bluuebunny/crossref_metadata_2025 huggingface dataset:
Distinguishing the Multifactorial Impacts on Ecosystem Services under the Long-Term Ecological Restoration in the Gonghe Basin of China
Authors: Jia Hong , Yang Siqi , Liu Lianyou , Wang Rui , Li Zeshi , Li Hang , Liu Jifu
Published: July 2024

The ongoing shifts in climate, coupled with human activities, are leading to significant land desertification; thus, understanding the long-term variations in ecosystem services as well as the driving factors has a significant value for ensuring ecological security in ecologically fragile arid regions. In this study, we used the RUSLE, RWEQ, CASA, and InVEST models to evaluate five typical ecosystem services (ESs) from 1990 to 2020 in the Gonghe Basin, including soil conservation, sand fixation, carbon sequestration, water yield, and habitat quality. Then, we analyzed the trade-offs between ESs and proposed scientific indications. Finally, we identified the driving mechanisms of ES spatiotemporal variations. The results showed that (1) the ecosystem services in the Gonghe Basin have, overall, improved over the past 30 years. Soil conservation, sand fixation, carbon sequestration, and water yield showed upward trends, while habitat quality showed a downward trend. (2) The relationships between ESs in the Gonghe Basin were characterized by strong synergies and weak trade-offs, with significant spatial heterogeneity in terms of the trade-off intensity. In addition, the implementation of ecological engineering may strengthen the intensity of the trade-offs. (3) Among all the factors (temperature, precipitation, wind speed, NDVI, land use type, slope, DEM and soil type) that affected ESs, NDVI had the greatest impact, and the explanatory power was 49%, followed by soil type. The explanatory power of the interactions between each factor was higher than that of a single factor, and the interaction between NDVI and soil type had the greatest impact. ESs increased by 12% mainly due to the implementation of ecological engineering projects and natural factors. The most suitable area for ESs was the southeastern edge of the Gonghe Basin. Our study will enrich the understanding of the mechanisms of ecosystem services in drylands and provide a scientific basis for the future implementation of ecological engineering on the Qinghai Tibet Plateau.
Limitations of a Multispectral UAV Sensor for Satellite Validation and Mapping Complex Vegetation
Authors: Cottrell Brendan , Kalacska Margaret , Arroyo-Mora Juan-Pablo , Lucanus Oliver , Inamdar Deep , Løke Trond , Soffer Raymond J.
Published: July 2024

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor’s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site.
Significant Increase in Global Steric Sea Level Variations over the Past 40 Years
Authors: Xie Jinpeng , Sun Zhangli , Zhou Shuaibo , Zhong Yulong , Sun Peijun , Xiong Yi , Tu Lin
Published: July 2024

Understanding global sea level variations and exploring their causes hold significant importance for future climate change predictions and the sustainable development of mankind, with the steric sea level (SSL) variations being one of the primary contributors to these changes. Here, we utilize four types of temperature and salinity products (i.e., EN4, IAP, SODA, and GDCSM) to investigate the spatiotemporal characteristics of global SSL changes from 1980 to 2020. We also explore the relationship between SSL changes and the El Niño-Southern Oscillation (ENSO) phenomenon. The findings reveal a rising trend of 0.64–0.97 mm/a in global SSL over the past 40 years, and the annual amplitudes of SSL time series are within the range of 0–50 mm. The SSL trend at a depth of 0–100 m exerts the greatest influence on the overall trend. The ENSO phenomenon has an obvious influence on sea level changes both in the equatorial Pacific region and global scale. The changes in the global sea level (GSL) associated with ENSO are primarily caused by changes in SSL. This study benefits the understanding of SSL changes and their connection to climate change, serves as a reference for comprehensively assessing sea level change mechanisms using diverse datasets and remote sensing technology, and further provides a scientific basis for the sustainable development of mankind in coastal areas.
DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation
Authors: Lu Junyu , Wang Yuedong , Zhu Yafei , Liu Jingtao , Xu Yang , Yang Honglei , Wang Yuebin
Published: July 2024

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.
A Secure Data Aggregation Algorithm Based on a Trust Mechanism
Authors: Liu Changtao , Ye Jun
Published: July 2024

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.
Development and Experiment of Semi-Physical Simulation Platform for Space Manipulator
Authors: Xu Jilong , Guo Yasheng , Liu Fucai , Huang Haoyu
Published: July 2024

