bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images
Authors: Kobat Sabiha Gungor , Baygin Nursena , Yusufoglu Elif , Baygin Mehmet , Barua Prabal Datta , Dogan Sengul , Yaman Orhan , Celiker Ulku , Yildirim Hakan , Tan Ru-San , Tuncer Turker , Islam Nazrul , Acharya U. Rajendra
Published: August 2022

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about >2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images.
Virtual Laboratories in Tertiary Education: Case Study Analysis by Learning Theories
Authors: Hassan Jahan , Devi Anamika , Ray Biplob
Published: August 2022

This paper examines and evaluates Virtual Laboratories (VLabs) in consideration of technology design, educational pedagogy, and outcome in tertiary education context for ICT courses. There is a growing demand for VLabs in tertiary education to support remote, flexible, and equitable learning. Most of the universities in Australia offer distance education to students who do not attend on-campus classes. On-line labs allowing access via an internet connection can offer learners the required infrastructure to complete their lab tasks without attending physical lab facilities. The onset of COVID-19 pandemic in early 2020 has seen further spike in demand for VLabs as accessing online lab facilities to undertake hands on activities from anywhere and anytime was imperative during lockdown periods. Despite their benefits, it is complex to choose an appropriate VLab design or type that ensures effective and improved learning process. This paper presents two case studies using commercial and custom-made VLabs that are analyzed through the lens of learning theories. The outcome of the analysis informs the readers that the teachers’ support (human mediator) and VLabs (teaching tool) are interlinked together in a dialectical way which is an important consideration to achieve successful learning outcome. This study will help educators to make an informed decision in choosing an appropriate VLab design for their teaching content to ensure effective learning outcome.
Implementing an Empowerment Framework: The Significance for Children’s Play Environments and Reflective Practice
Authors: Canning Natalie
Published: August 2022

This paper examines implementing a unique way of observing children’s play through an empowerment framework; considering children’s choices, decisions, and interactions through an empowerment lens. The study considers the different ways children are empowered through their play environments using participation, voice, and ownership as three guiding themes and the challenges of recording children’s empowerment for practitioners. Five nested case studies in two different UK-based childcare environments form the basis of the research. Thematic analysis scrutinised the data that were generated from observations of children’s play, interviews with practitioners and a focus group with both settings. A phenomenological theoretical framing allowed a layered picture of empowerment to emerge. Practitioners made subjective yet informed judgements about children’s empowerment in their observations, drawing on knowledge and understanding of children’s personalities. Empowering experiences were recorded under prompt questions focusing on the way in which and the influences of how children were experiencing empowering moments. Observing through an empowerment framework has changed practice and the way practitioners think about the opportunities provided for children in their play environment. The main challenges for practitioners relate to anxiety around moving away from outcome-based observations and time limitations on producing meaningful narratives around children’s empowerment.
Pharmacy Students’ Mental Health and Resilience in COVID-19: An Assessment after One Year of Online Education
Authors: Hammoudi Halat Dalal , Younes Samar , Safwan Jihan , Akiki Zeina , Akel Marwan , Rahal Mohamad
Published: August 2022

COVID-19 has impacted mental health and affected education due to the shift to remote learning. The purpose of the current study was to assess the mental health of pharmacy students one year following the onset of the pandemic. A descriptive cross-sectional questionnaire was distributed to pharmacy students. The severity of depression, anxiety, and stress was assessed by the Depression Anxiety Stress Scale (DASS-21), and resilience was assessed by the Brief Resilience Scale (BRS). COVID-19-related economic, educational, and health stressors, and students’ vaccine attitudes were surveyed. Descriptive, bivariate, and multivariable analysis were used, and a p-value of <0.05 was considered significant. A total of 561 students participated; 37% had mild-to-moderate depression, 37% had severe-to-extremely-severe anxiety, and 52% demonstrated normal stress levels. Severe depression, anxiety, and stress were associated with smoking and feeling isolated due to COVID-19. Around 40% of students had low resilience, associated with smoking, being in the third or fourth year of pharmacy study, and the consumption of caffeinated beverages. The mean score of satisfaction with online learning was 60.3 ± 21.3%. Only 5% of participants were vaccinated, of which 87% trusted the benefits of vaccines and their role in controlling the pandemic. One year after the onset of COVID-19, depression, anxiety, stress, and low resilience were observed among pharmacy students; the investigation of the long-term mental effects of the pandemic on university students is warranted.
Obsessive-Compulsive Disorder (OCD) Types and Social Media: Are Social Media Important and Impactful for OCD People?
Authors: Guazzini Andrea , Gursesli Mustafa Can , Serritella Elena , Tani Margherita , Duradoni Mirko
Published: August 2022

