bluuebunny/crossref_metadata_2025 huggingface dataset:
Urban Water Management with a Full Cost Recovery Policy: The Impact of Externalities on Pricing
Authors: Thomas Sanju John , Haribhau Bade Mukund , Sahoo Sudhansu Sekhar , Thomas Sheffy , Kumar Ajith , Awad Mohamed M.
Published: November 2022

Water has complex cost dimensions and is considered a scarce commodity under a reduced-recycle-reuse system with a full cost recovery strategy. The impact of externalities from the social, economic, and ecological aspects of exploiting water resources are often not accounted into the pricing mechanism. We discuss the current work model as well as a pricing strategy for a water infrastructure program with a full cost recovery strategy. Single and multi-block pricing models are created, and their effect on water pricing is discussed. The impact of externalities is accounted for, and respective cost components, namely, environmental cost, opportunity cost, and ecological imbalance cost are included in the water pricing, to analyze the impact on the cost of produced water. A comparison under the normalized, single-block and multi-block pricing strategy are discussed and the payback period is found. It is seen that the unit cost of potable and non-potable water is brought down from 0.94 USD/m3 and 0.51 USD/m3 to 0.62 USD/m3 and 0.29 USD/m3, respectively using a multi-block pricing strategy. It is recommended that policy interventions in a full cost recovery water pricing strategy should consider the cost of externalities with a multi-block pricing system for breakeven in water infrastructural investments.
Application of Six Sigma Methodology in an Automotive Manufacturing Company: A Case Study
Authors: Jou Yung-Tsan , Silitonga Riana Magdalena , Lin Ming-Chang , Sukwadi Ronald , Rivaldo Jovian
Published: November 2022

Continuous improvement is the prime requirement for all industries to sustain and grow in the competitive global market. This paper is a case study of a manufacturing industry in Taiwan, facing the problem of rejection in the brushless motor product. The DMAIC (Define, Measure, Analyze, Improve, and Control) cycle was used to improve processes to reduce the rejection rate. In the Define phase, anything related to processes, products, suppliers, customers, and customer needs related to good quality products were determined. SIPOC diagrams (Supplier, Input, Process, Output, and Customer) and Critical to Quality (CTQ) were identified. The Measure phase focused on data collection by determining the baseline of the process and determining the root causes of the process. DPMO and the Control Chart were applied in this phase. In the Analyze phase, the causes of production process failures that result in defective products were identified. Tools like Pareto Diagram, Fishbone Diagram, and FMEA were used in this phase. In the Improve phase, the improvement solutions in overcoming priority problems were determined by using the 5W + 1H tool. Several improvement solutions were implemented, such as improvement in inspection methods, re-selection for the supplier, increasing the number of workers, providing training to workers, and others. In the control phase, the Six Sigma values were improved. In January–May 2022, the Six Sigma level increased from 5.11 to 5.44.
Research on the Impact of the Industrial Cluster Effect on the Profits of New Energy Enterprises in China: Based on the Moran’s I Index and the Fixed-Effect Panel Stochastic Frontier Model
Authors: Sun Jialiang , Fan Pengxiang , Wang Ke , Yu Zixuan
Published: November 2022

The new energy industry is an imperative method through which to achieve sustainable development. Industrial clusters are one of the main states in the development of the new energy industry. However, few existing studies discuss the impact of industrial clusters on the relevant indicators of new energy enterprises. Based on panel data for the period 2011–2021 of 39 sample enterprises listed in China in 2011 and before, this empirical study first analyzes the spatial autocorrelation of the sample enterprises using the Global Moran’s I and Local Moran’s I, and then treats the Local Moran’s I of enterprises as a perturbation factor of the inefficiency term, using a fixed-effects panel stochastic frontier model to empirically analyze the effect of industrial clusters on the profits of the sample enterprises. The following is found: (1) The layout of new energy enterprises in China presents a specific physical spatial agglomeration phenomenon. Additionally, the layout of profit indicators shows spatial correlation to some extent. (2) When the homogeneity of clustering enterprises increases, the cluster effect can improve profits by reducing inefficiencies in enterprise production. This study provides valuable academic suggestions for the development of the new energy industry.
Catalytic Effect of Green Human Resource Practices on Sustainable Development Goals: Can Individual Values Moderate an Empirical Validation in a Developing Economy?
Authors: Liu Jiakun , Gao Xinxiang , Cao Yi , Mushtaq Naveed , Chen Jiuming , Wan Li
Published: November 2022

