bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Characterization, Antimicrobial Activity and In Vitro Antiproliferative Potential of Alchemilla vulgaris Auct Root Extract against Prostate (PC-3), Breast (MCF-7) and Colorectal Adenocarcinoma (Caco-2) Cancer Cell Lines
Authors: Ibrahim Omer H. M. , Abo-Elyousr Kamal A. M. , Asiry Khalid A. , Alhakamy Nabil A. , Mousa Magdi A. A.
Published: August 2022

Despite the proven biological activity of the aerial part extract of Alchemilla vulgaris, scarce information is available about the activity of the root extract. This encouraged us to initiate the current investigation to study the cytotoxic activity of A. vulgaris methanolic root extract against various cancer cell lines in vitro, along with its antimicrobial activity and phytochemical screening. MTT assay was applied to test the cytotoxic effect against the prostate (PC-3), breast (MCF-7) and colorectal adenocarcinoma (Caco-2), together with normal Vero cells. Flow cytometry was employed to assess cell cycle arrest and apoptosis vs. necrosis in PC-3 cells. The expression of apoptosis-related genes (BAX, BCL2 and P53) was quantified by qRT-PCR analysis. The obtained results showed strong antiproliferative activity on the three cancer cell lines and the normal Vero cells in a dose-dependent manner. A high selectivity index (SI) was recorded against the three cell lines with PC-3 cells showing the highest SI and the lowest IC50. This effect was associated with cell cycle arrest at G1 phase and induction of total apoptosis at 27.18% being mainly early apoptosis. Apoptosis induction was related to the upregulation of the proapoptotic genes P53 and BAX and the downregulation of the antiapoptotic gene BCL2. Additionally, the extract demonstrated in vitro antibacterial activity against Agrobacterium tumefaciens, Serratia marcescens and Acinetobacter johnsoni. Additionally, it showed antifungal activity against Rhizoctonia solani, Penicillium italicum and Fusarium oxysporium. Seven phenolic acids and seven flavonoids were detected. The predominant phenolic acids were cinnamic and caffeic acids, while hisperdin and querestin were the principal flavonoids. These findings provide clear evidence about the promising proapoptotic effect of A. vulgaris root extract, which contributes to laying the basis for broader and in-depth future investigations.
Advanced Characterization of Ceramic State Polymer Electrolyte at Radio Frequencies
Authors: Quan Wei , Afsar Mohammed Nurul
Published: August 2022

Two newly developed non-contact dielectric measurement techniques were applied to characterize the complex permittivity spectra of a ceramic state polymer electrolyte. The Capacitance Bridge was employed to measure the electrolyte sample in a frequency range from 50 Hz to 20 KHz with a novel air gap method. The newly designed in-waveguide measurement by VNA (vector network analyzer) was applied to measure the electrolyte in the frequency range from 8.2 GHz to 40 GHz. Both methods are newly developed non-contact techniques and there was no physical contact on the polymer sample film surface during the measurement. The weak contact error in traditional measurement could be completely avoided in these non-contact methods. The ionic conductivity and complex electrical conductivity can be derived from the accurate complex dielectric spectra.
Efficient, Recyclable, and Heterogeneous Base Nanocatalyst for Thiazoles with a Chitosan-Capped Calcium Oxide Nanocomposite
Authors: Khalil Khaled , Ahmed Hoda , Bashal Ali , Bräse Stefan , Nayl AbdElAziz , Gomha Sobhi
Published: August 2022

Calcium oxide (CaO) nanoparticles have recently gained much interest in recent research due to their remarkable catalytic activity in various chemical transformations. In this article, a chitosan calcium oxide nanocomposite was created by the solution casting method under microwave irradiation. The microwave power and heating time were adjusted to 400 watts for 3 min. As it suppresses particle aggregation, the chitosan (CS) biopolymer acted as a metal oxide stabilizer. In this study, we aimed to synthesize, characterize, and investigate the catalytic potency of chitosan–calcium oxide hybrid nanocomposites in several organic transformations. The produced CS–CaO nanocomposite was analyzed by applying different analytical techniques, including Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and field-emission scanning electron microscopy (FESEM). In addition, the calcium content of the nanocomposite film was measured using energy-dispersive X-ray spectroscopy (EDS). Fortunately, the CS–CaO nanocomposite (15 wt%) was demonstrated to be a good heterogeneous base promoter for high-yield thiazole production. Various reaction factors were studied to maximize the conditions of the catalytic technique. High reaction yields, fast reaction times, and mild reaction conditions are all advantages of the used protocol, as is the reusability of the catalyst; it was reused multiple times without a significant loss of potency.
Chitosan-Enriched Salicylic Acid Nanoparticles Enhanced Anthocyanin Content in Grape (Vitis vinifera L. cv. Red Sultana) Berries
Authors: Khalili Naser , Oraei Mehdi , Gohari Gholamreza , Panahirad Sima , Nourafcan Hassan , Hano Christophe
Published: August 2022

