bluuebunny/crossref_metadata_2025 huggingface dataset:
ADT-Det: Adaptive Dynamic Refined Single-Stage Transformer Detector for Arbitrary-Oriented Object Detection in Satellite Optical Imagery
Authors: Zheng Yongbin , Sun Peng , Zhou Zongtan , Xu Wanying , Ren Qiang
Published: July 2021

The detection of arbitrary-oriented and multi-scale objects in satellite optical imagery is an important task in remote sensing and computer vision. Despite significant research efforts, such detection remains largely unsolved due to the diversity of patterns in orientation, scale, aspect ratio, and visual appearance; the dense distribution of objects; and extreme imbalances in categories. In this paper, we propose an adaptive dynamic refined single-stage transformer detector to address the aforementioned challenges, aiming to achieve high recall and speed. Our detector realizes rotated object detection with RetinaNet as the baseline. Firstly, we propose a feature pyramid transformer (FPT) to enhance feature extraction of the rotated object detection framework through a feature interaction mechanism. This is beneficial for the detection of objects with diverse patterns in terms of scale, aspect ratio, visual appearance, and dense distributions. Secondly, we design two special post-processing steps for rotated objects with arbitrary orientations, large aspect ratios and dense distributions. The output features of FPT are fed into post-processing steps. In the first step, it performs the preliminary regression of locations and angle anchors for the refinement step. In the refinement step, it performs adaptive feature refinement first and then gives the final object detection result precisely. The main architecture of the refinement step is dynamic feature refinement (DFR), which is proposed to adaptively adjust the feature map and reconstruct a new feature map for arbitrary-oriented object detection to alleviate the mismatches between rotated bounding boxes and axis-aligned receptive fields. Thirdly, the focus loss is adopted to deal with the category imbalance problem. Experiments on two challenging satellite optical imagery public datasets, DOTA and HRSC2016, demonstrate that the proposed ADT-Det detector achieves a state-of-the-art detection accuracy (79.95% mAP for DOTA and 93.47% mAP for HRSC2016) while running very fast (14.6 fps with a 600 × 600 input image size).
Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector
Authors: Wang Yue , Wen Haijia , Sun Deliang , Li Yuechen
Published: July 2021

This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnerability maps utilizing landslide dataset from 2001 to 2016. The landslide dataset was built from historical records, satellite images and extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological factors, meteorological and hydrological factors and vegetation factors), 19 dominant factors were selected from 25 secondary conditioning factors based on the GeoDetector to form an evaluation factor library for the LSM. Subsequently, the random forest model (RF) was used to analyze landslide susceptibility. Then, the landslide hazard map was generated based on the landslide susceptibility mapping (LSM) for the study region. Thereafter, landslide vulnerability assessment was conducted using key elements (economic, material, community) and the weights were provided based on expert judgment. Finally, when risk equals vulnerability multiplied by hazard, the region was categorized as very low, low, medium, high and very high risk level. The results showed that most landslides distribute on both sides of the reservoir bank and the primary and secondary tributaries in the study area, which showed a spatial distribution pattern of more north than south. Elevation, lithology and groundwater type are the main factors affecting landslides. Fengjie County landslide risk level is mostly low (accounting for 73.71% of the study area), but a small part is high and very high risk level (accounting for 2.5%). The overall risk level shows the spatial distribution characteristics of high risk in the central and eastern urban areas and low risk in the southern and northern high-altitude areas. Secondly, it is necessary to strictly control the key risk areas, and carry out prevention and control zoning management according to local conditions. The study is conducted for a specific region but can be extended to other areas around the investigated area. The developed landslide risk map can be considered by relevant government officials for the smooth implementation of management at the regional scale.
Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery
Authors: Moura Marks Melo , de Oliveira Luiz Eduardo Soares , Sanquetta Carlos Roberto , Bastos Alexis , Mohan Midhun , Corte Ana Paula Dalla
Published: July 2021

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.
A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests
Authors: Grybas Heather , Congalton Russell G.
Published: July 2021

Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a sequence of mono-temporal and multi-temporal classifications were carried out. Kappa comparisons were conducted to ascertain whether the multi-temporal classifications significantly improved accuracy and whether there were significant differences between the RGB and multispectral classifications. The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used. Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time. The RGB sensor exhibited significantly higher accuracies, probably due to the blue band, which was found to be very important for classification accuracy and lacking in the multispectral sensor employed here.
Continuous Sensing of Water Temperature in a Reservoir with Grid Inversion Method Based on Acoustic Tomography System
Authors: Huang Haocai , Xu Shijie , Xie Xinyi , Guo Yong , Meng Luwen , Li Guangming
Published: July 2021

The continuous sensing of water parameters is of great importance to the study of dynamic processes in the ocean, coastal areas, and inland waters. Conventional fixed-point and ship-based observing systems cannot provide sufficient sampling of rapidly varying processes, especially for small-scale phenomena. Acoustic tomography can achieve the sensing of water parameter variations over time by continuously using sound wave propagation information. A multi-station acoustic tomography experiment was carried out in a reservoir with three sound stations for water temperature observation. Specifically, multi-path propagation sound waves were identified with ray tracing using high-precision topography data obtained with ship-mounted ADCP. A new grid inverse method is proposed in this paper for water temperature profiling along a vertical slice. The progression of water temperature variation in three vertical slices between acoustic stations was mapped by solving an inverse problem. The reliability and adaptability of the grid method developed in this research are verified by comparison with layer-averaged water temperature results. The grid method can be further developed for the 3D mapping of water parameters over time, especially in small-scale water areas, where sufficient multi-path propagation sound waves can be obtained.
Statistical Study of the Method of Radius-Change Sphericity Measurements
Authors: Stępień Krzysztof , Janecki Dariusz , Adamczak Stanisław
Published: July 2021

At the Kielce University of Technology, a concept of the accurate measurement of sphericity deviations of machine parts has been developed. The concept is based upon the measurement of roundness profiles in many clearly defined cross-sections of the workpiece. Measurements are performed with the use of a typical radius change measuring instrument equipped with a device for accurate positioning of the ball. This paper focuses on the statistical analysis of the differences between measurement results of spherical parts obtained by the new and traditional method. The differences were analyzed by applying preselected statistical parameters and diagrams. Results of the analysis revealed that that the new method may contribute to a much more reliable measurement of form deviations of spherical parts.
Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging
Authors: Robins Thomas , Camacho Jorge , Agudo Oscar Calderon , Herraiz Joaquin L. , Guasch Lluís
Published: July 2021

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.
A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control
Authors: Ha Jihyeon , Park Sangin , Im Chang-Hwan , Kim Laehyun
Published: July 2021

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.
Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications
Authors: Putra Karisma Trinanda , Chen Hsing-Chung , Prayitno  , Ogiela Marek R. , Chou Chao-Lung , Weng Chien-Erh , Shae Zon-Yin
Published: July 2021

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.
Male Sexual Health and Sexual Behaviors during the First National COVID-19 Lockdown in a Western Country: A Real-Life, Web-Based Study
Authors: Sansone Andrea , Mollaioli Daniele , Cignarelli Angelo , Ciocca Giacomo , Limoncin Erika , Colonnello Elena , Balercia Giancarlo , Reisman Yacov , Jannini Emmanuele A.
Published: July 2021

Restriction measures enacted during the COVID-19 pandemic had severe effects on male sexual and reproductive health. We aimed to investigate the real-life impact and perception of sexual function in 2020 in Italy, as the first Western country experiencing a national lockdown by measuring relative search volume for keywords pertaining to sexual health and behaviors provided by Google Trends and sales data for pro-erectile treatments. No significant change was observed for erectile dysfunction and premature ejaculation. Interest towards most phosphodiesterase type 5 inhibitors decreased significantly during lockdown (e.g., sildenafil, p = 0.0002; tadalafil p = 0.0016), then reverted to pre-lockdown levels (e.g., sildenafil, p < 0.0001; tadalafil p < 0.0001), mirroring a concomitant sales decrement (p = 0.0292). A subsequent recovery to previous levels at the end of the social confinement was also found (e.g., after vs. before lockdown, sildenafil, p = 0.8459; tadalafil p = 0.1166). During lockdown, interest towards pornography significantly increased during restrictions (p = 0.0053) and remained high (after vs. before lockdown, p = 0.0004), whereas interest towards paid and casual sex declined (p = 0.0015 and p = 0.049, respectively), then reverted to previous levels (after vs. before lockdown, p = 0.3969 and 0.8373, respectively). During the first Italian lockdown, we observed a transient but measurable decrease of sexual health-seeking behaviors and changes in sexual behaviors, with unknown impact for sexual medicine and public health.
Insights into the Impacts of Mega Transport Infrastructures on the Transformation of Urban Fabric: Case of BRT Lahore
Authors: Adeel Ahmad , Notteboom Bruno , Yasar Ansar , Scheerlinck Kris , Stevens Jeroen
Published: July 2021

