bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advances in Porous Bio-Polymer Composites for the Remediation of Organic Pollutants
Authors: Tadayoni Nayereh S. , Dinari Mohammad , Roy Aleena , Karimi Abdolmaleki Mahmood
Published: May 2024

The increasing awareness of the importance of a clean and sustainable environment, coupled with the rapid growth of both population and technology, has instilled in people a strong inclination to address the issue of wastewater treatment. This global concern has prompted individuals to prioritize the proper management and purification of wastewater. Organic pollutants are very persistent and due to their destructive effects, it is necessary to remove them from wastewater. In the last decade, porous organic polymers (POPs) have garnered interest among researchers due to their effectiveness in removing various types of pollutants. Porous biopolymers seem to be suitable candidates among POPs. Sustainable consumption and environmental protection, as well as reducing the consumption of toxic chemicals, are the advantages of using biopolymers in the preparation of effective composites to remove pollutants. Composites containing porous biopolymers, like other POPs, can remove various pollutants through absorption, membrane filtration, or oxidative and photocatalytic effects. Although composites based on porous biopolymers shown relatively good performance in removing pollutants, their insufficient strength limits their performance. On the other hand, in comparison with other POPs, including covalent organic frameworks, they have weaker performance. Therefore, porous organic biopolymers are generally used in composites with other compounds. Therefore, it seems necessary to research the performance of these composites and investigate the reasons for using composite components. This review exhaustively investigates the recent progress in the use of composites containing porous biopolymers in the removal of organic pollutants in the form of adsorbents, membranes, catalysts, etc. Information regarding the mechanism, composite functionality, and the reasons for using each component in the construction of composites are discussed. The following provides a vision of future opportunities for the preparation of porous composites from biopolymers.
Flexible and Multifunctional Composites with Highly Strain Sensing and Impact Resistance Properties
Authors: Wang Shu , Pu Jianyu , Xu Shuquan , Tian Yuanhao , Shu Qian , Zou Rui , Zhang Tonghua
Published: May 2024

The development of smart protective clothing will help detect injuries from contact sports, traffic collisions, and other accidents. The combination of ecoflex, spacer fabric, and graphene-based aerogel provides a multifunctional composite. It shows a strain sensitivity of 17.71 at the strain range of 40~55%, a pressure sensitivity of 0.125 kPa−1 at the pressure range of 0~15 kPa, and a temperature sensitivity of −0.648 °C−1. After 50 impact tests, its protection coefficient only dropped from 60% to 55%. Additionally, it shows thermal insulation properties. The compression and impact process results of finite element numerical simulation analysis are in good agreement with the experimental results. The ecoflex/aerogel/spacer fabric sensor exhibits a simple structure, large pressure strain, high sensitivity, flexibility, and ease of fabrication, making it a candidate for smart protective clothing resistant to impact loads.
Thermochromic Polymer Nanocomposites for the Heat Detection System: Recent Progress on Properties, Applications, and Challenges
Authors: Supian A. B. M. , Asyraf M. R. M. , Syamsir Agusril , Najeeb M. I. , Alhayek Abdulrahman , Al-Dala’ien Rayeh Nasr , Manar Gunasilan , Atiqah A.
Published: May 2024

