bluuebunny/crossref_metadata_2025 huggingface dataset:
On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
Authors: Alhaj Hasan Adnan , Kvasnikov Aleksey A. , Klyukin Dmitriy V. , Ivanov Anton A. , Demakov Alexander V. , Mochalov Dmitry M. , Kuksenko Sergei P.
Published: April 2023

This paper focuses on antenna modeling using wire-grid and surface triangulation as two of the most commonly used MoM-based approaches in this field. A comprehensive overview is provided for each of them, including their history, applications, and limitations. The mathematical background of these approaches is briefly presented. Two working algorithms were developed and described in detail, along with their implementations using acceleration techniques. The wire-grid-based algorithm enables modeling of arbitrary antenna solid structures using their equivalent grid of wires according to a specific modeling recommendation proposed in earlier work. On the other hand, the surface triangulation-based algorithm enables calculation of antenna characteristics using a novel excitation source model. Additionally, a new mesh generator based on the combined use of the considered algorithms is developed. These algorithms were used to estimate the characteristics of several antenna types with different levels of complexity. The algorithms computational complexities were also obtained. The results obtained using these algorithms were compared with those obtained using the finite difference time domain numerical method, as well as those calculated analytically and measured. The analysis and comparisons were performed on the example of a rectangle spiral, a spiral, rounded bow-tie planar antennas, biconical, and horn antennas. Furthermore, the validity of the proposed algorithms is verified using the Monte Carlo methodology.
Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
Authors: Kontogiannis Sotirios , Gkamas Theodosios , Pikridas Christos
Published: April 2023

Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors. This paper presents a new deep learning model called stranded-NN. This model uses a set of NN models of variable layer depths depending on the input. This way, the proposed model can classify different types of emergencies occurring in different time intervals; real-time, close-to-real-time, or periodic. The proposed stranded-NN model has been compared against existing fixed-depth MLPs and LSTM networks used by the industry. Experimentation has shown that the stranded-NN model can outperform fixed depth MLPs 15–21% more in terms of accuracy for real-time events and at least 10–14% more for close-to-real-time events. Regarding LSTMs of the same memory depth as the NN strand input, the stranded NN presents similar results in terms of accuracy for a specific number of strands. Nevertheless, the stranded-NN model’s ability to maintain multiple trained strands makes it a superior and more flexible classification and prediction solution than its LSTM counterpart, as well as being faster at training and classification.
Green Human Resource Management and Brand Citizenship Behavior in the Hotel Industry: Mediation of Organizational Pride and Individual Green Values as a Moderator
Authors: Elshaer Ibrahim A. , Azazz Alaa M. S. , Kooli Chokri , Fayyad Sameh
Published: April 2023

In recent years, there has been growing awareness of the need for sustainability in the hospitality industry. The hotel industry, in particular, has been identified as a significant contributor to environmental degradation. To address this issue, hotel managers have begun to adopt green human resource management (GHRM) practices to promote sustainable behavior among employees. This research paper explores the relationship between GHRM practices, brand citizenship behavior (BCBs), organizational pride, and individual green values in the hotel industry. The study examines how GHRM practices influence BCB through the mediation of organizational pride and the moderation of individual green values. A survey was conducted with 328 employees from five-star hotels and the obtained data were analyzed using PLS-SEM. The results indicate that GHRM practices positively affect BCB and that this relationship is partially mediated by organizational pride. Furthermore, individual green values were found to moderate the relationship between GHRM practices and BCB, indicating that employees with stronger green values are more likely to exhibit BCB. These findings contribute to the literature on GHRM and BCB and offer insights for hotel managers on how to enhance their sustainability efforts through effective GHRM practices.
Research on the Identification of Some Optimal Threshing and Separation Regimes in the Axial Flow Apparatus
Authors: Vlăduț Nicolae-Valentin , Ungureanu Nicoleta , Biriş Sorin-Ştefan , Voicea Iulian , Nenciu Florin , Găgeanu Iuliana , Cujbescu Dan , Popa Lorena-Diana , Boruz Sorin , Matei Gheorghe , Ekielski Adam , Teliban Gabriel-Ciprian
Published: April 2023

