bluuebunny/crossref_metadata_2025 huggingface dataset:
Curated Dataset for Red Blood Cell Tracking from Video Sequences of Flow in Microfluidic Devices
Authors: Cimrák Ivan , Tarábek Peter , Kajánek František
Published: June 2023

This work presents a dataset comprising images, annotations, and velocity fields for benchmarking cell detection and cell tracking algorithms. The dataset includes two video sequences captured during laboratory experiments, showcasing the flow of red blood cells (RBC) in microfluidic channels. From the first video 300 frames and from the second video 150 frames are annotated with bounding boxes around the cells, as well as tracks depicting the movement of individual cells throughout the video. The dataset encompasses approximately 20,000 bounding boxes and 350 tracks. Additionally, computational fluid dynamics simulations were utilized to generate 2D velocity fields representing the flow within the channels. These velocity fields are included in the dataset. The velocity field has been employed to improve cell tracking by predicting the positions of cells across frames. The paper also provides a comprehensive discussion on the utilization of the flow matrix in the tracking steps.
A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19
Authors: Carfora Maria Francesca , Orlando Albina
Published: June 2023

Mortality shocks, such as pandemics, threaten the consolidated longevity improvements, confirmed in the last decades for the majority of western countries. Indeed, just before the COVID-19 pandemic, mortality was falling for all ages, with a different behavior according to different ages and countries. It is indubitable that the changes in the population longevity induced by shock events, even transitory ones, affecting demographic projections, have financial implications in public spending as well as in pension plans and life insurance. The Short Term Mortality Fluctuations (STMF) data series, providing data of all-cause mortality fluctuations by week within each calendar year for 38 countries worldwide, offers a powerful tool to timely analyze the effects of the mortality shock caused by the COVID-19 pandemic on Italian mortality rates. This dataset, recently made available as a new component of the Human Mortality Database, is described and techniques for the integration of its data with the historical mortality time series are proposed. Then, to forecast mortality rates, the well-known stochastic mortality model proposed by Lee and Carter in 1992 is first considered, to be consistent with the internal processing of the Human Mortality Database, where exposures are estimated by the Lee–Carter model; empirical results are discussed both on the estimation of the model coefficients and on the forecast of the mortality rates. In detail, we show how the integration of the yearly aggregated STMF data in the HMD database allows the Lee–Carter model to capture the complex evolution of the Italian mortality rates, including the higher lethality for males and older people, in the years that follow a large shock event such as the COVID-19 pandemic. Finally, we discuss some key points concerning the improvement of existing models to take into account mortality shocks and evaluate their impact on future mortality dynamics.
Mechanical Properties of Glioblastoma: Perspectives for YAP/TAZ Signaling Pathway and Beyond
Authors: Pontes Bruno , Mendes Fabio A.
Published: June 2023

Glioblastoma is a highly aggressive brain tumor with a poor prognosis. Recent studies have suggested that mechanobiology, the study of how physical forces influence cellular behavior, plays an important role in glioblastoma progression. Several signaling pathways, molecules, and effectors, such as focal adhesions, stretch-activated ion channels, or membrane tension variations, have been studied in this regard. Also investigated are YAP/TAZ, downstream effectors of the Hippo pathway, which is a key regulator of cell proliferation and differentiation. In glioblastoma, YAP/TAZ have been shown to promote tumor growth and invasion by regulating genes involved in cell adhesion, migration, and extracellular matrix remodeling. YAP/TAZ can be activated by mechanical cues such as cell stiffness, matrix rigidity, and cell shape changes, which are all altered in the tumor microenvironment. Furthermore, YAP/TAZ have been shown to crosstalk with other signaling pathways, such as AKT, mTOR, and WNT, which are dysregulated in glioblastoma. Thus, understanding the role of mechanobiology and YAP/TAZ in glioblastoma progression could provide new insights into the development of novel therapeutic strategies. Targeting YAP/TAZ and mechanotransduction pathways in glioblastoma may offer a promising approach to treating this deadly disease.
IRSDD-YOLOv5: Focusing on the Infrared Detection of Small Drones
Authors: Yuan Shudong , Sun Bei , Zuo Zhen , Huang Honghe , Wu Peng , Li Can , Dang Zhaoyang , Zhao Zongqing
Published: June 2023

