bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process
Authors: Liu Yujun , Ni Dong , Wang Zongyi
Published: February 2024

Batch processing is a widely utilized technique in the manufacturing of high-value products. Traditional methods for quality assessment in batch processes often lead to productivity and yield losses because of offline measurement of quality variables. The use of soft sensors enhances product quality and increases production efficiency. However, due to the uneven batch data, the variation in processing times presents a significant challenge for building effective soft sensor models. Moreover, sensor failures, exacerbated by the manufacturing environment, complicate the accurate modeling of process variables. Existing soft sensor approaches inadequately address sensor malfunctions, resulting in significant prediction inaccuracies. This study proposes a fault-tolerant soft sensor algorithm that integrates two Long Short-Term Memory (LSTM) networks. The algorithm focuses on modeling process variables and compensating for sensor failures using historical batch quality data. It introduces a novel method for converting quality variables into process rates to align uneven batch data. A case study on simulated penicillin production validates the superiority of the proposed algorithm over conventional methods, showing its capacity for precise endpoint detection and effectiveness in addressing the challenges of batch process quality assurance. This study offers a robust solution to the issues of soft sensor reliability and data variability in industrial manufacturing.
Response Surface Methodology—Central Composite Design Optimization Sugarcane Bagasse Activated Carbon under Varying Microwave-Assisted Pyrolysis Conditions
Authors: Chen Xuexue , Pei Yunji , Wang Xinran , Zhou Wenlin , Jiang Li
Published: February 2024

Sugarcane bagasse (SB) is a widely available agro-industrial waste residue in China that has the potential to be converted into a cost-effective and renewable adsorbent. In this study, activated carbon (AC) was prepared from SB by microwave vacuum pyrolysis using H3PO4 as the activator. To enhance the sorption selectivity and yield, the pyrolysis process of SB-activated carbon (SBAC) should be well-designed. Central composite design was employed as an optimized experiment design, and response surface methodology was used to optimize the process parameters for maximized SBAC yield and its iodine number. The results showed that the optimized parameters obtained for the SBAC are 2.47 for the impregnation ratio (IR), 479.07 W for microwave power (MP), 23.86 mm for biomass bed depth, and 12.96 min for irradiation time, with responses of 868.7 mg/g iodine number and 43.88% yield. The anticipated outcomes were substantiated, revealing a marginal 5.4% variance in yield and a mere 1.9% discrepancy in iodine number from the forecasted values. The synthesized adsorbents underwent comprehensive characterization through instrumental methodologies, including FT-IR, BET, and SEM. The SBAC produced by the pyrolysis method contained a regular and homogeneous porous structure with a specific surface area of up to 1697.37 m2/g and a total 1.20 cm 3/g volume, which has favorable adsorption of toxic and harmful substances in the environment.
Retrieval of Snow Depths on Arctic Sea Ice in the Cold Season from FY-3D/MWRI Data
Authors: Yin Qianhui , He Yijun , Sun Deyong
Published: February 2024

Snow depth is a crucial factor in the formation of snow, and its fluctuations play a significant role in the Earth’s climate system. The existing snow depth algorithms currently lack systematic quantitative evaluation, and most of them are not suitable for direct application to Chinese satellites. Therefore, a quantitative evaluation of four existing snow depth algorithms from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was conducted by comparing their estimates with the measured dataset from the Operation IceBridge project (OIB). The study found that the algorithm developed by Rostosky et al. outperforms the other three algorithms in terms of correlation. However, it is unable to accurately retrieve both high and low snow depths. On the other hand, the algorithms developed by Comiso et al. and Li et al. demonstrated strong performance in correlation and statistical characteristics. Based on these results, these two algorithms were fused to enhance the accuracy of the final algorithm. The algorithm was applied to FengYun-3D/Microwave Radiation Imager (FY-3D/MWRI) data after calibration to develop a snow depth retrieval algorithm suitable for MWRI. Validation using the 2019 OIB data indicated that the algorithm had a bias and RMSE of 1 cm and 9 cm, respectively, for first-year ice (FYI) and 3 cm and 9 cm, respectively, for multi-year ice (MYI).
Resilient Cooperative Localization Based on Factor Graphs for Multirobot Systems
Authors: Wang Dongjia , Lian Baowang , Liu Yangyang , Gao Bo , Zhang Shiduo
Published: February 2024

