bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Phenanthrene Degradation Potential by Plant-Growth-Promoting Endophytic Strain Pseudomonas chlororaphis 23aP Isolated from Chamaecytisus albus (Hacq.) Rothm.
Authors: Karaś Magdalena Anna , Wdowiak-Wróbel Sylwia , Marek-Kozaczuk Monika , Sokołowski Wojciech , Melianchuk Krystsina , Komaniecka Iwona
Published: November 2023

Polycyclic aromatic hydrocarbons (PAHs) are common xenobiotics that are detrimental to the environment and human health. Bacterial endophytes, having the capacity to degrade PAHs, and plant growth promotion (PGP) may facilitate their biodegradation. In this study, phenanthrene (PHE) utilization of a newly isolated PGP endophytic strain of Pseudomonas chlororaphis 23aP and factors affecting the process were evaluated. The data obtained showed that strain 23aP utilized PHE in a wide range of concentrations (6–100 ppm). Ethyl-acetate-extractable metabolites obtained from the PHE-enriched cultures were analyzed by gas chromatography–mass spectrometry (GC-MS) and thin-layer chromatography (HPTLC). The analysis identified phthalic acid, 3-(1-naphthyl)allyl alcohol, 2-hydroxybenzalpyruvic acid, α-naphthol, and 2-phenylbenzaldehyde, and allowed us to propose that the PHE degradation pathway of strain 23aP is initiated at the 1,2-, 3,4-carbon positions, while the 9,10-C pathway starts with non-enzymatic oxidation and is continued by the downstream phthalic pathway. Moreover, the production of the biosurfactants, mono- (Rha-C8-C8, Rha-C10-C8:1, Rha-C12:2-C10, and Rha-C12:1-C12:1) and dirhamnolipids (Rha-Rha-C8-C10), was confirmed using direct injection–electrospray ionization–mass spectrometry (DI-ESI-MS) technique. Changes in the bacterial surface cell properties in the presence of PHE of increased hydrophobicity were assessed with the microbial adhesion to hydrocarbons (MATH) assay. Altogether, this suggests the strain 23aP might be used in bioaugmentation—a biological method supporting the removal of pollutants from contaminated environments.
Emerging Role of Plant-Based Bioactive Compounds as Therapeutics in Parkinson’s Disease
Authors: Kumari Nitu , Anand Santosh , Shah Kamal , Chauhan Nagendra Singh , Sethiya Neeraj K. , Singhal Manmohan
Published: November 2023

Neurological ailments, including stroke, Alzheimer’s disease (AD), epilepsy, Parkinson’s disease (PD), and other related diseases, have affected around 1 billion people globally to date. PD stands second among the common neurodegenerative diseases caused as a result of dopaminergic neuron loss in the midbrain’s substantia nigra regions. It affects cognitive and motor activities, resulting in tremors during rest, slow movement, and muscle stiffness. There are various traditional approaches for the management of PD, but they provide only symptomatic relief. Thus, a survey for finding new biomolecules or substances exhibiting the therapeutic potential to patients with PD is the main focus of present-day research. Medicinal plants, herbal formulations, and natural bioactive molecules have been gaining much more attention in recent years as synthetic molecules orchestrate a number of undesired effects. Several in vitro, in vivo, and in silico studies in the recent past have demonstrated the therapeutic potential of medicinal plants, herbal formulations, and plant-based bioactives. Among the plant-based bioactives, polyphenols, terpenes, and alkaloids are of particular interest due to their potent anti-inflammatory, antioxidant, and brain-health-promoting properties. Further, there are no concise, elaborated articles comprising updated mechanism-of-action-based reviews of the published literature on potent, recently investigated (2019–2023) medicinal plants, herbal formulations, and plant based-bioactive molecules, including polyphenols, terpenes, and alkaloids, as a method for the management of PD. Therefore, we designed the current review to provide an illustration of the efficacious role of various medicinal plants, herbal formulations, and bioactives (polyphenols, terpenes, and alkaloids) that can become potential therapeutics against PD with greater specificity, target approachability, bioavailability, and safety to the host. This information can be further utilized in the future to develop several value-added formulations and nutraceutical products to achieve the desired safety and efficacy for the management of PD.
Antidepressants as Autophagy Modulators for Cancer Therapy
Authors: He Leping , Fu Yuanfeng , Tian Yuxi , Wang Xiaofeng , Zhou Xuejun , Ding Ren-Bo , Qi Xingzhu , Bao Jiaolin
Published: November 2023

