bluuebunny/crossref_metadata_2025 huggingface dataset:
A Proposal to Perform High Contrast Imaging of Human Palatine Tonsil with Cross Polarized Optical Coherence Tomography
Authors: Sharma Gargi , Parmar Asha , Hoffmann Franziska , Geißler Katharina , von Eggeling Ferdinand , Guntinas-Lichius Orlando , Singh Kanwarpal
Published: April 2022

The palatine tonsils provide the first line of immune defense against foreign pathogens inhaled or ingested. However, a disruption in the epithelial layer within the tonsil crypts can lead to recurrent acute tonsillitis (RAT). Current imaging techniques suffer from poor resolution and contrast and do not allow a classification of the severity of RAT. We have developed a cross-polarized optical coherence tomography system. The system can detect a change in the polarization of the light after the light-tissue interaction. We demonstrate improved resolution and contrast in tonsil imaging with the developed method. Intensity, as well as retardance images of the excised tonsil tissue, were acquired. Features such as crypt epithelium, lymphoid follicles, and dense connective tissue were observed with improved contrast. Cross polarized optical coherence tomography can be a valuable tool in the clinic to evaluate palatine tonsils as it would allow visualizing common tonsil features without the need for any external contrast agent.
Impacts of Rapid Desiccation on Oxidative Status, Ultrastructure and Physiological Functions of Syzygium maire (Myrtaceae) Zygotic Embryos in Preparation for Cryopreservation
Authors: van der Walt Karin , Burritt David J. , Nadarajan Jayanthi
Published: April 2022

Syzygium maire is a highly threatened Myrtaceae tree species endemic to New Zealand. Due to its recalcitrant seed storage behaviour, cryopreservation is the only viable long-term ex situ conservation option for this species. This study investigated viability, oxidative stress, thermal properties, and ultrastructure of zygotic embryo axes (EAs) desiccated to various moisture contents (MC). Fresh EAs had a MC of c. 1.9 g/g with 100% viability but rapid desiccation to MC < 0.3 g/g significantly reduced viability and decreased the activities of the enzymatic antioxidants superoxide dismutase, catalase and glutathione peroxidase, with a sevenfold increase in the production of protein carbonyls and lipid peroxides. Differential Scanning Calorimetry analysis showed no thermal events in EAs desiccated to a MC of <0.2 g/g, indicating that all freezable water had been removed, but this was lethal to both EAs and enzymatic antioxidants. The ultrastructure of desiccated EAs showed signs of plasmolysis, while fully hydrated EAs exposed to cryogenic temperature had ultrastructural disintegration and membrane damage. The decline in enzymatic antioxidant activities and the increase in lipid peroxidation suggest that S. maire EA viability loss is due to oxidative stress rather than structural impacts.
Reevaluation of Parasynechococcus-like Strains and Genomic Analysis of Their Microsatellites and Compound Microsatellites
Authors: Tang Jie , Yao Dan , Zhou Huizhen , Du Lianming , Daroch Maurycy
Published: April 2022

Morphologically similar to Synechococcus, a large number of Parasynechococcus strains were misclassified, resulting in extreme underestimation of their genetic diversity. In this study, 80 Synechococcus-like strains were reevaluated using a combination of 16S rRNA phylogeny and genomic approach, identifying 54 strains as Parasynechococcus-like strains and showing considerably intragenus genetic divergence among the subclades identified. Further, bioinformatics analysis disclosed diversified patterns of distribution, abundance, density, and diversity of microsatellites (SSRs) and compound microsatellites (CSSRs) in genomes of these Parasynechococcus-like strains. Variations of SSRs and CSSRs were observed amongst phylotypes and subclades. Both SSRs and CSSRs were in particular unequally distributed among genomes. Dinucleotide SSRs were the most widespread, while the genomes showed two patterns in the second most abundant repeat type (mononucleotide or trinucleotide SSRs). Both SSRs and CSSRs were predominantly observed in coding regions. These two types of microsatellites showed positive correlation with genome size (p < 0.01) but negative correlation with GC content (p < 0.05). Additionally, the motif (A)n, (AG)n and (AGC)n was a major one in the corresponding category. Meanwhile, distinctive motifs of CSSRs were found in 39 genomes. This study characterizes SSRs and CSSRs in genomes of Parasynechococcus-like strains and will be useful as a prerequisite for future studies regarding their distribution, function, and evolution. Moreover, the identified SSRs may facilitate fast acclimation of Parasynechococcus-like strains to fluctuating environments and contribute to the extensive distribution of Parasynechococcus species in global marine environments.
Innovative Injection Molding Process for the Fabrication of Woven Fabric Reinforced Thermoplastic Composites
Authors: Jeong Euichul , Kim Yongdae , Hong Seokkwan , Yoon Kyunghwan , Lee Sunghee
Published: April 2022

