bluuebunny/crossref_metadata_2025 huggingface dataset:
On Valuation and Investments of Pension Plans in Discrete Incomplete Markets
Authors: Anthropelos Michail , Blontzou Evmorfia
Published: June 2023

We study the valuation of a pension fund’s obligations in a discrete time and space incomplete market model. The market’s incompleteness stems from the non-replicability of the wage process that finances the pension plan through time. The contingent defined-benefit liability of the pension fund is a function of the wages, which can be seen as the payoff of a path-dependent derivative security. We apply the notion of the super-hedging value and propose its difference from the current pension’s fund capital as a measure of distance to liability hedging. The induced closed-form expressions of the values and the related investment strategies provide insightful comparative statistics. Furthermore, we use a utility-based optimization portfolio to point out that in cases of sufficient capital, the application of a subjective investment criterion may result in heavily different strategies than the super-hedging one. This means that the pension fund will be left with some liability risk, although it could have been fully hedged. Finally, we provide conditions under which the effect of a possible early exit leaves the super-hedging valuation unchanged.
Regulation and De-Risking: Theoretical and Empirical Insights
Authors: Haar Lawrence , Gregoriou Andros
Published: June 2023

The purpose of the Bank for International Settlements regulatory agenda, as implemented by financial regulators globally, has been to make banks safer and reduce the likelihood of systemic events. Using an original model of bank profit maximisation under a regulatory constraint, we statistically examine how market risk exposure has interacted with financial performance and capital structure, to see if the Basel regulatory agenda concerning the quantity, quality and liquidity of capital, has prompted changes in banking behaviour as measured by exposure to market risk. Breaking new ground, we empirically explore how the regulatory agenda has affected the largest banks. We analyse if the regulatory agenda has succeeded in aligning the cost of capital with their exposure to market risk, measured by Value at Risk; or if regulations have induced changes to banking activities. We find rather than regulation inducing changes to the rate at which unchanged risk exposure is capitalised; it leads to changes in the nature of exposures. Risk has declined along with financial performance while the cost of capital is largely unchanged. A consequence of regulation may be to encourage the migration of riskier activities to organisations where it may be borne more cheaply.
Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics
Authors: Vásquez Roger A. Rojas , Heenkenda Muditha K. , Nelson Reg , Segura Serrano Laura
Published: June 2023

Remote sensing Vegetation Indices (VIs) are simple, effective, and widely used methods for quantitative and qualitative analysis of vegetation cover, vigor, and growth dynamics. This study developed and assessed a new vegetation index (VI) using Cyan, Orange, and Near Infrared (NIR) bands to assess Soybean growth dynamics. The study was conducted at Lakehead University Agriculture Research Station, Thunder Bay, Canada, over four reproductive stages of Soybean growth (R4–R7). Spectral profiles were created for each stage, and the correlation between each spectral band at different stages was tested. There was no linear correlation between different bands except the correlation between the Cyan and Orange bands at R5 and R6 stages. Existing VIs have also been explored using approximately similar band combinations. Based on this analysis, three VIs were proposed for this new camera, and their behavior at different stages was evaluated using Leaf Area Index (LAI). Cyan and Orange spectral values were relatively high in the first and last growing seasons, while NIR values increased dramatically in the mid-growing seasons and decreased in the last stage. VINIR,O,C index showed the best results for mid-growing seasons (correlation with LAI = 0.39 for R5 and R6). VIC,O index showed a high level of details visually (leaves and background) for R4 and R7 than the other indices and correlated highly with LAI (0.48 and −0.5, respectively). Overall, the study provided new VIs that can be used to effectively analyze Soybean growth dynamics, with different VIs showing reliability over different growing stages.
An Optimization Method for Collaborative Radar Antijamming Based on Multi-Agent Reinforcement Learning
Authors: Feng Cheng , Fu Xiongjun , Wang Ziyi , Dong Jian , Zhao Zhichun , Pan Teng
Published: June 2023

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar’s and jammer’s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar’s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively.
Gas Plume Target Detection in Multibeam Water Column Image Using Deep Residual Aggregation Structure and Attention Mechanism
Authors: Chen Wenguang , Wang Xiao , Yan Binglong , Chen Junjie , Jiang Tingchen , Sun Jialong
Published: June 2023

