bluuebunny/crossref_metadata_2025 huggingface dataset:
Furthering Automatic Feature Extraction for Fit-for-Purpose Cadastral Updating: Cases from Peri-Urban Addis Ababa, Ethiopia
Authors: Metaferia Mekonnen Tesfaye , Bennett Rohan Mark , Alemie Berhanu Kefale , Koeva Mila
Published: August 2023

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. This approach can be applied to both the initial establishment and on-going maintenance of the system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers’ land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions were utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of “boundary” and “non-boundary” outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) were undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file were used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artifacts were also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadastres. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4 m and 0.6 m, respectively, for the interactively delineated and reference boundaries. The study proposed publicly available software solutions and outlined a workflow to (semi-) automatically extract cadastral boundaries from aerial/satellite images. It further demonstrated the potentially significant role AFE could play in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.
Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile
Authors: Rodríguez-López Lien , Usta David Bustos , Duran-Llacer Iongel , Alvarez Lisandra Bravo , Yépez Santiago , Bourrel Luc , Frappart Frederic , Urrutia Roberto
Published: August 2023

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.
Integrated Approach for the Study of Urban Expansion and River Floods Aimed at Hydrogeomorphic Risk Reduction
Authors: Mandarino Andrea , Faccini Francesco , Luino Fabio , Bono Barbara , Turconi Laura
Published: August 2023

Urbanization in flood-prone areas is a critical issue worldwide. The historical floods, the urban expansion in terms of building footprint, the extent and construction period of inundated buildings with reference to two representative floods (5–6 November 1994 and 24–25 November 2016), and the ground effects and dynamics of these events were investigated in the cities of Garessio, Ceva, and Clavesana, along the Tanaro River (NW Italy). An integrated approach based on historical data analysis, photograph interpretation, field surveys, and GIS investigations was adopted, and novel metrics for quantitative analysis of urbanization and flood exposure at the individual-building scale were introduced. The considered cities were hit by damaging floods several times over the last centuries and experienced an increase in built-up surface after the mid-19th century, especially between the 1930s and 1994. The 1994 and 2016 high-magnitude floods highlighted that urban expansion largely occurred in flood-prone areas, and anthropogenic structures conditioned flood propagation. One of the rare Italian cases of the relocation of elements exposed to floods is documented. This research aims to emphasize the relevance of information on past floods and urbanization processes for land planning and land management and the need for land use planning for flood control to forbid new urban expansion in potentially floodable areas. The outcomes represent an essential knowledge base to define effective and sustainable management measures to mitigate hydrogeomorphic risk.
A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning
Authors: Fang Wei , Qin Hui , Liu Guanjun , Yang Xin , Xu Zhanxing , Jia Benjun , Zhang Qianyi
Published: August 2023

Reliable precipitation data are essential for studying water cycle patterns and climate change. However, there are always temporal or spatial errors in precipitation data from various sources. Most precipitation fusion methods are influenced by high-dimensional input features and do not make good use of the spatial correlation between precipitation and environmental variables. Thus, this study proposed a novel multi-source precipitation spatiotemporal fusion method for improving the spatiotemporal accuracy of precipitation. Specifically, the attention mechanism was used to first select critical input information to dimensionalize the inputs, and the Convolutional long-short-term memory network (ConvLSTM) was used to merge precipitation products and environmental variables spatiotemporally. The Yalong River in the southeastern part of the Tibetan Plateau was used as the case study area. The results show that: (1) Compared with the original precipitation products (IMERG, ERA5 and CHIRPS), the proposed method has optimal accuracy and good robustness, and its correlation coefficient (CC) reaches 0.853, its root mean square coefficient (RMSE) decreases to 3.53 mm/d and its mean absolute error (MAE) decreases to 1.33 mm/d. (2) The proposed method can reduce errors under different precipitation intensities and greatly improve the detection capability for strong precipitation. (3) The merged precipitation generated by the proposed method can be used to describe the rainfall–runoff relationship and has good applicability. The proposed method may greatly improve the spatiotemporal accuracy of precipitation in complex terrain areas, which is important for scientific management and the allocation of water resources.
Velocity-Related Magnetic Interference Compensation of Unmanned Underwater Vehicle
Authors: Luo Huihui , Pan Mengchun , Du Qingfa , Zhang Qi , Hu Jiafei , Ding Zengquan
Published: August 2023