Starting from the influencing parameters of threshing and separation and implicit seed losses that occur within this process, this paper searched for and identified the optimal threshing regimes to minimize losses depending on the process parameters. The evacuation losses (pev) depend on threshing rotor speed (n) and implicit rotor peripheral speed (vp), material feed speed (va), the space between the rotor and counter-rotor (δ), material feed flow (Q), material density (ρ), and the length of the threshing apparatus (L). As the parameters ρ and L are constant, the variation of losses in relation to each of the arguments was followed: vp, Q, ρ, and va, respectively, for the minimization of losses by the variation of the loss function by two arguments each (represented graphically); the four arguments targeted being: vp, va, ρ, and Q. Using these input parameters, it was possible to determine the optimal threshing regimes for the variation of losses in relation to the rotor peripheral speed, the feed flow, the space between the rotor and the counter-rotor, and the feed speed, so as to obtain a seed separation percentage (Ss) as close as possible to 100% (and implicitly the smallest threshing losses—towards zero) in relation to these parameters.
A Comprehensive Study on the Combustion of Sunflower Husk Pellets by Thermogravimetric and Kinetic Analysis, Kriging Method
Authors: Islamova Svetlana , Tartygasheva Anastasia , Karaeva Julia , Panchenko Vladimir , Litti Yuriy
Published: April 2023

The combustion of sunflower husk pellets was investigated by kinetic analysis supplemented by the Kriging method. The nonisothermal thermogravimetric experiments in air were carried out at the temperatures from 20 to 700 °C and heating rates of 5, 10, and 20 °C/min. Kinetic analysis was carried out using the model-free OFW (Ozawa–Flynn–Wall) method and Coats–Redfern (CR) method. The activation energy values, calculated by the OFW method, ranged from 116.44 to 249.94 kJ/mol. These data were used to determine the combustion mechanism by the CR method. The kinetic triplet (Eα, A, g(α)) was determined in the conversion interval 0.2 to 0.8. The model of the chemical reaction F8 was recommended to describe the mechanism of the thermochemical conversion process. The relationship between the kinetic parameters was analyzed using the Kriging method. The patterns between the kinetic parameters were represented as three-dimensional surfaces and two-dimensional projections. The distribution’s surfaces were uniform; there were local extremes as well as linear regions. A new approach to the data analysis will allow predicting parameters of a thermochemical conversion of the various raw materials and contributes to a deeper understanding of the characteristics and mechanism of biomass combustion.
Sustainable Development of Rural Human Settlements in the Information Age: Can Internet Use Drive Farmers to Participate in Garbage Classification?
Authors: Xu Dingde , Qing Chen , Chen Yang , He Jia , Zhang Fengwan
Published: April 2023

Garbage classification is significant to alleviate the pressure of household waste management in rural areas and promote green development. Based on the micro survey data of 2228 households in rural areas of Jiangsu Province, this paper discusses the impact of internet use on the garbage classification’s willingness and behavior based on the planned behavior theory. The results show that: (1) There is a deviation between willingness and behavior. Ninety percent of the surveyed farmers were willing to do garbage classification, but the garbage classification rate was only 53%. (2) Internet use has a positive effect on the willingness and behavior of farmers to classify garbage, and it can promote the willingness to change behavior and reduce the deviation between willingness and behavior. Specifically, internet use increased by 1 unit, the probability of farmers having neither willingness nor behavior, having both willingness and behavior, and only having willingness but not having the behavior decreased by 5.4%, increased by 13%, and decreased by 7.5%, respectively. (3) Further analysis according to different internet access methods shows that mobile internet access and mixed internet access can have a positive impact on farmers’ willingness and behavior in relation to garbage classification, while computer internet access has no significant impact on farmers’ willingness and behavior in relation to garbage classification. (4) Internet use can enhance farmers’ willingness and behavior in relation to garbage classification by improving their knowledge, behavioral, and environmental cognition. Specifically, the mediating effects of knowledge, behavioral, and environmental cognition on willingness were 71.48%, 21.72%, and 40.49%, respectively, and the mediating effects on behavior were 89.47%, 8.89%, and 18.81%, respectively. Based on this, this paper puts forward the policy recommendations of strengthening the hardware and software construction of the internet, adopting diversified propaganda methods of garbage classification, and improving the social supervision and restraint mechanism.
Calculation Method of Canopy Dynamic Meshing Division Volumes for Precision Pesticide Application in Orchards Based on LiDAR
Authors: Wang Mengmeng , Dou Hanjie , Sun Hongyan , Zhai Changyuan , Zhang Yanlong , Yuan Feixiang
Published: April 2023

