bluuebunny/crossref_metadata_2025 huggingface dataset:
Biocompatible Calcium Ion-Doped Magnesium Ferrite Nanoparticles as a New Family of Photothermal Therapeutic Materials for Cancer Treatment
Authors: Manivasagan Panchanathan , Ashokkumar Sekar , Manohar Ala , Joe Ara , Han Hyo-Won , Seo Sun-Hwa , Thambi Thavasyappan , Duong Hai-Sang , Kaushik Nagendra Kumar , Kim Ki Hyeon , Choi Eun Ha , Jang Eue-Soon
Published: May 2023

Novel biocompatible and efficient photothermal (PT) therapeutic materials for cancer treatment have recently garnered significant attention, owing to their effective ablation of cancer cells, minimal invasiveness, quick recovery, and minimal damage to healthy cells. In this study, we designed and developed calcium ion-doped magnesium ferrite nanoparticles (Ca2+-doped MgFe2O4 NPs) as novel and effective PT therapeutic materials for cancer treatment, owing to their good biocompatibility, biosafety, high near-infrared (NIR) absorption, easy localization, short treatment period, remote controllability, high efficiency, and high specificity. The studied Ca2+-doped MgFe2O4 NPs exhibited a uniform spherical morphology with particle sizes of 14.24 ± 1.32 nm and a strong PT conversion efficiency (30.12%), making them promising for cancer photothermal therapy (PTT). In vitro experiments showed that Ca2+-doped MgFe2O4 NPs had no significant cytotoxic effects on non-laser-irradiated MDA-MB-231 cells, confirming that Ca2+-doped MgFe2O4 NPs exhibited high biocompatibility. More interestingly, Ca2+-doped MgFe2O4 NPs exhibited superior cytotoxicity to laser-irradiated MDA-MB-231 cells, inducing significant cell death. Our study proposes novel, safe, high-efficiency, and biocompatible PT therapeutics for treating cancers, opening new vistas for the future development of cancer PTT.
Pharmacotherapeutic Potential of Aloe secundiflora against Colorectal Cancer Growth and Proliferation
Authors: Macharia John M. , Ngure Veronica , Emődy Barnabás , Király Bence , Káposztás Zsolt , Rozmann Nóra , Erdélyi Attila , Raposa Bence
Published: May 2023

Aloe species are widespread and diverse in African ecosystems, and this commonly correlates to their habitual use as reservoirs of herbal medicine. The side effects associated with chemotherapy and the development of antimicrobial resistance to empirically used antimicrobial drugs are substantial, paving the way for novel phytotherapeutic approaches. This comprehensive study aimed to evaluate and present Aloe secundiflora (A. secundiflora) as a compelling alternative with potential benefits in colorectal cancer (CRC) treatment. Important databases were systematically searched for relevant literature, and out of a large collection of 6421 titles and abstracts, only 68 full-text articles met the inclusion criteria. A. secundiflora possesses an abundant presence of bioactive phytoconstituents in the leaves and roots, including anthraquinones, naphthoquinones, phenols, alkaloids, saponins, tannins, and flavonoids, among others. These metabolites have proven diverse efficacy in inhibiting cancer growth. The presence of innumerable biomolecules in A. secundiflora signifies the beneficial effects of incorporating the plant as a potential anti-CRC agent. Nonetheless, we recommend further research to determine the optimal concentrations necessary to elicit beneficial effects in the management of CRC. Furthermore, they should be investigated as potential raw ingredients for making conventional medications.
Photonics Large-Survey Telescope Internal Motion Metrology System
Authors: An Qichang , Zhang Hanfu , Wu Xiaoxia , Wang Jianli , Chen Tao , Li Hongwen
Published: May 2023