Underwater magnetic detection based on unmanned underwater vehicles (UUVs) is an important approach for marine exploration and monitoring. Due to the interference of the carrier magnetic field, the detection accuracy is severely limited. To improve the performance of magnetic detection, various compensation methods have been developed. However, the compensation effectiveness of these methods is greatly diminished when UUVs sail at different velocities. In this paper, we propose a velocity-related interference magnetic compensation method that enhances the conventional Tolles–Lawson model. By introducing a velocity-related term and combining the ridge regression with the linear fitting algorithm, we determine the compensation parameters for various UUV velocities. Field experiments are conducted to verify the effectiveness of the proposed method. The results show that the root mean square error of the total magnetic field after compensation is reduced from 246.90 nT to 2.23 nT. Our study demonstrates that the velocity-related compensation method can significantly improve the accuracy of interference magnetic compensation under different UUV velocities, making it applicable to actual underwater magnetic detection.
High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model
Authors: Zhao Rongkun , Wang Yue , Li Yuechen
Published: August 2023

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.
Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
Authors: Phadke Abhishek , Medrano F. Antonio , Sekharan Chandra N. , Chu Tianxing
Published: August 2023

The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.
Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data
Authors: Tarsha Kurdi Fayez , Amakhchan Wijdan , Gharineiat Zahra , Boulaassal Hakim , El Kharki Omar
Published: August 2023

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature.
Robust PDF Watermarking against Print–Scan Attack
Authors: Li Lei , Zhang Hong-Jun , Meng Jia-Le , Lu Zhe-Ming
Published: August 2023

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print–scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print–scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print–scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.
Application of Deep Learning Techniques for Detection of Pneumothorax in Chest Radiographs
Authors: Deng Lawrence Y. , Lim Xiang-Yann , Luo Tang-Yun , Lee Ming-Hsun , Lin Tzu-Ching
Published: August 2023

With the advent of Artificial Intelligence (AI) and even more so recently in the field of Machine Learning (ML), there has been rapid progress across the field. One of the prominent examples is image recognition in the medical category, such as X-ray imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). It has the potential to alleviate a doctor’s heavy workload of sifting through large quantities of images. Due to the rising attention to lung-related diseases, such as pneumothorax and nodules, ML is being incorporated into the field in the hope of alleviating the already strained medical resources. In this study, we proposed a system that can detect pneumothorax diseases reliably. By comparing multiple models and hyperparameter configurations, we recommend a model for hospitals, as its focus on minimizing false positives aligns with the precision required by medical professionals. Through our cooperation with Poh-Ai Hospital, we acquired a total of over 8000 X-ray images, with more than 1000 of them from pneumothorax patients. We hope that by integrating AI systems into the automated process of scanning chest X-ray images with various diseases, more resources will be available in the already strained medical systems. Our proposed system showed that the best model that is used for transfer learning from our dataset performed with an AP of 51.57 and an AP75 of 61.40, with accuracy at 93.89%, a false positive of 1.12%, and a false negative of 4.99%. Based on the feedback from practicing doctors, they are more wary of false positives. For their use case, we recommend another model due to the lower false positive rate and higher accuracy compared with other models, which in our test shows a rate of only 0.88% and 95.68%, demonstrating the feasibility of the research. This promising result showed that it could be utilized in other types of diseases and expand to more hospitals and medical organizations, potentially benefitting more people.
A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment
Authors: Almuqren Latifah , Aljameel Sumayh S. , Alqahtani Hamed , Alotaibi Saud S. , Hamza Manar Ahmed , Salama Ahmed S.
Published: August 2023