To address the extended development cycle, high costs, and maintenance difficulties associated with existing microgravity simulation methods, this study has developed a semi-physical simulation platform for robotic arms tailored to different gravity environments and loading conditions. The platform represents difficult-to-model joints as physical objects, while the easily modeled components are simulated based on principles of similarity. In response to the strong coupling, nonlinearity, and excess force disturbance issues in the electric variable load loading system, a fractional-order linear active disturbance rejection control algorithm was employed. The controller parameters were tuned using an improved particle swarm algorithm with modified weight coefficients, and experimental results demonstrate that a fractional-order linear active disturbance rejection control improves response speed and disturbance rejection performance compared to linear sliding mode control. The study investigated the differences in the drive force of joint motors in space robotic arms under varying gravity environments and loading conditions. Experimental results indicate that load torque is the primary influencing factor on joint motor drive force, while radial force serves as a secondary influencing factor. Additionally, when the axis of the joint motor is perpendicular to the ground, it can, to some extent, simulate microgravity conditions on the ground.
Coupling Fault Diagnosis Based on Dynamic Vertex Interpretable Graph Neural Network
Authors: Wang Shenglong , Jing Bo , Pan Jinxin , Meng Xiangzhen , Huang Yifeng , Jiao Xiaoxuan
Published: July 2024

Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.
A 3 MHz Low-Error Adaptive Howland Current Source for High-Frequency Bioimpedance Applications
Authors: Nwokoye Ifeabunike I. , Triantis Iasonas F.
Published: July 2024

Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method’s applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The “Adaptive Howland Current Source” (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer.
A Comprehensive Study of Object Tracking in Low-Light Environments
Authors: Yi Anqi , Anantrasirichai Nantheera
Published: July 2024

Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance the tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN.
FireSonic: Design and Implementation of an Ultrasound Sensing-Based Fire Type Identification System
Authors: Wang Zheng , Wang Yanwen , Liao Mingyuan , Sun Yi , Wang Shuke , Sun Xiaoqi , Shi Xiaokang , Kang Yisen , Tian Mi , Bao Tong , Lu Ruiqi
Published: July 2024

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.
Memory-Augmented 3D Point Cloud Semantic Segmentation Network for Intelligent Mining Shovels
Authors: Cui Yunhao , Zhang Zhihui , An Yi , Zhong Zhidan , Yang Fang , Wang Junhua , He Kui
Published: July 2024

The semantic segmentation of the 3D operating environment represents the key to intelligent mining shovels’ autonomous digging and loading operation. However, the complexity of the operating environment of intelligent mining shovels presents challenges, including the variety of scene targets and the uneven number of samples. This results in low accuracy of 3D semantic segmentation and reduces the autonomous operation accuracy of the intelligent mine shovels. To solve these issues, this paper proposes a 3D point cloud semantic segmentation network based on memory enhancement and lightweight attention mechanisms. This model addresses the challenges of an uneven number of sampled scene targets, insufficient extraction of key features to reduce the semantic segmentation accuracy, and an insufficient lightweight level of the model to reduce deployment capability. Firstly, we investigate the memory enhancement learning mechanism, establishing a memory module for key semantic features of the targets. Furthermore, we address the issue of forgetting non-dominant target point cloud features caused by the unbalanced number of samples and enhance the semantic segmentation accuracy. Subsequently, the channel attention mechanism is studied. An attention module based on the statistical characteristics of the channel is established. The adequacy of the expression of the key features is improved by adjusting the weights of the features. This is done in order to improve the accuracy of semantic segmentation further. Finally, the lightweight mechanism is studied by adopting the deep separable convolution instead of conventional convolution to reduce the number of model parameters. Experiments demonstrate that the proposed method can improve the accuracy of semantic segmentation in the 3D scene and reduce the model’s complexity. Semantic segmentation accuracy is improved by 7.15% on average compared with the experimental control methods, which contributes to the improvement of autonomous operation accuracy and safety of intelligent mining shovels.
MTD-Diorama: Moving Target Defense Visualization Engine for Systematic Cybersecurity Strategy Orchestration
Authors: Lee Se-Han , Kim Kyungshin , Kim Youngsoo , Park Ki-Woong
Published: July 2024