With the rapid growth of the global drone market, a variety of small drones have posed a certain threat to public safety. Therefore, we need to detect small drones in a timely manner so as to take effective countermeasures. At present, the method based on deep learning has made a great breakthrough in the field of target detection, but it is not good at detecting small drones. In order to solve the above problems, we proposed the IRSDD-YOLOv5 model, which is based on the current advanced detector YOLOv5. Firstly, in the feature extraction stage, we designed an infrared small target detection module (IRSTDM) suitable for the infrared recognition of small drones, which extracted and retained the target details to allow IRSDD-YOLOv5 to effectively detect small targets. Secondly, in the target prediction stage, we used the small target prediction head (PH) to complete the prediction of the prior information output via the infrared small target detection module (IRSTDM). We optimized the loss function by calculating the distance between the true box and the predicted box to improve the detection performance of the algorithm. In addition, we constructed a single-frame infrared drone detection dataset (SIDD), annotated at pixel level, and published an SIDD dataset publicly. According to some real scenes of drone invasion, we divided four scenes in the dataset: the city, sky, mountain and sea. We used mainstream instance segmentation algorithms (Blendmask, BoxInst, etc.) to train and evaluate the performances of the four parts of the dataset, respectively. The experimental results show that the proposed algorithm demonstrates good performance. The AP50 measurements of IRSDD-YOLOv5 in the mountain scene and ocean scene reached peak values of 79.8% and 93.4%, respectively, which are increases of 3.8% and 4% compared with YOLOv5. We also made a theoretical analysis of the detection accuracy of different scenarios in the dataset.
Multi-Drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue
Authors: Xiong Tao , Liu Fang , Liu Haoting , Ge Jianyue , Li Hao , Ding Kai , Li Qing
Published: June 2023

In a three-dimensional (3D) disaster rescue mission environment, multi-drone mission assignments and path planning are challenging. Aiming at this problem, a mission assignment method based on adaptive genetic algorithms (AGA) and a path planning method using sine–cosine particle swarm optimization (SCPSO) are proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in disaster rescue environments are modeled, including mountains, transmission towers, and severe weather. Third, a cost–revenue function that considers factors such as drone performance, demand for mission points, elevation cost, and threat sources, is formulated to assign missions to multiple drones. Fourth, an AGA is employed to realize the multi-drone mission assignment. To enhance convergence speed and optimize performance in finding the optimal solution, an AGA using both the roulette method and the elite retention method is proposed. Additionally, the parameters of the AGA are adjusted according to the changes in the fitness function. Furthermore, the improved circle algorithm is also used to preprocess the mission sequence for AGA. Finally, based on the sine–cosine function model, a SCPSO is proposed for planning the optimal flight path between adjacent task points. In addition, the inertia and acceleration coefficients of linear weights are designed for SCPSO so as to enhance its performance to escape the local minimum, explore the search space more thoroughly, and achieve the purpose of global optimization. A multitude of simulation experiments have demonstrated the validity of our method.
Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies
Authors: James Nick , Menzies Max
Published: June 2023

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market’s collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a “best value” portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.
Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks
Authors: Sardar Nida , Khan Sundas , Hintze Arend , Mehra Priyanka
Published: June 2023

Deep learning models have achieved an impressive performance in a variety of tasks, but they often suffer from overfitting and are vulnerable to adversarial attacks. Previous research has shown that dropout regularization is an effective technique that can improve model generalization and robustness. In this study, we investigate the impact of dropout regularization on the ability of neural networks to withstand adversarial attacks, as well as the degree of “functional smearing” between individual neurons in the network. Functional smearing in this context describes the phenomenon that a neuron or hidden state is involved in multiple functions at the same time. Our findings confirm that dropout regularization can enhance a network’s resistance to adversarial attacks, and this effect is only observable within a specific range of dropout probabilities. Furthermore, our study reveals that dropout regularization significantly increases the distribution of functional smearing across a wide range of dropout rates. However, it is the fraction of networks with lower levels of functional smearing that exhibit greater resilience against adversarial attacks. This suggests that, even though dropout improves robustness to fooling, one should instead try to decrease functional smearing.
The Impact of Recent Economic Crises on Income Inequality and the Risk of Poverty in Greece
Authors: Petrakos George , Rontos Konstantinos , Vavoura Chara , Vavouras Ioannis
Published: June 2023

