bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Differentiation of Carbon Budgets and Carbon Balance Zoning in China Based on the Land Use Perspective
Authors: Wen Hui , Li Yi , Li Zirong , Cai Xiaoxue , Wang Fengxia
Published: October 2022

Carbon emission reduction in China is of great significance to curb global warming. Based on the land use perspective, the spatial characteristics of carbon emissions and carbon sinks in 30 Chinese provinces were analyzed and the carbon balance was partitioned by combining the economic contribution coefficient (ECC) and ecological support coefficient (ESC). It was found that (1) the intensity of land use in China is strong, being high in the southeast and low in the northwest, divided by the Heihe–Tengchong Line, and carbon source and carbon sink lands show clear spatial heterogeneity. (2) Total carbon emissions show clear spatial heterogeneity. Carbon emissions from construction land are the main source of carbon emissions. The carbon emission intensity and per capita carbon emissions are both high in the north and low in the south. (3) The total carbon sink is high in the north–south and low in the middle of the country, and woodland and grassland are the main sites of terrestrial carbon absorption. The overall carbon sink intensity shows a continuous decrease from southeast to northwest. (4) Based on the ECC and ESC indicators, 30 provinces were divided into four carbon zones and differentiated low-carbon development suggestions are proposed.
Diamond-α Hardy-Type Inequalities on Time Scales
Authors: El-Deeb Ahmed A. , Awrejcewicz Jan
Published: October 2022

In the present article, we prove some new generalizations of dynamic inequalities of Hardy-type by utilizing diamond-α dynamic integrals on time scales. Furthermore, new generalizations of dynamic inequalities of Hardy-type in two variables on time scales are proved. Moreover, we present Hardy inequalities for several functions by using the diamond-α dynamic integrals on time scales. The results are proved by using the dynamic Jensen inequality and the Fubini theorem on time scales. Our main results extend existing results of the integral and discrete Hardy-type inequalities. Symmetry plays an essential role in determining the correct methods to solve dynamic inequalities.
Activation-Inhibition Coordination in Neuron, Brain, and Behavior Sequencing/Organization: Implications for Laterality and Lateralization
Authors: Young Gerald
Published: October 2022

Activation-inhibition coordination is considered a dynamic process that functions as a common mechanism in the synchronization and functioning of neurons, brain, behavior, and their sequencing/organization, including over these different scales. The concept has broad applicability, for example, in applications to maladaptivity/atypicality. Young developed the hypothesis to help explain the efficacy of right-hand reaching to grasp in 1-month-olds, a study that implicated that the left hemisphere is specialized for activation-inhibition coordination. This underlying left-hemisphere function, noted to characterize the left hemisphere right from birth, can explain equally its language and fine motor skills, for example. The right hemisphere appears specialized for less complex inhibitory skills, such as outright damping/inhibition. The hypotheses related to inhibition and hemispheric specialization that appear in the literature typically refer to right hemisphere skills in these regards. The research to present also refers to excitation/inhibition balance/ratio in synaptic function, but not to coordination in the sense described here. Furthermore, it refers to the inhibitory function widely in neuronal networks. The paper presents a comprehensive literature review, framing the research in terms of the proposed concept. Further, the paper presents a broad model of activation-inhibition coordination that can help better understand neuron, brain, and behavior, generally, and left hemisphere specialization, specifically.
Deflection Angle and Shadow of the Reissner–Nordström Black Hole with Higher-Order Magnetic Correction in Einstein-Nonlinear-Maxwell Fields
Authors: Kumaran Yashmitha , Övgün Ali
Published: October 2022

Nonlinear electrodynamics is known as the generalizations of Maxwell electrodynamics at strong fields and presents interesting features such as curing the classical divergences present in the linear theory when coupled to general relativity. In this paper, we consider the asymptotically flat Reissner–Nordström black hole solution with higher-order magnetic correction in Einstein-nonlinear-Maxwell fields. We study the effect of the magnetic charge parameters on the black hole, viz., weak deflection angle of photons and massive particles using the Gauss–Bonnet theorem. Moreover, we apply Keeton–Petters formalism to confirm our results concerning the weak deflection angle. Apart from a vacuum, their influence in the presence of different media such as plasma and dark matter are probed as well. Finally, we examine the black hole shadow cast using the null-geodesics method and investigate its spherically in-falling thin accretion disk. Our inferences show how the magnetic charge parameter p affects the other physical quantities; so, we impose some constraints on this parameter using observations from the Event Horizon Telescope.
An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance
Authors: Jia Bingjing , Wang Chenglong , Zhao Haiyan , Shi Lei
Published: October 2022

In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.
Tutte Polynomials and Graph Symmetries
Authors: Chbili Nafaa , Alderai Noura , Ali Roba , AlQedra Raghd
Published: October 2022