A multibeam water column image (WCI) can provide detailed seabed information and is an important means of underwater target detection. However, gas plume targets in an image have no obvious contour information and are susceptible to the influence of underwater environments, equipment noises, and other factors, resulting in varied shapes and sizes. Compared with traditional detection methods, this paper proposes an improved YOLOv7 (You Only Look Once vision 7) network structure for detecting gas plume targets in a WCI. Firstly, Fused-MBConv is used to replace all convolutional blocks in the ELAN (Efficient Layer Aggregation Networks) module to form the ELAN-F (ELAN based on the Fused-MBConv block) module, which accelerates model convergence. Additionally, based on the ELAN-F module, MBConv is used to replace the 3 × 3 convolutional blocks to form the ELAN-M (ELAN based on the MBConv block) module, which reduces the number of model parameters. Both ELAN-F and ELAN-M modules are deep residual aggregation structures used to fuse multilevel features and enhance information expression. Furthermore, the ELAN-F1M3 (ELAN based on one Fused-MBConv block and three MBConv blocks) backbone network structure is designed to fully leverage the efficiency of the ELAN-F and ELAN-M modules. Finally, the SimAM attention block is added into the neck network to guide the network to pay more attention to the feature information related to the gas plume target at different scales and to improve model robustness. Experimental results show that this method can accurately detect gas plume targets in a complex WCI and has greatly improved performance compared to the baseline.
Improved General Polarimetric Model-Based Decomposition for Coherency Matrix
Authors: Li Yongzhen , Liu Yemin , Liu Xinghua , Xing Shiqi , Lv Hanfeng , Wu Guoqing
Published: June 2023

A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter β in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter β is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.
Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging
Authors: Daniels Louis , Eeckhout Eline , Wieme Jana , Dejaegher Yves , Audenaert Kris , Maes Wouter H.
Published: June 2023

The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.
Analytical Modeling of Electromagnetic Scattering and HRRP Characteristics from the 3D Sea Surface with a Plunging Breaker
Authors: Dong Chunlei , Meng Xiao , Zhang Jinpeng , Liu Yue , Wei Qihao , Guo Lixin
Published: June 2023

Electromagnetic (EM) scattering of sea surface may exhibit sea-spikespikes when there exist breaking waves. As sea-spikes are usually mistaken for targets, the investigation of EM scattering characteristics and high-range resolution profiles (HRRPs) of three-dimensional (3D) sea surfaces with a plunging breaker is meaningful for target detection and recognition. To describe the basic features of a plunging breaker, this paper developed a feasible and wind-related plunging breaker model. Here, profiles of a plunging breaker in its life cycle are modeled according to the wind speed and time factor, and the small-scale roughness is considered. Then, the sea surface and plunging breaker are combined to obtain the composite model. Additionally, a hybrid algorithm based on the Capillary Wave Modification Facet Scattering Model (CWMFSM) and ray tracing technique is developed to calculate the EM scattering of 3D sea surface with a plunging breaker. Simulation results show that the sea-spike phenomenon is more likely to occur for the upwind and large incident angles. The amplitude of the backscattering electric field from the plunging breaker is much stronger than that of the sea surface. Furthermore, the HRRPs of 3D sea surface with a plunging breaker and target are computed. Sharp peaks from the plunging breaker that exhibit obvious target-like features are observed.
Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China
Authors: Wang Jie , Xu Duanyang , Li Hongfei
Published: June 2023

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales.
Prediction of Land Surface Temperature Considering Future Land Use Change Effects under Climate Change Scenarios in Nanjing City, China
Authors: Tian Lei , Tao Yu , Li Mingyang , Qian Chunhua , Li Tao , Wu Yi , Ren Fang
Published: June 2023

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect.
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
Authors: Iqbal Faiza , Altaf Ayesha , Waris Zeest , Aray Daniel Gavilanes , Flores Miguel Angel López , Díez Isabel de la Torre , Ashraf Imran
Published: June 2023