Cancer is a major global public health problem with high morbidity. Depression is known to be a high-frequency complication of cancer diseases that decreases patients’ life quality and increases the mortality rate. Therefore, antidepressants are often used as a complementary treatment during cancer therapy. During recent decades, various studies have shown that the combination of antidepressants and anticancer drugs increases treatment efficiency. In recent years, further emerging evidence has suggested that the modulation of autophagy serves as one of the primary anticancer mechanisms for antidepressants to suppress tumor growth. In this review, we introduce the anticancer potential of antidepressants, including tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs). In particular, we focus on their autophagy-modulating mechanisms for regulating autophagosome formation and lysosomal degradation. We also discuss the prospect of repurposing antidepressants as anticancer agents. It is promising to repurpose antidepressants for cancer therapy in the future.
Composition and Electronic Structure of La2O3/CNFs@C Core-Shell Nanoparticles with Variable Oxygen Content
Authors: Suslova Evgeniya V. , Ulyanov Alexander N. , Kozlov Alexey P. , Shashurin Denis A. , Savilov Serguei V. , Chelkov Georgy A.
Published: November 2023

La2O3 nanoparticles stabilized on carbon nanoflake (CNF) matrix were synthesized and graphitized to produce core-shell structures La2O3/CNFs@C. Further oxidation of these structures by nitric acid vapors for 1, 3 or 6 h was performed, and surface-oxidized particles La2O3/CNFs@C_x (x = 1, 3, 6) were produced. Bulk and surface compositions of La2O3/CNFs@C and La2O3/CNFs@C_x were investigated by thermogravimetric analysis and X-ray photoelectron spectroscopy. With increasing the duration of oxidation, the oxygen and La2O3 content in the La2O3/CNFs@C_x samples increased. The electronic structures of samples were assessed by electron paramagnetic resonance. Two paramagnetic centers were associated with unpaired localized and mobile electrons and were registered in all samples. The correlation between bulk and surface compositions of the samples and their electronic structures was investigated for the first time. The impact of the ratio between sp2- and sp3-hybridized C atoms, the number and nature of oxygen-containing groups on the surface and the presence and proportion of coordinated La atoms on the EPR spectra was demonstrated.
The Effect of Vitamin D on Metabolic Bone Disease and Chronic Diseases
Authors: Minisola Salvatore , Merlotti Daniela
Published: November 2023

The history of vitamin D begins more than 100 years ago, with the initial documentation of rickets in industrialized cities of England [...]
Flood-Associated, Land-to-Sea Pathogens’ Transfer: A One Health Perspective
Authors: Di Guardo Giovanni
Published: November 2023

Similarly to many other countries across the globe, several floods have been recorded in Italy throughout the last few decades, including those of catastrophic magnitude that occurred in the Emilia-Romagna and Tuscany regions last May and a few weeks ago, respectively [...]
Assessment of Mastitis Patterns in Serbian Dairy Cows: Blood Serum Metabolic Profile and Milk Composition Parameters
Authors: Stanojević Jovan , Kreszinger Mario , Radinović Miodrag , Kladar Nebojša , Tomanić Dragana , Ružić Zoran , Kovačević Zorana
Published: November 2023

Mastitis is one of the most important diseases in dairy cows, leading to substantial economic losses associated with decreased milk production and quality. Early detection of changes in metabolic and milk parameters is crucial for maintaining animal welfare and milk quality. This study aimed to detect patterns in metabolic and milk composition parameters in Serbian dairy cows affected by mastitis. It also examined the relationship between these factors in cows with clinical and subclinical mastitis, as well as in healthy cows. This study included 60 Holstein-Friesian cows with the same body score condition that were in the same lactation phase. They were divided into three groups of 20: clinical and subclinical mastitis and a control group of healthy cows. The categorization was based on clinical udder health and the California mastitis test. Blood serum metabolic profiles were measured using a Rayto spectrophotometer (Shenzhen, China), and milk composition was determined using MilcoScanTM (Foss, Hilleroed, Denmark) and FossomaticTM (Foss, Hilleroed, Denmark) instruments. Significant increases in non-esterified fatty acids (NEFAs), beta-hydroxybutyrate (BHB), total protein, globulin, urea, total bilirubin, magnesium, and enzyme activity were noted in mastitis-affected cows compared to healthy ones. Additionally, mastitis-affected cows had higher total protein and globulin levels and increased somatic cell counts (SCCs), while albumin concentrations were decreased. Furthermore, a negative correlation between total protein and lactose suggested inflammation leading to reduced lactose levels due to cell damage, infection, and lactose use by mastitis pathogens. Hence, indicators of the energy and protein status of the metabolic profile, together with the chemical composition of milk, may be significant diagnostic tools for detecting, monitoring, and predicting the outcome of mastitis in cows.
Mapping the Landscape of Health Research Priorities for Effective Pandemic Preparedness in Human Mpox Virus Disease
Authors: Aggarwal Sumit , Agarwal Pragati , Nigam Kuldeep , Vijay Neetu , Yadav Pragya , Gupta Nivedita
Published: November 2023

