bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacokinetics and Anti-Diabetic Studies of Gliclazide Nanosuspension
Authors: Sampathi Sunitha , Prajapati Shubham , Junnuthula Vijayabhaskarreddy , Dyawanapelly Sathish
Published: September 2022

Gliclazide (GCZ), an antidiabetic medication, has poor solubility and limited oral bioavailability due to substantial first-pass metabolism. Thus, the purpose of the current study was to optimize and formulate a GCZ nanosuspension (NS) employing the antisolvent precipitation technique. A three-factor, three-level Box–Behnken design (BBD) was used to examine the impact of the primary formulation factors (drug concentration, stabilizer, and surfactant %) on particle size. The optimized NS contains 29.6 mg/mL drug, 0.739% lecithin, and 0.216% sodium dodecyl sulfate (SDS). Under scanning microscopy, the topography of NS revealed spherical particles. Furthermore, NS had a much better saturation solubility than the pure material, which resulted in a rapid dissolving rate, which was attributed to the amorphous structure and smaller particle size of the NS particles. Studies on intestinal permeability using the in vitro noneverted intestinal sac gut method (duodenum, jejunum, and ileum) and single-pass intestinal permeability (SPIP) techniques showed that the effective permeability was also increased by more than 3 fold. In the pharmacokinetic study, the Cmax and AUC0–t values of NS were approximately 3.35- and 1.9-fold higher than those of the raw medication and marketed formulation (MF). When compared to plain drug and commercial formulations, the antidiabetic efficacy of NS demonstrated that it had a significant impact on lowering glucose levels.
Predicting Transiently Expressed Protein Yields: Comparison of Transfection Methods in CHO and HEK293
Authors: Porosk Ly , Nebogatova Jekaterina , Härk Heleri Heike , Vunk Birgit , Arukuusk Piret , Toots Urve , Ustav Mart , Langel Ülo , Kurrikoff Kaido
Published: September 2022

Therapeutic proteins are currently at the apex of innovation in pharmaceutical medicine. However, their industrial production is technically challenging and improved methods for transient transfection of mammalian cell cultures are necessary. We aimed to find a fast, microliter-scale transfection assay that allows the prediction of protein expression in the transient production settings. We used an array of lipid, polymeric and cell-penetrating peptide transfection reagents, and compared their performance in various high throughput transfection assays to their performance in protein (antibody) expression in professional protein-producer cell lines. First, we show that some of the most frequently used microliter-scale transfection efficacy assays fail to predict performance in the protein production in milliliter and liter scale settings. We found that CHO suspension culture post-transfection EGFP(+) population and SEAP quantitation correlate with large-scale protein production, whereas the adhesion culture assays and transfection of pLuc are non-predictive. Second, we demonstrated that cell-penetrating peptide-based transfection achieves significantly higher protein yields compared to PEI and lipoplex methods in both CHO and HEK293 producer cell lines. In this work we demonstrate a CPP-based transient protein expression approach that significantly outperformed the current industry standard workhorse method of PEI.
Assessment of Acute Oral Toxicity of Thiolated Gum Ghatti in Rats
Authors: Puri Vivek , Sharma Ameya , Kumar Pradeep , Dua Kamal , Huanbutta Kampanart , Singh Inderbir , Sangnim Tanikan
Published: September 2022

Various drug delivery systems were developed using a modified form of gum ghatti. Modifying gum ghatti using thioglycolic acid improves its mucoadhesive property, and hence, it is a suitable approach for the fabrication and development of controlled drug delivery systems. In accordance with regulatory guidelines, namely, the Organization for Economic Co-operation and Development’s (OECD) 423 guidelines, an acute oral dose toxicity study was performed to examine the toxicological effects of gum ghattiin an animal (Wistar rat) after a single oral dose administration of pure gum ghatti and thiolated gum ghatti. Orally administered pure and thiolated gum ghatti do not reveal any considerable change in the behavioral pattern, food intake, body weight, hematology, or clinical symptoms of treated animals. Furthermore, histopathological studies showed no pathological mutations in the vital organs of Wistar rats after the oral administration of single doses of both types of gumghatti (i.e., 300 mg/kg and 2000 mg/kg body weight). Whole blood clotting studies showed the low absorbance value of the modified gum (thiolated gum ghatti) in contrast to the pure gum and control, hence demonstrating its excellent clotting capability. The aforementioned toxicological study suggested that the oral administration of a single dose of pure and thiolated gum ghatti did not produce any toxicological effects in Wistar rats. Consequently, it could be a suitable and safe candidate for formulating various drug delivery systems.
Insights into Stoichiometry Adjustments Governing the Performance of Flexible Foamed Polyurethane/Ground Tire Rubber Composites
Authors: Olszewski Adam , Kosmela Paulina , Żukowska Wiktoria , Wojtasz Paweł , Szczepański Mariusz , Barczewski Mateusz , Zedler Łukasz , Formela Krzysztof , Hejna Aleksander
Published: September 2022