The canopy volume of fruit trees is an important input for the precise and varying application of pesticides in orchards. The fixed mesh division method is mostly used to calculate canopy volumes with variable target-oriented spraying. To reduce the influence of the working speed on the detection accuracy under a fixed mesh width division, the cuboid accumulation of divided areas (CADAs), which is a light detection and ranging (LiDAR) online detection method for a fruit tree canopy volume based on dynamic mesh division, is proposed in this paper. In the method, the area is divided according to the number of unilateral nozzles of the sprayer in the canopy height direction of the fruit tree, and the mesh width is dynamically adjusted according to the change in the working speed in the moving direction of the sprayer. To verify the accuracy and applicability of the method, the simulation canopy and peach tree canopy detection experiments were carried out. The test results show that the CADA method can be used to calculate the contour and volume of the canopy. However, detection errors easily occur at the edge of the canopy, resulting in a detection error of 8.33% for the simulated canopy volume. The CADA method has a good detection accuracy under different moving speeds and fruit tree canopy sizes. At a speed of 1 m/s, the detection accuracy of the canopy volume reaches 99.18%. Compared with the existing canopy volume calculation methods based on the alpha-shape algorithm and canopy meshing-profile characterization (CMPC), the detection accuracy of the CADA method is 2.73% and 7.22% better, respectively. This method can not only reduce the influence of the moving speed on the detection accuracy of the canopy volume, but also improve the detection accuracy. Thus, this method can provide theoretical support for the research and development of target-oriented variable spraying control systems for orchards.
CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels
Authors: Li Dazhuang , Wang Jinglu , Zhang Ying , Lu Xianju , Du Jianjun , Guo Xinyu
Published: April 2023

The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An automatic CT image analysis pipeline was then developed to extract 20 traits related to the three-dimensional structure of kernel, embryo, endosperm, and cavity. The determination coefficients for five volume-related traits (embryo, endosperm, silty endosperm, embryo cavity, and endosperm cavity) were 0.95, 0.95, 0.77, 0.73, and 0.94, respectively. Further, we analyzed the phenotypic variations among a group of 303 inbred lines and conducted genome-wide association studies (GWAS). A total of 26 significant SNP loci were associated with these traits that are closely related to kernel volume, and 62 candidate genes were identified. Functional analysis revealed that most candidate genes corresponding to cavity traits encoded stress resistance proteins, while those corresponding to embryo and endosperm traits encoded proteins involved in regulating plant growth and development. These results will improve the understanding of the phenotypic traits of maize kernels and will provide new theoretical support for in-depth analysis of the genetic mechanism of kernel structure traits.
Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology
Authors: Rivas Pablo , Zhao Liang
Published: April 2023

ChatGPT is an AI-powered chatbot platform that enables human users to converse with machines. It utilizes natural language processing and machine learning algorithms, transforming how people interact with AI technology. ChatGPT offers significant advantages over previous similar tools, and its potential for application in various fields has generated attention and anticipation. However, some experts are wary of ChatGPT, citing ethical implications. Therefore, this paper shows that ChatGPT has significant potential to transform marketing and shape its future if certain ethical considerations are taken into account. First, we argue that ChatGPT-based tools can help marketers create content faster and potentially with quality similar to human content creators. It can also assist marketers in conducting more efficient research and understanding customers better, automating customer service, and improving efficiency. Then we discuss ethical implications and potential risks for marketers, consumers, and other stakeholders, that are essential for ChatGPT-based marketing; doing so can help revolutionize marketing while avoiding potential harm to stakeholders.
Male-Biased Parasitism of Brandt’s Voles (Lasiopodomys brandtii) in Inner Mongolia, China
Authors: Li Gaojian , Wang Qinghe , Zhang Min , Hu Bin , Han Shuyi , Xiang Chen , Yuan Guohui , He Hongxuan
Published: April 2023

The abundance and prevalence of parasitic infection often vary in different host sexes, and this phenomenon has been named sex-biased parasitism. Brandt’s voles are the dominant rodent species in typical steppe habitat and are widely distributed in Inner Mongolia, China, but the prevalence of parasites in Brandt’s voles are poorly reported. In this study, we investigated the prevalence of six intestinal parasites in Brandt’s voles in May, June, July, and August 2022 around the Xilingol Grassland in Inner Mongolia, China. The results showed that Syphacia obvelata, Aspiculuris tetraptera, and Trichostrongylidae family were the dominant intestinal parasites in Brandt’s voles that we captured in this study, and the infection rates of the three parasites were significantly higher in males than females, which showed obvious male-biased parasitism. Season and human activities such as grazing had no significant effect on the infection rates for different parasites, while the parasite reproduction level was higher when the ambient temperature was around 18 °C. Sexual size dimorphism was ubiquitous in Brandt’s voles, and it was mainly manifested by the differences in body weight and length between males and females. Simple linear regression analysis showed a significant positive correlation between bodyweight and parasite infection rates, so the sex-biased parasitism in Brandt’s voles could be explained by the body size hypothesis, as a larger body could provide more ecological niches for parasitic infection.
CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides
Authors: Bournez Colin , Riool Martijn , de Boer Leonie , Cordfunke Robert A. , de Best Leonie , van Leeuwen Remko , Drijfhout Jan Wouter , Zaat Sebastian A. J. , van Westen Gerard J. P.
Published: April 2023