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.
Creating an Autonomous Hovercraft for Bathymetric Surveying in Extremely Shallow Water (<1 m)
Authors: Troup Meghan L. , Hatcher Matthew , Barclay David
Published: August 2023

Coastal shallow water environments (<5 m) are extremely biodiverse and dynamic yet are often mapped too infrequently or at too low resolutions to capture the important processes occurring in these regions. Common forms of coastal surveying can leave gaps in data in the shallow water zone due to optical instrument capabilities and a vessel’s ability to navigate in this region. One solution to these issues is an autonomous hovercraft that can fly over land and water and begin surveying at sub-meter water depths, bridging the gap between common optical and acoustic surveying methods. The craft’s autonomy is tested via four autonomous flight paths, or missions, and the desired path is compared to both the observed heading and direction of motion. Although the accuracy for each track in the mission varies, most headings and directions of motion of the hovercraft are within 50 degrees of the desired direction. A single-beam echo sounder was used to map the bathymetry of the study site, showing a gently sloping beach.
Electrospun Rubber Nanofiber Web-Based Dry Electrodes for Biopotential Monitoring
Authors: Reza Mohammad Shamim , Jin Lu , Jeong You Jeong , Oh Tong In , Kim Hongdoo , Kim Kap Jin
Published: August 2023

This study aims to find base materials for dry electrode fabrication with high accuracy and without reducing electrode performance for long-term bioelectric potential monitoring after electroless silver plating. Most applications of dry electrodes that have been developed in the past few decades are restricted by low accuracy compared to commercial Ag/AgCl gel electrodes, as in our previous study of PVDF-based dry electrodes. In a recent study, however, nanoweb-based chlorinated polyisoprene (CPI) and poly(styrene-b-butadiene-b-styrene) (SBS) rubber were selected as promising candidates due to their excellent elastic properties, as well as their nanofibril nature, which may improve electrode durability and skin contact. The electroless silver plating technique was employed to coat the nanofiber web with silver, and silver nanoweb(AgNW)-based dry electrodes were fabricated. The key electrode properties (contact impedance, step response, and noise characteristics) for AgNW dry electrodes were investigated thoroughly using agar phantoms. The dry electrodes were subsequently tested on human subjects to establish their realistic performance in terms of ECG, EMG monitoring, and electrical impedance tomography (EIT) measurements. The experimental results demonstrated that the AgNW dry electrodes, particularly the SBS-AgNW dry electrodes, performed similarly to commercial Ag/AgCl gel electrodes and were outperformed in terms of long-term stability.
Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm
Authors: Shi Shuomang , Jiang Baiyu , Ludwig Simone , Xu Luyang , Wang Hao , Huang Ying , Yan Fei
Published: August 2023

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.
“GeSn Rule-23”—The Performance Limit of GeSn Infrared Photodiodes
Authors: Chang Guo-En , Yu Shui-Qing , Sun Greg
Published: August 2023

Group-IV GeSn photodetectors (PDs) compatible with standard complementary metal–oxide-semiconductor (CMOS) processing have emerged as a new and non-toxic infrared detection technology to enable a wide range of infrared applications. The performance of GeSn PDs is highly dependent on the Sn composition and operation temperature. Here, we develop theoretical models to establish a simple rule of thumb, namely “GeSn−rule 23”, to describe GeSn PDs’ dark current density in terms of operation temperature, cutoff wavelength, and Sn composition. In addition, analysis of GeSn PDs’ performance shows that the responsivity, detectivity, and bandwidth are highly dependent on operation temperature. This rule provides a simple and convenient indicator for device developers to estimate the device performance at various conditions for practical applications.
A Holistic Review of Cyber–Physical–Social Systems: New Directions and Opportunities
Authors: Sobb Theresa , Turnbull Benjamin , Moustafa Nour
Published: August 2023