Large survey telescopes are vital for mapping dark energy and dark matter in the deep universe. This study presents a fiber-linked internal motion metrology system that aligns the mirrors and large lenses in the telescopes to enhance alignment accuracy by improving the image quality at a lower weight, volume, power, and cost. The internal motion system comprises a photonic laser beam projector capable of projecting multiple Gaussian beams onto the detector of the telescope. The specific spatial frequency aberration component is determined by combining Gaussian beam location and the geometry model of the telescope. Furthermore, integrating the proposed system with the curvature-sensing wavefront system enables more precise alignment and camera sensing. In the experimental tests, the location precision was within 10 μm, and the rotation precision improved to 5 arcsecs, fulfilling the alignment and motion monitoring requirements of large survey telescopes. The results of this study can be used as a reference to improve the performance of closed-loop bandwidth systems and active camera optics.
Calcination-Enhanced Laser-Induced Damage Threshold of 3D Micro-Optics Made with Laser Multi-Photon Lithography
Authors: Gailevicius Darius , Zvirblis Rokas , Galvanauskas Karolis , Bataviciute Gintare , Malinauskas Mangirdas
Published: May 2023

Laser Direct Writing (LDW), also known as 3D multi-photon laser lithography of resins, is a promising technique for fabricating complex free-form elements, including micro-optical functional components. Regular organic or hybrid (organic–inorganic) resins are often used, with the latter exhibiting better optical characteristics, as well as having the option to be heat-treated into inorganic glass-like structures particularly useful for resilient micro-optics. This work is a continuation of our SZ2080™ calcination development of micro-optics, specifically studying the Laser-Induced Damage Threshold (LIDT). Such sol–gel-derived glass 3D micro-structures, particularly those that undergo heat treatment, have not been well-characterized in this respect. In this pilot study, we investigated the LIDT using the Series-on-One (S-on-1) protocol of functional micro-lenses produced via LDW and subsequently calcinated. Our results demonstrate that the LIDT can be significantly increased, even multiple times, by this approach, thus enhancing the resilience and usefulness of these free-form micro-optics. This work represents the first investigation in terms of LIDT into the impact of calcination on LDW-produced, sol–gel-derived glass micro-structures and provides important insights for the development of robust micro-optical devices.
Optical Chaos in Saturated Nonlinear Media
Authors: Li Fuqiang , Chen Ziyang , Song Jie , Li Meng , Lou Cibo
Published: May 2023

In the study of the evolution of Gaussian beam in saturated nonlinear media, it is found that the probability of optical rogue waves changes with the change of nonlinearity. The light intensity distribution on the exit surface of nonlinear medium can be characterized by scintillation index, and the change of rogue wave corresponds to the evolution of scintillation index. The rogue wave probability shows a complex trend with the evolution of nonlinearity. The Lyapunov exponent and power spectrum method are used to determine that the probability of rogue wave is chaotic with nonlinear evolution.
Enhancing the Performance of Triboelectric Generator: A Novel Approach Using Solid–Liquid Interface-Treated Foam and Metal Contacts
Authors: Nguyen Quang Tan , Vu Duy Linh , Le Chau Duy , Ahn Kyoung Kwan
Published: May 2023

This work introduces a novel approach for enhancing the performance of a triboelectric generator (TEG) by using a solid–liquid interface-treated foam (SLITF) as its active layer, combined with two metal contacts of different work functions. SLITF is made by absorbing water into a cellulose foam, which enables charges generated by friction energy during the sliding motion to be separated and transferred through the conductive path formed by the hydrogen-bonded network of water molecules. Unlike traditional TEGs, the SLITF-TEG demonstrates an impressive current density of 3.57 A/m2 and can harvest electric power up to 0.174 W/m2 with an induced voltage of approximately 0.55 V. The device generates a direct current in the external circuit, eliminating the limitations of low current density and alternating current found in traditional TEGs. By connecting six-unit cells of SLITF-TEG in series and parallel, the peak voltage and current can be increased up to 3.2 V and 12.5 mA, respectively. Furthermore, the SLITF-TEG has the potential to serve as a self-powered vibration sensor with high accuracy (R2 = 0.99). The findings demonstrate the significant potential of the SLITF-TEG approach for efficiently harvesting low-frequency mechanical energy from the natural environment, with broad implications for a range of applications.
Copolymer-Green-Synthesized Copper Oxide Nanoparticles Enhance Folate-Targeting in Cervical Cancer Cells In Vitro
Authors: Jagaran Keelan , Singh Moganavelli
Published: May 2023

