bluuebunny/crossref_metadata_2025 huggingface dataset:
Groundwater Level Data Imputation Using Machine Learning and Remote Earth Observations Using Inductive Bias
Authors: Ramirez Saul G. , Williams Gustavious Paul , Jones Norman L.
Published: November 2022

Sustainable groundwater management requires an accurate characterization of aquifer-storage change over time. This process begins with an analysis of historical water levels at observation wells. However, water-level records can be sparse, particularly in developing areas. To address this problem, we developed an imputation method to approximate missing monthly averaged groundwater-level observations at individual wells since 1948. To impute missing groundwater levels at individual wells, we used two global data sources: Palmer Drought Severity Index (PDSI), and the Global Land Data Assimilation System (GLDAS) for regression. In addition to the meteorological datasets, we engineered four additional features and encoded the temporal data as 13 parameters that represent the month and year of an observation. This extends previous similar work by using inductive bias to inform our models on groundwater trends and structure from existing groundwater observations, using prior estimates of groundwater behavior. We formed an initial prior by estimating the long-term ground trends and developed four additional priors by using smoothing. These prior features represent the expected behavior over the long term of the missing data and allow the regression approach to perform well, even over large gaps of up to 50 years. We demonstrated our method on the Beryl-Enterprise aquifer in Utah and found the imputed results follow trends in the observed data and hydrogeological principles, even over long periods with no observed data.
Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images
Authors: Ma Jiabao , Zhou Wujie , Qian Xiaohong , Yu Lu
Published: November 2022

The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RSIs of complex scenes is challenging, and (2) feature reconstruction for accurate segmentation is difficult. To solve these problems, we propose a deep-separation-guided progressive reconstruction network that achieves accurate RSI segmentation. First, we design a decoder comprising progressive reconstruction blocks capturing detailed features at various resolutions through multi-scale features obtained from various receptive fields to preserve accuracy during reconstruction. Subsequently, we propose a deep separation module that distinguishes various classes based on semantic features to use deep features to detect objects of different scales. Moreover, adjacent middle features are complemented during decoding to improve the segmentation performance. Extensive experimental results on two optical RSI datasets show that the proposed network outperforms 11 state-of-the-art methods.
Seasonal Variation in Land and Sea Surface Backscatter Coefficients at High Frequencies
Authors: Edwards Danielle , Cervera Manuel
Published: November 2022

Over the horizon radars (OTHR) rely on the propagation of high frequency (HF) radio waves via the ionosphere to successfully achieve their designated missions. Backscatter sounders (BSS) are environmental over-the-horizon radars which may be used to assess the ionospheric propagation conditions. However, high power observed by a BSS may be due to either good ionospheric propagation, a high surface backscatter coefficient, or a combination of both. Hence, an understanding of the surface backscatter coefficients and their temporal variation is essential to fully understand the ionospheric propagation conditions. A database of surface backscatter coefficients over a decade was created using backscatter ionogram data from four backscatter sounders in Australia. The temporal variations in the backscatter coefficients were investigated and it was found that the land backscatter coefficients were relatively constant over time, while the sea backscatter coefficients showed significant seasonal variation.
Measuring System for Determining the Quality of LED Light Sources and an Overview of LED Light Bulbs for Household Use
Authors: Markovič Matic , Orgulan Andrej , Sukič Primož
Published: November 2022

Modern LED light sources have many advantages, as well as some disadvantages. One of the disadvantages is the pulsating luminous flux, which, in some cases, affects people’s health and well-being negatively. The paper describes the design and making process of a measuring system for determining the quality of LED substitutes for conventional light bulbs and gives an overview of LED light bulbs for household use. The measurement system is controlled using the MATLAB software environment, in which data processing and plotting of the results are also performed. We acquired 59 different LED light bulbs from 37 manufacturers, and performed the measurements. The light bulbs are classified based on the percentage of fluctuations in the luminous flux, and the percentage of deviation of the measured luminous flux compared to the value stated on the packaging by the manufacturer.
Changes in Ultrasound Measurements of the Ulnar Nerve at Different Elbow Joint Positions in Patients with Cubital Tunnel Syndrome
Authors: Wolny Tomasz , Fernández-de-las-Peñas César , Granek Arkadiusz , Linek Paweł
Published: November 2022

