Search results for: threshold detecting

Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Vahab Behmanesh

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One of the topics that has always attracted the attention of sports medicine and sports science experts is the positive or negative effect of sports activities on the functioning of the body's immune system. In the present research, a course of aerobic running with spirulina consumption has been studied on the maximum oxygen consumption and the performance of some indicators of the immune system of men who have trained after one session of physical activity. In this research, 50 trained students were studied randomly in four groups, spirulina- aerobic, spirulina, placebo- aerobic, and control. In order to test the research hypotheses, one-way statistical method of variance (ANOVA) was used considering the significance level of a=0.005 and post hoc test (LSD). A blood sample was taken from the participants in the first stage test in fasting and resting state immediately after Bruce's maximal test on the treadmill until complete relaxation was reached, and their Vo2max value was determined through the aforementioned test. The subjects of the spirulina-aerobic running and placebo-aerobic running groups took three 500 mg spirulina and 500 mg placebo pills a day for six weeks and ran three times a week for 30 minutes at the threshold of aerobic stimulation. The spirulina and placebo groups also consumed spirulina and placebo tablets in the above method for six weeks. Then they did the same first stage test as the second stage test. Blood samples were taken to measure the number of CD4+, CD8+, NK, and the ratio of CD4+ to CD8+ on four occasions before and after the first and second stage tests. The analysis of the findings showed that: aerobic running and spirulina supplement alone increase Vo2max. Aerobic running and consumption of spirulina increases Vo2max more than other groups (P<0.05), +CD4 and hemoglobin of the spirulina-aerobic running group was significantly different from other groups (P=0.002), +CD4 of the groups together There was no significant difference, NK increased in all groups, the ratio of CD4+ to CD8+ between the groups had a significant difference (P=0.002), the ratio of CD4+ to CD8+ in the spirulina- aerobic group was lower than the spirulina and placebo groups. All in all, it can be concluded that the supplement of spirulina and aerobic exercise may increase Vo2max and improve safety indicators.

Keywords: spirulina (Q2), hemoglobin (Q3), aerobic exercise (Q3), residual activity (Q2), CD4+ to CD8+ ratio (Q3)

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Authors: S. Serhal, Y. Melinge, D. Rangeard, F. Hage Chehadeh

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Filling of estuaries is an international problem that can cause economic and environmental damage. This work aims the study of the rheological structuring mechanisms of natural sedimentary liquid-solid mixture in estuaries in order to better understand their filling. The estuary of the Rance river, located in Brittany, France is particularly targeted by the study. The aim is to provide answers on the rheological behavior of natural sediments by detecting structural factors influencing the rheological parameters. So we can better understand the fillings estuarine areas and especially consider sustainable solutions of ‘cleansing’ of these areas. The sediments were collected from the trap of Lyvet in Rance estuary. This trap was created by the association COEUR (Comité Opérationnel des Elus et Usagers de la Rance) in 1996 in order to facilitate the cleansing of the estuary. It creates a privileged area for the deposition of sediments and consequently makes the cleansing of the estuary easier. We began our work with a preliminary study to establish the trend of the rheological behavior of the suspensions and to specify the dormant phase which precedes the beginning of the biochemical reactivity of the suspensions. Then we highlight the visco-plastic character at younger age using the Kinexus rheometer, plate-plate geometry. This rheological behavior of suspensions is represented by the Bingham model using dynamic yield stress and viscosity which can be a function of volume fraction, granular extent, and chemical reactivity. The evolution of the viscosity as a function of the solid volume fraction is modeled by the Krieger-Dougherty model. On the other hand, the analysis of the dynamic yield stress showed a fairly functional link with the solid volume fraction.

Keywords: estuaries, rheological behavior, sediments, Kinexus rheometer, Bingham model, viscosity, yield stress

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Authors: Mathewos Temesgen, Lemi Geleta

