bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship between Complex Integration Indices and Inflation Indicators and Their Impact on the Development of Regional Cooperation between Countries to Reduce the Level of Inflationary Risks: Case of the SCO Member Countries
Authors: Bezpalov Valery V. , Lochan Sergey A. , Fedyunin Dmitry V. , Polozhentseva Irina V. , Gorina Tatiana V.
Published: December 2022

In this study, we focused on the development of cooperation between partner countries, which may affect the reduction of inflationary risks for partnership participants in the context of global and urgent changes in the world. This article aims to identify the relationship between inflation indicators and various types of globalization (complex integration indices) of each of the member countries of the Shanghai Cooperation Organization (SCO) in order to develop measures to contain inflation risks in these countries. The authors used the methods of pairwise linear regression, correlation analysis, and multiple linear regression. As variables, the authors used complex indicators that characterize six types of globalization: Economic, financial, demographic, industrial, information, and political indices. The authors concluded that China and India more effectively curb inflation and are less prone to inflation risks. The inflation rate and the independent variables have a close negative correlation, which indicates a strong degree of mutual influence and has a downward effect on the consumer price index. The most significant variables that have a strong influence on the inflation rate are the factors of financial and information integration. The impact of other types of integration considered in this study is not significant. In order to reduce the level of inflationary risks, the SCO member countries most vulnerable to the price volatility of raw materials (Uzbekistan, Tajikistan, and Kyrgyzstan) are encouraged to develop trade cooperation more actively, for example, by reducing or eliminating import duties on raw materials from the SCO countries.
Static Modeling of a Class of Stiffness-Adjustable Snake-like Robots with Gravity Compensation
Authors: Hu Jian , Liu Tangyou , Zeng Haijun , Chua Ming Xuan , Katupitiya Jayantha , Wu Liao
Published: December 2022

Stiffness-adjustable snake-like robots have been proposed for various applications, including minimally invasive surgery. Based on a variable neutral-line mechanism, previous works proposed a class of snake-like robots that can adjust their stiffness by changing the driving cables’ tensions. A constant curvature hypothesis was used to formulate such robots’ kinematics and was further verified by our previous work via rigorous force analysis and ADAMS simulations. However, all these models and analyses have ignored the effect of the robot links’ gravity, resulting in significant errors in real systems. In this paper, a static model considering gravity compensation is proposed for the stiffness-adjustable snake-like robots. The proposed model adopts a nonlinear Gauss–Seidel iteration scheme and consists of two parts: gravity update and pose estimation. In each iteration, the former updates the payload of each link caused by gravity, and the latter estimates the pose of the robot by refreshing the angle and position values. This iteration stops when the change in the tip position is less than a pre-set error ϵ. During the above process, the only dependent information is each cable’s tension. Simulations and experiments are carried out to verify the effectiveness of the proposed model. The impact of gravity is found to increase with growing material densities in the simulations. The experimental results further indicate that compared with a model without gravity compensation, our model reduces the tip estimation error by 91.5% on average.
The Influence of Visible Cables and Story Content on Perceived Autonomy in Social Human–Robot Interaction
Authors: Roesler Eileen , Steinhaeusser Sophia C. , Lugrin Birgit , Onnasch Linda
Published: December 2022

From teaching technical skills to telling bedtime stories, social robots support various edutainment tasks that require smooth communication. Previous studies often emphasized the importance of the autonomy of social robots for those tasks. However, the cabling of robots with power sources and/ or host computers is often required due to technical restrictions. However, it is currently unclear if the cabling of robots makes a difference in perceived autonomy. Therefore, this study examined the influence of visible cables in different tasks on the perception of a social robot. In an online survey, participants evaluated videos of a social robot that was either equipped with a cable or not and told either a story with technical educational content or socially entertaining content. No significant differences were revealed between the cabled and the non-cabled robot, neither for the perceived autonomy nor for the associated concepts of the Godspeed questionnaire series. In addition, the story content did not influence perceived autonomy. However, the robot that told the technical content was perceived as significantly more intelligent and tended to be perceived as more likable than the robot that told the social content. Moreover, the interaction effect of cabling and story content for perceived safety just failed to reach the conventional level of significance. In the social content condition, the non-cabled robot tended to be perceived as less safe than the cabled robot. This was not true for the technical content condition. In conclusion, the results showed the importance of considering story content. Due to methodological limitations of the current study, namely, the lack of gestures accompanying the storytelling and the video-based approach, the missing effect of cabling in regard to perceived autonomy should be investigated in the future via real-life interaction studies.
Radar Composite Reflectivity Reconstruction Based on FY-4A Using Deep Learning
Authors: Yang Ling , Zhao Qian , Xue Yunheng , Sun Fenglin , Li Jun , Zhen Xiaoqiong , Lu Tujin
Published: December 2022

Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.
Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT
Authors: Legros Quentin , Fourer Dominique
Published: December 2022

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions.
A 2D-GRAPPA Algorithm with a Boomerang Kernel for 3D MRI Data Accelerated along Two Phase-Encoding Directions
Authors: Shin Seonyeong , Han Yeji , Chung Jun-Young
Published: December 2022

For the reconstruction of 3D MRI data that are accelerated along the two phase-encoding directions, the 2D-generalized autocalibrating partially parallel acquisitions (GRAPPA) algorithm can be used to estimate the missing data in the k-space. We propose a new boomerang-shaped kernel based on theoretic and systemic analyses of the shape and dimensions of the kernel. The reconstruction efficiency of the 2D-GRAPPA algorithm with the proposed boomerang-shaped kernel (i.e., boomerang kernel (BK)-2D-GRAPPA) was compared with other 2D-GRAPPA algorithms that utilize different types of kernels (i.e., EX-2D-GRAPPA and SK-2D-GRAPPA) based on computer simulation, phantom and in vivo experiments. The proposed method was validated for different sets of ACS lines with acceleration factors from four to eight and various sizes of the kernels. A quantitative analysis was also performed by comparing the normalized root mean squared error (nRMSE) in the images and the undersampled edges. Computer simulation, in vivo and phantom experiments, and the quantitative analysis, showed that the proposed method could reduce aliasing artifacts without reducing the SNRs of the reconstructed images.
Three-Dimensional Division of Visible Light Communication Irradiation Area
Authors: Zhou Yang , Deng Yuanzhi , Wen Huajie , Chen Liting , Xu Gang
Published: December 2022

In this article, we divide the irradiated area of visible light communication (VLC) into three parts, according to the influence of diffuse reflection, the irradiance half angle at the source and the communication distance on VLC. We present a volume ratio method to quantitatively analyze each divided part. In this work, based on the Lambertian reflection model of the VLC system in line-of-sight channels, five factors affecting the VLC performance are compared and discussed. A VLC system of a single white-light-emitting diode in a 10 m line-of-sight channel indoors is designed by using the intensity modulation and direct detection technique, and a three-dimensional model of the irradiated area is established.By comparing the distribution of the bit error rate (BER) of the optical signal at different lampshade heights, the volume ratio method is used to calculate the volume percentage of the three irradiated areas. The experimental results show that area II with a volume ratio greater than 50% is the best signal receiving area when compared with areas I and III, having a volume ratio in the range 20∼30%.
An Embedded Framework for Fully Autonomous Object Manipulation in Robotic-Empowered Assisted Living
Authors: Mezzina Giovanni , De Venuto Daniela
Published: December 2022

Most of the humanoid social robots currently diffused are designed only for verbal and animated interactions with users, and despite being equipped with two upper arms for interactive animation, they lack object manipulation capabilities. In this paper, we propose the MONOCULAR (eMbeddable autONomous ObjeCt manipULAtion Routines) framework, which implements a set of routines to add manipulation functionalities to social robots by exploiting the functional data fusion of two RGB cameras and a 3D depth sensor placed in the head frame. The framework is designed to: (i) localize specific objects to be manipulated via RGB cameras; (ii) define the characteristics of the shelf on which they are placed; and (iii) autonomously adapt approach and manipulation routines to avoid collisions and maximize grabbing accuracy. To localize the item on the shelf, MONOCULAR exploits an embeddable version of the You Only Look Once (YOLO) object detector. The RGB camera outcomes are also used to estimate the height of the shelf using an edge-detecting algorithm. Based on the item’s position and the estimated shelf height, MONOCULAR is designed to select between two possible routines that dynamically optimize the approach and object manipulation parameters according to the real-time analysis of RGB and 3D sensor frames. These two routines are optimized for a central or lateral approach to objects on a shelf. The MONOCULAR procedures are designed to be fully automatic, intrinsically protecting sensitive users’ data and stored home or hospital maps. MONOCULAR was optimized for Pepper by SoftBank Robotics. To characterize the proposed system, a case study in which Pepper is used as a drug delivery operator is proposed. The case study is divided into: (i) pharmaceutical package search; (ii) object approach and manipulation; and (iii) delivery operations. Experimental data showed that object manipulation routines for laterally placed objects achieves a best grabbing success rate of 96%, while the routine for centrally placed objects can reach 97% for a wide range of different shelf heights. Finally, a proof of concept is proposed here to demonstrate the applicability of the MONOCULAR framework in a real-life scenario.
Fall Detection Algorithm Based on Inertial Sensor and Hierarchical Decision
Authors: Zheng Liang , Zhao Jie , Dong Fangjie , Huang Zhiyong , Zhong Daidi
Published: December 2022