Polyurethanes (PU) are widely applied in the industry due to their tunable performance adjusted by changes in the isocyanate index—stoichiometric balance between isocyanate and hydroxyl groups. This balance is affected by the incorporation of modifiers of fillers into the PU matrix and is especially crucial for PU foams due to the additional role of isocyanates—foaming of the material. Despite the awareness of the issue underlined in research works, the contribution of additives into formulations is often omitted, adversely impacting foams’ performance. Herein, flexible foamed PU/ground tire rubber (GTR) composites containing 12 different types of modified GTR particles differing by hydroxyl value (LOH) (from 45.05 to 88.49 mg KOH/g) were prepared. The impact of GTR functionalities on the mechanical, thermomechanical, and thermal performance of composites prepared with and without considering the LOH of fillers was assessed. Formulation adjustments induced changes in tensile strength (92–218% of the initial value), elongation at break (78–100%), tensile toughness (100–185%), compressive strength (156–343%), and compressive toughness (166–310%) proportional to the shift of glass transition temperatures (3.4–12.3 °C) caused by the additional isocyanates’ reactions yielding structure stiffening. On the other hand, formulation adjustments reduced composites’ thermal degradation onset due to the inferior thermal stability of hard segments compared to soft segments. Generally, changes in the composites’ performance resulting from formulation adjustments were proportional to the hydroxyl values of GTR, justifying the applied approach.
Facile Synthesis of Functionalised Hyperbranched Polymers for Application as Novel, Low Viscosity Lubricant Formulation Components
Authors: Goodwin Sophie R. , Stimpson Amy , Moon Richard , Cowie Lauren , Aragrag Najib , Filip Sorin V. , Smith Andrew G. , Irvine Derek J.
Published: September 2022

A novel, previously unreported, method for synthesising hyperbranched (HB) materials is detailed. Their use as additives to produce lubricant formulations that exhibit enhanced levels of wear protection and improved low-temperature oil viscosity and flow is also reported. The lubricant formulations containing HB additives were found to exhibit both significantly lower viscosities and improved in-use film-forming properties than the current industry standard formulations. To achieve this, alkyl methacrylate oligomers (predominantly dimers and trimers) were synthesised using catalytic chain transfer polymerisation. These were then used as functional chain transfer agents (CTA) to control the polymerisation of divinyl benzene (DVB) monomers to generate highly soluble, high polydispersity HB polymers. The level of dimer/trimer purification applied was varied to define its influence on both these HB resultant structures and the resultant HB additives’ performance as a lubricant additive. It was shown that, while the DVB acted as the backbone of the HB, the base oil solubility of the additive was imparted by the presence of the alkyl chains included in the structure via the use of the oligomeric CTAs.
Relationships between Size Distribution, Morphological Characteristics, and Viscosity of Cellulose Nanofibril Dispersions
Authors: Albornoz-Palma Gregory , Ching Daniel , Andrade Andrea , Henríquez-Gallegos Sergio , Teixeira Mendonça Regis , Pereira Miguel
Published: September 2022

Rheological parameters of cellulose nanofibril dispersions (CNF) are relevant and commonly used as quality control for producing of this type of material. These parameters are affected by morphological features and size distribution of the nanofibrils. Understanding the effect of size distribution is essential for analyzing the rheological properties, viscosity control, performance of CNFs, and potential dispersion applications. This study aims at comprehending how the morphological characteristics of the CNFs and their size distribution affect the rheological behavior of dispersions. The CNF dispersions were fractionated by size, obtaining six fractions of each, which were analyzed for their morphology and rheology (viscosity, intrinsic viscosity). In the dilute region, the viscosity and intrinsic viscosity behavior of CNF dispersions are linear concerning the size distribution present in the dispersion. In the semi-dilute region, the size of the fibrils and the fiber aggregates have a relevant effect on the viscosity behavior of CNF dispersions, which are satisfactorily related (R2 = 0.997) using the rule of logarithmic additivity of the dispersion viscosities of size fractions.
Improving the Heat and Ablation Resistance of Silicone Rubber Composites by Incorporating Hollow Microspheres
Authors: Tian Jinfeng , Yan Liwei , Zhang Hao , Zhou Shengtai , Xia Shuang , Zou Huawei
Published: September 2022