The global re-emergence of monkeypox (Mpox) in non-endemic regions in 2022 has highlighted the critical importance of timely virus detection and robust public health surveillance in assessing outbreaks and their impact. Despite significant Mpox research being conducted worldwide, there is an urgent need to identify knowledge gaps and prioritize key research areas in order to create a roadmap that maximizes the utilization of available resources. The present research article provides a comprehensive mapping of health research priorities aimed at advancing our understanding of Mpox and developing effective interventions for managing its outbreaks, and, as evidenced by the fact that achieving this objective requires close interdisciplinary collaboration. The key research priorities observed were identifying variants responsible for outbreaks; discovering novel biomarkers for diagnostics; establishing suitable animal models; investigating reservoirs and transmission routes; promoting the One Health approach; identifying targets for vaccination; gaining insight into the attitudes, experiences, and practices of key communities, including stigma; and ensuring equity during public health emergencies. The findings of this study hold significant implications for decision making by multilateral partners, including research funders, public health practitioners, policy makers, clinicians, and civil society, which will facilitate the development of a comprehensive plan not only for Mpox but also for other similar life-threatening viral infections.
Design and Research of Laser Power Converter (LPC) for Passive Optical Fiber Audio Transmission System Terminal
Authors: Zhou Yikai , Guan Chenggang , Lv Hui , Zhang Yihao , Zhou Ruling , Chu Wenxiu , Lv Puchu , Qin Haixin , Li Shasha , Li Xiaoqiang
Published: November 2023

In environments like coal mines and oil wells, electrical equipment carries the risk of disasters such as underground fires and methane gas explosions. However, communication equipment is essential for work. Our team has developed a long-range (approximately 25 km) audio transmission system that operates without the need for terminal power sources, thereby eliminating the risk of electrical sparks. This system leverages the reliability of optical fiber and employs a 1550 nm laser for analog audio transmission. After traveling through 25 km of optical fiber, the signal is converted back into electrical energy using a custom-designed Laser Power Converter (LPC). The optical fiber’s carrying capacity imposes limits on the light signal intensity, which, in turn, affects the signal transmission distance. To enable long-distance transmission, we have carefully chosen the optical wavelength with minimal loss. We observed that different LPC structures operating within the same wavelength band have an impact on the audio quality at the terminal. By comparing their characteristics, we have identified the key factors influencing audio output. The optimal LPC allows audio transmission over 25 km, with an output exceeding 12 mVrms.
Applications and Market of Micro-Organism-Based and Plant-Based Inputs in Brazilian Agriculture
Authors: Soares Cláudio Roberto Fonsêca Sousa , Hernández Anabel González , da Silva Emanuela Pille , de Souza Julia Emanuela Almeida , Bonfim Danyella Fernandes , Zabot Giovani Leone , Ferreira Paulo Ademar Avelar , Brunetto Gustavo
Published: November 2023