A Cyber–Physical–Social System (CPSS) is an evolving subset of Cyber–Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media’s influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work.
Analysis and Implementation of Controlled Semiconductor Switch for Ultra-Wideband Radar Sensor Applications
Authors: Jurik Patrik , Sokol Miroslav , Galajda Pavol , Drutarovsky Milos
Published: August 2023

All ultra-wideband (UWB) sensor applications require hardware designed directly for their specific application. The switching of broadband radio frequency and microwave signals is an integral part of almost every piece of high-frequency equipment, whether in commercial operation or laboratory conditions. The trend of integrating various circuit structures and systems on a chip (SoC) or in a single package (SiP) is also related to the need to design these integrated switches for various measuring devices and instruments in laboratories, paradoxically for their further development. Another possible use is switching high-frequency signals in telecommunications devices, whether mobile or fixed networks, for example, for switching signals from several antennas. Based on these requirements, a high-frequency semiconductor integrated switch with NMOS transistors was designed. With these transistors, it is possible to achieve higher integration than with bipolar ones. Even though MOSFET transistors have worse frequency characteristics, we can compensate them to some extent with the precise design of the circuit and layout of the chip. This article describes the analysis and design of a high-frequency semiconductor integrated switch for UWB applications consisting of three series-parallel switches controlled by CMOS logic signals. They are primarily intended for UWB sensor systems, e.g., when switching and configuring the antenna MIMO system or when switching calibration tools. The design of the switch was implemented in low-cost 0.35 µm SiGe BiCMOS technology with an emphasis on the smallest possible attenuation and the largest possible bandwidth and isolation. The reason for choosing this technology was also that other circuit structures of UWB systems were realized in this technology. Through the simulations, individual parameters of the circuit were simulated, the layout of the chip was also created, and the parameters of the circuit were simulated with the parasitic extraction and the inclusion of parasitic elements (post-layout simulations). Subsequently, the chip was manufactured and its parameters were measured and evaluated. Based on these measurements, the designed and fabricated UWB switch was found to have the following parameters: a supply current of 2 mA at 3.3 V, a bandwidth of 6 GHz, an insertion loss (at 1 GHz) of −2.2 dB, and isolation (at 1 GHz) of −33 dB, which satisfy the requirements for our UWB sensor applications.
Unveiling LoRa’s Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
Authors: Pinelo João , Rocha André Dionísio , Arvana Miguel , Gonçalves João , Cota Nuno , Silva Pedro
Published: August 2023

In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime sensing data. Specifically, we conduct an experimental assessment of the Azorean Long Range Wide Area Network (LoRaWAN) coverage. Our tests involve positioning the gateway at the island’s highest point and installing end nodes on medium-sized fishing vessels. Through measurements of received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and lines of sight (LOS), we showcase the potential of LoRaWAN transmissions to achieve communication distances exceeding 130 km in a LOS-free scenario over the ocean. These findings highlight the promising capabilities of LoRaWAN for reliable and long-range maritime communication of sensing data.
Development of a Semi-Empirical Model for Estimating the Efficiency of Thermodynamic Power Cycles
Authors: Bellos Evangelos
Published: August 2023

Power plants constitute the main sources of electricity production, and the calculation of their efficiency is a critical factor that is needed in energy studies. The efficiency improvement of power plants through the optimization of the cycle is a critical means of reducing fuel consumption and leading to more sustainable designs. The goal of the present work is the development of semi-empirical models for estimating the thermodynamic efficiency of power cycles. The developed model uses only the lower and the high operating temperature levels, which makes it flexible and easily applicable. The final expression is found by using the literature data for different power cycles, named as: organic Rankine cycles, water-steam Rankine cycles, gas turbines, combined cycles and Stirling engines. According to the results, the real operation of the different cases was found to be a bit lower compared to the respective endoreversible cycle. Specifically, the present global model indicates that the thermodynamic efficiency is a function of the temperature ratio (low cycle temperature to high cycle temperature). The suggested equation can be exploited as a quick and accurate tool for calculating the thermodynamic efficiency of power plants by using the operating temperature levels. Moreover, separate equations are provided for all of the examined thermodynamic cycles.
Potential Use of Agricultural Waste—Carob Kibbles (Ceratonia siliqua L.) as a Biosorbent for Removing Boron from Wastewater
Authors: Díaz Luz Adriana , Carlier Jorge Dias , Michalak Izabela , Costa María Clara
Published: August 2023

