bluuebunny/crossref_metadata_2025 huggingface dataset:
Revisiting Classical Controller Design and Tuning with Genetic Programming
Authors: García Carlos A. , Velasco Manel , Angulo Cecilio , Marti Pau , Camacho Antonio
Published: December 2023

This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm of control engineering. In contrast to already existing work, our GP-based approach operates exclusively in the time domain, incorporating differential operations such as derivatives and integrals without necessitating intermediate inverse Laplace transformations. This unique feature not only simplifies the design process but also ensures the practical implementability of the generated controllers within physical systems. Notably, the GP’s functional set extends beyond basic arithmetic operators to include a rich repertoire of mathematical operations, encompassing trigonometric, exponential, and logarithmic functions. This broad set of operations enhances the flexibility and adaptability of the GP-based approach in controller design. To rigorously assess the efficacy of our GP-based approach, we conducted an extensive series of tests to determine its limits and capabilities. In summary, our research establishes the GP-based approach as a promising solution for automating the controller design process, offering a transformative tool to address a spectrum of control problems across various engineering applications.
An Open-Source Wireless Electrophysiology System for In Vivo Neuronal Activity Recording in the Rodent Brain: 2.0
Authors: Erofeev Alexander , Antifeev Ivan , Vinokurov Egor , Bezprozvanny Ilya , Vlasova Olga
Published: December 2023

Current trends in neurobiological research focus on analyzing complex interactions within brain structures. To conduct relevant experiments, it is often essential to employ animals with unhampered mobility and utilize electrophysiological equipment capable of wirelessly transmitting data. In prior research, we introduced an open-source wireless electrophysiology system to surmount these challenges. Nonetheless, this prototype exhibited several limitations, such as a hefty weight for the wireless module, redundant system components, a diminished sampling rate, and limited battery longevity. In this study, we unveil an enhanced version of the open-source wireless electrophysiology system, tailored for in vivo monitoring of neural activity in rodent brains. This new system has been successfully tested in real-time recordings of in vivo neural activity. Consequently, our development offers researchers a cost-effective and proficient tool for studying complex brain functions.
Efficacy of Marker-Based Motion Capture for Respiratory Cycle Measurement: A Comparison with Spirometry
Authors: Shamantseva Natalia D. , Klishkovskaia Tatiana A. , Ananyev Sergey S. , Aksenov Andrey Y. , Moshonkina Tatiana R.
Published: December 2023

Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) based on a motion capture video system. Two analysis methods, the summation method and the triangle method were developed. The study focused on determining the optimal number of markers while achieving accuracy in respiratory parameter measurements. The results showed that most analysis methods showed a difference of ≤0.5 breaths per minute, with R2 ≥ 0.94 (p < 0.001) compared to spirometry. The best OEP methods for respiratory rate were the abdominal triangles and the sum of abdominal markers in all body positions. The study explored inspiratory and expiratory durations. The research found that 5–9 markers were sufficient to accurately determine respiratory time components in all body positions, reducing the marker requirements compared to previous studies. This interchangeability of OEP methods with standard spirometry demonstrates the potential of non-invasive methods for the simultaneous assessment of body segment movements, center of pressure dynamics, and respiratory movements. Future research is required to improve the clinical applicability of these methods.
Action Recognition Based on Multi-Level Topological Channel Attention of Human Skeleton
Authors: Hu Kai , Shen Chaowen , Wang Tianyan , Shen Shuai , Cai Chengxue , Huang Huaming , Xia Min
Published: December 2023

In action recognition, obtaining skeleton data from human poses is valuable. This process can help eliminate negative effects of environmental noise, including changes in background and lighting conditions. Although GCN can learn unique action features, it fails to fully utilize the prior knowledge of human body structure and the coordination relations between limbs. To address these issues, this paper proposes a Multi-level Topological Channel Attention Network algorithm: Firstly, the Multi-level Topology and Channel Attention Module incorporates prior knowledge of human body structure using a coarse-to-fine approach, effectively extracting action features. Secondly, the Coordination Module utilizes contralateral and ipsilateral coordinated movements in human kinematics. Lastly, the Multi-scale Global Spatio-temporal Attention Module captures spatiotemporal features of different granularities and incorporates a causal convolution block and masked temporal attention to prevent non-causal relationships. This method achieved accuracy rates of 91.9% (Xsub), 96.3% (Xview), 88.5% (Xsub), and 90.3% (Xset) on NTU-RGB+D 60 and NTU-RGB+D 120, respectively.
Fault Diagnosis Method for Human Coexistence Robots Based on Convolutional Neural Networks Using Time-Series Data Generation and Image Encoding
Authors: Choi Seung-Hwan , Park Jun-Kyu , An Dawn , Kim Chang-Hyun , Park Gunseok , Lee Inho , Lee Suwoong
Published: December 2023