Ultrasound imaging (US) is increasingly being used in the diagnosis of entrapment neuropathies. The aim of the current study was to evaluate changes in stiffness (shear modulus), cross-sectional area (CSA), and trace length (TRACE) of the ulnar nerve in patients with cubital tunnel syndrome (CuTS), with shear wave elastography (SWE). A total of 31 patients with CuTS were included. CSA, shear modulus, and TRACE examinations were performed in the SWE mode in four positions of the elbow: full extension, 45° flexion, 90° flexion, and maximum flexion. There were significant side-to-side differences in the ulnar nerve elasticity value at 45°, 90°, and maximal elbow flexion (all, p < 0.001) but not at elbow extension (p = 0.36). There were significant side-to-side differences in the ulnar nerve CSA value at each elbow position (all, p < 0.001). There were significant side-to-side differences in the ulnar nerve trace value at each elbow position (all, p < 0.001). The symptomatic ulnar nerve in patients with CuTS exhibited greater stiffness (shear modulus), CSA, and TRACE values, compared with the asymptomatic side. US examinations (shear modulus, CSA, and TRACE evaluation) of the ulnar nerve can be helpful in supporting and supplementing the diagnosis in patients with CuTS.
Classification of Holograms with 3D-CNN
Authors: Terbe Dániel , Orzó László , Zarándy Ákos
Published: November 2022

A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image.
Investigating the Overall Experience of Wearable Robots during Prototype-Stage Testing
Authors: Wang Jinlei , Yu Suihuai , Yuan Xiaoqing , Wang Yahui , Chen Dengkai , Wang Wendong
Published: November 2022

Wearable robots (WRs) might interact with humans in a similar manner to teammates to accomplish specific tasks together. However, the available data on WR user experience (UX) studies are limited, especially during the prototyping phase. Therefore, this study aims to examine the overall experience of WRs during the prototyping phase based on an exploratory research model. This theoretical model considered usability, hedonic quality, and attitude toward using WRs as key factors in explaining and predicting overall experience. To test the hypotheses inherent in the research model, quantitative empirical research was conducted and the data were analyzed by partial least squares structural equation modeling (PLS-SEM). The results from the PLS-SEM analysis revealed the significance level of correlations between the latent variables in the research model. The exploratory research model was able to explain up to 53.2% of the variance in the overall experience of using WRs, indicating medium predictive power. This research develops a new quantitative empirical research model that can be used to explain and predict the overall experience of interactive products such as WRs. Meanwhile, the model is needed during WR testing in the prototype phase.
Reinforcement Learning-Based Autonomous Driving at Intersections in CARLA Simulator
Authors: Gutiérrez-Moreno Rodrigo , Barea Rafael , López-Guillén Elena , Araluce Javier , Bergasa Luis M.
Published: November 2022

Intersections are considered one of the most complex scenarios in a self-driving framework due to the uncertainty in the behaviors of surrounding vehicles and the different types of scenarios that can be found. To deal with this problem, we provide a Deep Reinforcement Learning approach for intersection handling, which is combined with Curriculum Learning to improve the training process. The state space is defined by two vectors, containing adversaries and ego vehicle information. We define a features extractor module and an actor–critic approach combined with Curriculum Learning techniques, adding complexity to the environment by increasing the number of vehicles. In order to address a complete autonomous driving system, a hybrid architecture is proposed. The operative level generates the driving commands, the strategy level defines the trajectory and the tactical level executes the high-level decisions. This high-level decision system is the main goal of this research. To address realistic experiments, we set up three scenarios: intersections with traffic lights, intersections with traffic signs and uncontrolled intersections. The results of this paper show that a Proximal Policy Optimization algorithm can infer ego vehicle-desired behavior for different intersection scenarios based only on the behavior of adversarial vehicles.
A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study
Authors: Sarkar Nurul I. , Kavitha Asish Thomas , Ali Md Jahan
Published: November 2022

Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology’s Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2–20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.
Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders
Authors: Hendriks Maartje M. S. , Vos-van der Hulst Marije , Weijs Ralf W. J. , van Lotringen Jaap H. , Geurts Alexander C. H. , Keijsers Noel L. W.
Published: November 2022

The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery.
An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil
Authors: Farooq Muhammad , Amin Bilal , Kraśny Marcin J. , Elahi Adnan , Rehman Muhammad Riaz ur , Wijns William , Shahzad Atif
Published: November 2022

The wireless monitoring of key physiological parameters such as heart rate, respiratory rate, temperature, and pressure can aid in preventive healthcare, early diagnosis, and patient-tailored treatment. In wireless implantable sensors, the distance between the sensor and the reader device is prone to be influenced by the operating frequency, as well as by the medium between the sensor and the reader. This manuscript presents an ex vivo investigation of the wireless linkage between an implantable sensor and an external reader for medical applications. The sensor was designed and fabricated using a cost-effective and accessible fabrication process. The sensor is composed of a circular planar inductor (L) and a circular planar capacitor (C) to form an inductor–capacitor (LC) resonance tank circuit. The reader system comprises a readout coil and data acquisition instrumentation. To investigate the effect of biological medium on wireless linkage, the readout distance between the sensor and the readout coil was examined independently for porcine and ovine tissues. In the bench model, to mimic the bio-environment for the investigation, skin, muscle, and fat tissues were used. The relative magnitude of the reflection coefficient (S11) at the readout coil was used as a metric to benchmark wireless linkage. A readable linkage signal was observed on the readout coil when the sensor was held up to 2.5 cm under layers of skin, muscle, and fat tissue. To increase the remote readout distance of the LC sensor, the effect of the repeater coil was also investigated. The experimental results showed that the magnitude of the reflection coefficient signal was increased 3–3.5 times in the presence of the repeater coil, thereby increasing the signal-to-noise ratio of the detected signal. Therefore, the repeater coil between the sensor and the readout coil allows a larger sensing range for a variety of applications in implanted or sealed fields.
Multi-Feature Transformer-Based Learning for Continuous Human Motion Recognition with High Similarity Using mmWave FMCW Radar
Authors: Chen Yuh-Shyan , Cheng Kuang-Hung , Xu You-An , Juang Tong-Ying
Published: November 2022

Doppler-radar-based continuous human motion recognition recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of continuous human motion recognition (CHMR) using mmWave FMCW Radar are not considered the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS) problem, called as CHMR-HS, by using the modified Transformer-based learning model. As far as we know, this is the first result in the literature to investigate the continuous HMR with the high similarity. To obtain the clear FMCW radar images, the background and target signals of the detected human are separated through the background denoising and the target extraction algorithms. To investigate the effects of the spectral-temporal multi-features with different dimensions, Doppler, range, and angle signatures are extracted as the 2D features and range-Doppler-time and range-angle-time signatures are extracted as the 3D features. The 2D/3D features are trained into the adjusted Transformer-encoder model to distinguish the difference of the high-similarity human motions. The conventional Transformer-decoder model is also re-designed to be Transformer-sequential-decoder model such that Transformer-sequential-decoder model can successfully recognize the continuous human motions with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme are 95.2% and 94.5% if using 3D and 2D features, the simulation results also illustrates that our CHMR-HS scheme has advantages over existing CHMR schemes.
Prediction of Traffic Vibration Environment of Ancient Wooden Structures Based on the Response Transfer Ratio Function
Authors: Zhang Cheng , Zhang Nan , Zhang Yunshi , Liu Xiao
Published: November 2022