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.
Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning
Authors: do Nascimento Bendini Hugo , Fieuzal Rémy , Carrere Pierre , Clenet Harold , Galvani Aurelie , Allies Aubin , Ceschia Éric
Published: February 2024

Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r2) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha−1, surpassing results obtained from methods using single-image snapshots (r2 of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application.
Spatial Insights into Drought Severity: Multi-Index Assessment in Małopolska, Poland, via Satellite Observations
Authors: Staszel Jakub , Lupa Michał , Adamek Katarzyna , Wilkosz Michał , Marcinkowska-Ochtyra Adriana , Ochtyra Adrian
Published: February 2024

This study focuses on the assessment of drought severity, employing a comparative analysis between the normalized multi-band drought index (NMDI; calculated using Sentinel-2 imagery) and the combined drought indicator (CDI). The research aims to pinpoint the most accurate and reliable remote sensing techniques, which reflect ground-based measurements, thereby enhancing the precision of drought monitoring systems. By investigating the specific area of Małopolska, the study not only contributes to the global knowledge base on drought assessment methodologies but also addresses the regional needs for improved drought management practices. Through an exhaustive analysis correlating satellite-derived indices with in situ meteorological data, this research elucidates the potential of integrating NMDI and CDI for a comprehensive understanding of drought dynamics in Małopolska. In particular, the indices depict different dynamics of drought levels, as well as the location of regions more prone to its occurrence. The findings are poised to advance drought monitoring capabilities, offering significant insights for agricultural sustainability and water resource management in the region.
Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency
Authors: Zhang Jingcheng , Hu Yuncai , Li Fei , Fue Kadeghe G. , Yu Kang
Published: February 2024

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.
Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges
Authors: Huang Wenjun , Yu Anzhu , Xu Qing , Sun Qun , Guo Wenyue , Ji Song , Wen Bowei , Qiu Chunping
Published: February 2024

Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.
DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection
Authors: Chen Ming , Jiang Wanshou , Zhou Yuan
Published: February 2024

Deep learning has dramatically enhanced remote sensing change detection. However, existing neural network models often face challenges like false positives and missed detections due to factors like lighting changes, scale differences, and noise interruptions. Additionally, change detection results often fail to capture target contours accurately. To address these issues, we propose a novel transformer-based hybrid network. In this study, we analyze the structural relationship in bi-temporal images and introduce a cross-attention-based transformer to model this relationship. First, we use a tokenizer to express the high-level features of the bi-temporal image into several semantic tokens. Then, we use a dual temporal transformer (DTT) encoder to capture dense spatiotemporal contextual relationships among the tokens. The features extracted at the coarse scale are refined into finer details through the DTT decoder. Concurrently, we input the backbone’s low-level features into a contour-guided graph interaction module (CGIM) that utilizes joint attention to capture semantic relationships between object regions and the contour. Then, we use the feature pyramid decoder to integrate the multi-scale outputs of the CGIM. The convolutional block attention modules (CBAMs) employ channel and spatial attention to reweight feature maps. Finally, the classifier discriminates change pixels and generates the final change map of the difference feature map. Several experiments have demonstrated that our model shows significant advantages over other methods in terms of efficiency, accuracy, and visual effects.
Transfer-Learning-Based Human Activity Recognition Using Antenna Array
Authors: Ye Kun , Wu Sheng , Cai Yongbin , Zhou Lang , Xiao Lijun , Zhang Xuebo , Zheng Zheng , Lin Jiaqing
Published: February 2024

Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size and cross-scenario in neural network training, this paper proposes a WiFi human activity-recognition system based on transfer learning using an antenna array: Wi-AR. First, the Intel5300 network card collects CSI signal measurements through an antenna array and processes them with a low-pass filter to reduce noise. Then, a threshold-based sliding window method is applied to extract the signal of independent activities, which is further transformed into time–frequency diagrams. Finally, the produced diagrams are used as input to a pretrained ResNet18 to recognize human activities. The proposed Wi-AR was evaluated using a dataset collected in three different room layouts. The testing results showed that the suggested Wi-AR recognizes human activities with a consistent accuracy of about 94%, outperforming the other conventional convolutional neural network approach.
Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China
Authors: Nie Yonghui , Hu Yifan , Sa Rula , Fan Wenyi
Published: February 2024

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.
Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine
Authors: Wu Youfu , Shi Haijing , Yang Xihua
Published: February 2024