The use of plant-based and micro-organism-based biological inputs is a sustainable agricultural practice. It promotes a suitable and better utilization of non-renewable resources in the environment. The benefits of using micro-organisms are associated with direct and indirect mechanisms, mainly related to improvements in the absorption and availability of nutrients, resulting in a consequent impact on plant growth. The main benefits of using biochemical pesticides are the promotion of sustainability and the management of resistance to pests and diseases. Although the use of micro-organisms and botanical metabolites is a promising agricultural alternative, they are still primarily concentrated in grain crops. There is a huge opportunity to expand the plant-based and micro-organism-based biological inputs used in agriculture due to the wide range of mechanisms of action of those products. At a global level, several terminologies have been adopted to characterize biological inputs, but many terms used conflict with Brazilian legislation. This review will clarify the classes of biological inputs existing in Brazil as well as present the application and evolution of the market for microbiological and plant-based inputs.
Projection Stereolithography 3D-Printed Bio-Polymer with Thermal Assistance
Authors: Pu Hao , Guo Yuhao , Cheng Zhicheng , Chen Zhuoxi , Xiong Jing , Zhu Xiaoyang , Huang Jigang
Published: November 2023

A stereolithography process with thermal assistance is proposed in this work to address the tradeoff between the flowability and the high concentration of solute loadings at room temperature, through which the improved performance of polymers prepared using stereolithography 3D printing can be achieved. For the experiment, polyethylene glycol diacrylate (PEGDA) with a high molecular weight of 4000 is adopted to improve the mechanical properties of 2-Hydroxyethyl methacrylate (HEMA). For the polymer of HEMA, the highest soluble concentration of PEGDA is about 20 wt% at 25 °C (room temperature) while the concentration could be raised up to 40 wt% as the temperature increases to 60 °C. The 3D printing tests showed that the objects could be easily fabricated with the HEMA polymer loaded with 40 wt% of PEGDA through the thermally assisted projection stereolithography technology. By adding the 40 wt% of PEGDA, the Young’s modulus has been enhanced by nearly 390% compared to the HEMA resin without solute, of which the Young’s modulus is 63.31 ± 2.72 MPa. The results of the cell proliferation test proved that the HEMA resin loaded with PEGDA led to a better biocompatibility compared to the HEMA resin without the loading of the PEGDA solute. All of the results demonstrate that the polymer loaded with high solute is feasible to be precisely 3D-printed using the projection stereolithography process with thermal assistance, and the improved mechanical properties are beneficial for biomedical applications.
Optimization of Piezoresistive Response of Elastomeric Porous Structures Based on Carbon-Based Hybrid Fillers Created by Selective Laser Sintering
Authors: Rollo Gennaro , Ronca Alfredo , Cerruti Pierfrancesco , Xia Hesheng , Gruppioni Emanuele , Lavorgna Marino
Published: November 2023

Recently, piezoresistive sensors made by 3D printing have gained considerable interest in the field of wearable electronics due to their ultralight nature, high compressibility, robustness, and excellent electromechanical properties. In this work, building on previous results on the Selective Laser Sintering (SLS) of porous systems based on thermoplastic polyurethane (TPU) and graphene (GE)/carbon nanotubes (MWCNT) as carbon conductive fillers, the effect of variables such as thickness, diameter, and porosity of 3D printed disks is thoroughly studied with the aim of optimizing their piezoresistive performance. The resulting system is a disk with a diameter of 13 mm and a thickness of 0.3 mm endowed with optimal reproducibility, sensitivity, and linearity of the electrical signal. Dynamic compressive strength tests conducted on the proposed 3D printed sensors reveal a linear piezoresistive response in the range of 0.1–2 N compressive load. In addition, the optimized system is characterized at a high load frequency (2 Hz), and the stability and sensitivity of the electrical signal are evaluated. Finally, an application test demonstrates the ability of this system to be used as a real-time wearable pressure sensor for applications in prosthetics, consumer products, and personalized health-monitoring systems.
Recent Biotechnological Applications of Polyhydroxyalkanoates (PHA) in the Biomedical Sector—A Review
Authors: Diniz Matheus Silva da Fonseca , Mourão Murilo Moraes , Xavier Luciana Pereira , Santos Agenor Valadares
Published: November 2023