Cervical cancer is fast becoming a global health crisis, accounting for most female deaths in low- and middle-income countries. It is the fourth most frequent cancer affecting women, and due to its complexity, conventional treatment options are limited. Nanomedicine has found a niche in gene therapy, with inorganic nanoparticles becoming attractive tools for gene delivery strategies. Of the many metallic nanoparticles (NPs) available, copper oxide NPs (CuONPs) have been the least investigated in gene delivery. In this study, CuONPs were biologically synthesized using Melia azedarach leaf extract, functionalized with chitosan and polyethylene glycol (PEG), and conjugated to the targeting ligand folate. A peak at 568 nm from UV-visible spectroscopy and the characteristic bands for the functional groups using Fourier-transform infrared (FTIR) spectroscopy confirmed the successful synthesis and modification of the CuONPs. Spherical NPs within the nanometer range were evident from transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The NPs portrayed exceptional binding and protection of the reporter gene, pCMV-Luc-DNA. In vitro cytotoxicity studies revealed cell viability >70% in human embryonic kidney (HEK293), breast adenocarcinoma (MCF-7), and cervical cancer (HeLa) cells, with significant transgene expression, obtained using the luciferase reporter gene assay. Overall, these NPs showed favorable properties and efficient gene delivery, suggesting their potential role in gene therapy.
The Tailored Material Removal Distribution on Polyimide Membrane Can Be Obtained by Introducing Additional Electrodes
Authors: Wu Xiang , Fan Bin , Xin Qiang , Gao Guohan , Jiao Peiqi , Shao Junming , Luo Qian , Liang Zhaoyu
Published: May 2023

Reactive ion etching (RIE) is a promising material removal method for processing membrane diffractive optical elements and fabrication of meter-scale aperture optical substrates because of its high-efficiency parallel processing and low surface damage. However, the non-uniformity of the etching rate in the existing RIE technology will obviously reduce the machining accuracy of diffractive elements, deteriorate the diffraction efficiency and weaken the surface convergence rate of optical substrates. In the etching process of the polyimide (PI) membrane, additional electrodes were introduced for the first time to achieve the modulation of the plasma sheath properties on the same spatial surface, thus changing the etch rate distribution. Using the additional electrode, a periodic profile structure similar to the additional electrode was successfully processed on the surface of a 200-mm diameter PI membrane substrate by a single etching iteration. By combining etching experiments with plasma discharge simulations, it is demonstrated that additional electrodes can affect the material removal distribution, and the reasons for this are analyzed and discussed. This work demonstrates the feasibility of etching rate distribution modulation based on additional electrodes, and lays a foundation for realizing tailored material removal distribution and improving etching uniformity in the future.
Ageing-Oriented Prosthetic Treatment Plan: A Case Report
Authors: Ortensi Luca , Vitali Tommaso , Mirra Raffaele , Ortensi Marco , Borromeo Carlo
Published: May 2023

Today, older adults are routinely classified into different categories based on their degrees of independence. However, due to the extreme heterogeneity of this group, and the fact that ageing is a progressive process, older adults may require customized prosthetic treatment. Older people, during ageing, may quickly pass into a frail condition, with loss of independence and of the manual skills necessary for proper hygiene maintenance. With this in mind, complete arch rehabilitations can make an important contribution to a patient’s quality of life. New treatment strategies are needed which take into account the priorities of ageing. The aim of this case report is to show an adaptable prosthetic treatment protocol that is useful for older adults. This protocol provides individual satisfaction and also aids forecasting of the patient’s future functional and maintenance needs.
The Challenge of Muhammad Iqbal’s Philosophy of Khudi to Ibn ‘Arabi’s Metaphysical Anthropology
Authors: De Diego González Antonio
Published: May 2023

The period between the publication of Asrār-i Khūdī (Secrets of the Self) in 1915 and The Reconstruction of Religious Thought in Islam in 1930 marked the consolidation of the philosophy of khūdī (self) from the perspective of the Indian philosopher Muhammad Iqbal. A philosophical project for the contemporary Islamic world that sought to overcome, from the acceptance of science and few elements of Western philosophy, the limitations of the Islamic tradition and, above all, of Sufism, which the author labels as pantheism. Among the deep dialogues he maintains with Islamic tradition, Iqbal carried out a very special one with Muḥyī l-Dīn Ibn ʻArabī (1165–1240), who was one of the most notorious mystics and philosophers of Islam. A metahistorical dialogue, in the form of a critique, that invites us to see the convergences and divergences in metaphysical and anthropological aspects of both authors.
Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates
Authors: Colin Jérôme , Hagolle Olivier , Landier Lucas , Coustance Sophie , Kettig Peter , Meygret Aimé , Osman Julien , Vermote Eric
Published: May 2023