Woven fabric reinforced thermoplastic composites have been gaining significant attention as a lightweight alternative to metal in various industrial fields owing to their high stiffness and strength. Conventional manufacturing processes of woven fabric reinforced thermoplastic composites can be divided into two steps: first, the manufacturing of intermediate material, known as prepreg; then, the formation of the final products from the prepregs. This two-step process increases the manufacturing cost and time of the final composite products. This study demonstrated that woven fabric reinforced thermoplastic composites could be fabricated by an innovative injection molding process instead of the two-step process. A structure placing an extra mesh, which is a new and key component, on the mold-side of woven fabric was devised so that the thermoplastic matrix could be impregnated up to the surface of the woven fabric during injection molding. Tensile tests were performed in the direction parallel to the yarns of the fabric on the injection-molded composites to confirm their mechanical properties. As a result, it was possible to fabricate woven fabric reinforced thermoplastic composites with increased mechanical properties using injection molding without prepreg, and the composites could be molded with a much shorter cycle time than the conventional process, such as thermoforming or over-molding process.
Hybrid Materials Based on Nanoparticles Functionalized with Alkylsilanes Covalently Anchored to Epoxy Matrices
Authors: Salas Alexis , Jaramillo Andrés Felipe , Palacio Daniel Andrés , Díaz-Gómez Andrés , Rojas David , Medina Carlos , Pérez-Tijerina Eduardo , Solís-Pomar Francisco , Meléndrez Manuel Francisco
Published: April 2022

In this work, the surface modification of zinc oxide nanoparticles (ZnO-NPs) with 3-glycidyloxy-propyl-trimethoxysilane (GPTMS) was investigated. The ZnO-NPs were synthesized using the physical method of continuous arc discharge in controlled atmosphere (DARC-AC). The surface modification was carried out using a chemical method with constant agitation for 24 h at room temperature. This surface functionalization of zinc oxide nanoparticles (ZnO-NPs-GPTMS) was experimentally confirmed by infrared spectroscopy (FT-IR), TGA, and XRD, and its morphological characterization was performed with SEM. The increase in mechanical bending properties in the two final hybrid materials compared to the base polymers was verified. An average increase of 67% was achieved with a moderate decrease in ductility. In the case of compressive strength, they showed mixed results, maintaining the properties. With respect to thermal properties, it was observed that inorganic reinforcement conferred resistance to degradation on the base material, giving a greater resistance to high temperatures.
Short- and Long-Term Performance of Pipe Compounds Containing Polyethylene Post-Consumer Recyclates from Packaging Waste
Authors: Freudenthaler Paul J. , Fischer Joerg , Liu Yi , Lang Reinhold W.
Published: April 2022

The polymer industry is pushed to present solutions that lead to a circular plastics economy. High plastic packaging waste recycling targets will eventually lead to a high availability of packaging material recyclates. Although the use of polyethylene terephthalate (PET) recyclates is prescribed by regulations to be used in new PET bottles, no such regulation prescribes the use of polyethylene recyclate (rPE) in new products. One possibility of using rPE, which is considered by the European Union, is the use within pipe materials. Pipe applications demand a certain property profile, most prominently a high slow crack growth (SCG) resistance, which is not met by most packaging materials or recyclates made from it. Hence, this work investigates the use of commercially available post-consumer recyclates out of high-density polyethylene from packaging applications in compounds together with high SCG-resistant virgin PE pipe material with a PE100-RC specification. Two rPEs were acquired from German producers and blended to compounds consisting of 25 m%, 50 m% and 75 m% recyclate. These compounds, together with the pure recyclates and several virgin pipe materials acting as benchmarks were tested in terms of short- and long-term mechanical performance and with other basic characterization methods. Several compounds exceeded the performance of one tested virgin PE pipe material, an injection molding PE80 grade, in several categories. The content of recyclate needed to outperform this benchmark grade was mostly dependent on the resulting melt flow rate (MFR) of the compound and thus also of the MFR of the pure recyclate. Furthermore, different levels of polypropylene contaminations within the recyclates resulted in differently contaminated compounds. This is proved to influence the SCG resistance too, as compounds of similar MFRs but with different SCG resistances were found.
Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF
Authors: Nafees Afnan , Khan Sherbaz , Javed Muhammad Faisal , Alrowais Raid , Mohamed Abdeliazim Mustafa , Mohamed Abdullah , Vatin Nikolai Ivanovic
Published: April 2022