This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.
A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks
Authors: Yan Shaokai , Chen Fei , Yang Jiandong , Zhao Zhigao
Published: December 2023

Accurately predicting the changes in turbine vibration trends is a key part of the operational condition maintenance of hydropower units, which is of great significance for improving both the operational condition and operational efficiency of hydropower plants. In this paper, we propose a multistep prediction model for the vibration trend of a hydropower unit. This model is based on the theoretical principles of signal processing and machine learning, incorporating variational mode decomposition (VMD), stochastic configuration networks (SCNs), and the recursive strategy. Firstly, in view of the severe fluctuations of the vibration signal of the unit, this paper decomposes the unit vibration data into intrinsic mode function (IMF) components of different frequencies by VMD, which effectively alleviates the instability of the vibration trend. Secondly, an SCN model is used to predict different IMF components. Then, the predicted values of all the IMF components are superimposed to form the prediction results. Finally, according to the recursive strategy, a multistep prediction model of the HGU’s vibration trends is constructed by adding new input variables to the prediction results. This model is applied to the prediction of vibration data from different components of a unit, and the experimental results show that the proposed multistep prediction model can accurately predict the vibration trend of the unit. The proposed multistep prediction model of the vibration trends of hydropower units is of great significance in guiding power plants to adjust their control strategies to reach optimal operating efficiency.
Grid Impact of Wastewater Resource Recovery Facilities-Based Community Microgrids
Authors: Mohamed Ahmed Ali A. , Zafar Kirn , Vaidya Dhavalkumar , Salmeron Lizzette , Kanwhen Ondrea , Esa Yusef , Kamaludeen Mohamed
Published: December 2023

The overarching goal of this paper is to explore innovative ways to adapt existing urban infrastructure to achieve a greener and more resilient city, specifically on synergies between the power grid, the wastewater treatment system, and community development in low-lying coastal areas. This study addresses the technical feasibility, benefits, and barriers of using wastewater resource recovery facilities (WRRFs) as community-scale microgrids. These microgrids will act as central resilience and community development hubs, enabling the adoption of renewable energy and the provision of ongoing services under emergency conditions. Load flow modeling and analysis were carried out using real network data for a case study in New York City (NYC). The results validate the hypothesis that distributed energy resources (DERs) at WRRFs can play a role in improving grid operation and resiliency.
Surveillance Capitalism in Mental Health: When Good Apps Go Rogue (and What Can Be Done about It)
Authors: Gross Nicole , Mothersill David
Published: December 2023

Research shows that a large proportion of the world’s population has experience with mental health difficulties, and reliable as well as scalable care is urgently needed. Digital mental health seems to be an obvious solution to provide the better delivery of care but also the delivery of better care. With an imagined future of real-time information sharing, improved diagnosis and monitoring of mental health conditions, and remote care, supported by advances in artificial intelligence, many tech companies have emerged over the last three decades to plug the treatment gap and provide services. The evidence base seems compelling: some online treatments have the capability to treat individuals quite successfully. However, the introduction, utilisation, and expansion of digital mental health technologies have not always focused on public health only. Using a surveillance capitalism perspective, this paper approaches the democratisation–privatisation dichotomy in digital mental health with a critical lens. In particular, the paper details how (commercially valuable) mental health data are extracted, “shared”, and claimed as an asset by big tech companies. Reviewing the terms, conditions, and practices of ten popular mental health apps, the paper polemically argues that mental digital health cannot unlock real value for society—better treatment, good quality care, and efficient delivery—if power, politics, and profits remain in the hands of big tech companies. To conclude, the paper draws attention to contemporary discourses that seek to promote democracy and public value for digital mental health apps, technologies, and solutions.
Assessment of Entrepreneurial Potential in the Training of a New Generation of Change Agents in Spain
Authors: Cárdenas-Gutiérrez Antonio Ramón , Domínguez-Quintero Ana María , Bernal-Guerrero Antonio
Published: December 2023