Given the effects of salicylic acid (SA) on enhancing the phenolic compounds, flavonoids, and especially anthocyanins at higher doses in grapes as well as some toxic effects of SA at higher doses, the use of nano-carriers and nano-forms could assist SA in enhancing the accumulation of these compounds while reducing its toxic activity. Chitosan (CTS) has gained attention as a safe transporter and control releaser for a variety of chemicals, particularly in the agriculture industry. In this regard, the nano-form combination of SA and CTS (CTS-SA NPs) could boost the effectiveness of SA, particularly at lower dosages. Therefore, in the present study, SA (10, 20 mM), CTS (0.1%), and CTS-SA NPs (10, 20 mM) were applied on grape (Vitis vinifera L.) berries cv. Red Sultana at the pre-véraison stage to evaluate their actions on phenolic compounds, particularly anthocyanins. The CTS-SA NPs treatments provided the highest results in terms of the total phenolic compounds, flavonoids (10 mM), anthocyanins (in particular oenin, the main anthocyanin of red grapes) (10 and 20 mM), and PAL enzyme activity (20 mM). In conclusion, the CTS-SA NPs could be applied as a potential effective elicitor for phenolics, particularly anthocyanin enhancement of grape berries at pre- véraison stage with synergistic effects between SA and CTS in nano-forms predominantly at lower doses.
Development and Mechano-Chemical Characterization of Polymer Composite Sheets Filled with Silica Microparticles with Potential in Printing Industry
Authors: Siraj Sidra , Al-Marzouqi Ali H. , Iqbal Muhammad Z.
Published: August 2022

Polymer composite sheets using a low-cost filler (local natural sand) and polymer (high-density polyethylene, HDPE) as a replacement of the traditionally used wood-fiber-based sheets for paper-based applications were developed. The sand/polymer composite sheets were prepared by melt extrusion in a melt blender followed by compression molding. The effects of varying particle size, concentration, and the use of a compatibilizer (polyethylene-grafted maleic anhydride) was studied on the mechano-chemical performance properties of the composite sheets such as morphology, thermal and mechanical properties, and wettability characteristics used in the printing industry. In terms of thermal stability, filler (sand) or compatibilizer addition did not alter the crystallization, melting, or degradation temperatures significantly, thereby promoting good thermal stability of the prepared sheets. Compatibilization improved anti-wetting property with water. Additionally, for the compatibilized sheets prepared from 25 µm sand particles, at 35 wt%, the contact angle with printing ink decreased from 44° to 38.30°, suggesting improved ink-wetting performance. A decrease in the elastic modulus was also observed with the addition of the compatibilizer, with comparable results to commercial stone paper. Results from this study will be considered as a first step towards understanding compatibility of local natural sand and polymers for paper-based application.
Synthesis of Silver Nanocomposite Based on Carboxymethyl Cellulose: Antibacterial, Antifungal and Anticancer Activities
Authors: Salem Salem S. , Hashem Amr H. , Sallam Al-Aliaa M. , Doghish Ahmed S. , Al-Askar Abdulaziz A. , Arishi Amr A. , Shehabeldine Amr M.
Published: August 2022

Traditional cancer treatments include surgery, radiation, and chemotherapy. According to medical sources, chemotherapy is still the primary method for curing or treating cancer today and has been a major contributor to the recent decline in cancer mortality. Nanocomposites based on polymers and metal nanoparticles have recently received the attention of researchers. In the current study, a nanocomposite was fabricated based on carboxymethyl cellulose and silver nanoparticles (CMC-AgNPs) and their antibacterial, antifungal, and anticancer activities were evaluated. The antibacterial results revealed that CMC-AgNPs have promising antibacterial activity against Gram-negative (Klebsiella oxytoca and Escherichia coli) and Gram-positive bacteria (Bacillus cereus and Staphylococcus aureus). Moreover, CMC-AgNPs exhibited antifungal activity against filamentous fungi such as Aspergillus fumigatus, A. niger, and A. terreus. Concerning the HepG2 hepatocellular cancer cell line, the lowest IC50 values (7.9 ± 0.41 µg/mL) were recorded for CMC-AgNPs, suggesting a strong cytotoxic effect on liver cancer cells. As a result, our findings suggest that the antitumor effect of these CMC-Ag nanoparticles is due to the induction of apoptosis and necrosis in hepatic cancer cells via increased caspase-8 and -9 activities and diminished levels of VEGFR-2. In conclusion, CMC-AgNPs exhibited antibacterial, antifungal, and anticancer activities, which can be used in the pharmaceutical and medical fields.
First Insights into the Antiviral Activity of Chitosan-Based Bioactive Polymers towards the Bacteriophage Phi6: Physicochemical Characterization, Inactivation Potential, and Inhibitory Mechanisms
Authors: Plohl Olivija , Fric Katja , Filipić Arijana , Kogovšek Polona , Tušek Žnidarič Magda , Zemljič Lidija
Published: August 2022