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.
Overoxidation of Intrinsically Conducting Polymers
Authors: Holze Rudolf
Published: April 2022

Intrinsically conducting polymers may undergo significant changes of molecular structure and material properties when exposed to highly oxidizing conditions or very positive electrode potentials, commonly called overoxidation. The type and extent of the changes depend on the experimental conditions and chemical environment. They may proceed already at much lower rates at lower electrode potentials because some of the processes associated with overoxidation are closely related to more or less reversible redox processes employed in electrochemical energy conversion and electrochromism. These changes may be welcome for some applications of these polymers in sensors, extraction, and surface functionalization, but in many cases, the change of properties affects the performance of the material negatively, contributing to material and device degradation. This report presents published examples, experimental observations, and their interpretations in terms of both structural and of material property changes. Options to limit and suppress overoxidation are presented, and useful applications are described extensively.
Exploring the Impact of Chitosan Composites as Artificial Organs
Authors: Sivanesan Iyyakkannu , Hasan Nazim , Muthu Manikandan , Blessing Gowsalya , Gopal Judy , Chun Sechul , Shin Juhyun , Oh Jae-Wook
Published: April 2022

Chitosan and its allies have in multiple ways expanded into the medical, food, chemical, and biological industries and is still expanding. With its humble beginnings from marine shell wastes, the deacetylated form of chitin has come a long way in clinical practices. The biomedical applications of chitosan are truly a feather on its cap, with rarer aspects being chitosan’s role in tissue regeneration and artificial organs. Tissue regeneration is a highly advanced and sensitive biomedical application, and the very fact that chitosan is premiering here is an authentication of its ability to deliver. In this review, the various biomedical applications of chitosan are touched on briefly. The synthesis methodologies that are specific for tissue engineering and biomedical applications have been listed. What has been achieved using chitosan and chitosan composites in artificial organ research as well as tissue regeneration has been surveyed and presented. The lack of enthusiasm, as demonstrated by the very few reports online with respect to chitosan composites and artificial organs, is highlighted, and the reasons for this lapse speculated. What more needs be done to expand chitosan and its allies for a better utilization and exploitation to best benefit the construction of artificial organs and building of tissue analogs has been discussed.
Effect of Kenaf Fibre as Reinforcing Fillers in Corn Starch-Based Biocomposite Film
Authors: Hazrol M. , Sapuan S. , Zainudin E. , Wahab N. , Ilyas R.
Published: April 2022

Biocomposite films were prepared using corn starch (CS), sorbitol as a plasticiser, and multi-scale kenaf fibre as reinforcing filler. The microstructure and the physical, tensile, and water barrier properties of corn starch reinforced with kenaf fibre were characterised and investigated. The biocomposite films were developed via the solution casting technique using 10 g of CS with 0 to 8% kenaf fibre as filler treated with 30% (w/w, starch basis) of sorbitol. The increased amount of kenaf fibre introduced contributed to improvements in film thickness, weight, and density. Conversely, slight reductions in the biocomposite films’ moisture content, water absorption, and solubility rating were 9.86–5.88%, 163.13–114.68%, and 38.98–25.17%, respectively. An X-ray diffraction (XRD) test revealed that the films were amorphous and that there was no effect on the crystallinity structure of films with kenaf fibre reinforcement. Fourier transform infrared (FT-IR) and rheological analysis indicated that kenaf fibre could weaken the molecular interaction of the film matrix. Field emission scanning electron microscope (FESEM) revealed the arrangement and uniform distribution of kenaf fibre at 0.2–0.8%. The incorporation of kenaf increased the tensile strength, Young’s modulus, and elongation at break until (6% wt) of fibre. With the kenaf fibre incorporation, the optimal tensile strength, Young’s modulus, and elongation at break of the films reached 17.74 MPa, 1324.74 MPa, and 48.79%, respectively. Overall, the introduction of kenaf fibre as filler enhanced the physical and mechanical properties of CS films.
Discussion on the Reconstruction of Medium/Low-Permeability Gas Reservoirs Based on Seepage Characteristics
Authors: Gao Guangliang , Liu Wei , Zhu Shijie , He Haiyan , Wang Qunyi , Sun Yanchun , Xiao Qianhua , Yang Shaochun
Published: April 2022