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring.
A User-Friendly Tool to Increase Awareness about Impacts of Human Daily Life Activities on Carbon Footprint
Authors: Senese Antonella , Caspani Anna Claudia , Lombardo Lorenzo , Manara Veronica , Diolaiuti Guglielmina Adele , Maugeri Maurizio
Published: February 2024

In recent decades, climate change has demanded more and more attention. Consumers have the power to influence the carbon footprint of goods and services through their purchasing decisions, but to do this they need to learn more. To address this need, it is necessary to develop online questionnaires able to make people aware of which activities have a greater environmental impact in their daily lives. Focusing on this goal, we formulated two tools for quantifying an individual’s carbon footprint over a year. The innovativeness of these tools lies in being user-friendly and providing online open access to compilers, as well as using specific emission factors for the reference context. Specifically, we focused on the main emission sources: gas and electricity consumption, mobility, food, and waste. During these last years, the tools have been proposed to Italian students at different levels of education and to employees of Italian and international companies. The responses from 3260 users revealed an average annual direct carbon footprint per capita of about 5600 kg CO2-eq, which, integrated with the estimate of indirect emissions, provides an estimate in good agreement with the value provided by the Italian National Inventory of greenhouse gases. With the developed tools, people are able to observe which sectors have the greatest impact and consequently are stimulated to emit less by adopting more sustainable behaviors.
Source Apportionment of PM10 as a Tool for Environmental Sustainability in Three School Districts of Lecce (Apulia)
Authors: Siciliano Tiziana , De Donno Antonella , Serio Francesca , Genga Alessandra
Published: February 2024

Air pollution is a great threat to the sustainable development of the world; therefore, the improvement of air quality through the identification and apportionment of emission sources is a significant tool to reach sustainability. Single particle analysis, by means of a scanning electron microscope equipped with X-ray energy dispersive analysis (SEM-EDS), was used to identify the morphological and chemical properties of the PM10 particles in order to identify and quantify the main emission sources in three areas of Lecce, a city in the Apulia region of southern Italy. This type of characterization has not yet been performed for the Lecce site, but it is of particular importance to identify, based on the shape of the particles, the natural sources from the anthropogenic sources that are responsible for the serious health effects of the inhabitants. Three primary schools located in peripheral areas of the city were chosen for the sampling: “School 1” (A site), “School 2” (B site), and “School 3” (C site) to carry out a study of the air quality. The A site is characterized by a greater presence of calcium sulphates probably due both to construction activities present during sampling and to reactions between Ca particles and the sulfur present in the atmosphere. At the C site, there is a relative numerical abundance of different groups of particles that present, in the EDS spectrum, an enrichment in sulfur. At the B site, the number of particle groups is intermediate compared to that of the other two sites. With the source apportionment technique, ten emission sources were identified: combustion, soot, industry, soil, carbonates, sea salt, calcium sulfates, SIA, biological particles, and others. In PM10, the three sites are more affected by the soil source, with an effect greater than 60%.
Sustainability Assessment of the Upstream Bengawan Solo Watershed in Wonogiri Regency, Central Java Province, Indonesia
Authors: Rendrarpoetri Bunga Ludmila , Rustiadi Ernan , Fauzi Akhmad , Pravitasari Andrea Emma
Published: February 2024

The sustainability of watershed management is a key issue that must be considered to ensure the continuation of watershed services such as agriculture, food, and energy. This concern has also been raised in Presidential Regulation No. 2/2015 and No. 18/2020 regarding the National Medium-Term Development plans for the periods of 2015–2019 and 2020–2024, which mandate the restoration of priority watersheds, one of which is the Upstream Bengawan Solo Watershed. The purpose of this study is to fill this knowledge gap by measuring the sustainability of this watershed from a time dynamics perspective. However, several factors can influence the achievement of sustainable development. This paper assesses the sustainability of the watershed over several periods using MDS (Multidimensional Scaling) analysis with the assistance of modified Rapfish (Rapid Appraisal for Fisheries) software (2013 version). The information used in this case study was collected from 20 districts in relation to social, economic, and environmental dimensions. Our result shows that the average index of the social dimension increases from 2007 to 2019 and 2021, while the economic dimension tends to fluctuate. A decrease occurs from 2007 to 2019, and then increases from 2019 to 2021. This differs significantly from the environmental dimension, which decreases from 2007 to 2019 to 2021. The sustainability scores were then compared across regions. The lessons learned in this study can be incorporated into regional policies and actions to overcome challenges in the implementation phase.
Adaptability Evaluation of Human Settlements in Chengdu Based on ArcGIS
Authors: Li Hongyu , Wang Yuelin , Chen Wende , Li Hongfu , Tian Yue , Chen Ruojing
Published: February 2024