The Tutte polynomial is an isomorphism invariant of graphs that generalizes the chromatic and the flow polynomials. This two-variable polynomial with integral coefficients is known to carry important information about the properties of the graph. It has been used to prove long-standing conjectures in knot theory. Furthermore, it is related to the Potts and Ising models in statistical physics. The purpose of this paper is to study the interaction between the Tutte polynomial and graph symmetries. More precisely, we prove that if the automorphism group of the graph G contains an element of prime order p, then the coefficients of the Tutte polynomial of G satisfy certain necessary conditions.
Correlation between Deep Neural Network Hidden Layer and Intrusion Detection Performance in IoT Intrusion Detection System
Authors: Han Hyojoon , Kim Hyukho , Kim Yangwoo
Published: October 2022

As the Internet of Things (IoT) continues to grow, a vast amount of data is generated. The IoT environment is quite sensitive to security challenges because personal information may be leaked or sensor data may be manipulated, which could cause accidents. Because traditional intrusion detection system (IDS) studies are often designed to work well on datasets, it is unknown whether they would work well in a changing network environment. In addition, IDSs for protecting IoT environments have been studied, but their performance was verified using datasets unrelated to the IoT, so it is not known whether the performance would be effective in an IoT environment. In this study, we propose an intrusion detection hyperparameter control system (ID-HyConSys) that automates the IDS using proximal policy optimization (PPO) to solve these problems and reliably protect the IoT environment. ID-HyConSys consists of an intrusion detection module consisting of a deep neural network (DNN) feature extractor that extracts efficient features from a changing network environment, a k-means cluster that clusters the extracted data, and a PPO agent that automates the IDS through learning and control. Through experimentation, it was confirmed that the hidden layer configuration, the number of feature extractions by the DNN feature extractor, and the number of clusters in the k-means cluster significantly affected the intrusion detection performance. The PPO directly controls these hyperparameters and determines the optimized value itself. The performance of ID-HyConSys was evaluated using the CICIDS2017 and MQTTset datasets. An F1-score of 0.9707 on CICIDS2017 and an F1-score of 0.9973 on the MQTTset were obtained. Finally, we merged the two datasets and obtained an F1-score of 0.9901. The superiority of the ID-HyConSys proposed in this study was confirmed because ID-HyConSys showed high performance on each dataset and, at the same time, very high performance on complex merged datasets. ID-HyConSys is expected to protect the IoT environment more quickly and safely by automatically learning network changes and adjusting the intrusion detection module accordingly.
Symmetry Analysis of the Complex Polytypism of Layered Rare-Earth Tellurites and Related Selenites: The Case of Introducing Transition Metals
Authors: Charkin Dmitri O. , Dolgikh Valeri A. , Omelchenko Timofey A. , Vaitieva Yulia A. , Volkov Sergey N. , Deyneko Dina V. , Aksenov Sergey M.
Published: October 2022

Our systematic explorations of the complex rare earth tellurite halide family have added several new [Ln12(TeO3)12][M6X24] (M = Cd, Mn, Co) representatives containing strongly deficient and disordered metal-halide layers based on transition metal cations. The degree of disorder increases sharply with decrease of M2+ radius and the size disagreements between the cationic [Ln12(TeO3)12]+12 and anionic [M6Cl24]−12 layers. From the crystal chemical viewpoint, this indicates that the families of both rare-earth selenites and tellurites can be further extended; one can expect formation of some more complex structure types, particularly among selenites. Analysis of the polytypism of compounds have been performed using the approach of OD (“order–disorder”) theory.
Accurate Analytical Models of Armature Reaction Field for Multi-Segment Primaries Ironless PMLSM Based on Subdomain Method
Authors: Zhang Fugang , Yin Haibin , Li Yufeng
Published: October 2022

Multi-segment-primary (MSP) ironless Permanent Magnet Linear Synchronous Machine (PMLSM) can be widely applied in long primary, long stroke, and heavy load applications. Therefore, an accurate armature reaction field analysis is very important to control this novel topology motor. In order to simplify the research process, a two-segment-primary (TSP) ironless PMLSM in this article was proposed as the smallest unit. The analytical models of the armature reaction field of the motor based on the subdomain method (SDM) were established considering the finite length of the segment-primary (SP) and the interval distance between the TSP. Then, the coupling effect between the TSP and the end effect of the TSP on the armature reaction field were quantitatively analyzed. Furthermore, the coupling inductance between the TSP can be analytically calculated, which is influenced by the coupling effect. To validate the effectiveness of the proposed models, a prototype of the 24s/28p TSP ironless PMLSM was manufactured and tested. It was shown that the proposed models match well with the simulated and experimental results. As well, the maximum variation rate of the end coupling inductance was about 50.13%.
A Modified Regularization Method for a Spherically Symmetric Inverse Heat Conduction Problem
Authors: Cheng Wei , Liu Yi-Liang , Yang Fan
Published: October 2022