Utilizing the framework of the theory of planned behavior, a new model has been extracted and validated empirically to explore the role of green human resource management (GHRM) practices in the attainment of the sustainable development goals (SDGs) among textile manufacturing firms. Therefore, this research study is the first attempt to empirically investigate the impact of green skills and employee green attitudes that may proffer a better explanation of the nature of the relationship between GHRM and the SDGs, proposing an inclusive re`search model on the effect of GHRM on the SDGs in the hi-tech manufacturing industry. Questionnaires were distributed to 465 textile firms; however, of those returned, only 197 surveys completed in all respects were used for further empirical investigation. PLS-SEM was used to analyze the data to assess the validity and reliability of the instrument. The outcomes of the study affirmed the theoretical model according to which GHRM has a positive association with employee green attitudes, employee skills, decent work, and sustainable consumption and production behavior. Employee green attitudes and decent work had a positive effect on sustainable consumption and production behavior. It is also beneficial to identify significant moderators to explain the processes and circumstances through which the attitudes of employees are transformed into the desired behaviors. Therefore, individual green values were taken as moderators in the study to assess how they impacted the relationship between GHRM and employee green attitudes, as well as that between employee green attitudes and sustainable consumption and production behavior. The results also revealed that an employee’s green attitude acted as a partial mediator between GHRM and decent work. Moreover, employee green attitudes and employee skills fully mediated between GHRM and sustainable consumption and production behavior.
Forecasts of the Compound Coastal Erosion Risks Based on Time-Variant Assessment: A Case Study on Yunlin Coast, Taiwan
Authors: Huang Wei-Po , Ye Chun-Jhen , Hsu Jui-Chan
Published: November 2022

A coastal erosion risk assessment was framed as the basis for the intervention of coastal adaptation strategies under time-variant scenarios. The framework was devised to assess the influence of coastal erosion on coastal defense, the coastal inundation induced by the erosion-induced malfunction of defense, and risks using a downscaling analysis and the mechanism of the compound hazard interaction, which are innovative and practical for the application of coastal management in Taiwan. In addition, the vulnerable socio-economy was also taken into consideration in risk assessment. The adaptive strategy is proposed in terms of the risk origins and time-variance of risk forecasts, and the risk origins were assessed based on the Fuzzy Delphi Method and the analytic hierarchy process instead of subjective consideration. Within the erosion-induced hazard and risk assessments, this study considered erosion rates, decreases in defense elevation due to land subsidence, and population development in time-variant scenario analyses to estimate risk forecasts. Furthermore, a case study of the Yunlin coastal area was undertaken to demonstrate the feasibility of the proposed method. The presented results are informative for coastal hazard reduction and the promotion of the sustainable development of coastal zones.
A Study on the Acceptance of Mobile-Banking Applications in India—Unified Theory of Acceptance and Sustainable Use of Technology Model (UTAUT)
Authors: Samartha Vishal , Shenoy Basthikar Samarth , Hawaldar Iqbal Thonse , Spulbar Cristi , Birau Ramona , Filip Robert Dorin
Published: November 2022

This research makes an attempt to understand various factors that influence the adoption of mobile applications. Within the context of the “Unified theory of acceptance and use of technology” (UTAUT) modified model, considering the upcoming demand and increase in demand for mobile-banking applications, the researcher tried to explore the theoretical concept between random people of various states in India. The primary data was collected by preparing a questionnaire and circulating it using Google Forms. The collected data was further coded into Smart PLS 4 to understand the model and structural equation with reference to mobile-banking technological adoption and factors that had a significant impact. The conclusions derived from the study is that social influence, “effort expectancy”, and “trust” factors had a very strong influence on the “purchase intention”, whereas “effort” and “risk” factors had a negligible impact on purchase intent. It was also found that the UTAUT model is appropriate for evaluating the technological adoption of mobile-banking applications. With the advent of many players in the market and their unique banking management applications on mobile platforms, consumers are moving towards different third-party app than their origin bank in which they hold account. This has forced banking institutions to up the pace in the competition, introducing a lot of new features. It is also important to understand that, as a customer, there are a lot of attributes that he would be looking into for adoption. This paper is an attempt to understand the advancements in various variables that consumers would look at in the area of mobile-banking applications.
Sustainability in a Digitized Era Analyzing the Moderation Effect of Social Strata and Digital Capital Dependence on Digital Divide
Authors: Singh Swapnil , Singh Uma Shankar , Nermend Malgorzata
Published: November 2022

Although we have evolved digitally and technologically in the 21st century, disparities still exist in society. The research problem cited for the study is the need to assess the impact of the digital divide in the formation of digital capital, where the already stratified society based on variables such as age, gender, education, and region (rural–urban) will act as moderators to mitigate the digital inequalities for a sustainable world. The research objectives are to explore the underpinning reasons for the digital divide in reinforcing social inequalities, quantify the impact of the digital divide on digital capital statistically, and to evaluate the moderating effect of social strata variables comparatively. The idea of digital capital culminates into five hypotheses for this study as the digital divide impacts digital capital, as well as the assessment of moderating effects of age, education, and region. Statistical tools, specifically frequency, percentage, reliability, ANOVA, correlation, and regression, have been used to test the hypothesis and proposed conceptual model. The social strata dimension in the study revealed a higher variance of opinion. Digital capital is taken as the dependent variable and the digital divide is the independent variable, which shows Beta as 0.591 and B as 0.585, indicating a good relationship of 59.1% and an effect of 58.5%. Finally, the research reveals that there exists a digital divide and, hence, digital inequalities in India need to be addressed for attaining various Sustainable Development Goals. The study has significant implications for the leaders and policymakers to work towards inclusivity by bridging the digital divide and eliminating digital inequalities in India.
About Stability of Nonlinear Stochastic Differential Equations with State-Dependent Delay
Authors: Shaikhet Leonid
Published: November 2022