The outbreak of the worrisome coronavirus disease in 2019 has caused great concern among the global public, especially regarding the need for personal protective equipment with applied antiviral agents to reduce the spread and transmission of the virus. Thus, in our research, chitosan-based bioactive polymers as potential antiviral agents were first evaluated as colloidal macromolecular solutions by elemental analysis and charge. Three different types of low and high molecular weight chitosan (LMW Ch, HMW Ch) and a LMW Ch derivative, i.e., quaternary chitosan (quart-LMW Ch), were used. To explore their antiviral activity for subsequent use in the form of coatings, the macromolecular Chs dispersions were incubated with the model virus phi6 (surrogate for SARS-CoV-2), and the success of virus inactivation was determined. Inactivation of phi6 with some chitosan-based compounds was very successful (>6 log), and the mechanisms behind this were explored. The changes in viral morphology after incubation were observed and the changes in infrared bands position were determined. In addition, dynamic and electrophoretic light scattering studies were performed to better understand the interaction between Chs and phi6. The results allowed us to better understand the antiviral mode of action of Chs agents as a function of their physicochemical properties.
Physical Properties of Starches Modified by Phosphorylation and High-Voltage Electrical Discharge (HVED)
Authors: Ačkar Đurđica , Grec Marijana , Grgić Ivanka , Gryszkin Artur , Styczyńska Marzena , Jozinović Antun , Miličević Borislav , Šubarić Drago , Babić Jurislav
Published: August 2022

High-voltage electrical discharge (HVED) is considered as a novel, non-thermal process and is currently being researched regarding its effect on microorganisms (decontamination of food), waste water treatment, and modification of different compounds and food components. In this paper, four native starches (maize, wheat, potato, and tapioca) were treated with HVED, phosphorylated with Na2HPO4 and Na5P3O10, and modified by a combination of HVED with each phosphorylation reaction both prior and after chemical modification. Pasting properties, swelling power, solubility, gel texture, and particle size were analyzed. Although HVED induced lower contents of P in modified starches, it had an effect on analyzed properties. The results revealed that HVED treatment alone had a limited effect on pasting properties of starches, but it had an effect on properties of phosphorylated starches, both when it was conducted prior and after the chemical modification, reducing the influence of Na5P3O10 and Na2HPO4 on the decrease of pasting temperature. With minor exceptions, the gel strength of starches increased, and the rupture strength decreased by all modifications. HVED treatment resulted in a decrease of the particle size after the modification of maize and wheat starches, while potato and tapioca starches were not significantly influenced by the treatment.
Migration Behavior and Influencing Factors of Petroleum Hydrocarbon Phenanthrene in Soil around Typical Oilfields of China
Authors: Chen Hao , Lang Mengfan , Liao Changjun , Guo Xuetao
Published: August 2022

Petroleum spills and land contamination are becoming increasingly common around the world. Polycyclic aromatic hydrocarbons (PAHs) and other pollutants found in petroleum are constantly migrating underground, making their migration in soil a hot research topic. Therefore, it is of great significance to evaluate the migratory process of petroleum hydrocarbons in petroleum-polluted soil to clarify its ecological and environmental risks. In this study, Phenanthrene (PHE) was used as a typical pollutant of PAHs. The soil was gathered from three typical oilfields in China, and a soil column apparatus was built to simulate the vertical migration of PHE in the soil. The migration law and penetration effect of PHE in various environmental conditions of soil were investigated by varying the ionic strength (IS), pH, particle size, and type of soil. According to the literature, pH has no discernible effect on the migration of PHE. The migration of PHE was adversely and positively linked with changes in IS and soil particle size, respectively. The influence of soil type was mainly manifested in the difference of organic matter and clay content. In the Yanchang Oilfield (YC) soil with the largest soil particle size and the least clay content, the mobility of PHE was the highest. This study may reveal the migration law of PAHs in soils around typical oilfields, establish a new foundation for PAH migration in the soil, and also provide new ideas for the management and control of petroleum pollution in the soil and groundwater.
Existential Empathy: The Challenge of ‘Being’ in Therapy and Counseling
Authors: Vanhooren Siebrecht
Published: August 2022