Traffic−induced vibration is increasingly affecting people’s lives, which necessitates scrutiny of the environmental vibrations caused by traffic. This paper proposed a vibration prediction method suitable for the ancient wooden structures subjected to traffic−induced vibrations based on the multi−point response transfer ratio function. The accuracy of the proposed approach was also checked by comparing the predicted results with the measured results in the context of both the time domain and frequency domain. Subsequently, the environmental vibrations due to heavy−duty trucks passing at various speeds were measured, and the measurements were utilized as the input vibration excitation to assess the structural vibration of the Feiyun Pavilion. The structural safety was evaluated according to the “Technical specifications for protecting historic buildings against man−made vibration”. In order to meet the structural safety requirements of the Feiyun Pavilion, it is strongly recommended to limit the type and speed of vehicles in the nearby area.
Intrusion Detection in IoT Using Deep Learning
Authors: Banaamah Alaa Mohammed , Ahmad Iftikhar
Published: November 2022

Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning, compares the performance of different deep learning methods, and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). A standard dataset for intrusion detection in IoT is considered to evaluate the proposed model. Finally, the empirical results are analyzed and compared with the existing approaches for intrusion detection in IoT. The proposed method seemed to have the highest accuracy compared to the existing methods.
Green Technologies for Sustainable Water and Wastewater Treatment: Removal of Organic and Inorganic Contaminants
Authors: Lee Yong-Gu , Chon Kangmin
Published: November 2022

Green technology presents technology and science-based solutions that reduce negative effects on the environment and human beings in various industries, including agriculture, manufacturing, and energy [...]
Simulation of an Indoor Visible Light Communication System Using Optisystem
Authors: Poulose Alwin
Published: November 2022

Visible light communication (VLC ) is an emerging research area in wireless communication. The system works the same way as optical fiber-based communication systems. However, the VLC system uses free space as its transmission medium. The invention of the light-emitting diode (LED) significantly updated the technologies used in modern communication systems. In VLC, the LED acts as a transmitter and sends data in the form of light when the receiver is in the line of sight (LOS) condition. The VLC system sends data by blinking the light at high speed, which is challenging to identify by human eyes. The detector receives the flashlight at high speed and decodes the transmitted data. One significant advantage of the VLC system over other communication systems is that it is easy to implement using an LED and a photodiode or phototransistor. The system is economical, compact, inexpensive, small, low power, prevents radio interference, and eliminates the need for broadcast rights and buried cables. In this paper, we investigate the performance of an indoor VLC system using Optisystem simulation software. We simulated an indoor VLC system using LOS and non-line-of-sight (NLOS) propagation models. Our simulation analyzes the LOS propagation model by considering the direct path with a single LED as a transmitter. The NLOS propagation model-based VLC system analyses two scenarios by considering single and dual LEDs as its transmitter. The effect of incident and irradiance angles in an LOS propagation model and an eye diagram of LOS/NLOS models are investigated to identify the signal distortion. We also analyzed the impact of the field of view (FOV) of an NLOS propagation model using a single LED as a transmitter and estimated the bitrate (Rb). Our theoretical results show that the system simulated in this paper achieved bitrates in the range of 2.1208×107 to 4.2147×107 bits/s when the FOV changes from 30∘ to 90∘. A VLC hardware design is further considered for real-time implementations. Our VLC hardware system achieved an average of 70% data recovery rate in the LOS propagation model and a 40% data recovery rate in the NLOS propagation model. This paper’s analysis shows that our simulated VLC results are technically beneficial in real-world VLC systems.
Reading the Aftermath of Portuguese Colonialism: The Retorno in the Written Media of the 21st Century
Authors: Ribeiro Orquídea Moreira , Fonseca Daniela Monteiro da
Published: November 2022

The purpose of this work is to undertake an exploratory pilot study on the multiplicity of texts that strive to write or analyse the memories of the displaced individuals from the former Portuguese African colonies during the 1975–1976 forced migration to Portugal who came to be labelled retornados (returnees), rethink history and eliminate silences, to pave the way for postmemory and controlled affective ties through reparative readings and nostalgia. We undertook bibliographical research to determine the eligibility criteria and used nonprobability convenience sampling to select the texts to carry out a content analysis. An interdisciplinary approach is used to present and discuss the results. Although decades have passed since the retorno, the stigma of being a retornado still remains in the memory of Portuguese society as interest in the topic and the time distance allows for more rigorous studies. In the analysed texts, the results achieved establish that the relationship of the Portuguese people with the contemporary memory and history of the colonial era is comprised of silences and nonmemories that still have to be deconstructed to forge a positive future for the generation of postmemory.
Cultural Values, Heritage and Memories as Assets for Building Urban Territorial Identities
Authors: Cercleux Andreea-Loreta , Harfst Jörn , Ilovan Oana-Ramona
Published: November 2022