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products.
Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China
Authors: Cai Peihua , Chen Guanzhou , Yang Haobo , Li Xianwei , Zhu Kun , Wang Tong , Liao Puyun , Han Mengdi , Gong Yuanfu , Wang Qing , Zhang Xiaodong
Published: May 2023

In recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to improve the accuracy of PWD detection at the single-tree level. This study introduces a novel framework for PWD detection that combines high-resolution RGB drone imagery with free-access Sentinel-2 satellite multi-spectral imagery. The proposed approach includes an PWD-infected tree detection model named YOLOv5-PWD and an effective data augmentation method. To evaluate the proposed framework, we collected data and created a dataset in Xianning City, China, consisting of object detection samples of infected trees at middle and late stages of PWD. Experimental results indicate that the YOLOv5-PWD detection model achieved 1.2% higher mAP compared to the original YOLOv5 model and a further improvement of 1.9% mAP was observed after applying our dataset augmentation method, which demonstrates the effectiveness and potential of the proposed framework for PWD detection.
Landsat Satellite Image-Derived Area Evolution and the Driving Factors Affecting Hulun Lake from 1986 to 2020
Authors: Song Wei , A Yinglan , Wang Yuntao , Xue Baolin
Published: May 2023

The area fluctuation of lakes directly affects the stability of the surrounding ecological environment. Research on the area evolution of lakes and the driving factors affecting it plays an important role in sustainable water resource management. In this study, Hulun Lake, located in the Hulunbuir grassland, was taken as the research object. Based on remote sensing images of the Hulun Lake area from 1986 to 2020, MNDWI interpretation was used to obtain the change law of lake surface area over a long time frame. Combined with natural factors and anthropogenic factors, Pearson correlation analysis and principal component analysis were used to analyze the driving force. The results showed that (1) in the past 35 years, the water surface area of Hulun Lake has decreased significantly. The dynamic change in water area could be divided into four stages. The areas with dramatic changes in water area are distributed mainly in the northeast and south of Hulun Lake. (2) In terms of natural factors, the meteorological factors based on evaporation and relative humidity, the runoff of rivers entering the lake, and the vegetation with medium-high coverage and medium-low coverage had significant effects. In terms of anthropogenic factors, the population had the most significant impact. The artificial water diversion project had different degrees of influence on the response of the Hulun Lake area change to natural factors. (3) Anthropogenic factors were the main driving force causing the rapid change in the Hulun Lake area from 2000 to 2016, explaining 48% of the change in the Hulun Lake area. These research results can provide a scientific basis for the development and utilization of water resources and sustainable development in the Hulun Lake area.
Halide Perovskites Films for Ionizing Radiation Detection: An Overview of Novel Solid-State Devices
Authors: Falsini Naomi , Ubaldini Alberto , Cicconi Flavio , Rizzo Antonietta , Vinattieri Anna , Bruzzi Mara
Published: May 2023

Halide perovskites are a novel class of semiconductors that have attracted great interest in recent decades due to their peculiar properties of interest for optoelectronics. In fact, their use ranges from the field of sensors and light emitters to ionizing radiation detectors. Since 2015, ionizing radiation detectors exploiting perovskite films as active media have been developed. Recently, it has also been demonstrated that such devices can be suitable for medical and diagnostic applications. This review collects most of the recent and innovative publications regarding solid-state devices for the detection of X-rays, neutrons, and protons based on perovskite thin and thick films in order to show that this type of material can be used to design a new generation of devices and sensors. Thin and thick films of halide perovskites are indeed excellent candidates for low-cost and large-area device applications, where the film morphology allows the implementation on flexible devices, which is a cutting-edge topic in the sensor sector.
Methodological Considerations for Assessing Automatic Brightness Control in Endoscopy: Experimental Study
Authors: Ravichandran Nishitha , Sreelatha Premkumar Preejith , Sivaprakasam Mohanasankar
Published: May 2023

