bluuebunny/crossref_metadata_2025 huggingface dataset:
Pragmatic Encroachment, Phenomenology, and Religious Experience
Authors: Barber Michael D.
Published: July 2022

Aaron Rizzieri’s Pragmatic Encroachment, Religious Belief, andPractice (2013) is the fullest religious appropriation of a relatively new epistemological concept: pragmatic encroachment. To achieve this goal, Rizzieri rightly sees (1) how justification takes place within an encompassing pragmatic context and (2) how justification of religious belief establishes within a wider context less than absolute knowledge. While the first point can be supported by Alfred Schutz’s theory of action, often including multi-layered sub-acts, Schutz’s idea of a theoretical enclave can create a space for epistemic evidentialism, as an independent distinctive moment, with distinctive (justificatory) purposes, within an overarching practical action. Rizzieri’s book itself exemplifies such evidentialism, theoretically justifying pragmatic encroachment, after the fashion of Husserlian transcendental phenomenology. Rizzieri could also profit from Husserlian regional ontologies, on which he implicitly already relies to support religious knowledge. Husserl’s concept of bipolar intentionality would accommodate Rizzieri’s responsible internalism, while allowing for the action of objects upon us. This, in turn, opens the door to the evidence for religious knowledge that an account of religious experience such as Max Scheler’s could provide. Such an account could counter those who reduce religious experience to mere subjective projection—a critique to which internalism might be more vulnerable.
A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images
Authors: Ji Xinran , Huang Liang , Tang Bo-Hui , Chen Guokun , Cheng Feifei
Published: July 2022

This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image.
Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data
Authors: Ge Xingtong , Yang Yi , Peng Ling , Chen Luanjie , Li Weichao , Zhang Wenyue , Chen Jiahui
Published: July 2022

Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.
ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion
Authors: Qiu Mulan , Huang Liang , Tang Bo-Hui
Published: July 2022

Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model.
A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices
Authors: Han Yixiu , Tang Rui , Liao Zhenqi , Zhai Bingnian , Fan Junliang
Published: July 2022

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately determine an ideal combination of vegetation indices (VIs) for simulating wheat AGB. Five multispectral bands of the unmanned aerial vehicle platform and 56 types of VIs obtained based on the five bands were used to drive the new model. The GOA-XGB model was compared with many state-of-the-art models, for example, multiple linear regression (MLR), multilayer perceptron (MLP), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), random forest (RF), support vector machine (SVM), XGBoost, SVM optimization by particle swarm optimization (PSO), SVM optimization by the whale optimization algorithm (WOA), SVM optimization by the GOA (GOA-SVM), XGBoost optimization by PSO, XGBoost optimization by the WOA. The results demonstrated that MLR and GOA-MLR models had poor prediction accuracy for AGB, and the accuracy did not significantly improve when input factors were more than three. Among single-factor-driven machine learning (ML) models, the GPR model had the highest accuracy, followed by the XGBoost model. When the input combinations of multispectral bands and VIs were used, the GOA-XGB model (having 37 input factors) had the highest accuracy, with RMSE = 0.232 kg m−2, R2 = 0.847, MAE = 0.178 kg m−2, and NRMSE = 0.127. When the XGBoost feature selection was used to reduce the input factors to 16, the model accuracy improved further to RMSE = 0.226 kg m−2, R2 = 0.855, MAE = 0.172 kg m−2, and NRMSE = 0.123. Based on the developed model, the average AGB of the plot was 1.49 ± 0.34 kg.
Two-Stage Nested Array Direction of Arrival Estimation for Mixed Signals
Authors: Li Wanru , Deng Ke
Published: July 2022

In this paper, a novel two-stage subspace-based direction of arrival (DOA) estimation algorithm with the nested array is proposed for mixed signals containing circular and non-circular ones. By exploiting the difference between the two types of steering vectors, the DOAs of circular signals are estimated in the first stage. After eliminating the estimated information of circular signals by the covariance matrix reconstruction and oblique projection methods, the dimensions of the noise subspace are increased in estimating the DOAs of non-circular signals in the second stage. Through the two-stage estimation, the DOAs of the circular and non-circular signals are estimated separately and different types of signals with similar or the same DOAs can be distinguished. Furthermore, to avoid the two-dimensional (2-D) search with huge computational burden, a one-dimensional (1-D) search method exploiting the rank deficiency is proposed in the DOA estimation for non-circular signals. The simulation results show that the proposed algorithm can effectively improve the estimation accuracy and resolution probability, especially when circular and non-circular signals have similar DOAs.
Modulation Awareness Method for Dual-Hop Cooperative Transmissions over Frequency-Selective Channels
Authors: Marey Mohamed , Mostafa Hala
Published: July 2022