Social media (SM) are the new standard for social interaction and people with OCD use such platforms like everyone else. However, the research on these individuals provides limited, sporadic, and difficult-to-generalize data outside of social-media evidence for one specific context concerning how SM is experienced by people with OCD. Our cross-sectional study involved 660 participants (71.4% females, 28.6% males) with 22% of the sample surpassing the 90° percentile threshold to be identified as high-level OCD-symptomatic individuals. Our work highlighted that roughly all OCD types are affected by social media in terms of mood and that these individuals appeared to give SM more importance than non-OCD individuals. The evidence presented, although very narrow, can be conceived as the first building blocks to encourage future research considering how individuals with OCD experience social media, since they appear to be affected more by them compared to non-OCD individuals.
Adjacent Frame Difference with Dynamic Threshold Method in Underwater Flash Imaging LiDAR
Authors: Yang Gang , Tian Zhaoshuo , Bi Zongjie , Cui Zihao , Liu Qingcao
Published: August 2022

During the underwater LiDAR imaging process, the images achieved by the conventional constant threshold adjacent frame difference (AFD) method normally loses the distance information of targets. This is mainly due to the Gaussian distribution of the laser light intensity field, which leads to the inhomogeneous intensity distribution in the frame from the target acquired by intensity charge-coupled devices (ICCD). In order to overcome this issue, the novel dynamic threshold adjacent frame difference (DTAFD) method was proposed in this paper. The DTAFD method modifies the intensity threshold following the pixel intensities in the different parts of the single frame intensity image acquired by ICCD. After the detailed theoretical demonstration of the DTAFD method, with the purpose of verifying its feasibility, the self-developed range-gated flash imaging LiDAR has been employed to construct the distance images of the rectangular and circular shaped targets at different distances. The distance between the rectangular target and the LiDAR system is 25.7 m, and the circular target is 70 cm further away from the rectangular target. The full distance information of these two targets is obtained by the DTAFD method with an effectively suppressing noise and the PSNR is increased from 6.95±0.0426 dB to 7.62±0.0264 dB. The experimental results indicate that the DTAFD method efficiently solves the AFD method’s drawback on the target information loss caused by the unequal optical field distribution, which makes it more suitable for the scenarios with uneven laser distribution such as the underwater imaging environment.
Forward Warping-Based Video Frame Interpolation Using a Motion Selective Network
Authors: Heo Jeonghwan , Jeong Jechang
Published: August 2022

Recently, deep neural networks have shown surprising results in solving most of the traditional image processing problems. However, the video frame interpolation field does not show relatively good performance because the receptive field requires a vast spatio-temporal range. To reduce the computational complexity, in most frame interpolation studies, motion is first calculated with the optical flow, then interpolated frames are generated through backward warping. However, while the backward warping process is simple to implement, the interpolated image contains mixed motion and ghosting defects. Therefore, we propose a new network that does not use the backward warping method through the proposed max-min warping. Since max-min warping generates a clear warping image in advance according to the size of the motion and the network is configured to select the warping result according to the warped layer, using the proposed method, it is possible to optimize the computational complexity while selecting a contextually appropriate image. The video interpolation method using the proposed method showed 34.847 PSNR in the Vimeo90k dataset and 0.13 PSNR improvement compared to the Quadratic Video Interpolation method, showing that it is an efficient frame interpolation self-supervised learning.
Assessment of the Impact of Emitted Radiated Interference Generated by a Selected Rail Traction Unit on the Operating Process of Trackside Video Monitoring Systems
Authors: Paś Jacek , Rosiński Adam , Wetoszka Patryk , Białek Kamil , Klimczak Tomasz , Siergiejczyk Mirosław
Published: August 2022

The article presents a method for assessing the impact of radiated electromagnetic interference generated by a selected rail traction unit on the operational process of trackside video monitoring systems (VMS). VMSs operated throughout an extensive railway area are responsible for the safety of people and property transport processes. Emissions of radiated electromagnetic interference generated in an unintended manner by traction vehicles within a railway line lead to interference in the VMS operating process. Based on the knowledge of actual VMS operating process data, spectral characteristics and values of individual components of disturbing signals occurring in the emissions of radiated electromagnetic interference, it is possible to determine the parameters of damage intensities for the devices and elements of this system. Using that data enables determining the VMS reliability parameters within its operating system, for an extensive railway area. The article’s authors first discussed the basic issues associated with VMS, followed by analysing the topic’s current status. They also presented issues related to measuring interference radiated within a rail area, developed a selected operational process model, and determined selected operational indicators for the structures in question. The paper ends with conclusions.
Influence of Timber Moisture Content on the Ultrasonic Wave Velocity Measurement of Tectona grandis L.F. and Cupressus lusitanica M. from Costa Rica
Authors: Paniagua Viviana , Corrales Javier , Torres Cindy , González-Rodrigo Beatriz
Published: August 2022