The construction of underground gas storage mostly focuses on depleted gas reservoirs. However, the depleted gas reservoir used to build underground gas storage in China is located far from the main gas consumption economic zone. It is necessary to reconstruct underground gas storage using nearby reservoirs in order to meet the needs of economic development. The complex three-phase seepage characteristics encountered in the process of reconstruction of underground gas storage reservoirs seriously affect their storage and injection production capacities. Combined with the mechanism of multiphase seepage and the multicycle injection production mode during the process of gas storage construction, the feasibility of rebuilding gas storage in medium- and low-permeability reservoirs was evaluated through relative permeability experiments and core injection production experiments. The results showed that the mutual driving of two-phase oil–water systems will affect the storage space and seepage capacity, that the adverse effect will be weakened after multiple cycles, and that increasing the gas injection cycle can enhance the gas-phase seepage capacity and improve the crude oil recovery. Therefore, we found that it is feasible to reconstruct underground gas storage in medium- and low-permeability reservoirs, which lays a foundation for the development of underground gas storage in China.
Enzymatic Glucose and Xylose Production from Paper Mill Rejects
Authors: Rauzi Joseph , Tschirner Ulrike
Published: April 2022

Recycled paper fiber rejects have shown potential as a source of waste-to-resource carbohydrates for renewable chemicals production. This study examined three classes of recycled paper fines (old corrugated containers, old newspaper, and mixed office waste) and two industrial papermaking rejects streams from different recycling mills (one mill processes linerboard and the other old corrugated cardboard). The effect of chemical pretreatment using dilute sodium hydroxide, hot water and dilute sulfuric acid on enzymatic glucose and xylose yields was evaluated. Enzymatic hydrolysis results indicated that recycled fiber streams with more old corrugated cardboard have higher potential to produce carbohydrates. The recycled cardboard rejects produced more glucose and xylose per kilogram of rejects than the linerboard rejects under all untreated and pretreated conditions. The highest producing rejects sample was sodium hydroxide pretreated cardboard rejects with 373 g glucose and 61 g xylose produced per kilogram of rejects. However, a simple hot water pretreatment showed similar results, with 335 g glucose and 58 g xylose produced per kilogram of rejects. The hot water pretreatment is recommended due to its comparable yield and lower chemical addition.
Kids Reading Tanakh: The Child as Interpreter
Authors: Hassenfeld Ziva
Published: April 2022

This essay examines two fourth-grade students’ task-based read-aloud interviews on the biblical text of Numbers 13. Taking up the New London Group’s call for a pedagogy of multiliteracies this article examines how educators and adults might sensitize themselves to the interpretive identities children bring to their reading of biblical texts. This work is intricately tied to child development, as we move religious education from a deficit model and perspective towards the child to a more welcoming asset model and perspective.
Beyond the Mainland: An Introduction
Authors: Chia Jack Meng-Tat
Published: April 2022

Mention “Southeast Asian Buddhism” and what comes to mind is often Theravāda Buddhism, the dominant religion in the mainland Southeast Asian states of Cambodia, Laos, Myanmar, and Thailand [...]
Income Tax Progressivity and Nonreligion in Central and Eastern Europe: A Case of the Czech Republic
Authors: Čábelková Inna , Mareš David , Strielkowski Wadim , Zenchenko Svetlana , Radyukova Yana
Published: April 2022