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Lake Koka is one of the Ethiopian Central Rift Valleys lakes, where the absorbance of domestic, agricultural, and industrial waste from the nearby industrial and agro-industrial activities is very common. The aim of this research was to assess the heavy metal bioaccumulation in edible parts of common carp (Cyprinus carpio) in Lake Koka and the health risks associated with the dietary intake of the fish. Three sampling sites were selected randomly for primary data collection. Physicochemical parameters (pH, Total Dissolved Solids, Dissolved Oxygen and Electrical Conductivity) were measured in-situ. Four heavy metals (Cd, Cr, Pb, and Zn) in water and bio-accumulation in the edible parts of the fish were analyzed with flame atomic absorption spectrometry. The mean values of TDS, EC, DO and pH of the lake water were 458.1 mg/L, 905.7 µ s/cm, 7.36 mg/L, and 7.9, respectively. The mean concentrations of Zn, Cr, and Cd in the edible part of fish were also 0.18 mg/kg, ND-0.24 mg/kg, and ND-0.03 mg/kg, respectively. Pb was, however, not identified. The amount of Cr in the examined fish muscle was above the level set by FAO, and the accumulation of the metals showed marked differences between sampling sites (p<0.05). The concentrations of Cd, Pb and were below the maximum permissible limit. The results also indicated that Cr has a high transfer factor value and Zn has the lowest. The carcinogenic hazard ratio values were below the threshold value (<1) for the edible parts of fish. The estimated weekly intake of heavy metals from fish muscles ranked as Cr>Zn>Cd, but the values were lower than the Reference Dose limit for metals. The carcinogenic risk values indicated a low health risk due to the intake of individual metals from fish. Furthermore, the hazard index of the edible part of fish was less than unity. Generally, the water quality is not a risk for the survival and reproduction of fish, and the heavy metal contents in the edible parts of fish exhibited low carcinogenic risk through the food chain.

Keywords: bio-accumulation, cyprinus carpio, hazard index, heavy metals, Lake Koka

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Lavinia Flueckiger, Roselind Lieb, Andrea H. Meyer, Cornelia Witthauer, Jutta Mata

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Objective: Health behaviors tend to show a high variability over time within the same person. However, most existing research can only assess a snapshot of a person’s behavior and not capture this natural daily variability. Two intensive longitudinal studies examine the variability in health behavior over one academic year and their implications for other aspects of daily life such as affect and academic performance. Can already a single day of increased physical activity, snacking, or improved sleep have beneficial effects? Methods: In two intensive longitudinal studies with up to 65 assessment days over an entire academic year, university students (Study 1: N = 292; Study 2: N = 304) reported sleep quality, physical activity, snacking, positive and negative affect, and learning goal achievement. Results: Multilevel structural equation models showed that on days on which participants reported better sleep quality or more physical activity than usual, they also reported increased positive affect, decreased negative affect, and better learning goal achievement. Higher day-to-day snacking was only associated with increased positive affect. Both, increased day-to-day sleep quality and physical activity were indirectly associated with better learning goal achievement through changes in positive and negative affect; results for snacking were mixed. Importantly, day-to-day sleep quality was a stronger predictor for affect and learning goal achievement than physical activity or snacking. Conclusion: One day of better sleep or more physical activity than usual is associated with improved affect and academic performance. These findings have important implications for low-threshold interventions targeting the improvement of daily functioning.

Keywords: sleep quality, physical activity, snacking, affect, academic performance, multilevel structural equation model

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Authors: Eric Muñoz de la Torre, Julián González Trinidad, Efrén González Ramírez

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Rain varies greatly in its duration, intensity, and spatial coverage, it is important to have sub-daily rainfall data for various applications, including risk prevention. However, the ground measurements are limited by the low and irregular density of rain gauges. An alternative to this problem are the Satellite Precipitation Products (SPPs) that use passive microwave and infrared sensors to estimate rainfall, as IMERG, however, these SPPs have to be validated before their application. The aim of this study is to evaluate the performance of the IMERG: Integrated Multi-satellitE Retrievals for Global Precipitation Measurament final run V06B SPP in a semi-arid region of Mexico, using 4 automatic rain gauges (pluviographs) sub-daily data of October 2019 and June to September 2021, using the Minimum inter-event Time (MIT) criterion to separate unique rain events with a dry period of 10 hrs. for the purpose of evaluating the rainfall properties (depth, duration and intensity). Point to pixel analysis, continuous, categorical, and volumetric statistical metrics were used. Results show that IMERG is capable to estimate the rainfall depth with a slight overestimation but is unable to identify the real duration and intensity of the rain events, showing large overestimations and underestimations, respectively. The study zone presented 80 to 85 % of convective rain events, the rest were stratiform rain events, classified by the depth magnitude variation of IMERG pixels and pluviographs. IMERG showed poorer performance at detecting the first ones but had a good performance at estimating stratiform rain events that are originated by Cold Fronts.