State of the art mass transit systems, such as bus rapid transit (BRT), have appeared to be an innovative solution to meet the mobility needs of many world cities. However, their ability to transform surrounding urban fabric remains less explored in the developing world. This paper examines the impacts of BRT Lahore on land development patterns, considering the phenomena of land use revitalization, densification, and property reconfiguration. We have used quantitative approaches to gauge the changes in the urban fabric, with respect to the local neighborhood conditions and parcel level attributes, using multilevel logit models. The results of the logit models reveal heterogeneous impacts on conversions to commercial plazas, apartment buildings, and mixed-use buildings. The distance to the BRT station and size of the property is significantly associated with these conversions. Overall, one can notice a lack of a sense of place making and an absence of transit-oriented zoning around BRT stations. The desire of sustainable transit-oriented development to intensify land uses according to local conditions requires local urban designers and planners to think ahead of the curve by working on the regulatory and zoning restrictions governing the designs of built environment and also address the issues of gentrification and social inequity.
The Role of Geometry on a Self-Sustaining Bio-Receptive Concrete Panel for Facade Application
Authors: Mustafa Kazi Fahriba , Prieto Alejandro , Ottele Marc
Published: July 2021

Bio-receptivity refers to the aptitude of a material to allow for the natural growth of small plant species on stony surfaces with minimum external influence. Despite the numerous associated environmental benefits, the growth of mosses and lichens on facades has always been viewed as a negative phenomenon due to the random and shabby growth conditions. This research dealt with the design of a self-sustaining bio-receptive concrete facade system with an aim to create a more sustainable and green concrete for the construction industry. The research used surface geometry as a design variable to facilitate moss growth on concrete panels in an ordered and systematic manner. The exercise was an attempt to not only address the functional aspect of bio-receptivity but also its aesthetical quality, which has a primary influence on people’s perception of bio-receptivity and can promote mass use of this type of concrete material. The research was conducted in a top-down approach, where first, through design by research, six distinctly designed concrete panels were fabricated using adapted material composition (blast furnace cement with 75% slag, 0.6 water/cement, sand 0–4 mm and gravel 5–8 mm) as the boundary condition. The concrete mixture together with no curing policy resulted in highly porous concrete panels, suitable for bio-receptive properties. Next in the design validation phase, the influence of surface geometry/roughness on the water retention ability of the panels and the subsequent moss growth on the panels were evaluated through in vitro experiments. The water retention experiment of the panels was based on quantitative measurements for weight, relative humidity and temperature at several time intervals. The moss-growing experiment was carried out within an ideal greenhouse condition where the panels were initially inoculated with moss spores; the results were based on qualitative observation for a period of 4 months. According to the comparative analysis of these results, with the same material composition, Panel 2 showed the highest bio-colonization owing to its prominent surface geometry, whereas Panel 5 showed the least bio-colonization owing to its plain surface despite high absorption capacity. Thus, the role of geometry has been extensively proven in this research and as an outcome a set of general design guidelines have been formulated for a self-sustaining bio-receptive concrete facade panel, using geometry as a design variable for bio-receptivity.
Is Agent-Based Simulation a Valid Tool for Studying the Impact of Nature-Based Solutions on Local Economy? A Case Study of Four European Cities
Authors: Koppelaar Rembrandt , Marvuglia Antonino , Havinga Lisanne , Brajković Jelena , Rugani Benedetto
Published: July 2021