Our paper focuses on the tax progressivity and nonreligion in central and eastern Europe using an example of the Czech Republic, one of the most atheistic countries in the world. Religion might imply formal affiliation with a certain confession or active church attendance, but it might also become a determinant of taxation preferences. We employ ordinal regression analyses to study direct and mediation effects of both church affiliation and church attendance on a representative sample from the Czech Republic (n = 1924, 54.8% female, aged 18–95, M ± SD: 52.0 ± 16.9; 19.4% with higher education) controlling for employment status, social class and socio-demographics. The results suggest that neither church affiliation nor church attendance were related to desired income tax progressivity; social class plaid the most important role. The frequency of church attendance was significantly related to the perceived adequacy of taxation of higher incomes, where the more respondents attended the religious services, the more they believed that the taxes on the rich are too high. However, the churches’ ideas to help the needy were manifested in the preferences for international tax progressivity, where the frequent churchgoers were more inclined to the idea that the rich countries should pay additional taxes to help the poor countries. These controversial results may indicate the rivalrous position of the church and the state in the Czech Republic in terms of taxation of the wealthy. We suggest that under the condition of no church tax, the state taxation of the rich may be viewed as diverting funds, which could otherwise be directed to the church. These results might be of some interest to the state, the church and to the academic researchers alike and significantly contribute to the discussion on specific features of nonreligiosity in central and eastern Europe.
How Does COVID-19 Lockdown Impact Air Quality in India?
Authors: Hu Zhiyuan , Jin Qinjian , Ma Yuanyuan , Ji Zhenming , Zhu Xian , Dong Wenjie
Published: April 2022

Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National Clean Air Programme (NCAP) in January 2019. Meanwhile, the unexpected city-lockdown due to the COVID-19 pandemic in March 2020 in India greatly reduced human activities and thus anthropogenic emissions of gaseous and aerosol pollutants. The NCAP and the lockdown could provide an ideal field experiment for quantifying the extent to which various levels of human activity reduction impact air quality in the Indian subcontinent. Here, we study the improvement in air quality due to COVID-19 and the NCAP in the India subcontinent by employing multiple satellite products and surface observations. Satellite data shows significant reductions in nitrogen dioxide (NO2) by 17% and aerosol optical depth (AOD) by 20% during the 2020 lockdown with reference to the mean levels between 2005–2019. No persistent reduction in NO2 nor AOD is detectable during the NCAP period (2019). Surface observations show consistent reductions in PM2.5 and NO2 during the 2020 lockdown in seven cities across the Indian subcontinent, except Mumbai in Central India. The increase in relative humidity and the decrease in the planetary boundary layer also play an important role in influencing air quality during the 2020 lockdown. With the decrease in aerosols during the lockdown, net radiation fluxes show positive anomalies at the surface and negative anomalies at the top of the atmosphere over most parts of the Indian subcontinent. The results of this study could provide valuable information for policymakers in South Asia to adjust the scientific measures proposed in the NCAP for efficient air pollution mitigation.
Interferometric SAR Observation of Permafrost Status in the Northern Qinghai-Tibet Plateau by ALOS, ALOS-2 and Sentinel-1 between 2007 and 2021
Authors: Zou Lichuan , Wang Chao , Tang Yixian , Zhang Bo , Zhang Hong , Dong Longkai
Published: April 2022

With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world’s largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007–2009), 73 scenes of ALOS-2 data (2015–2020) and 284 scenes of Sentinel-1 data (2017–2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km2 in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from −20 to +20 mm/year during 2007–2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from −30 to 30 mm/year during 2015–2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV < −10 mm/year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters.
Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm
Authors: TayebiHaghighi Shahnaz , Koo Insoo
Published: April 2022

Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The proposed fuzzy Lyapunov-based computed ratio observer integrated with SVM has three main layers. In the preprocessing (first) layer, the resampled root mean square (RMS) signals are extracted from the original signals to the designed indirect observer. The second (observation) layer is the principal part with the proposed indirect fuzzy sensor-fault-classification technique. This layer has two sub-layers: signal modeling and estimation. The Gaussian autoregressive-Laguerre approach integrated with the fuzzy approach is designed for resampled RMS fuel-to-air-ratio normal signal modeling, while the subsequent sub-layer is used for resampled RMS fuel-to-air-ratio signal estimation using the proposed fuzzy Lyapunov-based computed ratio observer. The third layer, for residual signal generation and classification, is used to identify ICE sensor anomalies, where residual signals are generated by the difference between the original and estimated resampled RMS fuel-to-air-ratio signals. Moreover, SVM is suggested for residual signal classification. To test the effectiveness of the proposed method, the results are compared with two approaches: a Lyapunov-based computed ratio observer and a computed ratio observer. The results show that the accuracy of sensor anomaly classification by the proposed fuzzy Lyapunov-based computed ratio observer is 98.17%. Furthermore, the proposed scheme improves the accuracy of sensor fault classification by 8.37%, 2.17%, 6.17%, 4.57%, and 5.37% compared to other existing methods such as the computed ratio observer, the Lyapunov-based computed ratio observer, fuzzy feedback linearization observation, self-tuning fuzzy robust multi-integral observer, and Kalman filter technique, respectively.
Emotion Recognition from Physiological Channels Using Graph Neural Network
Authors: Wierciński Tomasz , Rock Mateusz , Zwierzycki Robert , Zawadzka Teresa , Zawadzki Michał
Published: April 2022