Keywords: IMERG, rainfall, rain gauge, remote sensing, statistical evaluation

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Authors: Nitin Kumar

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Purpose: The current research work focuses mainly on evolving a delivery system for ginseng extract (GE), which in turn will ameliorate the neuroprotective potential by means of enhancing the ginsenoside (Rb1) bio-availability (BA). For more noteworthy enhancement in oral bioavailability (OBA) along with pharmacological properties, the drug carriers’ performance can be strengthened by utilizing phytosomes-loaded microspheres (PM) delivery system. Methods: For preparing the disparate phytosome complexes (F1, F2, and F3), an aqueous extract of ginseng roots (GR) along with phospholipids were reacted in disparate ratio. Considering the outcomes, F3 formulation (spray-dried) was chosen for preparing the phytosomes powder (PP), PM, and extract microspheres (EM). PM was made by means of loading of F3 into Gum Arabic (GA) in addition to maltodextrin polymer mixture, whereas EM was prepared by means of the addition of extract directly into the same polymer mixture. For investigating the neuroprotective effect (NPE) in addition to their pharmacokinetic (PK) properties, PP, PM, and EM formulations were assessed. Results: F3 formulation gave enhanced entrapment efficiency (EE) (i.e., 50.61%) along with good homogeneity of spherical shaped particle size (PS) (42.58 ± 1.4 nm) with least polydispersity index (PDI) (i.e., 0.193 ± 0.01). The sustained release (up to 24 h) of ginsenoside Rb1 (GRb1) is revealed by the dissolution study of PM. A significantly (p < 0.05) greater anti-oxidant (AO) potential of PM can well be perceived as of the diminution in the lipid peroxidase level in addition to the rise in the glutathione superoxide dismutase (SOD) in addition to catalase levels. It also showed a greater neuroprotective potential exhibiting significant (p < 0.05) augmentation in the nociceptive threshold together with the diminution in damage to nerves. A noteworthy enhancement in the relative BA (157.94%) of GRb1 through the PM formulation can well be seen in the PK studies. Conclusion: It is exhibited that the PM system is an optimistic and feasible strategy to enhance the delivery of GE for the effectual treatment of neuropathic pain.

Keywords: ginseng, neuropathic, phytosome, pain

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Tomasz Raczyński

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Non-native and invasive species negatively transform ecosystems. Non-native fish species can displace native fish species through competition, predation, disrupting spawning, transforming ecosystems, or transmitting parasites. This influence is more and more noticeable in Poland and in the world. From December 2014 to October 2020, did catch of fishes by electrofishing method carried on 416 sites in various parts of Poland. Research was conducted in both running and stagnant freshwaters with the predominance of running waters. Only sites where the presence of fish was found were analysed. The research covered a wide spectrum of waters from small mountain streams, through drainage ditches to the largest Polish river - the Vistula. Single sites covered oxbow lakes, small ponds and lakes. Electrofishing was associated with ichthyofauna inventories and was mainly aimed at detecting protected species of fish and lampreys or included in the annexes to the EU Habitats Directive (Council Directive 92/43/EEC on the Conservation of natural habitats and of wild fauna and flora). The results of these catches were analysed for alien and invasive fish species. The analysis of the catch structure shows that in 71 out of 416 research sites was found alien and invasive fish species, belonging to 9 taxa. According to the above, alien species of fish are present in 17% of the study sites. The most frequently observed species was the Prussian carp Carassius gibelio, which was recorded on 43 sites. Stone moroko Pseudorasbora parva was found on 24 sites. Chinese sleeper Perccottus glenii was found on 6 sites, and Bullhead Ameiurus sp. was also found on 6 sites. Western tubenose goby Proterorhinus semilunaris was found at 5 sites and Rainbow trout Oncorhynchus mykiss at 3 sites. Monkey goby Neogobius fluviatilis, Round goby Neogobius melanostomus and Eurasian carp Cyprinus carpio was recorded on 2 sites.

Keywords: non-native species, invasive species, fish species, invasive fish species, native fish species

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Authors: Rong-Yang Lai, Jia-Yu Wu, Chih-Han Chang, Yung-Chung Chen

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The dental bridge is one of the clinical methods of the treatment for missing teeth. The dental bridge is generally designed for two layers, containing the inner layer of the framework(zirconia) and the outer layer of the porcelain-fused to framework restorations. The design of a conventional bridge is generally based on the antagonist tooth profile so that the framework evenly indented by an equal thickness from outer contour. All-ceramic dental bridge made of zirconia have well demonstrated remarkable potential to withstand a higher physiological occlusal load in posterior region, but it was found that there is still the risk of all-ceramic bridge failure in five years. Thus, how to reduce the incidence of failure is still a problem to be solved. Therefore, the objective of this study is to develop mechanical designs for all-ceramic dental bridges framework by reducing the stress and enhancing fracture resistance under given loading conditions by finite element method. In this study, dental design software is used to design dental bridge based on tooth CT images. After building model, Bi-directional Evolutionary Structural Optimization (BESO) Method algorithm implemented in finite element software was employed to analyze results of finite element software and determine the distribution of the materials in dental bridge; BESO searches the optimum distribution of two different materials, namely porcelain and zirconia. According to the previous calculation of the stress value of each element, when the element stress value is higher than the threshold value, the element would be replaced by the framework material; besides, the difference of maximum stress peak value is less than 0.1%, calculation is complete. After completing the design of dental bridge, the stress distribution of the whole structure is changed. BESO reduces the peak values of principle stress of 10% in outer-layer porcelain and avoids producing tensile stress failure.