We consider the impact of the two recent economic crises, one that resulted from the great recession of 2007–2009 and one following the COVID-19 pandemic, on income inequality and the risk of poverty in Greece. To this end, we also investigate the key macroeconomic variables affecting the Greek income distribution. We find that alternative measures of inequality and relative poverty have deteriorated during the years of crises, implying that the country’s response to economic shocks has been particularly harmful for its more disadvantaged citizens. Regarding the variables affecting the income distribution, we show that income inequality and the risk of poverty increase with growth, implying that the benefits of growth, and burdens of recession, are distributed unequally among members of the Greek society. Moreover, inequality appears to increase with unemployment and decrease with the share of wages and salaries in total income. This finding highlights the importance of labour market regulations and workers’ welfare for attaining equality. Finally, we provide evidence that, during pre-election periods, income inequality is reduced, meaning that the political budget cycles, which are characteristic of the Greek economy, generate gains in terms of equality, but these gains are short-lived and mainly benefit the middle bracket of the income distribution.
Effects of Signaling and Practice Types in Video-Based Software Training
Authors: Ragazou Vasiliki , Karasavvidis Ilias
Published: June 2023

Video tutorials are a popular means of learning software applications but their design and effectiveness have received little attention. This study investigated the effectiveness of video tutorials for software training. In addition, it examined whether two multimedia design principles, signaling and practice types, contribute to task performance, mental effort, and self-efficacy. The study participants were 114 undergraduate students from a nursing department. A two (no signals vs. signals) × two (video practice vs. video practice video) mixed factorial design was used for testing the main study hypotheses. The analysis revealed a unique contribution of signaling and practice types on task performance and self-efficacy. Contrary to expectations, however, no combined effect of signaling and practice types was found. This paper is concluded with a discussion of the findings and implications for future research.
Electrochemical Sensing of Amoxicillin Drug-Assisted Uropathogenic E. coli Bacteria Using Gold Nanostructures—A Preliminary Study
Authors: Sushmitha Jayaprakash , Nellaiappan Subramanian
Published: June 2023

The present study focuses on the electrochemical sensing of amoxicillin (AMX, as a model antibiotic drug) and its interaction with Uropathogenic E. coli (UPEC) bacteria (as a model pathogen) under physiological conditions. The electrochemical sensor probe is formulated by nanostructured gold wires (AuNWs) embedded in a carbon nanofiber–chitosan (CNF-CHIT) matrix. The synthesis of AuNWs is characterized by scanning electron microscopy (SEM), UV-Visible spectrophotometry, and X-ray photoelectron spectroscopy (XPS). The CNF-CHIT/AuNW-modified system is characterized by SEM and XPS. Initially, the CNF-CHIT/AuNW electrode was utilized for the sensing of AMX; later, in the antibiotic drug-assisted sensing of UPEC, i.e., in the presence of AMX, the interaction of UPEC was studied. The modified electrode showed appreciable sensitivity for AMX sensing; also, the interaction of AMX with UPEC is studied at two different conditions. One, at a fixed concentration of AMX (100 µM) and different concentrations of UPEC bacteria (0.6–1.2 × 106 CFU/mL), and another with incubation time (1 h–1 h 35 min) for bacterial reaction. The electrochemical antimicrobial resistance developed by UPEC, which is inherent in the sensing of AMX, is the key concept for the detection of pathogens.
Similarity of Heterogeneous Kinetics to Delay of Double-Layer Capacitance Using Chronoamperometry
Authors: Liu Yuanyuan , Aoki Koichi Jeremiah , Chen Jingyuan
Published: June 2023

Chronoamperometric curves for the oxidation of a ferrocenyl derivative via a potential step, calculated using the Cottrell equation, showed less diffusion-controlled currents on a platinum wire electrode. This lower deviation cannot be explained via Butler–Volmer heterogeneous kinetics, but was ascribed to the negatively capacitive current associated with a redox reaction. The deviation in fully oxidized electrical potential corresponds to the non-zero concentration at the electrode surface, which cannot be predicted using the Nernst equation. This equation expresses the relationship between the electrical potential and activity at the electrode surface rather than the concentration. The diffusion equation determines the relationship between the current and surface concentration rather than activity. Negative capacitance or a non-zero concentration may arise from structure formation on the electrode owing to dipole–dipole interactions, which are similar to the generation of double-layer capacitance, including frequency dispersion. Following this concept, we derive expressions for a lowered diffusion-controlled current and time-dependent surface concentration. The negatively capacitive current shows the time dependence of t−0.9, which is similar to the decay of double-layer capacitive currents. The surface concentration decays with t−0.4-dependence.
SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets
Authors: He Erheng , Chen Qianru , Zhong Qinghua
Published: June 2023