For thermal protection materials (TPMs) which are used to protect space vehicles from extreme thermomechanical environments, the thermal conductivity of the original material and the char layer that has formed during ablation plays a significant role in determining the ablation performance. In order to investigate this, in this study, we introduced glass hollow microspheres (GHMs), phenolic hollow microspheres (PHMs), and acrylonitrile-methyl methacrylate copolymer hollow microspheres (AMHMs) into silicone rubber (SR), and the ablation performance of these composites was systematically studied. The thermogravimetric results showed that the residue yield of the SR composites was increased with the incorporation of the hollow microspheres. Compared to the SR composites without the hollow microspheres, the residue weight values under 800 °C (R800) of the SR composites with the 30 parts of fumed silica per hundred of the SR (phr) addition of GHMs, PHMs, and AMHMs were increased from 10.11% to 21.70%, 18.31%, and 20.83%, respectively. The ablation tests showed that the addition of the AMHMs enhanced the ablation performance of the SR composites because the linear ablation rates and the backplane temperature were clearly decreased when compared to the SR composites without the hollow microspheres. This work provides an effective and potential method for preparing thermal protection materials with an improved ablation performance.
Few-Shot Aircraft Detection in Satellite Videos Based on Feature Scale Selection Pyramid and Proposal Contrastive Learning
Authors: Zhou Zhuang , Li Shengyang , Guo Weilong , Gu Yanfeng
Published: September 2022

To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance.
A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion
Authors: Gao Fei , Xu Jingming , Lang Rongling , Wang Jun , Hussain Amir , Zhou Huiyu
Published: September 2022

Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images’ features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.
Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal
Authors: Mohammadpour Pegah , Viegas Domingos Xavier , Viegas Carlos
Published: September 2022

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.
Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations
Authors: Wang Yuxin , He Xianqiang , Bai Yan , Li Teng , Wang Difeng , Zhu Qiankun , Gong Fang
Published: September 2022

The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (Rrsλ) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which Rrsλ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from Rrsλ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R2) was greater than 0.98, and the root mean squared error (RMSE) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.
A Repeater-Type SAR Deceptive Jamming Method Based on Joint Encoding of Amplitude and Phase in the Intra-Pulse and Inter-Pulse
Authors: Cheng Dongyang , Liu Zhenchang , Guo Zhengwei , Shu Gaofeng , Li Ning
Published: September 2022

Due to advantages such as low power consumption and high concealment, deceptive jamming against synthetic aperture radar (SAR) has received extensive attention in electronic countermeasures. However, the false targets generated by most of the deceptive jamming methods still have limitations, such as poor controllability and strong regularity. Inspired by the idea of waveform coding, this paper proposed a repeater-type SAR deceptive jamming method through the joint encoding of amplitude and phase in intra-pulse and inter-pulse, which can generate a two-dimensional controllable deceptive jamming effect. Specifically, the proposed method mainly includes two parts, i.e., grouping and encoding. The number of groups determines the number of false targets, and the presence of the phase encoding produces false targets. The amplitude encoding affects the amplitude of the false targets. For the intra-pulse cases, the proposed method first samples the intercepted SAR signal. Meanwhile, the sampling points are grouped in turn. For the inter-pulse cases, the grouped objects are the pulses. Subsequently, the joint encoding of amplitude and phase is performed on each group, which generates jamming signals with deceptive effects. In this paper, the imaging effect of the generated jamming signals is analyzed in detail, and the characteristics of false targets, including numbers, position, and amplitude, are derived. The simulation and experimental results verify the correctness of the theoretical analysis. In addition, the superiority of the proposed method is verified by comparing it with other methods.
TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral Image Denoising
Authors: Pang Li , Gu Weizhen , Cao Xiangyong
Published: September 2022