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.
SFBC Recognition over Orthogonal Frequency Division Multiplexing Schemes in the Presence of Inphase and Quadrature Phase Discrepancies for Cognitive Radio Applications
Authors: Marey Mohamed , Sedik Ahmed , Mostafa Hala
Published: June 2023

A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is implemented by an expectation maximization tool utilizing the error control decoders’ soft outputs. The simulations results reveal that the suggested strategy delivers a much greater recognition accuracy than the typical approaches outlined in the comparable literature. At a signal-to-noise ratio (SNR) of 14 dB, for example, the proposed approach achieves a bit error rate (BER) of 0.00002, which is very close to the case of perfect estimation and compensation for IQDs, outperforming the previous reported works which achieved BERs of 0.01 and 0.02.
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
Authors: Xiao Liang , Xu Jiaolong , Zhao Dawei , Shang Erke , Zhu Qi , Dai Bin
Published: June 2023

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large-scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STNs). The combined adversarial and random-transformation-based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Furthermore, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.
A 5.8 GHz 1.8 V +20 dBm 32.5% PAE Power Amplifier for a Short-Range Over-the-Air WPT Application
Authors: Kim Myonggwan , Rad Reza E. , Kim Sungjin , Pu Younggun , Jung Yeonjae , Huh Hyungki , Yoo Joonmo , Kim Seokkee , Lee Kangyoon
Published: June 2023

This paper presents a 5.8 GHz differential cascode power amplifier for an over-the-air wireless power transfer application. Over-the-air wireless power transfer provides a variety of benefits in several applications such as the Internet of Things and medical implantation applications. The proposed PA features two fully differentially active stages with a custom-designed transformer to provide a single-ended output. The custom-made transformer shows a high quality factor, as high as 11.6 and 11.2 for the primary and secondary sides at 5.8 GHz. Fabricated using a standard 180 nm CMOS process, the amplifier achieves input and output matching of −14.7 dB and −29.7 dB, respectively. To achieve a high power level and efficiency, accurate optimization through power matching, Power Added Efficiency (PAE), and the design of the transformer are carried out while the supply voltage is limited to 1.8 V. Measurement results show a 20 dBm output power with a PAE as high as 32.5%, which makes the PA suitable for application, and it can be implanted while arrayed with various antenna arrays. Finally, a FOM is introduced to compare the performance of the work with similar works in the literature.
Inductive Coupling of Bipolar Signals with a Conjugate Coil Pair for an Analog Passive ECG Sensor Using a PPy-Coated pvCNT Dry Electrodes
Authors: Abu-Saude Mohammad , Morshed Bashir I.
Published: June 2023

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair for a wireless resistive analog passive (WRAP) ECG sensor. Furthermore, we integrate this sensor with a new type of dry electrode, namely conductive polymer polypyrrole (PPy)-coated patterned vertical carbon nanotube (pvCNT) electrodes. The proposed circuit uses dual-gate depletion-mode MOSFETs to convert the differential biopotential signals to correlated drain-source resistance changes and the conjugate coil wirelessly transmits the differences of the two input signals. The circuit rejects (17.24 dB) common mode signals and passing only differential signals. We have integrated this novel design with our previously reported PPy-coated pvCNT dry ECG electrodes, fabricated on a stainless steel substrate with a diameter of 10 mm, which provided a zero-power (battery-less) ECG capture system for long duration monitoring. The scanner transmits an RF carrier signal at 8.37 MHz. The proposed ECG WRAP sensor uses only two complementary biopotential amplifier circuits, each of which has a single-depletion MOSFET. The amplitude-modulated RF signal is envelope-detected, filtered, amplified, and transmitted to a computer for signal processing. ECG signals are collected using this WRAP sensor and compared with a commercial counterpart. Due to the battery-less nature of the ECG WRAP sensor, it has the potential to be a body-worn electronic circuit patch with dry pvCNT electrodes that stably operate for a long period of time.
Enhancing Safety in Hyperbaric Environments through Analysis of Autonomic Nervous System Responses: A Comparison of Dry and Humid Conditions
Authors: Sánchez Carlos , Hernando Alberto , Bolea Juan , Izquierdo David , Rodríguez Germán , Olea Agustín , Lozano María Teresa , Peláez-Coca María Dolores
Published: June 2023

Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⊥¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as “normal” or “abnormal” was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets’ ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses.
An Improved Ambiguity Resolution Algorithm for Smartphone RTK Positioning
Authors: Jiang Yang , Gao Yuting , Ding Wei , Liu Fei , Gao Yang
Published: June 2023

Ambiguity resolution based on smartphone GNSS measurements can enable various potential applications that currently remain difficult due to ambiguity biases, especially under kinematic conditions. This study proposes an improved ambiguity resolution algorithm, which uses the search-and-shrink procedure coupled with the methods of the multi-epoch double-differenced residual test and the ambiguity majority tests for candidate vectors and ambiguities. By performing a static experiment with Xiaomi Mi 8, the AR efficiency of the proposed method is evaluated. Furthermore, a kinematic test with Google Pixel 5 verifies the effectiveness of the proposed method with improved positioning performance. In conclusion, centimeter-level smartphone positioning accuracy is achieved in both experiments, which is greatly improved compared with the float and traditional AR solutions.
Noniterative Generalized Camera Model for Near-Central Camera System
Authors: Choi Taehyeon , Yoon Seongwook , Kim Jaehyun , Sull Sanghoon
Published: June 2023

This paper proposes a near-central camera model and its solution approach. ’Near-central’ refers to cases in which the rays do not converge to a point and do not have severely arbitrary directions (non-central cases). Conventional calibration methods are difficult to apply in such cases. Although the generalized camera model can be applied, dense observation points are required for accurate calibration. Moreover, this approach is computationally expensive in the iterative projection framework. We developed a noniterative ray correction method based on sparse observation points to address this problem. First, we established a smoothed three-dimensional (3D) residual framework using a backbone to avoid using the iterative framework. Second, we interpolated the residual by applying local inverse distance weighting on the nearest neighbor of a given point. Specifically, we prevented excessive computation and the deterioration in accuracy that may occur in inverse projection through the 3D smoothed residual vectors. Moreover, the 3D vectors can represent the ray directions more accurately than the 2D entities. Synthetic experiments show that the proposed method can achieve prompt and accurate calibration. The depth error is reduced by approximately 63% in the bumpy shield dataset, and the proposed approach is noted to be two digits faster than the iterative methods.
Solar Business in an Oil-Rich Country? A Socio-Technical Investigation of Solar PV Businesses in Iran
Authors: Aghlimoghadam Leila
Published: June 2023

Market acceptance of renewable energy technologies involves both the demand and supply sides, though the main empirical literature has focused on the demand side under the titles like public, communities, users’, market or even social acceptance. In this study, I focus on solar businesses (niche actors) as the suppliers of solar PV services in Iran. My main research questions are: (i) which factors drive solar businesses to establish and do business in the solar PV field despite the fossil-based economy and energy policies in Iran? (ii) what are the practical barriers to solar business in Iran? and (iii) which roles do Iranian solar businesses play in bringing solar PV development forward? I collected the data via 20 semi-structured interviews with solar businesspeople in diverse Provinces in Iran. Taking an inductive approach (Grounded Theory) toward the data, my results lead to significant insights: the dominance of intrinsic behavioural drivers over the major extrinsic barriers among Iranian solar businesspeople, moreover to their key roles in educating people and driving the innovative deployment of solar PV. This research helps to inform first, policymakers about the existing potential among solar businesses, and second the solar businesses themselves about adopting better business strategies.
Fractional Differential Boundary Value Equation Utilizing the Convex Interpolation for Symmetry of Variables
Authors: Hussain Aftab
Published: June 2023

In this paper, we introduce a novel form of interpolative convex contraction and develop some new theorems by utilizing the progressive method of interpolative convex contractions. We also obtain some fixed point results for a Suzuki convex contraction in orbitally S-complete F-metric spaces. The second purpose of this research is to evaluate the effectiveness of the fixed point approach in solving fractional differential equations with boundary conditions.
An Assessment of the Effects of Food Districts on Sustainable Management of Land: The Case of Lombardia, Italy
Authors: Carillo Felicetta , Henke Roberto , Sturla Alberto
Published: June 2023