Although probably all psychotherapists and counselors care for the lives of their clients, not every therapist is invested in helping their clients make sense of their existence. Departing from the question if clients are actually bringing their existential concerns to therapy, studies actually show that a significant proportion of clients brings their ultimate concerns to the consultation room. However, therapists do not always feel comfortable with the existential concerns of their clients. Therapists seem to underestimate their clients’ existential needs. Furthermore, therapists and counselors report that the existential concerns of clients can be overwhelming and evoke an existential quest in therapists. Existential empathy, or the capacity to resonate with the client’s existential concerns and to communicate this empathy, could be enlarged in therapists in order to help clients find different avenues to be with their human condition. Inspired by Tillich, Rank, and Rogers, grounding in ‘being’ is suggested to help therapists being fully present with the clients’ ultimate concerns.
A 3-Stage Spectral-Spatial Method for Hyperspectral Image Classification
Authors: Chan Raymond H. , Li Ruoning
Published: August 2022

Hyperspectral images often have hundreds of spectral bands of different wavelengths captured by aircraft or satellites that record land coverage. Identifying detailed classes of pixels becomes feasible due to the enhancement in spectral and spatial resolution of hyperspectral images. In this work, we propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images. The method consists of three stages. In the first stage, the pre-processing stage, the Nested Sliding Window algorithm is used to reconstruct the original data by enhancing the consistency of neighboring pixels and then Principal Component Analysis is used to reduce the dimension of data. In the second stage, Support Vector Machines are trained to estimate the pixel-wise probability map of each class using the spectral information from the images. Finally, a smoothed total variation model is applied to ensure spatial connectivity in the classification map by smoothing the class probability tensor. We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral datasets with 10 to 50 training labels for each class. The results show that our method gives the overall best performance in accuracy even with a very small set of labeled pixels. Especially, the gain in accuracy with respect to other state-of-the-art algorithms increases when the number of labeled pixels decreases, and, therefore, our method is more advantageous to be applied to problems with a small training set. Hence, it is of great practical significance since expert annotations are often expensive and difficult to collect.
SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea
Authors: Rizaev Igor G. , Achim Alin
Published: August 2022

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46,080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate the classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classifications of vessel types based on their SAR signatures are highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets, respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.
Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique
Authors: Qiao Jiandong , Zhou Chen , Liu Yi , Zhao Jiaqi , Zhao Zhengyu
Published: August 2022

The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D) electron density nowcasting model for China and its adjacent regions was developed. Unlike the traditional Gaussian background covariance model, the error covariance of the IRI model, based on the IGS grid TEC data, is estimated in this work. Due to the large scale of the high-resolution covariance matrix, it cannot be stored and calculated directly on a personal computer. The covariance localization (CL) technique is introduced to sparse the covariance matrix while removing the pseudo-correlation of the covariance matrix. After localization, the covariance matrix can be converted into a sparse matrix for storage and calculation, which greatly reduces the computer memory requirement of the assimilation model and improves the calculation speed of the model. Based on this algorithm, a series of experiments were carried out in this work. The experimental results show that this algorithm can effectively assimilate the observed GNSS to the background field, make up for the temporal and spatial limitations of the observed data, and improve the accuracy of the ionospheric electron density nowcast. Compared with the digisonde observed foF2 (the critical frequency of the ionospheric F2 layer), the RMSE of the assimilation model is 0.44 MHz lower than that of the IRI model.
Explosive Paroxysmal Events at Etna Volcano of Different Magnitude and Intensity Explored through a Multidisciplinary Monitoring System
Authors: Calvari Sonia , Biale Emilio , Bonaccorso Alessandro , Cannata Andrea , Carleo Luigi , Currenti Gilda , Di Grazia Giuseppe , Ganci Gaetana , Iozzia Adriana , Pecora Emilio , Prestifilippo Michele , Sciotto Mariangela , Scollo Simona
Published: August 2022