Ultrasound is a reliable non-destructive method commonly used to evaluate the state of a piece of wood. The effect of the moisture content (MC) on the timber wave velocity, which is different depending on the wood species, must be considered. MC adjustment factors are important if accurate and comparable results are to be achieved. Thus, the goal of this study is to define a model that allows obtaining the adjustment factor to predict the standard velocity (12% of MC (V12) from velocity obtained at different MC for two Costa Rican commercial species: Tectona grandis L.F (teak) and Cupressus lusitanica M. (cypress). This effect was studied on small clean specimens during the desorption stages, from the fiber saturation point to the oven-dry state, controlling the specimen’s mass and MC on 62 specimens. With this data, the rate of change in ultrasound velocity per MC was modeled. Thus, the applicability of already published moisture adjustment models for conifers and hardwood tropical species was proved. The results showed that the proposed model coefficients adjust better than the ones obtained from the wood science literature, which makes them suitable to describe ultrasound velocity in different moisture conditions (VH).
Simulating Wind Disturbances over Rubber Trees with Phenotypic Trait Analysis Using Terrestrial Laser Scanning
Authors: Zhang Bo , Wang Xiangjun , Yuan Xingyue , An Feng , Zhang Huaiqing , Zhou Lijun , Shi Jiangong , Yun Ting
Published: August 2022

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a lifting steel truss carrying a large forced draft fan near a rubber plantation on Hainan Island, and we aligned three selected small rubber trees in a row in front of the fan (with separation distances from the forced draft fan outlet of approximately 1.3, 3.3, and 5.3 m) to explore the susceptibility of rubber trees to the mechanical loading of hurricane-level winds. By adjusting the power of the forced draft fan, four wind speeds were emitted: 0 m/s, 10.5 m/s, 13.5 m/s, and 17.5 m/s. Meanwhile, point clouds of the three rubber trees under different continuous wind speeds were acquired using two terrestrial laser scanners. Computer algorithms were applied to derive the key parameters of the three rubber trees, namely, the zenith and azimuth angles of each leaf, effective leaf area index (LAI), windward area of each tree, volume of the tree canopy, and trunk tilt angle, from these point clouds under all four wind speeds. The results show that by increasing the wind speed from 0 m/s to 17.5 m/s, the leaf zenith angles of the three rubber trees were unimodally distributed with the peak concentrated at 0°, while the leaf azimuth angles were bimodally distributed with the peaks concentrated at 0° and 360°. The effective LAI values of the three trees increased from 2.97, 4.77, and 3.63 (no wind) to 3.84, 5.9, and 4.29 (wind speed of 17.5 m/s), respectively, due to a decrease in the vertical crown projection area caused by the compression of the tree canopy. We also found that the effective LAI, windward area, and canopy volume of the third rubber tree (the tree farthest from the forced draft fan) varied less than those of the other two trees, reflecting the attenuation of the wind speed by the crowns of the two trees closer to the fan. The experimental results also indicate that the joint use of light detection and ranging (LiDAR) data with computer graphics algorithms to analyse the dynamic changes in tree phenotypic characteristics during the passage of a hurricane is promising, enabling the development of a novel strategy for mitigating wind hazards. The proposed method with the designed device capable of producing an adjustable wind speed also has the potential to study the impacts of wind damage under various forest conditions by further modifying the tree spacing and tree species.
Identification and Forecast of Potential Fishing Grounds for Anchovy (Engraulis ringens) in Northern Chile Using Neural Networks Modeling
Authors: Armas Elier , Arancibia Hugo , Neira Sergio
Published: August 2022