Reversible thermochromic polymers have emerged as compelling candidates in recent years, captivating attention for their application in heat detection systems. This comprehensive review navigates through the multifaceted landscape, intricately exploring both the virtues and hurdles inherent in their integration within these systems. Their innate capacity to change colour in response to temperature fluctuations renders reversible thermochromic nanocomposites promising assets for heat detection technologies. However, despite their inherent potential, certain barriers hinder their widespread adoption. Factors such as a restricted colour spectrum, reliance on external triggers, and cost considerations have restrained their pervasive use. For instance, these polymer-based materials exhibit utility in the domain of building insulation, where their colour-changing ability serves as a beacon, flagging areas of heat loss or inadequate insulation, thus alerting building managers and homeowners to potential energy inefficiencies. Nevertheless, the limited range of discernible colours may impede precise temperature differentiation. Additionally, dependency on external stimuli, such as electricity or UV light, can complicate implementation and inflate costs. Realising the full potential of these polymer-based materials in heat detection systems necessitates addressing these challenges head-on. Continuous research endeavours aimed at augmenting colour diversity and diminishing reliance on external stimuli offer promising avenues to enhance their efficacy. Hence, this review aims to delve into the intricate nuances surrounding reversible thermochromic nanocomposites, highlighting their transformative potential in heat detection and sensing. By exploring their mechanisms, properties, and current applications, this manuscript endeavours to shed light on their significance, providing insights crucial for further research and potential applications.
A Study of Plant-Filled Polymer Composites Based on Highly Plasticized Polyvinyl Chloride
Authors: Samuilova Evgeniia , Ponomareva Alina , Sitnikova Vera , Zhilenkov Anton , Kichigina Olga , Uspenskaya Mayya
Published: May 2024

To enhance the ecological properties of polyvinyl chloride (PVC) products, the fabrication of PVC-based composites using biofillers with acceptable performance characteristics could be considered. In this work, plant-filled PVC-based composite materials were fabricated and their optical, structural, thermal, and mechanical properties, depending on the nature of the filler, were studied. Spruce flour, birch flour, and rice husk were used as fillers. Optical measurements showed the selected technological parameters, allowing films with a uniform distribution of dispersed plant filler in the polymer matrix to be obtained. Using the plant fillers in PVC films leads to a reduction in strength characteristics; for instance, the tensile strength changed from 18.0 MPa (for pure PVC film) to ~7 MPa (for composites with 20 wt.% of fillers), and to ~5–6.2 MPa (for composites with 40 wt.% of fillers). Thermal investigations showed that the samples with plant fillers could be used at low temperatures without changing their operating characteristics. Thus, plant-filled PVC-based composite materials have a wide operating temperature range, from—65 °C to 150 °C. TGA analysis has demonstrated that the rice husk affected the thermal stability of the composites by increasing their thermal decomposition resistance. The ability to absorb water was observed during the investigation of water absorption of the samples. And the highest degree of water absorption (up to 160 mg/g) was detected for the sample with 40 wt.% of rice husk. In general, plant-filled polymer composites based on PVC can be used on an equal basis with unfilled PVC plastic compounds for some applications such as in construction (for example, for design tasks).
ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing Images with State Space Model and Spatial Context
Authors: Wang Yinxiao , Yuan Wei , Xie Fang , Lin Baojun
Published: May 2024

Super-resolution (SR) for satellite remote sensing images has been recognized as crucial and has found widespread applications across various scenarios. Previous SR methods were usually built upon Convolutional Neural Networks and Transformers, which suffer from either limited receptive fields or a lack of prior assumptions. To address these issues, we propose ESatSR, a novel SR method based on state space models. We utilize the 2D Selective Scan to obtain an enhanced capability in modeling long-range dependencies, which contributes to a wide receptive field. A Spatial Context Interaction Module (SCIM) and an Enhanced Image Reconstruction Module (EIRM) are introduced to combine image-related prior knowledge into our model, therefore guiding the process of feature extraction and reconstruction. Tailored for remote sensing images, the interaction of multi-scale spatial context and image features is leveraged to enhance the network’s capability in capturing features of small targets. Comprehensive experiments show that ESatSR demonstrates state-of-the-art performance on both OLI2MSI and RSSCN7 datasets, with the highest PSNRs of 42.11 dB and 31.42 dB, respectively. Extensive ablation studies illustrate the effectiveness of our module design.
Deep Multi-Order Spatial–Spectral Residual Feature Extractor for Weak Information Mining in Remote Sensing Imagery
Authors: Zhang Xizhen , Zhang Aiwu , Sun Yuan , Wang Juan , Pang Haiyang , Peng Jinbang , Chen Yunsheng , Zhang Jiaxin , Giannico Vincenzo , Legesse Tsegaye Gemechu , Shao Changliang , Xin Xiaoping
Published: May 2024