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers. These expressions provide visual cues to an individual’s emotions, which can be used in a broad range of potential applications such as lie detection and policing. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Unlike other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone Swin Transformer network, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach performs well on the MEGC 2021 spotting task, with an overall F1-score of 0.1824 and 0.1357 on the CAS(ME)2 and SAMM Long Videos, respectively. The code is publicly available on GitHub.
Accurate Multi-Channel QCM Sensor Measurement Enabled by FPGA-Based Embedded System Using GPS
Authors: Bourennane Adrien , Tanougast Camel , Diou Camille , Gorse Jean
Published: June 2023

This paper presents a design and implementation proposal for a real-time frequency measurement system for high-precision, multi-channel quartz crystal microbalance (QCM) sensors using a field programmable gate array (FPGA). The key contribution of this work lies in the integration of a frequency measurement and mass resolution computation based on Global Positioning System (GPS) signals within a single FPGA chip, utilizing Input/Output Blocks to incorporate logic QCM oscillator circuits. The FPGA design enables parallel processing, ensuring accurate measurements, faster calculations, and reduced hardware complexity by minimizing the need for external components. As a result, a cost-effective and accurate multi-channel sensor system is developed, serving as a reconfigurable standalone measurement platform with communication capabilities. The system is implemented and tested using the FPGA Xilinx Virtex-6, along with multiple QCM sensors. The implementation on a Xilinx XC6VLX240T FPGA achieves a maximum frequency of 324 MHz and consumes a dynamic power of 120 mW. Notably, the design utilizes a modest number of resources, requiring only 188 slices, 733 flip-flops, and 13 IOBs to perform a double-channel sensor microbalance. The proposed system meets the precision measurement requirements for QCM sensor applications, exhibiting low measurement error when monitoring QCM frequencies ranging from 1 to 50 MHz, with an accuracy of 0.2 ppm and less than 0.1 Hz.
Virtual Armature Resistance-Based Control for Fault Current Limiting in a High-Order VSG and the Impact on Its Transient Stability
Authors: Carletti Daniel , Amorim Thiago , Encarnação Lucas
Published: June 2023

This article proposes a fault ride through (FRT) technique for a high-order virtual synchronous generator (VSG) that adjusts its virtual armature resistance. When a fault is detected by a dedicated algorithm, the proposed control adjusts the resistance parameter accordingly. The main contribution of this article is to adjust the virtual resistance directly in the machine model to limit the current during faults, unlike other techniques proposed in the literature that add another control loop to produce the virtual impedance effects. To validate the effectiveness of the proposed control, a hardware-in-the-loop real-time simulation platform was adopted using a Typhoon HIL 402 device and a Texas Instruments F28379D digital controller. The results demonstrate that the control effectively limits the converter’s current while still contributing to raising the system’s critical clearing time (CCT) and improving transient stability. The proposed FRT strategy is validated in a three-phase fault scenario in which a 500 kVA–480 V converter’s peak fault current is reduced from 5 kA to 1.4 kA, depending on the resistance value adjusted. The transient stability is also analyzed in 30 different scenarios and the VSG support on the CCT is reduced by 23 ms on average. However, when compared to the baseline scenario without the VSG, the system still sees an increase in CCT with the current limiting control applied. Additionally, the control allows the VSG to smoothly transition to island mode in a scenario where the fault is cleared and the grid is disconnected by a protection system.
Impact of Atrium Glazing with and without BIPV on Energy Performance of the Low-Rise Building: A Central European Case Study
Authors: Marchwiński Janusz , Starzyk Agnieszka , Kopyłow Ołeksij , Kurtz-Orecka Karolina
Published: June 2023