This study establishes the evaluation index system of Chengdu’s habitat suitability based on three dimensions (the ecological livability environment, economic development environment, and social security environment) and quantitatively investigates the habitat suitability of Chengdu using the Analytic Hierarchy Process (AHP) and ArcGIS10.8 tools. Additionally, it analyzes the spatial pattern characteristics of Chengdu’s habitat suitability to provide insights into the rational optimization of Chengdu’s habitat system, and show that (1) the adaptability index of Chengdu’s human settlement is between 15.69 to 75.56, and the habitat suitability exhibits a high spatial distribution in the central area and a low spatial distribution in the surrounding regions, with obvious differences between hot spots and cold spots. (2) According to the suitability index from high to low, the habitat of Chengdu is divided into five regions: the most suitable area (895.62 km2, 6.25%), highly suitable area (2136.82 km2,14.91%), moderately suitable area (5755.80 km2,40.15%), low-suitability area (4580.61 km2, 31.95%) and the unsuitable area (966.15 km2, 6.74%). (3) The spatial distribution of habitat suitability in Chengdu demonstrates a certain coupling relationship with the city’s circular social development model. Moreover, the spatial distribution characteristics of each area exhibit good consistency with population density, natural environment, economic conditions, and social conditions. (4) The influence of each indicator factor shows spatial heterogeneity, with variations in different subregions. Additionally, different regions have their own advantages and disadvantages. The results show that there are obvious regional characteristics with the suitability of human settlements in Chengdu; the main urban area exhibits the highest degree of habitat suitability, while the western Longmen Mountain area shows relatively lower suitability in its habitat. Considering these spatial characteristics, future development should explore corresponding development modes for each region based on local conditions, aiming to reduce spatial differences and promote the integrated development of urban and rural areas.
Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior
Authors: Qin Minglu , Xu Haibin , Huang Jiantuan
Published: February 2024

Cycling is a flexible way of traveling that can promote the development of urban public transportation. Previous studies on the influence of cycling have focused more on the cyclists themselves, ignoring the influences of the features of natural environments, such as streetscapes and land surface temperatures (LSTs), on cycling behavior. Therefore, in this study, street view image data and Landsat 8 imagery were utilized to extract streetscape and LST features; in particular, a framework was established for a single-indicator analysis and a multiple-indicator interaction analysis based on the random forest model with GeoDetector. The model was used to explore the effects of streetscapes and surface temperatures on cycling behavior. The results of this study for the main urban area of Beijing show that (1) high-density buildings and high population activity exacerbated the heat island effect at the city center and certain areas in the east, with the highest LST reaching 46.93 °C. In contrast, the greenery and water bodies in the northwestern and northeastern areas reduced the LST, resulting in a minimum temperature of 11.61 °C. (2) The optimal analysis scale was a 100 m buffer pair, and the regression fitting accuracy reached 0.83, confirming the notable influences of streetscape and LST characteristics on cycling behavior. (3) The random forest (RF) model results show that the importance of LST features and vegetation and sky conditions exceeded 0.07, and a reasonable sky openness and open building ventilation became the first choices for promoting cycling behavior. (4) According to the GeoDetector model, the LST features alone exhibited an importance of more than 0.375 for cycling behavior, while interactions with streetscapes greatly reduced the negative effect of LST on cycling behavior. The interaction between walls and plants reached 0.392, while the interaction between multiple environmental factors and greenery and favorable ventilation counteracted the negative impact of high-temperature heat waves on the residents’ choice of bicycles.
A Parametric Integrated Design Approach for Life Cycle Zero-Carbon Buildings
Authors: Kamel Ehsan , Pittau Francesco , Dal Verme Laura Mora , Scatigna Piergiorgio , Iannaccone Giuliana
Published: February 2024