Implementing nature-based solutions (NBSs) in cities, such as urban forests, can have multiple effects on the quality of life of inhabitants, acting on the mitigation of climate change, and in some cases also enhancing citizens’ social life and the transformation of customer patterns in commercial activities. Assessing this latter effect is the aim of this paper. An agent-based model (ABM) was used to assess change in commercial activities by small and midsize companies in retail due to the development of parks. The paper focuses on the potential capacity of NBS green spaces to boost retail companies’ business volumes, thus increasing their revenues, and at the same time create a pleasant feeling of space usability for the population. The type of NBS is not specified but generalized into large green spaces. The simulation contains two types of agents: (1) residents and (2) shop owners. Factors that attract new retail shops to be established in an area are simplified, based on attractor points, which identify areas such as large green spaces within and around which shops can form. The simulations provided insights on the number of retail shops that can be sustained based on the purchasing behavior of citizens that walk in parks. Four European cities were explored: Szeged (Hungary), Alcalá de Henares (Spain), Çankaya Municipality (Turkey) and Milan (Italy). The model allowed analyzing the indirect economic benefit of NBSs (i.e., large green spaces in this case) on a neighborhood’s economic structure. More precisely, the presence of green parks in the model boosted the visits of customers to local small shops located within and around them, such as cafés and kiosks, allowing for the emergence of 5–6 retail shops (on average, for about 800 walking citizens) in the case of Szeged and an average 12–14 retail shops for a simulated population of 2900 persons that walk in parks in the case of Milan. Overall, results from this modeling exercise can be considered representative for large urban green areas usually visited by a substantial number of citizens. However, their pertinence to support for local policies for NBS implementation and other decision-making related activities of socioeconomic nature is hampered by the low representativeness of source data used for the simulations.
Food Security, Environmental Health, and the Economy in Mexico: Lessons Learned with the COVID-19
Authors: Monroy-Torres Rebeca , Castillo-Chávez Ángela , Carcaño-Valencia Erika , Hernández-Luna Marco , Caldera-Ortega Alex , Serafín-Muñoz Alma , Linares-Segovia Benigno , Medina-Jiménez Karen , Jiménez-Garza Octavio , Méndez-Pérez Monserrat , López-Briones Sergio
Published: July 2021

The COVID-19 pandemic showed an impact mainly on the health of people and the economy of households. The levels of food security in the world’s households, especially in Mexico, have decreased. When people do not have food security, their health is compromised and they have financial problems; on the other hand, environmental deterioration has a link with food security. The purpose of this review is to analysis of the current situation in Mexico of food security, environmental health and economy, the main lessons learned in these areas and their proposals integrating public policies. A review was carried out in the main databases (MEDLINE, Embase, CINAHL Plus, Web of Science, CAB Abstracts y PAIS Index) with the following keywords and according to the MeSH terms: Food security, food insecurity, environmental health, public policies, environmental, production, integrating the word COVID-19 in English and Spanish. Only 44.5% of Mexican households presented food security. For food insecurity, 22.6% had moderate and severe food insecurity, while 32.9% had mild insecurity. Food insecurity and the health impacts of environmental origin (waste management during the coronavirus pandemic, water contaminated by bacteria, viruses, and toxins; air pollution) generates impacts on economic activity by not offering food that meets health regulations. Without the application of cost-effective measures and interventions for the prevention and control of patients with obesity, the direct costs for 2023 will amount to 9 million dollars, which worsens the household economy. Despite having laws and policies on the right to food, a healthy environment (water), and opportunities for economic growth, these human rights are not fulfilled. The conclusion is that it is necessary to use a health and agroecological model to promote public policies (health, environment, and economy) that aims to prevent the discussed issues, with multidisciplinary and intersectoral interventions (government, academia, researchers, civil society organizations, industry, and population). This upholds the human right that all people should enjoy an adequate, healthy environment and have access to high-quality food.
On the Relation between Green Entrepreneurship Intention and Behavior
Authors: Amankwah Joseph , Sesen Harun
Published: July 2021

This study investigated the relationship between green entrepreneurship intention (GEI) and green entrepreneurship behavior (GEB). The study explored how university education support and green consumption commitment (GCC) moderate the relationship between green entrepreneurship intention and the GEB of university students in Ghana. The researcher used a quantitative approach. A total of 420 responses from the university campuses in Ghana were acquired through a purposive sampling method. A structural equation model was established with the help of AMOS 18. Confirmatory factor analysis was used to assess the goodness of fit of the hypothesized model, the construct validity was evaluated, and the model had an acceptable fit. The results indicated that GEI positively influenced GEB. High university education support yielded a more substantial effect. This finding supports the notion that UES and GCC moderate the relationship between GEI and GEB. The research was limited to a survey questionnaire. The researcher acknowledges that the research was carried out in Ghana, and for generalization purposes it is recommended that the pool of university students surveyed should be extended to cover more countries in Africa, or a comparative study should be conducted that includes Ghana and some European countries.
Awakenings: An Authentic Leadership Development Program to Break the Glass Ceiling
Authors: Martínez-Martínez Miryam , Molina-López Manuel , Mateos de Cabo Ruth , Gabaldón Patricia , González-Pérez Susana , Izquierdo Gregorio
Published: July 2021