Modulation awareness and cooperative transmissions have individually received a significant amount of research in the scholarly literature. However, a limited number of works are principally concerned with the combination of the two topics, and they are restricted to frequency-flat wireless channels. In this study, we propose a new modulation awareness method applicable to dual-hop amplify-and-forward cooperative broadcasts. The suggested method is built on the creation of theoretical representations of cross-correlation functions of the received signals. We conceptually prove that a family of modulation types generates spikes for certain cross-correlation functions, while others do not. We create a numerous layer hypothesis evaluation for the purpose of making judgments centered on this attribute. The suggested method has a number of benefits, such as the ability to operate on both frequency-flat and frequency-selective channels, as well as the absence of the necessity of channel awareness or noise power. Computer simulations analyze the performance of the proposed method, which delivers adequate awareness performance in a variety of operational scenarios.
IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
Authors: Nasir Muhammad Umar , Khan Safiullah , Mehmood Shahid , Khan Muhammad Adnan , Rahman Atta-ur , Hwang Seong Oun
Published: July 2022

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.
Morphological Fabrication of Equilibrium and Auditory Sensors through Electrolytic Polymerization on Hybrid Fluid Rubber (HF Rubber) for Smart Materials of Robotics
Authors: Shimada Kunio
Published: July 2022

The development of auditory sensors and systems is essential in smart materials of robotics and is placed at the strategic category of mutual communication between humans and robots. We designed prototypes of the rubber-made equilibrium and auditory sensors, mimicking hair cells in the saccule and the cochlea at the vestibule of the human ear by utilizing our previously proposed technique of electrolytic polymerization on the hybrid fluid rubber (HF rubber). The fabricated artificial hair cells embedded with mimicked free nerve endings and Pacinian corpuscles, which are well-known receptors in the human skin and have already been elucidated effective in the previous study, have the intelligence of equilibrium and auditory sensing. Moreover, they have a voltage that is generated from built-in electricity caused by the ionized particles and molecules in the HF rubber due to piezoelectricity. We verified the equilibrium and auditory characteristics by measuring the changes in voltage with inclination, vibration over a wide frequency range, and sound waves. We elucidated experimentally that the intelligence has optimum morphological conditions. This work has the possibility of advancing the novel technology of state-of-the-art social robotics.
Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM
Authors: Liu Wei , Liu Xu , Hu Yuan , Shi Jie , Chen Xinqiang , Zhao Jiansen , Wang Shengzheng , Hu Qingsong
Published: July 2022

Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.
Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose
Authors: Labanska Malgorzata , van Amsterdam Sarah , Jenkins Sascha , Clarkson John P. , Covington James A.
Published: July 2022

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.
Raman Scattering Study of Amino Acids Adsorbed on a Silver Nanoisland Film
Authors: Skvortsov Alexey , Babich Ekaterina , Lipovskii Andrey , Redkov Alexey , Yang Guang , Zhurikhina Valentina
Published: July 2022

We studied the surface-enhanced Raman spectra of amino acids D-alanine and DL-serine and their mixture on silver nanoisland films (SNF) immersed in phosphate-buffered saline (PBS) solution at millimolar amino acid concentrations. It is shown that the spectra from the amino acid solutions differ from the reference spectra for microcrystallites due to the electrostatic orientation of amino acid zwitterions by the metal nanoisland film. Moreover, non-additive peaks are observed in the spectrum of the mixture of amino acids adsorbed on SNF, which means that intermolecular interactions between adsorbed amino acids are very significant. The results indicate the need for a thorough analysis of the Raman spectra from amino acid solutions, particularly, in PBS, in the presence of a nanostructured silver surface, and may also be of interest for studying molecular properties and intermolecular interactions.
BREEZE—Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles
Authors: Elmakis Oren , Shaked Tom , Fishbain Barak , Degani Amir
Published: July 2022

Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent.
Multiple Groups of Agents for Increased Movement Interference and Synchronization
Authors: Meneses Alexis , Mahzoon Hamed , Yoshikawa Yuichiro , Ishiguro Hiroshi
Published: July 2022

We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, or three agents, as well as human or robot avatars, and finally, the agent moving biologically or linearly. We found the main effect on movement interference was the number of agents; namely, three agents had significantly more influence on movement interference than one agent. These results suggest that the number of agents is more influential on movement interference than other avatar characteristics. For the synchronization, the main effect of the type of the agent was revealed, showing that the human agent kept more synchronization compared to the robotic agent. In this experiment, we introduced an additional paradigm on the interference which we called synchronization, discovering that a group of agents is able to influence this behavioral level as well.
An IoT Measurement System Based on LoRaWAN for Additive Manufacturing
Authors: Fedullo Tommaso , Morato Alberto , Peserico Giovanni , Trevisan Luca , Tramarin Federico , Vitturi Stefano , Rovati Luigi
Published: July 2022

The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible.
HLNet Model and Application in Crop Leaf Diseases Identification
Authors: Xu Yanlei , Kong Shuolin , Gao Zongmei , Chen Qingyuan , Jiao Yubin , Li Chenxiao
Published: July 2022

Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture.
The Moderating Role of Age in the Effect of Video Playback Speed on Urgency Perception in the Context of Climate Change
Authors: Sheng Xushan , Mo Tiantian , Zhou Xinyue
Published: July 2022

Urgency perception plays a vital role in addressing the issue of climate change. However, little is known about how to promote the perceived urgency of climate change and its potential influence on proenvironmental intention and behavior. This research focuses on a potentially significant but less studied factor in video communication: video playback speed. The current research explores the effectiveness of video playback speed as a subtle behavioral nudge to influence urgency perception and proenvironmental response in the context of climate change. We conducted two survey-embedded experiments in which participants watched a climate change video playing at either normal or fast speed and then completed measurements. Data were collected first in an undergraduate sample (n = 75) and then in a general population sample (n = 300) and analyzed using Mann–Whitney U tests, chi-squared tests, and moderation analysis in SPSS. The results reveal that a fast playback speed of climate change video decreases the perceived urgency of climate change for younger consumers, not for older consumers. However, video playback speed does not influence proenvironmental intention and behavior. These findings enhance understanding of when video playback speed affects urgency perception and proenvironmental tendency, and provide valuable insights for climate change communication.
Conceptualising the Factors Influencing Community Health Workers’ Preparedness for ICT Implementation: A Systematised Scoping Review
Authors: Ratshidi Lilies , Grobbelaar Sara , Botha Adele
Published: July 2022

Background: Globally, community health workers (CHW) are increasingly viewed as an integral part of the health system as opposed to simply being an extension of it. Given this view, most low- or middle-income countries (LMICs) have refocused their efforts on reorganising CHW initiatives to maximise their impact. The ongoing endeavours to augment the practice of community health workers using technological solutions are characterised by as many challenges as opportunities. In low- and middle-income countries, including South Africa, information and communication technology (ICT) has become a promising development in the enhancement of the equitable coverage of health services by community health workers. However, there has not been a wide-scale implementation and adoption of ICT; most technology initiatives fail to scale up during the implementation stage, which is attributable to human and context-related factors. Although there has been an effort to develop solutions to address ICT infrastructure and technical barriers, conceptualising an evidence-based understanding of the contextual and user-related factors that influence the efficacy of technology adoption by CHWs within their multidimensional system remains critical. Objective: The purpose of the study is to conceptualise the social factors to consider when implementing a bespoke ICT solution suited to the specific demands of CHWs in primary healthcare in developing contexts, with a particular focus on the South African context. Methodology: The methodology involves synthesizing, extracting, and consolidating the findings of a systematised scoping review into concepts and factors. The review adapts Arksey and O’Malley’s scoping review approach to the study and incorporates 59 relevant articles. Results: Although the knowledge base on CHWs is extensive, there is a considerable gap in addressing structural challenges within the community system, which contributes significantly to the overall performance of CHW programs. Factors promoting policy adaptations, common practice within the health system, CHW competencies, a community’s development in terms of knowledge and economic advancement, safety and security, the environment, and the socio-cultural context all play a significant role in facilitating or impeding the success of health interventions from the individual to the national level. Conclusions: Within several practical limitations, the study shows that despite the efforts of various developing countries to promote technology adoption, the barriers to ICT adoption outweigh the benefits gained in developing countries. The paper argues that addressing these challenges before and during implementation is critical. The authors conclude with some essential concerns about assisting CHWs towards realising the desired health outcomes through ICT.
Modeling the Assessment of Intersections with Traffic Lights and the Significance Level of the Number of Pedestrians in Microsimulation Models Based on the PTV Vissim Tool
Authors: Ziemska-Osuch Monika , Osuch Dawid
Published: July 2022