The article aims to analyze whether a larger diffusion of institutional–private co-operation in farming systems, such as Italian food districts (FDs), is helpful in pursuing goals of sustainable land use in agriculture. The paper focuses on the case of Lombardia in Italy, a region where this form of public–private partnership is widespread throughout the regional territory. Combining differences-in-differences (DiD) and propensity score matching (PSM) methods to reduce the estimation bias, we assessed and quantified a “district effect” on the sustainable management of lands. Specifically, using several land-use and land-use change proxies as outcome measures, we verified whether there are significant differences in such outcomes between two different groups of municipalities: those involved in FDs and those not. Our analysis shows that there is an “FD effect” on the persistence of agricultural activity and, although this does not necessarily translate into more landscape diversity, it can at least counteract detrimental tendencies such as the loss of natural elements, the loss of landscape diversity due to intensive farming, and land abandonment.
Leading Role of Big Data Analytic Capability in Innovation Performance: Role of Organizational Readiness and Digital Orientation
Authors: Binsaeed Rima H. , Grigorescu Adriana , Yousaf Zahid , Condrea Elena , Nassani Abdelmohsen A.
Published: June 2023

The advancement of technology offers various opportunities for business organizations to achieve sustainable growth. Through emerging technologies, business organizations are able to collect and analyze essential information vital for the acceleration of innovation. Therefore, this study investigated how big data contribute to the innovation activities of manufacturing entrepreneurs in terms of big data analytic capability (BDAC). The aim of this study was to relate BDAC to organizational readiness and innovation performance (IP). Moreover, we examined the mediating role of organizational readiness between BDAC and IP. We also examined the strengthening role of digital orientation. To collect the study data, we approached 494 frontline managers of the manufacturing sector of Saudi Arabia. The collected data were analyzed using statistical techniques such as descriptive, correlation, and hierarchical regression techniques. We found that BDAC plays a vital role in developing organizational readiness and IP. The findings also proved that organizational readiness has a significant effect on IP. The results revealed that organizational readiness mediates between BDAC and IP.
Effect of Temperature and Ionic Substitutions on the Tegumental Potentials of Protoscoleces of Echinococcus granulosus
Authors: Carabajal Mónica Patricia Antonella , Fernández Salom María José , Olivera Santiago , Cantiello Horacio F.
Published: June 2023

The protoscolex (PSC) is generated by asexual reproduction at the larval stage of taeniid Echinococcus granulosus that causes cystic echinococcosis or hydatidosis, a worldwide zoonosis. The PSC is enveloped by a complex cellular syncytial tegument responsible for ionic movements and the hydroelectrolytic balance of the parasite. We recently reported on two electrical potentials in bovine lung protoscoleces (PSCs) that reflect differences in ionic movements between the parasite’s invaginated and evaginated developmental stages. Here, we explored the effect of temperature and ionic substitutions on the tegumental potentials of bovine lung PSCs of Echinococcus granulosus by microelectrode impalements. We observed that the transient peak potential was temperature-dependent, consistent with an active transport component in the invaginated state only. Further changes in the electrical potentials by high K+ depolarization, low external Ca2+, and addition of the diuretic amiloride are in agreement with the presence of a Ca2+-sensitive cation-selective electrodiffusional pathway in the outer surface of the parasite. Variations in electrical potential differences through the tegument provide an accessible and valuable parameter for studying ionic transport mechanisms and, therefore, potential targets for developing novel antiparasitic drugs.
Molnupiravir Inhibits Porcine Epidemic Diarrhea Virus Infection In Vitro
Authors: Huang Zi-Xin , Zhou Shu-Ting , Yang Zhi-Biao , Wang Zhe
Published: June 2023