With the advancement in information and communication technology, modern society has relied on various computing systems in areas closely related to human life. However, cyberattacks are also becoming more diverse and intelligent, with personal information and human lives being threatened. The moving target defense (MTD) strategy was designed to protect mission-critical systems from cyberattacks. The MTD strategy shifted the paradigm from passive to active system defense. However, there is a lack of indicators that can be used as a reference when deriving general system components, making it difficult to configure a systematic MTD strategy. Additionally, even when selecting system components, a method to confirm whether the systematic components are selected to respond to actual cyberattacks is needed. Therefore, in this study, we surveyed and analyzed existing cyberattack information and MTD strategy research results to configure a component dataset. Next, we found the correlation between the cyberattack information and MTD strategy component datasets and used this to design and implement the MTD-Diorama data visualization engine to configure a systematic MTD strategy. Through this, researchers can conveniently identify the attack surface contained in cyberattack information and the MTD strategies that can respond to each attack surface. Furthermore, it will allow researchers to configure more systematic MTD strategies that can be used universally without being limited to specific computing systems.
Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete
Authors: Beskopylny Alexey N. , Stel’makh Sergey A. , Shcherban’ Evgenii M. , Razveeva Irina , Kozhakin Alexey , Meskhi Besarion , Chernil’nik Andrei , Elshaeva Diana , Ananova Oksana , Girya Mikhail , Nurkhabinov Timur , Beskopylny Nikita
Published: July 2024

The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study’s objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the “critical/uncritical” format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.
A Lightweight Cross-Layer Smoke-Aware Network
Authors: Wang Jingjing , Zhang Xinman , Zhang Cong
Published: July 2024

Smoke is an obvious sign of pre-fire. However, due to its variable morphology, the existing schemes are difficult to extract precise smoke characteristics, which seriously affects the practical applications. Therefore, we propose a lightweight cross-layer smoke-aware network (CLSANet) of only 2.38 M. To enhance the information exchange and ensure accurate feature extraction, three cross-layer connection strategies with bias are applied to the CLSANet. First, a spatial perception module (SPM) is designed to transfer spatial information from the shallow layer to the high layer, so that the valuable texture details can be complemented in the deeper levels. Furthermore, we propose a texture federation module (TFM) in the final encoding phase based on fully connected attention (FCA) and spatial texture attention (STA). Both FCA and STA structures implement cross-layer connections to further repair the missing spatial information of smoke. Finally, a feature self-collaboration head (FSCHead) is devised. The localization and classification tasks are decoupled and explicitly deployed on different layers. As a result, CLSANet effectively removes redundancy and preserves meaningful smoke features in a concise way. It obtains the precision of 94.4% and 73.3% on USTC-RF and XJTU-RS databases, respectively. Extensive experiments are conducted and the results demonstrate that CLSANet has a competitive performance.
The Volatile Compounds Composition of Different Parts of Wild Kazakhstan Sedum ewersii Ledeb.
Authors: Kobylina Tatyana , Novikov Andriy , Sadyrova Gulbanu , Kyrbassova Elzira , Nazarbekova Saltanat , Imanova Elmira , Parmanbekova Meruyert , Tynybekov Bekzat
Published: July 2024

The chemical composition of Sedum ewersii Ledeb., a plant indigenous to Kazakhstan and traditionally utilized in folk medicine, was comprehensively investigated, with a focus on its various plant parts. Fresh samples collected in May 2023 from the Almaty region underwent hydrodistillation to extract volatile components, followed by analysis using gas chromatography coupled with mass spectrometric detection, which identified a total of 71 compounds across different plant parts, including the root (underground part), root (aerial part), leaf, stem, and flowering aerial part. The predominant biologically active compound identified across all plant parts was Ethyl α-D-glucopyranoside. Monoterpenes, recognized as primary secondary metabolites, were notably abundant in each plant part, with varying compositions: the root (underground part) contained 28.58% aliphatic monoterpenes, 54.41% oxygenated monoterpenoids, 1.42% diterpenoids, and 15.59% other compounds; the root (aerial part) exhibited 1.34% aliphatic monoterpenes, 31.28% oxygenated monoterpenoids, 6.16% diterpenoids, and 61.22% other compounds; the stem and leaves showed 3.06% aliphatic monoterpenes, 21.49% oxygenated monoterpenoids, 17.99% diterpenoids, and 57.46% other compounds; and the flowering aerial part displayed 8.20% aliphatic monoterpenes, 53.18% oxygenated monoterpenoids, 23.75% diterpenoids, and 14.87% other compounds. Diterpenes, particularly Phytol, were prominently present in the leaf, stem, and flowering aerial parts. Additionally, a diverse array of organic acids, ketones, and phenolic compounds were identified across the plant parts, each potentially offering distinct pharmacological benefits. The presence of exclusive compounds in specific plant parts, such as Dihydroxyacetone in the root (aerial part), underscored the pharmacological diversity of S. ewersii. This study provides valuable insights into the chemical diversity and pharmacological potential of S. ewersii, suggesting promising applications in pharmaceutical and medicinal fields. Further research aimed at elucidating the individual and synergistic pharmacological effects of these compounds is crucial to fully harness the therapeutic benefits of this plant.
Despite Food Insecurity, University Students Remain Committed to Achieving Their Academic Goals: Cross-Sectional Single-Center Study in Saudi Arabia
Authors: Kahtan Rawan K. , Hanbazaza Mahitab A.
Published: July 2024