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.
Improved Speech Spatial Covariance Matrix Estimation for Online Multi-Microphone Speech Enhancement
Authors: Kim Minseung , Cheong Sein , Song Hyungchan , Shin Jong Won
Published: December 2022

Online multi-microphone speech enhancement aims to extract target speech from multiple noisy inputs by exploiting the spatial information as well as the spectro-temporal characteristics with low latency. Acoustic parameters such as the acoustic transfer function and speech and noise spatial covariance matrices (SCMs) should be estimated in a causal manner to enable the online estimation of the clean speech spectra. In this paper, we propose an improved estimator for the speech SCM, which can be parameterized with the speech power spectral density (PSD) and relative transfer function (RTF). Specifically, we adopt the temporal cepstrum smoothing (TCS) scheme to estimate the speech PSD, which is conventionally estimated with temporal smoothing. Furthermore, we propose a novel RTF estimator based on a time difference of arrival (TDoA) estimate obtained by the cross-correlation method. Furthermore, we propose refining the initial estimate of speech SCM by utilizing the estimates for the clean speech spectrum and clean speech power spectrum. The proposed approach showed superior performance in terms of the perceptual evaluation of speech quality (PESQ) scores, extended short-time objective intelligibility (eSTOI), and scale-invariant signal-to-distortion ratio (SISDR) in our experiments on the CHiME-4 database.
Design of a Ku-Band Monopulse Antenna with a Truncated Reflector and an Open-Ended Waveguide Feed
Authors: Monebi Ayodeji Matthew , Lee Chan-Soo , Ahn Bierng-Chearl , Choi Seong-Gon
Published: December 2022

This paper presents a design for a monopulse reflector antenna with asymmetric beamwidths for radar applications at the Ku band. The proposed design features a rectangular waveguide monopulse feed and a truncated parabolic reflector. An array of four open-ended rectangular waveguides were employed to realize a compact monopulse feed. The reflector is cut in the H plane of the feed producing a wider beam in the azimuth plane. This type of pattern is useful in applications such as projectile tracking and airport surveillance. The design parameters for optimum performances are chosen at all stages of the design. The design and analysis have been carried out using the commercial simulation tool CST Studio Suite 2022. The directivity of the sum, elevation difference and azimuth difference channels of the reflector antenna are 32.1, 28.1, and 26.4 dB at 14 GHz; 30.9, 29, and 27.3 dB at 15 GHz; 31.7, 29.6, and 27.6 dB at 16 GHz; 31.6, 29.9, and 27.8 dB at 17 GHz.
A Multi-Port Hardware Energy Meter System for Data Centers and Server Farms Monitoring
Authors: Conti Giuseppe , Jimenez David , del Rio Alberto , Castano-Solis Sandra , Serrano Javier , Fraile-Ardanuy Jesus
Published: December 2022

Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) intelligence in the energy workflow is gaining importance. All these approaches have a common need: data. Data that should be collected and provided in a reliable, accurate, secure, and efficient way. For this purpose, sensing technologies that enable ubiquitous data acquisition and the new communication infrastructure that ensure low latency and high density are the key. This article presents a sensing solution devoted to the precise gathering of energy parameters such as voltage, current, active power, and power factor for server farms and datacenters, computing infrastructures that are growing meaningfully to meet the demand for network applications. The designed system enables disaggregated acquisition of energy data from a large number of devices and characterization of their consumption behavior, both in real time. In this work, the creation of a complete multiport power meter system is detailed. The study reports all the steps needed to create the prototype, from the analysis of electronic components, the selection of sensors, the design of the Printed Circuit Board (PCB), the configuration and calibration of the hardware and embedded system, and the implementation of the software layer. The power meter application is geared toward data centers and server farms and has been tested by connecting it to a laboratory server rack, although its designs can be easily adapted to other scenarios where gathering the energy consumption information was needed. The novelty of the system is based on high scalability built upon two factors. Firstly, the one-on-one approach followed to acquire the data from each power source, even if they belong to the same physical equipment, so the system can correlate extremely well the execution of processes with the energy data. Thus, the potential of data to develop tailored solutions rises. Second, the use of temporal multiplexing to keep the real-time data delivery even for a very high number of sources. All these ensure compatibility with standard IoT networks and applications, as the data markup language is used (enabling database storage and computing system processing) and the interconnection is done by well-known protocols.
5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems
Authors: Firouzi Ramin , Rahmani Rahim
Published: December 2022

Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.
Comparative Study of Various Procedures for Extracting Doxorubicin from Animal Tissue Samples
Authors: Maliszewska Olga , Treder Natalia , Roszkowska Anna , Olędzka Ilona , Kowalski Piotr , Bączek Tomasz , Plenis Alina
Published: December 2022

This article presents a comparative study of selected deproteinization-, liquid–liquid-extraction- (LLE), and solid-phase-extraction (SPE)-based procedures for the isolation of doxorubicin (DOX) and daunorubicin (DAU) as an internal standard (IS) from rat tissue samples. During the experiments, all samples were analyzed via liquid chromatography coupled with fluorescence detection (LC-FL), with analytes being monitored at excitation and emission wavelengths of 487 and 555 nm, respectively. The absolute recoveries of the sample-preparation procedure were then calculated and compared, and the advantages and disadvantages of each approach were considered in depth. Ultimately, SPE with hydrophilic–lipophilic balanced (HLB) sorbents was selected as the most effective extraction procedure as it enabled the absolute recovery of DOX from tissue samples at a level of 91.6 ± 5.1%. Next, the selected HLB-SPE protocol was coupled with LC-FL separation and the resultant method was validated according to FDA and ICH requirements. The validation data confirmed that the developed procedure met all required criteria for bioanalytical methods, with a limit of detection (LOD) and limit of quantification (LOQ) of 0.005 µg/g and 0.01 µg/g, respectively. Finally, the developed protocol was successfully tested on various rat tissues enriched with DOX, confirming its potential as an interesting alternative to previously reported protocols for pharmacokinetic studies and clinical investigations aimed at analysis of the level and biodistribution of DOX in tissue samples after systemic administration of this drug.
The Most Active Child Is Not Always the Fittest: Physical Activity and Fitness Are Weakly Correlated
Authors: Lupo Corrado , De Pasquale Paolo , Boccia Gennaro , Ungureanu Alexandru Nicolae , Moisè Paolo , Mulasso Anna , Brustio Paolo Riccardo
Published: December 2022

The present cross-sectional study aimed to evaluate the impact of physical activity level (PA) on physical fitness by controlling for individual characteristics in Italian children. A total of 329 children (girls n = 155, 42.6%; from five primary schools, 17 classes) aged 8–10 filled out the Physical Activity Questionnaire for Older Children (PAQ-C) to assess their PA level and performed anthropometric measurements (body mass, height, and BMI) and physical tests for measuring sprint (20 m sprint), cardiorespiratory fitness (shuttle-run test), balance (single-leg stance), handgrip strength (handgrip), lower-limb power (standing long-jump), peak force (countermovement jump), and low-back flexibility (sit-and-reach) skills. Linear mixed-effects models were applied to determine the relationship between physical fitness and PAQ-C score controlling for individual characteristics (i.e., gender, age, BMI). Results reported significant relationships between PAQ-C scores and sit-and-reach, shuttle-run, long-jump, and sprint tests. All considered physical tests were correlated with gender, age, and BMI, except for sit-and-reach from BMI. The variance in age, gender, BMI, and PAQ-C score accounted altogether for 30.0% of the variance in handgrip, 23.0% in single-leg stance, 26% in sit-and-reach, 36% in shuttle-run, 31% in long-jump, 34% in sprint, and 31% in countermovement jump. Therefore, the relationship between PA and fitness is not absolute and depends on the test and children’s characteristics.
Decisions and Coordination of the Green Supply Chain with Retailers’ Fairness Concerns
Authors: Hu Haiju , Li Yakun , Li Yingyue , Li Mengdi , Yue Xiao , Ding Yu
Published: December 2022