Between 13 December 2020 and 21 February 2022, Etna volcano produced a sequence of 66 paroxysmal explosive eruptions, with Strombolian activity at the summit craters climaxing in lava fountains and eruption columns extending several kilometers above the craters, accompanied by minor and short-lasting lava flows from the crater rim. We selected three of these episodes that occurred within a short space of time, between 13 December 2020 and 12 March 2021, of different magnitude (i.e., erupted volume) and intensity (i.e., mass eruption rate or instantaneous eruption rate), and analyzed them from a multidisciplinary perspective. The aim was to gain insights into those parameters that mostly reveal the eruptive process for hazard assessment purposes. The multidisciplinary data consist of calibrated visible images, thermal images, seismic and infrasound data, ground deformation detected from the strainmeters, as well as satellite SEVIRI images. From these data, we obtained the timing of each paroxysmal event, the erupted volume in terms of tephra and lava flows, and the corresponding deflation of the source region, together with the development of the lava fountains and eruption columns with time. The results enabled determining that the smallest episode was that of 13 December 2020, which comprised three distinctive pulses but did not produce an eruptive column detectable from either monitoring cameras or satellites. The 28 February 2021 episode was remarkable for the short amount of time required to reach the climax, and was the most intense, whereas the 12 March 2021 event showed the longest duration but with an intensity between that of the previous two. Our results show that these three paroxysmal events display a typical trend, with the first event also being the smallest in terms of both erupted volume and intensity, the second being the most intense, and the third the one of greatest magnitude but less intense than the second. This is coherent with the end of the first paroxysmal phase on 1 April 2021, which was followed by 48 days of eruptive pause before starting again. In this context, the end of the paroxysmal phase was anticipated by a more effusive episode, thus heralding a temporary decline in the gas content within the feeding magma batch.
Assessing the Impact of Urbanization and Eco-Environmental Quality on Regional Carbon Storage: A Multiscale Spatio-Temporal Analysis Framework
Authors: Niu Lu , Zhang Zhengfeng , Liang Yingzi , Huang Yanfen
Published: August 2022

Understanding the mechanisms, intensity, and spatio-temporal heterogeneity of the impacts of urbanization and eco-environmental quality on carbon storage is crucial for achieving carbon neutrality goals. This study constructed a multiscale spatio-temporal analysis framework using multi-source remote sensing data, the InVEST model, and the multiscale geographically weighted regression (MGWR) model. Then, the effects of multiple factors on regional carbon storage were assessed in an empirical study involving 199 counties in Beijing-Tianjin-Hebei. The results showed that the carbon storage loss in the Beijing-Tianjin-Hebei region from 2010 to 2018 was 58.87 Tg C, with an annual relative loss rate of 0.16%. The MGWR model used in this study explained more than 98% of the spatial variation in regional carbon storage. In contrast, the impacts of various urbanization and eco-environmental indicators on regional carbon storage varied with the spatial and temporal variation. Overall, urban land structure and vegetation growth strongly influenced regional carbon storage resulting from urbanization and eco-environmental quality, respectively. In addition, based on an analysis of spatial context, MGWR suggests that the northwestern mountains in the Beijing-Tianjin-Hebei region have a greater potential to store more carbon than the other regions. This study also details the impact of future sustainable land use on regional carbon storage. Our findings can provide a scientific reference for formulating relevant carbon storage conservation policies.
Reduction of Signal Drift in a Wavelength Modulation Spectroscopy-Based Methane Flux Sensor
Authors: Seymour Scott P. , Festa-Bianchet Simon A. , Tyner David R. , Johnson Matthew R.
Published: August 2022

Accurately quantifying unsteady methane venting from key oil and gas sector sources such as storage tanks and well casing vents is a critical challenge. Recently, we presented an optical sensor to meet this need that combines volume fraction and Doppler shift measurements using wavelength modulation spectroscopy with 2f harmonic detection to quantify mass flux of methane through a vent line. This paper extends the previous effort through a methodical component-by-component investigation of potential sources of thermally-induced measurement drift to guide the design of an updated sensor. Test data were analyzed using an innovative signal processing technique that permitted quantification of background wavelength modulation spectroscopy signal drift linked to specific components, and the results were successfully used to design a drift-resistant sensor. In the updated sensor, background signal strength was reduced, and stability improved, such that the empirical methane-fraction dependent velocity correction necessary in the original sensor was no longer required. The revised sensor improves previously reported measurement uncertainties on flow velocity from 0.15 to 0.10 m/s, while markedly reducing thermally-induced velocity drift from 0.44 m/s/K to 0.015 m/s/K. In the most general and challenging application, where both flow velocity and methane fraction are independently varying, the updated design reduces the methane mass flow rate uncertainty by more than a factor of six, from ±2.55 kg/h to ±0.40 kg/h. This new design also maintains the intrinsic safety of the original sensor and is ideally suited for unsteady methane vent measurements within hazardous locations typical of oil and gas facilities.
Impact of Lithium-Ion Battery State of Charge on In Situ QAM-Based Power Line Communication
Authors: Koshkouei Mahyar J. , Kampert Erik , Moore Andrew D. , Higgins Matthew D.
Published: August 2022