We propose a new deep neural network termed TRQ3DNet which combines convolutional neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network consists of two branches. One is built by 3D quasi-recurrent blocks, including convolution and quasi-recurrent pooling operation. Specifically, the 3D convolution can extract the spatial correlation within a band, and spectral correlation between different bands, while the quasi-recurrent pooling operation is able to exploit global correlation along the spectrum. The other branch is composed of a series of Uformer blocks. The Uformer block uses window-based multi-head self-attention (W-MSA) mechanism and the locally enhanced feed-forward network (LeFF) to exploit the global and local spatial features. To fuse the features extracted by the two branches, we develop a bidirectional integration bridge (BI bridge) for better preserving the image feature information. Experimental results on synthetic and real HSI data show the superiority of our proposed network. For example, in the case of Gaussian noise with sigma 70, the PSNR value of our method significantly increases about 0.8 compared with other state-of-the-art methods.
MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms
Authors: Cao Huimin , Luo Xiaobo , Peng Yidong , Xie Tianshou
Published: September 2022

Obtaining high-spatial–high-temporal (HTHS) resolution remote sensing images from a single sensor remains a great challenge due to the cost and technical limitations. Spatiotemporal fusion (STF) technology breaks through the technical limitations of existing sensors and provides a convenient and economical solution for obtaining HTHS resolution images. At present, most STF methods use stacked convolutional layers to extract image features and then obtain fusion images by using a summation strategy. However, these convolution operations may lead to the loss of feature information, and the summation strategy results in poorly fused images due to a lack of consideration of global spatial feature information. To address these issues, this article proposes a STF network architecture based on multiscale and attention mechanisms (MANet). The multiscale mechanism module composed of dilated convolutions is used to extract the detailed features of low-spatial resolution remote sensing images at multiple scales. The channel attention mechanism adaptively adjusts the weights of the feature map channels to retain more temporal and spatial information in the upsampling process, while the non-local attention mechanism adjusts the initial fusion images to obtain more accurate predicted images by calculating the correlation between pixels. We use two datasets with different characteristics to conduct the experiments, and the results prove that the proposed MANet method with fewer parameters obtains better fusion results than the existing machine learning-based and deep learning-based fusion methods.
Ghostformer: A GhostNet-Based Two-Stage Transformer for Small Object Detection
Authors: Li Sijia , Sultonov Furkat , Tursunboev Jamshid , Park Jun-Hyun , Yun Sangseok , Kang Jae-Mo
Published: September 2022

In this paper, we propose a novel two-stage transformer with GhostNet, which improves the performance of the small object detection task. Specifically, based on the original Deformable Transformers for End-to-End Object Detection (deformable DETR), we chose GhostNet as the backbone to extract features, since it is better suited for an efficient feature extraction. Furthermore, at the target detection stage, we selected the 300 best bounding box results as regional proposals, which were subsequently set as primary object queries of the decoder layer. Finally, in the decoder layer, we optimized and modified the queries to increase the target accuracy. In order to validate the performance of the proposed model, we adopted a widely used COCO 2017 dataset. Extensive experiments demonstrated that the proposed scheme yielded a higher average precision (AP) score in detecting small objects than the existing deformable DETR model.
Expectation–Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems
Authors: Chen Lu , Chen Zhigang , Ji Zhi
Published: September 2022

In this paper, we proposed a novel expectation–maximization-based simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) communication systems. By fully exploiting the geometric relationship among the access point (AP) positions, the angle difference of arrival (ADOA) from the APs and the mobile terminal (MT) position, and regarding the MT positions as the latent variable of the AP positions, the proposed algorithm first reformulates the SLAM problem as the maximum likelihood joint estimation over both the AP positions and the MT positions in a latent variable model. Then, it employs a feasible stochastic approximation expectation–maximization (EM) method to estimate the AP positions. Specifically, the stochastic Monte Carlo approximation is employed to obtain the intractable expectation of the MT positions’ posterior probability in the E-step, and the gradient descent-based optimization is used as a viable substitute for estimating the high-dimensional AP positions in the M-step. Further, it estimates the MT positions and constructs the indoor map based on the estimated AP topology. Due to the efficient processing capability of the stochastic approximation EM method and taking full advantage of the abundant spatial information in the crowd-sourcing ADOA data, the proposed method can achieve a better positioning and mapping performance than the existing geometry-based mmW SLAM method, which usually has to compromise between the computation complexity and the estimation performance. The simulation results confirm the effectiveness of the proposed algorithm.
A Comprehensive Survey of Depth Completion Approaches
Authors: Khan Muhammad Ahmed Ullah , Nazir Danish , Pagani Alain , Mokayed Hamam , Liwicki Marcus , Stricker Didier , Afzal Muhammad Zeshan
Published: September 2022