A nonlinear stage-structured population model with a state-dependent delay under stochastic perturbations is investigated. Delay-independent and delay-dependent conditions of stability in probability for two equilibria of the considered system are obtained via the general method of Lyapunov functionals construction and the method of linear matrix inequalities (LMIs). The model under consideration is not the aim of the work and was chosen only to demonstrate the proposed research method, which can be used for the study of other types of nonlinear systems with a state-dependent delay.
Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
Authors: Akhtar Muhammad Shoaib , Feng Tao
Published: November 2022

Cyber-attacks on the numerous parts of today’s fast developing IoT are only going to increase in frequency and severity. A reliable method for detecting malicious attacks such as botnet in the IoT environment is critical for reducing security risks on IoT devices. Numerous existing methods exist for mining IoT networks for previously discovered patterns that may be exploited to improve security. This study used a hybrid deep learning approach, namely the CNN-LSTM technique, to detect botnet attacks. Any software that infiltrates a computer system or is installed there without the administrators’ knowledge or permission is malicious. There is a wide range of viruses that cyber-criminals use to further their nefarious ends. A revolutionary deep learning system has been developed to counteract the increasing quantity of harmful programs. The system takes advantage of NLP methods as a baseline, mixes CNNs and LSTM neurons to capture local spatial correlations, and learns from successive long-term dependencies. Spatial invariance, often known as symmetry, is the property wherein the dataset size remains constant throughout iterations of an algorithm while undergoing various transformations. Therefore, automated extraction of high-level abstractions and representations aids in the malware categorization process. When compared to its predecessor research study, the current level of categorization accuracy is significantly greater than 0.81. The proposed CNN-LSTM method obtained an R2 = 99.19% in the dataset, with a correlation coefficient for the CNN-LSTM technique of R2 = 100% utilizing the provided dataset. The symmetry correlation of the CNN-LSTM, which illustrates that the CNN-LSTM method has the highest detection accuracy, at 99%, among the other malware detection methods such as the SVM and DT. The rest of classifiers had an accuracy of 98% for DT, and 95% for SVM. The accuracy of the LSTM model is 99%, the precision of the CNN-LSTM is 99%, recall is 99% and F1 score is 1.
Sandstorm Image Enhancement Using Image-Adaptive Eigenvalue and Brightness-Adaptive Dark Channel Network
Authors: Lee Hosang
Published: November 2022

Sandstorm images suffer from dust particles and the attenuation of light. Degraded sandstorm images have a reddish or yellowish color cast owing to dust particles, which have a certain color. Because of this phenomenon, distorted sandstorm images have imbalanced red and blue channels. Existing sandstorm enhancement methods improve the image by only focusing on dehazing, due to sandstorm images having similar features to hazy or dusty images. However, the enhanced images using previous methods have a color cast. Therefore, to enhance the sandstorm image naturally, a balancing procedure of color components is needed. This paper proposes a color-balancing method using image-adaptive eigenvalues. Because eigenvalues describe image characteristics, improved images have balanced color components. However, these enhanced images have dusty features. Therefore, to enhance balanced images a dehazing procedure is needed, something which this paper applies using a multiscale convolution neural network, thus generating a transmission map with brightness-adaptive features. Images enhanced using the proposed method compare well with state-of-the-art methods in subjective and objective measures of quality.
The Arcsine Kumaraswamy-Generalized Family: Bayesian and Classical Estimates and Application
Authors: Emam Walid , Tashkandy Yusra
Published: November 2022

In this paper, by including a trigonometric function, we propose a family of heavy-tailed distribution called the arcsine Kumaraswamy generalized-X family of distributions. Based on the proposed approach, a four-parameter extension of the Lomax distribution called the arcsine Kumaraswamy generalized Lomax (ASKUG-LOMAX) distribution is discussed in detail. Maximum likelihood, bootstrap, and Bayesian estimation are used to estimate the model parameters. A simulation study is used to evaluate ASKUG-LOMAX performance. The flexibility and usefulness of the proposed ASKUG-LOMAX distribution to predict unique symmetric and asymmetric patterns is demonstrated by analyzing real data. The results show that the ASKUG-LOMAX model is a good candidate for analyzing claims based on heavy-tailed data.
Mathematical Entropy Analysis of Natural Convection of MWCNT—Fe3O4/Water Hybrid Nanofluid with Parallel Magnetic Field via Galerkin Finite Element Process
Authors: Ghali Djellouli , Redouane Fares , Abdelhak Roubi , Belhadj Mahammed Amine , Zineb Chikr Djaoutsi , Jamshed Wasim , Eid Mohamed R. , Eldin Sayed M. , Musa Awad , Mohd Nasir Nor Ain Azeany
Published: November 2022