Implementing net-zero carbon design is a crucial step towards decarbonizing the built environment during the entire life cycle of a building, encompassing both embodied and operational carbon. This paper presents a novel computational approach to designing life cycle zero-carbon buildings (LC-ZCBs), utilizing parametric integrated modeling through the versatile Grasshopper platform. A residential building located at the New York Institute of Technology, optimized to fulfill the LC-ZCB target, serves as a case study for this comprehensive study. Four main influencing design parameters are defined, and three hundred design combinations are evaluated through the assessment of operational carbon (OC) and embodied carbon (EC). By incorporating biobased materials in the design options (BIO) as a replacement for conventional insulation (OPT), the influence of biogenic carbon is addressed by utilizing the GWPbio dynamic method. While both OPT and BIO registered similar OC, with values ranging below 0.7 kg CO2eq/m2a, the EC is largely different, with negative values ranging between −0.64 and −0.54 kg CO2eq/m2a only for BIO alternatives, while the OPT ones achieved positive values (2.25–2.45 kg CO2eq/m2a). Finally, to account for potential climate changes, future climate data, and 2099 weather conditions are considered during the scenario assessments. The results show that OC tends to slightly decrease due to the increasing productivity of PV panels. Thus, the life cycle emissions for all OPT alternatives decrease, moving from 2.4–3.0 kg CO2eq/m2a to 2.2–2.4, but none of them achieve the LC-ZCB target, while BIO alternatives are able to achieve the target with negative values between −0.15 and −0.60 kg CO2eq/m2a. There is potential for achieving LC-ZCBs when fast-growing biobased materials are largely used as construction materials, fostering a more environmentally responsible future for the construction industry.
A New Subclass of Analytic Functions Associated with the q-Derivative Operator Related to the Pascal Distribution Series
Authors: Yang Ying , Srivastava Rekha , Liu Jin-Lin
Published: February 2024

A new subclass TXq[λ,A,B] of analytic functions is introduced by making use of the q-derivative operator associated with the Pascal distribution. Certain properties of analytic functions in the subclass TXq[λ,A,B] are derived. Some known results are generalized.
Myocardial Strain for the Differentiation of Myocardial Involvement in the Post-Acute Sequelae of COVID-19—A Multiparametric Cardiac MRI Study
Authors: Ibrahim El-Sayed H. , Rubenstein Jason , Sosa Antonio , Stojanovska Jadranka , Pan Amy , North Paula , Rui Hallgeir , Benjamin Ivor
Published: February 2024

Myocardial involvement was shown to be associated with an unfavorable prognosis in patients with COVID-19, which could lead to fatal outcomes as in myocardial injury-induced arrhythmias and sudden cardiac death. We hypothesized that magnetic resonance imaging (MRI) myocardial strain parameters are sensitive markers for identifying subclinical cardiac dysfunction associated with myocardial involvement in the post-acute sequelae of COVID-19 (PASC). This study evaluated 115 subjects, including 65 consecutive COVID-19 patients, using MRI for the assessment of either post-COVID-19 myocarditis or other cardiomyopathies. Subjects were categorized, based on the results of the MRI exams, as having either ‘suspected’ or ‘excluded’ myocarditis. A control group of 50 matched individuals was studied. Along with parameters of global cardiac function, the MRI images were analyzed for measurements of the myocardial T1, T2, extracellular volume (ECV), strain, and strain rate. Based on the MRI late gadolinium enhancement and T1/T2/ECV mappings, myocarditis was suspected in 7 out of 22 patients referred due to concern of myocarditis and in 9 out of 43 patients referred due to concern of cardiomyopathies. The myocardial global longitudinal, circumferential, and radial strains and strain rates in the suspected myocarditis group were significantly smaller than those in the excluded myocarditis group, which in turn were significantly smaller than those in the control group. The results showed significant correlations between the strain, strain rate, and global cardiac function parameters. In conclusion, this study emphasizes the value of multiparametric MRI for differentiating patients with myocardial involvement in the PASC based on changes in the myocardial contractility pattern and tissue structure.
Hazardous Effects of Pesticides on Human Health
Authors: Ádám Balázs , Cocco Pierluigi , Godderis Lode
Published: February 2024

Pesticides, a major group of biocides, are designed to control harmful and/or unwanted organisms [...]
Pertussis Vaccines Scarcely Provide Protection against Bordetella parapertussis Infection in Children—A Systematic Review and Meta-Analysis
Authors: Remesh Arun Thachappully , Alagarasu Kalichamy , Jadhav Santoshkumar , Prabhakar Meera , Viswanathan Rajlakshmi
Published: February 2024