Power line communication within a lithium-ion battery allows for high fidelity sensor data to be transferred between sensor nodes of each instrumented cell within the battery pack to an external battery management system. In this paper, the changing characteristics of the lithium-ion cell at various states of charge are measured, analysed, and compared to understand their effectiveness on the communication channel of a power line communication system for carrier frequencies of 10 MHz to 6 GHz. Moreover, the use of quadrature amplitude modulation (QAM) is investigated to determine its effectiveness as a state-of-the-art modulation method for the same carrier frequency range. The overall results indicate that certain carrier frequencies and QAM orders may not be suitable for the in situ battery pack power line communication due to changes in battery impedance with certain lithium-ion cell states of charge, which cause an increase in error vector magnitude, bit error ratio, and symbol error ratio. Recommendations and trends on the impact of these changing characteristics based upon empirical results are also presented in this paper.
An Aeromagnetic Compensation Method for Suppressing the Magnetic Interference Generated by Electric Current with Vector Magnetometer
Authors: Zhang Chao , Du Changping , Peng Xiang , Han Qi , Guo Hong
Published: August 2022

In aeromagnetic detection, the magnetic interference conducted by electric currents in onboard electronic (OBE) equipment is gradually being taken seriously with the development of aeromagnetic compensation technology. Here, we propose a compensation method based on the synthetically total magnetic field (STMF) measured by an onboard fluxgate vector magnetometer. In this method, a compensation model is firstly built to suppress the electric current magnetic interference (ECMI) which is jointly measured by a scalar magnetometer and a fluxgate vector magnetometer. The singular spectrum analysis (SSA) method is introduced to accurately extract the characteristic signal of the ECMI from the compensated STMF. In addition, in order to better suppress the geomagnetic gradient interference, the International Geomagnetic Reference Field (IGRF) model is introduced to modify the existing geomagnetic gradient compensation model. Based on these, a novel compensation model including the traditional aeromagnetic compensation model, modified geomagnetic gradient model, and ECMI compensation model is proposed. The results in the field experiment show that this model has better compensation performance than the TLG model, which is extended from the T–L compensation model.
An Approach to Task Representation Based on Object Features and Affordances
Authors: Gajewski Paul , Indurkhya Bipin
Published: August 2022

Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot’s understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations.
A Fast Weighted Fuzzy C-Medoids Clustering for Time Series Data Based on P-Splines
Authors: Xu Jiucheng , Hou Qinchen , Qu Kanglin , Sun Yuanhao , Meng Xiangru
Published: August 2022

The rapid growth of digital information has produced massive amounts of time series data on rich features and most time series data are noisy and contain some outlier samples, which leads to a decline in the clustering effect. To efficiently discover the hidden statistical information about the data, a fast weighted fuzzy C-medoids clustering algorithm based on P-splines (PS-WFCMdd) is proposed for time series datasets in this study. Specifically, the P-spline method is used to fit the functional data related to the original time series data, and the obtained smooth-fitting data is used as the input of the clustering algorithm to enhance the ability to process the data set during the clustering process. Then, we define a new weighted method to further avoid the influence of outlier sample points in the weighted fuzzy C-medoids clustering process, to improve the robustness of our algorithm. We propose using the third version of mueen’s algorithm for similarity search (MASS 3) to measure the similarity between time series quickly and accurately, to further improve the clustering efficiency. Our new algorithm is compared with several other time series clustering algorithms, and the performance of the algorithm is evaluated experimentally on different types of time series examples. The experimental results show that our new method can speed up data processing and the comprehensive performance of each clustering evaluation index are relatively good.
The Uses of Coffee in Highly Demanding Work Contexts: Managing Rhythms, Sleep, and Performance
Authors: Pegado Elsa , Rodrigues Carla , Raposo Hélder , Fernandes Ana I.
Published: August 2022

This paper presents a sociological approach to coffee consumption as a performance management strategy in work contexts, particularly in professions with intense work rhythms and highly responsive demands. Focusing on the daily work of three professional groups (nurses, police officers, and journalists), we analyze the social expression of coffee and how it is mobilized to deal with sleep problems, fatigue, concentration, or stress. For this purpose, three intertwined dimensions are explored: (1) the nature of these professionals’ work and the pressures for certain forms and levels of performance; (2) sleep problems as both a result of those work characteristics and a constraint on performance; and (3) the role of coffee in managing professional imperatives. The use of coffee appears as a legitimate practice in everyday working routines, due to its socializing markers, whereas additional benefits are attributed to the pharmacological properties of caffeine, given the perceived improvement in performance. The empirical data derive from a study carried out in Portugal on the use of medicines and food supplements for performance management, following a mixed methods approach. In particular, data from a questionnaire survey in a sample of 539 workers and information collected through seven focus groups with a total of 33 participants were used.
Deriving the Optimal Strategy for the Two Dice Pig Game via Reinforcement Learning
Authors: Zhu Tian , Ma Merry H.
Published: August 2022