This article aims to investigate the impact exerted by different types of covering an atrium with glazing on the energy performance of a kindergarten building, provided by the authors as a conceptual design. The considered types of atria included an open atrium, a glazed atrium, and an atrium that operated as a hybrid system. Additionally, the following aspects were taken into consideration: the effect of a glazing-integrated PV system (BIPV); the variety of thermal features represented by the inner boundary between the conditioned and the unconditioned space (Uiu); and the atrium space air-exchange ratio (nue) on the energy balance of the building. Energy performance indicators, including energy demands for space heating and cooling (Eu), delivered energy (Ed), and primary energy (Ep) indicators for heating and cooling mode were estimated for the moderate climates and two locations of the building model, i.e., for Warsaw (Central Poland) and Ahlbeck (Northern Germany). The research results prove that the glazed atrium exerts the most beneficial impact on the energy performance of the building. Nevertheless, certain variables must be considered, especially the air-exchange ratio of the atrium space, as they significantly influence the total annual energy performance. The results obtained with regard to the effect exerted by the presence of BIPV systems differ from those usually expected. This is due to the effect of the total solar-energy-transmittance value (g) modulation caused by the system and, finally, by a significant reduction in passive solar-gain harvesting, which is important for heating-mode results in examined climate conditions. Taking the present analysis into account, it can be concluded that the energy and environmental effects of the glazed integrated PV systems in temperate climates are strongly influenced by the environmental conditions, and, in some cases, these solutions may prove to be not efficient enough in terms of the energy and costs.
Landfill Biogas Recovery and Its Contribution to Greenhouse Gas Mitigation
Authors: Pheakdey Dek Vimean , Noudeng Vongdala , Xuan Tran Dang
Published: June 2023

This study assesses the biomethane (CH4) generation and greenhouse gas (GHG) emissions resulting from municipal solid waste landfilling in Phnom Penh, Cambodia, with a focus on the impact of fugitive CH4 emissions and operation processes in four landfilling scenarios: simple dumping (S1), improved management with leachate treatment (S2), engineered landfill with flaring (S3), and engineered landfill with energy recovery (S4). The study also considered the environmental benefits of carbon sequestration and landfill gas utilization. The LandGEM and IPCC FOD models were used to calculate CH4 generation over the period of 2009–2022, and it was found that approximately 18 and 21 M kg/year of CH4 were released, respectively. The energy potential from CH4 recovery was 51–61 GWh/year. Overall, GHG emissions in S2 were the highest, amounting to 409–509 M kg CO2-eq/year, while S1 had lower emissions at 397–496 M kg CO2-eq/year. Flaring-captured CH4 in S3 could reduce GHG emissions by at least 55%, and using captured CH4 for electricity production in S4 could mitigate at least 83% of GHG emissions. Electricity recovery (S4) could avoid significant amounts of GHG emissions (−52 to −63 kg CO2-eq/tMSW). The study suggests that landfill gas-to-energy could significantly reduce GHG emissions.
Modeling of an Integrated Renewable-Energy-Based System for Heating, Cooling, and Electricity for Buildings
Authors: Pilou Marika , Kosmadakis George , Meramveliotakis George
Published: June 2023

An integrated numerical model that describes the operation of a renewable-energy-based system for a building’s heating, cooling, and domestic hot water needs is described in this study. The examined energy system includes a vapor compression multi-source heat pump, PVT collectors, borehole thermal energy storage, and water tanks. Energy balance equations for the collectors and the tanks are coupled with correlations for the heat pump and the piping losses within a thermal network approach. The non-linear system of equations that arises is solved by employing in-house software developed in Python v. 3.7.3. The performance of the numerical tool is validated against measurements collected during the pilot operation of such a system installed in Athens (Greece) for two 5-day periods (summer and winter). It is shown that the proposed model can predict, both qualitatively and quantitatively, the building’s energy system performance, whereas limited deviations from the experimental findings are mostly observed when highly transient phenomena occur. The numerical tool is designed with flexibility in mind and can be easily adapted to accommodate additional energy-system configurations and operational modes. Thus, it can be utilized as a supporting decision tool for new energy systems’ designs and the optimization of existing ones.
Effects of Mean Stress and Multiaxial Loading on the Fatigue Life of Springs
Authors: Kobelev Vladimir
Published: June 2023