Background: Pertussis, or whooping cough, is a global public health concern. Pertussis vaccines have demonstrated good protection against Bordetella pertussis infections, but their effectiveness against Bordetella parapertussis remains debated due to conflicting study outcomes. Methods: A systematic review and meta-analysis were conducted to assess the effectiveness of pertussis vaccines in protecting children against B. parapertussis infection. A comprehensive search of PubMed, Web of Science, and Scopus databases was conducted, and randomized controlled trials (RCTs) and observational studies that met inclusion criteria were included in the analysis. Results: The meta-analysis, involving 46,533 participants, revealed no significant protective effect of pertussis vaccination against B. parapertussis infection (risk ratio: 1.10, 95% confidence interval: 0.83 to 1.44). Subgroup analyses by vaccine type and study design revealed no significant protection. The dearth of recent data and a limited pool of eligible studies, particularly RCTs, underscore a critical gap that warrants future research in the domain. Conclusions: These findings offer crucial insights into the lack of effectiveness of pertussis vaccines against B. parapertussis. Given the rising incidence of cases and outbreaks, coupled with the lack of cross-protection by the existing vaccines, there is an urgent need to develop vaccines that include specific antigens to protect against B. parapertussis.
Evaluation of the Effect of a Recent Comparative Intradermal Tuberculin Test on the Humoral Diagnosis of Paratuberculosis Using Serum and Milk Samples from Goats
Authors: Velasco Carlos , Ortega Javier , Gómez-Buendía Alberto , Grau Anna , López Marisol , Álvarez Julio , Romero Beatriz , de Juan Lucía , Bezos Javier
Published: February 2024

Paratuberculosis (PTB) and tuberculosis (TB) are two mycobacterial diseases with a severe economic and health impact on domestic ruminants. The ante mortem diagnosis of PTB is hampered, among other factors, by the limited sensitivity of all the available diagnostic techniques. Since TB-infected goats subjected to the comparative intradermal tuberculin test (CITT) may experience a booster effect on their antibody titer and a potential enhancement to the sensitivity of humoral techniques for tuberculosis, in the present study we aimed to evaluate this diagnostic strategy on the humoral diagnosis of PTB in serum and milk samples collected from a caprine herd that was TB free and PTB infected. The results from 120 goats indicated a significant increase (p < 0.001) in the quantitative response detected using an ELISA technique, conducted using serum and milk samples taken 15 and 30 days after performing a CITT (day 0 of the study); although, it did not translate into a significant increase in the number of reactors during any of the testing events (0, 3,15, 30 and 60 days post-CITT). Additionally, the number of ELISA-positive animals was higher for the serum versus the milk samples at both 15 and 30 days post-CITT. The increase in the quantitative ELISA result suggested a diagnostic strategy that maximizes ELISA sensitivity, mainly using serum samples, in PTB-infected herds; although, it may depend on individual differences and the interpretation criteria.
Preliminary Planning and Optimization Approach for Wastewater Infrastructure for Regions with Low Data Availability
Authors: Khurelbaatar Ganbaatar , Ramos Rodriguez Sara Paola , Aubron Thomas , Rahman Khaja Zillur , Khalil Nadeem , van Afferden Manfred , Breulmann Marc , Friesen Jan , Müller Roland Arno
Published: February 2024

For decades, there has been ongoing discussion about whether centralized or decentralized wastewater management systems are better. Decision-makers need to define the best option but do not always have the necessary tools to develop, compare, and identify the most appropriate solution. To address this, studies have been conducted on a settlement level. In this study, the main focus was to develop and optimize wastewater management scenarios for a region containing rural areas, where data scarcity was an issue, by extracting scenario-relevant information from the region using a satellite image and its calibration using locally available data. We selected a study region in India containing 184 villages with a total population of around 210,000 and covering an area of around 400 km2. The study considered three different scenarios for the study area: centralized, decentralized, and an optimized scenario, which consists of a hybrid system involving partly decentralized and partly semi-centralized (clustered) infrastructure. The study developed a systematic approach for defining an optimized cluster of villages by considering the cost trade-off between the wastewater treatment plant (WWTP) capacity and sewer network layout. The results showed that the clustered and decentralized scenarios were nearly equal in terms of cost (around EUR 118 million), while the centralized scenario showed a relatively high cost of EUR 168 million. Potential applications and further development of the method were also considered. The proposed methodology may aid global wastewater management by estimating and optimizing infrastructure costs needed to fulfill Sustainable Development Goal 6 (SDG#6) in rural regions.
A Methodology to Predict the Impact of a Marine Structure on Longshore Dynamics and Shoreline Evolution
Authors: Araújo Maria Amélia V. C. , Wallbridge Steven , Fernand Liam
Published: February 2024