Heat transfer in a symmetrical cavity with two semi-cylinders was explored in this study. Several parameters, such as (103≤Ra≤106), (10−5≤Da≤10−2), (0.02≤ϕ≤0.08), (0.2≤ε≤0.8), and (0≤Ha≤100) were selected and evaluated in this research. The outcome of the magnetic field and the temperature gradient on the nanofluid flow is considered. The geometric model is therefore described using a symmetry technique. The flow issue for the governing equations has been solved using the Galerkin finite element method (G-FEM), and these solutions are presented in dimensionless form. The equations for energy, motion, and continuity were solved using the application of the COMSOL Multiphysics® software computer package. According to the results, there is a difference in the occurrence of the magnetic parameter and an increase in heat transmission when the right wall is recessed inward. The heat transmission is also significantly reduced when the right wall is exposed to the outside. The number of Nusselt grows directly proportional to the number of nanofluids in the environment. In contrast, all porous media with low Darcy and Hartmann numbers, high porosity, and low volume fraction have high Nusselt numbers. It is found that double streamlines for the hot side and single cooling for Darcy, Rayleigh, and Hartmann numbers. A cold isotherm at various physical parameters is needed in the top cavity. Rayleigh’s number and a solid volume fraction raise Darcy’s number, increasing heat transmission inside the cavity and thermal entropy determines entropy components.
Does Mobility of the Ankle Joint Depends on Length of the Free Part of the Achilles Tendon?
Authors: Wawrzyński Tomasz , Pietrzak Bernadeta Angelika , Mika Anna
Published: November 2022

The aim of our study was to evaluate whether the length of the free part of the Achilles tendon affects the mobility of the ankle joint in active motion without a load, as well as in functional motion with a body-weight load. We examined 36 healthy people, aged 21 to 30 years, and divided them into two groups: 1 (n = 15)—participants with a normal range of dorsiflexion in the ankle joint (20° or more), and 2 (n = 21)—participants with a reduced range of dorsiflexion in the ankle joint (below 20°). The length of the free part of the Achilles tendon was measured using ultrasonography. Ankle joint range of dorsiflexion was assessed, and a weight-bearing lunge test (WBLT) was conducted. Group 1 performed the WBLT better and demonstrated significantly greater Achilles tendon length compared to Group 2. A moderate, significant correlation was observed between ankle joint range of dorsiflexion and Achilles tendon length (r = 0.53, p < 0.05); between WBLT and Achilles tendon length (r = 0.61, p < 0.05); as well as between ankle joint range of dorsiflexion and WBLT (r = 0.63, p < 0.05). Thus, we can suggest that both the length of the tendon (measured by USG) and the ankle range of motion under a body-weight load (measured by WBLT) are good indicators regarding the range of foot dorsiflexion, but only up to specific values (6 cm of tendon length and 11 cm of WBLT reach). Therefore, Achilles tendon length, e.g., after injury and during tendon healing, may be monitored using the method of ultrasound imaging presented in this study.
Modelling of Red-Mud Particle-Solid Distribution in the Feeder Cup of a Thickener Using the Combined CFD-DPM Approach
Authors: Fedorova Elmira , Pupysheva Elena , Morgunov Vladimir
Published: November 2022

The paper evaluates the behavior of a red-mud solid fraction in a thickener feeder cup, aiming to identify the main characteristics of particle distribution in the flocculation zone and to determine the dependencies affecting the further process taking place in the particle-free sedimentation zone in the thickener-thickening unit. This work used mathematical and numerical modeling to study the influence of such parameters as the flow rate of the feed pulp in the thickener, the flow rate of the flocculant, the density of pulp at the inlet to the unit, and the viscosity and temperature of the pulp on the particle-size distribution from under the feeder cup. The results and dependencies obtained are intended to be used as nominal values in the red-mud thickening process performed on a lab-scale unit.
Radar-Jamming Classification in the Event of Insufficient Samples Using Transfer Learning
Authors: Hou Yanbin , Ren Huidong , Lv Qinzhe , Wu Lili , Yang Xiaodong , Quan Yinghui
Published: November 2022

Radar has played an irreplaceable role in modern warfare. A variety of radar-jamming methods have been applied in recent years, which makes the electromagnetic environment more complex. The classification of radar jamming is critical for electronic counter-countermeasures (ECCM). In the field of signal classification, machine learning-based methods take great effort to find proper features as well as classifiers, and deep learning-based methods depend on large training datasets. For the above reasons, an efficient transfer learning-based method is proposed in this paper. Firstly, one-dimensional radar signals were transformed into time–frequency images (TFIs) using linear and bilinear time–frequency analysis, which is inspired by symmetry theory. Secondly, pretrained AlexNet and SqueezeNet networks were modified to classify the processed TFIs. Finally, performance of this method was evaluated and compared using a simulated data set with nine types of radar-jamming signals. The results demonstrate that our proposed classification method performs well in accuracy and efficiency at a 1% training ratio, which is practical for anti-jamming.
SPM: Sparse Persistent Memory Attention-Based Model for Network Traffic Prediction
Authors: Ma Xue-Sen , Jiang Gong-Hui , Zheng Biao
Published: November 2022