The development of a green economy has become a global consensus. More and more manufacturers are greening their production to build green supply chains. At the same time, retailers are employing green marketing efforts for green products. In addition, members who are followers of the green supply chain are prone to fairness-concern behavior. To investigate the impact of fairness concerns on green supply chain decisions, this study develops a two-tier green supply chain in which the manufacturer makes green input and the retailer makes green marketing effort input. The retailer in the follower position of the supply chain has fairness concerns. Stackelberg game models are constructed and compared in three scenarios: one without fairness concerns, one where the manufacturer considers the retailer’s fairness concerns, and one where the manufacturer does not consider the retailer’s fairness concerns. After the manufacturer decides whether to consider the retailer’s fairness concerns, a two-part tariff contract is used to coordinate the green supply chain based on optimal decision-making and profit under a centralized decision-making condition. The study found that: (1) when the manufacturer considers the retailer’s fairness concerns, fairness concerns will negatively impact greenness, green marketing efforts, wholesale price, and retail price. When the manufacturer does not consider the retailer’s fairness concerns, fairness concerns will not impact greenness or wholesale price, but will negatively impact green marketing efforts; (2) the retailer’s fairness concerns have a negative impact on the green supply chain’s profit, and from the supply chain perspective, the retailer should abandon its fairness concerns; (3) it will be more beneficial if the manufacturer can consider the retailer’s fairness concerns; (4) when the manufacturer does not consider the retailer’s fairness concerns, the retailer can make fairness concerns according to the parameter conditions; (5) when a fixed fee meets a certain range, the two-part tariff contract can coordinate the green supply chain when the retailer has fairness concerns. The results will help manufacturers and retailers better understand fairness concerns and provide them with decision-making guidance and coordinated choices.
Effects of Background Turbulence on the Relaxation of Ion Temperature Anisotropy Firehose Instability in Space Plasmas
Authors: Navarro Roberto E. , Moya Pablo S.
Published: December 2022

Turbulence in space plasmas usually exhibits an energy cascade in which large-scale magnetic fluctuations are dominated by non-linear MHD wave–wave interactions following a Kolmogorov-like power-law spectrum. In addition, at scales at which kinetic effects take place, the magnetic spectrum follows a steeper power-law k−α shape given by a spectral index α>5/3. In a recent publication, a quasilinear model was used to study the evolution of ion temperatures in a collisionless plasma in which electromagnetic waves propagate along the background magnetic field, and it was found that the interaction between the plasma and a turbulent spectrum of ion-cyclotron waves may lead the plasma to states out of thermal equilibrium characterized by enhanced temperature anisotropies T⊥>T‖ and with a reduction in the parallel proton beta, which is consistent with space observations. Here, we complement such studies by analyzing the quasilinear interaction between plasma and a solar-wind-like turbulent spectrum of fast magnetosonic waves, and study the role of firehose instability (FHI) in the regulation of temperature anisotropy. Our results show that the presence of turbulence significantly modifies the FHI marginal stability threshold, as predicted from linear theory. Moreover, depending on the value of the plasma β, a turbulent magnetosonic spectrum may lead an initially thermally isotropic plasma to develop anisotropic states in which T⊥<T‖.
Classification of Solar Radio Spectrum Based on Swin Transformer
Authors: Chen Jian , Yuan Guowu , Zhou Hao , Tan Chengming , Yang Lei , Li Siqi
Published: December 2022

Solar radio observation is a method used to study the Sun. It is very important for space weather early warning and solar physics research to automatically classify solar radio spectrums in real time and judge whether there is a solar radio burst. As the number of solar radio burst spectrums is small and uneven, this paper proposes a classification method for solar radio spectrums based on the Swin transformer. First, the method transfers the parameters of the pretrained model to the Swin transformer model. Then, the hidden layer weights of the Swin transformer are frozen, and the fully connected layer of the Swin transformer is trained on the target dataset. Finally, parameter tuning is performed. The experimental results show that the method can achieve a true positive rate of 100%, which is more accurate than previous methods. Moreover, the number of our model parameters is only 20 million, which is 80% lower than that of the traditional VGG16 convolutional neural network with more than 130 million parameters.
Determination of Conformational and Functional Stability of Potential Plague Vaccine Candidate in Formulation
Authors: Athirathinam Krubha , Nandakumar Selvasudha , Verma Shailendra Kumar , Kandasamy Ruckmani
Published: December 2022

Generally, protein-based vaccines are available in liquid form and are highly susceptible to instability under elevated temperature changes including freezing conditions. There is a need to create a convenient formulation of protein/peptides that can be stored at ambient conditions without loss of activity or production of adverse effects. The efficiency of naturally occurring biocompatible polymer dextran in improving the shelf-life and biological activity of a highly thermally unstable plague vaccine candidate protein called Low Calcium Response V antigen (LcrV), which can be stored at room temperature (30 ± 2 °C), has been evaluated. To determine the preferential interactions with molecular-level insight into solvent–protein interactions, analytical techniques such asspectroscopy, particle size distribution, gel electrophoresis, microscopy, and thermal analysis have been performed along with the evaluation of humoral immune response, invivo. The analytical methods demonstrate the structural stability of the LcrV protein by expressing its interaction with the excipients in the formulation. The invivo studies elicited the biological activity of the formulated antigen with a significantly higher humoral immune response (p-value = 0.047) when compared to the native, adjuvanted antigen. We propose dextran as a potential biopolymer with its co-excipient sodium chloride (NaCl) to provide protein compactness, i.e., prevent protein unfolding by molecular crowding or masking mechanism using preferential hydrophobic interaction for up to three weeks at room temperature (30 ± 2 °C).
Iron Acquisition Proteins of Pseudomonas aeruginosa as Potential Vaccine Targets: In Silico Analysis and In Vivo Evaluation of Protective Efficacy of the Hemophore HasAp
Authors: Hamad Abdelrahman S. , Edward Eva A. , Sheta Eman , Aboushleib Hamida M. , Bahey-El-Din Mohammed
Published: December 2022