Petroleum-derived plastics are materials of great importance for the contemporary lifestyle, and are widely used commercially because they are low cost, resistant, malleable, and weightless, in addition to their hydrophobic character. However, some factors that confer the qualities of these materials also cause problems, mainly environmental, associated with their use. The COVID-19 pandemic aggravated these impacts due to the high demand for personal protective equipment and the packaging sector. In this scenario, bioplastics are environmentally positive alternatives to these plastics due to their applicability in several areas ranging from packaging, to biomedicine, to agriculture. Polyhydroxyalkanoates (PHAs) are biodegradable biopolymers usually produced by microorganisms as an energy reserve. Their structural variability provides a wide range of applications, making them a viable option to replace polluting materials. PHAs can be applied in various biotechnology sectors, such as producing drug carriers and scaffolds for tissue engineering. This review aimed to survey works published in the last five years on the study and biotechnological application of PHAs in the biomedical sector, exploring the versatility and advantages of their use and helping to understand how to enhance their application.
Application of the Quality by Design Concept (QbD) in the Development of Hydrogel-Based Drug Delivery Systems
Authors: Mohseni-Motlagh S. Farid , Dolatabadi Roshanak , Baniassadi Majid , Baghani Mostafa
Published: November 2023

Hydrogel-based drug delivery systems are of interest to researchers for many reasons, such as biocompatibility, high diversity, and the possibility of administration from different routes. Despite these advantages, there are challenges, such as controlling the drug release rate and their mechanical properties during the manufacturing of these systems. For this reason, there is a need for the production and development of such drug delivery systems with a scientific strategy. For this reason, the quality by design (QbD) approach is used for the development of drug delivery systems. This approach, by identifying the most effective factors in the manufacturing of pharmaceutical products and controlling them, results in a product with the desired quality with the least number of errors. In this review article, an attempt is made to discuss the application and method of applying this approach in the development of hydrogel-based drug delivery systems. So that for the development and production of these systems, according to the type of drug delivery system, what target characteristics should be considered (QTPP) and what factors, such as material properties (CMA) or process parameters (CPP), should be taken into account to reach the critical quality attributes of the product (CQA).
Numerical Simulation of Heat Transfer of Roller Slag in Centrifugal Preparation of Inorganic Fiber
Authors: Liu Chunyu , Wang Weixing , Qi Xiwei , Wang Baoxiang , Chen Wei , Zhao Kai , Zhen Jie , Zhang Qiaorong
Published: November 2023

The synergistic preparation of aluminum silicate ceramic fibers from dust removal ash and fly ash is a newly developed process that achieves green and high added value treatment of solid waste. In this process, the centrifugal fiber forming method is used to treat molten slag to obtain aluminum silicate ceramic fibers. During the production process, the centrifugal roller, as a key component in fiber forming, is in long-term contact with high-temperature slag. The heat transfer between the two causes a huge temperature gradient inside the roller material, causing significant thermal stress inside the material, which has a significant impact on the stability of the centrifugal roller structure and its working condition. This article mainly conducts numerical simulation research on the heat transfer between the roller and the slag during the centrifugal fiber forming process, providing theoretical support for ensuring the structural stability of the roller and improving its service life. The research was carried out using the FLUENT module of the ANSYS software (V2021R1), and the heat transfer model of the slag and roller was established. The effects of the internal circulating water, different slag temperatures, different slag film widths, and different boundary layer thicknesses on the heat transfer of the roller were analyzed. The results show that the water temperature at the outlet is about 6 °C higher than that at the inlet on average; when the temperature of the slag increases by 1 °C, the temperature of the roller surface in contact with the slag increases by 0.91 °C; when the width of the slag on the roll surface is 11–17 mm and the slag thickness (boundary layer thickness) is less than 1 mm, it is beneficial for fiber production.
Mental Health Peer-Led Cafés—A Complementary Approach to Traditional Crisis Care: A Protocol for a Systematic Scoping Review
Authors: Norton Michael John
Published: November 2023