The present article contains a microsimulation analysis of the impact of the number of pedestrians on pedestrian crossings controlled by traffic lights. To analyze the level of freedom of movement using the HCM 2010 method based on the level of service (LOS) implemented in the PTV VISSIM tool, a simulation of two interconnected intersections is performed. These crossings differ in the number of inlets as well as in the intensity of vehicles at each of the inlets. The microsimulation model was based on real data on the intensity of vehicles from an intelligent traffic control system as well as real traffic light programs. Eleven different variants of the same initial conditions were tested in which the number of pedestrians at pedestrian crossings was increased every 50 and the time of the right turn and the LOS of the right turn were compared. The result shows the impact of the number of pedestrians on the assessment of LOS traffic conditions at the entire intersection. The results consider the ranges in which the number of pedestrians has a significant impact and change the assessment of the entire intersection to the next worse level of freedom of movement. The article shows how it can be a mistake to overlook adding the exact number of pedestrians at traffic light intersections with PTV Viswalk in the microsimulation model.
Impact of Farmers’ Participation in Community-Based Organizations on Adoption of Flood Adaptation Strategies: A Case Study in a Char-Land Area of Sirajganj District Bangladesh
Authors: Faruk Md Omar , Maharjan Keshav Lall
Published: July 2022

Community-based organizations (CBOs) are gaining popularity in Bangladesh as a tool for relaying flood risk information and adaptation strategies. However, to our knowledge, no attempts have been made to determine the impact of CBOs on farmers’ adoption of flood adaptation strategies. Therefore, in this paper, we identify the determinants that influence farmers’ decisions to participate in CBOs and how this participation impacts farmers’ adoption of flood adaptation strategies. A multistage sampling procedure was employed to select 359 farmers for the study. An endogenous switching regression model was applied to control for possible selection bias due to unobserved factors, while propensity score matching (PSM) and inverse probability-weighted regression adjustment (IPWRA) were employed to test for the robustness of the results. The results reveal a positive selection bias, indicating that farmers with above-average flood adaptation strategies are more willing to participate in CBOs. Farmers’ flood experience, having children under 10 years, distance to the village center, and access to information mainly determine the participation in CBOs. It is also found that CBO participation significantly increases farmers’ adoption of flood adaptation strategies. ESR results show that farmers who participated in CBOs have 3.76 higher average flood adaptation strategies compared to CBO non-participation, and this finding is also consistent with PSM and IPWRA results. Therefore, policy intervention aimed at further strengthening and institutionalizing CBOs is necessary for successful flood adaptation.
Research on Popular Science Tourism Based on SWOT-AHP Model: A Case Study of Koktokay World Geopark in China
Authors: Long Cheng , Lu Song , Zhu Yiting
Published: July 2022