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the earlier approaches focused on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review in detail different state-of-the-art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.
GRAPE: Grammatical Algorithms in Python for Evolution
Authors: de Lima Allan , Carvalho Samuel , Dias Douglas Mota , Naredo Enrique , Sullivan Joseph P. , Ryan Conor
Published: September 2022

GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.
Simulation of Artificial Lighting Using Leading Software to Evaluate Lighting Conditions in the Absence of Daylight in a University Classroom
Authors: Králiková Ružena , Džuňová Laura , Lumnitzer Ervin , Piňosová Miriama
Published: September 2022

This paper focuses on evaluating lighting conditions in a university classroom used for day and evening classes. The evaluation was made with regard to the legislation requirements for evening classes and adult education. The practical part of this paper consists of illumination measuring and simulation. A RadioLux 111 luxmeter was used to measure the illumination during the absence of daylight, which often occurs, especially in winter during the later hours. The simulation was performed in the DIALux EVO 10.1 and ReluxDesktop lighting design software. In both software, a 3D model of the classroom was created to represent its actual state. The results of the measurement and both simulations were compared with each other and with the requirements of the legislation. The requirements for evening classes have not been met in all three cases; therefore, the lighting simulation tools were used to improve the lighting system design concerning its energy efficiency and costs. This paper points out the benefits of using freely available computer software in the process of the calculation and improvement of lighting systems.
Identification of Applicable YouTubers for Hotels: A Case Study of Integrated Hybrid MCDM Model
Authors: Wu Lee-Chun , Chang Kuei-Lun , Chuang Tung-Lin , Chen You-Shyang , Tsai Jung-Fa
Published: September 2022

The coronavirus disease 2019 (COVID-19) pandemic has caused a serious business recession in various walks of life, particularly in the full-service hotel industry. YouTube has one billion active users and is undoubtedly a social media platform that companies use to build relationships with customers and create value for brands. Marketers should be aware of YouTubers’ significant influence on complex decision-making processes. Given the above reasons, identifying a YouTuber attracts the concerns of various industries; thus, this important issue is focused on and offered the study’s rationality. This study proposes an integrated hybrid MCDM model to organize the four key techniques of FDM, DEMATEL, ANP, and TOPSIS to identify YouTubers for hotels. Consequently, 12 key criteria and four core dimensions were identified to improve the decision of optimal YouTubers for promoting sustainable development and increasing the efficiency of decision-making. From the limited literature review, the proposed hybrid model was not observed regarding YouTuber identification of hotels; thus, this study provides a superior application contribution to address this important and interesting topic for academicians and practitioners.
Analysis of Spatial Dynamic Correlation and Influencing Factors of Atmospheric Pollution in Urban Agglomeration in China
Authors: Wei Liangli , Li Xia
Published: September 2022

The fluidity of air pollution makes a cross-regional joint effort to control pollution inevitable. Exploring the dynamic correlation and affecting factors of air pollution in urban agglomerations is conducive to improving the effectiveness of pollution control and promoting the high-quality development of the regional economy. Based on daily data on PM2.5 concentration, the article identifies the dynamic association relationship of atmospheric pollution in urban agglomerations of Beijing–Tianjin–Hebei (BTH) air pollution transmission channel under the framework of the vector autoregressive model, building the spatial correlation network of atmospheric pollution in urban agglomerations of BTH atmospheric pollution transmission channel, investigating the structure characteristics and influencing factors. The results show that the atmospheric pollution in BTH cities has a general dynamic correlation, which shows a stable multithreaded complex network structure; the overflow direction of air pollution is highly consistent with the weight matrix of northwest wind direction; economic development level, population density, openness degree, geographical location, and the relationship of wind direction are the important factors affecting the spatial association network of atmospheric pollution. We should actively explore the construction mode of urban agglomeration under the constraint of atmospheric pollution and improve the cross-regional collaborative governance mechanism.
Exploring the Role of Community Empowerment in Urban Poverty Eradication in Kuala Lumpur, Malaysia
Authors: Dass Mahaganapathy , Kunasekaran Puvaneswaran , Kaur Charanjit , Gill Sarjit S.
Published: September 2022