In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman’s model while the accuracy of the Circumplex model is similar to the baseline methods.
A Formal Validation Approach for XACML 3.0 Access Control Policy
Authors: Caserio Carmine , Lonetti Francesca , Marchetti Eda
Published: April 2022

Access control systems represent a security mechanism to regulate the access to system resources, and XACML is the standard language for specifying, storing and deploying access control policies. The verbosity and complexity of XACML syntax as well as the natural language semantics provided by the standard make the verification and testing of these policies difficult and error-prone. In the literature, analysis techniques and access control languages formalizations are provided for verifiability and testability purposes. This paper provides three contributions: it provides a comprehensive formal specification of XACML 3.0 policy elements; it leverages the existing policy coverage criteria to be suitable for XACML 3.0; and it introduces a new set of coverage criteria to better focus the testing activities on the peculiarities of XACML 3.0. The application of the proposed coverage criteria to a policy example is described, and hints for future research directions are discussed.
A Comprehensive Survey on RF Energy Harvesting: Applications and Performance Determinants
Authors: Sherazi Hafiz Husnain Raza , Zorbas Dimitrios , O’Flynn Brendan
Published: April 2022

There has been an explosion in research focused on Internet of Things (IoT) devices in recent years, with a broad range of use cases in different domains ranging from industrial automation to business analytics. Being battery-powered, these small devices are expected to last for extended periods (i.e., in some instances up to tens of years) to ensure network longevity and data streams with the required temporal and spatial granularity. It becomes even more critical when IoT devices are installed within a harsh environment where battery replacement/charging is both costly and labour intensive. Recent developments in the energy harvesting paradigm have significantly contributed towards mitigating this critical energy issue by incorporating the renewable energy potentially available within any environment in which a sensor network is deployed. Radio Frequency (RF) energy harvesting is one of the promising approaches being investigated in the research community to address this challenge, conducted by harvesting energy from the incident radio waves from both ambient and dedicated radio sources. A limited number of studies are available covering the state of the art related to specific research topics in this space, but there is a gap in the consolidation of domain knowledge associated with the factors influencing the performance of RF power harvesting systems. Moreover, a number of topics and research challenges affecting the performance of RF harvesting systems are still unreported, which deserve special attention. To this end, this article starts by providing an overview of the different application domains of RF power harvesting outlining their performance requirements and summarizing the RF power harvesting techniques with their associated power densities. It then comprehensively surveys the available literature on the horizons that affect the performance of RF energy harvesting, taking into account the evaluation metrics, power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting. Finally, it summarizes the available literature associated with RF powered networks and highlights the limitations, challenges, and future research directions by synthesizing the research efforts in the field of RF energy harvesting to progress research in this area.
Analytical Determination of Allergenic Fragrances in Indoor Air
Authors: Balducci Catia , Cerasa Marina , Avino Pasquale , Ceci Paolo , Bacaloni Alessandro , Garofalo Martina
Published: April 2022