In this paper, the effects of mean stress and damage accumulation on the fatigue life of springs are theoretically studied. The study examines the fatigue life of homogeneously stressed material subjected to cyclic loading. The mean stress of a load cycle is non-zero. Goodman and Haigh diagrams are commonly used for estimating fatigue life in engineering applications. Alternatively, conventional hypotheses by Smith–Watson–Topper, Walker and Bergmann have been successfully used to describe uniaxial cyclic fatigue with non-zero mean value over the whole range of the fatigue life. However, the physical characteristics of the mean stress sensitivities in these hypotheses are different. The mean stress sensitivity according to Smith–Watson–Topper is identical for all materials and stress levels. This weakness reduces the applicability of the Smith–Watson–Topper parameter. At first glance, the mean stress sensitivities according to Walker and Bergmann are diverse. The mean stress sensitivities depend upon two different additional correction parameters, namely the Bergmann parameter and the Walker exponent. The possibility of fitting the mean stress sensitivity in these hypotheses overcomes the significant drawback of the Smith–Watson–Topper schema. The principal task of this actual study is to reveal the dependence between the Bergmann parameter and the Walker exponent, which leads to a certain mean stress sensitivity. The manuscript establishes the simple relationship between both fitting parameters, which causes the equivalent mean stress sensitivity for the Bergmann and Walker criteria. As known from the state of the technology, fabrication and operation yield several impacts with a significant influence on the fatigue life of springs. One effect deals with the sequence of low and high stress amplitudes and amplitude-dependent damage accumulation. Particularly, during the load cycle a certain microscopical creep occurs. This creep causes damage. The accumulation hypothesis for creep damage is introduced. The hypothesis can be verified experimentally.
Effects of Hemlock Woolly Adelgid Control Using Imidacloprid on Leaf-Level Physiology of Eastern Hemlock
Authors: McDonald Kelly M. , Seiler John R. , Wang Bingxue , Salom Scott M. , Rhea Rusty J.
Published: June 2023

Widespread mortality of eastern hemlock (Tsuga canadensis [L.] Carr.) has been occurring due to the introduction of hemlock woolly adelgid (Adelges tsugae Annand) (HWA), threatening millions of hectares of hemlock-dominated forests in the eastern United States. HWA feeds at the base of needles and removes stored carbohydrates, which can impact leaf-level physiology, contributing to the decline of the tree. However, these physiological mechanisms in HWA-infested hemlocks are still not clearly understood. We investigated hemlock leaf physiology year-round at three forested sites with various degrees of infestation. At each site, half the trees were treated with imidacloprid (Merit® 2 F, Bayer, Kansas City, MO, USA) while the rest were left untreated. Imidacloprid is widely used to control HWA but can itself have phytotoxic effects. After one growing season, there was an increase in photosynthetic rates (7.5%, p = 0.0163) and stomatal conductance (7.1%, p = 0.0163) across sites in the trees treated with imidacloprid. After two years, the imidacloprid treatment also increased bud break from 22.5% to 88.7% at Fishburn (the most severely impacted site) and from 22.7% to 58.9% at Mountain Lake (the least impacted site), and slightly increased chlorophyll fluorescence for treated trees at Fishburn. Chemical treatment also slightly increased water use efficiency at Mountain Lake. These results suggest that HWA is causing tree mortality largely through a reduction in leaf area caused by decreasing bud break and also by a slight, but significant, reduction in leaf-level photosynthesis and stomatal conductance.
Research and Application of Improved Multiple Imputation Based on R Language in Fire Prediction
Authors: Wang Jie , Yang Meilin , Li Tianming , Jiang Xuepeng , Lu Kaihua
Published: June 2023

An improved multiple imputation based on R language is proposed to deal with the miss of data in a fire prediction model, which can affect the accuracy of the prediction results. Hazard and operability (HAZOP) is used to accurately find the data related to the research purpose, and exclude data with a missing rate greater than 80% and small differences in characteristics. Then, by changing the m value in the mice package under the R language (R-mice), the relevant parameters of the complete filling factor set under different m values are obtained. The value of m is determined after observing and comparing the parameters. The proposed method fully considers the randomness of filling and the difference between the generated dataset. Taking Hubei Province as an example, the data processed by this method are used as the input of the Bayesian network, and the fire trend is used as the output. The results show that the improved multiple imputation based on R-mice can solve the problem of missing data very well, and have a high prediction effect (AUC = 94.0800). In addition, the results of the predictive reasoning and sensitivity analysis show that the government’s supervision has a vital influence on the trend of fires in Hubei Province.
Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History
Authors: Jones Kate , Vukomanovic Jelena
Published: June 2023

Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days.
Lionfish Diet Composition at Three Study Sites in the Aegean Sea: An Invasive Generalist?
Authors: Batjakas Ioannis E. , Evangelopoulos Athanasios , Giannou Maria , Pappou Sofia , Papanikola Eleftheria , Atsikvasi Maria , Poursanidis Dimitris , Gubili Chrysoula
Published: June 2023

The diet of the lionfish (Pterois miles), an invasive species in the Aegean Sea, was examined by collecting stomach content data from fish collected in three study sites in the Aegean Sea (southern Crete, Kastellorizo, and Nysiros islands). Prey composition in terms of numerical abundance and frequency of occurrence was used to compare lionfish’s diet between these sites. Lionfish largely preyed upon teleosts (4% to 83% numerical abundance and 16% to 58% frequency of occurrence, depending on the site) and decapods (12% to 95% numerical abundance and 11% to 81% frequency of occurrence). The most important teleost families in lionfish’s diet were Gobiidae, Labridae, and Scorpaenidae, while decapods and especially the family Scyllaridae and the genus Plesionika were the dominant decapod prey items. The lionfish was found to be an especially successful generalist across the study sites, an opportunistic, predatory species overall, and at the same time, at a local level, it seems to be an equally successful specialist that could increase the predation mortality of already stressed prey populations and can be a serious threat to endemic, critically endangered, and/or commercially important species.
ChIP-Seq Analysis of SlAREB1 Downstream Regulatory Network during Tomato Ripening
Authors: He Yanan , Wu Qiong , Cui Chunxiao , Tian Qisheng , Zhang Dongdong , Zhang Yurong
Published: June 2023

SlAREB1, a member of the abscisic acid (ABA) response element-binding factors (AREB/ABFs) family, was reported to play a crucial role in the expression of ABA-regulated downstream genes and affect the ripening of tomato fruit. However, the downstream genes of SlAREB1 are still unclear. Chromatin immunoprecipitation (ChIP) is a powerful tool and a standard method for studying the interactions between DNA and proteins at the genome-wide level. In the present study, SlAREB1 was proved to continually increase until the mature green stage and then decrease during the ripening period, and a total of 972 gene peaks were identified downstream of SlAREB1 by ChIP-seq analysis, mainly located in the intergenic and promoter regions. Further gene ontology (GO) annotation analysis revealed that the target sequence of SlAREB1 was the most involved in biological function. Kyoto Encylopaedia of Genes and Genomes (KEGG) pathway analysis showed that the identified genes were mainly involved in the oxidative phosphorylation and photosynthesis pathways, and some of them were associated with tomato phytohormone synthesis, the cell wall, pigment, and the antioxidant characteristic of the fruit as well. Based on these results, an initial model of SlAREB1 regulation on tomato fruit ripening was constructed, which provided a theoretical basis for further exploring the effects of the regulation mechanism of SlAREB1 and ABA on tomato fruit ripening.
Consumer Preference of Traditional Korean Soy Sauce (Ganjang) and Its Relationship with Sensory Attributes and Physicochemical Properties
Authors: Byeon Yang Soo , Heo JeongAe , Park Kwon , Chin Young-Wook , Hong Sang-pil , Lim Sang-Dong , Kim Sang Sook
Published: June 2023

This study aimed to investigate the physicochemical characteristics, sensory attributes, and consumer acceptance of the Certification of Quality of Traditional Food (CQT) ganjang samples produced in different provinces of Korea. Wide variations in physicochemical properties were found among the samples, especially in lipids, total nitrogen, acidity, and reducing sugar. Traditional fermented foods are known to be closely tied to regional features, but the composition and characteristics of CQT ganjangs might be influenced much more by individual ganjang producers than by region. Preference mapping was performed to understand consumer behavior towards ganjang, and most consumers tended to have similar preferences, implying shared a common sensory ideal. The results of the partial least squares regression revealed drivers of liking for ganjang among sensory attributes, free amino acids, and organic acids. Overall, sensory attributes such as sweetness and umami were positively associated with acceptability, while the terms related to fermentation were negatively associated. In addition, amino acids, such as threonine, serine, proline, glutamate, aspartate, and lysine, and organic acids, such as lactate and malate, were positively associated with consumer acceptance. The important implications of the findings of this study for the food industry can be utilized to develop and optimize traditional foods.
Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy
Authors: Ping Fengjiao , Yang Jihong , Zhou Xuejian , Su Yuan , Ju Yanlun , Fang Yulin , Bai Xuebing , Liu Wenzheng
Published: June 2023