In this paper, we investigate a spherically symmetric inverse heat conduction problem, which determines the internal surface temperature distribution of the hollow sphere from measured data at the fixed location inside it. This problem is ill-posed, and a conditional stability result of its solution is given. A modified quasi-boundary value method is proposed to solve the ill-posed problem. Two Ho¨lder-type error estimates between the approximation solution and its exact solution are obtained under an a priori and an a posteriori regularization parameter selection rule, respectively.
An Integrated Scheduling Algorithm Based on a Process End Time-Driven and Long-Time Scheduling Strategy
Authors: Zhan Xiaojuan , Xie Zhiqiang , Yao Dengju
Published: October 2022

The integrated scheduling problem is a classical combinatorial optimization problem. The existing integrated scheduling algorithms generally adopt the short-time scheduling strategy that does not fully consider the impact of the degree of process parallelism on scheduling results. In order to further optimize the total processing time of a product and the utilization rate of a device, an integrated scheduling algorithm based on a process end time-driven and the long-time scheduling strategy is proposed. The proposed integrated scheduling algorithm sets up a separate candidate process queue for each device and determines the scheduling order for each scheduling queue on the premise of satisfying the constraint conditions of the process tree. Driven by the process end time, the algorithm finds schedulable processes for each device. If the schedulable process is unique, it is scheduled. Otherwise, if the schedulable process is not unique, the process with long-path and long-time is scheduled. In particular, the scheduling strategies of the scheduling queues of different devices are symmetric, and the constraint relationships between the processes in different queues are asymmetric. The case analysis results show that the proposed integrated scheduling algorithm is better than some existing algorithms in terms of the total processing time of a product and the average utilization rate of devices. Therefore, the proposed algorithm provides a new idea for processing the scheduling of a single complex product.
Recent Developments in Heterogeneous Photocatalysts with Near-Infrared Response
Authors: Cao Nan , Xi Meilan , Li Xiaoli , Zheng Jinfang , Qian Limei , Dai Yitao , Song Xizhong , Hu Shengliang
Published: October 2022

Photocatalytic technology has been considered as an efficient protocol to drive chemical reactions in a sustainable and green way. With the assistance of semiconductor-based materials, heterogeneous photocatalysis converts solar energy directly into chemical energy that can be readily stored. It has been employed in several fields including CO2 reduction, H2O splitting, and organic synthesis. Given that near-infrared (NIR) light occupies 47% of sunlight, photocatalytic systems with a NIR response are gaining more and more attention. To enhance the solar-to-chemical conversion efficiency, precise regulation of the symmetric/asymmetric nanostructures and band structures of NIR-response photocatalysts is indispensable. Under the irradiation of NIR light, the symmetric nano-morphologies (e.g., rod-like core-shell shape), asymmetric electronic structures (e.g., defect levels in band gap) and asymmetric heterojunctions (e.g., PN junctions, semiconductor-metal or semiconductor-dye composites) of designed photocatalytic systems play key roles in promoting the light absorption, the separation of electron/hole pairs, the transport of charge carriers to the surface, or the rate of surface photocatalytic reactions. This review will comprehensively analyze the four main synthesis protocols for the fabrication of NIR-response photocatalysts with improved reaction performance. The design methods involve bandgap engineering for the direct utilization of NIR photoenergy, the up-conversion of NIR light into ultraviolet/visible light, and the photothermal effect by converting NIR photons into local heat. Additionally, challenges and perspectives for the further development of heterogeneous photocatalysts with NIR response are also discussed based on their potential applications.
An IoT-Platform-Based Deep Learning System for Human Behavior Recognition in Smart City Monitoring Using the Berkeley MHAD Datasets
Authors: Khalifa Othman O. , Roubleh Adil , Esgiar Abdelrahim , Abdelhaq Maha , Alsaqour Raed , Abdalla Aisha , Ali Elmustafa Sayed , Saeed Rashid
Published: October 2022

Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model’s simulation results achieved 98% accuracy for the citizen behavior recognition system.
Household Registration, Old-Age Insurance, and Consumption: Evidence from China
Authors: Zou Jin , Fu Xu , Gong Chi , Ye Lin Yi
Published: October 2022

Understanding migrants’ status and socio-economic outcomes can help develop immigrant societies. Household registration in China plays a vital role in migrants’ status to provide an excellent research environment. Based on panel data from the Institute of Social Science Survey (CFPS) from 2012 to 2020, this paper investigates the impact of various old-age insurance schemes on urban residents’ consumption with the difference in household registration. The result shows that insurance increases consumption more in a higher benefit scheme due to a lower level of precautionary saving motivation. Household registration reduces consumption by causing disparities in insurance coverage. Even after matching propensity scores to adjust for baseline differences between agricultural and non-agricultural households in cities, residents with agricultural registration participate more in a lower benefit scheme of old-age insurance. Furthermore, agricultural households consume less than non-agricultural households despite receiving the same old-age insurance scheme, owing to a greater incentive for precautionary savings. This finding sheds light on the relationship between economic behavior and social conditions with significant policy implications for the economic assimilation of migrants.
A Learning-Based Decision Tool towards Smart Energy Optimization in the Manufacturing Process
Authors: El Mazgualdi Choumicha , Masrour Tawfik , Barka Noureddine , El Hassani Ibtissam
Published: October 2022