A geopark is the best place for scientific knowledge dissemination, recreation and regional economic development. However, research on science tourism in China’s national geoparks is still in its infancy and the theory is lagging behind practical development needs. For the purpose of exploring the shortcomings and optimization paths of geoparks in science tourism development, here, we used Koktokay World Geopark as the research object and examined the internal advantages, internal disadvantages, external opportunities and external challenges. Then, using 17 indicators, such as natural resource endowment, a SWOT-AHP model was constructed to systematically analyze the advantages, disadvantages, opportunities and threats. The analysis found that advantages > disadvantages > opportunities > threats. The SWOT four-quadrant strategy was positioned at the first quadrant, and the advantage–opportunity strategy should be based on its internal advantages and make full use of the external opportunity conditions. Based on the results, suggestions were made to maintain resource advantages, obtain policy opportunities and improve the transportation conditions and the interpretation system; hence, in this study, a reasonable path for the development of science tourism in Koktokay World Geopark was explored and theoretical references for the science tourism and sustainable development of other geoparks were provided.
Analysis of Drill-String Nonlinear Dynamics Using the Lumped-Parameter Method
Authors: Khajiyeva Lelya A. , Andrianov Igor V. , Sabirova Yuliya F. , Kudaibergenov Askar K.
Published: July 2022

This work aims at studying the nonlinear dynamics of drill strings using the lumped-parameter method (LPM). The study is based on the good consistency of the results of the test problem where the model of the longitudinal vibrations of a horizontal drill string with a static compressive load at the left end is considered. In this paper, this method is applied to discretize linear and nonlinear models of the lateral vibrations of a vertical drill string under the effect of a supersonic gas flow. The obtained results are verified with the previously published data. The optimal number of the drill-string partitions is determined using the developed application, which allows us to estimate the accuracy of the loaded data. The numerical solution of the model is obtained using the fourth-order Runge–Kutta method. The optimization of the numerical algorithm using parallel-programming tools is carried out, and the efficiency of the method is analyzed.
Incidence, Trends, and Seasonality of Paediatric Injury-Related Emergency Department Presentations at a Large Level 1 Paediatric Trauma Centre in Australia
Authors: Lystad Reidar P. , Fyffe Andrew , Orr Rhonda , Browne Gary
Published: July 2022

This retrospective cohort study aimed to examine the characteristics, incidence, temporal trends, and seasonality of paediatric injury-related Emergency Department (ED) presentations at a large metropolitan paediatric hospital. It included children aged ≤15 years who presented to the ED at The Children’s Hospital at Westmead, Sydney Australia, with a principal diagnosis of injury during the ten-year period from 1 January 2010 to 31 December 2019. Descriptive statistics were used to describe the characteristics of the cohort and the distribution of ED presentations by mode of arrival, triage category, discharge status, injury diagnosis. Negative binomial regression was used to examine percentage change in annual incidence. Seasonality was examined with Seasonal and Trend decomposition using Loess (STL). There were 134,484 (59.7% male children) paediatric injury-related ED presentations during the ten-year period, of which 23,224 (17.3%) were admitted to hospital. Head injury accounted for more than one-quarter (26.8%) of ED presentations. The average annual increase in incidence was more pronounced during the first five years (5.6% [95%CI 4.1% to 7.1%]) than in the last five years (0.8% [95%CI 0.2% to 1.5%]). The monthly incidence of ED presentations had a bimodal distribution with peaks during autumn (March–May) and spring (October–November) seasons. The mean number of ED presentations per day was higher on weekends (40.8 ± 0.3) than weekdays (35.3 ± 0.8). During 2010 to 2019, there was a significant increase in the annual incidence of injury-related ED presentations for children aged ≤15 years, with head injury accounting for more than one-quarter of the ED presentations. The incidence of paediatric injury-related ED presentations was higher during autumn and spring seasons and at weekends. These data will inform health resource planning and priority-setting and advocacy for child injury prevention strategies in Australia.
An Impact Factor for Tropical Medicine and Infectious Disease
Authors: Leggat Peter A. , Frean John
Published: July 2022

Tropical Medicine and Infectious Disease (TMID or TropicalMed) was established in 2016 [...]
Dengue, Chikungunya, and Zika: Spatial and Temporal Distribution in Rio de Janeiro State, 2015–2019
Authors: de Almeida Paula Maria Pereira , Nobre Aline Araújo , Câmara Daniel Cardoso Portela , Costa Luciana Moura Martins , dos Reis Izabel Cristina , Ribeiro Mário Sérgio , Dias Cristina Maria Giordano , Ayllón Tania , Honório Nildimar Alves
Published: July 2022