Porcine epidemic diarrhea virus (PEDV) is a swine coronavirus that is highly infectious and prone to variation. Vaccines derived from traditional PEDV strains provide less protection against PEDV-variant strains. Furthermore; there is a complex diversity of sequences among various PEDV-variant strains. Therefore; there is an urgent need to develop alternative antiviral strategies to defend against PEDV. Molnupiravir is a nucleotide analogue that could replace natural nucleosides to restrain viral RNA replication. Our study provided evidence for the dose-dependent inhibition of PEDV replication by molnupiravir in Vero cells. Molnupiravir also exhibited a strong inhibitory effect on viral RNA and protein production. Our results demonstrated that molnupiravir inhibits PEDV RNA-dependent RNA polymerase (RdRp) activity and induces a high frequency of mutations in the PEDV genome. Further studies revealed that molnupiravir can reverse changes in the transcriptome caused by viral infection. In conclusion, our results indicated that molnupiravir has the potential to be an effective treatment for PEDV infection.
Immunogenicity and Blocking Efficacy of Norovirus GII.4 Recombinant P Protein Vaccine
Authors: Yu Zhendi , Shao Qingyi , Xu Zhangkai , Chen Chenghao , Li Mingfan , Jiang Yi , Cheng Dongqing
Published: June 2023

Noroviruses (NoVs) are the main cause of acute gastroenteritis in all ages worldwide. The aim of this study was to produce the recombinant P protein of norovirus and to demonstrate its blocking effect. In this study, the engineered strains were induced to express the P protein of NoVs GII.4, which was identified using SDS-PAGE and ELISA as having the capacity to bind to histo-blood group antigens (HBGAs). Rabbits were immunized to obtain neutralizing antibodies. ELISA and ISC-RT-qPCR were used to determine the blocking efficacy of the neutralizing antibody to human norovirus (HuNoV) and murine norovirus (MNV). The recombinant P protein (35 KD) was obtained, and the neutralizing antibody was successfully prepared. The neutralizing antibody could block the binding of the P protein and HuNoV to HBGAs. Neutralizing antibodies can also block MNV invasion into host cells RAW264.7. The recombinant P protein expressed in E. coli can induce antibodies to block HuNoV and MNV. The recombinant P protein of NoVs GII.4 has the value of vaccine development.
Respiratory Syncytial Virus (RSV) Disease and Prevention Products: Knowledge, Attitudes, and Preferences of Kenyan Healthcare Workers in Two Counties in 2021
Authors: Nyawanda Bryan O. , Opere Victor A. , Nyiro Joyce U. , Vodicka Elisabeth , Fleming Jessica A. , Baral Ranju , Khan Sadaf , Pecenka Clint , Ayugi Jorim O. , Atito Raphael , Ougo James , Bigogo Godfrey , Emukule Gideon O. , Otieno Nancy A. , Munywoki Patrick K.
Published: June 2023

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infection (LRTI) among infants under 6 months of age. Yet, in Kenya, little is known about healthcare workers’ (HCWs) knowledge, attitudes, and perceptions around RSV disease and the prevention products under development. Between September and October 2021, we conducted a mixed methods cross-sectional survey to assess HCWs’ knowledge, attitudes, and perceptions of RSV disease and RSV vaccinations in two counties. We enrolled HCWs delivering services directly at maternal and child health (MCH) departments in selected health facilities (frontline HCWs) and health management officers (HMOs). Of the 106 respondents, 94 (88.7%) were frontline HCWs, while 12 were HMOs. Two of the HMOs were members of the Kenya National Immunization Technical Advisory Group (KENITAG). Of the 104 non-KENITAG HCWs, only 41 (39.4%) had heard about RSV disease, and 38/41 (92.7%) felt that pregnant women should be vaccinated against RSV. Most participants would recommend a single-dose vaccine schedule (n = 62, 58.5%) for maximal adherence and compliance (n = 38/62, 61.3%), single dose/device vaccines (n = 50/86, 58.1%) to prevent wastage and contamination, and maternal vaccination through antenatal care clinics (n = 53, 50%). We found the need for increased knowledge about RSV disease and prevention among Kenyan HCWs.
Impact and Cost-Effectiveness of Alternative Human Papillomavirus Vaccines for Preadolescent Girls in Mozambique: A Modelling Study
Authors: Guimarães Esperança Lourenço , Chissaque Assucênio , Pecenka Clint , Debellut Frédéric , Schuind Anne , Vaz Basília , Banze Arlindo , Rangeiro Ricardina , Mariano Arlete , Lorenzoni Cesaltina , Carrilho Carla , Martins Maria do Rosário Oliveira , de Deus Nilsa , Clark Andrew
Published: June 2023