Introduction: Psychiatric services are gradually becoming more recovery-orientated. With such orientation came the peer movement, a process which began during the civil rights movement of the 1960s and 1970s and has grown exponentially since. In June 2020, the Irish Department of Health released ‘Sharing the Vision’, a policy which envisioned the implementation of crisis cafés as part of mental health service provision. In addition to this, recovery and peer support movements allowed for the timely establishment of peer-led services, thus raising the question: can such crisis cafés as posited in current Irish mental health policy be peer-run? Methods and Analysis: A systematic scoping review is proposed within this paper, the aim of which is to investigate the effectiveness of peer-led cafés on an individual’s recovery journey along with its impact on mental health, safety, and coercion-based outcomes within mental health crisis care. To ensure a transparent and empirically sound approach to the search and reporting of this systematic scoping review, the PRISMA guidelines will be adhered to in this review. Additionally, Arksey and O’Malley’s original framework will be employed to support the adherence to the relevant methodological processes when reporting on this kind of review. Arksey and O’Malley’s original framework presents scoping reviews through a five-step process, which includes stating search terms and searching databases (CINAHL, psycINFO, psycARTICLES, and PubMed) and repositories (Google, Cochrane Online Library, and ETHos), for papers to be included, based on a pre-determined inclusion/exclusion criteria. Discussion: This protocol is the first, to the authors’ knowledge, that reports the qualitative, quantitative, and mixed method evidence into peer-run cafés for mental health crisis management. The use of the PRISMA guidelines and Arksey and O’Malley’s methodological framework enhances the methodological rigour, transparency, reproducibility, and accuracy of this review. The review is limited, as it does not report on study quality or risk of bias. This will not occur in order to maintain its adherence to Arksey and O’Malley’s original framework. The preprint protocol was added to the OSF Registries and is freely available. It will be updated once the paper is published.
Migrating 120,000 Legacy Publications from Several Systems into a Current Research Information System Using Advanced Data Wrangling Techniques
Authors: Lappalainen Yrjö , Lassila Matti , Heikkilä Tanja , Nieminen Jani , Lehtilä Tapani
Published: November 2023

This article describes a complex CRIS (current research information system) implementation project involving the migration of around 120,000 legacy publication records from three different systems. The project, undertaken by Tampere University, encountered several challenges in data diversity, data quality, and resource allocation. To handle the extensive and heterogenous dataset, innovative approaches such as machine learning techniques and various data wrangling tools were used to process data, correct errors, and merge information from different sources. Despite significant delays and unforeseen obstacles, the project was ultimately successful in achieving its goals. The project served as a valuable learning experience, highlighting the importance of data quality and standardized practices, and the need for dedicated resources in handling complex data migration projects in research organizations. This study stands out for its comprehensive documentation of the data wrangling and migration process, which has been less explored in the context of CRIS literature.
Perspectives on Religious History in Early Modern Portugal: Problems, Historiographic Production and Challenges
Authors: Mendes Paula Almeida
Published: November 2023

This article aims to outline a panoramic view of the paths taken by researchers, in the last 15 years, in the field of the religious history of the Modern Period in Portugal. Starting from the identification of the scientific production and activities that have achieved a more relevant place in the framework of the study of the religious history in early modern Portugal, since the 1950s, this article draws attention to a set of subjects that urgently need to be debated. It will be argued that research in the area in question continues to constitute a challenge. The focus of this article is the European space of Portugal, not considering productions and research about Portuguese imperial spaces, namely Asia and Brazil.
Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds
Authors: Petrova Mariana , Todorov Teodor
Published: November 2023

The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund “Golden Lev Index 30” stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the “EF Rapid” investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds.
Country Risk and Financial Stability: A Focus on Commercial Banks in Africa
Authors: Oyetade Damilola , Muzindutsi Paul-Francois
Published: November 2023

This paper employs dynamic panel models to investigate the impact of country risk on the financial stability of banks in Africa. Using country risk and bank specific data for 10 African countries over the period of 2000 and 2021, the results reveal that African countries have a high country risk exposure. The country risk negatively and significantly affects African bank stability. The study findings suggest that compliance with at least Basel II capital requirements is needed to protect African banks from the negative effects of country risk on their stability in the short run. However, the adverse effects of prolonged country risk are mitigated by the compliance with higher Basel capital requirements in the long run. The results further show that an efficient legal and regulatory framework is essential to complement the capital buffer against country risk. Policies must be introduced to reduce country risk to enable African banks to adequately support the African economy in good and challenging times. Overall, country risk remains a threatening factor for bank stability, and consequently, banks need adequate capital to reduce the impact of country risk on bank stability in Africa.
Research on Reliable Long-Baseline NRTK Positioning Method Considering Ionospheric Residual Interpolation Uncertainty
Authors: Liu Hao , Gao Wang , Miao Weiwei , Pan Shuguo , Meng Xiaolin , Qiao Longlei
Published: November 2023