Games of chance have historically played a critical role in the development and teaching of probability theory and game theory, and, in the modern age, computer programming and reinforcement learning. In this paper, we derive the optimal strategy for playing the two-dice game Pig, both the standard version and its variant with doubles, coined “Double-Trouble”, using certain fundamental concepts of reinforcement learning, especially the Markov decision process and dynamic programming. We further compare the newly derived optimal strategy to other popular play strategies in terms of the winning chances and the order of play. In particular, we compare to the popular “hold at n” strategy, which is considered to be close to the optimal strategy, especially for the best n, for each type of Pig Game. For the standard two-player, two-dice, sequential Pig Game examined here, we found that “hold at 23” is the best choice, with the average winning chance against the optimal strategy being 0.4747. For the “Double-Trouble” version, we found that the “hold at 18” is the best choice, with the average winning chance against the optimal strategy being 0.4733. Furthermore, time in terms of turns to play each type of game is also examined for practical purposes. For optimal vs. optimal or optimal vs. the best “hold at n” strategy, we found that the average number of turns is 19, 23, and 24 for one-die Pig, standard two-dice Pig, and the “Double-Trouble” two-dice Pig games, respectively. We hope our work will inspire students of all ages to invest in the field of reinforcement learning, which is crucial for the development of artificial intelligence and robotics and, subsequently, for the future of humanity.
Changes in Physicochemical Properties of Typical Subtropical Soils under Different Treated Domestic Wastewater Irrigation Modes
Authors: Lu Jiayu , Wang Hui , Hu Chuanwang
Published: August 2022

Irrigation with treated domestic wastewater (TWW) affects the physicochemical properties of soils, but little research has been conducted for different soils in subtropical regions. Consequently, in order to evaluate the effects of domestic wastewater (treated by A2/O process) irrigation on the hydraulic properties, soil salinity and sodicity of four typical subtropical agricultural soils, a soil-column experiment was conducted for one year with eight cycles of wetting and drying. A clay soil (red soil), a silty clay soil (aquic soil), a loamy clay soil (purple soil), and a silty clay loam soil (paddy soil) were subjected to three irrigation modes: (a) cyclic irrigation with TWW (W1), (b) alternating irrigations with TWW and distilled water (W2), and (c) irrigation with distilled water as a control (CK). Our results indicated that EC values increased significantly (p < 0.05) with TWW irrigation for all the soils. TWW irrigation increased the concentrations of Na+, K+, Ca2+, Mg2+, and SAR, ESP values in the red soil, for the W2 treatment especially. In contrast, it decreased Na+, K+, Ca2+, Mg2+ accumulation and ESP values in the aquic soil. Moreover, the soil physical properties exhibited correlations with soil salinity. The saturated hydraulic conductivity (Ks) of the red soil, purple soil, and paddy soil were lower under the W1 and W2 treatments than CK, but the Ks of the aquic soil for W1 were 183.7% higher than that under CK. The unsaturated hydraulic conductivity (Kψ) of the purple soil and paddy soil decreased after TWW irrigation, but those of the aquic soil increased. The water-retention capacities (WRCs) of the TWW-irrigated red soil and aquic soil decreased as a consequence of a shift in the pore-size distribution toward wider pores, but the those of the purple soil and paddy soil improved, associated with narrower pores. The W2 treatment alleviated the impact of TWW on the aquic soil, purple soil, and paddy soil but negatively affected the physical properties, salinity, and sodicity of the red soil. Our results will provide useful information for managing soil and water under TWW irrigation in subtropical regions.
Acceptance of ELV Management: The Role of Social Influence, Knowledge, Attitude, Institutional Trust, and Health Issues
Authors: Sitinjak Charli , Ismail Rozmi , Tahir Zurinah , Fajar Rizqon , Simanullang Wiyanti Fransisca , Bantu Edward , Samuel Karuhanga , Rose Rosniza Aznie Che , Yazid Muhamad Razuhanafi Mat , Harun Zambri
Published: August 2022

The growth of Indonesia’s automotive sector has increased the number of end-of-life vehicles (ELVs), making ELV waste management a major issue. Most countries, such as Japan, China, and Europe, manage ELV waste well, but developing countries still do not. In developing countries, little is known about ELV social admissions. This study analyzes ELV management social acceptance in developing countries. Three hundred nine respondents from Jakarta, Bogor, Depok, Tangerang, and Bekasi (the district in Indonesia) were surveyed in a cross-sectional. A set of questions was designed to determine social acceptance (attitude, knowledge, social influence, institutional trust, health issues, and acceptance). After passing validity and reliability tests, the hypothesized research model was estimated using structural equitation. According to this study, social influence, attitude, knowledge, institutional trust, and health issues influenced public acceptance. The health issues variable was also a good moderator (Adj. R2 = 0.173, p < 0.001, average path coefficient = 0.299). The analysis of social acceptance models related to ELV management found that social influence, attitude, knowledge, and institutional trust play a role in one’s desire to accept a new rule, and health issues can strengthen a person in the admission process.
Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand
Authors: Shanmuganathan Jaikumar , Victoire Aruldoss Albert , Balraj Gobu , Victoire Amalraj
Published: August 2022