Companies are vital agents in achieving the United Nations’ Sustainable Development Goals. One key role that businesses can play in achieving the 5th Sustainable Development Goal on gender equality is implementing training programs for their women executives so they can reach top corporate leadership positions. In this paper, we test the effectiveness of an Authentic Leadership Development (ALD) program for women executives. By interviewing 32 participants from this ALD program and building on authentic leadership theory, we find that this program lifts women participants’ self-efficacy perception, as well as their self-resolution to take control of their careers. The driver for both results is a reflective thinking process elicited during the program that leads women to abandon the stereotype of a low status role and lack of self-direction over time. Through the relational authenticity developed during the program, women participants develop leadership styles that are more congenial with their gender group, yet highly accepted by the in-group leader members, which enhances their social capital. After the program, the women participants flourished as authentic leaders, were able to activate and foster their self-esteem and social capital, and enhanced their agency in career advancement, increasing their likelihood of breaking the glass ceiling.
Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube
Authors: Bhattacharyya Suvanjan , Sarkar Debraj , Roy Rahul , Chakraborty Shramona , Goel Varun , Almatrafi Eydhah
Published: July 2021

In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.
Biocontrol Activity of Aureubasidium pullulans and Candida orthopsilosis Isolated from Tectona grandis L. Phylloplane against Aspergillus sp. in Post-Harvested Citrus Fruit
Authors: Sukmawati Dalia , Family Nurul , Hidayat Iman , Sayyed R. , Elsayed Elsayed , Dailin Daniel , Hanapi Siti , Wadaan Mohammad , Enshasy Hesham
Published: July 2021

This study aimed to isolate and identify moulds from rotten Citrus sinensis post-harvests and to investigate the activity of antagonist and biocontrol activity moulds that cause citrus fruit rotting. A total of 12 mould isolates were obtained. Following the pathogenicity test, two representative mould isolates were selected and identified based on the sequence analyses of internal transcribed spacer (ITS) regions of the rDNA. Methods used in this study include isolation of fungal postharvest diseases, pathogenicity assay, antagonism assay, growth curve analysis, in vitro biocontrol assay, and molecular phylogenetic analysis. Two isolates of fungal postharvest diseases were determined as the most destructive pathogens. The biocontrol assay showed that isolates of Y1 and Y10 were capable to reduce the growth of fungal isolates K6 and K9 and mitigate up to 100% of the damage of sweet citrus fruits after 7 days of incubation. The moulds were identified as K6 (Aspergillus flavus sensu lato) and K9 (Aspergillus niger sensu lato). Phylogenetic analysis showed that the Y10 yeast isolate was identified as Candida orthopsilosis, whereas the Y1 isolate had a close genetic relationship with Aureobasidium pullulans and possibly belongs to a new species. Further analysis is necessary to confirm this finding.
Implementation of an Expanded Decision-Making Technique to Comment on Sweden Readiness for Digital Tourism
Authors: Sorooshian Shahryar
Published: July 2021

Tourism provides many advantages for Sweden and the whole world, as well as its travelers. Since almost all types of tourism are currently in crisis as a result of the current COVID-19 pandemic, information and communication technology is expected to play a role, not only during the crisis but also in the post-COVID-19 era. Thus, with no expectations from types of tourism, Sweden needs to broaden its digital tours. As a result, this letter aims to classify the transition readiness of industry clusters for this digitalization move. An extended version of the TOPSIS technique was formulated and validated, plus a new framework for measuring digitalization readiness for this purpose. Lastly, analysis of the collected data proves that business tourism could lead the change, though adventure and rural tourism are at the farthest point from being considered ready to change.
Development of Piezoresistive Sensor Yarn to Monitor Local Fabric Elongation
Authors: Barthod-Malat Benjamin , Cochrane Cédric , Boussu François
Published: July 2021