We developed a self-optimizing decision system that dynamically minimizes the overall energy consumption of an industrial process. Our model is based on a deep reinforcement learning (DRL) framework, adopting three reinforcement learning methods, namely: deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) algorithms, combined with a self-predicting random forest model. This smart decision system is a physics-informed DRL that sets the key industrial input parameters to optimize energy consumption while ensuring the product quality based on desired output parameters. The system is self-improving and can increase its performances without further human assistance. We applied the approach to the process of heating tempered glass. Indeed, the identification and control of tempered glass parameters is a challenging task requiring expertise. In addition, optimizing energy consumption while dealing with this issue is of great value-added. The evaluation of the decision system under the three configurations has been performed and consequently, outcomes and conclusions have been explained in this paper. Our intelligent decision system provides an optimized set of parameters for the heating process within the acceptance limits while minimizing overall energy consumption. This work provides the necessary foundations to address energy optimization issues related to process parameterization from theory to practice and providing real industrial application; further research opens a new horizon towards intelligent and sustainable manufacturing.
Modeling the Enablers to FinTech Innovation in Saudi Arabia: A Hybrid Approach Using ISM and ANP
Authors: Makki Anas A. , Alqahtani Ammar Y.
Published: October 2022

Financial technology (FinTech) has emerged as a significant financial sector breakthrough due to the sharing economy, new legislation, and IT advances, contributing to its fast growth. Under a new national policy, Saudi Arabia intends to increase the number of FinTech firms. Thus, it is necessary to develop a more profound understanding of what critically enables FinTech innovation, how these enablers are interconnected, and their priorities. This research study aims to identify and model the critical enablers of FinTech innovation by exploring contextual relationships among them and their importance. A hybrid approach was followed using interpretive structural modeling (ISM) and an analytic network process (ANP) to achieve the objective. Eleven enablers and their essential components were extracted from the literature and confirmed by Saudi FinTech experts who provided input data on their linkages and relative importance through interviews and a designed questionnaire. The developed model reveals the enablers’ structure in terms of their driving and dependence powers and classifies them into six levels with relative importance to each other. The developed model in this research puts forward a holistic perspective on FinTech and innovation, assisting decision-makers, regulators, policy designers, practitioners, and technology developers to create effective ways to safeguard the FinTech industry’s growth.
Data Model Design to Support Data-Driven IT Governance Implementation
Authors: Biagi Vittoria , Russo Angela
Published: October 2022

Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.
Prostate Cancer Biochemical Recurrence Resulted Negative on [68Ga]Ga-PSMA-11 but Positive on [18F]Fluoromethylcholine PET/CT
Authors: Laudicella Riccardo , La Torre Flavia , Davì Valerio , Crocè Ludovica , Aricò Demetrio , Leonardi Giuseppe , Russo Simona , Minutoli Fabio , Burger Irene A. , Baldari Sergio
Published: October 2022

For prostate cancer (PCa) biochemical recurrence (BCR), the primarily suggested imaging technique by the European Association of Urology (EAU) guidelines is prostate-specific membrane antigen (PSMA) positron emission tomography/computer tomography (PET/CT). Indeed, the increased detection rate of PSMA PET/CT for early BCR has led to a fast and wide acceptance of this novel technology. However, PCa is a very heterogeneous disease, not always easily assessable with the highly specific PSMA PET with around 10% of cases occuring without PSMA expression. In this paper, we present the case of a patient with PCa BCR that resulted negative on [68Ga]Ga-PSMA-11 PET/CT, but positive on [18F]Fluoromethylcholine (Choline) PET/CT.
Prediction of Ablation Volume in Percutaneous Lung Microwave Ablation: A Single Centre Retrospective Study
Authors: Ierardi Anna Maria , Grillo Pasquale , Bonanno Maria Chiara , Coppola Andrea , Vespro Valentina , Andrisani Maria Carmela , Tosi Davide , Mendogni Paolo , Franzi Sara , Venturini Massimo , Carrafiello Gianpaolo
Published: October 2022