The development of entrepreneurial potential in the training of school-age pupils is relevant for carrying out projects of an innovative and transformative nature. Entrepreneurial training is enacted through Spanish educational regulations, in relation to the development of the key entrepreneurial competence. Thus, a training programme in entrepreneurial potential for school-age students, who are considered as agents of change, was evaluated under the approach of “enterprise education pedagogy”. For this purpose, an experimental research study, pre-test and post-test, with a control and experimental group, was designed. The sample consisted of 1036 participants from eight autonomous communities in Spain. The data analysis was carried out by means of a t-test to compare the mean before and after the application of the programme on the total number of participants, as well as on the subgroups with and without entrepreneurial intentions. The results show that the PEIEO programme had a positive effect on entrepreneurial potential. The experimental group, compared to the control group, significantly increased their total score in the t-test, as well as for each dimension of entrepreneurial potential. Similarly, the participants considered to have entrepreneurial intentions further increased their entrepreneurial potential compared to the group considered to have no entrepreneurial intentions. Consequently, the results indicate that entrepreneurship education, in a holistic sense, has a relevant impact on entrepreneurial potential in a programme aimed at training agents of change.
Addressing the Microplastic Dilemma in Soil and Sediment with Focus on Biochar-Based Remediation Techniques: Review
Authors: Elbasiouny Heba , Elbehiry Fathy
Published: December 2023

Microplastic (MP) pollution is a widespread global environmental concern, representing an emerging contaminant with major implications for ecosystems and human well-being. While extensive research has focused on MPs in aquatic environments, their impact on sediments and soils remains inadequately explored. Studies have confirmed the harmful effects of MPs on soil and sediment biota, as well as on the properties of these ecosystems. Furthermore, the long-term persistence of MPs within the environment contributes to their accumulation in terrestrial and marine food chains, with potential consequences for groundwater quality. Although several methods have been applied to mitigate MP pollution, some methods have drawbacks and some are not studied well, necessitating the urgent exploration of novel, sustainable, and eco-friendly approaches. Biochar is a remarkable solution for pollution removal; recently it has been used in addressing the increasingly concerning issue of microplastic contamination. This review aims to shed light on the difficulty posed by MPs in soils and sediments, while highlighting the remediation methods and the potential advantages of utilizing BC as an environmentally friendly solution for MP removal and remediation.
A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min–Max Normalization
Authors: Shantal Mohammed , Othman Zalinda , Bakar Azuraliza Abu
Published: December 2023

In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets.
Research on the Measurement and Characteristics of Virtual Agglomeration Based on Social Network Analysis: Evidence from 29 Manufacturing Industries in China
Authors: Zhang Qing , Ru Shaofeng , Cheng Yiyang
Published: December 2023

(1) Background: Virtual agglomeration reshapes the organizational form and drives the sustainable development of the manufacturing industry. How to measure the virtual agglomeration level of the manufacturing industry is an important and difficult problem for current research. (2) Methods: In this study, we constructed a social network for the virtual agglomeration of the manufacturing industry, with each industry representing a node in the network. We also measured the virtual agglomeration level of the overall manufacturing industry using the network edge number and network density indicators in the social network analysis method. Each sub-industry virtual agglomeration level was measured using the point centrality index. Furthermore, the virtual agglomeration characteristics of the manufacturing industry were examined through cluster analysis and core–periphery analysis. The data sources include the supply chain statistics and virtual agglomeration text data of manufacturing enterprises. The virtual agglomeration text data were obtained with the help of Python crawler technology. Two types of data were matched, and the virtual agglomeration data of 29 manufacturing industries in China from 2012 to 2022 was obtained. (3) Results: The virtual agglomeration level of the overall manufacturing industry is constantly improving, but there are large differences among different industries. Moreover, the virtual agglomeration of the manufacturing industry has the characteristics of both specialization and diversification. The virtual agglomeration social network of the manufacturing industry is experiencing an evolution process from a “core–periphery” structure to a “core–semi-periphery–periphery” structure. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the virtual agglomeration level of the manufacturing industry.
Identification of Innovative Opportunities Based on Product Scenario Evolution
Authors: Wang Fanfan , Tan Runhua , Peng Qingjin , Wang Kang , Dong Yafan
Published: December 2023

Innovation is a key factor for product development. Identifying innovative opportunities is the first step in innovative product design. Traditional methods of identifying innovative opportunities, such as market surveys and brainstorming, are limited by product users’ and designers’ experiences and lack systematic approaches to generate breakthrough innovations. This paper proposes a method to identify innovative opportunities based on product scenario evolution. The method models a product scenario based on product scenario elements, states, and behaviors. A Type II hierarchical function model is constructed based on the transformation and abstraction hierarchy of the product function model to identify target elements for the scenario evolution. Based on the theory of basic element extension and needs evolution characteristics, the method of extending target scenario elements is proposed. Based on the new scenario element sets and their impact, diffusion, identification, and evaluation methods are proposed for innovation opportunities. Potential opportunities are explored for product innovation from a scenario evolutionary perspective, which updates knowledge and technology reserves and finds new market opportunities for industries. The feasibility and effectiveness of the method are verified using the innovative design of a polyethylene (PE) pipeline hot-melt welding machine.
The Relationship between the Rhizothrichidae Por (Copepoda: Harpacticoida) and the Cletodoidea Bowman & Abele, Including the Establishment of a New Genus and the Description of a New Species
Authors: George Kai Horst , Zey Andreina , Packmor Jana
Published: December 2023