Urban culture has undergone significant transformations under the impact of globalization in the last decades [...]
Remote-Workers and Their Furry Co-Workers: A Multimethod Exploration of New Avenues for Work-Related Exhaustion and Job Satisfaction
Authors: Scholtz Salomé Elizabeth
Published: November 2022

The COVID-19 pandemic fast-tracked the remote-working trend and placed many employees in a unique situation: conducting work in the presence of household pets. Despite research on pet-friendly workplaces, little work is available on the impact pets may have on remote-working owners. A simultaneous multimethod study was conducted to explore the impact of pets on remote-working employees’ work-related exhaustion and job satisfaction. The current study reports on the qualitative findings of the multimethod study. Using purposive sampling data were collected from remote-working pet owners (n = 77) through an online survey. Qualitative content analysis shows that pets served as a social resource to remote workers and influenced participants’ willingness to continue remote working. Some remote workers also saw their pets as a demand. This study provides preliminary insight into pets’ role in job satisfaction and work-related exhaustion through social support.
Custom-Made 3D Titanium Plate for Mandibular Reconstruction in Surgery of Ameloblastoma: A Novel Case Report
Authors: Chakraborty Somangshu , Guha Rajdeep P. , Naskar Sukanya , Banerjee Rajarshi
Published: November 2022

Ameloblastoma is a benign yet locally invasive odontogenic neoplasm, characterised by slow growth and painless swelling. The treatment for ameloblastoma varies from curettage to en bloc resection, with recurrence commonly occurring. The safety margin of resection is hence essential to avoid recurrence. Understanding the three-dimensional anatomy for reconstruction of mandibular defects after tumour resection often poses problems for head and neck surgeons. Historically, various autografts and alloplastic materials have been used in the reconstruction of these types of defects. Over time, advances in technology with computed tomography scanners and three-dimensional images enhance the surgical planning and management of maxillofacial tumours. The development of new prototyping systems provides accurate 3D biomodels on which surgery can be simulated, especially in cases of ameloblastoma, in which the safety margin is vital for the clinical outcome. The objective of this paper was to report a clinical case of employing these methodologies for reconstruction after an extensive mandibular resection. The clinical outcomes were observed. A case of follicular ameloblastoma of the mandible is depicted in the following paper, where a 3D biomodel was used throughout the surgery. A 3D printed patient-specific titanium implant was manufactured and placed intraoperatively for reconstruction. The treatment had satisfactory postoperative results without complications. Titanium implants being bioinert, customisable and easily workable, especially with the help of 3D virtual planning techniques, can be considered as ideal alloplastic materials for mandibular reconstruction.
Sustainable Development of Energy, Water and Environment Systems (SDEWES)
Authors: Sahin Oz , Bertone Edoardo
Published: November 2022

Earth’s natural systems provide essential resources such as water, food, energy, and materials that we all are dependent upon [...]
Toward a More Personalized MOOC: Data Analysis to Identify Drinking Water Production Operators’ Learning Characteristics—An Ecuador Case
Authors: Bustamante-León Martín , Herrera Paúl , Domínguez-Granda Luis , Schellens Tammy , Goethals Peter L. M. , Alejandro Otilia , Valcke Martin
Published: November 2022