Background: Percutaneous Microwave Ablation (MWA) of lung malignancies is a procedure with many technical challenges, among them the risk of residual disease. Recently, dedicated software able to predict the volume of the ablated area was introduced. Cone-beam computed tomography (CBCT) is the imaging guidance of choice for pulmonary ablation in our institution. The volumetric prediction software (VPS) has been installed and used in combination with CBCT to check the correct position of the device. Our study aimed to compare the results of MWA of pulmonary tumours performed using CBCT with and without VPS. Methods: We retrospectively reviewed 1-month follow-up enhanced contrast-enhanced computed tomography (CECT) scans of 10 patients who underwent ablation with the assistance of VPS (group 1) and of 10 patients who were treated without the assistance of VPS (group 2). All patients were treated for curative purposes, the maximum axial diameter of lesions ranged between 5 and 22 mm in group 1 and between 5 and 25 mm in group 2. We compared the presence of residual disease between the two groups. Results: In group 1 residual disease was seen in only 1 patient (10%) in which VPS had ensured complete coverage of the tumour. In group 2 residual disease was found in 3 patients (30%). Conclusions: Using this software during MWA of lung malignancies could improve the efficacy of the treatment compared to the conventional only CBCT guidance.
Regression Model Decreasing the Risk of Femoral Neurovascular Bundle Accidental Puncture
Authors: Valera-Calero Juan Antonio , Varol Umut , Plaza-Manzano Gustavo , Fernández-de-las-Peñas César , Agudo-Aguado Adolfo
Published: October 2022

Although most of the adverse events derived from dry needling are minor, avoiding potential hazards for patients including accidental invasion of vessels, ganglia, and nerves is essential to ensure patients’ safety. We aimed to investigate the contribution of predictors explaining the variance of sartorius muscle depth limit at proximal third and middle thigh as these locations lead to an augmented risk of neurovascular bundle invasion during dry needling application. A diagnostic study was conducted on 84 subjects to calculate the accuracy of a prediction model for sartorius depth, as assessed with ultrasound imaging, based on sex, age, height, weight, body mass index (BMI), thigh perimeter, and length. After calculating a correlation matrix, a multiple linear regression analysis was performed to detect those variables contributing to the sartorius deep limit in both locations. Although males showed greater thigh perimeter than women (p < 0.001), the deep limit of the sartorius muscle was significantly more superficial for both the proximal third (p = 0.003) and the mid-third (p = 0.004) points. No side-to-side anthropometric differences were found (p > 0.05). In addition, we found sartorius muscle depth to be associated with the proximal and mid-third girth, gender, height, and BMI (all, p < 0.01). Gender, proximal-third girth, and BMI explained 51.1% and 42.6% of the variance for the sartorius deep limit at the proximal and the mid-third, respectively. This study analyzed whether anthropometric features could predict sartorius muscle depth in healthy participants for assisting clinicians in choosing the optimal needle length to avoid accidental femoral bundle puncture.
Fukushima and Chernobyl: Similarities and Differences of Radiocesium Behavior in the Soil–Water Environment
Authors: Konoplev Alexei
Published: October 2022

In the wake of Chernobyl and Fukushima accidents, radiocesium has become a radionuclide of most environmental concern. The ease with which this radionuclide moves through the environment and is taken up by plants and animals is governed by its chemical forms and site-specific environmental characteristics. Distinctions in climate and geomorphology, as well as 137Cs speciation in the fallout, result in differences in the migration rates of 137Cs in the environment and rates of its natural attenuation. In Fukushima areas, 137Cs was strongly bound to soil and sediment particles, with its bioavailability being reduced as a result. Up to 80% of the deposited 137Cs on the soil was reported to be incorporated in hot glassy particles (CsMPs) insoluble in water. Disintegration of these particles in the environment is much slower than that of Chernobyl-derived fuel particles. The higher annual precipitation and steep slopes in Fukushima-contaminated areas are conducive to higher erosion and higher total radiocesium wash-off. Among the common features in the 137Cs behavior in Chernobyl and Fukushima are a slow decrease in the 137Cs activity concentration in small, closed, and semi-closed lakes and its particular seasonal variations: increase in the summer and decrease in the winter.
Comparative Study on the Distribution of Essential, Non-Essential Toxic, and Other Elements across Trophic Levels in Various Edible Aquatic Organisms in Sri Lanka and Dietary Human Risk Assessment
Authors: Wickrama-Arachchige Anura Upasanta-Kumara , Guruge Keerthi S. , Tani Hinako , Dharmaratne Tilak Siri , Kumara Marappullige P. , Niizuma Yasuaki , Ohura Takeshi
Published: October 2022

Thirty-six elements are categorized as essential but toxic in excess amount (EBTEs), non-essential toxic (NETs), and Other in 29 different edible aquatic species dwelling in offshore pelagic, and coastal and estuarine (CE) ecosystems were investigated in Sri Lanka. Elements were analyzed using an energy-dispersive X-ray fluorescence (EDXRF) spectrometer, and an NIC MA-3000 Mercury Analyzer. EBTEs showed a negative relationship, whereas NETs showed a positive relationship between the concentration (mg/kg wet weight) and trophic levels in both ecosystems. EBTEs showed trophic dilution, whereas NETs showed trophic magnification. Some elements in a few organisms exceeded the maximum allowable limit which is safe for human consumption. There was a positive relationship (R2 = 0.85) between the concentration of mercury and body weight of yellowfin tuna (YFT). For the widely consumed YFT, the calculated hazard index (HI) for the non-carcinogenic health and exposure daily intake of NETs for adults were 0.27 and 9.38 × 10−5 mg/kg bw/day, respectively. The estimated provisional tolerable weekly intake (PTWI) (μg/kg bw/w) was 0.47 for arsenic and 0.05 for antimony, cadmium, mercury, and lead. The HI and PTWI values were below the recommended limits; thus, consumption of YFT does not pose any health risk for Sri Lankan adults.
Blood Lead Levels among Non-Occupationally Exposed Pregnant Women in Southern Thailand
Authors: Waeyeng Donrawee , Khamphaya Tanaporn , Pouyfung Phisit , Vattanasit Udomratana , Yimthiang Supabhorn
Published: October 2022

