bluuebunny/crossref_metadata_2025 huggingface dataset:
Transfection Models to Investigate Plasmodium vivax-Type Dormant Liver Stage Parasites
Authors: Voorberg-van der Wel Annemarie , Zeeman Anne-Marie , Kocken Clemens H. M.
Published: August 2023

Plasmodium vivax causes the second highest number of malaria morbidity and mortality cases in humans. Several biological traits of this parasite species, including the formation of dormant stages (hypnozoites) that persist inside the liver for prolonged periods of time, present an obstacle for intervention measures and create a barrier for the elimination of malaria. Research into the biology of hypnozoites requires efficient systems for parasite transmission, liver stage cultivation and genetic modification. However, P. vivax research is hampered by the lack of an in vitro blood stage culture system, rendering it reliant on in vivo-derived, mainly patient, material for transmission and liver stage culture. This has also resulted in limited capability for genetic modification, creating a bottleneck in investigations into the mechanisms underlying the persistence of the parasite inside the liver. This bottleneck can be overcome through optimal use of the closely related and experimentally more amenable nonhuman primate (NHP) parasite, Plasmodium cynomolgi, as a model system. In this review, we discuss the genetic modification tools and liver stage cultivation platforms available for studying P. vivax persistent stages and highlight how their combined use may advance our understanding of hypnozoite biology.
Special Issue “Ethnopharmacology in Latin America”
Authors: Alonso-Castro Angel Josabad
Published: August 2023

Latin America is a multicultural region encompassing 43 countries, with 665 million inhabitants with a mean age of 31 years old, 84% of whom live in urban areas [...]
Engineered Extracellular Vesicles for Drug Delivery in Therapy of Stroke
Authors: Ahmed Waqas , Kuniyan Muhammed Shibil , Jawed Aqil Mohammad , Chen Lukui
Published: August 2023

Extracellular vesicles (EVs) are promising therapeutic modalities for treating neurological conditions. EVs facilitate intercellular communication among brain cells under normal and abnormal physiological conditions. The potential capability of EVs to pass through the blood–brain barrier (BBB) makes them highly promising as nanocarrier contenders for managing stroke. EVs possess several potential advantages compared to existing drug-delivery vehicles. These advantages include their capacity to surpass natural barriers, target specific cells, and stability within the circulatory system. This review explores the trafficking and cellular uptake of EVs and evaluates recent findings in the field of EVs research. Additionally, an overview is provided of the techniques researchers utilize to bioengineer EVs for stroke therapy, new results on EV–BBB interactions, and the limitations and prospects of clinically using EVs for brain therapies. The primary objective of this study is to provide a comprehensive analysis of the advantages and challenges related to engineered EVs drug delivery, specifically focusing on their application in the treatment of stroke.
Automated Segmentation and Morphometry of Zebrafish Anterior Chamber OCT Scans
Authors: Ramos-Soto Oscar , Jo Hang Chan , Zawadzki Robert J. , Kim Dae Yu , Balderas-Mata Sandra E.
Published: August 2023

Zebrafish (Danio rerio) eyes are widely used in modeling studies of human ophthalmic diseases, including glaucoma and myopia. These pathologies cause morphological variations in the anterior chamber elements, which can be quantitatively measured using morphometric parameters, such as the corneal curvature, central corneal thickness, and anterior chamber angle. In the present work, an automated method is presented for iris and corneal segmentation, as well as the determination of the above-mentioned morphometry from optical coherence tomography (OCT) scans of zebrafish. The proposed method consists of four stages; namely, preprocessing, segmentation, postprocessing, and extraction of morphometric parameters. The first stage is composed of a combination of wavelet and Fourier transforms as well as gamma correction for artifact removal/reduction. The segmentation step is achieved using the U-net convolutional neural network. The postprocessing stage is composed of multilevel thresholding and morphological operations. Finally, three algorithms are proposed for automated morphological extraction in the last step. The morphology obtained using our automated framework is compared against manual measurements to assess the effectiveness of the method. The obtained results show that our scheme allows reliable determination of the morphometric parameters, thereby allowing efficient assessment for massive studies on zebrafish anterior chamber morphology using OCT scans.
Anomalous Spectral Shift of o-Modes in Multilayer Photonic Structure Induced by Homeotropic–Homeoplanar Transition in Chiral–Nematic Defect Layer
Authors: Gunyakov Vladimir A. , Sutormin Vitaly S. , Timofeev Ivan V. , Shabanov Vasily F. , Zyryanov Victor Ya.
Published: August 2023