Food insecurity is a pressing global concern affecting individuals across all age groups. Despite its widespread impact, a notable gap remains in the literature, particularly in reference to the literature concerning university students. This study aimed to assess the prevalence of food insecurity and examine its associated factors, including mental and general health and academic performance, among university students in Jeddah, Saudi Arabia. A total of 421 students from King Abdulaziz University participated in this study, completing a questionnaire that measured food insecurity, academic performance, and mental and general health. About 40% of the respondents experienced some level of food insecurity, which was categorized as mild (20.4%), moderate (13.0%), or severe (5.6%). Significant associations were observed between food insecurity and monthly household income (p < 0.001) and general health (p < 0.005), respectively. However, the associations were weaker between food insecurity and perceived stress (p < 0.066) and the impact of COVID-19. Notably, no significant correlation was found between food security and socio-demographic characteristics or academic performance. This study highlights the prevalence of food insecurity among university students in Saudi Arabia and emphasizes the need for further research to gain a better understanding of its complexities. Such insights can empower universities and policymakers to implement targeted interventions, thereby enhancing the learning experience and future success of university students.
Contested Terrains: Mega-Event Securities and Everyday Practices of Governance
Authors: De Lisio Amanda , Silk Michael , Hubbard Philip
Published: July 2024

Sport mega-events (SMEs) remake cities as global brandscapes of leisured consumption; reliant in part upon securitization designed to create an atmosphere free from disturbance and render invisible those “abject” populations who might puncture the tourist bubble that surrounds stadia and fan-zones. Yet, such “shiny” cityspaces are not devoid of complexity, contestation, and compunction. In this paper, we draw on extensive ethnographic- and community-based participatory research in Rio de Janeiro, Brazil (prior to, during, and after two SMEs) collected in collaboration with sex workers, working in areas of SME intervention. Our focus is on the contingent nature of securitization amidst the contested terrains and trajectories of SME urbanism. Our analysis resonates with observations from other host cities, challenging dominant myths that the sport mega-event creates impermeable securitized cityscapes by revealing the fluid topography of formality and informality, contestation and negotiation, and oppression and power.
Comparing the Effects of Erosion and Accretion along the Eastern Coast of Río de Janeiro and Guanabara Bay in Brazil
Authors: González Rodríguez Silvia V. , Negro Valdecantos Vicente , del Campo José María , Torrodero Numpaque Vanessa
Published: July 2024

This paper presents a case study of a general research project investigating the coastal landscapes’ evolution in different port cities of the American continent with a common historical background: colonization. The general aim is to ascertain whether the evolution of coastal landscapes in the selected port cities is influenced by the origins of their colonizers, who initiated changes that shaped these cities into repositories of artistic, cultural, and historical heritage. The paper presents a study of the coastal landscape transformation in Rio de Janeiro and Guanabara Bay, Brazil. The study employs a comparative approach, utilizing cartographic maps from disparate historical periods, with the objective of identifying and quantifying changes in the coastline. The results indicate that accretion exceeded erosion over the study period. The erosion is associated with the loss of mangroves, underscoring the necessity for environmental protection and reconstruction efforts in affected regions. The research contributes to the existing body of knowledge by providing a comprehensive analysis of the coastal transformation in Rio de Janeiro and Guanabara Bay and its impact on the environment. Furthermore, it allows for a comparison with the port cities of the other case studies in the overall research.
Tourist Attitudes toward Heritage of a County in Western Hungary
Authors: Darabos Ferenc , Kundi Viktória , Kőmíves Csaba
Published: July 2024