Mozambique has one of the highest rates of cervical cancer in the world. Human papillomavirus (HPV) vaccination was introduced in 2021. This study evaluated the health and economic impact of the current HPV vaccine (GARDASIL® hereafter referred to as GARDASIL-4) and two other vaccines (CECOLIN® and CERVARIX®) that could be used in the future. A static cohort model was used to estimate the costs and benefits of vaccinating girls in Mozambique over the period 2022–2031. The primary outcome measure was the incremental cost per disability-adjusted life-year averted from a government perspective. We conducted deterministic and probabilistic sensitivity analyses. Without cross-protection, all three vaccines averted approximately 54% cervical cancer cases and deaths. With cross-protection, CERVARIX averted 70% of cases and deaths. Without Gavi support, the discounted vaccine program costs ranged from 60 million to 81 million USD. Vaccine program costs were approximately 37 million USD for all vaccines with Gavi support. Without cross-protection, CECOLIN was dominant, being cost-effective with or without Gavi support. With cross-protection and Gavi support, CERVARIX was dominant and cost-saving. With cross-protection and no Gavi support, CECOLIN had the most favorable cost-effectiveness ratio. Conclusions: At a willingness-to-pay (WTP) threshold set at 35% of Gross Domestic Product (GDP) per capita, HPV vaccination is cost-effective in Mozambique. The optimal vaccine choice depends on cross-protection assumptions.
Application of the DMD Approach to High-Reynolds-Number Flow over an Idealized Ground Vehicle
Authors: Misar Adit , Tison Nathan A. , Korivi Vamshi M. , Uddin Mesbah
Published: June 2023

This paper attempts to develop a Dynamic Mode Decomposition (DMD)-based Reduced Order Model (ROMs) that can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle’s trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time dynamics. This ROM can then also be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7×106. The high-dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using the Menter’s Shear Stress Transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in the literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper, which includes: (a) a requirement of higher sampling rate to obtain a higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high-Reynolds-number, separation-dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and they were found to offer significant improvement.
Biofilm-Producing Ability of Staphylococcus aureus Obtained from Surfaces and Milk of Mastitic Cows
Authors: Vargová Mária , Zigo František , Výrostková Jana , Farkašová Zuzana , Rehan Ibrahim F.
Published: June 2023

This study was conducted to evaluate the incidence of mastitis in 153 dairy cows and to evaluate the kinetics of adhesion of isolates obtained from surfaces and milk in comparison with the reference strain (RS), CCM 4223. The surfaces of the floor, teat cup, and cow restraints were aseptically swabbed in three replicates (n = 27). Of the total number of infected cows (n = 43), 11 samples were found to be positive for Staphylococcus aureus, 12 samples tested positive for non-aureus staphylococci, 6 samples tested positive for Streptococcus spp., and 11 samples tested positive for other bacteria (Escherichia coli, Pseudomonas spp.) or a mixed infection. The most represented pathogen in milk (11/43) and on surfaces (14/27) was S. aureus. The kinetics of adhesion of the reference strain and isolates of S. aureus on stainless steel surfaces were determined after 3, 6, 9, 12, 24, and 48 h, and 3, 6, 9, 12, and 15 days of incubation. All strains reached counts higher than 5 Log10 CFU/cm2 needed for biofilm formation, except RS (4.40 Log10 CFU/cm2). The isolates of S. aureus revealed a higher capability to form biofilm in comparison with RS during the first 3 h (p < 0.001). Thus, there is a significant difference between the occurrence of S. aureus on monitored surfaces—floor, teat cup, and cow restraints—and the frequency with which mastitis is caused by S. aureus (p < 0.05). This finding raises the possibility that if various surfaces are contaminated by S. aureus, it can result in the formation of biofilm, which is a significant virulence factor.