Lead (Pb) is a heavy metal that is toxic to humans, especially children and pregnant women. In Thailand, guidelines exist to minimize lead exposure in pregnant women working in lead-related occupations. However, no guidelines exist for pregnant women who are not employed in these economic sectors. This cross-sectional study aimed to examine blood lead levels (BLLs) and related risk factors among 80 non-occupationally exposed pregnant women from the general population living in Nakhon Si Thammarat province, Southern Thailand. BLLs were determined by graphite furnace atomic absorption spectrophotometry. A validated questionnaire was adopted to interview participants which included demographic, consumer goods, supplement intake, and health factors. The mean BLL was 4.68 ± 1.55 µg/dL (95% CI 4.33–5.02) and 42.50% had BLLs ≥ 5 µg/dL. Higher education was the only demographic factor associated with BLLs ≥ 5 µg/dL (aOR 0.16, 95% CI 0.03–0.80, p = 0.027). Systolic blood pressure was also associated with BLLs ≥ 5 µg/dL (aOR 5.00, 95% CI 1.23–17.16, p = 0.023). However, consumer goods and supplement intake were not associated with BLLs. Our results indicate that pregnant women from the general population who were not in the risk exposure group had lead in their bodies. Except for education, demographics were not associated with pregnant women with BLLs. However, with health factors, even low BLLs had a small effect on systolic blood pressure. These data suggest a need for promoting health education and health interventions to prevent the dangers of lead exposure, especially for pregnant women and children.
Application of Non-Aflatoxigenic Aspergillus flavus for the Biological Control of Aflatoxin Contamination in China
Authors: Zhang Wan , Dou Jianpeng , Wu Zidan , Li Qiu , Wang Shanshan , Xu Huiru , Wu Wenfu , Sun Changpo
Published: October 2022

Biological control through the application of competitive non-aflatoxigenic Aspergillus flavus (A. flavus) to the soil during peanut growth is a practical method for controlling aflatoxin contamination. However, appropriate materials need to be found to reduce the cost of biocontrol products. In this study, a two-year experiment was conducted under field conditions in China, using a native non-aflatoxigenic strain to explore its effect. After three months of storage under high humidity, aflatoxin levels remained low in peanuts from fields treated with the biocontrol agent. Three types of substrates were tested with the biocontrol agent: rice grains, peanut meal (peanut meal fertilizer) and peanut coating. Compared to untreated fields, these formulations resulted in reductions of 78.23%, 67.54% and 38.48%, respectively. Furthermore, the ratios of non-aflatoxigenic A. flavus recovered in the soils at harvest in the treated fields were between 41.11% and 96.67% higher than that in untreated fields (25.00%), indicating that the rice inoculum was the most effective, followed by the peanut meal fertilizer and peanut coating. In 2019, the mean aflatoxin content of freshly harvested peanuts in untreated fields was 19.35 µg/kg higher than that in the fields treated with 7.5 kg/ha rice inoculum, which was 1.37 µg/kg. Moreover, no aflatoxin was detected in the two other plots treated with 10 and 15 kg/ha rice inoculum. This study showed that the native Chinese non-aflatoxigenic strain of A. flavus (18PAsp-zy1) had the potential to reduce aflatoxin contamination in peanuts. In addition, peanut meal can be used as an alternative substrate to replace traditional grains, reducing the cost of biocontrol products.
Construction, Characterization, and Application of a Nonpathogenic Virus-like Model for SARS-CoV-2 Nucleocapsid Protein by Phage Display
Authors: Wu Yuting , Liu Bing , Liu Zhiwei , Zhang Pengjie , Mu Xihui , Tong Zhaoyang
Published: October 2022

With the outbreak and spread of COVID-19, a deep investigation of SARS-CoV-2 is urgent. Direct usage of this virus for scientific research could provide reliable results and authenticity. However, it is strictly constrained and unrealistic due to its high pathogenicity and infectiousness. Considering its biosafety, different systems and technologies have been employed in immunology and biomedical studies. In this study, phage display technology was used to construct a nonpathogenic model for COVID-19 research. The nucleocapsid protein of SARS-CoV-2 was fused with the M13 phage capsid p3 protein and expressed on the M13 phages. After validation of its successful expression, its potential as the standard for qPCR quantification and affinity with antibodies were confirmed, which may show the possibility of using this nonpathogenic bacteriophage to replace the pathogenic virus in scientific research concerning SARS-CoV-2. In addition, the model was used to develop a system for the classification and identification of different samples using ATR–FTIR, which may provide an idea for the development and evaluation of virus monitoring equipment in the future.
Realized Heritability, Risk Assessment, and Inheritance Pattern in Earias vittella (Lepidoptera: Noctuidae) Resistant to Dipel (Bacillus thuringiensis Kurstaki)
Authors: Ahmad Syed Faisal , Gulzar Asim , Abbas Naeem , Tariq Muhammad , Ali Intazar , Hafez Abdulwahab M.
Published: October 2022