A chiral nematic is embedded between multilayer mirrors to obtain voltage-inducible polarized resonance spectra. Initially, the nematic director is uniformly oriented perpendicular to the mirrors’ surfaces because the chiral nematic helix is completely untwisted due to the homeotropic boundary conditions specified by the adsorbed cations. Then, a voltage is applied to remove the layer of surface-active cations from the input mirror. The obtained twisted homeoplanar configuration has a helix pitch exceeding the layer’s thickness. The twisting leads to the anomalous blue shift of the o-modes in the transmittance spectrum of the photonic structure. This blue shift can be effectively compensated by repulsion of spectral peaks as a result of mode coupling in the vicinity of the virtual avoided crossing point. The experimental results obtained are confirmed numerically using the 4 × 4 transfer matrix method and explained with the contribution of a geometric phase.
A Straightforward Method to Produce Multi-Nanodrug Delivery Systems for Transdermal/Tympanic Patches Using Electrospinning and Electrospray
Authors: Azimi Bahareh , Ricci Claudio , Macchi Teresa , Günday Cemre , Munafò Sara , Maleki Homa , Pratesi Federico , Tempesti Veronika , Cristallini Caterina , Bruschini Luca , Lazzeri Andrea , Danti Serena , Günday-Türeli Nazende
Published: August 2023

The delivery of drugs through the skin barrier at a predetermined rate is the aim of transdermal drug delivery systems (TDDSs). However, so far, TDDS has not fully attained its potential as an alternative to hypodermic injections and oral delivery. In this study, we presented a proof of concept of a dual drug-loaded patch made of nanoparticles (NPs) and ultrafine fibers fabricated by using one equipment, i.e., the electrospinning apparatus. Such NP/fiber systems can be useful to release drugs locally through the skin and the tympanic membrane. Briefly, dexamethasone (DEX)-loaded poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBHV) fiber meshes were decorated with rhodamine (RHO)-loaded poly(lactic-co-glycolic acid) (PLGA) NPs, with RHO representing as a second drug model. By properly tuning the working parameters of electrospinning, DEX-loaded PHBHV fibers (i.e., by electrospinning mode) and RHO-loaded PLGA NPs (i.e., by electrospray mode) were successfully prepared and straightforwardly assembled to form a TDDS patch, which was characterized via Fourier transform infrared spectroscopy and dynamometry. The patch was then tested in vitro using human dermal fibroblasts (HDFs). The incorporation of DEX significantly reduced the fiber mesh stiffness. In vitro tests showed that HDFs were viable for 8 days in contact with drug-loaded samples, and significant signs of cytotoxicity were not highlighted. Finally, thanks to a beaded structure of the fibers, a controlled release of DEX from the electrospun patch was obtained over 4 weeks, which may accomplish the therapeutic objective of a local, sustained and prolonged anti-inflammatory action of a TDDS, as is requested in chronic inflammatory conditions, and other pathological conditions, such as in sudden sensorineural hearing loss treatment.
Impact of Bark-Sourced Building Blocks as Substitutes for Fossil-Derived Polyols on the Structural, Thermal, and Mechanical Properties of Polyurethane Networks
Authors: Arshanitsa Alexandr , Ponomarenko Jevgenija , Pals Matiss , Jashina Lilija , Lauberts Maris
Published: August 2023

The hydrophilic extractives isolated from black alder (Alnus glutinosa) bark through hot water extraction were characterized as novel renewable macromonomers capable of forming polyurethane (PU) networks based on a commercial polyisocyanate, with partial or complete replacement of petroleum-derived polyol polyether. The bark-sourced bio-polyol mainly consists of the xyloside form of the diarylheptanoid oregonin, along with oligomeric flavonoids and carbohydrates, resulting in a total OH group content of 15.1 mmol·g−1 and a molecular weight (Mn) of approximately 750 g∙mol−1. The 31P NMR data confirmed a similar proportion of aliphatic OH and phenolic groups. Three-component PU compositions were prepared using polyethylene glycol (Mn = 400 g∙mol−1), bio-polyol (up to 50%), and polymeric diphenylmethane diisocyanate, which were pre-polymerized in tetrahydrofuran (THF) solution with tin organic and tertiary amine catalysts. The resulting mixture was cast and subjected to thermal post-curing. Calculation and experimental data confirmed the crosslinking activity of the bark-sourced bio-polyol in PU, leading to an increase in glass transition temperature (Tg), a decrease in sol fraction yield upon leaching of cured PU networks in THF, a significant increase in Young’s modulus and tensile strength. The macromonomers derived from bark promoted char formation under high temperature and oxidative stress conditions, limiting heat release during macromolecular network degradation compared to bio-polyol-free PU. It was observed that amine catalysts, which are active in urethane formation with phenolic groups, promoted the formation of PU with higher Tg and modulus at tensile but with less limitation of heat liberation during PU macromolecular structure degradation. The high functionality of the bark-derived bio-polyol, along with the equal proportion of phenolic and aliphatic OH groups, allows for further optimization of PU characteristics using three variables: increasing the substitution extent of commercial polyethers, decreasing the NCO/OH ratio, and selecting the type of catalyst used.
A Fast Workflow for Automatically Extracting the Apparent Attitude of Fractures in 3-D Digital Core Images
Authors: Zhou Ying , Chang Deshuang , Zheng Jianxiong , Zhu Douxing , Nie Xin
Published: August 2023