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.
Factors Influencing Inappropriate Use of Antibiotics in Infants under 3 Years of Age in Primary Care: A Qualitative Study of the Paediatricians’ Perceptions
Authors: Arnau-Sánchez José , Jiménez-Guillén Casimiro , Alcaraz-Quiñonero Manuel , Vigueras-Abellán Juan José , Garnica-Martínez Beatriz , Soriano-Ibarra Juan Francisco , Martín-Ayala Gema
Published: April 2023

Antibiotic consumption in infants of less than three years is higher than average the average consumption for general population. The aim of this study was to explore paediatricians’ opinions regarding factors influencing inappropriate use of antibiotics in early infancy in primary care. A qualitative study based on the grounded theory using convenience sampling was conducted in Murcia Region, Spain. Three focal discussion groups were developed with 25 participants from 9 health areas (HA) of Murcia Region. Paediatricians perceived that health care pressure was an influential factor in the prescribing behaviour, forcing them to prescribe antibiotics for a rapid cure in unjustified circumstances. Participants believed that antibiotic consuming was related to parents’ self-medication due to their perceptions about the curative potential of antibiotics together with facilities to obtain these agents from pharmacies without prescription. The misuse of antibiotics by paediatricians was associated to the lack of education on antibiotic prescription and the limited use of clinical guidelines. Not prescribing an antibiotic in the presence of a potentially severe disease generated more fear than an unnecessary prescription. The clinical interaction asymmetry was more evident, when paediatricians use trapping risk strategies as a mechanism to justify a restrictive prescribing behaviour. The rational model of clinical decision-making in antibiotic prescribing among paediatricians was determined by factors associated with health care management, social awareness and knowledge of the population and pressure of families’ demands. The present findings have contributed to the design and implementation of health interventions in the community for improving awareness of the appropriate use of antibiotics, as well as for a better quality of prescription by peadiatricians.
Comprehensive Approach to Medical Nutrition Therapy in Patients with Type 2 Diabetes Mellitus: From Diet to Bioactive Compounds
Authors: Barrea Luigi , Vetrani Claudia , Verde Ludovica , Frias-Toral Evelyn , Ceriani Florencia , Cernea Simona , Docimo Annamaria , Graziadio Chiara , Tripathy Devjit , Savastano Silvia , Colao Annamaria , Muscogiuri Giovanna
Published: April 2023

In the pathogenesis of type 2 diabetes mellitus (T2DM), diet plays a key role. Individualized medical nutritional therapy, as part of lifestyle optimization, is one of the cornerstones for the management of T2DM and has been shown to improve metabolic outcomes. This paper discusses major aspects of the nutritional intervention (including macro- and micronutrients, nutraceuticals, and supplements), with key practical advice. Various eating patterns, such as the Mediterranean-style, low-carbohydrate, vegetarian or plant-based diets, as well as healthy eating plans with caloric deficits have been proven to have beneficial effects for patients with T2DM. So far, the evidence does not support a specific macronutrient distribution and meal plans should be individualized. Reducing the overall carbohydrate intake and replacing high glycemic index (GI) foods with low GI foods have been shown as valid options for patients with T2DM to improve glycemic control. Additionally, evidence supports the current recommendation to reduce the intake of free sugars to less than 10% of total energy intake, since their excessive intake promotes weight gain. The quality of fats seems to be rather important and the substitution of saturated and trans fatty acids with foods rich in monounsaturated and polyunsaturated fats lowers cardiovascular risk and improves glucose metabolism. There is no benefit of supplementation with antioxidants, such as carotene, vitamins E and C, or other micronutrients, due to the lack of consistent evidence showing efficacy and long-term safety. Some studies suggest possible beneficial metabolic effects of nutraceuticals in patients with T2DM, but more evidence about their efficacy and safety is still needed.
Nano-Sized Selenium Maintains Performance and Improves Health Status and Antioxidant Potential While Not Compromising Ultrastructure of Breast Muscle and Liver in Chickens
Authors: Bień Damian , Michalczuk Monika , Łysek-Gładysińska Małgorzata , Jóźwik Artur , Wieczorek Anna , Matuszewski Arkadiusz , Kinsner Misza , Konieczka Paweł
Published: April 2023