Preserving and passing on folk traditions and gastronomic culture to the next generation has always been an important task of elderly people in every single historic period. The main goal of this study is to provide a clear picture of travelers’ attitudes toward gastronomy and cultural values in a western Hungarian county. The presence of the Szigetköz Nature Park and some World Heritage sites (like Fertő-Hanság National Park and Abbey of Pannonhalma) requires responsible tourism planning that is environmentally, socially, and economically sustainable. When conducting quantitative research (survey based on questionnaires), special attention was paid to tourists’ knowledge of gastronomy values and architectural, cultural, and natural attractions. Four hypotheses were formulated in this study. The data collection took place between 1 September 2023 and 30, October 2023 (N = 666). The sample is not representative since the respondents were randomly selected. As opposed to previous studies, the most important novelty of this research is that tourists’ visits to different destinations were assessed while previous studies mainly focused on the interests of tourists.
Integrating Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to Evaluate the Economic Benefits of Designing Aging-In-Place Homes at the Conceptual Stage
Authors: Rostamiasl Vafa , Jrade Ahmad
Published: July 2024

This paper presents a methodology of integrating Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to evaluate the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the rising global aging population, there is a growing demand for housing tailored to elderly needs. This study emphasizes the importance of early design phases, offering a semi-automated model to estimate life cycle costs from design to disposal. The model enables comprehensive economic assessments, highlighting the long-term feasibility of design decisions by considering life cycle costs early in the process. Investing in accessible and universal design features upfront can lead to long-term savings by reducing the need for extensive future retrofits. The model allows for comparisons among different design alternatives, assessing the financial impact of features such as wider doorways, accessible bathrooms, and elevators. This study provides valuable insights for designers and homeowners, supporting efficient decision-making during the early design stages of AIP homes.
Precision Farming Multimodal Technologies Using Optical Sensors for the Detection of Citrus Tristeza Virus Endemics
Authors: Argyriou Athanasios V. , Tektonidis Nikolaos , Alevizos Evangelos , Ferentinos Konstantinos P. , Kourgialas Nektarios N. , Mathioudakis Matthaios M.
Published: July 2024

Citrus trees and their fruits have significant nutritional value and contain antioxidants that are important components of the Mediterranean diet. However, pathogenic diseases pose a threat to citriculture by reducing crop yield and quality. Therefore, there is a need for novel technologies to maintain healthy citrus crops and enable early and accurate detection of the related pathogens, such as the citrus tristeza virus (CTV). Remote sensing offers a non-destructive, cost effective and efficient method for assessing plant health dynamics. It can provide insights into chlorophyll content, water stress and disease presence. This study provides new insights by integrating a combination of remote sensing approaches (FCCs, NDVI, PCA), optical and proximal techniques with in situ field data collection as well as various serological/molecular technologies to detect CTV effectively and evaluate its temporal epidemiology pattern. In addition, the integration of the adopted techniques in case studies of known fields being infected by CTV provides the basis for remote sensing procedures, such as random forest machine learning algorithm, to become powerful in verifying and identifying new CTV-infected fields in a broader extent coverage area, reaching 89.7% accuracy assessment. Thus, it offers decision-makers a robust approach that contributes to CTV epidemiology monitoring and can aid in the development of effective and sustainable disease management strategies.
Renaturing for Urban Wellbeing: A Socioecological Perspective on Green Space Quality, Accessibility, and Inclusivity
Authors: Russo Alessio
Published: July 2024

This perspective examines renaturing cities, the strategic reintroduction of nature, as a potential solution to the negative health impacts of rapid urbanisation. By utilising nature-based solutions to maximise ecosystem services and reintegrate human and natural systems, renaturing offers access to high-quality, accessible green spaces. Studies suggest such access is associated with reduced depression, high blood pressure, and cardiovascular disease risk. Renaturing also presents opportunities for physical activity and daily exposure to nature, which can further enhance well-being and happiness. However, challenges include ensuring equitable access to these spaces and avoiding gentrification. To achieve this equitable distribution and ensure the project truly benefits the community, a bottom-up approach involving residents in the planning process is crucial. Longitudinal studies and evidence from design projects are necessary to understand which types of nature-based solutions can most effectively improve wellbeing and happiness for disadvantaged groups. Further research is needed to better refine the renaturing concept, as it is sometimes used interchangeably with other similar but distinct approaches.
Sustainable Methods of Soybean Cultivation in Poland
Authors: Panasiewicz Katarzyna , Faligowska Agnieszka , Szymańska Grażyna , Ratajczak Karolina , Kłosowicz Monika , Wolna-Maruwka Agnieszka
Published: July 2024