Only 35% of the Ecuadorian population consumes drinking water of “assured quality”. One of the causes is related to the deficiencies in the technical ability of the operators due to their lack of education, technical training, and experience. Massive open online courses (MOOCs) responsive to characteristics and learning needs are an option to strengthen the skills of operators. The goal of the present study is therefore to describe a methodology that includes the application of a survey and the use of statistical methods such as categorical principal component analysis (CATPCA) and cluster analysis to identify and assess learning characteristics. The results present the most frequent variables in the personal, academic, emotional, social, and cognitive aspects. They also show the preferences and learning needs of the operators. Finally, it is concluded that this study identifies common learning characteristics, needs, and preferences that are relevant for the creation of a quality personalized instructional design in MOOCs.
Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System
Authors: Subahi Ahmad F. , Khalaf Osamah Ibrahim , Alotaibi Youseef , Natarajan Rajesh , Mahadev Natesh , Ramesh Timmarasu
Published: November 2022

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.
Study on Dynamic Response Characteristics of Circular Extended Foundation of Large Wind Turbine Generator
Authors: Deng Zong-Wei , Fan Zi-Jian , Zhou Yan-Ming , Deng Pei-Yu
Published: November 2022

In order to study the dynamic response characteristics of circular extended foundation of wind turbine in mountainous areas, a 1:10 scaled model test was carried out on the circular extended foundation of 2MW wind turbine, and the deformation characteristics of wind turbine foundation under random wind load were analyzed by ABAQUS numerical calculation. The results show that: (1) The wind turbine foundation has different stress types on the windward side and the leeward side. The components of the windward side foundation are subjected to tensile stress, while the components of the wind turbine leeward side foundation are subjected to compressive stress. (2) The strain of the foundation bolt, the strain of the foundation ring, and the strain of the foundation plate are within the allowable range of material deformation, but the relative deformation of the windward side and the leeward side is quite different. (3) The numerical calculation results of wind turbine foundation under strong wind load are compared with the failure results of scale model experiment, which shows that the overall overturning failure of foundation is a dynamic response mode of wind turbine foundation. In the design and construction, it is necessary to strengthen the research on the windward side and the leeward side and strengthen the anti-overturning design of the wind turbine expansion foundation.
The Use of Cluster Analysis to Assess the Threats of Poverty or Social Exclusion in EU Countries: The Case of People with Disabilities Compared to People without Disabilities
Authors: Frączek Bożena
Published: November 2022

The main research objective was to assess the range of threats related to poverty or social exclusion in EU countries among people with disabilities as compared to people without disabilities. The research used the available poverty determinants, including the percentage of low work intensity, the percentage of low income, the percentage of material deprivation, the poverty risk rate, the percentage of severe disability, living conditions and population income, and the overall risk of household poverty or exclusion, which are available in Eurostat databases. The data used in the research relates to 2018 and was published in 2021. The research used cluster analysis, more specifically one of the agglomeration clustering methods, i.e., Ward’s method. Separate cluster analysis using Ward’s method was carried out for people with disabilities and for people without disabilities. The analysis identifies two clusters among people with disabilities and two clusters among people without disabilities. In the group of people with disabilities, cluster 2 includes 19 countries with a higher risk of poverty or social exclusion, while cluster 1 includes eight countries with a lower risk of poverty or social exclusion. In turn, in the group of people without disabilities, cluster 2 includes nine countries with a higher risk of poverty or social exclusion, while cluster 1 includes 18 countries with a lower risk of poverty or social exclusion.
National Park or Cultural Landscape Preservation? What the Soil Seed Bank Reveals for Plant Diversity Conservation
Authors: Drissen Tim , Faust Christopher , Treitler Julia T. , Stadtmann Robin , Zerbe Stefan , Mantilla-Contreras Jasmin
Published: November 2022

National parks play an important role in the conservation of biodiversity, mainly excluding human influence following the IUCN approach. However, in Europe, they are often characterized by a high percentage of traditional cultural landscape elements, which require active management. This calls into question whether the national park protection strategy is always appropriate. Here, we follow this question by taking the soil seed bank of various habitats of the Asinara National Park (Sardinia, Italy) as an example. Asinara is a suitable model region, as the island mainly consists of traditional cultural landscape elements, but the main conservation goals include afforestation plans and nature development promotion, which creates a trade-off between the conservation of forest vs. cultural landscapes. We investigated the soil seed bank, standing vegetation, and environmental factors in different cultural and natural habitats. Since the highest species richness and diversity were revealed for cultural vegetation units, they need to be of primary concern regarding the preservation of the island’s phytodiversity. Given the main objective of the conservation of biodiversity in the Asinara National Park, we conclude that a biosphere reserve with an adapted sustainable land-use management might be more suitable than a national park to account for both natural and cultural landscape preservation. This conclusion applies to many other European national parks.
Relationship between Disintegration Characteristics and Intergranular Suction in Red Soil
Authors: Sun Yinlei , Tang Liansheng , Xie Jianbin
Published: November 2022