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes’ quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (a*) and Chroma (C*) and soluble solids (SSC) content and decreasing values in color of lightness (L*), yellowness/blueness (b*) and Hue angle (h*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.
Block-Centered Finite-Difference Methods for Time-Fractional Fourth-Order Parabolic Equations
Authors: Zhang Taixiu , Yin Zhe , Zhu Ailing
Published: June 2023

The block-centered finite-difference method has many advantages, and the time-fractional fourth-order equation is widely used in physics and engineering science. In this paper, we consider variable-coefficient fourth-order parabolic equations of fractional-order time derivatives with Neumann boundary conditions. The fractional-order time derivatives are approximated by L1 interpolation. We propose the block-centered finite-difference scheme for fourth-order parabolic equations with fractional-order time derivatives. We prove the stability of the block-centered finite-difference scheme and the second-order convergence of the discrete L2 norms of the approximate solution and its derivatives of every order. Numerical examples are provided to verify the effectiveness of the block-centered finite-difference scheme.
Gender and Risk Aversion: Evidence from a Natural Experiment
Authors: Pacheco Luís , Lobão Júlio , Coelho Sílvia
Published: June 2023

The theoretical literature on risk aversion and Expected Utility Theory is extensive; however, the analysis of this behaviour with natural experiments could be more comprehensive. In this paper, we use data from 120 episodes of the Portuguese version of the TV game show The Price is Right, namely from The Wheel game, to explore risk aversion as well as the impact of gender in decision-making. The Wheel game has straightforward rules and huge expected payoffs. All contestants have access to the same information and distributions of uncertainty, making it a unique field laboratory to conduct an experimental test of rational decision theory. The objective is to infer the risk aversion levels of decision-makers from their choice to turn the wheel and the influence of gender on risk attitudes. There is a widespread view that women are more risk-averse than men. However, we could not reject the hypothesis that women and men have the same level of risk aversion. Nevertheless, we have evidence that contestants are more risk-averse than risk-seeking. The omission bias, loss aversion and regret can explain that behaviour.
Nanocomposite Hydrogels with Polymer Grafted Silica Nanoparticles, Using Glucose Oxidase
Authors: Mohammed Ali A. , Li Siwei , Sang Tian , Jones Julian R. , Pinna Alessandra
Published: June 2023

Nanocomposite hydrogels offer remarkable potential for applications in bone tissue engineering. They are synthesized through the chemical or physical crosslinking of polymers and nanomaterials, allowing for the enhancement of their behaviour by modifying the properties and compositions of the nanomaterials involved. However, their mechanical properties require further enhancement to meet the demands of bone tissue engineering. Here, we present an approach to improve the mechanical properties of nanocomposite hydrogels by incorporating polymer grafted silica nanoparticles into a double network inspired hydrogel (gSNP Gels). The gSNP Gels were synthesised via a graft polymerization process using a redox initiator. gSNP Gels were formed by grafting 2-acrylamido-2-methylpropanesulfonic acid (AMPS) as the first network gel followed by a sequential second network acrylamide (AAm) onto amine functionalized silica nanoparticles (ASNPs). We utilized glucose oxidase (GOx) to create an oxygen-free atmosphere during polymerization, resulting in higher polymer conversion compared to argon degassing. The gSNP Gels showed excellent compressive strengths of 13.9 ± 5.5 MPa, a strain of 69.6 ± 6.4%, and a water content of 63.4% ± 1.8. The synthesis technique demonstrates a promising approach to enhance the mechanical properties of hydrogels, which can have significant implications for bone tissue engineering and other soft tissue applications.
Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs
Authors: Zhou Peng , Yin Chang , Wang Yuwei , Yin Zongjun , Liu Yang
Published: June 2023

The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations—500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1—which has been linked to weight loss in mice and is potentially influential for AGE100—and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes—namely, PDZRN4, LIPM, and ANKRD22—which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.