The network traffic prediction (NTP) model can help operators predict, adjust, and control network usage more accurately. Meanwhile, it also reduces network congestion and improves the quality of the user service experience. However, the characteristics of network traffic data are quite complex. NTP models with higher prediction accuracy tend to have higher complexity, which shows obvious asymmetry. In this work, we target the conflict between low complexity and high prediction performance and propose an NTP model based on a sparse persistent memory (SPM) attention mechanism. SPM can accurately capture the sparse key features of network traffic and reduce the complexity of the self-attention layer while ensuring prediction performance. The symmetric SPM encoder and decoder replace the high complexity feed-forward sub-layer with an attention layer to reduce the complexity. In addition, by adding an attention layer to persistently memorize key features, the prediction performance of the model could be further improved. We evaluate our method on two real-world network traffic datasets. The results demonstrate that the SPM-based method outperforms the state-of-the-art (SOTA) approaches in NTP results by 33.0% and 21.3%, respectively. Meanwhile, the results of RMSE and R2 are also optimal. When measured by temporal performance, SPM reduces the complexity and reduces the training time by 22.2% and 30.4%, respectively, over Transformer.
Positive Solutions for a High-Order Riemann-Liouville Type Fractional Integral Boundary Value Problem Involving Fractional Derivatives
Authors: Wang Wuyang , Ye Jun , Xu Jiafa , O’Regan Donal
Published: November 2022

In this paper, under some super- and sub-linear growth conditions, we study the existence of positive solutions for a high-order Riemann–Liouville type fractional integral boundary value problem involving fractional derivatives. Our analysis methods are based on the fixed point index and nonsymmetric property of the Green function. Additionally, we provide some valid examples to illustrate our main results.
Rogue Waves Generator and Chaotic and Fractal Behavior of the Maccari System with a Resonant Parametric Forcing
Authors: Maccari Attilio
Published: November 2022

Using the Asymptotic Perturbation (AP) method we can find approximate solutions for the Maccari equation with a parametric resonant forcing acting over the frequency of a generic mode. Taking into account its nonlocal behavior and applying symmetry considerations, a system with two coupled equations for the phase and amplitude modulation can be obtained. The system can be solved, and we demonstrate the existence of a big modulation in the wave amplitude, producing a rogue waves train and, in this case, these waves are not isolated. We then obtain a rogue waves generator, being able of producing and controlling the rogue waves’ amplitude. Another important finding is the existence of chaotic or fractal solutions, because of the presence of an arbitrary function in the solution.
How Does Philanthropy Influence Innovation Management Systems? A Moderated Mediation Model with a Social Exchange Perspective
Authors: He Lu , Jiang Minghui
Published: November 2022

Social philanthropy and innovation systems both require significant resource investment—how can organizations achieve both? This paper combines social exchange theory, signaling theory and imprinting theory to analyze the intrinsic dynamics mechanism from the perspective of government technology resource acquisition and to explore the influential role of generalist CEOs. Based on the hand-collected multidimensional career experience dataset of CEOs and the quantitative analysis of 3651 enterprises in China’s A-share listed manufacturing industry from 2010–2019, the results show that: philanthropic donation has a systematic contribution to corporate innovation investment, in which government science and technology (S&T) resource acquisition plays a partially mediating role; generalist CEOs significantly enhance the effect of philanthropic donations and government S&T resource acquisition on innovation investment. Finally, the statistical results remain stable in the lagged effects experiment and the IV-2SLS model analysis using exogenous variables. The contribution of this study lies in expanding the research on maximizing the combined benefits of economic value creation and social performance from a social exchange perspective, and helping us understand why the share of generalists in the global managerial market big data has been steadily increasing.
Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model
Authors: Zhao Yinglan , Li Yi , Feng Chen , Gong Chi , Tan Hongru
Published: November 2022

In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect.
Determination of Zearalenone and Its Derivatives in Feed by Gas Chromatography–Mass Spectrometry with Immunoaffinity Column Cleanup and Isotope Dilution
Authors: Luo Sunlin , Liu Ying , Guo Qi , Wang Xiong , Tian Ying , Yang Wenjun , Li Juntao , Chen Yiqiang
Published: November 2022

In this study, a gas chromatography–mass spectrometry (GC-MS) method was established for the determination of zearalenone and its five derivatives in feed, including zearalanone, α-zearalanol, β-zearalanol, α-zearalenol, and β-zearalenol. An effective immunoaffinity column was prepared for sample purification, which was followed by the silane derivatization of the eluate after an immunoaffinity chromatography analysis for target compounds by GC-MS. Matrix effects were corrected by an isotope internal standard of zearalenone in this method. The six analytes had a good linear relationship in the range of 2–500 ng/mL, and the correlation coefficients were all greater than 0.99. The limits of detection (LODs) and limits of quantification (LOQs) were less than 1.5 μg/kg and 5.0 μg/kg, respectively. The average spike recoveries for the six feed matrices ranged from 89.6% to 112.3% with relative standard deviations (RSDs) less than 12.6%. Twenty actual feed samples were analyzed using the established method, and at least one target was detected. The established GC-MS method was proven to be reliable and suitable for the determination of zearalenone and its derivatives in feed.
The Indispensability of Snail Control for Accelerating Schistosomiasis Elimination: Evidence from Zanzibar
Authors: Muhsin Mtumweni Ali , Wang Xinyao , Kabole Fatma Mohammed , Zilabumba January , Yang Kun
Published: November 2022