The release of boron (B) into the environment as a result of anthropogenic activity modifies sustainable natural conditions, thus affecting ecosystems. To meet water quality regulations, commercial and natural boron adsorbents are available to reduce its concentrations in industrial effluents, with the former being not only more expensive but also less sustainable. In the publication, the biosorption parameters of carob kibbles (Ceratonia siliqua L.) were optimized in order to remove boron from aqueous solutions using batch experiments. The biosorbent used in the present research was agro-waste biomass provided by the local locust-beam gum industry. Boron removal by carob kibbles was favored at high initial pH values, and this capacity was found to be a function of boron initial concentration, biosorbent content in the solution, and particle size. The change in temperature did not affect the potential of biomass to remove boron. The highest boron removal efficiency (55.1%) was achieved under the following optimal conditions: 50 g/L biosorbent dose (Cs), with particle size range 0.025–0.106 mm, for the initial concentration (C0) of boron in the solution of 100 mg/L, at an initial pH of 11.5, for 5 h at 25 °C. This investigation suggests that carob kibble agro-waste can be valorized as a biosorbent to remove boron from wastewater, and the boron-loaded residue may eventually be explored as a new boron-fertilizer.
When Kinship Caregivers Became Teachers: Role Stress and Strain from Remote Learning during COVID-19
Authors: Klein-Cox Amanda , Tobin Angela , Denby Ramona
Published: August 2023

For caregivers of school-age children, schooling at home was a significant stressor during the COVID-19 pandemic. Research demonstrates the emotional burden of taking on the role of teacher while trying to balance responsibilities related to work, household, and childcare. Yet little is known about the well-being of kinship caregivers during this time, although this population is at heightened risk for role stress and strain and emotional distress. This article shares the results of focus groups conducted as part of a larger qualitative study, the purpose of which was to understand the factors that influenced the well-being of kinship families during the pandemic, from the perspective of kinship caregivers and child welfare professionals. From these conversations, the challenges of remote learning emerged as a prominent theme, particularly for caregivers raising children with special healthcare needs, who comprised the majority of the sample. Given their pre-pandemic role stress and strain due to caregiving responsibilities, most caregivers struggled with managing their child’s learning, working from home, and caring for children with trauma histories, often with limited support and access to needed services. These findings suggest that this population needs support—potentially in the form of wrap-around services and/or social safety net provisions—to reduce their role stress and strain and improve well-being.
An Evaluation of the Caudal End Deviation of the Nasal Septum Using the Quantitative Analysis of Computed Tomography
Authors: Hirai Tomohisa , Ueda Tsutomu , Ishino Takashi , Takeno Sachio
Published: August 2023

Objectives: This study was designed to determine objective surgical indications of correcting caudal end deviation of the nasal septum. Methods: We employed quantitative computed tomographic (CT) analysis and assessed the validity by comparing this with anterior rhinoscopic findings (AR findings). The study population consisted of 300 patients. The archived CT data were transferred to a workstation, and 3D CT volume-rendered images were generated using computer graphics tools. In the plane of the nostril entrance, we calculated ratios of the cross-sectional area of the convex side (narrower side) and the concave side (wider side), which is abbreviated as the N/W ratio. We also examined the presence of laterality between the right and the left cross-sectional area of the nasal valve based on the AR findings. Surgical procedures for whether to expose the caudal end were planned based on the AR findings and the N/W ratio. Results: A significant correlation was found between the AR findings and the N/W ratio. After surgery, the average N/W ratio improved from 0.53 ± 0.15 to 0.81 ± 0.15, and the average values of VAS scaling for nasal obstruction improved from 8.1 ± 0.2 to 1.0 ± 0.1. Conclusions: The quantitative CT analysis proposed in the study is a useful modality to objectively determine the surgical indications of managing the caudal end of the nasal septum.
Analyst Coverage and Corporate ESG Performance
Authors: Zhang Chunying , Wu Xiaohui
Published: August 2023