Red soil is widely distributed in South America, Africa and Southeast Asia (approximately between the 30° S and 30° N latitudes). These soils are typically formed by the weathering of carbonate or other rocks rich in iron and aluminium oxides in hot-humid climates and susceptible to a large reduction in disintegration upon wetting or other conditions. This paper provides a state-of-the-art review on the red soil disintegration mechanism and intergranular suction with reference to red soil. The present paper reviews the research progress that has been made regarding the disintegration characteristics and testing methods of the intergranular suction of red soil, including (1) influencing factors of disintegration and the mechanism of action, (2) advantages and disadvantages of each intergranular suction testing method and (3) the intrinsic relationship between disintegration and intergranular suction. The study indicated that the research on the disintegration mechanism of red soil is still in the macro stage, involving the different moisture contents, temperatures and other directly influential conditions. Soil disintegration characteristics have not been extensively analysed from the micro perspective (including pore structure and distribution, fracture development degree and particle arrangements). When these problems are solved, disintegration studies of red soil will be advanced. Some limitations of the previous research are highlighted at the end of the paper, and suggestions are made for future research.
Bisphenol A and 17α-Ethinylestradiol Removal from Water by Hydrophobic Modified Acicular Mullite
Authors: Zhou Qiuhong , Luo Xi , He Jing , Guo Jinsong , Xu Chengjian , Wan Yanlei , Xiong Guangcheng , Xu Qi
Published: November 2022

The hydrophilicity and hydrophobicity of adsorbents have an important influence on organic pollutants adsorption. To effectively remove bisphenol A (BPA) and 17-acetylene estradiol (EE2) from water, acicular mullite was modified by cetyl trimethyl ammonium bromide (CTMAB) to increase the hydrophobicity of the mullite. The adsorption process and mechanism of BPA and EE2 by modified acicular mullite were studied in detail. Results indicated that the concentration of CTMAB solution was related to the contact angle of CTMAB-modified mullite (CTMAB-M). The optimal concentration of CTMAB was 4 mmol/L. The CTMAB-M could adsorb more hydrophobic organic pollutants than virgin acicular mullite. Due to the electrostatic attraction and hydrophobic partitioning, the adsorption amount of BPA and EE2 on CTMAB-M increased with increasing pH. The adsorption amounts of BPA and EE2 on CTMAB-M increase with increasing ionic strength. The adsorption kinetics of BPA and EE2 adsorption on CTMAB-M could be best described by the pseudo second-order kinetics model. Thermodynamic analysis showed that the low temperature favored the adsorption of BPA and EE2 on CTMAB-M, and the adsorption was driven by entropy increase. Site energy studies indicated that BPA and EE2 firstly occupy high-energy adsorption sites and then switch to low-energy sites during the adsorption process. The average adsorption site energy μ(E*) of EE2 on CTMAB-M is smaller than BPA. CTMAB modification can significantly improve the removal efficiency of ceramsite on EDCs.
Environmental Contamination of a Biodiversity Hotspot—Action Needed for Nature Conservation in the Niger Delta, Nigeria
Authors: Ansah Christabel Edena , Abu Itohan-Osa , Kleemann Janina , Mahmoud Mahmoud Ibrahim , Thiel Michael
Published: November 2022

The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.
Stevia rebaudiana under a CO2 Enrichment Atmosphere: Can CO2 Enrichment Overcome Stomatic, Mesophilic and Biochemical Barriers That Limit Photosynthesis?
Authors: Pompelli Marcelo F. , Espitia-Romero Carlos A. , de Diós Jaraba-Navas Juán , Rodriguez-Paez Luis Alfonso , Jarma-Orozco Alfredo
Published: November 2022