Schistosomiasis is a serious and neglected global tropical disease, affecting upwards of 230 million people, with more than 95% of infections concentrated in Africa. For many years, the main schistosomiasis control strategy in Africa focused on mass drug administration (MDA). The aim of this study was to compare the difference between MDA alone and alongside another intervention, namely snail control, by exploring effective measures for eliminating schistosomiasis. Retrospective data of human prevalence on Schistosoma haematobium and major control measures were collected from the China-Zanzibar-WHO Cooperation Project for Schistosomiasis Elimination (CZW) and the Zanzibar Elimination of Schistosomiasis Transmission (ZEST) project since 2012. The optimal order polynomial regression fitting model and joinpoint regression model (JRM) were used to analyze trends in schistosomiasis prevalence and the consistency of change points with strengthening of the control measures. In Unguja Island, the main control measure was MDA, and prevalence decreased to a nadir in 2019, and then rebounded. The R2 value of the optimal fitting model was 0.6641. There was a single JRM changepoint in 2019, the annual percent change (APC) was −19.3% (p < 0.05) from 2012 to 2019, and the APC was 59.7% (p > 0.05) from 2019 to 2021. In Pemba Island, the main control measures until 2016 was MDA, while integrated measures of MDA and snail control were implemented from 2017, the prevalence continuously decreased, and the R2 value was 0.8673. There was also a single JRM changepoint in 2017, the APC was −22.2% (p < 0.05) from 2012 to 2017, and was maintained at −8.6% (p > 0.05) from 2017 to 2021. Our data indicate that, while it is challenging to eliminate schistosomiasis by MDA alone, integrated measures, including both MDA and snail control, can prevent reinfection and help to eliminate the diseases in Africa.
Development of an Anti-Zika and Anti-Dengue IgM ELISA Assay: Evaluation of Cross Reactivity and Validation
Authors: Cerutti Helena , Tesi Giulia , Soldatini Claudia , Bandini Tommaso , Castria Marinunzia , Brogi Alessandra
Published: November 2022

Zika and dengue viruses (ZIKV and DENV) have been considered major global threats to humans in the past decade. The two infections display similar epidemiological and clinical manifestations. They are transmitted by the same primary vector, accounting for the co-circulation of the two viruses in regions where they are endemic. Highly specific and sensitive serological assays that are able to detect ZIKV and DENV antibodies (Abs) during the acute and convalescent phases of infections would help to improve clinical management and disease control. We report the development and characterisation of two monoclonal Abs, the ZIKV 8-8-11 and the DENV 8G2-12-21, which recognise the Zika non-structural protein 1 (NS1) and the dengue virus type 2 envelope protein, respectively. Both mAbs were used to set up enzyme-linked immunosorbent assays (ELISAs) specific for the detection of anti-Zika immunoglobulin M (IgM) and anti-dengue IgM and whose performance was similar to commercially available kits. These kits, intended to be used with the CHORUS Instruments, are rapid and require ≤50 µL of human serum. These tests could represent an affordable and reliable option for the rapid diagnosis of both ZIKV and DENV infections in developing countries, where these flaviviruses are endemic.
Substance Use and Adherence to Antiretroviral Therapy among People Living with HIV in the United States
Authors: Przybyla Sarahmona , Ashare Rebecca L. , Cioffi Loriann , Plotnik Isabella , Shuter Jonathan , Seng Elizabeth K. , Weinberger Andrea H.
Published: November 2022

People with HIV (PWH) report substance use at higher rates than HIV-uninfected individuals. The potential negative impact of single and polysubstance use on HIV treatment among diverse samples of PWH is underexplored. PWH were recruited from the Center for Positive Living at the Montefiore Medical Center (Bronx, NY, USA) from May 2017-April 2018 and completed a cross-sectional survey with measures of substance use, antiretroviral therapy (ART) use, and ART adherence. The overall sample included 237 PWH (54.1% Black, 42.2% female, median age 53 years). Approximately half of the sample reported any current substance use with 23.1% reporting single substance use and 21.4% reporting polysubstance use. Polysubstance use was more prevalent among those with current cigarette smoking relative to those with no current smoking and among females relative to males. Alcohol and cannabis were the most commonly reported polysubstance combination; however, a sizeable proportion of PWH reported other two, three, and four-substance groupings. Single and polysubstance use were associated with lower ART adherence. A thorough understanding of substance use patterns and related adherence challenges may aid with targeted public health interventions to improve HIV care cascade goals, including the integration of substance use prevention into HIV treatment and care settings.
Potential Nosocomial Infections by the Zika and Chikungunya Viruses in Public Health Facilities in the Metropolitan Area of Recife, Brazil
Authors: Krokovsky Larissa , Guedes Duschinka Ribeiro Duarte , Santos Fabiana Cristina Fulco , Sales Kamila Gaudêncio da Silva , Bandeira Daniela Anastácio , Pontes Claudenice Ramos , Leal Walter Soares , Ayres Constância Flávia Junqueira , Paiva Marcelo Henrique Santos
Published: November 2022