During expedition EMB-238 of RV EM BORGESE to the Fehmarn Belt (Baltic Sea, Germany) in 2020, specimens of a new species of the Rhizothrichidae Por (Crustacea, Copepoda, Harpacticoida) were found. Currently, Rhizothrichidae enclose two genera: Rhizothrix Sars and Tryphoema Monard. The assignment of the new species is based on the following commonly derived characters: the formation of a strong spinulose (sub)apical element on the last segment of the female A1, and the formation of a long brush seta on the last exopodal and endopodal segments of the P1. The new species could not be assigned to either Rhizothrix or Tryphoema because it lacks their autapomorphies but presents exclusive derived characters, including the absence of the abexopodal seta on the A2 allobasis, the possession of only one seta on the endopods of P2–P4, and the fusion of the female P5 exopod and baseoendopod. Therefore, it is assigned to a new genus. Besides the species description, its phylogenetic position in the Rhizothrichidae as well as its possible relationship with Rhizothrix and Tryphoema are discussed. Furthermore, the affiliation of the Rhizothrichidae with the Cletodoidea is justified on the basis of 17 apomorphies. A key to the rhizothrichid genera is provided.
Door-Opening Technologies: Search for Affordable Assistive Technology
Authors: Shaikh-Mohammed Javeed , Alharbi Yousef , Alqahtani Abdulrahman
Published: December 2023

To the authors’ knowledge, currently, there is no review covering the different technologies applied to opening manual doors. Therefore, this review presents a summary of the various technologies available on the market as well as those under research and development for opening manual doors. Four subtopics—doorknob accessories, wheelchair-mounted door-opening accessories, door-opening robots, and door-opening drones—were used to group the various technologies for manually opening doors. It is evident that opening doors is a difficult process, and there are different ways to solve this problem in terms of the technology used and the cost of the end product. The search for an affordable assistive technology for opening manual doors is ongoing. This work is an attempt to provide wheelchair users and their healthcare providers with a one-stop source for door-opening technologies. At least one of these door-opening solutions could prove beneficial to the elderly and some wheelchair users for increased independence. The ideal option would depend on an individual’s needs and capabilities, and occupational therapists could assess and recommend the right solutions.
Amlodipine Therapy in β-Thalassemia Patients: A Systematic Review and Meta-Analysis on Ferritin Levels and Liver MRI T2*
Authors: Aliasgharian Aily , Karami Hossein , Zahedi Mohammad , Jahanshahi Reza , Bakhtiari-Dovvombaygi Hossein , Nasirzadeh Amirreza , Naderisorki Mohammad , Kosaryan Mehrnoush , Salehifar Ebrahim , Ghazaiean Mobin , Bitaraf Saeid , Darvishi-Khezri Hadi
Published: December 2023

Background and aim: We conducted a review to determine the efficacy of amlodipine alongside iron chelators on serum ferritin levels and liver T2-weighted magnetic resonance imaging (MRI T2*) in β-thalassemia patients. Methods: Systematic search was conducted in multiple databases, including Web of Science, PubMed, Scopus, Embase, Cochrane Library, ClinicalTrials.gov, the Iranian Registry of Clinical Trials (IRCT), ProQuest, OpenGrey, and Web of Science Conference Proceedings Citation Index. The search was closed in January 2023. Primary outcomes were comprised of liver MRI T2* (millisecond (msec)) and serum ferritin levels (ng/mL). Results: Seven studies (n = 227) were included in the study. The pooled Cohen’s d for serum ferritin was estimated at −0.46, 95% confidence interval (CI) −1.11 to 0.19 and p = 0.16 (I2 86.23%, p < 0.0001). The pooled mean difference for serum ferritin was −366.44 ng/mL, 95% CI −844.94 to 112.05, and p = 0.13 (I2 81.63%, p < 0.0001). After a meta-regression based on the length of using amlodipine, a coefficient for the mean difference was also −23.23 ng/mL and 95% CI −155.21 to 108.75. The coefficient obtained from a meta-regression as per the amlodipine dose at 5 mg/day than 2.5 to 5 mg/day anchored at −323.49 ng/mL and 95% CI −826.14 to 1473.12. A meta-regression according to the baseline values of serum ferritin discovered a coefficient of 1.25 ng/mL and 95% CI 0.15 to 2.35. Based on two included studies (n = 96), the overall Cohen’s d for liver MRI T2* was 2.069, 95% CI −0.896 to 5.035, and p = 0.17 (I2 96.31%, p< 0.0001). The synthesized mean difference for liver MRI T2* was 8.76 msec, 95% CI −4.16 to 21.67, and p = 0.18 (I2 98.38%, p < 0.000). Conclusion: At a very low level of evidence, probably using amlodipine at a dose of 2.5 to 5 mg a day, up to a year, alongside iron chelators slightly decreases serum ferritin levels in iron-overloaded thalassemia cases by nearly 366 ng/mL (23 ng/mL per month). The liver MRI T2* might also rise to 8.76 msec upon co-therapy with amlodipine.
Ecotoxicology of Polymetallic Nodule Seabed Mining: The Effects of Cobalt and Nickel on Phytoplankton Growth and Pigment Concentration
Authors: Ou Rimei , Huang Hao , He Xuebao , Lin Shuangshuang , Ou Danyun , Li Weiwen , Qiu Jinli , Wang Lei
Published: December 2023