Engraulis ringens (E. ringens) is a small pelagic fish of which the geographic and bathymetric distribution is conditioned by fluctuations in oceanographic conditions at different time scales (daily, weekly, monthly, annually, supra-annually, and longer) and by fishing. Understanding the organism−environment interactions and predicting the spatial distribution of its schools can improve conservation actions and fishery management, along with the operation of the fleets targeting E. ringens. There is an important fishery of E. ringens in Northern Chile (18°21′ S–26°00′ S), which provides about 80% of the purse seine catch. To identify and predict potential fishing grounds for E. ringens in this system, we implemented a predictive model of fishing grounds based on neural networks, which was trained with the georeferenced data of daily catches by industrial purse sein ships from 2003 to 2020 and information on oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height, and currents) obtained from the Copernicus Marine Enviroment Monitoring Service (CMEMS program). The neural network model had a very good performance (86%). Longitude (23%) was the most relevant variable for identifying potential fishing grounds, followed by the mixed layer depth (18%), latitude (15%), sea surface temperature (12%), month (12%), sea height (9%), salinity (9%), and the zonal and meridional components of the current velocity (2%). The neural network model classified correctly the majority of the areas with and without fishing potential; thus, its use is recommended to predict fishing grounds for E. ringens in the study area. Its application could increase by 88% of the probability of capture anchovy by the purse seine fleet of Northern Chile.
Small-Scale Rotor Aeroacoustics for Drone Propulsion: A Review of Noise Sources and Control Strategies
Authors: Candeloro Paolo , Ragni Daniele , Pagliaroli Tiziano
Published: August 2022

In the last decade, the drone market has grown rapidly for both civil and military purposes. Due to their versatility, the demand for drones is constantly increasing, with several industrial players joining the venture to transfer urban mobility to the air. This has exacerbated the problem of noise pollution, mainly due to the relatively lower altitude of these vehicles and the proximity of their routes to extremely densely populated areas. In particular, both the aerodynamic and aeroacoustic optimization of the propulsive system and of its interaction with the airframe are key aspects of unmanned aerial vehicle design that can signify the success or the failure of their mission. The industrial challenge involves finding the best performance in terms of loading, efficiency and weight, and, at the same time, the most silent configuration. For these reasons, research has focused on an initial localization of the noise sources and, on further analysis, of the noise generation mechanism, focusing particularly on directivity and scattering. The aim of the present study is to review the noise source mechanisms and the state-of-the-art control strategies, available in the literature, for its suppression, focusing especially on the fluid-dynamic aspects of low Reynolds numbers of the propulsive system and on the interaction of the propulsive system flow with the airframe.
Pasta with Kiwiberry (Actinidia arguta): Effect on Structure, Quality, Consumer Acceptance, and Changes in Bioactivity during Thermal Treatment
Authors: Osoś Agata , Jankowska Patrycja , Drożdżyńska Agnieszka , Różańska Maria Barbara , Biegańska-Marecik Róża , Baranowska Hanna Maria , Ruszkowska Millena , Kačániová Miroslava , Tomkowiak Agnieszka , Kieliszek Marek , Kowalczewski Przemysław Łukasz
Published: August 2022

In this study, kiwiberry lyophilizate (KBL) was incorporated into pasta at different levels (5%, 10%, and 15% w/w). Kiwiberry fruits’ characteristics (ascorbic acid, carotenoids, phenolic compounds, and antioxidant activity determination) as well as physical (cooking properties, color, microscopic structure determination, texture, and water molecular dynamics analysis by low-field NMR) and chemical analyses (proximate composition phenolic compounds composition and antioxidant activity) of KBL-enriched pasta were investigated. The replacement of semolina with KBL in the production of pasta significantly changed its culinary properties. Results showed that the addition of KBL leads to a reduction in optimal cooking time and cooking weight (47.6% and 37.3%, respectively). Additionally, a significant effect of the KBL incorporation on the color of both fresh and cooked pasta was observed. A significant reduction in the L* value for fresh (27.8%) and cooked (20.2%) pasta was found. The KBL-enriched pasta had a different surface microstructure than the control pasta and reduced firmness (on average 44.7%). Low-field NMR results have confirmed that the ingredients in kiwiberry fruit can bind the water available in fresh pasta. The heat treatment resulted in increasing the availability of phenolic compounds and the antioxidant activity (64.7%) of cooked pasta. Sensory evaluation scores showed that the use of 5–10% of the KBL additive could be successfully accepted by consumers.
Sustainable Development and Storage Stability of Orange By-Products Extract Using Natural Deep Eutectic Solvents
Authors: Gómez-Urios Clara , Viñas-Ospino Adriana , Puchades-Colera Pablo , López-Malo Daniel , Frígola Ana , Esteve María José , Blesa Jesús
Published: August 2022