Simultaneous spatial circulation of urban arboviral diseases, such as dengue, chikungunya, and Zika, is a major challenge. In this ecological study of urban arboviruses performed from 2015 to 2019, we analyzed the spatial and temporal dynamics of these arboviruses in all 92 municipalities and nine health regions of Rio de Janeiro state. Annual cumulative incidences are presented for all three arboviruses throughout the study period. Spatial analyses of the three studied arboviruses showed distinct behaviors among municipalities and health regions. Co-circulation of the three arboviruses in the state and a heterogeneous spatiotemporal pattern was observed for each disease and region, with dengue having a higher annual incidence during the five years of the study, as well as two consecutive epidemic years in the state. The increase in transmission in different regions of the state in one year culminated in an epidemic in the state in the following year. A high annual cumulative incidence of chikungunya occurred in municipalities from 2017 to 2019 and of Zika only in 2016. Some municipalities with higher population densities showed higher incidences for some arboviruses and appeared to contribute to the dissemination to cities of lower demographic density and maintenance of these urban arboviruses. Thus, regions recording increased incidences of the three diseases in their territories for long periods should be considered municipal poles, as they initiated and sustained high transmission within their region.
SARS-CoV-2 Omicron Induces Enhanced Mucosal Interferon Response Compared to other Variants of Concern, Associated with Restricted Replication in Human Lung Tissues
Authors: Alfi Or , Hamdan Marah , Wald Ori , Yakirevitch Arkadi , Wandel Ori , Oiknine-Djian Esther , Gvili Ben , Knoller Hadas , Rozendorn Noa , Golan Berman Hadar , Adar Sheera , Vorontsov Olesya , Mandelboim Michal , Zakay-Rones Zichria , Oberbaum Menachem , Panet Amos , Wolf Dana G.
Published: July 2022

SARS-CoV-2 Omicron variant has been characterized by decreased clinical severity, raising the question of whether early variant-specific interactions within the mucosal surfaces of the respiratory tract could mediate its attenuated pathogenicity. Here, we employed ex vivo infection of native human nasal and lung tissues to investigate the local-mucosal susceptibility and innate immune response to Omicron compared to Delta and earlier SARS-CoV-2 variants of concern (VOC). We show that the replication of Omicron in lung tissues is highly restricted compared to other VOC, whereas it remains relatively unchanged in nasal tissues. Mechanistically, Omicron induced a much stronger antiviral interferon response in infected tissues compared to Delta and earlier VOC-a difference, which was most striking in the lung tissues, where the innate immune response to all other SARS-CoV-2 VOC was blunted. Notably, blocking the innate immune signaling restored Omicron replication in the lung tissues. Our data provide new insights to the reduced lung involvement and clinical severity of Omicron.
Photo-Fenton Degradation of Ciprofloxacin by Novel Graphene Quantum Dots/α-FeOOH Nanocomposites for the Production of Safe Drinking Water from Surface Water
Authors: Pervez Md. Nahid , Ma Shengjia , Huang Siqi , Naddeo Vincenzo , Zhao Yaping
Published: July 2022

In the current work, novel graphene quantum dots (GQDs)-doped goethite (α-FeOOH) nanocomposites (GQDs/α-FeOOH) were prepared by following a feasible hydrolysis method and applied for ciprofloxacin (CIP) removal. Results showed that the CIP degradation efficiency was significant (93.73%, 0.0566 min−1) in the GQDs/α-FeOOH + H2O2 + Vis system using much lower amounts of H2O2 (0.50 mM), which is 3.9 times the α-FeOOH + H2O2 + Vis system. It was found that •OH, O2•−, and 1O2 were mainly responsible for CIP degradation in the GQDs/α-FeOOH photo-Fenton system. GQDs/α-FeOOH demonstrated broad-spectrum UV–vis-IR responsiveness in the degradation of ciprofloxacin as a function of the doping of GQDs. Additionally, GQDs/α-FeOOH showed outstanding durability (recyclability up to 3 cycles with a lower iron leaking amount, 0.020 mg L−1), a broad range of application pH, and a pretty acceptable catalytic efficacy in a variety of surface water matrices. Overall, GQDs/α-FeOOH have been shown to be an effective photocatalyst for the remediation of emerging contaminants via the workable exploitation of solar energy.
Gender Perspective of Flood Early Warning Systems: People-Centered Approach
Authors: Shah Ashfaq Ahmad , Ullah Ayat , Khan Nasir Abbas , Pal Indrajit , Alotaibi Bader Alhafi , Traore Abou
Published: July 2022