The immense growth and penetration of electric vehicles has become a major component of smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the environment. With the increased volumes of electric vehicles (EV) in the past few years, the charging demand of these vehicles has also become an immediate requirement. Due to which, the prediction of the demand of electric vehicle charging is of key importance so that it minimizes the burden on the electric grids and also offers reduced costs of charging. In this research study, an attempt is made to develop a novel deep learning (DL)-based long-short term memory (LSTM) recurrent neural network predictor model to carry out the forecasting of electric vehicle charging demand. The parameters of the new deep long-short term memory (DLSTM) neural predictor model are tuned for its optimal values using the classic arithmetic optimization algorithm (AOA) and the input time series data are decomposed so as to maintain their features using the empirical mode decomposition (EMD). The novel EMD—AOA—DLSTM neural predictor modeled in this study overcomes the vanishing and exploding gradients of basic recurrent neural learning and is tested for its superiority on the EV charging dataset of Georgia Tech, Atlanta, USA. At the time of simulation, the best results of 97.14% prediction accuracy with a mean absolute error of 0.1083 and a root mean square error of 2.0628 × 10−5 are attained. Furthermore, the mean absolute error was evaluated to be 0.1083 and the mean square error pertaining to 4.25516 × 10−10. The results prove the efficacy of the prediction metrics computed with the novel deep learning LSTM neural predictor for the considered dataset in comparison with the previous techniques from existing works.
The Spatial Pattern of the Prefecture-Level Carbon Emissions and Its Spatial Mismatch in China with the Level of Economic Development
Authors: Yang Zedong , Sun Hui , Yuan Weipeng , Xia Xuechao
Published: August 2022

Now, more and more attention is being paid to the deviation between economic benefits and environmental costs. Therefore, the research investigated the temporal variation in, and the spatial pattern of, carbon emissions systematically from the prefecture-level cities in China, and the spatial mismatch with the level of economic development from 2005 to 2020 by the spatial autocorrelation, the barycenter model, the spatial mismatch index and the standard deviation ellipse. The research results showed that: (1) The prefecture-level carbon emissions showed the characteristics of continuous expansion in time, decreasing from the north to the south and increasing from the southeast to the north-west in space. (2) The spatial distribution of high and low values of the prefecture-level carbon emissions in China showed significant spatial aggregation and positive correlation. Among them, the high-high (H-H) regions were mainly distributed in the city agglomerations of the mid-southern Liaoning, Beijing-Tianjin-Hebei, Huebaoyu, central Shanxi, Shandong Peninsula, and Yangtze River Delta. Low-low (L-L) regions continued to accelerate to the middle reaches of the Yangtze River, Guangdong, Fujian, Zhejiang and Macao, Pearl River Delta, Beibu Gulf, central Yunnan, central Guizhou, Chengdu-Chongqing, and other city agglomerations. (3) The barycenter of the carbon emissions has always been located in the north of the barycenter of economic development, and the spatial mismatch distance between the two showed the change characteristic of the type of N. It can be seen that the spatial mismatch was apparent. (4) The mismatch index and the mismatch contribution of the cities showed a significant regional differentiation pattern, decreasing from the east to the west.
How Does Green Search Promote Green Innovation? The Mediating Role of Green Control
Authors: Li Yahui , Zheng Wenli , Zhao Yunhui
Published: August 2022

The importance of a knowledge search for green innovation has been recognized, but our understanding of the influence of green search is still unclear. We used 198 Chinese manufacturing data to sort the influential mechanism of green search on green innovation based on resource-based theory, information processing theory, and transaction cost theory. We also explored the intermediate role of green control (i.e., output and behavior control) between the two dimensions of green search (i.e., search breadth and depth) and green innovation (i.e., exploratory and exploitative innovation). The results show that the diversified green information searched from outside must be controlled through the behavior process and results in improving the quality of existing green products and developing new green products. A continuous and deep search for green knowledge can achieve the results of green innovation by setting goals and monitoring processes. When Chinese manufacturing enterprises are searching for knowledge to produce green products, they must pay attention to the supervision and control of the knowledge integration process and results to ensure the effective utilization and transformation of knowledge.