The citrus industry produces large amounts of waste rich in bioactive compounds that have important effects on human health. Their extraction was performed using organic solvents, and a greener alternative to those solvents are natural deep eutectic solvents (NADES). The present study aimed to obtain and optimize extracts rich in polyphenols and flavonoids from orange peels using NADES and monitor polyphenol stability in the extracts for 30 days. The software COSMOtherm (conductor-like screening model) was used to screen fourteen NADES. The most promising solvents were lactic acid:glucose (LA:Glu) with an extraction yield of 1932 ± 7.83 mgGAE/100 gdw for TPC (total polyphenol content) and 82.7 ± 3.0 mg/100 gdw for TFC (total flavonoid content) and in the case of L-proline:malic acid (LP:MA) was 2164 ± 5.17 mgGAE/100 gdw for TPC and 97.0 ± 1.65 mg/100 gdw for TFC. The extraction process using LA:Glu and LP:MA was optimized, and the results showed that the selected variables (%NADES, solid:liquid ratio, and extraction time) had a significant influence on the extraction of TPC and TFC. Results showed that NADES improve the stability of TPC. These findings revealed that NADES are efficient for the extraction of bioactive compounds from orange by-products, and these extracts can represent an alternative for the food industry to enrich food products with natural ingredients.
How Consumer Expertise Influences Preference for Customized Food
Authors: Li Han , Liao Fen , Qing Ping
Published: August 2022

The strategy of food customization has increasingly aroused widespread interest among marketing managers and scholars, but most of them focus on the direct effect of customization on purchase intention. According to the research on self-image-consistent product perceptions, customization may also have an indirect amplification effect. Current research finds food customization will prompt individuals to incorporate their expertise in their perceptions of focal product attributes (taste perception). The findings of two studies demonstrate that food customization and consumer expertise have an interaction effect on consumers’ purchase intention. Specifically, consumers with higher (lower) expertise prefer customized food (standard food). Consumers’ taste perception mediates the interaction effect between food customization and consumer expertise on purchase intention. Finally, these findings provide guidance for marketing managers to adopt customized strategies.
Changes in Triacylglycerols Content and Quality Control Implications of Coix Seeds during Processing and Storage
Authors: Tang Weiwei , Wang Jiancheng , Li Wei , Zhang Chaojun , Li Ping , Chen Jun
Published: August 2022

Coix seed is a kind of widespread cereal, and it is used as a folk medicine in China. The present work focuses on the analysis of changes in triacylglycerols (TAGs) content and mycotoxins of coix seed during the processing and storage period for its quality control. Using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS) and high-performance liquid chromatography coupled with evaporative light-scattering detector (HPLC-ELSD) techniques, 42 lipid components in coix seeds were identified, and seven molecular species of TAG in coix seeds from different localities in China were measured and compared, respectively. A correlation analysis between the morphological features and TAGs contents revealed the integrity instead of the particle size of the seed, displaying a highly positive correlation with its quality. The higher contents of TAGs in hulled coix seed than in polished coix seed proposed an alternative processing way. During storage, the changes in TAGs contents of seeds indicated that the storage period should be less than 3 months, and the intact seeds could maintain lipid stability better than the powder. Furthermore, the air humidity and temperature should be controlled during coix seed storage to prevent the production of mycotoxins. These results provide significant insight into the effective control of coix seed quality during processing and storage.
Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning
Authors: Hartley Faith M. , Maxwell Aaron E. , Landenberger Rick E. , Bortolot Zachary J.
Published: August 2022

This study investigates the mapping of forest community types for the entire state of West Virginia, United States, using Global Land Analysis and Discovery (GLAD) Phenology Metrics, Analysis Ready Data (ARD) derived from Landsat time series data, and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study was to explore the use of globally consistent ARD for operational forest type mapping over a large spatial extent. Mean overall accuracy calculated from 50 model replicates for differentiating seven forest community types using only variables selected from the 188 GLAD Phenology Metrics used in the study resulted in an overall accuracy (OA) of 54.3% (map-level image classification efficacy (MICE) = 0.433). Accuracy increased to a mean OA of 64.8% (MICE = 0.496) when the Oak/Hickory and Oak/Pine classes were combined into an Oak Dominant class. Once selected terrain variables were added to the model, the mean OA for differentiating the seven forest types increased to 65.3% (MICE = 0.570), while the accuracy for differentiating six classes increased to 76.2% (MICE = 0.660). Our results highlight the benefits of combining spectral data and terrain variables and also the enhancement of the product’s usefulness when probabilistic predictions are provided alongside a hard classification. The GLAD Phenology Metrics did not provide an accuracy comparable to those obtained using harmonic regression coefficients; however, they generally outperformed models trained using only summer or fall seasonal medians and performed comparably to those trained using spring medians. We suggest further exploration of the GLAD Phenology Metrics as input for other spatial predictive mapping and modeling tasks.
Elastic Solutions to 2D Plane Strain Problems: Nonlinear Contact and Settlement Analysis for Shallow Foundations
Authors: Taylor Adam G. , Chung Jae H.
Published: August 2022