Natural hazards early warning systems (EWS) are built on a solid technical and scientific foundation. However, a significant focus must be on those at risk and a systems approach that considers all the key risk factors. A people-centered flood EWS has proved to be more successful at conveying risk messages and protecting lives during times of crisis. The present study intends to analyze the gender perspective of flood early warning systems in Pakistan by using four components of a people-centered approach. The data acquired from the different sources in this study (including institutional key informant interviews = 30 and community focus groups = 32) is analyzed using a paradigm of gender analysis. The findings regarding gender analysis revealed that there was a lack of participation from women in the risk assessment process, and the institutions did not pay enough attention to the conventional knowledge and views of women and men. In most cases, women were not given hazard alerts since they only receive information about possible risks from men. Women were still reluctant to participate in the planning process for disaster response and capacity building because of societal norms and impediments. In addition, the study revealed that the EWS did not satisfy most of the requirements specified in a people-centered gender-sensitive EWS owing to the bureaucratic approach, lack of residents’ engagement, communication breakdown between people (at risk), and official risk messages.
Experimental Investigation of the Earth Pressure on Horizontally Composite Breakwaters According to Different Shoulder Widths of Rubble Mounds
Authors: Lee Jae-Sung , Lee Dal Soo , Oh Sang-Ho
Published: July 2022

A series of physical experiments were carried out to investigate the characteristics of the horizontal active earth pressure exerted by rubble stones placed in front of horizontally composite breakwaters. Typically, the shoulder width of rubble mounds is shorter than the failure wedge assumed by Rankine’s earth pressure theory; therefore, it is not appropriate to apply the theory for the estimation of the horizontal pressure of rubble stones on the caisson. Considering this, physical experiments were conducted to evaluate the horizontal earth pressure with rubble stones having different shoulder widths in front of the caisson. The experimental results showed that the horizontal pressure was considerably lower than that obtained by Rankine’s theory when the shoulder width was shorter than the failure wedge width. Even when the shoulder width was sufficiently large to apply the theory, the earth pressure was approximately 17% lower than the value calculated byRankine’s theory. Based on these analyses, an empirical equation is proposed that can estimate the earth pressure on the caisson for a wide range of shoulder widths of rubble mounds.
Evaluation of Microalgal Bacterial Dynamics in Pig-Farming Biogas Digestate under Impacts of Light Intensity and Nutrient Using Physicochemical Parameters
Authors: Pham Anh Le , Luu Khanh Duy , Duong Thuy Thi , Dinh Thanh Mai Thi , Nguyen Son Quoc , Nguyen Toan Khanh , Duong Hung Cong , Le Quynh Phuong Thi , Le Thu Phuong
Published: July 2022

Determination of the dynamics between microalgae and bacteria in pig farming biogas digestate is vital for a consistent and reliable application towards sustainable wastewater treatment and biofuel production. This study assesses the reliability of using physicochemical parameters as indicators for the rapid evaluation of microalgal bacterial dynamics in real digestate under impacts of light, nutrient loads, and N:P ratios. The relationship between variation profiles of nutrients, biomass and physicochemical properties in each experiment was analyzed. High light and high nutrient load enhanced biomass growth and nutrient removal rate. Ammonium addition (high N:P ratio) elevated NH3 level which inhibited the growth of microalgae, subsequently reducing the biomass growth and nutrient removal. Low N:P ratio triggered the accumulation of phosphorus and the growth of chlorophyll-a but exerted little influence on treatment. Variation profiles of dissolved oxygen, nutrient and biomass were highly consistent in every experiment allowing us to identify the shift from microalgal to bacterial predomination under unfavorable conditions including low light intensity and high N:P ratio. Strong linear correlation was also found between total nitrogen removal and electrical conductivity (R2 = 0.9754). The results show the great potential of rapid evaluation of microalgal bacterial dynamics for large scale system optimization and modelling.