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs.
Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model
Authors: Lv Guangyu , Li Xuan , Fang Lei , Peng Yanbo , Zhang Chuanxing , Yao Jianyu , Ren Shilong , Chen Jinyue , Men Jilin , Zhang Qingzhu , Wang Guoqiang , Wang Qiao
Published: May 2024

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.
Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil
Authors: Verdelho Fernanda F. , Beneti Cesar , Pavam Luis G. , Calvetti Leonardo , Oliveira Luiz E. S. , Zanata Alves Marco A.
Published: May 2024

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.
Classification of Crop Area Using PALSAR, Sentinel-1, and Planet Data for the NISAR Mission
Authors: Anconitano Giovanni , Kim Seung-Bum , Chapman Bruce , Martinez Jessica , Siqueira Paul , Pierdicca Nazzareno
Published: May 2024

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop.
Tracing State Structure for Ecological Processes in Soil Including Greenhouse Gas Exchange with Lower Atmosphere
Authors: Sirola Miki , Koskinen Markku , Polvinen Tatu , Pihlatie Mari
Published: May 2024

Exploring data aids in the comprehension of the dataset and the system’s essence. Various approaches exist for managing numerous sensors. This study perceives operational states to clarify the physical dynamics within a soil environment. Utilizing Principal Component Analysis (PCA) enables dimensionality reduction, offering an alternative perspective on the spring soil dataset. The K-means algorithm clusters data densities, forming the groundwork for an operational state description. Soil data, integral to an ecosystem, entails evident attributes. Employing dynamic visualization, including animations, constitutes a vital exploration angle. Greenhouse gas variables have been added to PCA to achieve more understanding in the interconnection of gas exchange and soil properties. Pit data and flux data are analysed both separately and together using a data-driven approach. The results look promising, showing the potential to add new values and more detailed state structures to ecological models. All experiments are conducted within the Jupyter programming environment, utilizing Python 3. The relevant literature on data visualization is examined. Through combined techniques and tools, the potential features of the soil ecosystem are observed and identified.
Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis
Authors: Tarcsay Bálint Levente , Bárkányi Ágnes , Németh Sándor , Chován Tibor , Lovas László , Egedy Attila
Published: May 2024

In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.
Exploring the Use of Cold Atmospheric Plasma for Sound and Vibration Generation
Authors: Ghaderi Nasser , Hasheminejad Navid , Dirckx Joris , Vanlanduit Steve
Published: May 2024

In this study, we investigate the potential of cold atmospheric plasma (CAP) as a non-contact excitation device, comparing its performance with an ultrasound transmitter. Utilizing a scanning Laser Doppler Vibrometer (LDV), we visualize the acoustic wavefront generated by a CAP probe and an ultrasound sensor within a designated 50 mm × 50 mm area in front of each probe. Our focus lies in assessing the applicability of a CAP probe for exciting a small polymethyl methacrylate (PMMA) sample. By adjusting the dimensions of the sample to resonate at the excitation frequency of the probe, we can achieve high vibrational velocities, enabling further mechanical analysis. In contrast with traditional vibration excitation techniques such as electrodynamical shakers and hammer impact excitation, a plasma probe can offer distinct advantages without altering the structure’s dynamics since it is contactless. Furthermore, in comparison with laser excitation, plasma excitation provides a higher power level. Additionally, while pressurized air systems are applicable for limited low frequencies, plasma probes can perform at higher frequencies. Our findings in this study suggest that CAP is comparable with acoustic excitation, indicating its potential as an effective mechanical excitation method.
Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems
Authors: Alalhareth Mousa , Hong Sung-Chul
Published: May 2024