The classical Neumann boundary value problem of an isotropic, homogeneous elastic half-plane under plane strain conditions is readdressed as the limiting case of the fully three-dimensional problem. Analytical solutions of the stress and strain tensors are obtained by taking the limit from known three-dimensional solutions. It is shown that the displacement fields for the plane strain problem are not well defined. A small number of simple expressions are developed, which provide a general solution for linearly-varying traction over arbitrary regions on the boundary. A simple, efficient, and rapidly convergent algorithm is developed which uses these solutions as analytic elements and provides a solution approach to the general boundary value problem. The method is verified against known solutions for Hertzian contact between parallel cylinders. Two numerical examples are presented for the analysis of shallow foundation systems. In the first, the boundary conditions are informed by analytical elastoplastic calculations and a strain influence analysis is performed and compared with the Schmertmann method. Subsequently, empirical laboratory contact traction distributions measured by Bauer et al., in both the normal and tangential directions are employed as boundary conditions for an analysis of the underlying stress field.
The Impact of COVID-19 Crisis on the Control and Management of Radiography Practice in the United Arab Emirates
Authors: Salih Suliman , Alkatheeri Ajnas , Almarri Bashayer , Shamsi Nouf Al , Jaafari Osama , Alshammari Majedh
Published: August 2022

The present study aimed to assess the impact of the COVID-19 crisis on radiology practices in Abu Dhabi, UAE. An electronic survey (Google form) was distributed among Abu Dhabi government and private hospitals. The survey included general X–ray services, which were only provided in the radiology departments. The diagnostic radiographers who reported changes in the number and type of radiology procedures (37%) reported that the changes reached 61–80% compared to the number of procedures being conducted prior to the outbreak of COVID-19. While infection control was challenging due to the shortage of personal protective equipment (PPE), 51.2% of the participants were affected. The healthcare workers in the radiology departments in Abu Dhabi are exposed to a high number of COVID-19–infection patients, which increases their chances of contracting the disease. A total of 90% of employees were infected with COVID-19 during the crisis. COVID-19 has resulted in changes in clinical working patterns, such as the type and number of procedures performed daily. Additionally, PPE shortages, staff infection during the pandemic, an increase in workplace–related difficulties, and staff well–being are common consequences of the pandemic. It is vital to enhance coping strategies in order to support staff well–being. However, the psychological effects caused as a result of the pandemic should not be ignored, and providing professional support to workers is recommended.
Systolic Blood Pressure Trajectories and the Progression of Arterial Stiffness in Chinese Adults
Authors: Liu Run , Li Dankang , Yang Yingping , Hu Yonghua , Wu Shouling , Tian Yaohua
Published: August 2022

Evidence on the association between systolic blood pressure (SBP) trajectories and arterial stiffness progression is scarce. The current study aimed to identify the association between SBP trajectories and the progression of arterial stiffness over time in Chinese adults. This study included 30,384 adult participants. Latent mixture modeling was used to identify the SBP trajectory patterns from 2006 to 2010. The brachial–ankle pulse wave velocity (baPWV) was used to measure arterial stiffness. The associations between SBP trajectories and the progression of arterial stiffness were explored using multiple linear regression models. We identified five distinct SBP trajectories and took the low-stable group as the reference. In the cross-sectional analysis, the four SBP trajectories were significantly associated with higher baPWV levels (p < 0.001) compared with the reference. In the longitudinal analysis, after adjusting for covariates and the baseline baPWV, the SBP trajectories were significantly associated with the progression of the baPWV, with corresponding β (95% CI) values of 23.3 (17.2–29.5) cm/s per year for the moderate-stable group, 44.8 (36.6–52.9) cm/s per year for the moderate-increasing group, 54.6 (42.2–67.0) cm/s per year for the elevated-decreasing group, and 66.8 (54.7–79.0) cm/s per year for the elevated-stable group. Similar significant results were also observed in the non-hypertensive population. In conclusion, SBP trajectories were related to the baseline baPWV and the progression of the baPWV. Blood pressure control may be considered a therapeutic target to further reduce the risk of arterial stiffness.
Renin as a Marker of Tissue Perfusion, Septic Shock and Mortality in Septic Patients: A Prospective Observational Study
Authors: Leśnik Patrycja , Łysenko Lidia , Krzystek-Korpacka Małgorzata , Woźnica-Niesobska Ewa , Mierzchała-Pasierb Magdalena , Janc Jarosław
Published: August 2022