In order to improve the understanding of the environmental impacts of polymetallic nodule mining, ecotoxicological studies were conducted on the growth of model phytoplankton species Skeletonema costatum and Prorocentrum donghaiense using cobalt and nickel. This study evaluated various physiological and ecological indicators, such as cell proliferation, chlorophyll a, pigments, total protein, and antioxidant enzyme markers. The results show that the introduction of low amounts of cobalt or nickel increased the growth rate of phytoplankton. The phytoplankton benefited from low concentrations of cobalt and nickel stress. The increased protein levels and decreased activity of antioxidant enzymes considerably impacted physiological responses during the promotion of cell abundance. High concentrations of cobalt or nickel resulted in decreased light-absorbing pigments, increased photoprotective pigments, an inactive chlorophyll content, decreased total proteins, and maximal antioxidant enzyme activity in phytoplankton. Throughout the experiment, both the phytoplankton protein and enzyme activity declined with prolonged stress, and the cells underwent age-induced damage. Thus, seabed mining’s repercussions on phytoplankton could result in both short-term growth promotion and long-term damage. These consequences depend on the impurity concentrations infiltrating the water, their duration, and the organism’s physiological responses.
Hormesis and Low Toxic Effects of Three Lanthanides in Microfungi Isolated from Rare Earth Mining Waste in Northwestern Russia
Authors: Kasatkina Elena A. , Shumilov Oleg I. , Kirtsideli Irina Y. , Makarov Dmitry V.
Published: December 2023

The low-dose toxicity of chloride and nitrate salts of three lanthanides (La, Ce and Nd) was tested on six microfungal species. Five of them (Geomyces vinaceus, Aspergillus niveoglaucus, Pseudogymnoascus pannorum, Penicillium simplicissimum and Umbelopsis isabellina) were isolated from the loparite ore tailings on the Kola Peninsula, northwestern Russia. Sydowia polyspora was a control strain. In the case of nitrate salts, the toxicity of REEs to four of six microorganisms was significantly (p < 0.5) lower compared to chloride salts. In this case, nitrates can play the role of exogenous nutrients, compensating for the toxic effect of REEs. Interestingly, U. isabellina only showed an opposite response, indicating the highest toxicity of nitrate (IC5 = 9–20 mg/L) REEs’ salts compared to chlorides (IC5 = 80–195 mg/L) at low concentration levels. In addition, treatment with lanthanides showed a “hormesis effect” on fungal growth with stimulation at low doses and inhibition at high doses. However, U. isabellina and S. polyspora demonstrated the absence of hormetic response under the treatment of REEs’ nitrate salt. Taking into account the specific hormetic responses and high tolerance of P. simplicissimum and U. isabellina to lanthanides, our findings may be useful in the assessment of the potential application of the selected fungi to bioremediation and REE bioleaching.
Geodesic Structure of Generalized Vaidya Spacetime through the K-Essence
Authors: Majumder Bivash , Khlopov Maxim , Ray Saibal , Manna Goutam
Published: December 2023

This article investigates the radial and non-radial geodesic structures of the generalized K-essence Vaidya spacetime. Within the framework of K-essence geometry, it is important to note that the metric does not possess conformal equivalence to the conventional gravitational metric. This study employs a non-canonical action of the Dirac–Born–Infeld kind. In this work, we categorize the generalized K-essence Vaidya mass function into two distinct forms. Both the forms of the mass functions have been extensively utilized to analyze the radial and non-radial time-like or null geodesics in great detail inside the comoving plane. Indications of the existence of wormholes can be noted during the extreme phases of spacetime, particularly in relation to black holes and white holes, which resemble the Einstein–Rosen bridge. In addition, we have also detected a distinctive indication of the quantum tunneling phenomenon around the singularity (r→0). Furthermore, we have found that for certain types of solutions, there exist circular orbits through the event horizon as well as quasicircular orbits. Also, we have noted that there is no central singularity in our spacetime where both r and t tend towards zero. The existence of a central singularity is essential for any generalized Vaidya spacetime. This indicates that spacetime can be geodesically complete, which correlates with the findings of Kerr’s recent work (2023).
Novel Tri-Segmented Rhabdoviruses: A Data Mining Expedition Unveils the Cryptic Diversity of Cytorhabdoviruses
Authors: Bejerman Nicolas , Dietzgen Ralf , Debat Humberto
Published: December 2023