Fractures play a crucial role as fluid conduits and reservoir spaces in reservoirs. The distribution and characteristics of fractures determine the presence of high-quality reservoirs. To accurately analyze and observe fracture parameters, three-dimensional (3-D) digital cores generated from computed tomography (CT) are utilized. However, the current process of extracting fracture properties from these digital cores is time-consuming and labor-intensive. This paper introduces a new, fast, and automatic workflow for extracting the apparent dip angle and direction of fractures from 3-D digital core images. The proposed workflow involves several steps. Firstly, two perpendicular cross-sections are obtained from the digital core and converted into binary images. Next, the coordinates of four fracture feature points within the core image are automatically extracted. The fracture plane is then fitted using the least squares method based on the extracted coordinates. Finally, the apparent dip angle and direction of the fracture are calculated using the plane’s normal vector. By comparing and analyzing the proposed workflow with the original method, it becomes evident that the method proposed in this paper allows for quick, automated, and accurate extraction of the apparent dip angle and direction of fractures. The application of this workflow to extract fracture attitudes in 3-D micro-CT and full-hole digital core images significantly enhances efficiency.
Fretting Wear and Corrosion-Related Risk Factors in Total Hip Replacement: A Literature Review on Implant Retrieval Studies and National Joint Replacement Registry Reports
Authors: Ghadirinejad Khashayar , Day Christopher W. , Milimonfared Roohollah , Taylor Mark , Solomon Lucian B. , Hashemi Reza
Published: August 2023

Fretting corrosion is a known failure mechanism of total hip replacement (THR) that can lead to revision surgery. Implant retrieval studies have thoroughly documented the occurrence of fretting corrosion in THR implants and its correlation with implant- and patient-related factors. Although implant retrieval studies benefit both clinicians and implant manufacturers, the limitations of these types of studies need to be acknowledged. For example, while some factors are routinely investigated for a possible correlation with failure due to fretting corrosion, other factors are often assumed to have no effect. To improve on these limitations, this review investigates the most significant patient- and implant-related risk factors for fretting corrosion of THR implants for both published retrieval studies and joint replacement registries. The findings and limitations are discussed critically. It is concluded that retrieval studies add significant insight into implant failure mechanisms and should be used in conjunction with joint replacement registry reports. It is suggested that the development of reliable predictive models based on implant failure risk factors and decision-making support systems could lead to enhanced implant longevity.
Treatment of Brain Fog of Long COVID Syndrome: A Hypothesis
Authors: Norman Trevor R.
Published: August 2023

The emergence of the SARS-CoV-2 (COVID-19) virus has exacted a significant toll on the global population in terms of fatalities, health consequences, and economics [...]
Spatial Distribution Characteristics and the Evolution of Buddhist Monasteries in Xi’an City Area
Authors: Song Hui , Meng Qingwen , Wang Chenyang
Published: August 2023

Buddhist monasteries have played important roles in the development of both the culture of and urban planning in ancient Chinese cities. In this paper, the Buddhist monasteries in the city of Xi’an, Shaanxi Province, during the Song (宋), Yuan (元), Ming (明), and Qing (清) dynasties are collated from historical documents. The characteristics of the spatial distribution of Buddhist monasteries are analyzed by using kernel density estimation (KDE), and the evolution of that spatial distribution is explored by documentary analysis. The results show that Buddhist monasteries are closely surrounded by cultural buildings and warehouses, discretely surrounded by administrative buildings. The spatial distribution evolution of Buddhist monasteries has evolved evenly during the expansion of the Xi’an city area, through the Song, Yuan, Ming, and Qing dynasties. This study provides a reference for the preservation of Buddhist monastery spaces in the historical context of Xi’an city area.
Radar Active Jamming Recognition under Open World Setting
Authors: Zhang Yupei , Zhao Zhijin , Bu Yi
Published: August 2023

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.
Assessing the Nonlinear Changes in Global Navigation Satellite System Vertical Time Series with Environmental Loading in Mainland China
Authors: Zhang Jie , Li Zhicai , Zhang Peng , Yang Fei , Wu Junli , Liu Xuchun , Wang Xiaoqing , Tan Qianchi
Published: August 2023