Sepsis is a life-threatening organ dysfunction caused by the dysregulation of the host’s response to an infection, where the dominant mechanism is tissue hypoperfusion. Currently, the marker used to define tissue disorders is lactate levels, which may be elevated in other disease states as well. Renin is an essential hormone for the proper functioning of the renin-angiotensin-aldosterone (RASS) system. It is secreted in the glomerular apparatus in response to hypoperfusion. This study aimed to assess the usefulness of renin as a marker of tissue hypoperfusion in patients with sepsis and septic shock. A final group of 48 patients treated for sepsis and septic shock in the intensive care unit was included. Blood samples for renin quantification were collected in the morning as a part of routine blood analysis on the first, third, and fifth days. Sepsis was diagnosed in 19 patients (39.6%), and septic shock was diagnosed in 29 patients (60.4%). There was no significant difference in renin concentration between patients who received and did not receive continuous renal replacement therapy (CRRT) on any study day. Therefore, all samples were analyzed together in subsequent analyses. There was a significant difference in renin concentration between sepsis survivors and non-survivors on the third (31.5 and 119.9 pg/mL, respectively) and fifth (18.2 and 106.7 pg/mL, respectively) days. As a survival marker, renin was characterized by 69% and 71% overall accuracy if determined on the third and fifth days, respectively. There was a significant difference in renin concentration between sepsis and septic shock patients on the first (45.8 and 103.4 pg/mL, respectively) and third (24.7 and 102.1 pg/mL, respectively) days. At an optimal cut-off of 87 pg/mL, renin had very good specificity and a positive likelihood ratio. Renin was a strong predictor of mortality in patients with sepsis and septic shock. Further, the level of renin in patients with septic shock was significantly higher than in patients with sepsis. In combination with the assessment of lactate concentration, renin seems to be the optimal parameter for monitoring tissue hypoperfusion and could be helpful for septic shock diagnosis, as well as for identifying candidate patients for CRRT.
Molecular Mimicry of the Rheumatoid Arthritis-Related Immunodominant T-Cell Epitope within Type II Collagen (CII260-270) by the Bacterial L-Asparaginase
Authors: Moten Dzhemal , Teneva Ivanka , Apostolova Desislava , Batsalova Tsvetelina , Dzhambazov Balik
Published: August 2022

The etiology of most autoimmune diseases, including rheumatoid arthritis (RA), remains unclear. Both genetic and environmental factors are believed to be involved in pathogenesis. Molecular mimicry is considered one of the mechanisms for the occurrence of autoimmune diseases. The aim of the study was to determine whether the bacterial peptide L-ASNase67-81, which mimics the immunodominant T-cell epitope CII259-273, can induce T-cell reactivity in blood samples from RA patients and healthy subjects through molecular mimicry. Using bioinformatic molecular modeling methods, we first determined whether the L-ASNase67-81 peptide binds to the HLA-DRB1*04:01 molecule and whether the formed MHCII–peptide complex interacts with the corresponding T-cell receptor. To validate the obtained results, leukocytes isolated from early RA patients and healthy individuals were stimulated in vitro with L-ASNase67-81 and CII259-273 peptides as well as with bacterial L-asparaginase or human type II collagen (huCII). The activated T cells (CD4+CD154+) were analyzed by flow cytometry (FACS), and the levels of cytokines produced (IL-2, IL-17A/F, and IFN-γ) were measured by ELISA. Our in silico analyses showed that the bacterial peptide L-ASNase67-81 binds better to HLA-DRB1*04:01 compared to the immunodominant T-cell epitope CII259-273, mimicking its structure and localization in the binding groove of MHCII. Six contact points were involved in the molecular interaction of the peptide with the TCR. FACS data showed that after in vitro stimulation with the L-ASNase67-81 peptide, the percentage of activated T cells (CD154+CD4+) was significantly increased in both cell cultures isolated from ERA patients and those isolated from healthy individuals, as higher values were observed for the ERA group (9.92 ± 0.23 vs. 4.82 ± 0.22). Furthermore, the ELISA assays revealed that after stimulation with L-ASNase67-81, a significant increase in the production of the cytokines IL-2, IL-17A/F, and IFN-γ was detected in the group of ERA patients. Our data showed that the bacterial L-ASNase67-81 peptide can mimic the immunodominant T-cell epitope CII259-273 and activate HLA-DRB1*04:01-restricted T cells as well as induce cytokine production in cells isolated from ERA patients. These results are the first to demonstrate that a specific bacterial antigen could play a role in the pathogenesis of RA, mimicking the immunodominant T-cell epitope from type II collagen.
From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction
Authors: Stătescu Cristian , Anghel Larisa , Tudurachi Bogdan-Sorin , Leonte Andreea , Benchea Laura-Cătălina , Sascău Radu-Andy
Published: August 2022