Since 2015, the Dengue, Zika, and Chikungunya viruses gained notoriety for their impact in public health in many parts of the globe, including Brazil. In Recife, the capital of Pernambuco State, the introduction of ZIKV impacted human population tremendously, owing to the increase in the number of neurological cases, such as the Guillain–Barré and congenital Zika disorders. Later, Recife was considered to be the epicenter for ZIKV epidemics in Brazil. For arboviral diseases, there are some risk factors, such as climate changes, low socioeconomic conditions, and the high densities of vectors populations, that favor the broad and rapid dispersion of these three viruses in the city. Therefore, continuous arbovirus surveillance provides an important tool for detecting these arboviruses and predicting new outbreaks. The purpose of the present study was to evaluate the circulation of DENV, ZIKV, and CHIKV by RT-qPCR in mosquitoes collected in health care units from the metropolitan area of Recife (MAR), during 2018. A total of 2321 female mosquitoes (357 pools) belonging to two species, Aedes aegypti and Culex quinquefasciatus, were collected from 18 different healthcare units, distributed in five cities from the MAR. Twenty-three pools were positive for ZIKV, out of which, seventeen were of C. quinquefasciatus and six were of A. aegypti. Positive pools were collected in 11/18 health care units screened, with Cq values ranging from 30.0 to 37.4 and viral loads varying from 1.88 × 107 to 2.14 × 109 RNA copies/mL. Nosocomial Aedes- and Culex-borne transmission of arbovirus are widely ignored by surveillance and vector control programs, even though healthcare-associated infections (HAI) are considered a serious threat to patient safety worldwide. Although the results presented here concern only the epidemiological scenario from 2018 in MAR, the potential of hospital-acquired transmission through mosquito bites is being overlooked by public health authorities. It is, therefore, of the ultimate importance to establish specific control programs for these locations.
Mechanistic Contributions of lncRNAs to Cellular Signaling Pathways Crucial to the Lifecycle of Human Papillomaviruses
Authors: Arman Warda , Munger Karl
Published: November 2022

Papillomaviruses are ubiquitous epitheliotropic viruses with double-stranded circular DNA genomes of approximately 8000 base pairs. The viral life cycle is somewhat unusual in that these viruses can establish persistent infections in the mitotically active basal epithelial cells that they initially infect. High-level viral genome replication (“genome amplification”), the expression of capsid proteins, and the formation of infectious progeny are restricted to terminally differentiated cells where genomes are synthesized at replication factories at sites of double-strand DNA breaks. To establish persistent infections, papillomaviruses need to retain the basal cell identity of the initially infected cells and restrain and delay their epithelial differentiation program. To enable high-level viral genome replication, papillomaviruses also need to hold the inherently growth-arrested terminally differentiated cells in a replication-competent state. To provide ample sites for viral genome synthesis, they target the DNA damage and repair machinery. Studies focusing on delineating cellular factors that are targeted by papillomaviruses may aid the development of antivirals. Whilst most of the current research efforts focus on protein targets, the majority of the human transcriptome consists of noncoding RNAs. This review focuses on one specific class of noncoding RNAs, long noncoding RNAs (lncRNAs), and summarizes work on lncRNAs that may regulate the cellular processes that are subverted by papillomavirus to enable persistent infections and progeny synthesis.
CXCL10 Chemokine: A Critical Player in RNA and DNA Viral Infections
Authors: Elemam Noha , Talaat Iman , Maghazachi Azzam
Published: November 2022

Chemokines constitute a group of small, secreted proteins that regulate leukocyte migration and contribute to their activation. Chemokines are crucial inflammatory mediators that play a key role in managing viral infections, during which the profile of chemokine expression helps shape the immune response and regulate viral clearance, improving clinical outcome. In particular, the chemokine ligand CXCL10 and its receptor CXCR3 were explored in a plethora of RNA and DNA viral infections. In this review, we highlight the expression profile and role of the CXCL10/CXCR3 axis in the host defense against a variety of RNA and DNA viral infections. We also discuss the interactions among viruses and host cells that trigger CXCL10 expression, as well as the signaling cascades induced in CXCR3 positive cells.
Intra-Patient Evolution of HIV-2 Molecular Properties
Authors: Palm Angelica A. , Esbjörnsson Joakim , Kvist Anders , Månsson Fredrik , Biague Antonio , Norrgren Hans , Jansson Marianne , Medstrand Patrik
Published: November 2022