In the past few decades, network real-time kinematic (NRTK) positioning technology has developed rapidly. Generally, in the continuously operating reference stations (CORS) network, within a moderate baseline length, e.g., 80–100 km, atmospheric delay can be effectively processed through regional modeling and, thus, can support almost instantaneous centimeter-level NRTK positioning. However, in long-baseline CORS networks, especially during the active period of the ionosphere, ionospheric delays cannot be fully eliminated through modeling, leading to decreased NRTK positioning accuracy. To address this issue, this study proposes a long-baseline NRTK positioning method considering ionospheric residual interpolation uncertainty (IRIU). The method utilizes the ionospheric residual interpolation standard deviation (IRISTD) calculated during atmospheric delay modeling, then fits an IRISTD-related stochastic model through the fitting of the absolute values of the ionospheric delay modeling residuals and IRISTD. Finally, based on the ionosphere-weighted model, the IRISTD processed by the stochastic model is used to constrain the ionospheric pseudo-observations. This method achieves good comprehensive performance in handling ionospheric delay and model strength, and the advantage is validated through experiments using CORS data with baseline lengths ranging from 54 km to 106 km in western China and from 84 km to 180 km in AUSCORS data. Quantitative results demonstrate that, across the three sets of experiments, the proposed ionosphere-weighted model achieves an average increase in the fixed rate of 16.9% compared to the ionosphere-fixed model and 25.6% compared to the ionosphere-float model. In terms of positioning accuracy, the proposed model yields average improvements of 67.4%, 76.4%, and 66.0% in the N/E/U directions, respectively, compared to the ionosphere-fixed model, and average improvements of 21.0%, 32.0%, and 24.4%, respectively, compared to the ionosphere-float model. Overall, the proposed method can achieve better NRTK positioning performance in situations where ionospheric delay modeling is inaccurate, such as long baselines and ionospheric activity.
Image Segmentation and Filtering of Anaerobic Lagoon Floating Cover in Digital Elevation Model and Orthomosaics Using Unsupervised k-Means Clustering for Scum Association Analysis
Authors: Vien Benjamin Steven , Kuen Thomas , Rose Louis Raymond Francis , Chiu Wing Kong
Published: November 2023

In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance.
Driving Environment Inference from POI of Navigation Map: Fuzzy Logic and Machine Learning Approaches
Authors: Li Yu , Metzner Martin , Schwieger Volker
Published: November 2023

To adapt vehicle control and plan strategies in a predictive manner, it is usually desired to know the context of a driving environment. This paper aims at efficiently inferring the following five driving environments around vehicle’s vicinity: shopping zone, tourist zone, public station, motor service area, and security zone, whose existences are not necessarily mutually exclusive. To achieve that, we utilize the Point of Interest (POI) data from a navigation map as the semantic clue, and solve the inference task as a multilabel classification problem. Specifically, we first extract all relevant POI objects from a map, then transform these discrete POI objects into numerical POI features. Based on these POI features, we finally predict the occurrence of each driving environment via an inference engine. To calculate representative POI features, a statistical approach is introduced. To composite an inference engine, three inference systems are investigated: fuzzy inference system (FIS), support vector machine (SVM), and multilayer perceptron (MLP). In total, we implement 11 variants of inference engine following two inference strategies: independent and unified inference strategies, and conduct comprehensive evaluation on a manually collected dataset. The result shows that the proposed inference framework generalizes well on different inference systems, where the best overall F1 score 0.8699 is achieved by the MLP-based inference engine following the unified inference strategy, along with the fastest inference time of 0.0002 millisecond per sample. Hence, the generalization ability and efficiency of the proposed inference framework are proved.
Correction: Dang et al. Augmented Hearing of Auditory Safety Cues for Construction Workers: A Systematic Literature Review. Sensors 2022, 22, 9135
Authors: Dang Khang , Elelu Kehinde , Le Tuyen , Le Chau
Published: November 2023

In the original publication [...]
Development of High-Precision Urban Flood-Monitoring Technology for Sustainable Smart Cities
Authors: Jang Bong-Joo , Jung Intaek
Published: November 2023

Owing to rapid climate change, large-scale floods have occurred yearly in cities worldwide, causing serious damage. We propose a real-time urban flood-monitoring technology as an urban disaster prevention technology for sustainable and secure smart cities. Our method takes advantage of the characteristic that water flow is regularly detected at a certain distance with a constant Doppler velocity within the radar observation area. Therefore, a pure flow energy detection algorithm in this technology can accurately and immediately detect water flow due to flooding by effectively removing dynamic obstacles such as cars, people, and animals that cause changes in observation distance, and static obstacles that do not cause Doppler velocities. Specifically, in this method, the pure flow energy is detected by generating a two-dimensional range–Doppler relation map using 1 s periodic radar observation data and performing statistical analysis on the energy detected on the successive maps. Experiments to verify the proposed technology are conducted indoors and in real river basins. As a result of conducting experiments in a narrow indoor space that could be considered an urban underpass or underground facility, it was found that this method can detect flooding situations with centimeter-level accuracy by measuring water level and flow velocity in real time from the time of flood occurrence. And the experimental results in various river environments showed that our technology could accurately detect changes in distance and flow speed from the river surface. We also confirmed that this method could effectively eliminate moving obstacles within the observation range and detect only pure flow energy. Finally, we expect that our method will be able to build a high-density urban flood-monitoring network and a high-precision digital flood twin.
Development of a Sandwiched Piezoelectric Accelerometer for Low-Frequency and Wide-Band Seismic Exploration
Authors: Shen Hengguang , Zhu Zhaolin , Lu Haotian , Ju Haonan , Huang Jinliang , Chen Zhihao
Published: November 2023

A sandwiched piezoelectric accelerometer is developed and optimized for acquiring low-frequency, wide-band seismic data. The proposed accelerometer addresses the challenges of capturing seismic signals in the low-frequency range while maintaining a broad frequency response through the design of multi-stage charge amplifiers and a sandwiched structure. The device’s design, fabrication process, and performance evaluation are discussed in detail. Experimental results demonstrate its performance in amplitude and phase response characteristics.
On Encapsulated Dielectric Barrier Discharge Plasma Sources for Radar Cross Section Reduction in Mobile Environments
Authors: Choi Minsu , You Shin-Jae , Jung Jinwoo , Cho Changseok , Lee Yongshik , Kim Cheonyoung , Ha Jungje , Lee Hyunsoo , Seol Youbin
Published: November 2023

This paper deals with the practical application of Radar Cross Section (RCS) reduction technology using plasma. Although various plasma application technologies for RCS reduction have been studied, there are still many issues to be addressed for practical implementation. In order to achieve actual application, the discharge should be sustained regardless of the external environment of the aircraft. It is also important to investigate the actual plasma parameters to determine the expected RCS reduction effect. Building upon previous studies that optimized the electrodes for RCS reduction, this study fabricates a Dielectric Barrier Discharge (DBD) source suitable for dynamic environments and verifies the power consumption during one cycle of plasma generation. The obtained results are expected to contribute to the optimization of DBD electrodes for plasma RCS reduction.
A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5
Authors: Yang Jingyi , Wang Qinjun , Chang Dingkun , Xu Wentao , Yuan Boqi
Published: November 2023

Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies.
Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data
Authors: Heo Youngju , Kim Jangkyum , Choi Seong Gon
Published: November 2023

This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.
Fast Stray Light Performance Evaluation Based on BSDF and Radiative Transfer Theory
Authors: Zeng Chaoli , Xia Guangqing , Zhong Xing , Li Lei , Qu Zheng , Yang Qinhai , Wang Yuanhang
Published: November 2023

Evaluating the stray light cancellation performance of an optical system is an essential step in the search for superior optical systems. However, the existing evaluation methods, such as the Monte Carlo method and the ray tracing method, suffer from the problems of vast arithmetic and cumbersome processes. In this paper, a method for a rapid stray light performance evaluation model and quantitatively determining high-magnitude stray light outside the field of view are proposed by adopting the radiative transfer theory based on the scattering property of the bidirectional scattering distribution function (BSDF). Under the global coordinates, based on the derivation of the light vector variation relationship in the near-linear system, the specific structural properties of the off-axis reflective optical system, and the specular scattering properties, a fast quantitative evaluation model of the optical system’s stray light elimination capability is constructed. A loop nesting procedure was designed based on this model, and its validity was verified by an off-axis reflective optical system. It successfully fitted the point source transmittance (PST) curve in the range of specular radiation reception angles and quantitatively predicted the prominence due to incident stray light outside the field of view. This method does not require multiple software to work in concert and requires only 10–5 orders of magnitude of computing time, which is suitable for the rapid stray light assessment and structural screening of off-axis reflective optical systems with a good symmetry. The method is promising for improving imaging radiation accuracy and developing lightweight space cameras with low stray light effects.