Due to the desire to live a healthier lifestyle, the search for nonglycosidic sweeteners has increased stevioside production in recent years. The main goal of this study was to demonstrate that S. rebaudiana grown in a CO2-enriched environment can overcome stomatic, mesophilic and biochemical barriers that limit photosynthesis (AN). We show that in an environment with a CO2-enriched atmosphere (800 and 1200 µmol CO2 mol−1), the genotype 16 (G16) shows an increase of 17.5% in AN and 36.2% in stomatal conductance in plants grown in 800 µmol CO2 mol−1 when compared to non-enriched plants. In conjunction with this issue, the plants show an efficient mechanism of dissipating excess energy captured by the photosystems. Photosystem II efficiency was increased at 1200 µmol CO2 mol−1 when compared to non-enriched plants, both in genotype 4 (25.4%) and G16 (211%). In addition, a high activity of Calvin–Benson enzymes, a high production of sugars and an enhanced production of steviosides were combined with high horticultural yield. Both genotypes (G4 and G16) showed excellent physiological indicators, with high superiority in G16. Thus, our study has demonstrated that S. rebaudiana could adapt to global climate change scenarios with higher temperatures caused by higher atmospheric CO2.
Leveraging Buyers’ Interest in ESG Investments through Sustainability Awareness
Authors: Khalil Muhammad Khuram , Khalil Rashid
Published: November 2022

Currently, organizations often take into consideration the environmental, social, and governance (ESG) principles by following a more inclusive approach together with potential other emerging practices that attract more consumers who have particular affiliations with the environment. This study investigates the influence of social and environmental sustainability awareness practices on impulse buying through a proposed set of hypotheses. The hypothetical model is based on (441) valid responses from Omani buyers via an online research survey. Data were processed using CFA and SEM techniques. Results indicate that social and environmental sustainability awareness has a productive relationship with green trust that strengthens impulse buying. Environmental sustainability awareness positively links with green altruism, which further enhances the impulse. By taking gender as a moderator, it has an encouraging impact on social and environmental sustainability awareness. Environmental sustainability awareness and green altruism have a constructive relationship within males rather than females. Based on the unique connection between sustainability practices and impulse buying, the current study’s results indicate that if firms take appropriate steps to implement sustainability practices in their operations, sustainability commitment can promote the environmental and social well-being of consumers and the establishment of trust. Such practices enable companies to achieve their sustainability reporting goals and SDGs. Social and environmental sustainability practices protect from the harmful effects of social and environmental influences in mitigating the uncertainty of consumers’ buying behavior. Such productive sustainability practices compel buyers to purchase products impulsively at premium prices. The study strongly recommends policymakers and marketers focusing on environmental and social sustainability awareness and green altruism.
Climate Disaster Losses and Foreign Exchange Reserve Dynamics: Evidence of East Asia Pacific
Authors: Wang Yawen , Wang Qing , Xing Zhaopeng
Published: November 2022

This paper examines the relationship between foreign exchange reserves (FERs) and climate disaster losses (CDLs) in the East Asia Pacific region. To guide the empirical work, we use the bootstrap Granger rolling window estimation to capture the dynamic relationship between the two variables. It is suggested that CDLs positively affect the central banks’ FERs in East Asia Pacific countries, but this relationship appears to be weakening recently. FERs are shown to reduce CDLs. The results are supported by the small open economy model in which the central bank decides jointly on FERs and external debt. With the balance of payments deteriorating, CDLs can lead to a sudden stop of international capital flows, which is destructive to economic development. Therefore, when severe climate disasters are anticipated, the central bank accumulates FERs in advance. If unexpected climate disasters occur, central banks may become more precautious and increase FERs. Therefore, the central bank should consider the risk of climate change and hold an appropriate amount of FERs but FERs are not the more the better; the government should strengthen infrastructure construction to resist climate disasters.