In recent years, environmental, social, and governance factors (ESG) have played an increasingly significant role in the practice of corporate development of widespread concern. For corporate ESG, it is still necessary to consider the factors that influence the development of corporate ESG. This paper performed fixed-effect panel model analysis to investigate the relationship between analyst coverage and corporate ESG performance using data from China’s listed firms from 2011 to 2021. Our results showed that analyst coverage improves corporate ESG performance, especially the environmental (E) and social (S) dimensions, proving that analyst coverage is an important driving force behind corporate ESG engagement. The results were shown to be valid through a series of endogeneity and robustness checks. In the heterogeneity analysis, we showed that the promotion effects are more significant for state-owned firms and firms faced with greater financial constraints and higher information asymmetry. Furthermore, analyst coverage improves corporate ESG performance through the potential channels of attracting media attention and conducting site visits. Our study enriches the existing literature on the determinants of corporate ESG performance, and highlights the role analysts play in shaping corporate non-financial behavior and promoting corporate sustainable development.
Privacy Concerns in Social Commerce: The Impact of Gender
Authors: Mutambik Ibrahim , Lee John , Almuqrin Abdullah , Zhang Justin Zuopeng , Baihan Mohammed , Alkhanifer Abdulrhman
Published: August 2023

Today, social commerce is one of the most rapidly growing subsectors of e-commerce, creating new opportunities for brands of all types and sizes. However, despite its popularity and potential, social commerce faces significant challenges, including issues of privacy, trust and ethics. This paper sets out to identify key aspects of privacy which influence ongoing user engagement with social commerce, so that social media, and other social commerce, platforms can more effectively address the issue. In particular, the paper seeks to determine the extent to which these aspects of privacy are a function of gender and, therefore, to increase our understanding of the role of gender in determining a user’s likelihood of sustainable engagement with s-commerce. To explore these issues, the study deploys a mixed methodology (semi-structured interviews and questionnaires) to examine the views of a broad demographic of s-commerce users in Saudi Arabia. The results allowed us to identify three distinct aspects of online privacy that significantly influence the likelihood of engaging in s-commerce and also demonstrated that the relative importance of these aspects is a function of gender. The study enhances current understanding of the role of gender in intention to use s-commerce and provides a framework for further research. The findings of the study will be of interest to all parties involved in the design and provision of s-commerce services, including social media platforms.
The Classification of Riparian Habitats and Assessment of Fish-Spawning Habitat Suitability: A Case Study of the Three Gorges Reservoir, China
Authors: Mao Zhihui , Ding Fang , Yuan Lilai , Zhang Yan , Ni Zhaohui , Li Yingren , Wang Lin , Li Yunfeng
Published: August 2023