Among all the emerging contaminants, fragrances are gaining more relevance for their proven allergenic and, in some cases, endocrine-disrupting properties. To date, little information exists on their concentration in the air. This study aims to fill this gap by developing a method for the determination of semivolatile fragrances in the indoor gaseous phase with sampling protocols usually adopted for the collection of atmospheric particulate matter (sampling time 24 h, flow rate 10 L min−1) and instrumental analysis by gas chromatography coupled with mass spectrometry. The method was developed on 66 analytes and tested at three concentration levels: 20 compounds showed analytical recoveries ≥72% with percentage standard deviations always better than 20%. For most compounds, negligible sampling breakthroughs were observed. The method was then applied to real samples collected in a coffee bar and in a private house. Considering the fragrances for which the method has shown good effectiveness, the highest concentrations were observed for carvone in the coffee bar (349 ng m−3) and camphor in the house (157 ng m−3). As concerns certain or suspected endocrine disruptors, lilyal and galaxolide were detected at both sites, α-isomethylionone was the second most concentrated compound in the house (63.2 ng m−3), musk xylene and musk ketone were present at lower concentration (≈ 1 or 2 ng m−3).
Exploring Determinants Shaping Recycling Behavior Using an Extended Theory of Planned Behavior Model: An Empirical Study of Households in Sabah, Malaysia
Authors: Juliana Natasha , Lada Suddin , Chekima Brahim , Abdul Adis Azaze-Azizi
Published: April 2022

Household waste and the exploitation of the environment are among the alarming and unsustainable consumption patterns that are harmful to the ecosystem. Hence, this study aims to determine the factors influencing recycling behavior by extending the theory of planned behavior (TPB) to an analysis of how recycling practices are fostered among citizens and how sustainable development goals (SDG) are upheld. The extended TPB model includes moral norms, convenience, and the cost of recycling. A total of 170 responses were received via the purposive sampling technique. The research model was analyzed using the partial least square structural equation modeling technique (PLS-SEM). Results show that subjective norms, moral norms, convenience, and the cost to recycle positively influenced recycling behavior. Environmental awareness was found to heavily influence attitudes, but attitudes were not found to translate into behavior. This research provides significant insight and a better understanding of recycling behavior and adds new momentum to the growing literature. The implications of these findings are further discussed.
Assessment of Multiple Intelligences in First-Year Engineering Students in Northeast Mexico
Authors: Chavarría-Garza Wendy Xiomara , Santos-Guevara Ayax , Morones-Ibarra José Rubén , Aquines-Gutiérrez Osvaldo
Published: April 2022

In sustainable education, it is important to analyze student diversity in order to create strategies that allow for the implementation of inclusive education based on the differences observed among students. To achieve this, a sample of 321 first-year engineering students (107 females and 214 males) at a private university in northeast Mexico was analyzed during the 2020 academic year. Students were classified according to their gender, engineering program, and the development of their multiple intelligences according to Howard Gardner theory of multiple intelligences. To verify the effect of gender and program factors on the development of multiple intelligences, Kruskal–Wallis tests were performed with α = 0.05. The analysis of the effects of gender identified significant differences in four intelligences: linguistic and interpersonal (for which the female students obtained higher mean scores) and mathematical and visual (for which the male students obtained higher mean scores). The analysis of the effects of the engineering program identified significant differences in five intelligences: mathematical, visual, and musical (for which civil engineering students obtained a higher mean score than the students in the other programs); kinesthetic (for which computer science students obtained a lower mean score than students in the other programs); and naturalistic (for which sustainability engineering students obtained a higher mean score than students in the other programs). These differences allowed us to observe the characteristics of the students and to develop more inclusive courses in order to make the teaching and learning process more optimal and sustainable.
Economic Viability of Energy Communities versus Distributed Prosumers
Authors: Petrichenko Lubov , Sauhats Antans , Diahovchenko Illia , Segeda Irina
Published: April 2022

As distribution grids are made to accommodate significant amounts of renewable energy resources, the power system evolves from a classical producer-consumer scheme to a new one that includes individual prosumers or energy communities. This article contributes to the exploration of the solution to the dilemma of whether to be a distributed prosumer or an energy community prosumer by comparing the profitability of these two business models. To achieve this goal, a high-resolution methodology is created for measuring economic performance via proposed indices under different development scenarios of renewable proliferation and various network configurations. The developed methodology considers today’s electricity billing and renewable support scheme net metering. The results indicate that, first, the energy community is a more profitable framework than the individual distributed prosumer: avoided costs for energy community are, on average, 20% higher than for the individual, resulting in a payback period of the energy community that is about two times shorter than for owners of rooftop installations. Such promising results should encourage ordinary consumers to be members of energy communities. Second, the energy losses in the power distribution system are slightly higher for the case of energy communities rather than individual prosumers, yet the difference is insignificant, about 0.2%. Third, regulatory barriers shall be removed to enable participation of Latvian prosumers and distribution system operators to the energy communities, as it will benefit all the stakeholders and facilitate economically efficient energy transition. The results of this study could be adopted by decision-makers, such as government agencies, companies, and solar and wind turbine owners.
Examining the Nexus between the Vs of Big Data and the Sustainable Challenges in the Textile Industry
Authors: Marquesone Rosangela de Fátima Pereira , Carvalho Tereza Cristina Melo de Brito
Published: April 2022