Earias vittella Fabricius is a potential cotton and okra pest in South Asia. The realized heritability, risk assessment, and inheritance mode of Bacillus thuringiensis Kurstaki (Btk) resistance were determined in the Dipel-selected (DIPEL-SEL) E. vittella. The DIPEL-SEL strain had a 127.56-fold rise in Dipel resistance after nine generations compared to the laboratory reference strain (LAB-PK). The overlapping of 95% fiducial limits in the median lethal concentrations (LC50s) of the F1 (DIPEL-SEL♂ × LAB-PK♀) and F1ǂ (DIPEL-SEL♀ × LAB-PK♂) suggested a lack of sex linkage and an autosomal Dipel resistance. The dominance (DLC) values for the F1 (0.86) and F1ǂ (0.94) indicated incompletely dominant resistance to Dipel. Backcrossing of the F1♀ × Lab-PK♂ revealed a polygenic response of resistance to Dipel. The realized heritability estimation (h2) of resistance to Dipel was 0.19. With 20% to 90% selection mortality, the generations required for a tenfold increase in LC50 of Dipel were 4.7–22.8, 3.1–14.9, and 2.3–11.1 at h2 of 0.19, 0.29, and 0.39, respectively, and a constant slope of 1.56. At slope values of 2.56 and 3.56 with a constant h2 = 0.19, 7.7–37.4 and 10.6–52.0 generations were needed to increase the tenfold LC50 of Dipel in the DIPEL-SEL E. vittella. It is concluded that the DIPEL-SEL E. vittella has an autosomal, incompletely dominant, and polygenic nature of resistance. The h2 of 0.19 suggested that a high proportion of phenotypic variation for the Dipel resistance in E. vittella was heritable genetic variation. The present results will support the creation of an effective and suitable resistance management plan for better control of E. vittella.
Quantitative Proteomic Analysis of Zearalenone Exposure on Uterine Development in Weaned Gilts
Authors: Liu Xinglin , Wang Zengchun , Jiang Yanping , Huang Libo , Yuan Xuejun , Li Yang , Jiao Ning , Yang Weiren , Jiang Shuzhen
Published: October 2022

The aim of this study was to explore the effect of zearalenone (ZEA) exposure on uterine development in weaned gilts by quantitative proteome analysis with tandem mass spectrometry tags (TMT). A total of 16 healthy weaned gilts were randomly divided into control (basal diet) and ZEA3.0 treatments groups (basal diet supplemented with 3.0 mg/kg ZEA). Results showed that vulva size and uterine development index were increased (p < 0.05), whereas serum follicle stimulation hormone, luteinizing hormone and gonadotropin-releasing hormone were decreased in gilts fed the ZEA diet (p < 0.05). ZEA, α-zearalenol (α-ZOL) and β-zearalenol (β-ZOL) were detected in the uteri of gilts fed a 3.0 mg/kg ZEA diet (p < 0.05). The relative protein expression levels of creatine kinase M-type (CKM), atriopeptidase (MME) and myeloperoxidase (MPO) were up-regulated (p < 0.05), whereas aldehyde dehydrogenase 1 family member (ALDH1A2), secretogranin-1 (CHGB) and SURP and G-patch domain containing 1 (SUGP1) were down-regulated (p < 0.05) in the ZEA3.0 group by western blot, which indicated that the proteomics data were dependable. In addition, the functions of differentially expressed proteins (DEPs) mainly involved the cellular process, biological regulation and metabolic process in the biological process category. Some important signaling pathways were changed in the ZEA3.0 group, such as extracellular matrix (ECM)-receptor interaction, focal adhesion and the phosphoinositide 3-kinase–protein kinase B (PI3K-AKT) signaling pathway (p < 0.01). This study sheds new light on the molecular mechanism of ZEA in the uterine development of gilts.
Blazar Jets as Possible Sources of Ultra-High Energy Photons: A Short Review
Authors: Bhatta Gopal
Published: October 2022