The poultry industry is looking for the most effective sources of selenium (Se) for commercial use. Over the past five years, nano-Se has attracted a great deal of attention in terms of its production, characterisation and possible application in poultry production. The objective of this study was to evaluate the effects of dietary levels of inorganic and organic Se, selenised yeast and nano forms of selenium on breast meat quality, liver and blood markers of antioxidants, the ultrastructure of tissue and the health status of chickens. A total of 300 one-day-old chicks Ross 308 were divided into 4 experimental groups, in 5 replications, with 15 birds per replication. Birds were fed the following treatments: a standard commercial diet containing inorganic Se in the form of inorganic Se at the level of 0.3 mg/kg diet and an experimental diet with an increased level of Se (0.5 mg/kg diet). The use of other forms of Se (nano-Se) versus sodium selenate significantly influences (p ≤ 0.05) a higher collagen content and does not impair physico-chemical properties in the breast muscle or the growth performance of the chickens. In addition, the use of other forms of selenium at an increased dose versus sodium selenate affected (p ≤ 0.01) the elongation of sarcomeres in the pectoral muscle while reducing (p ≤ 0.01) mitochondrial damage in hepatocytes and improving (p ≤ 0.05) oxidative indices. The use of nano-Se at a dose of 0.5 mg/kg feed has high bioavailability and low toxicity without negatively affecting the growth performance and while improving breast muscle quality parameters and the health status of the chickens.
The Action of Tornadoes on the Structural Elements of a Wooden Low-Rise Building Roof and Surrounding Objects—Review and Case Study
Authors: Lamparski Tomasz , Dutkiewicz Maciej
Published: April 2023

This article presents the phenomena of tornado models with statistical data on the number of tornadoes in various regions of the world. Classifications are presented of the intensity of weather phenomena caused by gusts of wind in terms of the amount of damage to buildings and nearby objects. The main aim of the article is to analyze a damaged single-family house and its surroundings after the tornado in Fairburn (2018, Georgia, USA), to estimate the peak wind speed that caused the damage, based on the ultimate and serviceable limit states, and to compare the obtained results with the tornado scales. The article draws attention to the various possibilities of damage and analyzes several places where potential damage could begin. The low-trength capacity of the joints was the direct cause of the roof structure failure, and this case is analyzed and discussed. FinallyFinally, the calculations performed are summarized and conclusions resulting from the visible trends are proposed. The novelty of this article lies in the following: the validation of the numerical model describing the response of the roof structure to the tornado; the estimation of the wind speed based on the degree of damage to the building based on the EF scale; and the analysis of the ultimate and serviceable limit states of the rafter to the horizontal beam joint.
A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes
Authors: Liu Qiang , Wei Jiaxing , Liu Hao , Ji Yimu
Published: April 2023

Graph summarization techniques are vital in simplifying and extracting enormous quantities of graph data. Traditional static graph structure-based summarization algorithms generally follow a minimum description length (MDL) style, and concentrate on minimizing the graph storage overhead. However, these methods also suffer from incomprehensive summary dimensions and inefficiency problems. In addition, the need for graph summarization techniques often varies among different graph applications, but an ideal summary method should generally retain the important characteristics of the key nodes in the final summary graph. This paper proposes a novel method based on ranking nodes, called HRNS, that follows a hierarchical parallel graph summarization approach. The HRNS first preprocesses the node ranking using a hybrid weighted importance strategy, and introduces the node importance factor into traditional MDL-based summarization algorithms; it then leverages a hierarchical parallel process to accelerate the summary computation. The experimental results obtained using both real and simulated datasets show that HRNS can efficiently extract nodes with high importance, and that the average importance over six datasets ranges from 0.107 to 0.167; thus, HRNS can achieve a significant performance gain on speedups, as the sum error ratios are also lower than the methods traditionally used.
The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
Authors: Wang Chenming , Huang Bo
Published: April 2023