This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model’s superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.
Using a Sensor-Embedded Baseball to Identify Finger Characteristics Related to Spin Rate and Pitching Velocity in Pitchers
Authors: Yeh Ming-Chia , Yang Wen-Wen , Hung Yu-Hsuan , Liu Ya-Chen , Kung Jung-Tang , Ma Hsi-Pin , Liu Chiang
Published: May 2024

Background: Previous investigations have shown a positive relationship between baseball pitching velocity and the kinetic chain involved in pitching motion. However, no study has examined the influence of finger characteristics on pitching velocity and rate of spin via a sensor-embedded baseball. Methods: Twenty-one pitchers volunteered and were recruited for this study. An experimental baseball embedded with a force sensor and an inertial measurement unit was designed for pitching performance measurement. Finger length and strength were measured as dependent variables. Spin rate and velocity were independent variables. Pearson product–moment correlations (r) and intraclass correlation coefficients (ICCs) determined the relationship between finger characteristics and pitching performance. Results: Finger length discrepancy, two-point pinch strength, index finger RFD (rate of force development), middle finger impulse, and force discrepancy had significant correlations with spin rate (r = 0.500~0.576, p ≤ 0.05). Finger length discrepancy, two-point pinch, three-point pinch strength, index and middle finger RFD, middle finger impulse, and force combination had significant correlations with fastball pitching velocity (r = 0.491~0.584, p ≤ 0.05). Conclusions: Finger length discrepancy, finger pinch strength, and pitching finger force including maximal force and RFD may be factors that impact fastball spin rate and fastball pitching velocity.
Study on Sensing Urine Concentrations in Water Using a Microwave Sensor Based on Hilbert Structure
Authors: Abdulsattar Rusul Khalid , Al-Kaltakchi Musab T. S. , Mocanu Iulia Andreea , Al-Behadili Amer Abbood , Abdu Hassain Zaid A.
Published: May 2024

In this study, a two-port network-based microwave sensor for liquid characterization is presented. The suggested sensor is built as a miniature microwave resonator using the third iteration of Hilbert’s fractal architecture. The suggested structure is used with the T-resonator to raise the sensor quality factor. The suggested sensor is printed on a FR4 substrate and has a footprint of 40×60×1.6mm3. Analytically, a theoretical investigation is made to clarify how the suggested sensor might function. The suggested sensor is created and put to the test in an experiment. Later, two pans to contain the urine Sample Under Test (SUT) are printed on the sensor. Before loading the SUT, it is discovered that the suggested structure’s frequency resonance is 0.46 GHz. An 18 MHz frequency shift is added to the initial resonance after the pans are printed. They monitor the S-parameters in terms of S12 regarding the change in water content in the urine samples, allowing for the sensing component to be completed. As a result, 10 different samples with varying urine percentages are added to the suggested sensor to evaluate its ability to detect the presence of urine. Finally, it is discovered that the suggested process’ measurements and corresponding simulated outcomes agreed quite well.
Bibliometric Analysis of Weather Radar Research from 1945 to 2024: Formations, Developments, and Trends
Authors: Liu Yin
Published: May 2024

In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field’s rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point.
Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
Authors: Rezaei Sina , Maier Angelina , Arefi Hossein
Published: May 2024

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.
A Review of Image Sensors Used in Near-Infrared and Shortwave Infrared Fluorescence Imaging
Authors: Zhu Banghe , Jonathan Henry
Published: May 2024

To translate near-infrared (NIR) and shortwave infrared (SWIR) fluorescence imaging into the clinic, the paired imaging device needs to detect trace doses of fluorescent imaging agents. Except for the filtration scheme and excitation light source, the image sensor used will finally determine the detection limitations of NIR and SWIR fluorescence imaging systems. In this review, we investigate the current state-of-the-art image sensors used in NIR and SWIR fluorescence imaging systems and discuss the advantages and limitations of their characteristics, such as readout architecture and noise factors. Finally, the imaging performance of these image sensors is evaluated and compared.
The Electric Vehicle Market in Brazil: A Systematic Literature Review of Factors Influencing Purchase Decisions
Authors: Schvartz Marceli Adriane , Lange Salvia Amanda , Londero Brandli Luciana , Leal Filho Walter , Veiga Avila Lucas
Published: May 2024