Background: Pseudomonas aeruginosa (PA) is a Gram-negative pathogen responsible for fatal nosocomial infections worldwide. Iron is essential for Gram-negative bacteria to establish an infection. Therefore, iron acquisition proteins (IAPs) of bacteria are attractive vaccine targets. Methodology: A “Reverse Vaccinology” approach was employed in the current study. Expression levels of 37 IAPs in various types of PA infections were analyzed in seven previously published studies. The IAP vaccine candidate was selected based on multiple criteria, including a high level of expression, high antigenicity, solubility, and conservation among PA strains, utilizing suitable bioinformatics analysis tools. The selected IAP candidate was recombinantly expressed in Escherichia coli and purified using metal affinity chromatography. It was further evaluated in vivo for protection efficacy. The novel immune adjuvant, naloxone (NAL), was used. Results and discussion: HasAp antigen met all the in silico selection criteria, being highly antigenic, soluble, and conserved. In addition, it was the most highly expressed IAP in terms of average fold change compared to control. Although HasAp did excel in the in silico evaluation, subcutaneous immunization with recombinant HasAp alone or recombinant HasAp plus NAL (HasAP-NAL) did not provide the expected protection compared to controls. Immunized mice showed a low IgG2a/IgG1 ratio, indicating a T-helper type 2 (Th2)-oriented immune response that is suboptimal for protection against PA infections. Surprisingly, the bacterial count in livers of both NAL- and HasAp-NAL-immunized mice was significantly lower than the count in the HasAp and saline groups. The same trend was observed in kidneys and lungs obtained from these groups, although the difference was not significant. Such protection could be attributed to the enhancement of innate immunity by NAL. Conclusions: We provided a detailed in silico analysis of IAPs of PA followed by in vivo evaluation of the best IAP, HasAp. Despite the promising in silico results, HasAp did not provide the anticipated vaccine efficacy. HasAp should be further evaluated as a vaccine candidate through varying the immunization regimens, models of infection, and immunoadjuvants. Combination with other IAPs might also improve vaccination efficacy. We also shed light on several highly expressed promising IAPs whose efficacy as vaccine candidates is worthy of further investigation.
Fear, Risk Perception, and Engagement in Preventive Behaviors for COVID-19 during Nationwide Lockdown in Nepal
Authors: Khanal Ashok , GC Sulochan , Panthee Suresh , Paudel Atmika , Ghimire Rakesh , Neupane Garima , Gaire Amrit , Sitaula Rukmini , Bhattarai Suman , Khadka Shubhechchha , Khatri Bibek , Khanal Aashis , Panthee Bimala , Wasti Sharada P , GC Vijay S
Published: December 2022

The world has faced huge negative effects from the COVID-19 pandemic between early 2020 and late 2021. Each country has implemented a range of preventive measures to minimize the risk during the COVID-19 pandemic. This study assessed the COVID-19-related fear, risk perception, and preventative behavior during the nationwide lockdown due to COVID-19 in Nepal. In a cross-sectional study, conducted in mid-2021 during the nationwide lockdown in Nepal, a total of 1484 individuals completed measures on fear of COVID-19, COVID-19 risk perception, and preventive behavior. A multiple linear regression analysis was used to identify factors associated with COVID-19 fear. The results revealed significant differences in the fear of COVID-19 in association with the perceived risk of COVID-19 and preventive behaviors. Age, risk perception, preventive behavior, and poor health status were significantly positively related to fear of COVID-19. Perceived risk and preventive behaviors uniquely predicted fear of COVID-19 over and above the effects of socio-demographic variables. Being female and unmarried were the significant factors associated with fear of COVID-19 among study respondents. Higher risk perception, poor health status, and being female were strong factors of increased fear of COVID-19. Targeted interventions are essential to integrate community-level mental health care for COVID-19 resilience.
Prognostic Factors for the Efficiency of Radiation Therapy in Dogs with Oral Melanoma: A Pilot Study of Hypoxia in Intraosseous Lesions
Authors: Noguchi Shunsuke , Yagi Kohei , Okamoto Nanako , Wada Yusuke , Tanaka Toshiyuki
Published: December 2022