The performance of bulletproof vests is mainly based on the energy absorption capacity of the flexible fibrous reinforcements. To understand the in situ behaviour of these textile structures during a ballistic impact, we find the use of sensor yarns integrated into these fibrous reinforcements to be a non-invasive and reliable solution. Measurements of the dynamic deformation of the sensor yarns will provide a new and useful source of information. The design and manufacturing stages of a sensor yarn, made with the same structural yarns of the fabric, are detailed successively. Then, different batches of sensor yarns were designed, and electromechanical tensile tests were performed in quasi-static mode. These experiments provide encouraging results for the measurement of the deformation of a textile structure subject to a dynamic impact.
Comparison of Lethal and Nonlethal Mouse Models of Orientia tsutsugamushi Infection Reveals T-Cell Population-Associated Cytokine Signatures Correlated with Lethality and Protection
Authors: Luce-Fedrow Alison , Chattopadhyay Suchismita , Chan Teik-Chye , Pearson Gregory , Patton John B. , Richards Allen L.
Published: July 2021

The antigenic diversity of Orientia tsutsugamushi as well as the interstrain difference(s) associated with virulence in mice impose the necessity to dissect the host immune response. In this study we compared the host response in lethal and non-lethal murine models of O. tsutsugamushi infection using the two strains, Karp (New Guinea) and Woods (Australia). The models included the lethal model: Karp intraperitoneal (IP) challenge; and the nonlethal models: Karp intradermal (ID), Woods IP, and Woods ID challenges. We monitored bacterial trafficking to the liver, lung, spleen, kidney, heart, and blood, and seroconversion during the 21-day challenge. Bacterial trafficking to all organs was observed in both the lethal and nonlethal models of infection, with significant increases in average bacterial loads observed in the livers and hearts of the lethal model. Multicolor flow cytometry was utilized to analyze the CD4+ and CD8+ T cell populations and their intracellular production of the cytokines IFNγ, TNF, and IL2 (single, double, and triple combinations) associated with both the lethal and nonlethal murine models of infection. The lethal model was defined by a cytokine signature of double- (IFNγ-IL2) and triple-producing (IL2-TNF-IFNγ) CD4+ T-cell populations; no multifunctional signature was identified in the CD8+ T-cell populations associated with the lethal model. In the nonlethal model, the cytokine signature was predominated by CD4+ and CD8+ T-cell populations associated with single (IL2) and/or double (IL2-TNF) populations of producers. The cytokine signatures associated with our lethal model will become depletion targets in future experiments; those signatures associated with our nonlethal model are hypothesized to be related to the protective nature of the nonlethal challenges.
Genetic and Chemical Capsid Modifications of Adenovirus Vectors to Modulate Vector–Host Interactions
Authors: Weklak Denice , Pembaur Daniel , Koukou Georgia , Jönsson Franziska , Hagedorn Claudia , Kreppel Florian
Published: July 2021

Adenovirus-based vectors are playing an important role as efficacious genetic vaccines to fight the current COVID-19 pandemic. Furthermore, they have an enormous potential as oncolytic vectors for virotherapy and as vectors for classic gene therapy. However, numerous vector–host interactions on a cellular and noncellular level, including specific components of the immune system, must be modulated in order to generate safe and efficacious vectors for virotherapy or classic gene therapy. Importantly, the current widespread use of Ad vectors as vaccines against COVID-19 will induce antivector immunity in many humans. This requires the development of strategies and techniques to enable Ad-based vectors to evade pre-existing immunity. In this review article, we discuss the current status of genetic and chemical capsid modifications as means to modulate the vector–host interactions of Ad-based vectors.
Bispecific Antibodies: A Smart Arsenal for Cancer Immunotherapies
Authors: You Gihoon , Won Jonghwa , Lee Yangsoon , Moon Dain , Park Yunji , Lee Sang Hoon , Lee Seung-Woo
Published: July 2021