Technological advancement has propelled global economic growth, but it has also led to high rates of pollution emissions, underscoring the urgency of environmental issues. The transportation sector, particularly the rise in individual vehicle ownership, significantly contributes to atmospheric pollution. In this context, Brazil faces growing challenges, largely due to the high number of individual motor vehicles. Transitioning to electric vehicles (EVs) is seen as a sustainable alternative to reduce emissions of polluting gases. However, it is important to consider that EVs may indirectly generate pollution due to energy production in thermal power plants. Nevertheless, EVs are often sought by countries to reduce dependence on imported fuels and to mitigate urban pollution. This study aims to understand the factors influencing the purchase decision of EVs in Brazil through a systematic literature review and validation by specialists using the fuzzy Delphi method. The results indicate 37 variables, grouped into five main blocks: psychological factors, performance factors, environmental factors, barriers, and prospects for the automotive sector. The validation of these variables by specialists provides a solid foundation for the next stage of the research, which involves administering the questionnaire to the general community. Considering these results will lead to strategies aimed at promoting EV adoption, in line with global sustainability challenges and the reduction in CO2 emissions.
Decreasing Access to Water and Coping Strategies for Shortage in the Informal Settlements of Calabar, Nigeria
Authors: Nchor Julius Uti , Ukam Leonard Edadi
Published: May 2024

Calabar is a city in which 65 per cent of people live in poverty in informal settlements that have low and decreasing access to improved water. In the low-income informal areas of the city, residents are being made responsible for securing their own water supply through the use of several coping strategies. This paper explores the decreasing access to water and coping strategies for shortage in Calabar, Nigeria. It analyses two complementary pieces of data: (i) the households’ coping strategies and (ii) satisfaction with improved water services. A mixed method was introduced, and data were collected via fieldwork at three settlements in Calabar. A household survey of 360 respondents and 27 in-depth interviews were undertaken, and focus groups were conducted. The findings show that households engaged in different coping strategies with which to access water, including conservation (changing routine/reusing), purchasing (spending a significant proportion of their income on buying water) and pumping. The household’s tenure, socioeconomic status and water accessibility determine their decision to move if services are not provided. A crucial differentiation exists between a larger population being granted less access to water versus certain individuals receiving access to a greater quantity of water. The study suggests that improved access to water be secured by mainstreaming approaches and policy interventions that align with the needs of marginalized informal residents and by concerted efforts to effectively manage the water supply for sustainable development.
Unlocking the Potential of Plant Growth-Promoting Rhizobacteria to Enhance Drought Tolerance in Egyptian Wheat (Triticum aestivum)
Authors: Salem Mahmoud A. , Ismail Menattallah A. , Radwan Khaled H. , Abd-Elhalim Haytham M.
Published: May 2024

Plant growth-promoting rhizobacteria (PGPRs) represent a promising strategy for enhancing plant resilience and yields under drought-stress conditions. This study isolated and characterized PGPR from wheat rhizosphere soil in Egypt. Four PGPR strains were evaluated for an array of plant growth-promoting traits, including IAA production, biofilm formation, siderophore production, nitrogen fixation, ACC deaminase activity, phosphate solubilization, and antagonistic potential. Molecular identification via 16S rRNA sequencing classified three isolates (MMH101, MMH102, and MMH103) within the Bacillus genus and one isolate (MMH104) as Myroides sp. Greenhouse experiments examined the effects of PGPR inoculation on the drought-stressed Egyptian wheat cultivar, Gimmeza-9. Wheat plants inoculated with PGPR isolates showed dramatic improvements in growth parameters and stress tolerance indicators compared to non-inoculated controls when subjected to a 10-day drought period, with Bacillus rugosus (MMH101) inoculation resulting in increases of 61.8% in fresh biomass, 77.2% in dry biomass, 108.5% shoot length, and 134.9% root length. PGPR treatments also elevated the chlorophyll and proline content while reducing malondialdehyde levels. The findings demonstrate the effectiveness of PGPR inoculation in enhancing the morphology, physiology, and drought stress resilience of wheat. Isolated PGPR strains hold promise as biofertilizers for improving cereal productivity under water-deficit conditions.
New Design Options for Container Barges with Improved Navigability on the Danube
Authors: Duldner-Borca Bianca , Hoerandner Laura , Bieringer Bernhard , Khanbilverdi Reza , Putz-Egger Lisa-Maria
Published: May 2024