Cytorhabdoviruses (genus Cytorhabdovirus, family Rhabdoviridae) are plant-infecting viruses with enveloped, bacilliform virions. Established members of the genus Cytorhabdovirus have unsegmented single-stranded negative-sense RNA genomes (ca. 10–16 kb) which encode four to ten proteins. Here, by exploring large publicly available metatranscriptomics datasets, we report the identification and genomic characterization of 93 novel viruses with genetic and evolutionary cues of cytorhabdoviruses. Strikingly, five unprecedented viruses with tri-segmented genomes were also identified. This finding represents the first tri-segmented viruses in the family Rhabdoviridae, and they should be classified in a novel genus within this family for which we suggest the name “Trirhavirus”. Interestingly, the nucleocapsid and polymerase were the only typical rhabdoviral proteins encoded by those tri-segmented viruses, whereas in three of them, a protein similar to the emaravirus (family Fimoviridae) silencing suppressor was found, while the other predicted proteins had no matches in any sequence databases. Genetic distance and evolutionary insights suggest that all these novel viruses may represent members of novel species. Phylogenetic analyses, of both novel and previously classified plant rhabdoviruses, provide compelling support for the division of the genus Cytorhabdovirus into three distinct genera. This proposed reclassification not only enhances our understanding of the evolutionary dynamics within this group of plant rhabdoviruses but also illuminates the remarkable genomic diversity they encompass. This study not only represents a significant expansion of the genomics of cytorhabdoviruses that will enable future research on the evolutionary peculiarity of this genus but also shows the plasticity in the rhabdovirus genome organization with the discovery of tri-segmented members with a unique evolutionary trajectory.
Structural Studies of Bacteriophage Φ6 and Its Transformations during Its Life Cycle
Authors: Heymann J. Bernard
Published: December 2023

From the first isolation of the cystovirus bacteriophage Φ6 from Pseudomonas syringae 50 years ago, we have progressed to a better understanding of the structure and transformations of many parts of the virion. The three-layered virion, encapsulating the tripartite double-stranded RNA (dsRNA) genome, breaches the cell envelope upon infection, generates its own transcripts, and coopts the bacterial machinery to produce its proteins. The generation of a new virion starts with a procapsid with a contracted shape, followed by the packaging of single-stranded RNA segments with concurrent expansion of the capsid, and finally replication to reconstitute the dsRNA genome. The outer two layers are then added, and the fully formed virion released by cell lysis. Most of the procapsid structure, composed of the proteins P1, P2, P4, and P7 is now known, as well as its transformations to the mature, packaged nucleocapsid. The outer two layers are less well-studied. One additional study investigated the binding of the host protein YajQ to the infecting nucleocapsid, where it enhances the transcription of the large RNA segment that codes for the capsid proteins. Finally, I relate the structural aspects of bacteriophage Φ6 to those of other dsRNA viruses, noting the similarities and differences.
Ginseng Polysaccharide Enhances the Humoral and Cellular Immune Responses to SARS-CoV-2 RBD Protein Subunit Vaccines
Authors: Zhang Jing , Feng Jing , Huang Yang , Zhou Boyan , Li Bing , Zhang Rongxin
Published: December 2023

The COVID-19 pandemic remarkably accelerated vaccine research progress. The role of adjuvants in enhancing vaccine immune intensity and influencing immune types has been considered. Ginseng polysaccharide (GPS) has been demonstrated to have strong immunoregulatory properties. It is important to explore the feasibility of adding GPS to vaccine adjuvant components to improve the immune response effect of RBD vaccines. Here, we prepared a SARS-CoV-2 RBD antigen using the Escherichia coli expression system and determined that subcutaneous administration of GPS at a dose of 40 mg/kg could effectively activate dendritic cells (DCs) and macrophages (MΦ) in mice. Compared with the RBD group, the RBD+GPS triggered stronger and persistent antibody responses. It is also notable that higher levels of RBD-specific IgG and IgA were distributed in the lungs of RBD+GPS-immunized BALB/c mice. In addition, the RBD+GPS also resulted in lower percentages of IFN-γ+ CD4+ T cells and higher percentages of IFN-γ+ CD8+ T cells and CD8+ Tcm cells. These results suggest that GPS could be a promising vaccine immuno-enhancer for SARS-CoV-2 RBD subunit vaccines to establish stronger systemic and pulmonary mucosal protective immunity.
Effectiveness of Adapted COVID-19 Vaccines and Ability to Establish Herd Immunity against Omicron BA.1 and BA4-5 Variants of SARS-CoV-2
Authors: Plans-Rubió Pedro
Published: December 2023

