bluuebunny/crossref_metadata_2025 huggingface dataset:
The Citrus Mutant Jedae-unshiu Induced by Gamma Irradiation Exhibits a Unique Fruit Shape and Increased Flavonoid Content
Authors: Eun Chang-Ho , Kim In-Jung
Published: May 2022

Satsuma mandarin (Citrus unshiu Marc. cv. Miyagawa-wase) is the most widely cultivated citrus variety in Korea. Although most varieties are imported from Japan, efforts have focused on developing new domestic varieties. We produced mutants by irradiating C. unshiu Marc. cv. Miyagawa-wase scions with gamma rays and grafting them onto C. unshiu Marc. cv. Miyagawa-wase branches. We compared the characteristics of these mutants with Miyagawa-wase fruit as a control. A mutant line named Jedae-unshiu with a unique fruit shape was selected for investigation in detail. The phenotype of Jedae-unshiu fruit demonstrated vertical troughs on the flavedo, smooth albedo without rough protruding fibers, and good adhesion between peel and flesh. In addition, Jedae-unshiu had thicker peels and higher fruit hardness than the control. Higher levels of hesperetin and narirutin, representative flavonoids, accumulated in the peel and flesh of Jedae-unshiu than those of the control. Cellular-level microscopic observations of the mature fruit peels demonstrated epidermal cell disruption in the control but not in Jedae-unshiu. Our results suggest that Jedae-unshiu has high possibility for development as a good storage variety containing large amounts of flavonoids, in addition to potential for ornamental value due to the unique shape of the fruit.
Solute Diffusivity and Local Free Volume in Cross-Linked Polymer Network: Implication of Optimizing the Conductivity of Polymer Electrolyte
Authors: Tsai Yi-Chen , Chiu Chi-Cheng
Published: May 2022

The diffusion of small molecules or ions within polymeric materials is critical for their applications, such as polymer electrolytes. Cross-linking has been one of the common strategies to modulate solute diffusivity and a polymer’s mechanical properties. However, various studies have shown different effects of cross-linking on altering the solute transports. Here, we utilized coarse-grained molecular dynamics simulation to systematically analyze the effects of cross-linking and polymer rigidity of solute diffusive behaviors. Above the glass transition temperature Tg, the solute diffusion followed the Vogel–Tammann–Fulcher (VTF) equation, D = D0 e−Ea/R(T−T0). Other than the conventional compensation relation between the activation energy Ea and the pre-exponential factor D0, we also identified a correlation between Ea and Vogel temperature T0. We further characterized an empirical relation between T0 and cross-linking density. Integrating the newly identified correlations among the VTF parameters, we formulated a relation between solute diffusion and the cross-linking density. The combined results proposed the criteria for the optimal solute diffusivity in cross-linked polymers, providing generic guidance for novel polymer electrolyte design.
Magneto-Dielectric Effects in Polyurethane Sponge Modified with Carbonyl Iron for Applications in Low-Cost Magnetic Sensors
Authors: Bica Ioan , Iacobescu Gabriela-Eugenia
Published: May 2022

In this study, magnetizable polyurethane sponges (MSs) were obtained from commercial absorbent polyurethane sponges (PSs) doped with carbonyl iron microparticles (CIPs). Based on MSs, we manufactured cylindrical capacitors (CCs). The CCs were subjected to both a magnetic field and an alternating electric field, with a frequency of f=1 kHz. Using an RLC bridge, we measured the series electric capacitance, Cs, and the tangent of the loss angle, Ds. From the functions Cs=Cs(δ)CCs and Ds=Ds(δ)CCs, we extracted the components of the complex dielectric permittivity. It was found that the CIPs embedded in the MS matrix aggregated, leading to magneto-dielectric effects such as the enhancement of the complex dielectric permittivity components when applying the magnetic field as a principal effect and the enhancement of the electric capacitance and time constant of the capacitors as a secondary effect. The obtained results represent landmarks in the realization of low-cost magnetic field sensors, deformation and mechanical stress transducers in the robotics industry, etc.
Extraction of Cellulose-Based Polymers from Textile Wastes
Authors: Felgueiras Helena P. , Padrão Jorge , Antunes Joana C.
Published: May 2022

The extraction and exploration of cellulose-based polymers is an exciting area of research [...]
Religion, Animals, and Contemplation
Authors: Komjathy Louis
Published: May 2022

Animals teach each other. For humans open to trans-species and inter-species dialogue and interaction, animal-others offer important insights into, invocations of and models for diverse and alternative modes of perceiving, experiencing, relating, and being. They in turn challenge anthropocentric conceptions of consciousness and offer glimpses of and perhaps inspiration for increased awareness and presence. Might the current academic vogue of “equity, diversity, and inclusion” (EDI; or whichever order you prefer) even extend to “non-human” animals? Might this also represent one essential key to the human aspiration for freedom, wellness, and justice? The present article explores the topic of “religion and animals” through the complementary dimension of “contemplation”. Developing a fusion of Animal Studies, Contemplative Studies, Daoist Studies, and Religious Studies, I explore the topic with particular consideration of the indigenous Chinese religion of Daoism with a comparative and cross-cultural sensibility. I draw specific attention to the varieties of Daoist animal engagement, including animal companionship and becoming/being animal. Theologically speaking, this involves recognition of the reality of the Dao (sacred) manifesting through each and every being, and the possibility of inter/trans-species communication, relationality, and even identification. In the process, I suggest that “animal contemplation”, a form of contemplative practice and contemplative experience that places “the animal question” at the center and explores the possibility (actuality) of “shared animality”, not only offers important opportunities for becoming fully human (animal), but also represents one viable contribution to resolving impending (ongoing) ecological collapse, or at least the all-too-real possibility of a world without butterflies, bees, and birdsong.
Astronomy and Calendrical Science in Early Mikkyō in Japan: Challenges and Adaptations
Authors: Kotyk Jeffrey
Published: May 2022

This study examines the use, adaptation, modification and omission of astronomical and calendrical elements in early Japanese Mikkyō (ninth century) in large part from the perspective of exact sciences. Shingon and Tendai inherited a Sinicized system of Indian astrology from their respective beginnings, but the significance of this fact in the study of Japanese religions is underrecognized despite the reality that astrology was both studied and technically required in Mikkyō. This study will examine how Mikkyō negotiated the demand for orthopraxical use of Indian models with the contingent realities of only possessing in practice a Chinese calendar and system of observational astronomy. Japanese monks were compelled to observe Indian astrology according to their own scriptures, which by extension necessitated knowledge of Indian astronomy, but substitutions and omissions had to be made in the absence of the required resources and knowledge.
A Dataset of Overshooting Cloud Top from 12-Year CloudSat/CALIOP Joint Observations
Authors: Li Haoyang , Wei Xiaocheng , Min Min , Li Bo , Nong Ziqi , Chen Lin
Published: May 2022

A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product of strong updrafts in convective storms, which can penetrate the tropopause and enter the lower stratosphere. OT is closely related to severe weather and can influence water vapor transport and the material exchange between the troposphere and stratosphere. Therefore, the timely detection of OT can help improve the accuracy of forecasting. In this study, we develop a new objective OT detection algorithm based on geostationary satellite observations from 2006 to 2017. The accuracy of the new algorithm in identifying OT is verified by manually comparing it with the radar echo images and the cloud images of MODIS 250 m. Then, the OT is statistically analyzed in a long time series. It is found that OT events are mainly concentrated in equatorial and low latitude regions, with higher frequency in summer. There are obvious differences between OT events on land and sea. Additionally, this dataset also reveals the close connection between the seasonal shift of OT and the seasonal average precipitation distribution around the globe. This study provides a scientific basis for determining the geographical characteristics of OT frequency and explores the application of this OT objective detection algorithm in the operational forecast of strong convective weather. We hope this study can benefit OT monitoring in operational weather forecasting.
GapLoss: A Loss Function for Semantic Segmentation of Roads in Remote Sensing Images
Authors: Yuan Wei , Xu Wenbo
Published: May 2022

At present, road continuity is a major challenge, and it is difficult to extract the centerline vector of roads, especially when the road view is obstructed by trees or other structures. Most of the existing research has focused on optimizing the available deep-learning networks. However, the segmentation accuracy is also affected by the loss function. Currently, little research has been published on road segmentation loss functions. To resolve this problem, an attention loss function named GapLoss that can be combined with any segmentation network was proposed. Firstly, a deep-learning network was used to obtain a binary prediction mask. Secondly, a vector skeleton was extracted from the prediction mask. Thirdly, for each pixel, eight neighboring pixels with the same value of the pixel were calculated. If the value was 1, then the pixel was identified as the endpoint. Fourth, according to the number of endpoints within a buffered range, each pixel in the prediction image was given a corresponding weight. Finally, the weighted average value of the cross-entropy of all the pixels in the batch was used as the final loss function value. We employed four well-known semantic segmentation networks to conduct comparative experiments on three large datasets. The results showed that, compared to other loss functions, the evaluation metrics after using GapLoss were nearly all improved. From the predicted image, the road prediction by GapLoss was more continuous, especially at intersections and when the road was obscured from view, and the road segmentation accuracy was improved.
Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020
Authors: Opedes Hosea , Mücher Sander , Baartman Jantiene E. M. , Nedala Shafiq , Mugagga Frank
Published: May 2022

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km2. The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park.
Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene Classification
Authors: Li Peng , Chen Peng , Zhang Dezheng
Published: May 2022

The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number of object-level semantic classes in big remote-sensing pictures. Complex label-space makes it hard to detect all the targets and perceive corresponding semantics in the typical scene, thereby weakening the sensing ability. Even worse, the preparation of a labeled dataset for the training of deep networks is more difficult due to multiple labels. In order to mine object-level visual features and make good use of label dependency, we propose a novel framework in this article, namely a Cross-Modal Representation Learning and Label Graph Mining-based Residual Multi-Attentional CNN-LSTM framework (CM-GM framework). In this framework, a residual multi-attentional convolutional neural network is developed to extract object-level image features. Moreover, semantic labels are embedded by language model and then form a label graph which can be further mapped by advanced graph convolutional networks (GCN). With these cross-modal feature representations (image, graph and text), object-level visual features will be enhanced and aligned to GCN-based label embeddings. After that, aligned visual signals are fed into a bi-LSTM subnetwork according to the built label graph. The CM-GM framework is able to map both visual features and graph-based label representations into a correlated space appropriately, using label dependency efficiently, thus improving the LSTM predictor’s ability. Experimental results show that the proposed CM-GM framework is able to achieve higher accuracy on many multi-label benchmark datasets in remote sensing field.
TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images
Authors: Xu Yongyang , Luo Wei , Hu Anna , Xie Zhong , Xie Xuejing , Tao Liufeng
Published: May 2022

Resolution is a comprehensive reflection and evaluation index for the visual quality of remote sensing images. Super-resolution processing has been widely applied for extracting information from remote sensing images. Recently, deep learning methods have found increasing application in the super-resolution processing of remote sensing images. However, issues such as blurry object edges and existing artifacts persist. To overcome these issues, this study proposes an improved generative adversarial network with self-attention and texture enhancement (TE-SAGAN) for remote sensing super-resolution images. We first designed an improved generator based on the residual dense block with a self-attention mechanism and weight normalization. The generator gains the feature extraction capability and enhances the training model stability to improve edge contour and texture. Subsequently, a joint loss, which is a combination of L1-norm, perceptual, and texture losses, is designed to optimize the training process and remove artifacts. The L1-norm loss is designed to ensure the consistency of low-frequency pixels; perceptual loss is used to entrench medium- and high-frequency details; and texture loss provides the local features for the super-resolution process. The results of experiments using a publicly available dataset (UC Merced Land Use dataset) and our dataset show that the proposed TE-SAGAN yields clear edges and textures in the super-resolution reconstruction of remote sensing images.
Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection
Authors: Luz Andréa Eliza O. , Negri Rogério G. , Massi Klécia G. , Colnago Marilaine , Silva Erivaldo A. , Casaca Wallace
Published: May 2022

The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.
Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
Authors: Kim Ye-Eun , Kim Yea-Sul , Kim Hwankuk
Published: May 2022

The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.
Wearable Pulse Oximeter for Swimming Pool Safety
Authors: Kałamajska Elżbieta , Misiurewicz Jacek , Weremczuk Jerzy
Published: May 2022

The purpose of this research was to develop an algorithm for a wearable device that would prevent people from drowning in swimming pools. The device should detect pre-drowning symptoms and alert the rescue staff. The proposed detection method is based on analyzing real-time data collected from a set of sensors, including a pulse oximeter. The pulse oximetry technique is used for measuring the heart rate and oxygen saturation in the subject’s blood. It is an optical method; subsequently, the measurements obtained this way are highly sensitive to interference from the subject’s motion. To eliminate noise caused by the subject’s movement, accelerometer data were used in the system. If the acceleration sensor does not detect movement, a biosensor is activated, and an analysis of selected physiological parameters is performed. Such a setup of the algorithm allows the device to distinguish situations in which the person rests and does not move from situations in which the examined person has lost consciousness and has begun to drown.
Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
Authors: Everett Tim , Taylor Trey , Lee Dong-Kyeong , Akos Dennis M.
Published: May 2022

The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions.
Clustering Approach for Detecting Multiple Types of Adversarial Examples
Authors: Choi Seok-Hwan , Bahk Tae-u , Ahn Sungyong , Choi Yoon-Ho
Published: May 2022

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture. Especially, defense methods using adversarial example detection architecture have been actively studied. This is because defense methods using adversarial example detection architecture do not make wrong decisions for the legitimate input data while others do. In this paper, we note that current defense methods using adversarial example detection architecture can classify the input data into only either a legitimate one or an adversarial one. That is, the current defense methods using adversarial example detection architecture can only detect the adversarial examples and cannot classify the input data into multiple classes of data, i.e., legitimate input data and various types of adversarial examples. To classify the input data into multiple classes of data while increasing the accuracy of the clustering model, we propose an advanced defense method using adversarial example detection architecture, which extracts the key features from the input data and feeds the extracted features into a clustering model. From the experimental results under various application datasets, we show that the proposed method can detect the adversarial examples while classifying the types of adversarial examples. We also show that the accuracy of the proposed method outperforms the accuracy of recent defense methods using adversarial example detection architecture.
Development and Applications of a Pressurized Water-Filled Impedance Tube
Authors: Shen Zong-You , Huang Ching-Jer , Liu Kuan-Wen
Published: May 2022

In this study, a pressurized, water-filled impedance tube (WFIT) was developed to measure the reflection coefficients of sound-absorbing materials under various hydrostatic pressures. The developed WFIT was calibrated using a two-microphone, three-parameter calibration method (3PCM). The accuracy and repeatability of the measured reflection coefficients for the water–air interface in the WFIT were determined by comparing these coefficients with corresponding theoretical reflection coefficients. The WFIT was then used to measure the acoustic reflection coefficient of a porous rubber specimen on three dates, and the corresponding measurement results exhibited satisfactory repeatability. The aforementioned impedance tube was also used to measure the reflection coefficient of a porous rubber specimen under a hydrostatic pressure of 4 Patm three times on the same day, and one time each on three days, using the same experimental setup and measurement procedure. The results obtained in the aforementioned tests also exhibited satisfactory repeatability. Finally, the WFIT was used to measure the reflection coefficients of porous rubber specimens with various thicknesses under different hydrostatic pressures. The results of this study indicate that the developed WFIT calibrated with the 3PCM can achieve suitable repeatability in the measurement of the reflection coefficients of sound-absorbing materials under various hydrostatic pressures.
Assisting Personalized Healthcare of Elderly People: Developing a Rule-Based Virtual Caregiver System Using Mobile Chatbot
Authors: Miura Chisaki , Chen Sinan , Saiki Sachio , Nakamura Masahide , Yasuda Kiyoshi
Published: May 2022

To assist personalized healthcare of elderly people, our interest is to develop a virtual caregiver system that retrieves the expression of mental and physical health states through human–computer interaction in the form of dialogue. The purpose of this paper is to implement and evaluate a virtual caregiver system using mobile chatbot. Unlike the conventional health monitoring approach, our key idea is to integrate a rule-based virtual caregiver system (called “Mind Monitoring” service) with the physical, mental, and social questionnaires into the mobile chat application. The elderly person receives one question from the mobile chatbot per day, and answers it by pushing the optional button or using a speech recognition technique. Furthermore, a novel method is implemented to quantify the answers, generate visual graphs, and send the corresponding summaries or advice to the specific elder. In the experimental evaluation, we applied it to eight elderly subjects and 19 younger subjects within 14 months. As main results, its effects were significantly improved by the proposed method, including the above 80% in the response rate, the accurate reflection of their real lives from the responses, and high usefulness of the feedback messages with software quality requirements and evaluation. We also conducted interviews with subjects for health analysis and improvement.
Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning
Authors: Rahman Atta-ur , Alqahtani Abdullah , Aldhafferi Nahier , Nasir Muhammad Umar , Khan Muhammad Farhan , Khan Muhammad Adnan , Mosavi Amir
Published: May 2022

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
Exploration of Fulvic Acid as a Co-Former in Crystal Engineering
Authors: Gnananath Kattamanchi , Kumar Kolli Prabhanjan , Prasad Yejella Rajendra , Nataraj Kalakonda Sri , Taleuzzaman Mohamad , Ansari Mohammad Javed , Mirza Mohd. Aamir
Published: May 2022

The aim of the project was to investigate Peat-derived Fulvic acid for its propensity to form co-crystals with quercetin and curcumin and characterize it by using different analytical techniques. The formation of co-crystals generally enhances water solubility and the overall bioavailability of molecules. Co-crystals were synthesized using a 1:1 stoichiometric ratio of fulvic acid with quercetin and curcumin, respectively, using solvent crystallization techniques taking tetrahydrofuran and water in a 1:1 v/v ratio. The co-crystals were characterized by spectroscopic methods, FTIR and Differential scanning calorimetry. Further confirmation was made by morphological studies using SEM. A structural analysis was also carried out, using 13C solid-state NMR analysis. The studies confirmed the formation of semi crystalline forms. Furthermore, the saturation solubility displayed the enhancement in solubility of up to 10, 5-folds for Quercetin and Curcumin, respectively. The in vitro dissolution results showed that T50% was achieved within 30 min for both the drugs. The literature supports that the nutraceutical co-crystals offer advantages, particularly in the improvement of biopharmaceutical properties and addressing the challenges of the lab and manufacturing scale process. Both the semi crystalline powders exhibited enhanced solubility and a better dissolution profile.
An Innovative Approach for Subnational Climate Adaptation of Biodiversity and Ecosystems: The Case Study of a Regional Strategy in Italy
Authors: Pollo Alessandra , Piccini Irene , Chiara Jacopo , Porro Elena , Chiantore Daniela , Gili Fabrizio , Alba Riccardo , Barbi Andrea , Bogliani Giuseppe , Bagliani Marco , Doretto Alberto , Ruffino Carlo , Malenotti Elisa , Garazzino Agostina , Pelosini Renata , Siniscalco Consolata , Bonelli Simona
Published: May 2022

Since climate change impacts are already occurring, urgent adaptive actions are necessary to avoid the worst damages. Regional authorities play an important role in adaptation, but they have few binding guidelines to carry out strategies and plans. Sectoral impacts and adaptive measures strongly differ between regions; therefore, specific results for each territory are needed. Impacts are often not exhaustively reported by literature, dataset and models, thus making it impossible to objectively identify specific adaptive measures. Usual expert elicitation helps to fill this gap but shows some issues. For the Piedmont Strategy, an innovative approach has been proposed, involving experts of private and public bodies (regional authorities, academia, research institutes, parks, associations, NGOs, etc.). They collaborated in two work group, first to identify current and future impacts on biodiversity and ecosystems, and secondly to elaborate and prioritize measures. Involving 143 experts of 46 affiliations, it was possible to quickly edit a cross-validated list of impacts (110) and measures (92) with limited costs. Lastly, a public return of results took place. This approach proved to be effective, efficient and influenced the policymakers, overcoming the tendency to enact long-term actions to face climate change. It could be used internationally by subnational authorities also in other sectors.
Spatiotemporal Patterns and Dominant Factors of Urban Particulate Matter Islands: New Evidence from 240 Cities in China
Authors: Peng Ziqiang , Cao Shisong , Du Mingyi , Yang Meizi , Lu Linlin , Cai Yile , Mo You , Zhao Wenji
Published: May 2022

With rapid urbanization and industrialization, PM2.5 pollution exerts a significant negative impact on the urban eco-environment and on residents’ health. Previous studies have demonstrated that cities in China are characterized by urban particulate matter island (UPI) phenomena, i.e., higher PM2.5 concentrations are observed in urban areas than in rural settings. How, though, nature and socioeconomic environments interact to influence UPI intensities is a question that still awaits a general explanation. To fill this knowledge gap, this study investigates spatiotemporal variations in UPI effects with respect to different climatic settings and city sizes in 240 cities in China from 2000 to 2015 using remotely sensed data and explores the effective mechanism of human–environmental factors on UPI dynamics based upon the Geographically Weighted Regression (GWR) model. In particular, a conceptual framework that considers natural environments, technology, population, and economics is proposed to explore the factors influencing UPIs. The results show (1) that about 70% of the cities in China selected exhibited UPI effects from 2000 to 2015. In addition, UPI intensities and the number of UPI-related cities decreased over time. It is noteworthy that PM2.5 pollution shifted from urban to rural areas. (2) Elevation was the most efficient driving factor of UPI variations, followed by precipitation, population density, NDVI, per capita GDP, and PM2.5 emission per unit GDP. (3) Climatic backgrounds and city sizes influenced the compositions and performance of dominant factors regarding UPI phenomena. This study provides valuable a reference for PM2.5 pollution mitigation in cities experiencing global climate change and rapid urbanization and thus can help sustainable urban developments.
Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications
Authors: Ross Edwin , Wagterveld Martijn , Mayer Mateo , Stigter Hans , Højris Bo , Li Yang , Keesman Karel
Published: May 2022

As chlorate concentrations have been found to be harmful to human and animal health, governments are increasingly demanding strict control of the chlorate concentration in drinking water. Since there are no chlorate sensors available, the current solution is sampling and laboratory analysis. This is costly and time consuming. The aim of this work was to investigate Sensor Data Fusion (SDF) as an alternative approach, with a focus on chlorate formation in the electrochlorination process, and design an observer for the real-time estimation of chlorate. The pH, temperature and UV-a absorption were measured in real time. A reduced-order nonlinear model was derived, and it was found to be detectable. An Extended Kalman Filter (EKF), based on this model, was then used to estimate the chlorate formation. The EKF algorithm was verified experimentally and was found to be capable of accurately estimating chlorate concentrations in real time. Electrochlorination is an emerging and efficient method of disinfecting drinking water. Soft sensing of chlorate concentrations, as proposed in this paper, may help to better control and manage the process of electrochlorination.
Workers’ Unsafe Actions When Working at Heights: Detecting from Images
Authors: Hu Qijun , Bai Yu , He Leping , Huang Jie , Wang Haoyu , Cheng Guangran
Published: May 2022

Working at heights causes heavy casualties among workers during construction activities. Workers’ unsafe action detection could play a vital role in strengthening the supervision of workers to avoid them falling from heights. Existing methods for managing workers’ unsafe actions commonly rely on managers’ observation, which consumes a lot of human resources and impossibly covers a whole construction site. In this research, we propose an automatic identification method for detecting workers’ unsafe actions, considering a heights working environment, based on an improved Faster Regions with CNN features (Faster R-CNN) algorithm. We designed and carried out a series of experiments involving five types of unsafe actions to examine their efficiency and accuracy. The results illustrate and verify the method’s feasibility for improving safety inspection and supervision, as well as its limitations.
Engaging Students in Sustainable Science Education
Authors: Grabau Larry J.
Published: May 2022

Students who find science to be enjoyable are more likely to engage in their science classes [...]
Practical Challenges and Opportunities for Marine Plastic Litter Reduction in Manila: A Structural Equation Modeling
Authors: Borongan Guilberto , NaRanong Anchana
Published: May 2022

Land-based plastic pollution has increased to the level of an epidemic due to improper plastic waste management, attributed to plastic waste flux into the marine environment. The extant marine plastic litter (MPL) literature focuses primarily on the monitoring and assessment of the problem, but it fails to acknowledge the link between the challenges and opportunities for MPL reduction. The study aimed to examine the practical challenges and opportunities influencing the reduction of marine plastic litter in Manila in the Philippines. Data collected through an online survey from 426 barangays were analyzed using structural equation modeling (SEM) and were then validated using interviews and focused group discussions. Good internal consistency (0.917) and convergent and discriminant validity were achieved. The empirical study has established structural model fit measures of RMSEA (0.021), SRMR (0.015), CFI (0.999), and TLI (0.994), with a good parsimonious fit of the chi-square/degrees of freedom ratio of 1.190. The findings revealed that environmental governance regarding waste management policies and guidelines, COVID-19 regulations for waste management, community participation, and socio-economic activities have positively affected marine plastic litter leakage and solution measures. Environmental governance significantly and partially mediates the effects of, e.g., COVID-19-related waste and socio-economic activities on MPL leakage. However, there is no relationship between the waste management infrastructure and environmental governance. The findings shed light on how to enhance environmental governance to reduce marine plastic litter and address Manila’s practical challenges.
Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data
Authors: An Liqiang , Zhang Jingfa
Published: May 2022

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population.
Prioritization of Factors Impacting Lecturer Research Productivity Using an Improved Fuzzy Analytic Hierarchy Process Approach
Authors: Hue Truong Thi , Tuan Nguyen Anh , Van Luu Huu , Lien Luong Thuy , Huong Do Dieu , Anh Luong Tram , Huy Nghiem Xuan , Dat Luu Quoc
Published: May 2022

Improving the scientific research productivity of lecturers is an important strategy contributing to improving the reputation of universities, attracting external funding sources, and improving the credibility of both domestic and international students. This study was carried out with the aim of determining the priority of the university’s governance factors that affect lecturers’ scientific research productivity. Six university governance factors were considered, including (i) research objectives and strategies, (ii) decentralization, (iii) leadership, (iv) support for research activities, (v) policy towards lecturers, and (vi) resources for research activities. In this study, an improved analytic hierarchy process method using generalized triangular fuzzy numbers and a centroid index was proposed. The research data were collected via in-depth interviews with experts and administrators at Vietnam National University, Hanoi (VNU). The results indicate that “resources for research activities” constitute the most important factor affecting the research productivity of lecturers at VNU, followed by research objectives and strategies and leadership.
A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study
Authors: Cheng Huibing , Zheng Shanshui , Feng Jianghong
Published: May 2022

This study is motivated by the Zhuhai municipal government, which needs to select a sustainable ferry operator. Previous research has ignored the evaluation and selection of ferry operators. In addition, since ferry operator evaluation involves conflicting qualitative and quantitative criteria, and there may be uncertainty and ambiguity in the evaluation of criteria by experts, a fuzzy multi-criteria decision-making (MCDM) approach is required to address this challenge. To this end, this paper proposes an integrated MCDM framework model to evaluate and select the best ferry operator. First, a ferry operator evaluation index system with 15 sub-criteria is constructed according to literature and expert opinions; then the fuzzy analytic hierarchy process (FAHP) is used to determine the subjective weight of the criteria, and the entropy weight (EW) method is used to calculate the objective weight of the criteria. We use the linear weighting method to obtain the comprehensive weights of the criteria; finally, the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) method is adapted to determine the best ranking of the alternatives. This paper takes the Wanshan Islands in Zhuhai as a real case study to verify the proposed FAHP-EW-FTOPSIS method. The results show that the proposed method can be effectively applied to the evaluation and selection of ferry operators. Sensitivity analysis of criteria weights demonstrates the effectiveness and robustness of the proposed framework model. Key findings based on the research provide management insights that can benefit relevant stakeholders. This is the first paper to study the evaluation and selection of ferry operators. Hence, the evaluation index system and integrated framework model proposed in this paper can make important contributions to the evaluation of ferry operators.
Recycling Model Selection for Electronic Products Considering Platform Power and Blockchain Empowerment
Authors: Wang Xue , Ma Deqing , Hu Jinsong
Published: May 2022

This paper considers a dynamic platform-based, closed-loop supply chain consisting of a manufacturer and an online platform. As an online distributor of the manufacturer, the platform expands the market scale by exerting the platform power. At the same time, to solve the problem of inconsistency between the actual recycling amount and the theoretical recycling amount in the recycling process of waste electronic products, the whole-process supervision of waste products is carried out with the help of blockchain technology, which is difficult to tamper with and is traceable. With the help of differential game theory, four differential game models of manufacturer recycling and platform recycling with and without blockchain are established. The state feedback strategies are derived from Bellman’s continuous dynamic programming theory. Through analytical results and comparative analysis, the adoption conditions of blockchain and the impact of blockchain on the selection of recycling models are obtained. The results illustrated that the introduction of blockchain technology effectively improves the real recycling rate of waste electronics, building trust in consumers, which benefits corporations in certain conditions. However, it amplifies the double marginal effect of the CLSC. Nevertheless, the implementation of blockchain is still beneficial to consumers, as the adverse impact of the double marginal effect is compensated by the improvement in consumer surplus. In addition, the study shows that the implementation of the blockchain incentivizes members, who benefit on the same recycling model when the fixed cost of the blockchain and the share ratio of the residual value of waste electronics are between certain thresholds. That is, both the manufacturer and the platform are better off in a manufacturer recycling model enabled by blockchain. Moreover, in this model, the social welfare and the recycling rate of waste electronics are increased, which enable the CLSC to achieve benefits related to economy, environment, and society.