Despite its substantial economic power, the textile industry currently faces environmental and social challenges, such as continuous extraction of natural resources, extensive water consumption and contamination, greenhouse gas emissions, increasing generation of waste, and inadequate working conditions. In this context, the literature indicates that Big Data contributes to solving these challenges, enabling the extraction of insights and the improvement of decision-making processes from the volume, variety and velocity of data. However, there is still a gap in the literature regarding the directions of how Big Data must be applied by an organization to achieve this goal. Therefore, this article aims to explore this gap, presenting an analysis regarding the nexus between Big Data and sustainability challenges of the textile industry. To this end, a set of 12 textile industry challenges were extracted from an assessment of 108 case studies. These challenges were categorized and contextualized according to Big Data dimensions, and a discussion of the applicability of Big Data to solving each challenge was presented. From this approach, this article contributes to the textile industry by presenting a categorization of sustainable challenges of the industry and also by providing directions regarding the resolution of such challenges from a data-driven perspective.
A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
Authors: Franco Patricia , Martínez José M. , Kim Young-Chon , Ahmed Mohamed A.
Published: April 2022

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control plane representing the decision-making process. We survey the main technologies and techniques implemented in the recent years for each of the stages, reviewing and identifying the cutting-edge challenges that the research community are currently facing. For the Acquisition part, we define a metering device according to the IEC TS 63297:2021 Standard. We analyze the communication infrastructure as part of beyond 2030 communication era (5G and 6G), and discuss the Analytics stage as the cyber part of the CPS-based HEMS. To conclude, we present a case study in which, using real data collected in an experimental environment, we validate proposed architecture of HEMS in monitoring tasks. Results revealed an accuracy of 99.2% in appliance recognition compared with the state-of-the-art proposals.
Review of Design-Oriented Green Information Systems Research
Authors: Brendel Alfred Benedikt , Chasin Friedrich , Mirbabaie Milad , Riehle Dennis M. , Harnischmacher Christine
Published: April 2022

Green IS (GIS) research addresses environmental challenges brought on by climate change and the need to preserve the natural environment. Within this scope, design-oriented research, most notably within the Design Science Research (DSR) community, aims to provide solutions to these environmental challenges in the form of novel artifacts. The resulting IS solutions are valuable instruments for reducing emissions, increasing energy efficiency, and mitigating waste. Over the past 14 years, the IS research community was called upon multiple times to focus on designing solutions suitable for facilitating sustainability. However, it is unclear how these calls for action resonated within the design-oriented research community. Against this background, we analyzed the landscape of design-oriented GIS research by looking at 60 different GIS studies that have designed and evaluated an artifact. By analyzing these publications, we were able to make six observations. Based on these observations, we discuss how design-oriented GIS research can evolve to live up to the expectations of creating an immediate positive environmental impact.
Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies
Authors: Du Lijing , Li Xiaohuan , Gan Yuan , Leng Kaijun
Published: April 2022

To reduce distribution risk and improve the efficiency of medical materials delivery under major public health emergencies, this paper introduces a drone routing problem with time windows. A mixed-integer programming model is formulated considering contactless delivery, total travel time, and customer service time windows. Utilizing Dantzig–Wolfe decomposition, the proposed optimization model is converted into a path-based master problem and a pricing subproblem based on an elementary shortest path problem with resource constraints. We embed the pulse algorithm into a column generation framework to solve the proposed model. The effectiveness of the model and algorithm is verified by addressing different scales of Solomon datasets. A case study on COVID-19 illustrates the application of the proposed model and algorithm in practice. We also perform a sensitivity analysis on the drone capacity that may affect the total distribution time. The experimental results enrich the research related to vehicle routing problem models and algorithms under major public health emergencies and provide optimized relief distribution solutions for decision-makers of emergency logistics.