity coefficient can reach more than 90% and the detection probability of the radar is significantly enhanced at a low signal-to-noise ratio.
A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction
Authors: Khoshkangini Reza , Tajgardan Mohsen , Lundström Jens , Rabbani Mahdi , Tegnered Daniel
Published: June 2023

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.
Molecularly Imprinted Polymer-Based Sensors for the Detection of Skeletal- and Cardiac-Muscle-Related Analytes
Authors: Ostrovidov Serge , Ramalingam Murugan , Bae Hojae , Orive Gorka , Fujie Toshinori , Hori Takeshi , Nashimoto Yuji , Shi Xuetao , Kaji Hirokazu
Published: June 2023

Molecularly imprinted polymers (MIPs) are synthetic polymers with specific binding sites that present high affinity and spatial and chemical complementarities to a targeted analyte. They mimic the molecular recognition seen naturally in the antibody/antigen complementarity. Because of their specificity, MIPs can be included in sensors as a recognition element coupled to a transducer part that converts the interaction of MIP/analyte into a quantifiable signal. Such sensors have important applications in the biomedical field in diagnosis and drug discovery, and are a necessary complement of tissue engineering for analyzing the functionalities of the engineered tissues. Therefore, in this review, we provide an overview of MIP sensors that have been used for the detection of skeletal- and cardiac-muscle-related analytes. We organized this review by targeted analytes in alphabetical order. Thus, after an introduction to the fabrication of MIPs, we highlight different types of MIP sensors with an emphasis on recent works and show their great diversity, their fabrication, their linear range for a given analyte, their limit of detection (LOD), specificity, and reproducibility. We conclude the review with future developments and perspectives.
Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data
Authors: Islam Umar , Alwageed Hathal Salamah , Farooq Malik Muhammad Umer , Khan Inayat , Awwad Fuad A. , Ali Ijaz , Abonazel Mohamed R.
Published: June 2023

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.
Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks
Authors: Huang Biaozhang , Li Xinde
Published: June 2023

Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm.
Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning
Authors: Yang Zhenglin , Zhao Yadian , Xu Chao
Published: June 2023

The development of an accurate and efficient method for detecting missing bolts in engineering structures is crucial. To this end, a missing bolt detection method that leveraged machine vision and deep learning was developed. First, a comprehensive dataset of bolt images captured under natural conditions was constructed, which improved the generality and recognition accuracy of the trained bolt target detection model. Second, three deep learning network models, namely, YOLOv4, YOLOv5s, and YOLOXs, were compared, and YOLOv5s was selected as the bolt target detection model. With YOLOv5s as the target recognition model, the bolt head and bolt nut had average precisions of 0.93 and 0.903, respectively. Third, a missing bolt detection method based on perspective transformation and IoU was presented and validated under laboratory conditions. Finally, the proposed method was applied to an actual footbridge structure to test its feasibility and effectiveness in real engineering scenarios. The experimental results showed that the proposed method could accurately identify bolt targets with a confidence level of over 80% and detect missing bolts under different image distances, perspective angles, light intensities, and image resolutions. Moreover, the experimental results on a footbridge demonstrated that the proposed method could reliably detect the missing bolt even at a shooting distance of 1 m. The proposed method provided a low-cost, efficient, and automated technical solution for the safety management of bolted connection components in engineering structures.
Vehicular Visible Light Communication for Intersection Management
Authors: Vieira M. A. , Vieira M. , Louro P. , Vieira P. , Fantoni A.
Published: June 2023

An innovative treatment for congested urban road networks is the split intersection. Here, a congested two-way–two-way traffic light-controlled intersection is transformed into two lighter intersections. By reducing conflict points and improving travel time, it facilitates smoother flow with less driver delay. We propose a visible light communication system based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications able to safely manage vehicles crossing through an intersection, leveraging Edge of Things (EoT) facilities. Headlights, street lamps, and traffic signals are used by connected vehicles to communicate with one another and with infrastructure. Through internally installed Driver Agents, an Intersection Manager coordinates traffic flow and interacts with vehicles. For the safe passage of vehicles across intersections, request/response mechanisms and time and space relative pose concepts are used. A virtual scenario is proposed, and a “mesh/cellular” hybrid architecture used. Light signals are emitted by transmitters by encoding, modulating, and converting data. Optical sensors with light-filtering properties are used as receivers and decoders. The VLC request/response concept uplink and downlink communication between the infrastructure and the vehicles is tested. Based on the results, the short-range mesh network provides a secure communication path between street lamp controllers and edge computers through neighbor traffic light controllers that have active cellular connections, as well as peer-to-peer communication, allowing V-VLC ready cars to exchange information.
Statistical Analysis of Wood Durability Data and Its Effect on a Standardised Classification Scheme
Authors: Brischke Christian , Haase Felix , Bächle Lea , Bollmus Susanne
Published: June 2023

The biological durability of wood is an important property for outdoor applications of wood-based products. In temperate climate zones, the most critical biological hazard is wood-destroying fungi, and the European standard EN 350 in combination with EN 113-2 provide guidance on sampling, testing, and classifying wood durability against brown and white rot fungi. However, in their latest revised versions, both standards recommend the use of probability density functions for fitting mass loss data (ML). Subsequently, the durability of wood and its variability should be further characterised. The aim of this study was to statistically analyse the ML data from laboratory agar plate tests with different European-grown wood species and to examine the effect of different statistical treatments on the standardised classification scheme of wood durability. It was concluded that more precise guidance is needed on the sampling procedure since significant differences in durability exist between stem zones. The assignment of dispersion indicators requires a revision to ensure clear, unmistakable, and reproducible durability classification of wood. Deficits in the description of the proposed statistical treatments in both standards became evident. It can be questioned whether the application of probability density functions provides additional information about the variability of wood durability.