Endoscopy is a critical application that requires adaptable illumination to adjust to varying imaging conditions. Automatic brightness control (ABC) algorithms ensure optimal brightness throughout the image with rapid but smooth response and render the true colours of the biological tissue under examination. To achieve good image quality, high-quality ABC algorithms are necessary. In this study, we propose a three-assessment method approach for objectively evaluating ABC algorithms based on (1) image brightness and its homogeneity, (2) controller response and response time, and (3) colour rendition. We conducted an experimental study to assess the effectiveness of ABC algorithms in one commercial and two developmental endoscopy systems using the proposed methods. The results showed that the commercial system achieved good, homogeneous brightness within 0.4 s, and its damping ratio was 0.597, indicating a stable system, but its colour rendition was suboptimal. The developmental systems had control parameter values that resulted in either a sluggish response (over 1 s) or a fast (about 0.3 ms) but unstable response with damping ratios above 1, causing flickers. Our findings indicate that the interdependency among the proposed methods can establish tradeoffs in the overall ABC performance better than single-parameter approaches. The study establishes that comprehensive assessments using the proposed methods can contribute to designing new ABC algorithms and optimising already implemented ones for efficient performance in endoscopy systems.
Confocal Laser Scanning Microscope Imaging of Custom-Made Multi-Cylinder Phantoms: Theory and Experiment
Authors: Hevisov David , Glöckler Felix , Ott Felix , Kienle Alwin
Published: May 2023

In this work, the image formation in a confocal laser scanning microscope (CLSM) is investigated for custom-made multi-cylinder phantoms. The cylinder structures were fabricated using 3D direct laser writing and consist of parallel cylinders with radii of 5 and 10 μm for the respective multi-cylinder phantom, with overall dimensions of about 200×200×200 μm3. Measurements were performed for different refractive index differences and by varying other parameters of the measurement system, such as pinhole size or numerical aperture (NA). For theoretical comparison, the confocal setup was implemented in an in-house developed tetrahedron-based and GPU-accelerated Monte Carlo (MC) software. The simulation results for a cylindrical single scatterer were first compared with the analytical solution of Maxwell’s equations in two dimensions for prior validation. Subsequently, the more complex multi-cylinder structures were simulated using the MC software and compared with the experimental results. For the largest refractive index difference, i.e., air as the surrounding medium, the simulated and measured data show a high degree of agreement, with all the key features of the CLSM image being reproduced by the simulation. Even with a significant reduction in the refractive index difference by the use of immersion oil to values as low as 0.005, a good agreement between simulation and measurement was observed, particularly with respect to the increase in penetration depth.
C2RL: Convolutional-Contrastive Learning for Reinforcement Learning Based on Self-Pretraining for Strong Augmentation
Authors: Park Sanghoon , Kim Jihun , Jeong Han-You , Kim Tae-Kyoung , Yoo Jinwoo
Published: May 2023

Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a self-supervised learning framework with data augmentation in the reinforcement learning architecture can promote generalization to a certain extent. However, excessively large changes in the input images may disturb reinforcement learning. Therefore, we propose a contrastive learning method that can help manage the trade-off relationship between the performance of reinforcement learning and auxiliary tasks against the data augmentation strength. In this framework, strong augmentation does not disturb reinforcement learning and instead maximizes the auxiliary effect for generalization. Results of experiments on the DeepMind Control suite demonstrate that the proposed method effectively uses strong data augmentation and achieves a higher generalization than the existing methods.
RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults
Authors: Uwineza Jean-Bernard , Farrell Jay A.
Published: May 2023

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement. For any fault scenario affecting h measurements, the eigenvalue problem that defines the worst-case fault is expressed and analyzed in terms of these orthogonal subspaces, which enables further analysis. For h>(m−n), where n is the number of estimated variables, it is known that there always exist faults that are undetectable from the residual vector, yielding an infinite value for the failure mode slope. This article uses the range space and its complement to explain: (1) why, for fixed h and n, the failure mode slope decreases with m; (2) why, for a fixed n and m, the failure mode slope increases toward infinity as h increases; (3) why a failure mode slope can become infinite for h≤(m−n). A set of examples demonstrate the results of the paper.
An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection
Authors: Toma Tabassum Islam , Choi Sunwoong
Published: May 2023

Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.
Design of a Narrow Band Filter Based on a Photonic Crystal Cavity for CO2 Sensing Application
Authors: Jannesari Reyhaneh , Grille Thomas , Stocker Gerald , Jakoby Bernhard
Published: May 2023

This paper investigates the use of a miniaturized filter based on a triangular lattice of holes in a photonic crystal (PhC) slab. The plane wave expansion method (PWE) and finite-difference time-domain (FDTD) techniques were utilized to analyze the dispersion and transmission spectrum, as well as the quality factor and free spectral range (FSR) of the filter. A 3D simulation has demonstrated that for the designed filter, an FSR of more than 550 nm and a quality factor of 873 can be attained by adiabatically coupling light from a slab waveguide into a PhC waveguide. This work designs a filter structure that is implemented into the waveguide and is suitable for a fully integrated sensor. The small size of the device provides a strong potential for the realization of large arrays of independent filters on a single chip. The fully integrated character of this filter has further advantages such as reducing power loss in coupling light from sources to filters and also from filters to waveguides. The ease of fabrication is another benefit of completely integrating the filter.
Natech Accidents Triggered by Heat Waves
Authors: Ricci Federica , Casson Moreno Valeria , Cozzani Valerio
Published: May 2023

Natech accidents have an increasing relevance due to the growing number of such events and to their severe consequences. Climate change and global warming are intensifying the occurrence and the magnitude of climate-related natural events, further increasing the risk of cascading sequences triggered by natural disasters impacting industrial installations. The present study focuses on Natech triggered by heat waves. The features of this specific category of Natech events were investigated by past accident analysis, collecting an extended dataset of past events. The dataset analysis allowed the identification of the key factors that characterize these accident scenarios, such as the direct causes, the technological scenario that occurred, the substance categories, and the equipment items more frequently involved. The main direct cause of accidents resulted in an internal pressure increase, exceeding equipment design limits. Fire scenarios represent the most important category of technological scenarios that occurred. Besides equipment items handling liquid and gaseous hydrocarbons, waste storage and processing systems also resulted frequently in accidents, due to the self-decomposition and self-ignition phenomena. The analysis of past accidents also allowed identifying some lessons learned, useful to identify specific actions aimed at preventing and/or mitigating the possible occurrence of these accident scenarios.
Dialogue Act Classification via Transfer Learning for Automated Labeling of Interviewee Responses in Virtual Reality Job Interview Training Platforms for Autistic Individuals
Authors: Adiani Deeksha , Colopietro Kelley , Wade Joshua , Migovich Miroslava , Vogus Timothy J. , Sarkar Nilanjan
Published: May 2023

Computer-based job interview training, including virtual reality (VR) simulations, have gained popularity in recent years to support and aid autistic individuals, who face significant challenges and barriers in finding and maintaining employment. Although popular, these training systems often fail to resemble the complexity and dynamism of the employment interview, as the dialogue management for the virtual conversation agent either relies on choosing from a menu of prespecified answers, or dialogue processing is based on keyword extraction from the transcribed speech of the interviewee, which depends on the interview script. We address this limitation through automated dialogue act classification via transfer learning. This allows for recognizing intent from user speech, independent of the domain of the interview. We also redress the lack of training data for a domain general job interview dialogue act classifier by providing an original dataset with responses to interview questions within a virtual job interview platform from 22 autistic participants. Participants’ responses to a customized interview script were transcribed to text and annotated according to a custom 13-class dialogue act scheme. The best classifier was a fine-tuned bidirectional encoder representations from transformers (BERT) model, with an f1-score of 87%.
Burnout through the Lenses of Equity/Equality, Diversity and Inclusion and Disabled People: A Scoping Review
Authors: Wolbring Gregor , Lillywhite Aspen
Published: May 2023