One of the measures set forth by the European Green Deal to decarbonize the freight transport sector is the promotion of inland waterway transport (IWT), and particularly intermodal transport in Europe. To facilitate intermodal transport on the Danube, we developed six new barge designs for the transport of 45′ pallet-wide high-cube containers using a four-step approach. Our approach consisted of detailed desk research, followed by the design and further analysis of the identified barge types, considering, for example, sightlines and stability. Their container carrying capacity reaches up to 90 containers in three layers, which is double the capacity of existing standard barges on the Danube. Nevertheless, three-layer transport is hardly feasible in several cases, due to restrictions regarding sightlines and stability. We conclude that each loading condition must be evaluated separately to determine the best barge design option for each case. This study is limited by its geographical scope and the type of container used to develop the new barge designs. A possible direction for further research could be using other container types and/or extending the geographical scope to extend the usability of our container barge designs.
A Blessing or a Curse? Highway Connection and the Entry of Polluting Firms in China
Authors: Meng Xuechen , Sun Yaqi , Xu Xiaoshu
Published: May 2024

We investigate how highway connections influenced the entry of polluting firms based on panel data from 257 Chinese prefectures from 1998 to 2012. We also investigate the heterogeneity and influencing channels. We use the generalized difference-in-difference method as a tool in the empirical analysis. We have three main findings. First, highway connections increased the entry of polluting firms by 54.5 percent. Second, the heterogeneity analysis shows that this effect is mainly caused by prefectures on provincial borders, prefectures in central China, and prefectures with more than 1 million residents in urban districts. Third, highway connections reduced the concern about environmental protection by local governments of prefectures on provincial boundaries but increased concern about local governments of prefectures in interior regions. The concern about employment increased regardless of a prefecture’s location in a province. These results indicate that less developed regions, i.e., those on provincial boundaries, valued the benefit of job creation for welfare improvement more than the harm of pollution. Hence, they intentionally reduced environmental regulation to induce polluting firms’ entry. Our results provide new and insightful insights into the transition of economic development to a sustainable path, especially in developing countries.
Patterns of Mangrove Resource Uses within the Transboundary Conservation Area of Kenya and Tanzania
Authors: Kamau Anne Wanjiru , Shauri Halimu , Hugé Jean , Van Puyvelde Karolien , Koedam Nico , Kairo James Gitundu
Published: May 2024

Mangrove forests provide a wide range of goods and services that sustain communities around the world. This paper explores utilization patterns of extractable mangrove resources by communities within the proposed Kenya–Tanzania transboundary conservation area (TBCA). Some 152 household surveys and 12 nominal group technique discussions were carried out. At least 16 direct-use products were reported to be extracted from the mangroves, with 90% of households found to use mangrove products. Changing patterns of mangrove use in the areas and accessibility point towards unsustainable utilization and promote illegal activities. Mangrove use patterns also show a high level of mangrove dependence by communities for their livelihood. Overharvesting of mangrove wood products is reported to be the major threat facing the forests. There is a need to address the problems of open access through capacity building and awareness creation, development and implementation of harvest plans, and introduction of alternative livelihood options. The results of this study can be used to inform development of the proposed TBCA as well as the establishment of a conservation policy in order to contribute to sustainable utilization of mangroves. These findings are important within the TBCA as well as similar environments around the world.
The Minimum Dietary Level and Mix Ratio of Krill Meal and Fish Meal to Elicit Feed Intake and Growth Performance in Juvenile Penaeus vannamei
Authors: Nunes Alberto J. P. , Leite Jordana Sampaio , Gomes Caio Gabriel Dantas , Dragøy Ragnhild , Burri Lena
Published: May 2024