This study investigated the nonlinear changes in the vertical motion of 411 GNSS reference stations situated in mainland China and assessed the influence of the environmental load on their vertical displacement. The researchers evaluated the effect of environmental load by calculating the change in annual cycle amplitude before and after its removal, focusing on its impact across regions with distinct foundation types. The results demonstrate that removing the environmental load led to a considerable reduction of approximately 50.25% in the annual cycle amplitude of vertical motion for GNSS reference stations in mainland China. This reduction in amplitude improved the positioning accuracy of the stations, with the highest WRMS reduction being 2.72 mm and an average reduction of 1.03 mm. The most significant impact was observed in the southwestern, northern, and northwestern regions, where the amplitude experienced a notable decrease. Conversely, the southeastern region exhibited a corresponding increase in amplitude. This article innovatively explored the effects of environmental loads on diverse foundation types. When categorizing GNSS reference stations based on their foundation type, namely, bedrock, 18 m soil layer, and 4–8 m soil layer stations, this study found that removing the environmental load resulted in reductions in annual cycle amplitudes of 49.37%, 59.61%, and 46.48%, respectively. These findings indicate that 18 m soil layer stations were more susceptible to environmental load-induced vertical motion. In conclusion, the impact of the environmental load was crucial when analyzing the vertical motion of GNSS reference stations in mainland China, as it was essential for establishing a high-precision coordinate reference framework and studying the tectonic structure of the region.
An Efficient Detection Framework for Aerial Imagery Based on Uniform Slicing Window
Authors: Yang Xin , Song Yong , Zhou Ya , Liao Yizhao , Yang Jinqi , Huang Jinxiang , Huang Yiqian , Bai Yashuo
Published: August 2023

Drone object detection faces numerous challenges such as dense clusters with overlapping, scale diversity, and long-tail distributions. Utilizing tiling inference through uniform sliding window is an effective way of enlarging tiny objects and meanwhile efficient for real-world applications. However, merely partitioning input images may result in heavy truncation and an unexpected performance drop in large objects. Therefore, in this work, we strive to develop an improved tiling detection framework with both competitive performance and high efficiency. First, we formulate the tiling inference and training pipeline with a mixed data strategy. To avoid truncation and handle objects at all scales, we simultaneously perform global detection on the original image and local detection on corresponding sub-patches, employing appropriate patch settings. Correspondingly, the training data includes both original images and the patches generated by random online anchor-cropping, which can ensure the effectiveness of patches and enrich the image scenarios. Furthermore, a scale filtering mechanism is applied to assign objects at diverse scales to global and local detection tasks to keep the scale invariance of a detector and obtain optimal fused predictions. As most of the additional operations are performed in parallel, the tiling inference remains highly efficient. Additionally, we devise two augmentations customized for tiling detection to effectively increase valid annotations, which can generate more challenging drone scenarios and simulate the practical cluster with overlapping, especially for rare categories. Comprehensive experiments on both public drone benchmarks and our customized real-world images demonstrate that, in comparison to other drone detection frameworks, the proposed tiling framework can significantly improve the performance of general detectors in drone scenarios with lower additional computational costs.
Roles of Extracellular Vesicles in Multiple Sclerosis: From Pathogenesis to Potential Tools as Biomarkers and Therapeutics
Authors: Pistono Cristiana , Osera Cecilia , Cuccia Mariaclara , Bergamaschi Roberto
Published: August 2023

Extracellular vesicles (EVs) are involved in the regulation of immune system functioning and central nervous system (CNS) homeostasis, suggesting a possible role in multiple sclerosis (MS). Indeed, by carrying several types of mediators, such as cytokines, enzymes, and RNAs, EVs can display both anti- and pro-inflammatory roles on the innate and adaptive immune system, and are involved in several CNS functions, including neuronal plasticity, trophic support, disposal of cellular components, axonal maintenance and neuroprotection. In this review, we provide an overview of the studies carried out to understand the role of EVs in the compromised immune system and CNS functioning typical of MS. Moreover, we also highlight the potential of EVs for the diagnosis of this disorder, thanks to their ability to cross the blood-brain barrier (BBB). In addition, we describe the advances in the use of EVs as therapeutic agents by describing their therapeutic potential.
Stakeholders’ Experiences and Perceptions of the Provision and Practice of Language Support for Ethnic Minority School Children in Japan
Authors: Saki Michi
Published: August 2023

This study examines experiences and perceptions concerning the provision of Japanese language support for ethnic minority school children between the ages of 6 and 12 enrolled in public elementary schools in a city located in the Kansai region of Japan (hereinafter referred to as “City M”). This paper will focus in particular on interpreting the experiences and perspectives of language support teachers, volunteer interpreters, mother-tongue language supporters (hereafter referred to as MTLS) as well as three principals of three public elementary schools located in particularly different areas of the City M. Each of the school’s history, backgrounds and current situations are varied and unique. One-on-one interviews of 40 to 60 min in length were conducted with a total of 9 participants consisting of public elementary school principals and Japanese language support teachers. These results of the data collection provided a deeper understanding and explanation of the reasons behind current trends and challenges regarding the accessibility, implementation, provision, and practice of language learning support for ethnic minority school children. The findings from this research will increase awareness of current issues faced by practitioners supporting ethnic minority children in their learning. The research findings also provide insight into what obstacles need to be overcome in order to provide adequate, sufficient, and sustainable educational support for children from different cultural and linguistic backgrounds within the mainstream Japanese education system.
Sustainable Educational Metaverse Content and System Based on Deep Learning for Enhancing Learner Immersion
Authors: Lee Jaekyu , Kim Yeichang
Published: August 2023

Social distancing has encouraged the use of various non-face-to-face services utilizing information and communication technology, especially in the education sector. Educators and learners are increasingly utilizing online technology to conduct non-face-to-face classes, which has resulted in an increased use of EduTech. Virtual education is expected to expand continuously. However, students involved in virtual education find it difficult to focus and participate in the classes. Hence, we propose a system that can improve learners’ focus and immersion in metaverse-based education. In this paper, we propose a sustainable educational metaverse content and system based on deep learning that can enhance learners’ immersion. We built an AI-based simulation that judges learning activities based on the learning behavior rather than on the learner’s device and program events and allows the user to proceed to the next level of education. In the simulation implemented in this study, virtual reality educational contents were created for 12 educational activities, and the effectiveness of four learning models in assessing the learning effectiveness of learners was evaluated. From the four models, an ensemble model with boosting was adopted considering its accuracy, complexity, and efficiency. The F1-score and specificity of the adopted learning model were confirmed. This model was applied to the system in a simulation.
Central Asia: Exploring Insights on Gender Considerations in Climate Change
Authors: Kovaleva Marina , Leal Filho Walter , Borgemeister Christian , Komagaeva Julia
Published: August 2023

More than three decades of independence of Central Asia (CA) countries have been marked by socio-economic, political, and legal reforms. Growing climate change impacts threaten the wellbeing and livelihood of the already vulnerable local population, more than half of which comprises women. In this context, it is essential to adequately include both women’s and men’s needs in multiple efforts taken by national governments to overcome climate change challenges. This paper explores how gender/women considerations have been already addressed in climate change in CA using bibliometric analysis, an expert-driven assessment approach, and a comprehensive analysis of thematically relevant development projects. The findings demonstrated a significant prevalence of grey literature implying much lower interest from academia. The experts outlined the importance of women’s participation and consideration of their experiences, which were different from men’s, in climate change decision making. The comprehensive analysis of the selected development projects revealed the leadership/empowerment domain of climate change–gender interconnections as being mostly addressed. The implications of this paper regarding the current knowledge on the topic related to the CA region are threefold. Firstly, it highlights a strong need for further scientific research that could be implemented through international research initiatives and national institutional programs. Secondly, it calls for increased input from women representation in climate action at all levels. Thirdly, it outlines areas that require stronger cooperation with international donors to mainstream gender/women considerations among a wide range of stakeholders engaged in climate change and its related fields. Overall, the paper lays a basis for further steps towards advancing gender-sensitive and -responsive approaches in CA, particularly in those climate-change-related areas that are often perceived as being gender-neutral.
An Empirical Study of SETA Program Sustaining Educational Sector’s Information Security vs. Information Systems Misuse
Authors: Zheng Binglong , Tse Daniel , Ma Jiajing , Lang Xuanyi , Lu Yinli
Published: August 2023

Information systems misuse and data breaches are among the most common information security threats at the organisational and individual levels. Security, Education, Training and Awareness (SETA) program can be effective tools in addressing and preventing such risks for sustaining the educational sector’s information security, although it is costly to implement and achieves limited results. Several studies have shown that SETA implementation can improve corporate employees’ information security protection behaviours. This study adopts the method of quantitative research, deterrence theory with selected perceived cost and information security awareness (ISA) as intermediate variables and explores how SETA programs affect information system abuse on campuses. The results show that implementing the SETA program positively impacts perceived cost and ISA; perceived cost and information security positively impact reducing misuse behaviour of information systems. At last, we provide rationalisation suggestions for individual students and schools to help SETA programs to be better implemented.
Social Landscape, Peripheral Inclusion and Un-Practice: Concepts for Understanding Social Housing Daily Life in Open Spaces
Authors: Donoso Veronica Garcia , Queiroga Eugenio Fernandes
Published: August 2023

This article presents new concepts for discussing urban social space, named “social landscape”, “peripheral inclusion” and “un-practice”. These concepts are based on the analysis of social practices in vulnerable neighborhoods with a high number of social housing blocks in South America. The aim of the article is to show that the complexity of social practices in vulnerable urban areas is not only the result of the urban environment, which combines social inequality, marginalization and insecurity, but also and above all of the management and maintenance of this inequality. The research method combines bibliographical research with the method of non-participant systematic observation, the latter analyzing everyday life in social housing areas of São Paulo-SP (Brazil) and Santiago (Chile). The discussion and results will lead the reader to understand not only the concepts, but also the idea that open spaces have an important role in social practices, especially public spaces. It seeks to demonstrate the importance of linking public spaces and housing in public policies for the creation of social housing, as opposed to housing policies that focus on the production of architecture disconnected from the urban and social reality.
Pathways to Enhancing Analysis of Irrigation by Remote Sensing (AIRS) in Urban Settings
Authors: Capener Annelise M. , Sowby Robert B. , Williams Gustavious P.
Published: August 2023

In contrast to agricultural settings, irrigation of residential properties in urban settings is typified by small and irregular areas, many untrained water users, limited end-use metering, and differing groundcover. This makes analyzing irrigation patterns to promote efficient water use challenging. We explore the use of remote sensing tools and data sets to help characterize urban irrigation use in the United States. Herein, we review available multispectral imagery datasets and discuss tradeoffs among spatial resolution, collection frequency, and historical availability. We survey options for evapotranspiration data at various spatial and temporal scales that could be paired with the multispectral imagery to estimate irrigation demand. We call the general approach Analysis of Irrigation by Remote Sensing (AIRS). We discuss the potential of drones to capture higher-resolution temporal or spatial data in study areas and/or multiple flights in a single season to provide ground truth or establish patterns. We present data and analysis options that may be suitable depending on specific project objectives. Through a case study scenario, we illustrate some tradeoffs. As a starting point, we recommend public 1 m National Agriculture Imagery Program (NAIP) images for irrigated area estimates and normalized difference vegetation index (NDVI) calculations, combined with open-source OpenET for evapotranspiration, to provide historical snapshots of water use, vegetation quality, and general irrigation efficiency in urban areas. The method is most effective when paired with optional water use data and can provide information with which to design more optimal studies.
Advancing the Application of a Multidimensional Sustainable Urban Waste Management Model in a Circular Economy in Mexico City
Authors: Jacintos Nieves Antonio , Delgado Ramos Gian Carlo
Published: August 2023

The increase in global municipal solid waste (MSW) generation, with a significant portion being improperly managed, has resulted in detrimental socio-ecological effects. This paper addresses the challenge of implementing effective waste management practices for achieving sustainability, particularly in urban areas where the majority of global waste is generated. It emphasizes the need for a multidimensional, multiscale, and long-term approach that surpasses local solutions and linear strategies. The approach recognizes the broader impacts of waste management beyond city boundaries and promotes circularity by incorporating waste reduction, reuse, recycling, and proper disposal practices. Through an analysis of the literature on waste from 1992 to 2022, this paper aims to identify the key concepts, propose solutions, and explore sustainable waste management scenarios. This paper introduces the m-SWM4Cities model for sustainable waste management in urban areas, highlighting its methodology and application in Mexico City (mD-SWM4CDMX). The models presented in this paper incorporate essential elements and interactions, providing a comprehensive understanding of the transition to sustainable waste management. The findings highlight the importance of monitoring waste management indicators and offer insights into the waste system of Mexico City. The m-SWM4Cities model can be adapted to address specific city contexts and thus serves as a valuable tool for assessing and improving waste management practices.
Optimization Method of Subway Station Guide Sign Based on Pedestrian Walking Behavior
Authors: Suo Yifei , Lei Bin , Xun Tianxiang , Li Na , Lei Dongbo , Luo Linlin , Cao Xiaoqin
Published: August 2023