Collaborative filtering (CF) usually suffers from data sparsity and cold starts. Knowledge graphs (KGs) are widely used to improve recommendation performance. To verify that knowledge graphs can further alleviate the above problems, this paper proposes an end-to-end framework that uses attentive knowledge graph perceptual propagation for recommendations (AKGP). This framework uses a knowledge graph as a source of auxiliary information to extract user–item interaction information and build a sub-knowledge base. The fusion of structural and contextual information is used to construct fine-grained knowledge graphs via knowledge graph embedding methods and to generate initial embedding representations. Through multi-layer propagation, the structured information and historical preference information are embedded into a unified vector space, and the potential user–item vector representation is expanded. This article used a knowledge perception attention module to achieve feature representation, and finally, the model was optimized using the stratified sampling joint learning method. Compared with the baseline model using MovieLens-1M, Last-FM, Book-Crossing and other data sets, the experimental results demonstrate that the model outperforms state-of-the-art KG-based recommendation methods, and the shortcomings of the existing model are improved. The model was applied to product design data and historical maintenance records provided by an automotive parts manufacturing company. The predictions of the recommended system are matched to the product requirements and possible failure records. This helped reduce costs and increase productivity, helping the company to quickly determine the cause of failures and reduce unplanned downtime.
Strike: Stream Cipher Based on Stochastic Lightning Strike Behaviour
Authors: Suwais Khaled , Almanasra Sally
Published: April 2023

There is an increasing need for secure and fast encryption algorithms to support applications and communication protocols, and business models. In this paper, we present an alternative stream cipher (Strike) inspired by the stochastic behaviour of lightning strike phenomena. The novelty and originality of Strike stem from the utilisation of lightning strike behaviour as a source for generating random keystreams for encryption and decryption. Strike consists of three main functions: a function for setting up the security attributes, a function for generating lightning strikes and converting them to a keystream, and a function for plaintext encryption. The proposed stream cipher was tested against several cryptanalysis and statistical attacks in addition to other performance tests. The results show that Strike achieves high throughput on both high- and low-speed devices. Additionally, security analysis shows that our cipher is resistant to cryptanalysis and statistical attacks.
Laccase in Biorefinery of Lignocellulosic Biomass
Authors: Tiwari Ashutosh , Chen Chiu-Wen , Haldar Dibyajyoti , Patel Anil Kumar , Dong Cheng-Di , Singhania Reeta Rani
Published: April 2023

Biorefinery has emerged in recent years as an alternative to petrorefinery, as biofuels have all the potential to replace fossil fuels for the sustainable development of human society. From this aspect, lignocellulosic biomasses are the most important, since these are the most abundant ubiquitous most raw material on earth, which can be converted into biofuels such as bioethanol, biobutanol, biohydrogen, biogas, etc. There are several strategies for conversion, such as biochemical, thermochemical, and microbial conversions of biomasses to biofuels; however, each of the strategies has its own consequences. Enzymatic conversion of biomasses into sugars, and thereby into bioethanol, is considered as the most sustainable way. However, biomass recalcitrance to enzymatic hydrolysis is the biggest challenge, as cellulose, hemicellulose, and lignin are intricately attached to each other making their separation a tedious task. Pretreatment is necessary to partially remove or change the form of lignin to make cellulose and hemicellulose accessible to enzymes. Most of the pretreatment methods are designed to target lignin, as it is the major component responsible for recalcitrance nature of biomasses. Laccase is a versatile lignin-degrading or lignin-modifying enzyme which is secreted by filamentous fungi and bacteria, and is reported for the biological pretreatment of biomasses, which is the most sustainable way of pretreatment. However, the rate of the reaction is extremely slow making it less attractive. This article will give an insight into the biorefinery of biomasses, with the special significance to laccase.
Pilot Scale Production of Single Cell Oil by Apiotrichum brassicae and Pichia kudriavzevii from Acetic Acid and Propionic Acid
Authors: Burgstaller Lukas , Oliver Laura , Dietrich Thomas , Neureiter Markus
Published: April 2023

Volatile fatty acids can be used as a cheap carbon source for biotechnological lipid production with oleaginous yeasts, but one factor limiting their large-scale use is their inherent cytotoxicity. Developing a suitable cultivation strategy can help mitigate the adverse effect volatile fatty acids have, since these effects are strongly dependent on concentration and pH. This work shows that, by employing a pH-stat fed-batch approach for the cultivation of Apiotrichum brassicae V134 and Pichia kudriavzevii V194, lipid contents above 56 g/100 g dry cell weight and dry cell weight concentrations above 30 g/L can be reached. Furthermore, volumetric lipid productivities up to 0.29 g/Lh could be achieved using acetic and propionic acid as a sole carbon source. It was also demonstrated that the developed process is robust and scalable. Scale-up to the 500 L scale resulted in a similar lipid yield, dry cell weight (31–37 g/L), and single cell oil content (56 g/100 g dry cell weight–58 g/100 g dry cell weight). The main fatty acid present in the produced lipids was oleic acid (36–43%), but also odd-numbered fatty acids, especially heptadecanoic acid (7–15%), were present. Additionally, different methods for the pretreatment of biomass prior to lipid extraction were assessed, and the iodine value (48), peroxide value (7.3), and acid value (4.3) of the extracted single cell oil were determined.
Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
Authors: Xie Lisiqi , He Kangjian , Xu Dan
Published: April 2023

Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment.
Characterization and Assessment of Performance of Innovative Lime Mortars for Conservation of Building Heritage: Paimogo’s Fort, a Case Study
Authors: Santos Ana Rita , Veiga Maria do Rosário , Santos Silva António
Published: April 2023

Along the Portuguese coastline, several military fortifications were built with the intention to protect the territory from the constant military threat from the sea. These constructions have been subjected, during centuries, to a very aggressive environment; the renders, whose main function is the protection of walls, are particularly exposed to such actions. Nossa Senhora dos Anjos de Paimogo’s Fort, better known as the Paimogo’s Fort, is one of these fortifications, built in 1674 and classified of public interest since 1957. Within the scope of the “Coast Memory Fort” Project of EEA Grants Culture Programme 2014–2021, promoted by the Municipality of Lourinhã, repair mortars are being developed for the preservation of the Fort, considering the physical–mechanical and chemical characteristics of the pre-existing mortars and of the substrate, as well as the aggressive environmental conditions. In this work, several mortar compositions, compatible with the original mortars and designed to resist the aggressive environment, are briefly described and their main physical and mechanical characteristics are analysed and compared in successive ages. Different binder mixes were used, and a fine-tuning of the aggregate was carried out. Assessment of sequential wetting/drying cycles’ effect on the mortar’s behaviour is also presented. The laboratory results reveal that mortars with additions of 30% of quicklime present the best behaviour (with the lowest water absorption and highest strength). Moreover, the substitution of part of the siliceous sand by limestone aggregate, in general, increases the mortars’ mechanical strength; however, the drying occurs slower, which could compromise the durability of these mortars if a good balance is not achieved.
Multi-Scale Feature Fusion with Attention Mechanism Based on CGAN Network for Infrared Image Colorization
Authors: Ai Yibo , Liu Xiaoxi , Zhai Haoyang , Li Jie , Liu Shuangli , An Huilong , Zhang Weidong
Published: April 2023

This paper proposes a colorization algorithm for infrared images based on a Conditional Generative Adversarial Network (CGAN) with multi-scale feature fusion and attention mechanisms, aiming to address issues such as color leakage and unclear semantics in existing infrared image coloring methods. Firstly, we improved the generator of the CGAN network by incorporating a multi-scale feature extraction module into the U-Net architecture to fuse features from different scales, thereby enhancing the network’s ability to extract features and improving its semantic understanding, which improves the problems of color leakage and blurriness during colorization. Secondly, we enhanced the discriminator of the CGAN network by introducing an attention mechanism module, which includes channel attention and spatial attention modules, to better distinguish between real and generated images, thereby improving the semantic clarity of the resulting infrared images. Finally, we jointly improved the generator and discriminator of the CGAN network by incorporating both the multi-scale feature fusion module and attention mechanism module. We tested our method on a dataset containing both infrared and near-infrared images, which retains more detailed features while also preserving the advantages of existing infrared images. The experimental results show that our proposed method achieved a peak signal-to-noise ratio (PSNR) of 16.5342 dB and a structural similarity index (SSIM) of 0.6385 on an RGB-NIR (Red, Green, Blue-Near Infrared) testing dataset, representing a 5% and 13% improvement over the original CGAN network, respectively. These results demonstrate the effectiveness of our proposed algorithm in addressing the issues of color leakage and unclear semantics in the original network. The proposed method in this paper is not only applicable to infrared image colorization but can also be widely applied to the colorization of remote sensing and CT images.
GIS-Based Identification of Locations in Water Distribution Networks Vulnerable to Leakage
Authors: Alzarooni Eisa , Ali Tarig , Atabay Serter , Yilmaz Abdullah Gokhan , Mortula Md. Maruf , Fattah Kazi Parvez , Khan Zahid
Published: April 2023