Many countries in Europe are struggling with a shortage of feed protein; moreover, efforts are being made to limit the import of post-extraction soybean meal, most often from GMO crops. To achieve the above assumptions, varietal progress is necessary and, above all, breeding work should aim at greater adaptation to regional conditions. This study was designed to evaluate the potential for growing Ukrainian soybean ‘Annushka’ in the southeastern Baltic Sea area, in accordance with the application of mineral nitrogen fertilizer and the inoculation of seeds with Bradyrhizobium japonicum. Soybean ‘Annushka’ yielded 0.98–1.68 t ha−1 in the conditions of central Poland. Our experiments have shown significant variations in seed, protein, and fat yields over the years. The maximum amounts of these characteristics were recorded in 2017. Nitrogen fertilization combined with seed inoculation with B. japonicum has proven to be an important factor in improving soybean yields; however, it slightly modified the content of organic compounds in seeds. Improvement in seed and protein yields relative to the control amounted, respectively, to Nitragina + 30 kg N ha−1 (58.8%; 72.6%), HiStick® Soy + 30 kg N ha−1 (57.6%; 68.3%), and Nitroflora + 60 kg N ha−1 (57.6%; 71.9%).
Facilities Sites Selection Optimization for Food Emergency Logistics to Meet Urgent Demands
Authors: Zeng Xiaoqing , Chen Yanping , Liu Liming
Published: July 2024

Effective emergency logistics facility site selection is vital for ensuring prompt and fair food supply during crises. This study tackles the intricate task of choosing optimal sites for emergency food logistics facilities by considering varying urgency levels of needs, uncertain demands, and potential facility interruptions. A novel weighted Mahalanobis distance–gray relational analysis–TOPSIS method is devised to evaluate demand urgency and guide site selection decisions. The proposed location model aims to minimize total cost and unmet demand while integrating discrete scenario strategies to address interruption events. Leveraging the Social Network Search (SNS) algorithm, the model is solved, and its effectiveness is validated through a case study analysis. The results highlight the accuracy of the urgency level determination method in capturing demand characteristics and the model’s provision of an objective and practical framework for formulating rational facility location strategies. This approach holds significant promise for enhancing the promptness and fairness of food supply assurance during emergencies.
Synthesis, Characterization, and Study of the Antimicrobial Potential of Dimeric Peptides Derived from the C-Terminal Region of Lys49 Phospholipase A2 Homologs
Authors: Bicho Gabriel F. H. , Nunes Letícia O. C. , Fiametti Louise Oliveira , Argentin Marcela N. , Candido Vitória T. , Camargo Ilana L. B. C. , Cilli Eduardo M. , Santos-Filho Norival A.
Published: July 2024

Currently, the search for new alternatives to conventional antibiotics to combat bacterial resistance is an urgent task, as many microorganisms threaten human health due to increasing bacterial resistance to traditional medicines. Thus, new molecules such as antimicrobial peptides have emerged as promising alternatives because of their low induction of resistance and broad spectrum of action. In this context, in the past few years, our research group has synthesized and characterized a peptide derived from the C-terminal region of the Lys49 PLA2-like BthTX-I, named p-BthTX-I. After several studies, the peptide (p-BthTX-I)2K was proposed as the molecule with the most considerable biotechnological potential. As such, the present work aimed to evaluate whether the modifications made on the peptide (p-BthTX-I)2K can be applied to other molecules originating from the C-terminal region of PLA2-like Lys49 from snake venoms. The peptides were obtained through the solid-phase peptide synthesis technique, and biochemical and functional characterization was carried out using dichroism techniques, mass spectrometry, antimicrobial activity against ESKAPE strains, hemolytic activity, and permeabilization of lipid vesicles. The antimicrobial activity of the peptides was promising, especially for the peptides (p-AppK)2K and (p-ACL)2K, which demonstrated activity against all strains that were tested, surpassing the model molecule (p-BthTX-I)2K in most cases and maintaining low hemolytic activity. The modifications initially proposed for the (p-BthTX-I)2K peptide were shown to apply to other peptides derived from Lys49 PLA2-like from snake venoms, showing promising results for antimicrobial activity. Future assays comparing the activity of the dimers obtained through this strategy with the monomers of these peptides should be carried out.
Evolution of RNA Viruses: Reasons for the Existence of Separate Plus, Minus, and Double-Strand Replication Strategies
Authors: Park Hyunjin , Higgs Paul G.
Published: July 2024