Burnout is a problem within the workplace including in higher education, the activity of activism, and in reaction to experiencing systemic discrimination in daily life. Disabled people face problems in all of these areas and therefore are in danger of experiencing “disability burnout”/”disablism burnout”. Equity/equality, diversity, and inclusion” (EDI) linked actions are employed to improve the workplace, especially for marginalized groups including disabled people. How burnout is discussed and what burnout data is generated in the academic literature in relation to EDI and disabled people influences burnout policies, education, and research related to EDI and to disabled people. Therefore, we performed a scoping review study of academic abstracts employing SCOPUS, the 70 databases of EBSCO-HOST and Web of Science with the aim to obtain a better understanding of the academic coverage of burnout concerning disabled people and EDI. We found only 14 relevant abstracts when searching for 12 EDI phrases and five EDI policy frameworks. Within the 764 abstracts covering burnout and different disability terms, a biased coverage around disabled people was evident with disabled people being mostly mentioned as the cause of burnout experienced by others. Only 30 abstracts covered the burnout of disabled people, with eight using the term “autistic burnout”. Disabled activists’ burnout was not covered. No abstract contained the phrase “disability burnout”, but seven relevant hits were obtained using full-text searches of Google Scholar. Our findings suggest that important data is missing to guide evidence-based decision making around burnout and EDI and burnout of disabled people.
Assessment of Economic Efficiency and Its Determents for Mixed Crop Livestock Production under Dryland Agriculture System in the Western Zone of Tamil Nadu, India
Authors: Balaji G. Arun , Geethalakshmi Vellingiri , Senthil Alagarsamy , Prahadeeswaran Mockaisamy , Iswarya Sivakumarasamy , Rajavel Marimuthu , Bhuvaneswari Kulanthaivel , Natarajan Balakrishnan , Senthilraja Kandasamy , Gowtham Ramasamy , Priyanka Shanmugavel
Published: May 2023

In order to accomplish economic, social, and environmental objectives, farm diversification is a significant feature in India’s agricultural systems and rural development. It is crucial to examine factors impacting farm productivity, such as socio, structural, and Circular Economy (CE) factors, in order to ensure the sustainability of varied farms. A decision to follow a farm circularity approach is required to increase agricultural production, return on investment, and achieve sustainability, so the current study was designed to move forward in identifying farm efficiency and their influencing drivers in production systems for the successful execution of circularity in future farms. The objective of this paper is to find the existing operational efficiency of farms, variables that impact a farm’s efficiency, the disparities in farm efficiency, and variables dominating farm efficiency. The efficiency behaviours of Indian farms operating in 2020–2022 were studied and regressed against the factors influencing farm performance. A two-step technique and survey data were employed. To assess variations in the relevance of drivers, an input-oriented Data Envelopment Analysis was employed; in the second stage, efficiency ratings were used as a dependent variable in a Tobit regression analysis. Explanatory variables for enhancing farm performance included social, structural, and Circular Economy (CE) drivers. The age of the farmer, total farm area, and dryland area as socio-structural variables, and reduce, recycle, and redesign as CE variables, have proven to affect the farm’s efficiencies significantly. Managerial implications in terms of technical, allocative, cost efficiency, and their drivers have been drawn from study findings. CE has been confirmed to be the strategy over which the farmer has complete control and has been found to have a more significant impact on farm efficiency. Social structural determents are second in terms of their impact. Farmers in the region have cushy access to inputs; however, they are not utilized to their maximum effect. Therefore, the study indicates that inefficient dryland farmers could increase their production by concentrating on a circular economy strategy.
Sustainable Cruise Tourism: Systematic Literature Review and Future Research Areas
Authors: Kulkov Ignat , Hellström Magnus , Tsvetkova Anastasia , Malmberg Johan
Published: May 2023