The detection of leakages in Water Distribution Networks (WDNs) is usually challenging and identifying their locations may take a long time. Current water leak detection methods such as model-based and measurement-based approaches face significant limitations that impact response times, resource requirements, accuracy, and location identification. This paper presents a method for determining locations in the WDNs that are vulnerable to leakage by combining six leakage-conditioning factors using logistic regression and vulnerability analysis. The proposed model considered three fixed physical factors (pipe length per junction, number of fittings per length, and pipe friction factor) and three varying operational aspects (drop in pressure, decrease in flow, and variations in chlorine levels). The model performance was validated using 13 district metered areas (DMAs) of the Sharjah Electricity and Water Authority (SEWA) WDN using ArcGIS. Each of the six conditioning factors was assigned a weight that reflects its contribution to leakage in the WDNs based on the Analytic Hierarchy Process (AHP) method. The highest weight was set to 0.25 for both pressure and flow, while 0.2 and 0.14 were set for the chlorine and number of fittings per length, respectively. The minimum weight was set to 0.08 for both length per junction and friction factor. When the model runs, it produces vulnerability to leakage maps, which indicate the DMAs’ vulnerability classes ranging from very high to very low. Real-world data and different scenarios were used to validate the method, and the areas vulnerable to leakage were successfully identified based on fixed physical and varying operational factors. This vulnerability map will provide a comprehensive understanding of the risks facing a system and help stakeholders develop and implement strategies to mitigate the leakage. Therefore, water utility companies can employ this method for corrective maintenance activities and daily operations. The proposed approach can offer a valuable tool for reducing water production costs and increasing the efficiency of WDN.
Restenosis Investigation of Two-Stent Placement in the Artery Bifurcation with Different Stenting Techniques Using Computational Fluid Dynamics Analysis
Authors: Chen Bang-Sia , Wu Rong-Ching , Liu Pao-Hsin
Published: April 2023

The in-stent restenosis may still occur in patients with a coronary bifurcation stenting. The purpose of this study is to investigate the restenosis effect in the relationships between the bifurcation angles of the coronary artery and three types of stenting techniques (T-stenting, Culotte, and Kissing techniques), for comparing two kinds of arterial shapes using computational fluid dynamics (CFD) analysis. The finite element models of two types of the coronary arteries were reconstructed using stereolithography (STL) data of an artificial artery from reverse engineering laser scanning, and then each artery was further modified to add three kinds of the bifurcation angles, including 80°, 113°, and 142°. The low TAWSS of the two arteries were mainly concentrated at the LCX branch; a more essential discovery was that the low TAWSS area ration was in proportion to the bifurcation angle. The smallest low TAWSS of the T-stenting and Kissing techniques was detected in the arteries A and B, respectively. Both Culotte and Kissing techniques always showed a higher OSI than the T-stenting, and a high OSI was meaningfully located at the LCX branch. The CFD study concluded that the large bifurcation angle of an artery using a long stent at the LCX side branch with the T-stenting technique has a high risk of stenting restenosis.
Prediction System for Overhead Cranes Based on Digital Twin Technology
Authors: Gao Pei , Wang Zongyan , Zhang Yuting , Li Menglong
Published: April 2023

To solve the problems of inaccurate quality inspection and poor safety maintenance of traditional overhead cranes, this study has developed a prediction system for overhead cranes based on digital twin technology. First, interworking of the data flow between the control port of the overhead crane and the digital twin system is realized. Then, finite element technology is exploited to calculate the stress of the crane bridge, thereby performing stress rendering of the digital twin system. Next, the life of bridge cranes is predicted by the stress and strain of their main girders. Finally, synchronous overhead-crane communication combines the virtual twin system and the actual control system of the crane. The study results provide a reference for developing crane software based on digital twins.
Inversion Analysis of the In Situ Stress Field around Underground Caverns Based on Particle Swarm Optimization Optimized Back Propagation Neural Network
Authors: Yan Hong-Chuan , Liu Huai-Zhong , Li Yao , Zhuo Li , Xiao Ming-Li , Chen Ke-Pu , Wu Jia-Ming , Pei Jian-Liang
Published: April 2023

The in situ stress distribution is one of the driving factors for the design and construction of underground engineering. Numerical analysis methods based on artificial neural networks are the most common and effective methods for in situ stress inversion. However, conventional algorithms often have some drawbacks, such as slow convergence, overfitting, and the local minimum problem, which will directly affect the inversion results. An intelligent inverse method optimizing the back-propagation (BP) neural network with the particle swarm optimization algorithm (PSO) is applied to the back analysis of in situ stress. The PSO algorithm is used to optimize the initial parameters of the BP neural network, improving the stability and accuracy of the inversion results. The numerical simulation is utilized to calculate the stress field and generate training samples. In the application of the Shuangjiangkou Hydropower Station underground powerhouse, the average relative error decreases by about 3.45% by using the proposed method compared with the BP method. Subsequently, the in situ stress distribution shows the significant tectonic movement of the surrounding rock, with the first principal stress value of 20 to 26 MPa. The fault and the lamprophyre significantly influence the in situ stress, with 15–30% localized stress reduction in the rock mass within 10 m. The research results demonstrate the reliability and improvement of the proposed method and provide a reference for similar underground engineering.