Following the clinical success of cancer immunotherapies such as immune checkpoint inhibitors blocking B7/CTLA-4 or PD-1/PD-L1 signaling and ongoing numerous combination therapies in the clinic,3 bispecific antibodies (BsAbs) are now emerging as a growing class of immunotherapies with the potential to improve clinical efficacy and safety further. Here, we describe four classes of BsAbs: (a) immune effector cell redirectors; (b) tumor-targeted immunomodulators; (c) dual immunomodulators; and (d) dual tumor-targeting BsAbs. This review describes each of these classes of BsAbs and presents examples of BsAbs in development. We reviewed the biological rationales and characteristics of BsAbs and summarized the current status and limitations of clinical development of BsAbs and strategies to overcome limitations. The field of BsAb-based cancer immunotherapy is growing, and more data from clinical trials are accumulating. Thus, BsAbs could be the next generation of new treatment options for cancer patients.
Preliminary Data on Post Market Safety Profiles of COVID 19 Vaccines in Rheumatic Diseases: Assessments on Various Vaccines in Use, Different Rheumatic Disease Subtypes, and Immunosuppressive Therapies: A Two-Centers Study
Authors: Rotondo Cinzia , Cantatore Francesco Paolo , Fornaro Marco , Colia Ripalta , Busto Giuseppe , Rella Valeria , Sciacca Stefania , Lops Lucia , Cici Daniela , Maruotti Nicola , D’Onofrio Francesca , Iannone Florenzo , Corrado Addolorata
Published: July 2021

An increased risk of developing severe infections has been evidenced in rheumatic disease (RD) patients, and anti-COVID-19 vaccination is strictly recommended for RD patients. However, up to now, no data are available on safety, immunogenicity and efficacy of COVID-19 vaccinations in RD patients. The possible development of adverse events (AEs), including the flare-up of underlying RD, represents a matter of growing importance. The aim of our study is to assess, in RD patients, the safety profile of different types of approved vaccines and the possible influence of immunosuppressive therapies and clinical or demographic characteristics of RD patients on development of AEs. Participants (n = 185; 30.7%) received anti-COVID-19 vaccinations, 137 with autoimmune/chronic inflammatory RD (Au/cIn-RD) and 48 with nonautoimmune/chronic inflammatory RD (no-Au/cIn-RD). AEs were recorded in 42% of patients after the first dose of vaccine, and in 26% of patients after the second dose. The most common reported AEs after anti-COVID 19 vaccines were site injection pain (17%), headache (12%), fever (12%), myalgia (10%) and fatigue (10%). Relapses of the underlying Au/c-In-RD were recorded in 2.2% of patients after the first dose of vaccine. In Au/c-In-RD the risk of developing AEs after the first dose of vaccine was lower in older patients (OR = 0.95; p = 0.001), and in the group of patients with complete control of RD (OR: 0.2; p = 0.010). A lower percentage of AEs was observed in patients with complete control of their Au/cIn-RD (29%) compared to those with low (57%) or moderate-high disease activity (63%) (p = 0.002 and p = 0.006 respectively). In this study all types of COVID-19 vaccines in use in Italy seemed safe in RD patients. The results of this study might provide reassuring information for Au/cIn RD patients and clinicians and could strengthen the data on vaccine safety to guide the use of COVID-19 vaccines in Au/cIn-RD on immunosuppressive agents.
Sero-Monitoring of Horses Demonstrates the Equivac® HeV Hendra Virus Vaccine to Be Highly Effective in Inducing Neutralising Antibody Titres
Authors: Halpin Kim , Graham Kerryne , Durr Peter A.
Published: July 2021

Hendra virus (HeV) is a high consequence zoonotic pathogen found in Australia. The HeV vaccine was developed for use in horses and provides a One Health solution to the prevention of human disease. By protecting horses from infection, the vaccine indirectly protects humans as well, as horses are the only known source of infection for humans. The sub-unit-based vaccine, containing recombinant HeV soluble G (sG) glycoprotein, was released by Pfizer Animal Health (now Zoetis) for use in Australia at the end of 2012. The purpose of this study was to collate post-vaccination serum neutralising antibody titres as a way of assessing how the vaccine has been performing in the field. Serum neutralization tests (SNTs) were performed on serum samples from vaccinated horses submitted to the laboratory by veterinarians. The SNT results have been analysed, together with age, dates of vaccinations, date of sampling and location. Results from 332 horses formed the data set. Provided horses received at least three vaccinations (consisting of two doses 3–6 weeks apart, and a third dose six months later), horses had high neutralising titres (median titre for three or more vaccinations was 2048), and none tested negative.
Detailed Dissection and Critical Evaluation of the Pfizer/BioNTech and Moderna mRNA Vaccines
Authors: Xia Xuhua
Published: July 2021