The cruise shipping industry has existed for centuries. However, sustainability is a relatively new trend that could make a big difference and someday impact the industry. A growing body of research on sustainable cruise tourism includes studying changes among industry stakeholders, internal and external processes, and more. However, until now, there have been no comprehensive and systematic reviews of the academic literature on this topic and proposals for future research areas. The sample for our research consists of 56 articles structured into the following themes and subthemes: (1) corporate social responsibility (public interaction and emissions management); (2) territory management (collaboration with stakeholders and infrastructure development); (3) training in sustainable behavior (passengers, ship personnel, and other stakeholders). These themes fully explore the various use cases for sustainable cruises, forming a conceptual framework for understanding trends for the industry’s sustainable development. We direct the attention of other researchers to the following areas for further research: GHG emissions of cruising; biodiversity impacts; quantitative understanding of the target audience and their participation in sustainability financing; in-depth understanding of the reasons for cooperation between stakeholders; identifying the true motivation for participation in sustainable development; the long-term trends; and how the shipping industry is adapting to the effects of the COVID-19 pandemic.
Potentials of Biomass Waste Valorization: Case of South America
Authors: Sampaolesi Sofía , Briand Laura Estefanía , Saparrat Mario Carlos Nazareno , Toledo María Victoria
Published: May 2023

Various surveys carried out by the government and scientific projects on the availability of direct and indirect waste biomass in South America have reported that Brazil and Colombia produce 97% of the total waste biomass in the region, directly obtained from their extensive plantations of sugarcane. In addition, Argentina generates 45% of the total indirect biomass, followed by Brazil, Peru, Chile and Paraguay. The major source of those residues comprises sub-products of the wood (43%) and alimentary industries (20% from sugarcane and 11% from tea). Meaningful quantities of agricultural waste originate from soybean and corn, as the continent produces 50% and 11% of the global harvest of these crops. The higher content of cellulose in eucalyptus and willow waste (49%), among woody residues, along with their low lignin levels, makes them more suitable for delignification and exploitation as a biorefinery feedstock. Regarding the remains of agroindustrial activities, sugarcane bagasse (53%), corn cob (40%), wheat straw (49%) and banana hulls (38%) are the remarkable ones. In this context, the latest research concerning the use of commercial enzymatic cocktails for cellulose and hemicellulose deconstruction and the consequent feedstock hydrolysis is reviewed. In addition, we introduce the potential applications of cellulases isolated from native Latin American microbiota explored by South American research groups.
A Study on the Relationship between Tourists’ Experience and Experience Value and Satisfaction in Taiwan’s Farmer’s Markets
Authors: Tseng Shih-Chi , Wang Dan , Shen Ching-Cheng , Chung Hsiao-Ping
Published: May 2023

This study explores the effects of four types of visitor experiences on experience value and satisfaction. Farmer’s markets indirectly have an educational function through disseminating food and agricultural knowledge. They significantly shorten farm product distribution channels, providing more economic benefits for participating producers. After the pre-test, a questionnaire was officially distributed using convenience sampling and analyzed in SPSS. The study primarily found that the entertainment factor has the most significant impact on the experience value and satisfaction of the visitor experience. Moreover, through quantitative analysis, this research offers practical recommendations to enhance consumer experience value and satisfaction for farmer’s market stallholders and managers.
Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models
Authors: Almasoudi Fahad M.
Published: May 2023

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.
Evaluated Post-Disaster and Emergency Assembly Areas Using Multi-Criteria Decision-Making Techniques: A Case Study of Turkey
Authors: Atmaca Ediz , Aktaş Esra , Öztürk Hafsa Nur
Published: May 2023

Disaster is a general term for events that result in physical, economic, and social losses for people and occur when insufficient resources exist. Natural disasters, particularly earthquakes, are frequent occurrences worldwide. Although plans are made for both pre-and post-disaster periods, the painful experience of the recent major earthquake on the Eastern Anatolian Fault Line in Turkey has highlighted the inadequacy of these plans in terms of feasibility. Preventing future disasters is possible by implementing a good disaster management plan. Identifying emergency assembly areas after a disaster is one of the most critical issues in disaster management. This study used four multi-criteria decision-making techniques—AHP, TOPSIS, COPRAS, and BORDA—to evaluate the selection and suitability of emergency assembly areas for the Gölbaşı district of Ankara, Turkey. The AHP method was used to weight criteria, which were then applied in various decision-making approaches. Finally, utilizing the BORDA method, the alternative ranking derived from the AHP, TOPSIS, and COPRAS procedures was rated as more effective. According to the findings, the Sacrificial Slaughter Area, Green Area, Şehir Park, Muhsin Yazıcıoğlu Sports Complex, and the Türkiye Muhasebeciler Association (İncek and Taşpınar) are determined the five most effective emergency assembly areas in the Gölbaşı district.