The appropriate distribution of the guide signs in subway stations can allow passengers to obtain direction and wayfinding information quickly and accurately. The existing setting methods of subway station guide signs mainly consider the locations of facilities and pedestrian flowlines in the station. Therefore, this research proposes an optimization method based on pedestrian walking behavior to set guide signs more realistically and increase the guidance efficiency of signs. Firstly, through on-site investigation, this research analyzed the walking behavior characteristics of pedestrians and measured the average speed of pedestrians in different spaces to quantify the walking characteristics. Then, the guidance level of guide signs was defined to describe the guide ability of each sign and was the basis for determining the distribution of the guide signs. The constraint of the distance between two adjacent guide signs was added according to the short-term memory of pedestrian walking identification. Moreover, the variable of the number of floors between the two adjacent signs was added to avoid calculating floor-by-floor. Finally, Xi’an Xiaozhai Station was selected as an example to verify the proposed optimization method using the AnyLogic 8.7.6. The results show that the guide sign optimization method based on pedestrian walking behavior proposed in this paper can obtain an accurate distribution of guide signs, which can decrease the outbound time by 18.51s at the most, and the thickness at the bottleneck decreases by 5.90%.
Effects of Mobility Restrictions on Air Pollution in the Madrid Region during the COVID-19 Pandemic and Post-Pandemic Periods
Authors: Bañuelos-Gimeno Jorge , Sobrino Natalia , Arce-Ruiz Rosa María
Published: August 2023

Air quality is one of the problems cities face today. The COVID-19 pandemic provided a unique opportunity to study the influence of traffic reduction on air quality during 2020, 2021, and 2022. The aim of this paper is to analyze the impacts and relationship between mobility restrictions in six COVID-19 wave periods and air pollution and evolution in the post-pandemic period differentiating Madrid city from its metropolitan area. We tested whether the changes produced for NO2, NOx, PM2.5, PM10, and O3 in the urban traffic and suburban traffic air quality stations data in comparison to the 2019 reference period were significant. The findings of this study show that the periods with the greatest reduction in pollutant concentrations were the first and third COVID-19 waves, when mobility restrictions were most stringent: there was strict confinement for the first wave (i.e., 47% reduction in daily average NO2 concentration), while severe weather forced a reduction in traffic in the region in the third wave period (i.e., 41% reduction in daily average NOx concentration). With the return to normal activity in the last period, pollutant concentrations began to exceed pre-pandemic levels. At the urban level, the reductions were more noticeable in relation to NO2 and NOx, while at the suburban level, changes were less prominent, except for the O3. The results are particularly inspiring for designing future mobility strategies for improving air quality in urban and metropolitan areas.
Structural Diffusion Model and Urban Green Innovation Efficiency—A Hybrid Study Based on DEA-SBM, NCA, and fsQCA
Authors: Li Fanbo , Zhang Hongfeng , Zhang Di , Yan Haoqun
Published: August 2023

This research is based on structural theory and innovation diffusion theory, exploring the theoretical foundations and influencing factors of urban green innovation to provide theoretical support for the realization of the world’s sustainable development goals (SDGs). By using the methods of Data Envelopment Analysis with Slacks-Based Measure (DEA) non-expected model, Necessary Condition Analysis of Research Methods (NCA), and Fuzzy Set Qualitative Comparative Analysis (fsQCA) in combination, the research analyzes the variables influencing the capability of urban green innovation. The study finds that the level of urban culture and absorptive capacity are necessary conditions for urban green innovation, with urban absorptive capacity having a high level of influence. The main paths for urban green innovation are a comprehensive cultural innovation path, an open cultural inclusion path, an open participation innovation integration path, and an outcome transformation to drive the innovation path. In addition, the research discovered patterns of cultural influence that go beyond institutional and resource-based structural factors, subject action processes, and transformation models guided by absorption and sustainable participation. The research results have important significance for understanding the driving factors and promotion paths of urban green innovation, providing empirical evidence for the realization of the world’s SDGs.
VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions
Authors: Perumal Ganeshkumar , Subburayalu Gopalakrishnan , Abbas Qaisar , Naqi Syed Muhammad , Qureshi Imran
Published: August 2023

Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems.
Analysis of the Mercury Content in Fish for Human Consumption in Poland
Authors: Brodziak-Dopierała Barbara , Fischer Agnieszka
Published: August 2023

Mercury (Hg) is a metal with toxic effects on the environment, including living organisms. Organic Hg derivatives readily penetrate biological membranes and pose a particular health risk. Food of aquatic origin is the main source of human exposure to methylmercury (MeHg). In Poland, the consumption of fishery and aquaculture products has been gradually increasing. The aim of this study was to determine the content of Hg in fish intended for human consumption and purchased in Poland. The total Hg content of the edible parts of different species of marine and freshwater fish was analysed. The range of Hg content in all fish was 0.004–0.827 mg/kg, with an arithmetic mean of 0.084 mg/kg. The concentration of Hg in marine fish (0.100 mg/kg) was higher than in freshwater fish (0.063 mg/kg). The highest concentration of Hg was found in tuna. The Estimated Hazard Quotient (EHQ) calculated for the tuna samples analysed was >1. This may represent a potential health risk for consumers. The consumption of the other fish analysed was considered safe on the basis of the EHQ. The Hg content of the analysed fish samples did not exceed the current limits for food.
Novel Free Differential Algebras for Supergravity
Authors: Grassi Pietro Antonio
Published: August 2023