Shrimp feeds depend on high levels of digestible protein and essential amino acids, which can be sourced from various commercially available feed ingredients. Marine proteins can be used to partially fulfill the requirements of these and other important nutrients. Their utilization is further influenced by their palatability and growth-promoting effect. However, marine ingredients can significantly drive costs in feed formulation depending on the type and dietary inclusion level. This study aimed to determine the minimum dietary level of fish meal (FML) and krill meal (KRM) and their mix ratio to elicit feed intake and growth performance in juvenile Penaeus vannamei. Ten diets were formulated with graded FML (90, 60, 30 g kg−1) in combination with 15, 30, and 45 g kg−1 KRM and a control diet with 120 g kg−1 FML. Shrimp (1.28 ± 0.08 g body weight, BW) were stocked in seventy 1-m3 tanks (135 animals m−2), and after 88 days, their growth performance was determined. Feed preference was assessed through two-by-two comparisons in twenty 0.5 m3 tanks over four weeks. No significant differences in survival (93.9 ± 4.5%), gained yield (1235 ± 92 g m−2), and feed conversion ratio (1.47 ± 0.09) were observed. Diets with 60 g kg−1 FML led to faster growth and higher feed intake than 30, 90, and 120 g kg−1 FML. Shrimp on 30 g kg−1 FML diets had the lowest BW, especially with 30–15 (FML-KRM) and 30–30 diets. Diets with 90 g kg−1 FML outperformed 30 g kg−1 FML. The control diet delivered a higher shrimp BW than diets 30–15 and 30–30, showing similar results to other diets except 60–15. Feed preference was influenced by KRM inclusion, with 15 g kg−1 KRM resulting in higher apparent feed intake than 30 and 45 g kg−1. The findings indicate that FML can be effectively reduced by up to 75% when combined with lower levels of KRM. This corresponds with the industry’s ongoing trend to achieve greater sustainability and cost efficiency through the reduced utilization of critical resources.
Extraction of Geolocations from Site Maps in the Context of Traffic Counting
Authors: Schering Johannes , Säfken Pascal , Marx Gómez Jorge
Published: May 2024

The further promotion of cycling is a key component for each city to reach its sustainability goals. To make decisions to improve comfort or safety for cyclists, the amount of motorized traffic should be taken into account. Therefore, traffic data play a crucial role not only in the construction of roads but also in cycling planning. This data source provides insights essential for road infrastructure development and optimizing various modes of transportation, such as bike paths. However, processing municipal traffic data becomes a challenge when stationary traffic-counting stations lack geo-referencing in relational databases. In this case, the locations of traffic counters are solely displayed on a PDF-based site map without inherent geo-referencing, and the geo-coordinates are not stored in any relational database. The absence of geo-references poses a significant hurdle for traffic-planning experts in decision-making processes. Hence, this study aims to address this issue by finding a suitable approach to extract the geo-coordinates from the site maps. Several potential solutions are discussed and compared in terms of time dimension, usability, extensibility, error treatment and the accuracy of results. Leveraging the open-source tool QGIS, geo-coordinates may be successfully extracted from the PDF-based site maps, resulting in the creation of a GeoTIFF file incorporating coordinates and the rotated site map. Geo-coordinates can then be derived from the GeoTIFF files using x and y coordinates, computed through the rotation matrix formula. Over 1400 measurement locations may be extracted based on the preferred approach, facilitating more informed decision-making in traffic planning.
Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques
Authors: Alam Waseem , Wang Haiyan , Pervez Amjad , Safdar Muhammad , Jamal Arshad , Almoshaogeh Meshal , Al-Ahmadi Hassan M.
Published: May 2024

Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety.