bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Surfactant Characteristics on Fuel Properties of Emulsions of Alternative Engine Fuel through the Phase Inversion Method
Authors: Lin Cherng-Yuan , Lin Keng-Hung , Yang Hsuan
Published: June 2023

Emulsions that mix two or more immiscible phases are broadly applied in pharmaceutics, chemistry, and industries. The phase inversion temperature (PIT) method is an emulsifying approach to preparing an emulsion with low energy consumption and cheap equipment. The effects of surfactant characteristics and processes of cooling or heating on the fuel properties of emulsions composed of silicone oil by the emulsifying method, such as mean droplet sizes of the de-ionized water phase, were considered herein. The application of the silicone oil emulsion as engine fuel was first evaluated. The results show that the emulsions added with the polyol surfactant mixture appeared to have a larger mean water-droplet size, a larger number of dispersed water droplets, a wider range of dispersed-water sizes, and lower kinematic viscosity than those with Brij 30 surfactant. Increasing the surfactant concentration of either Tween 20 mixed with Span 80 or Brij 30 surfactant increased kinematic viscosity and the number of dispersed droplets while decreasing mean droplet sizes. After being subjected to fast heating and then fast cooling, the silicone oil emulsion appeared to form many smaller dispersed droplets than those being proceeded with slow cooling. The emulsion of silicone oil was found to have adequate engine fuel properties.
The Role of Microbiota Pattern in Anxiety and Stress Disorders—A Review of the State of Knowledge
Authors: Krupa-Kotara Karolina , Gwioździk Weronika , Nandzik Sandra , Grajek Mateusz
Published: June 2023

Interest in the human microbiome in terms of mental health has increased with the rise in psychiatric diseases and disorders. The digestive system, the immune system, the brain, and the autonomic nervous system can all suffer from long-term lack of sleep and relaxation brought on by stress. There is little doubt that stress affects the human intestinal microbiota’s health and encourages problems with its composition, according to scientific studies. Chronic stress exposure raises the risk of both physical and mental illnesses. Therefore, this review’s goal was to support the theory that diseases including anxiety and stress are influenced by microbiome patterns. A total of 8600 sources directly relevant to this study’s topic were chosen from the 236,808 records returned by the literature search, and those with the highest scientific value were then selected based on bibliometric impact factors, language, and year of publication. A total of 87 sources, the most recent scientific output, were finally used for the literature review’s final analysis. The small number of studies on the subject indicates that it is still a developing problem, according to the literature study.
Review of Current Software for Analyzing Total X-ray Scattering Data from Liquids
Authors: Gallington Leighanne C. , Wilke Stephen K. , Kohara Shinji , Benmore Chris J.
Published: June 2023

The popularity of the pair distribution function (PDF) analysis of X-ray total scattering data has steadily grown as access to ex situ synchrotron data has expanded. Due to the broadening of the PDF user community, there is a growing demand for software that can be used to extract PDFs and is accessible to non-expert users. While user-friendly options have been developed over the past decade for fast, streamlined data analysis, care must be taken in both processing the data and understanding any limitations, especially in the case of liquids. In this review, the same scattering data are analyzed using different total X-ray scattering software, in order to compare the accuracy of the extracted structure factors and associated pair distribution functions. The goal is to assess the best practices for extracting the most accurate liquid data for each software package. The importance of absolute normalization and the application of the most appropriate corrections are emphasized via quantitative comparisons between liquid sulfur and water. Additionally, an awareness of the competing conventions used to define the PDF in crystallography and liquids/glasses is crucial for both the downstream analyses of the data and a comparison with the previous results in the literature.
Regina Coeli—Doctrine and Iconography of the Virgin Mary’s Heavenly Royalty
Authors: Salvador-González José María
Published: June 2023

This article aims to highlight the privileged status granted by Christianity to the Virgin Mary when considering her the Queen of Heaven. From the very early centuries of our era, this sublime title was assigned to the Virgin Mary, for her condition of Mother of God, by an increasing number of Church Fathers and theologians. Later, it was expounded by an uncountable number of medieval liturgical hymns. Finally, from the 12th century onwards, these textual proclamations were shaped in sculptures and paintings according to various iconographic types. The author will proceed by first analyzing a large corpus of texts by many Church Fathers, theologians, and hymnographers. Then, he will consider twenty sculptures and paintings which reflect the heavenly royalty of Mary according to five iconographic types.
A Study of Energy-Efficient and Optimal Locomotion in a Pneumatic Artificial Muscle-Driven Snake Robot
Authors: Lopez Marcela , Haghshenas-Jaryani Mahdi
Published: June 2023

This paper presents a study of energy efficiency and kinematic-based optimal design locomotion of a pneumatic artificial muscle (PAM)-driven snake-like robot. Although snake-like robots have several advantages over wheeled and track-wheeled mobile robots, their low energy-locomotion has limited their applications in long-range and outdoor fields. This work continues our previous efforts in designing and prototyping a muscle-driven snake-like robot to address their low energy efficiency limitation. An electro-pneumatic control hardware was developed to control the robot’s locomotion and a control algorithm for generating the lateral undulation gait. The energy efficiency of a single muscle (i.e., PAM), a single 2-link module of the robot, and a 6-link snake robot were also studied. Moreover, the power consumption was derived for the snake locomotion to determine the cost of transportation as the index for measuring the performance of the robot. Finally, the performance of the robot was analyzed and compared to similar models. Our analysis showed that the power consumption efficiency for our robot is 0.21, which is comparable to the reported range of 0.016–0.32 from other robots. In addition, the cost of transportation for our robot was determined to be 0.19 compared to the range of 0.01–0.75 reported for the other mobile robots. Finally, the range of motion for the joints of the robot is ±30∘, which is comparable to the reported range of motion of other snake-like robots, i.e., 25∘–45∘.
An Assessment of Negative Samples and Model Structures in Landslide Susceptibility Characterization Based on Bayesian Network Models
Authors: Khabiri Sahand , Crawford Matthew M. , Koch Hudson J. , Haneberg William C. , Zhu Yichuan
Published: June 2023

Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, remain inadequately addressed in the literature. In this study, we employed a physically based susceptibility model, PISA-m, to generate four different non-landslide data scenarios and combine them with mapped landslides from Magoffin County, Kentucky, for model training. We utilized two Bayesian network model structures, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN), to produce LSMs based on regional geomorphic conditions. After internal validation, we evaluated the robustness and reliability of the models using an independent landslide inventory from Owsley County, Kentucky. The results revealed considerable differences between the most effective model in internal validation (AUC = 0.969), which used non-landslide samples extracted exclusively from low susceptibility areas predicted by PISA-m, and the models’ unsatisfactory performance in external validation, as manifested by the identification of only 79.1% of landslide initiation points as high susceptibility areas. The obtained results from both internal and external validation highlighted the potential overfitting problem, which has largely been overlooked by previous studies. Additionally, our findings also indicate that TAN models consistently outperformed NB models when training datasets were the same due to the ability to account for variables’ dependencies by the former.
Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data
Authors: Wang Yanjun , Wang Mengjie , Teng Fei , Ji Yiye
Published: June 2023

The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection.
Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm
Authors: Wei Peng , Ye Huichun , Qiao Shuting , Liu Ronghao , Nie Chaojia , Zhang Bingrui , Song Lijuan , Huang Shanyu
Published: June 2023

Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sentinel-2 time-series data and the random forest classification algorithm. Crop information extraction was then performed. Following the analysis of the remote sensing classification features by the random forest importance approach and the subsequent normalization, the optimal features greater than or equal to 0.5 have yielded quite results in early crop mapping, and their overall accuracy was the highest in early-season mapping. The overall accuracy was observed to improve by 5% for 10 to 20 days of delay. In addition, rice, maize, and soybean were mapped at the irrigation transplanting period (10 May), jointing stage (9 July) and flowering (29 July), with an overall accuracy of 90.4%, 90.0% and 90.9%, respectively. This study shows that features suitable for early crop classification can be selected by random forest importance analysis as well as the ability of remote sensing to extract crop acreage information within the reproductive period.
Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives
Authors: Manga Sharanya , Muthavarapu Neha , Redij Renisha , Baraskar Bhavana , Kaur Avneet , Gaddam Sunil , Gopalakrishnan Keerthy , Shinde Rutuja , Rajagopal Anjali , Samaddar Poulami , Damani Devanshi N. , Shivaram Suganti , Dey Shuvashis , Mitra Dipankar , Roy Sayan , Kulkarni Kanchan , Arunachalam Shivaram P.
Published: June 2023

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Authors: Yu Yaze , Cao Yang , Wang Gong , Pang Yajun , Lang Liying
Published: June 2023

Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a hurdle. In this work, we propose an optical diffractive convolutional neural network (ODCNN) that is capable of performing image processing tasks in computer vision at the speed of light. We explore the application of the 4f system and the diffractive deep neural network (D2NN) in neural networks. ODCNN is then simulated by combining the 4f system as an optical convolutional layer and the diffractive networks. We also examine the potential impact of nonlinear optical materials on this network. Numerical simulation results show that the addition of convolutional layers and nonlinear functions improves the classification accuracy of the network. We believe that the proposed ODCNN model can be the basic architecture for building optical convolutional networks.
StethAid: A Digital Auscultation Platform for Pediatrics
Authors: Arjoune Youness , Nguyen Trong N. , Salvador Tyler , Telluri Anha , Schroeder Jonathan C. , Geggel Robert L. , May Joseph W. , Pillai Dinesh K. , Teach Stephen J. , Patel Shilpa J. , Doroshow Robin W. , Shekhar Raj
Published: June 2023

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid—a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics—that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still’s murmur identification and (2) wheeze detection. The platform has been deployed in four children’s medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still’s murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.
Boosting 3D Object Detection with Density-Aware Semantics-Augmented Set Abstraction
Authors: Zhang Tingyu , Wang Jian , Yang Xinyu
Published: June 2023

In recent years, point cloud-based 3D object detection has seen tremendous success. Previous point-based methods use Set Abstraction (SA) to sample the key points and abstract their features, which did not fully take density variation into consideration in point sampling and feature extraction. The SA module can be split into three parts: point sampling, grouping and feature extraction. Previous sampling methods focus more on distances among points in Euclidean space or feature space, ignoring the point density, thus making it more likely to sample points in Ground Truth (GT) containing dense points. Furthermore, the feature extraction module takes the relative coordinates and point features as input, while raw point coordinates can represent more informative attributes, i.e., point density and direction angle. So, this paper proposes Density-aware Semantics-Augmented Set Abstraction (DSASA) for solving the above two issues, which takes a deep look at the point density in the sampling process and enhances point features using onefold raw point coordinates. We conduct the experiments on the KITTI dataset and verify the superiority of DSASA.
Role of Reference Frames for a Safe Human–Robot Interaction
Authors: Borboni Alberto , Pagani Roberto , Sandrini Samuele , Carbone Giuseppe , Pellegrini Nicola
Published: June 2023

Safety plays a key role in human–robot interactions in collaborative robot (cobot) applications. This paper provides a general procedure to guarantee safe workstations allowing human operations, robot contributions, the dynamical environment, and time-variant objects in a set of collaborative robotic tasks. The proposed methodology focuses on the contribution and the mapping of reference frames. Multiple reference frame representation agents are defined at the same time by considering egocentric, allocentric, and route-centric perspectives. The agents are processed to provide a minimal and effective assessment of the ongoing human–robot interactions. The proposed formulation is based on the generalization and proper synthesis of multiple cooperating reference frame agents at the same time. Accordingly, it is possible to achieve a real-time assessment of the safety-related implications through the implementation and fast calculation of proper safety-related quantitative indices. This allows us to define and promptly regulate the controlling parameters of the involved cobot without velocity limitations that are recognized as the main disadvantage. A set of experiments has been realized and investigated to demonstrate the feasibility and effectiveness of the research by using a seven-DOF anthropomorphic arm in combination with a psychometric test. The acquired results agree with the current literature in terms of the kinematic, position, and velocity aspects; use measurement methods based on tests provided to the operator; and introduce novel features of work cell arranging, including the use of virtual instrumentation. Finally, the associated analytical–topological treatments have enabled the development of a safe and comfortable measure to the human–robot relation with satisfactory experimental results compared to previous research. Nevertheless, the robot posture, human perception, and learning technologies would have to apply research from multidisciplinary fields such as psychology, gesture, communication, and social sciences in order to be prepared for positioning in real-world applications that offer new challenges for cobot applications.
Wideband TDoA Positioning Exploiting RSS-Based Clustering
Authors: Fuchs Andreas , Wielandner Lukas , Neunteufel Daniel , Arthaber Holger , Witrisal Klaus
Published: June 2023

The accuracy of radio-based positioning is heavily influenced by a dense multipath (DM) channel, leading to poor position accuracy. The DM affects both time of flight (ToF) measurements extracted from wideband (WB) signals—specifically, if the bandwidth is below 100 MHz—as well as received signal strength (RSS) measurements, due to the interference of multipath signal components onto the information-bearing line-of-sight (LoS) component. This work proposes an approach for combining these two different measurement technologies, leading to a robust position estimation in the presence of DM. We assume that a large ensemble of densely-spaced devices is to be positioned. We use RSS measurements to determine “clusters” of devices in the vicinity of each other. Joint processing of the WB measurements from all devices in a cluster efficiently suppresses the influence of the DM. We formulate an algorithmic approach for the information fusion of the two technologies and derive the corresponding Cramér-Rao lower bound (CRLB) to gain insight into the performance trade-offs at hand. We evaluate our results by simulations and validate the approach with real-world measurement data. The results show that the clustering approach can halve the root-mean-square error (RMSE) from about 2 m to below 1 m, using WB signal transmissions in the 2.4 GHz ISM band at a bandwidth of about 80 MHz.
An Underwater Image Enhancement Method for a Preprocessing Framework Based on Generative Adversarial Network
Authors: Jiang Xiao , Yu Haibin , Zhang Yaxin , Pan Mian , Li Zhu , Liu Jingbiao , Lv Shuaishuai
Published: June 2023

This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.
A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar
Authors: Zhang Jingwen , Qi Qingjie , Cheng Huifeng , Sun Lifeng , Liu Siyun , Wang Yue , Jia Xinlei
Published: June 2023

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.
Research on the Evaluation of Green Technology Renovation Measurement for Multi-Storey Houses in Severe Cold Regions Based on Entropy-Weight-TOPSIS
Authors: Wang Anqi , An Yanhua , Yu Shuhua
Published: June 2023

In the context of global warming and the energy crisis, a large amount of energy consumed by the construction industry has a negative impact on climate change. Green retrofitting for older multi-story houses can significantly reduce energy consumption and pollutant gas emissions. However, a comprehensive evaluation method is needed to select suitable retrofitting methods. This paper established a green renovation evaluation system, including five dimensions from the green concept, and 16 evaluation indexes. Making an old multi-story house in Shenyang the research object, 16 transformation plans were generated through orthogonal experiments. The total weights of each index were derived through entropy weighting and expert scoring, and then the best retrofit solution was selected using the improved TOPSIS method. The best retrofit solution was evaluated to reduce energy consumption by 3016.4 kWh/m2 and pollutant gas emissions by 3934.3 tons over the remaining 35 years of its life cycle while generating an economic benefit of approximately 1.58 million yuan. These study results show that the retrofitting of external walls significantly affects the building’s energy consumption; exterior windows present the most negligible impact. The photovoltaic facilities provide significant energy savings.
TMR-Array-Based Pipeline Location Method and Its Realization
Authors: Wu Zhenning , Huang Hanyang , Zhao Guangdong , Liu Jinhai
Published: June 2023

Pipeline inspection is important to ensure the safe operation of pipelines. Obtaining the location of an underground pipeline is a prerequisite for most inspection technologies. Existing pipeline location methods can find a pipeline’s location, but they require multiple measurements and cannot be used by automatic inspection robots. In this paper, a tunnel magnetoresistance (TMR)-sensor-array-based pipeline location method is proposed to solve this problem. Firstly, a detection probe is designed using a TMR sensor array. It is calibrated by the improved ellipsoid fitting method to measure the magnetic field around the pipeline accurately. Secondly, a relative pipeline-position-locating method is proposed by detecting the phases of the magnetic induction signals at different frequencies. Thirdly, a three-dimensional pipeline location method is proposed. The horizontal and vertical distances and the angle between the pipeline and the probe are calculated by measuring the magnetic induction amplitude. Finally, a simulation model and a test platform are established, and the experimental results illustrate that, by adopting the TMR array, the three-dimensional pipeline location method can locate a pipeline in real time in three dimensions with good accuracy.
Co-Gasification Performance of Low-Quality Lignite with Woody Wastes Using Greenhouse Gas CO2—A TG–MS Study
Authors: Vamvuka Despina , Tsagris George , Loulashi Christia
Published: June 2023

The carbon dioxide gasification performance of low-quality lignite-agroindustrial/forest waste blends was investigated in terms of reactivity, conversion, cold gas efficiency, product gas composition and heating value. The experiments were conducted in a fixed bed unit and a thermal analysis mass spectrometer system. Raw materials, chars, liquids and gases were quantitatively analyzed and their energy content was determined. Synergetic effects and the role of minerals were examined, the latter through chemical and fusibility analyses of the ashes. Ahlada lignite (AL) was of low quality, with a calorific value of 8.9 MJ/kg. The biomass materials, ginning cotton waste (GCW) and pine needles (PN) had calorific values 16.6 MJ/kg and 20.1 MJ/kg, respectively. The slagging/fouling propensity of AL ash was low, whereas that of biomass wastes was medium to high. Thermal treatment of the samples and their blends prior to gasification produced upgraded fuels. A Boudourd reaction occurred above 750 °C. Gasification reactivity followed the order: GCW > PN > AL. AL/PN mixtures presented additivity effects; however, AL/GCW mixtures presented synergy effects. When the lignite was blended with the biochars studied, its conversion increased from 90% to 94.5% and its cold gas efficiency from 31.8% to 35%. Generated gas attained a heating value of about 12 MJ/m3.
Microplastic Contamination in Different Marine Species of Bintaro Fish Market, Indonesia
Authors: Widyastuti Sri , Abidin Angga Susmana , Hikmaturrohmi Hikmaturrohmi , Ilhami Bq Tri Khairina , Kurniawan Nanda Sofian Hadi , Jupri Ahmad , Candri Dining Aidil , Frediansyah Andri , Prasedya Eka Sunarwidhi
Published: June 2023

Indonesia is among the largest fish-producing countries. The West Nusa Tenggara (WNT) region is one of the highest producers of fish in Indonesia. Hence, the levels of MP contamination in commercial fish should be assessed to ensure food safety, food security, and socio-economic sustainability. This study investigates MP contamination in commercial fish in one of the largest fish markets in the WNT region, the Bintaro fish market. Three commercial fish species were evaluated for MP contamination in this study, Nasso thynnoides, Auxis rochei, and Caesio teres. The highest number of MPs was detected in A. rochei (21.60 ± 8.70 MPs/100 g). The other pelagic fish species, N. thynnoides, also shown considerably high MP contamination (18.17 ± 7.93 MPs/100 g). On the other hand, the midwater fish Caesio teres showed the least MP contamination (7.07 MPs/100 g). In addition, most of the MPs detected in all three fish species were fiber MPs of small sizes (100–500 µm). Based on FTIR analyses, the MP samples from all three fish species mainly consisted of polyamide (PA), which is the polymer used to form fiber for textiles. These results potentially reveal the degree of microplastic pollution in not only coastal areas of WNT, but also Sulawesi and also East Nusa Tenggara (ENT) since the fish distributed at the Bintaro fish market came from these regions. Better solid waste management in Indonesia is needed to reduce plastic waste management, particularly household waste, which is the potential major source of fiber MPs.
Preventing Oil Shock Inflation: Sustainable Development Mechanisms vs. Islamic Mechanisms
Authors: Benhamed Adel , Gassouma Mohamed Sadok
Published: June 2023

This paper discusses the effects of both sustainable development and an Islamic system on the transmission of oil shocks into inflation. Sustainable development in this context includes wealth distribution equity, public governance effectiveness and alternative energy creation for the promotion of economic growth. These factors have been revised by Askari and Mirakhor under an Islamic approach using a set of Islamic indices to reflect a social, economic, political and ecological system that is in compliance with the Sharia principles. Using the ARDL model, we tested whether these Islamic mechanisms could have played the role of sustainable development in the fight against the transmission of oil shocks into inflation in MENA countries during the period 2000–2018. The results show that the Islamic system can play the role of sustainable development better than the traditional one, through fostering social equity, fighting against corruption, promoting humanity in employability and facilitating growth of the real economy. These Islamic values slow down the inflationary phenomenon caused by oil shocks.
Community Perceptions on the Critical Success Factors of Hotels’ Community-Based Corporate Social Responsibility
Authors: Kasim Azilah , Jailani Siti Noormala , Mokhtar Muhammad Fauzi , Radha Jasmine Zea Raziah Radha Rashid , Khuadthong Bussalin , Fong Maebel Leong Sai
Published: June 2023

This paper examines community as the consumer of community-related corporate social responsibility (CSR). It empirically investigates the critical success factors of CSR within the context of tourism from the community perspective because, for CSR activities that are targeted towards them, understanding their perspective could not be more necessary. The study narrowed the gap of knowledge by testing twelve critical success factors proposed in the literature on 267 target respondents in Langkawi, Malaysia and analysed the data using PLS. The sample was drawn using a qualifying question from communities known to have participated in previous hotel CSR activities. The findings revealed that, from a community standpoint, just four of the recommended twelve variables were crucial to the performance of community-related CSR in Langkawi’s hotel industry. These variables were general community development, law and social norms, project management committee, and financial preparedness. The findings provide theoretical as well as managerial implications, which are explained in the later sections of the paper.
The Impact of the COVID-19 Pandemic on College Students’ Food Choice Motives in Greece
Authors: Skalkos Dimitris , Kalyva Zoi C. , Kosma Ioanna S.
Published: June 2023

We are already more than year away from the pandemic period of COVID-19; its effects and the changes it caused in our lives are becoming ever clearer, and these effects include our food choices and motives. In this study, we investigated changes in food choice motives due to the COVID-19 in college students in Greece; we used the 10 key food motives, namely health, convenience, sensory pleasure, appeal, nutritional quality, moral concerns, weight control, mood and stress, familiarity, price, and shopping frequency and behavior. A sample of 1017 college students answered the questionnaire survey through the Google platform, conducted in January to February 2023. The collected data were analyzed with statistical tools, combining cross and chi-square tests. The students exhibited subtle and very important preferences in terms of health, convenience, weight control, and mood and stress. Food choices related to sensory appeal, nutritional quality, and familiarity were of less importance for the students, similarly to ethical concerns; concern regarding the environmental impact of the food was greater than expected. The motive which continues to be of the highest concern for students before and after the pandemic is price; the students look for value for money in food (88.8%), inexpensiveness (80.7%), and cheapness (78.7%). The shopping frequency and behavior motives, which changed during the pandemic, have now returned to the preferences of the pre-pandemic period, with the purchasing of foods distributed between supermarkets (29%), local grocery stores (37.6%), online (12.3%), and by delivery services (20.4%), weekly or every two weeks. College students’ preference for cooking full meals at home is now very high, reaching 74.4%; students mostly avoid eating at restaurants or eating fast food (only 27%). Our findings indicate that students have already returned to their food choice motives of the period before COVID-19, except with regard to home-cooked food which now ranks higher in their preferences.
Experimental and Numerical Investigation of the Heat Transfer of Honeycomb-Structured Tubes
Authors: Trampe Eileen , Büschgens Dominik , Pfeifer Herbert
Published: June 2023

Tube bundle recuperators are generally designed to operate with smooth tubes. Structured tubes can be used to increase the efficiency of recuperators. Compared to smooth tubes, the surface for heat transfer is increased and thus heat transfer is enhanced. This effect is accompanied by an increased pressure loss, which must be kept as low as possible. Four tube geometries with different honeycomb structures are examined. The results are compared with the performance of a smooth tube. The investigations were carried out both numerically and experimentally at different off-gas and combustion air velocities. The experimental results show that the highest heat transfer is achieved with the concave 6 mm structured tube. The greatest pressure loss also occurs here. The validation of the numerical model has shown issues in resolving the turbulence.
Visualization of Metastatic Lung Cancer with TiNIR
Authors: Mun Seul-Ki , Sim Hyun Bo , Han Ji Yeon , Kim Hyeongyeong , Park Dae-Han , Chang Dong-Jo , Yee Sung-Tae , Chang Young-Tae , Kim Jong-Jin
Published: June 2023

The development of efficient biomarkers and probes for monitoring and treating cancer, specifically metastatic cancer, is a critical research area that can have a significant impact on both patient outcomes and drug discovery. In this context, TiNIR has been developed to detect tumor-initiating cells (TICs), with heme oxygenase 2 (HO2) as a promising therapeutic biomarker for tumor-initiating cells. In this study, TiNIR has demonstrated its effectiveness as an in vivo metastatic lung cancer tracker, highlighting its potential as a valuable tool in cancer research and therapy. The development of innovative approaches that selectively target metastatic cancers represents a promising avenue for improving survival rates and enhancing the quality of life of cancer patients.
Toxic Effects of Gemcitabine and Paclitaxel Combination: Chemotherapy Drugs Exposure in Zebrafish
Authors: D’Iglio Claudio , Famulari Sergio , Capparucci Fabiano , Gervasi Claudio , Cuzzocrea Salvatore , Spanò Nunziacarla , Di Paola Davide
Published: June 2023

Pharmaceuticals are widely recognized as potentially hazardous to aquatic ecosystems. In the last two decades, the constant intake of biologically active chemicals used in human healthcare has been related to the growing release of these agents into natural environments. As reported by several studies, various pharmaceuticals have been detected, mainly in surface water (seas, lakes, and rivers), but also in groundwater and drinking water. Moreover, these contaminants and their metabolites can show biological activity even at very low concentrations. This study aimed to evaluate the developmental toxicity of exposure to the chemotherapy drugs gemcitabine and paclitaxel in aquatic environments. Zebrafish (Danio rerio) embryos were exposed to doses of gemcitabine 15 μM in combination with paclitaxel 1 μM from 0 to 96 h post-fertilization (hpf) using a fish embryo toxicity test (FET). This study highlights that both gemcitabine and paclitaxel exposure at single non-toxic concentrations affected survival and hatching rate, morphology score, and body length after exposure in combination. Additionally, exposure significantly disturbed the antioxidant defense system and increased ROS in zebrafish larvae. Gemcitabine and paclitaxel exposure caused changes in the expression of inflammation-related, endoplasmic reticulum stress-related (ERS), and autophagy-related genes. Taken together, our findings underline that gemcitabine and paclitaxel increase developmental toxicity in zebrafish embryos in a time-dependent manner.
Exposure to Methylmercury at Juvenile Stage Worsens Autism-like Symptoms in Adult BTBR T+tf/J Mice Due to Lack of Nuclear Factor Erythroid 2-Related Factor 2 Signaling Upregulation in Periphery and Brain
Authors: Algahtani Mohammad M. , Ahmad Sheikh F. , Alkharashi Layla A. , Al-Harbi Naif O. , Alanazi Wael A. , Alhamed Abdullah S. , Attia Sabry M. , Bakheet Saleh A. , Ibrahim Khalid E. , Nadeem Ahmed
Published: June 2023

Autism spectrum disorder (ASD) is a multifaceted developmental condition that first appears in infancy. The condition is characterized by recurrent patterns in behavior and impairments in social and vocalization abilities. Methylmercury is a toxic environmental pollutant, and its derivatives are the major source of organic mercury to human beings. Inorganic mercury, which is released from a variety of pollutants into oceans, rivers, and streams, is transformed into methylmercury by bacteria and plankton in the water, which later builds up in fish and shellfish, and then enters humans through the consumption of fish and shellfish and increases the risk of developing ASD by disturbing the oxidant–antioxidant balance. However, there has been no prior research to determine the effect of juvenile exposure of methylmercury chloride on adult BTBR mice. Therefore, the current study evaluated the effect of methylmercury chloride administered during the juvenile stage on autism-like behavior (three-chambered sociability, marble burying, self-grooming tests) and oxidant–antioxidant balance (specifically Nrf2, HO-1, SOD-1, NF-kB, iNOS, MPO, and 3-nitrotyrosine) in the peripheral neutrophils and cortex of adult BTBR and C57BL/6 (B6) mice. Our results show that exposure to methylmercury chloride at a juvenile stage results in autism-like symptoms in adult BTBR mice which are related to a lack of upregulation of the Nrf2 signaling pathway as demonstrated by no significant changes in the expression of Nrf2, HO-1, and SOD-1 in the periphery and cortex. On the other hand, methylmercury chloride administration at a juvenile stage increased oxidative inflammation as depicted by a significant increase in the levels of NF-kB, iNOS, MPO, and 3-nitrotyrosine in the periphery and cortex of adult BTBR mice. This study suggests that juvenile exposure to methylmercury chloride contributes to the worsening of autism-like behavior in adult BTBR mice through the disruption of the oxidant–antioxidant balance in the peripheral compartment and CNS. Strategies that elevate Nrf2 signaling may be useful to counteract toxicant-mediated worsening of ASD and may improve quality of life.
The Importance of Entomo-Virological Investigation of Yellow Fever Virus to Strengthen Surveillance in Brazil
Authors: Cruz Ana Cecília Ribeiro , Hernández Leonardo Henrique Almeida , Aragão Carine Fortes , da Paz Thito Yan Bezerra , da Silva Sandro Patroca , da Silva Fábio Silva , de Aquino Ana Alice , Cereja Glennda Juscely Galvão Pereira , Nascimento Bruna Lais Sena do , Rosa Junior José Wilson , Elias Carmeci Natalina , Nogueira Cristiano Gomes , Ramos Daniel Garkauskas , Fonseca Vagner , Giovanetti Marta , Alcantara Luiz Carlos Junior , Nunes Bruno Tardelli Diniz , Vasconcelos Pedro F. da Costa , Martins Livia Carício , Nunes-Neto Joaquim Pinto
Published: June 2023

The largest outbreak of sylvatic yellow fever virus (YFV) in eight decades was recorded in Brazil between 2016–2018. Besides human and NHP surveillance, the entomo-virological approach is considered as a complementary tool. For this study, a total of 2904 mosquitoes of the Aedes, Haemagogus and Sabethes genera were collected from six Brazilian states (Bahia, Goiás, Mato Grosso, Minas Gerais, Pará, and Tocantins) and grouped into 246 pools, which were tested for YFV using RT-qPCR. We detected 20 positive pools from Minas Gerais, 5 from Goiás, and 1 from Bahia, including 12 of Hg. janthinomys and 5 of Ae. albopictus. This is the first description of natural YFV infection in this species and warns of the likelihood of urban YFV re-emergence with Ae. albopictus as a potential bridge vector. Three YFV sequences from Hg. janthinomys from Goiás and one from Minas Gerais, as well as one from Ae. albopictus from Minas Gerais were clustered within the 2016–2018 outbreak clade, indicating YFV spread from Midwest and its infection in a main and likely novel bridging vector species. Entomo-virological surveillance is critical for YFV monitoring in Brazil, which could highlight the need to strengthen YFV surveillance, vaccination coverage, and vector control measures.