After the completion of the Three Gorges Reservoir (TGR), there was a significant and drastic transformation of the original river habitat. These changes led to the loss of the original fish habitat and the emergence of a new habitat. To effectively classify and assess fish-spawning habitats in the TGR, a novel coastal complexity index (CCI) was developed. The CCI was formulated utilizing satellite remote sensing data and considering the river coastal line and river centerline on the river-reach scale. By integrating the CCI with river morphology, five river habitats were identified: the backwater bay, point bar, straight river channel, convex-bank point bar, and concave-bank deep pool. In order to evaluate the suitability of these habitats for sticky-egg-spawning fish, a single-factor habitat suitability curve was constructed using three key habitat factors: the CCI, slope, and vegetation coverage. This process involved the employment of two distinct methods: the habitat utilization method and the habitat preference method. The former only considered the survey data of spawning grounds, while the latter integrated the overall distribution of habitats in the TGR. Subsequently, a habitat suitability index (HSI) was established to assess the overall suitability of the identified habitats for sticky-egg-spawning fish. The results demonstrated a high classification accuracy, with the backwater bay representing the most prevalent habitat type, accounting for 43.31% of the total habitat types. When considering slope and vegetation coverage, the optimal ranges obtained through the two habitat suitability analysis methods were similar. However, for the CCI, there were variations in the optimal ranges obtained using the two methods. The habitat utilization method indicated an optimal interval of 2–4, while the habitat preference method provided an optimal interval of 4–8. Nonetheless, the assessment results for the spawning habitats’ suitability using both methods yielded essentially identical outcomes. Specifically, the backwater bay, convex-bank point bar, and concave-bank deep pool habitats exhibited higher suitability for spawning than point bar and straight river channel habitats. Further analysis revealed that approximately 75% of the 230 identified backwater bays were categorized as high-quality or higher-quality spawning habitats. In the time since this research was conducted, its findings have served as a theoretical foundation for the protection of aquatic biological resources and habitats.
Comparing Design Schemes and Infection Risk Assessment of Negative Pressure Isolation Cabin
Authors: Zhou Shuwen , Zan Yixin , Liu Xiaolong
Published: August 2023

At present, various public health emergencies have forced a deeper study of measures to prevent infectious diseases. To prevent the spread of infectious diseases on large cruise ships, the use of negative pressure isolation cabins is an effective method. However, existing cruise ships rarely use negative pressure isolation cabins or use them with shortcomings such as unreasonable layout of air inlets, which do not effectively reduce the risk of infection for medical staff while isolating patients. To solve this problem, first, the method of active air interference and the principle of proximity of the fresh air outlet were examined in this paper, and four groups of optimization schemes were designed. Second, by analyzing the diffusion of droplets in three breathing modes (coughing, sneezing, and talking while wearing or not wearing a mask), the direction of droplets and the efficiency of pollutant discharge under the condition of coughing were compared. Finally, in this paper, the infection risk of contact transmission and aerosol transmission was optimized by using the linear quantitative evaluation method and MSDR method, respectively. The results showed that the auxiliary air intake layout of optimal scheme 2 can effectively reduce the risk of infection for medical personnel in negative pressure isolation cabins. This study provides a useful reference for the design and optimization of negative pressure isolation cabins in future cruise ships.
Water Reuse—Analysis of the Possibility of Using Reclaimed Water Depending on the Quality Class in the European Countries
Authors: Ramm Klara , Smol Marzena
Published: August 2023

In 2020, the European Commission (EC) defined a legal requirement for water reuse for agricultural purposes in the European Union (EU). EU Regulation (2020/741) on minimum requirements for water reuse should mobilize member states to implement solutions for the use of reclaimed water. This paper aims to examine the state of implementation of the provisions of this Regulation at the time of its entry into force. Based on desk research, the legal status of water reuse in EU countries, with particular emphasis on the issues of reclaimed water quality and its applications, was analyzed. The state of implementation of solutions regulating water reuse varies significantly across the EU’s countries. Central and Eastern European (e.g., Poland, Germany, Latvia, Lithuania, Estonia) countries are in no rush to regulate water reuse in agriculture; some will take advantage of the derogation to gain more time to consider it. Southern countries (e.g., Greece, Italy, France, Spain) are the most advanced and have experience in practical implementations gained before the Regulation was introduced. However, they use different quality control parameters. For now, France, Greece, Portugal, and Spain have fully implemented EC Regulation (2020/741); Belgium, Hungary, and Italy have partially implemented it; Malta has implemented it practically but not formally; and Cyprus has implemented it in distributed regulations. It should be pointed out that the potential for water reuse in the EU is significant, and this process is needed due to climate change consequences for Europe’s water resources. Therefore, further initiatives are expected, including the implementation by other countries of the provisions of Regulation (2020/741) in the coming decades.