The main purpose of this study was to holistically understand the role of empowerment in urban poverty eradication of the Indian community in the urban areas of the Klang Valley, Malaysia. The poverty eradication effectiveness was tested by analysing community empowerment domains and MyKasih programme run by a non-governmental organisation. There are numerous studies conducted to understand the issues of poverty in Malaysia. However, few studies have so far focused on the minority community in Malaysia. Moreover, there is no recent study to test the effectiveness of any governmental or NGO’s poverty eradication efforts on this minority community. This study utilised a qualitative approach and an in-depth interview was used to gather the data. The respondents were single mothers living in a poverty-stricken area in the capital city of Malaysia.
Guizhou Karst Carbon Sink and Sustainability—An Overview
Authors: Zhang Min , Yang Wu , Yang Mingxing , Yan Jun
Published: September 2022

Global warming and climate change are becoming the most popular topics among scientists. In this century, the research focusing on the process and mechanism of the carbon cycle, especially the research of reducing the concentration of atmospheric CO2 (carbon sink and carbon sequestration technology), are the core issues of global change. The karst carbon sink was neglected due to the consideration that it was a geological process with a long timescale. Recently, studies have proven that carbonate rock weathering is a rapid and sensitive process, and the Fifth Climate Change Assessment Report of the United Nations Intergovernmental Panel on Climate Change confirmed this. Guizhou, as the center of karst in China, has the unique advantages of the karst carbon sink, and is the core area of the karst carbon sink. On the basis of summarizing the development and evolution trend of karst carbon sinks, through data collection and field research, the Carbon Neutralization Research Group of Guizhou Institute of Technology has conducted an exploratory investigation on karst carbon sinks in Guizhou Province, and basically identified the mechanism, influencing factors and measurement methods of karst carbon sinks in Guizhou karst areas. The results show that the potential of the karst carbon sink in Guizhou is huge. Vegetation restoration, soil improvement, irrigation with external water and the cultivation of aquatic plants are important ways to increase the karst carbon sink by artificial intervention. A series of achievements have been made in the theory, technology and platform construction of the karst carbon cycle geological survey and carbon sink effect evaluation. It is worth noting that there are still great challenges in karst carbon sink measurement and verification, and in the demonstration of artificial intervention in carbon sequestration and sink enhancement, which need to be continuously tackled and improved in the “14th Five-Year Plan” period and beyond, so as to meet the needs of the carbon neutralization target of geological carbon sink services.
Systems Dynamics and the Analytical Network Process for the Evaluation and Prioritization of Green Projects: Proposal That Involves Participative Integration
Authors: Castrillon-Gomez Julian Andres , Olivar-Tost Gerard , Valencia-Calvo Johnny
Published: September 2022

This research proposes a methodology for evaluating and prioritizing green projects based on an integrated approach between system dynamics modeling and the analytical network process. The methodology is presented in three stages: First of all, we show the citizen factors and obtain the data from the zone through community participation. Then, in the second stage, the model of system dynamics is consolidated and calibrated, which allows the generation of relevant information for experts by simulating model variables. In the third stage, considering the dependency and feedback relationships of the system, the model is translated to a complex network of many opinions which makes the decision making through peer review easier. The application of the methodology is presented using a case study undertaken in the California county that belongs to the Magdalena region in Colombia. The results allow to conclude that the proposed methodology makes the evaluation process and the prioritization of the projects easier because it is possible to advise the experts with respect to the variables that maximize investments and based on this select environmental initiatives that maximize investments and the environmental, social, or economic initiatives that best respond to the needs of the community. Likewise, we can demonstrate that the methodology can be applied to any rural community adjusting parameters and calibration variables to reflect the new conditions, both for natural resources and for particular policies and actions in such a way that the most appropriate project can be selected.
Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review
Authors: Rasheed Muhammad Waseem , Tang Jialiang , Sarwar Abid , Shah Suraj , Saddique Naeem , Khan Muhammad Usman , Imran Khan Muhammad , Nawaz Shah , Shamshiri Redmond R. , Aziz Marjan , Sultan Muhammad
Published: September 2022

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.
Analysis and Research on the Use of Bulk Recycled Materials for Sound Insulation Applications
Authors: Badida Miroslav , Moravec Marek , Pinosova Miriama , Andrejiova Miriam , Pástor Kristián , Nováková Alžbeta , Dzuro Tibor
Published: September 2022

The application of recycled materials from the automotive industry in the field of the construction industry is a suitable alternative application for these materials and the use of their acoustic and thermal insulation properties. The output of recycling is granular, or chopped materials that can be used as a substitute for conventional materials. One of the important features of building materials is their acoustic properties. The measurement and evaluation of acoustic properties is carried out using an impedance tube as equipment. Measuring compact materials is quite simple and requires the preparation of a sample. Measuring the acoustic properties of granular bulk materials is more complicated and requires the development and production of a special test cartridge. Recycled bulk materials from the automotive industry such as rubber granules and chopped textiles can be applied as fillings for dividing structures. The aim of this paper was to assess the acoustic properties of different fractions of recycled rubber granules and textile chopped material and to compare acoustic properties with compact rubber and textile panels. To evaluate and compare sound absorption coefficient (α) and sound transmission loss (R) parameters, we used basic statistical methods and hypothesis testing methods. The production of compact panels is quite expensive since it is necessary to use special synthetic binders in production, and the content of these substances can also have negative effects on the environment. Based on the results of measuring the acoustic properties of bulk recycled materials and comparing them with compact materials, we can conclude that bulk recycled rubber and textile materials have very good values for their acoustic properties, which enables them to be used in several areas of industry.
Sensory Perception Nudge: Insect-Based Food Consumer Behavior
Authors: Martins Oliva M. D. , Bucea-Manea-Țoniș Rocsana , Coelho Ana Sofia , Simion Violeta-Elena
Published: September 2022

The negative impact that animal protein sources have on the environment is a critical world problem. Finding new acceptable alternatives is crucial. Nevertheless, numerous factors influence the decision to try an unknown food. By adopting a consumer behavior perspective as well as approaching the possibility of overcoming neophobia, this research examined the influence of sensory perception on consumer behavior with regard to the experimentation with new foods, focusing on entomophagy. A theoretical model was developed, and path analysis and factor influence were based on the structural equation model (PLS-SEM), designed in SmartPLS, to test the model relationships. Despite the low level of awareness concerning the benefits of entomophagy, this study considered that many aspects influence experimentation with new food, specifically our sensorial system. Sensory perception is founded on the senses, such as the tactile, olfactory, visual, and gustatory senses, which can influence perception. In line with these assumptions, this research identified the three most important and decisive factors that can influence individuals’ sensory perceptions: preparation, visual and related aspects, and the presentation of the shape of food have an influence on sensory perception regarding entomophagy consumer behavior. People like to know the method of preparation as well as the ingredients and the color of the food. These findings are crucial to food business practitioners, policymakers, and marketers, who can adopt some food process strategies following sensory perception, that will contribute to changing the habits of consumers.
Evaluating the Regional Economic Impacts of High-Speed Rail and Interregional Disparity: A Combined Model of I/O and Spatial Interaction
Authors: Sugimori Shuji , Hayashi Yoshitsugu , Takeshita Hiroyuki , Isobe Tomohiko
Published: September 2022

Among the benefits of high-speed rails (HSRs) discussed from various aspects, indirect benefits may contribute to medium- and long-term economic impacts such as an increase in service supply and gross regional product (GRP). In order to estimate the economic impacts, we modeled I/O–spatial interaction by combining the inter–industrial transactions shown on the I/O table with the geospatial distance decay of economic mass through passenger transportation. In addition, the regional economic impacts, as a part of the indirect benefits, were evaluated by the model applied to the Mumbai–Ahmedabad High-Speed Rail (MAHSR) corridor in India, which is an emerging country with remarkable economic growth. The results showed the economic impacts on each zone and each industry along the MAHSR corridor as a relative distribution. The unique feature of this approach is that it is possible to evaluate the geographic distributions and interregional disparity of economic impacts by combining the industrial I/O relationships with the changes in passenger accessibility associated with a large-scale transportation project such as an HSR. Moreover, this method can be applied to various countries and regions where detailed I/O statistical data, such as interregional I/O tables, are difficult to obtain, as well as various transportation project evaluations taking into account interregional equity.