Globally, due to climate change and increased rates of coastal erosion, along with the need to protect sensitive habitats, there is an increasing requirement for a sustainable approach which considers both the effect of the marine environment on critical infrastructure and the impact of that infrastructure on the local environment. This paper presents a methodology for assessing the impact of a marine structure on longshore dynamics and shoreline evolution by using an external coupling of the phase-resolving ARTEMIS wave model and the shoreline evolution model, UNIBEST. The methodology can be applied at any coastal location, but for this study, it was applied to Sizewell Bay on the east coast of England to assess the impact of a shore-normal jetty on a gently curving stretch of shoreline with relatively complex offshore geomorphology. Results from simulating 22 years of shoreline evolution have shown that, at the jetty location, the shoreline at Sizewell will migrate seaward up to 45 m compared with the no-jetty case. Immediately south of the jetty, the shoreline was predicted to retreat landward by about 15 m. This behaviour is similar to observed changes at other locations, and the predicted longshore transport rates are in agreement with findings from previous studies, validating the methodology.
Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method
Authors: Li Houlu , Hu Bill X. , Lin Bo , Zhu Sihong , Meng Fanqi , Li Yufei
Published: February 2024

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.
Changes in the Hydrological Characteristics of the Attabad Landslide-Dammed Lake on the Karakoram Highway
Authors: Li Yousan , Yang Hongkui , Qi Youhui , Ye Wenqian , Cao Guangchao , Wang Yanhe
Published: February 2024

Understanding the evolving hydrological characteristics of landslide-induced barrier lakes is crucial for flood control, forecasting, early warning, and safety measures in reservoir areas. This study examines the changes in the hydrological characteristics of the Attabad landslide-dammed lake over the past decade after the occurrence of the landslide, focusing on lake area dynamics and sediment concentration. High-resolution satellite images from QuickBird, Pleiades, and WorldView2 over seven periods were analyzed. The findings indicate that the lake area has gradually decreased, with the center of mass shifting towards the lake dam, indicating a trend towards stability. The suspended sediment in the barrier lake is distributed in a strip running from north to south, then northeast to southwest, with the sediment concentration decreasing from the lake entrance to the dam and from the lake bank to the center. Over time, the average sediment concentration has decreased from 2010 to 2020, with higher concentrations in summer than in winter. Notably, during the 2017–2020 period, the lower-middle parts of the lake experienced a higher sediment concentration, while the dam area witnessed lower concentrations, thereby reducing the sediment impact on the dam. Furthermore, the sediment content in the middle of the dammed lake is relatively high, which may lead to the formation of a new dammed dam in the middle and the division of the original dammed lake into two smaller lakes, which will affect the stability of the dammed lake.
Groundwater Temperature Stripes: A Simple Method to Communicate Groundwater Temperature Variations Due to Climate Change
Authors: Lasagna Manuela , Egidio Elena , De Luca Domenico Antonio
Published: February 2024

As our planet faces the complex challenges of global climate change, understanding and effectively communicating critical environmental indicators have become critical. This study explores the importance of reporting groundwater temperature data as a key component in understanding the broader implications of climate change with the use of new graphical tools. More specifically, the use of the groundwater temperature (GWT) stripes and bi-plots of GWT anomalies vs. time was proposed. For an in-depth examination of this subject, monitoring wells situated in the Piedmont Po plain (NW Italy) were selected, with available daily groundwater temperature data dating back to 2010. All data refer to the groundwater of the shallow unconfined aquifer within alluvial deposits. From the analyses of both GWT stripes and the bi-plot of GWT anomalies vs. time, it was possible to identify a general increase in the positive anomaly, corresponding to an increase in GWT in time in almost all of the monitoring points of the Piedmont plain. Furthermore, the utilisation of GWT stripes demonstrated the capability to effectively portray the trend of the GWT data relative to a specific point in a readily understandable manner, facilitating easy interpretation, especially when communicating to a non-scientific audience. The findings underline the urgent need to improve GWT data search and communication strategies to disseminate valuable information to policy makers, researchers, and society. By illustrating the intricate interplay between groundwater temperature and climate change, this research aims to facilitate informed decision-making and promote a proactive approach towards climate resilience.