The design of Pfizer/BioNTech and Moderna mRNA vaccines involves many different types of optimizations. Proper optimization of vaccine mRNA can reduce dosage required for each injection leading to more efficient immunization programs. The mRNA components of the vaccine need to have a 5′-UTR to load ribosomes efficiently onto the mRNA for translation initiation, optimized codon usage for efficient translation elongation, and optimal stop codon for efficient translation termination. Both 5′-UTR and the downstream 3′-UTR should be optimized for mRNA stability. The replacement of uridine by N1-methylpseudourinine (Ψ) complicates some of these optimization processes because Ψ is more versatile in wobbling than U. Different optimizations can conflict with each other, and compromises would need to be made. I highlight the similarities and differences between Pfizer/BioNTech and Moderna mRNA vaccines and discuss the advantage and disadvantage of each to facilitate future vaccine improvement. In particular, I point out a few optimizations in the design of the two mRNA vaccines that have not been performed properly.
Humoral Response to SARS-Cov-2 Vaccination in Liver Transplant Recipients–A Single-Center Experience
Authors: Rashidi-Alavijeh Jassin , Frey Alexandra , Passenberg Moritz , Korth Johannes , Zmudzinski Jaqueline , Anastasiou Olympia E. , Saner Fuat H. , Jahn Michael , Lange Christian M. , Willuweit Katharina
Published: July 2021

Vaccination against SARS-CoV-2 infection is currently approved and shows favorable outcomes, but little known about antibody responses in solid organ transplant recipients, since these patients are known to have an impaired immune response upon vaccination and have not been included in admission studies. We therefore analyzed immunogenicity in 43 liver transplant (LT) recipients in a median of 15 days (IQR, 12–24) after receiving two doses of the mRNA-based SARS-CoV-2 vaccine BNT162b2 following the standard protocol, and compared these results to a control group consisting of 20 healthcare workers (HCWs). Thirty-four of the 43 (79%) LT recipients developed antibodies, compared to 20 out of 20 (100%) in the control group (p = 0.047). The median SARS-CoV-2 IgG titer was significantly lower in the LT recipients compared to the control group (216 vs. >2080 BAU/mL, p = 0.0001). Age and sex distribution was similar in the LT patients that developed antibodies after vaccination compared to those who did not. Interestingly, the patients who received mycophenolate mofetil exhibited a reduced vaccination response compared to the other LT patients (5 of 11 (45.5%) vs. 29 of 32 (90.6%), p = 0.004). In conclusion, our data reveal lower immunogenicity of SARS-CoV-2 vaccine BNT162b2 in LT patients compared to the control group, but still show superior results compared to other solid organ transplant recipients reported so far.
Exploiting Pan Influenza A and Pan Influenza B Pseudotype Libraries for Efficient Vaccine Antigen Selection
Authors: Del Rosario Joanne Marie M. , da Costa Kelly A. S. , Asbach Benedikt , Ferrara Francesca , Ferrari Matteo , Wells David A. , Mann Gurdip Singh , Ameh Veronica O. , Sabeta Claude T. , Banyard Ashley C. , Kinsley Rebecca , Scott Simon D. , Wagner Ralf , Heeney Jonathan L. , Carnell George W. , Temperton Nigel J.
Published: July 2021

We developed an influenza hemagglutinin (HA) pseudotype library encompassing Influenza A subtypes HA1-18 and Influenza B subtypes (both lineages) to be employed in influenza pseudotype microneutralization (pMN) assays. The pMN is highly sensitive and specific for detecting virus-specific neutralizing antibodies against influenza viruses and can be used to assess antibody functionality in vitro. Here we show the production of these viral HA pseudotypes and their employment as substitutes for wildtype viruses in influenza neutralization assays. We demonstrate their utility in detecting serum responses to vaccination with the ability to evaluate cross-subtype neutralizing responses elicited by specific vaccinating antigens. Our findings may inform further preclinical studies involving immunization dosing regimens in mice and may help in the creation and selection of better antigens for vaccine design. These HA pseudotypes can be harnessed to meet strategic objectives that contribute to the strengthening of global influenza surveillance, expansion of seasonal influenza prevention and control policies, and strengthening pandemic preparedness and response.