The emergence of novel SARS-CoV-2 variants has raised concerns about the ability of COVID-19 vaccination programs to establish adequate herd immunity levels in the population. This study assessed the effectiveness of adapted vaccines in preventing SARS-CoV-2 infection and the ability of the adapted vaccines to establish herd immunity against emerging Omicron variants. A systematic literature review was conducted to estimate the absolute vaccine effectiveness (aVE) in preventing SARS-CoV-2 infection using adapted vaccines targeting Omicron variants. The ability of the adapted vaccines to establish herd immunity was assessed by taking into account the following factors: aVE, Ro values of SARS-CoV-2 and the use of non-pharmacological interventions (NPIs). This study found meta-analysis-based aVEs in preventing severe disease and SARS-CoV-2 infection of 56–60% and 36–39%, respectively. Adapted vaccines could not establish herd immunity against the Omicron BA.1 and BA.4-5 variants without using non-pharmacological interventions (NPIs). The adapted vaccines could establish herd immunity only by achieving >80% vaccination coverage, using NPIs with greater effectiveness and when 20–30% of individuals were already protected against SARS-CoV-2 in the population. New adapted COVID-19 vaccines with greater effectiveness in preventing SARS-CoV-2 infection must be developed to increase herd immunity levels against emerging SARS-CoV-2 variants in the population.
Microbiota Characterization of the Cow Mammary Gland Microenvironment and Its Association with Somatic Cell Count
Authors: Liu Jing , Liu Huan , Cao Guangjie , Cui Yifang , Wang Huanhuan , Chen Xiaojie , Xu Fei , Li Xiubo
Published: December 2023

Subclinical mastitis is a common disease that threatens the welfare and health of dairy cows and causes huge economic losses. Somatic cell count (SCC) is the most suitable indirect index used to evaluate the degree of mastitis. To explore the relationship between SCC, diversity in the microbiome, and subclinical mastitis, we performed next-generation sequencing of the 16S rRNA gene of cow’s milk with different SCC ranges. The data obtained showed that the microbiota was rich and coordinated with SCC below 2 × 105. SCC above 2 × 105 showed a decrease in the diversity of microbial genera. When SCC was below 2 × 105, the phylum Actinobacteriota accounted for the most. When SCC was between 2 × 105 and 5 × 105, Firmicutes accounted for the most, and when SCC exceeded 5 × 105, Firmicutes and Proteobacteria accounted for the most. Pathogenic genera such as Streptococcus spp. were absent, while SCC above 2 × 105 showed a decrease in the diversity of microbial genera. SCC was positively correlated with the percentage of Romboutsia, Turicibacter, and Paeniclostridium and negatively correlated with the percentage of Staphylococcus, Psychrobacter, Aerococcus, and Streptococcus. Romboutsia decreased 6.19 times after the SCC exceeded 2 × 105; the SCC increased exponentially from 2 × 105 to 5 × 105 and above 1 × 106 in Psychrobacter. Analysis of the microbiota of the different SCC ranges suggests that the development of mastitis may not only be a primary infection but may also be the result of dysbiosis in the mammary gland.
Modal Analysis of 15 MW Semi-Submersible Floating Wind Turbine: Investigation on the Main Influences in Natural Vibration
Authors: Harger Arthur , Carmo Lucas H. S. , Gay Neto Alfredo , Simos Alexandre N. , Franzini Guilherme R. , Vieira Guilherme Henrique Rossi
Published: December 2023