Unresectable oral melanoma is often treated with radiation therapy (RT) and may show a temporary response to therapy. The clinical stage is one of the well-known prognostic factors for canine oral melanoma. However, the factors that directly affect the response to RT have remained unclear. This study aimed to validate the risk factors for recurrence after RT. Sixty-eight dogs with oral melanomas were included in this study. All dogs were treated with palliative RT using a linear accelerator without adjuvant therapies. After RT, the time to local recurrence (TTR) and overall survival (OS) were evaluated using the log-rank test. As a result, clinical stage and response to therapy were the significant independent prognostic factors in the multivariate analysis. The presence of local bone lysis and non-combination with cytoreductive surgery were associated with a worse response to RT. Immunohistochemical analysis for hypoxia-inducible factor-1α indicated that tumor cells invading the bone are under hypoxic conditions, which may explain a poorer efficiency of RT in dogs with bone lysis. In conclusion, clinical stage and combination with debulking surgery were needed to improve the efficiency of RT.
Evaluation of the Richness, Species Diversity, and Biosafety of Saprotrophic Fungal Communities in Constructed Wetlands and Biological Wastewater Ponds
Authors: Korniłłowicz-Kowalska Teresa , Bohacz Justyna , Rybczyńska-Tkaczyk Kamila , Jóźwiakowski Krzysztof
Published: December 2022

The present study was focused on the characterization of the biocenotic diversity of saprotrophic fungi, taking into account the impact of various treatment systems and technological stages as well as the physicochemical properties of wastewater. The biodegradation potential, the hazard posed to humans, animals, and the environment, and the effectiveness of elimination of propagules were evaluated. The most effective elimination of fungal propagules was recorded in hybrid constructed wetland systems with horizontal (HF-CW) and vertical (VF-CW) wastewater flow, especially in the VF-HF objects. The fungal communities present in wastewater from small constructed wetland (CW) and wastewater stabilization ponds (WSP) were dominated by ubiquitous terrestrial molds accompanied by a minimal number of yeasts. The similarity of the species composition of the fungal communities between the treatment plants was generally low, whereas the species diversity together with the population size was very high at the various stages of wastewater treatment. Species with potential pathogenicity to humans and animals accounted for over 45%, i.e., were classified as BioSafety Level 1 and 2 (BSL-1 and BSL-2 groups), and potentially phytopathogenic fungi represented 31.5% of the mycobiota species composition. The dynamics of fungal growth were correlated with the content of organic pollutants and nutrients (nitrogen and phosphorus) and with oxygen deficiency. The accumulation of nitrates corresponded to the decline in the frequency of fungi in treated wastewater. The lowest efficiency of the removal of fungi was exhibited by the biological wastewater stabilization ponds.
Comparative Biology of Daphniopsis tibetana from Different Habitats under Seawater Acclimation
Authors: Zhang Wan , Zhao Wen , Zou Jingjing , Wei Jie , Wang Shan , Yin Dongpeng
Published: December 2022

In this paper, we used experimental ecology methods and third-generation transcriptome sequencing to see the differences in growth, development, and reproduction of three strains of Daphniopsis tibetana Sars, 1903 from different locations in Tibet (Lake Namukacuo, NMKC; Lake Pengcuo, PC; and Lake Zigetangcuo, ZGTC). We also wanted to determine if the genes had changed after seawater-domesticated D. tibetana was reared in a laboratory. The results showed that at 15–16 ppt salinity and 15 ± 0.5 °C, the NMKC strain exhibited the highest survival rate of 26 d, and the ZGTC strain had the lowest survival rate at 53 days of culture. The body length was observed in NMKC (153.6 ± 12.1%), followed by PC (136.4 ± 16.1%), and then ZGTC (86.2 ± 7.6%). Combined, wild-type and seawater-acclimated D. tibetana produced 7252 DEGs, of which 4146 were up-regulated and 3106 were down-regulated. DEGs were subjected to gene ontology enrichment analysis. The DEGs were mainly enriched in single-organism localization, transporter activity, macromolecule localization, and organic substance transport. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis was also performed and revealed that the RNA transport, protein digestion and absorption, and protein processing in the endoplasmic reticulum pathways were highly enriched. The data mined can provide a reference for follow-up research.
Evaluating Curve Number Implementation Alternatives for Peak Flow Predictions in Urbanized Watersheds Using SWMM
Authors: Xiao Han , Vasconcelos Jose G.
Published: December 2022