We develop the theory of Free Integro-Differential Algebras (FIDA) extending the powerful technique of Free Differential Algebras constructed by D. Sullivan. We extend the analysis beyond the superforms to integral- and pseudo-forms used in supergeometry. It is shown that there are novel structures that might open the road to a deeper understanding of the geometry of supergravity. We apply the technique to some models as an illustration and we provide a complete analysis for D = 11 supergravity. There, it is shown how the Hodge star operator for supermanifolds can be used to analyze the set of cocycles and to build the corresponding FIDA. A new integral form emerges which plays the role of the truly dual to 4-form F(4) and we propose a new variational principle on supermanifolds.
Innate Immune Gene Polymorphisms and COVID-19 Prognosis
Authors: Bakaros Evangelos , Voulgaridi Ioanna , Paliatsa Vassiliki , Gatselis Nikolaos , Germanidis Georgios , Asvestopoulou Evangelia , Alexiou Stamatia , Botsfari Elli , Lygoura Vasiliki , Tsachouridou Olga , Mimtsoudis Iordanis , Tseroni Maria , Sarrou Styliani , Mouchtouri Varvara A. , Dadouli Katerina , Kalala Fani , Metallidis Simeon , Dalekos George , Hadjichristodoulou Christos , Speletas Matthaios
Published: August 2023

COVID-19 is characterized by a heterogeneous clinical presentation and prognosis. Risk factors contributing to the development of severe disease include old age and the presence of comorbidities. However, the genetic background of the host has also been recognized as an important determinant of disease prognosis. Considering the pivotal role of innate immunity in the control of SARS-CoV-2 infection, we analyzed the possible contribution of several innate immune gene polymorphisms (including TLR2-rs5743708, TLR4-rs4986790, TLR4-rs4986791, CD14-rs2569190, CARD8-rs1834481, IL18-rs2043211, and CD40-rs1883832) in disease severity and prognosis. A total of 249 individuals were enrolled and further divided into five (5) groups, according to the clinical progression scale provided by the World Health Organization (WHO) (asymptomatic, mild, moderate, severe, and critical). We identified that elderly patients with obesity and/or diabetes mellitus were more susceptible to developing pneumonia and respiratory distress syndrome after SARS-CoV-2 infection, while the IL18-rs1834481 polymorphism was an independent risk factor for developing pneumonia. Moreover, individuals carrying either the TLR2-rs5743708 or the TLR4-rs4986791 polymorphisms exhibited a 3.6- and 2.5-fold increased probability for developing pneumonia and a more severe disease, respectively. Our data support the notion that the host’s genetic background can significantly affect COVID-19 clinical phenotype, also suggesting that the IL18-rs1834481, TLR2-rs5743708, and TLR4-rs4986791 polymorphisms may be used as molecular predictors of COVID-19 clinical phenotype.
COPD-Related Mortality before and after Mass COVID-19 Vaccination in Northern Italy
Authors: Fedeli Ugo , Casotto Veronica , Barbiellini Amidei Claudio , Vianello Andrea , Guarnieri Gabriella
Published: August 2023

Background/Objective: Little is known about the impact of the COVID-19 pandemic on mortality from COPD at the population level. The objective was to investigate COPD-related mortality throughout different epidemic waves in Italy before and after the vaccination campaign, which started in late December 2020 and initially targeted the population aged ≥80 years. Methods: Death certificates of residents in Veneto (Northeastern Italy) aged ≥40 years between 2008 and 2021 were analyzed. Age-standardized morality rates were computed for death certificates with any mention of COPD. Generalized estimating equation (GEE) models were fitted to estimate the expected mortality during the pandemic. The results were stratified by age groups of 40–79 and ≥80 years, main comorbidities, and place of death. Results: COPD was mentioned in 3478 death certificates in 2020 (+14% compared to the 2018–2019 average) and in 3133 in 2021 (+3%). Age-standardized mortality rates increased in all age and sex groups in 2020; in 2021, mortality returned to pre-pandemic levels among the elderly but not in the population aged 40–79 years (+6%). GEE models confirmed this differential trend by age. COPD-related mortality peaks were observed, especially in the first pandemic waves, with COVID-19 identified as the underlying cause of death in a relevant proportion (up to 35% in November 2020–January 2021). Mortality with comorbid diabetes and hypertensive diseases slightly increased during the pandemic. Conclusion: COPD-related mortality increased at the beginning of the pandemic, due to deaths from COVID-19. The start of the vaccination campaign was associated with an important decline in COPD-related mortality, especially among the elderly, who first benefited from COVID-19 vaccines. The study findings show the role of mass vaccination in reducing COPD-related deaths during the later phases of the pandemic.