Limited data are available on the pathogenesis of HIV-2, and the evolution of Env molecular properties during disease progression is not fully elucidated. We investigated the intra-patient evolution of molecular properties of HIV-2 Env regions (V1–C3) during the asymptomatic, treatment-naïve phase of the infection in 16 study participants, stratified into faster or slower progressors. Most notably, the rate of change in the number of potential N-linked glycosylation sites (PNGS) within the Env (V1–C3) regions differed between progressor groups. With declining CD4+ T-cell levels, slower progressors showed, on average, a decrease in the number of PNGSs, while faster progressors showed no significant change. Furthermore, diversity increased significantly with time in faster progressors, whereas no such change was observed in slower progressors. No differences were identified between the progressor groups in the evolution of length or charge of the analyzed Env regions. Predicted virus CXCR4 use was rare and did not emerge as a dominating viral population during the studied disease course (median 7.9 years, interquartile range [IQR]: 5.2–14.0) in either progressor groups. Further work building on our observations may explain molecular hallmarks of HIV-2 disease progression and differences in pathogenesis between HIV-1 and HIV-2.
Viral Metagenomics for Identification of Emerging Viruses in Transfusion Medicine
Authors: Slavov Svetoslav Nanev
Published: November 2022

Viral metagenomics has revolutionized our understanding for identification of unknown or poorly characterized viruses. For that reason, metagenomic studies gave been largely applied for virus discovery in a wide variety of clinical samples, including blood specimens. The emerging blood-transmitted virus infections represent important problem for public health, and the emergence of HIV in the 1980s is an example for the vulnerability of Blood Donation systems to such infections. When viral metagenomics is applied to blood samples, it can give a complete overview of the viral nucleic acid abundance, also named “blood virome”. Detailed characterization of the blood virome of healthy donors could identify unknown (emerging) viral genomes that might be assumed as hypothetic transfusion threats. However, it is impossible only by application of viral metagenomics to assign that one viral agent could impact blood transfusion. That said, this is a complex issue and will depend on the ability of the infectious agent to cause clinically important infection in blood recipients, the viral stability in blood derivatives and the presence of infectious viruses in blood, making possible its transmission by transfusion. This brief review summarizes information regarding the blood donor virome and some important challenges for use of viral metagenomics in hemotherapy for identification of transfusion-transmitted viruses.
Updating the National Antigen Bank in Korea: Protective Efficacy of Synthetic Vaccine Candidates against H5Nx Highly Pathogenic Avian Influenza Viruses Belonging to Clades 2.3.2.1 and 2.3.4.4
Authors: Kang Yong-Myung , Cho Hyun-Kyu , An Sung-Jun , Kim Hyun-Jun , Lee Youn-Jeong , Kang Hyun-Mi
Published: November 2022

Since 2018, Korea has been building an avian influenza (AI) national antigen bank for emergency preparedness; this antigen bank is updated every 2 years. To update the vaccine strains in the antigen bank, we used reverse genetics technology to develop two vaccine candidates against avian influenza strains belonging to clades 2.3.2.1d and 2.3.4.4h, and then evaluated their immunogenicity and protective efficacy in SPF chickens challenged with H5 viruses. The two vaccine candidates, named rgCA2/2.3.2.1d and rgES3/2.3.4.4h, were highly immunogenic, with hemagglutination inhibition (HI) titers of 8.2–9.3 log2 against the vaccine strain, and 7.1–7.3 log2 against the lethal challenge viruses (in which the HA genes shared 97% and 95.4% homology with that of rgCA2/2.3.2.1d and rgES3/2.3.4.4h, respectively). A full dose of each vaccine candidate provided 100% protection against the challenge viruses, with a reduction in clinical symptoms and virus shedding. A 1/10 dose provided similar levels of protection, whereas a 1/100 dose resulted in mortality and virus shedding by 7 dpi. Moreover, immunity induced by the two vaccines was long lasting, with HI titers of >7 log2 against the vaccine strain remaining after 6 months. Thus, the two vaccine candidates show protective efficacy and can be used to update the AI national antigen bank.
Recent Progress on Vaccines Produced in Transgenic Plants
Authors: Gaobotse Goabaone , Venkataraman Srividhya , Mmereke Kamogelo M. , Moustafa Khaled , Hefferon Kathleen , Makhzoum Abdullah
Published: November 2022

The development of vaccines from plants has been going on for over two decades now. Vaccine production in plants requires time and a lot of effort. Despite global efforts in plant-made vaccine development, there are still challenges that hinder the realization of the final objective of manufacturing approved and safe products. Despite delays in the commercialization of plant-made vaccines, there are some human vaccines that are in clinical trials. The novel coronavirus (SARS-CoV-2) and its resultant disease, coronavirus disease 2019 (COVID-19), have reminded the global scientific community of the importance of vaccines. Plant-made vaccines could not be more important in tackling such unexpected pandemics as COVID-19. In this review, we explore current progress in the development of vaccines manufactured in transgenic plants for different human diseases over the past 5 years. However, we first explore the different host species and plant expression systems during recombinant protein production, including their shortcomings and benefits. Lastly, we address the optimization of existing plant-dependent vaccine production protocols that are aimed at improving the recovery and purification of these recombinant proteins.