Plus, minus, and double-strand RNA viruses are all found in nature. We use computational models to study the relative success of these strategies. We consider translation, replication, and virion assembly inside one cell, and transmission of virions between cells. For viruses which do not incorporate a polymerase in the capsid, transmission of only plus strands is the default strategy because virions containing minus strands are not infectious. Packaging only plus strands has a significant advantage if the number of RNA strands produced per cell is larger than the number of capsids. In this case, by not packaging minus strands, the virus produces more plus-strand virions. Therefore, plus-strand viruses are selected at low multiplicity of infection. However, at high multiplicity of infection, it is preferable to package both strands because the additional minus virions produced are helpful when there are multiple infections per cell. The fact that plus-strand viruses are widespread while viruses that package both strands are not seen in nature suggests that RNA strands are indeed produced in excess over capsids, and that the multiplicity of infection is not sufficiently high to favor the production of both kinds of virions. For double-strand viruses, we show that it is advantageous to produce only plus strands from the double strand within the cell, as is observed in real viruses. The reason for the success of minus-strand viruses is more puzzling initially. For viruses that incorporate a polymerase in the virion, minus virions are infectious. However, this is not sufficient to explain the success of minus-strand viruses, because in this case, viruses that package both strands outcompete those that package only minus or only plus. Real minus-strand viruses make use of replicable strands that are coated by a nucleoprotein, and separate translatable plus strands that are uncoated. Here we show that when there are distinct replicable and translatable strands, minus-strand viruses are selected.
Refractory/Resistant Cytomegalovirus Infection in Transplant Recipients: An Update
Authors: Royston Léna , Papanicolaou Genovefa A. , Neofytos Dionysios
Published: July 2024

Despite the significant progress made, CMV infection is one of the most frequent infectious complications in transplant recipients. CMV infections that become refractory or resistant (R/R) to the available antiviral drugs constitute a clinical challenge and are associated with increased morbidity and mortality. Novel anti-CMV therapies have been recently developed and introduced in clinical practice, which may improve the treatment of these infections. In this review, we summarize the treatment options for R/R CMV infections in adult hematopoietic cell transplant and solid organ transplant recipients, with a special focus on newly available antiviral agents with anti-CMV activity, including maribavir and letermovir.
Hydrochemical Characteristics and Water Quality Evaluation of Groundwater in the Luohe Formation of Binchang Mining Area, China
Authors: Wang Xu , Sun Kui , Ma Wanchao , Peng Jie , Liu Ruiping , Chen Jianping , Zhang Kun , Gao Shuai , Li Cheng , Zhang Penghua
Published: July 2024

The groundwater of the Luohe Formation in Binchang mining area is the main source of water for industrial and agricultural use and for drinking water for residents in the area. In order to study the hydrochemical characteristics and water-quality status of Luohe Formation groundwater in the mining area, statistical analysis, Piper three-line diagram, ion ratio relationship, and other methods were used to study the hydrochemical characteristics and formation factors of the groundwater. The Nemerow index evaluation method and the fuzzy comprehensive evaluation method based on principal component analysis were used to evaluate the groundwater quality in the mining area. The results show that the groundwater is weakly acidic as a whole, and the content of SO42− and Cl− have strong variability in terms of spatial distribution. The groundwater chemical type gradually evolves from SO4 • HCO3 • Cl–Na, SO4–Na and SO4 • Cl–Na-type water in the north of the mining area to SO4 • HCO3 • Cl–Na • Ca, HCO3 • SO4–Na • Mg, and SO4 • Cl–Na • Ca • Mg-type water in the south. The formation of the hydrochemical composition of groundwater in the study area may be related to multiple factors such as cation-alternating adsorption, carbonate and sulfate dissolution, and hydraulic exchange with the groundwater of the upper Huachi Formation. Comparing the evaluation results of the Nemerow index method and the principal component analysis method, the latter’s evaluation results can take into account the contribution of each indicator to the overall groundwater quality, and to a certain extent can weaken the control effect of a certain pollution indicator, exceeding the limit on the entire evaluation result. Therefore, the evaluation results based on the principal component analysis method are more credible.