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.
A Tailored Particle Swarm and Egyptian Vulture Optimization-Based Synthetic Minority-Oversampling Technique for Class Imbalance Problem
Authors: Rout Subhashree , Mallick Pradeep Kumar , V. N. Reddy Annapareddy , Kumar Sachin
Published: August 2022

Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper/unguided collection of data samples, etc. The uneven distribution of class samples among classes may affect the classification accuracy of the developed model. The main motivation behind this study is the design and development of methodologies for handling class imbalance problems. In this study, a new variant of the synthetic minority oversampling technique (SMOTE) has been proposed with the hybridization of particle swarm optimization (PSO) and Egyptian vulture (EV). The proposed method has been termed SMOTE-PSOEV in this study. The proposed method generates an optimized set of synthetic samples from traditional SMOTE and augments the five datasets for verification and validation. The SMOTE-PSOEV is then compared with existing SMOTE variants, i.e., Tomek Link, Borderline SMOTE1, Borderline SMOTE2, Distance SMOTE, and ADASYN. After data augmentation to the minority classes, the performance of SMOTE-PSOEV has been evaluated using support vector machine (SVM), Naïve Bayes (NB), and k-nearest-neighbor (k-NN) classifiers. The results illustrate that the proposed models achieved higher accuracy than existing SMOTE variants.
Logarithmic Negation of Basic Probability Assignment and Its Application in Target Recognition
Authors: Xu Shijun , Hou Yi , Deng Xinpu , Chen Peibo , Zhou Shilin
Published: August 2022

The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application.
Predators or Herbivores: Cockroaches of Manipulatoridae Revisited with a New Genus from Cretaceous Myanmar Amber (Dictyoptera: Blattaria: Corydioidea)
Authors: Li Xinran , Huang Diying
Published: August 2022

Manipulator modificaputis Vršanský and Bechly, 2015 (Manipulatoridae, Corydioidea) is a purported predatory cockroach from Cretaceous Myanmar amber, based on a single male. It is distinctive by the nimble head, elongate pronotum and legs, and particularly by the extraordinarily long maxillary palpi. In the present study, we redescribe Manipulator modificaputis based on six new fossils including males and females, and comment on the original description. The closely related Manipulatoides obscura gen. & sp. nov. is proposed on the basis of five fossils, including males and females. It differs from Manipulator in weaker spination of the legs, including the type-C forefemoral spination instead of the type-A of Manipulator. Some undetermined adults and nymphs are also described. We discuss the ethology of Manipulatoridae and speculate that they might feed on flowers. They are unlikely to be specialized predators since they lack necessary weaponry for capturing prey; in contrast, their unique morphotype appears to be suitable for efficient foraging and locomotion amid flowering twigs. The possibility of being kleptoparasites of the spider-web is also discussed. In addition, regenerated four-segmented tarsi are found from the new species.
Haptic Perception Training Programs on Fine Motor Control in Adolescents with Developmental Coordination Disorder: A Preliminary Study
Authors: Wuang Yee-Pay , Huang Chien-Ling , Wu Ching-Shan
Published: August 2022

Somatosensory and haptic perception deficit was commonly found in developmental coordination disorder (DCD) and was closely related to fine motor functions, and the intervention strategies should thus emphasize improving the underlying haptic functions. This study was intended to investigate the effects of haptic perception training programs on fine motor functions in adolescents with DCD. A total of 82 DCD participants were assigned to either the haptic perception training program (HTP; n = 42, 16 females, mean age = 13.32 ± 2.85 years) or standard occupational therapy (SOT; n = 40, 16 females, mean age = 13.41 ± 3.8 years) group. Both groups were given 12-week training, twice a week, 30 min per session. Outcome measures were: Jebsen-Taylor Hand Function Test (JTFHT), Bruininks–Oseretsky Test of Motor Proficiency-Second Edition (BOT-2), Test of Visual-Perceptual Skills- Fourth Edition (TVPS-4), and Vineland Adaptive Behavior Scale -Chinese Version (VABS-C). After the intervention, the HTP group outscored the SOT group on most fine motor control (JTFHT and BOT-2) tasks and all TVPS-4 and VABS-C items. The HTP group had greater pre–post changes on fine motor integration, fine motor precision, manual dexterity, and writing. TVPS-4 reached significant intervention gains on visual spatial relations, visual memory, and visual sequential memory. The haptic perception training programs demonstrated benefits in enhancing fine motor control in adolescents with DCD. It could be used as an easy and effective alternative to hospital or school-based therapy during the pandemic.