One of the sources of sustainable energy with great, still untapped potential is wind power. One way to harness such potential is to develop technology for offshore use, more specifically at high depths with floating turbines. It is always critical that their structural designs guarantee that their natural frequencies of vibration do not match the frequencies of the most important oscillatory loads to which they will be subjected. This avoids resonance and its excessive undesired oscillatory responses. Based on that, a 3D finite element model of a 15 MW semi-submersible floating offshore wind turbine was developed in the commercial software ANSYS Mechanical ® to study its dynamic behavior and contribute to the in-depth analysis of structural modeling of FOWTs. A tower and floating platform were individually modeled and coupled together. The natural frequencies and modes of vibration of the coupled system and of its components were obtained by modal analysis, not only to verify the resonance, but also to investigate the determinant factors affecting such behaviors, which are not extensively discussed in literature. It was found that there is strong coupling between the components and that the tower affects the system as a result of its stiffness, and the floater as a result of its rotational inertia. The platform’s inertia comes mainly from the ballast and the effects of added mass, which was considered to be a literal increase in mass and was modeled in two manners: first, it was approximately calculated and distributed along the submerged flexible platform members and then as a nodal inertial element with the floater being considered as a rigid body. The second approach allowed an iterative analysis for non-zero frequencies of vibration, which showed that a first approximation with an infinite period is sufficiently accurate. Furthermore, the effects of the mooring lines was studied based on a linear model, which showed that they do not affect the boundary conditions at the bottom of the tower in a significant way.
Interdisciplinary approach to the diagnosis of dentofacial deformities and speech disorders in preschoolers
Authors: Danilova M. A. , Goncharova-Tverskaya O. N. , Zalazaeva E. A. , Prokoshev P. A.
Published: December 2023

Relevance. Maxillofacial deformities and speech disorders are common types of oral pathology detected in children under seven years. Comprehensive rehabilitation of patients with a combination of these forms of pathology is a difficult task since there is no situational analysis of their prevalence; motivated specialists work in different institutions (education, healthcare) and have different guidelines for the treatment (correction) of existing diseases (disorders), which complicates planning and administering treatment, and predicting results.

Material and methods. To identify the nature of the relationship between various forms of orthodontic and speech disorders in children, we conducted a comprehensive clinical dental examination of 80 children aged 4-6 years in a preschool educational institution, where children with speech disorders receive speech therapy assistance.

Results. Maxillofacial deformities appeared to be in 56.25% of children with some speech sound disorder. Children with identified speech sound disorders combined with dentofacial deformities showed not only skeletal but also soft tissue defects: shifted midline (86.67 ± 0.10%), distal occlusion (42.22 ± 0.30%), deep overbite (46.68%), as well as tongue and upper lip ties in 51.10% and 22.20% of cases, respectively. On examination, 44.45% of patients demonstrated clinical signs of mouth breathing, 32.5% – reverse swallowing, and 53.33% – inefficient chewing.

Conclusion. Analysis of the results of a comprehensive speech pathology assessment and dental examination allowed us to conclude that there is a need to rethink the methodology of diagnosis, prevention and treatment of combined speech and dental disorders due to the high frequency of their comorbidity in preschool children.
Big Pride, Little Pride, and the Cunning of (In)Visibility in the Southern Caribbean
Authors: McNeal Keith E.
Published: December 2023

This essay explores the history, politics, and practice of global-style Pride in Trinidad and Tobago. It distinguishes between “Big” and “Little” forms of Pride and emphasizes that Pride did not emerge with the advent of a parade and day of high-profile visibility in 2018 after a successful High Court challenge to remove homophobic anti-sodomy and related laws in this twin-island postcolonial Republic, but developed over decades of organizing and intervention in lower-profile and more inwardly community-oriented ways, setting the foundation for the new institutional form and full-scale public orientation of #PrideTT. Once conjured, Trinbagonian Big Pride was paradoxically successful vis-à-vis the nation at large, yet also beset by tension, conflict, and schism within. I show how the pattern and structure of this dissension and debate embody wider global currents in the geopolitics of late modern sexuality as well as reflect local parameters and vicissitudes that make the politics of visibility and representation tricky business and far from an unalloyed good. #PrideTT is symptomatic of the changing and contested global political economy of queer visibility and sexual citizenship dramatized by being played in local keys and rhythms, thereby undermining the assumption that Pride is progress.
Enhancing Security and Privacy of Patient Data in Healthcare: A SmartPLS Analysis of Blockchain Technology Implementation
Authors: Handayani Indri , Apriani Desy , Mulyati Mulyati , Rahmania Az Zahra Achani , Yusuf Natasya Aprila
Published: December 2023

Security and privacy of patient data are critical concerns in the healthcare system. This research aims to investigate the impact of implementing blockchain technology on enhancing the security and privacy of patient data in healthcare. Using the SmartPLS analysis method, this study empirically examines the relationships between blockchain technology implementation and data security and data privacy within the healthcare system. The research sample consists of healthcare organizations that have implemented blockchain technology for data management. Data is collected through surveys and analyzed using SmartPLS to assess the effects of blockchain technology on data security and privacy. The findings reveal the positive influence of blockchain technology implementation on enhancing the security and privacy of patient data. The study also identifies challenges, such as scalability and interoperability, that need to be addressed for successful implementation. This research contributes to the existing literature by providing empirical evidence on the benefits and challenges of implementing blockchain technology to safeguard patient data in healthcare systems.