The application of hydrologic modeling tools to represent urban watersheds is widespread, and calculation of infiltration losses is an essential component of these models. The curve number (CN) method is widely used in such models and is implemented in US EPA’s Storm Water Management Model (SWMM 5). SWMM 5 models can be created either using CN values computed only for the pervious fraction of subcatchments, or using the entire subcatchment area, but choice is not clearly understood. The present work evaluates the differences between these approaches in CN computation within SWMM through a comparison with field data collected in an urban watershed in Alabama and with WinTR-55. Four approaches to computing CN were considered in which the impervious fractions varied according to a threshold CN value. Results indicated that a Fully Composite approach, which computed CN from all subcatchment areas, yielded the best results for the sub-watershed with higher average CN. It was also observed that results from the approaches using CN Cut-off values of 90 and 93 were better for subcatchments with lower average CN. The comparison between SWMM 5 and WinTR-55 indicated that SWMM 5 hydrographs had larger peak flow rates, but these differences decreased with larger intensity rain events. Research findings are useful to hydrologic modelers, and in particular for setting up SWMM 5 models using CN method.
The 50- and 100-year Exceedance Probabilities as New and Convenient Statistics for a Frequency Analysis of Extreme Events: An Example of Extreme Precipitation in Israel
Authors: Osetinsky-Tzidaki Isabella , Fredj Erick
Published: December 2022

The misinterpreted statistics of extreme events’ probability in drainage design codes can lead to an insufficient capacity of drainage systems. This is one of the main factors of frequent floods, especially in coastal cities. Heavy rainfalls, the so-called “1-in-50-year” or “1-in-100-year” events followed by floods, may occur there every 2–3 years. We hope to contribute to the correct understanding of extreme events’ statistics. To achieve this goal, we first recall the technique of calculating the exceedance probability of the extreme event over a period of years. Then, to illustrate the theoretical results (among which is the well-known 63.2% minimal exceedance probability over a period of N years for an event with a 1/N annual exceedance probability), we use the rainfall intensity data archived at the Israel Meteorological Service for 1938–2021. We estimate their exceedance probabilities over periods of 50 and 100 years and offer the latter as convenient measures for comparative analysis of the intensity of extreme events. These illustrations highlight the importance of considering return periods that are significantly longer than the 50- or 100-year periods currently used in drainage design, to reduce the risk of flooding and damage caused by heavy rainfalls.
An Evaluation Framework for Urban Pluvial Flooding Based on Open-Access Data
Authors: Reyes-Silva Julian D. , Novoa Diego , Helm Björn , Krebs Peter
Published: December 2022

Identifying the location and estimating the magnitude of urban pluvial flooding events is essential to assess their impacts, particularly in areas where data are unavailable. The present work focused on developing and exemplifying a tool to evaluate urban pluvial flooding based on open-access information. The tool has three separate submodules: (1) sewer network generation and design; (2) hydrodynamic model development; (3) urban pluvial flood evaluation. Application of the first two modules in two catchments and comparison of these results with real data indicated that the tool was able to generate systems with realistic layouts and hydraulic properties. Hydrodynamic models derived from this data were able to simulate realistic flow dynamics. The third module was evaluated for one of the study cases. The results of this indicated that the current approach could be used to identify flood areas and associated flood depths during different rainfall scenarios. The outcomes of this study could be used in a wide variety of contexts. For example, it could provide information in areas with data scarcity or uncertainty or serve as a tool for prospective planning, design, and decision making.
Evaluación de la economía circular en una empacadora de camarón en Ecuador
Authors: Sablón-Cossío Neyfe , Bailón Arévalo Evelyn Yaritza
Published: December 2022

El presente estudio se basa en el análisis de una empacadora de camarón, con el objeto de evaluar el paradigma de la economía circular. Para esto, se realizó un enfoque de información con el objetivo de plantear mejoras para el reciclaje y reutilización de los residuos con la aplicación de métodos basados en la economía circular dentro de la empresa. El tipo de investigación que se utilizó es la descriptiva y de campo porque se caracterizó parte de la producción de camarón y las variables de economía circular. Se dispuso de una lista de chequeo para su evaluación. En los resultados obtenidos, se determinó que la producción de la empacadora de camarón está trabajando en una economía lineal, y que la empresa tiene una economía circular en un nivel medio de aplicación. Esto permitiría mejoras en la sostenibilidad incrementando más la economía circular.