In this paper, I present a qualitative discussion on the prospect of production of ultra-high photons in blazars. The sources are a subclass of active galactic nuclei which host supermassive black holes and fire relativistic jets into the intergalactic medium. The kpc-scale jets are believed to be dominated by Poynting flux and constitute one of the most efficient cosmic particle accelerators, that potentially are capable of accelerating the particles up to EeV energies. Recent IceCube detection of astrophysical neutrino emissions, in coincidence with the enhanced gamma-ray from Tev blazar TXS 0506 + 056, further supports hadronic models of blazar emissions in which particle acceleration processes, such as relativistic shocks, magnetic re-connection, and relativistic turbulence, could energize hadrons, e.g., protons, up to energies equivalent to billions of Lorentz factors. The ensuing photo-pionic processes may then result in gamma-rays accompanied by neutrino flux. Furthermore, the fact that blazars are the dominant source of observed TeV emission encourages search for signatures of acceleration scenarios that would lead to the creation of ultra-high-energy photons.
The Phase Space Analysis of Interacting K-Essence Dark Energy Models in Loop Quantum Cosmology
Authors: Chen Bohai , Wu Yabo , Chi Jianan , Liu Wenzhong , Hu Yiliang
Published: October 2022

The present work deals with two kinds of k-essence dark energy models within the framework of loop quantum cosmology (LQC). The two kinds of k-essence models originates from two forms of Lagrangians, i.e., L1=F(X)V(ϕ) and L2=F(X)−V(ϕ), where F(X) and V(ϕ) stand for the kinetic term and potential of the scalar field ϕ, respectively. Two models are based on different phase variables settings, and the general form of autonomous dynamical system is deduced for each Lagrangian. Then, the dynamical stabilities of the critical points in each model are analysed in different forms of F(X) and V(ϕ). Model I is a 3-dim system with four stable points, and Model II is a 4-dim system but reduced to a 3-dim system using the symmetry analysis, which has five stable points. Moreover, the corresponding cosmological quantities, such as Ωϕ, wϕ and q, are calculated at each critical point. To compare these with the case of the classical Einstein cosmology (EC), the dynamical evolutionary trajectories in the phase space and evolutionary curves of the cosmological quantities are drawn for both EC and LQC cases, which shows that the loop quantum gravity effects diminish in the late-time universe but are significant in the early time. Further, the effects of interaction Q=αHρm on the evolutions of the universe are discussed. With the loop quantum gravity effects, bouncing universe is achieved in both models for different initial values of ϕ0, ϕ˙0, H0, ρ0 and coupling parameter α, which helps to avoid singularities. However, the interaction has little effect on bounce, although it is important to the stability of some critical points.
The Possibility of Mirror Planet as Planet Nine in the Solar System
Authors: Wang Pei , Tang Yuchen , Zu Lei , Chen Yuanyuan , Feng Lei
Published: October 2022

A series of dynamical anomalies in the orbits of distant trans-Neptunian objects points to a new celestial body (usually named Planet Nine) in the solar system. In this draft, we point out that a mirror planet captured from the outer solar system or formed in the solar system is also a possible candidate. The introduction of the mirror matter model is due to an unbroken parity symmetry and is a potential explanation for dark matter. This mirror planet has null or fainter electromagnetic counterparts with a smaller optical radius and might be explored through gravitational effects.
Predictors of COVID-19 Vaccine Acceptance among Healthcare Workers in Nigeria
Authors: Nnaemeka Victory Chizaram , Onwe Reuben Ogba , Ekwedike Adaku Lydia , Oyedele Oluwakemi Elizabeth , Tsiterimam Thomas Sambo , Ochepo Ogbole Emmanuel , Nwokoye Nkiru Nenye , Ike Anthony Chibuogwu
Published: October 2022

Healthcare workers (HCWs) are regarded as role models regarding health-related issues, including vaccination. Therefore, it is essential to identify the predictors for COVID-19 vaccine acceptance among them. A cross-sectional study to assess the risk perception, attitudes and knowledge of HCWs toward COVID-19 vaccination was carried out. A total of 710 responses were received between September 2021 and March 2022, from HCWs in the Northern, Western and Eastern regions of Nigeria. Cross tabulations were performed to determine statistical relations between sociodemographic variables, knowledge, attitudes and risk perceptions concerning COVID-19 vaccine acceptance. Multinomial logistic regression analysis was performed to determine the predictive variables for COVID-19 vaccine acceptance. Statistical analyses were performed and P-values less than 0.05 were considered statistically significant at a CI of 95%. Results showed that 59.3% of the participants were amenable to COVID-19 vaccines. Multinomial regression analysis identified 14 variables at α < 0.05 as predictors for vaccine acceptance. Male HCWs were 2.8 times more likely to accept the vaccine than their female counterparts. HCWs that were knowledgeable of the different kinds of vaccines, were willing to recommend the vaccines to their patients, believed that the timing of COVID-19 vaccination was appropriate and had recent vaccination history within three years were 1.6, 24.9, 4.4 and 3.1 times more likely to take COVID-19 vaccine than those not sure. The study found a relatively high trust (51.3%) in the Nigerian Center for Disease Control (NCDC) for information regarding COVID-19 vaccines. Therefore, the NDCD should disseminate more robust insights regarding the safety profiles of various COVID-19 vaccines.