Keywords: dental bridge, finite element analysis, framework, automatic program

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Authors: Hamidullah Binol, Abdullah Bal

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Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

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Authors: Asrat M.Belachew, Tiago Pereira, Institute of Mathematics, Computer Sciences, Avenida Trabalhador São Carlense, 400, São Carlos, 13566-590, Brazil

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Infectious diseases are among the most prominent threats to human beings. They cause morbidity and mortality to an individual and collapse the social, economic, and political systems of the whole world collectively. Mathematical models are fundamental tools and provide a comprehensive understanding of how infectious diseases spread and designing the control strategy to mitigate infectious diseases from the host population. Modeling the spread of infectious diseases using a compartmental model of inhomogeneous populations is good in terms of complexity. However, in the real world, there is a situation that accounts for heterogeneity, such as ages, locations, and contact patterns of the population which are ignored in a homogeneous setting. In this work, we study how classical an SEIR infectious disease spreading of the compartmental model can be extended by incorporating the mobility of population between heterogeneous cities during an outbreak of infectious disease. We have formulated an SEIR multi-cities epidemic spreading model using a system of 4k ordinary differential equations to describe the disease transmission dynamics in k-cities during the day and night. We have shownthat the model is epidemiologically (i.e., variables have biological interpretation) and mathematically (i.e., a unique bounded solution exists all the time) well-posed. We constructed the next-generation matrix (NGM) for the model and calculated the basic reproduction number R0for SEIR-epidemic spreading model with cities mobility. R0of the disease depends on the spectral radius mobility operator, and it is a threshold between asymptotic stability of the disease-free equilibrium and disease persistence. Using the eigenvalue perturbation theorem, we showed that sending a fraction of the population between cities decreases the reproduction number of diseases in interconnected cities. As a result, disease transmissiondecreases in the population.

Keywords: SEIR-model, mathematical model, city mobility, epidemic spreading

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

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Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Jou-An Chung

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The purpose of this study is to investigate the verb bias of the Mandarin verbs in children’s reading materials and provide the criteria for categorization. Verb bias varies cross-linguistically. As Mandarin and English are typological different, this study hopes to shed light on Mandarin verb bias with the use of corpus and provide thorough and detailed criteria for analysis. Moreover, this study focuses on children’s reading materials since it is a significant issue in understanding children’s sentence processing. Therefore, investigating verb bias of Mandarin verbs in children’s reading materials is also an important issue and can provide further insights into children’s sentence processing. The small corpus is built up for this study. The corpus consists of the collection of school textbooks and Mandarin Daily News for children. The files are then segmented and POS tagged by JiebaR (Chinese segmentation with R). For the ease of analysis, the one-word character verbs and intransitive verbs are excluded beforehand. The total of 20 high frequency verbs are hand-coded and are further categorized into one of the three types, namely DO type, SC type and other category. If the frequency of taking Other Type exceeds the threshold of 25%, the verb is excluded from the study. The results show that 10 verbs are direct object bias verbs, and six verbs are sentential complement bias verbs. The paired T-test was done to assure the statistical significance (p = 0.0001062 for DO bias verb, p=0.001149 for SC bias verb). The result has shown that in children’s reading materials, the DO biased verbs are used more than the SC bias verbs since the simplest structure of sentences is easier for children’s sentence comprehension or processing. In sum, this study not only discussed verb bias in child's reading materials but also provided basic coding criteria for verb bias analysis in Mandarin and underscored the role of context. Sentences are easier for children’s sentence comprehension or processing. In sum, this study not only discussed verb bias in child corpus, but also provided basic coding criteria for verb bias analysis in Mandarin and underscored the role of context.

Keywords: corpus linguistics, verb bias, child language, psycholinguistics

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Authors: Alina Smalinskiene Janina Petkeviciene, Jurate Klumbiene, Vilma Kriaucioniene, Vaiva Lesauskaite

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The worldwide prevalence of obesity has been increasing dramatically in the last few decades, and Lithuania is no exception. In 2012, every fifth adult (19% of men and 20.5 % of women) was obese and every third was overweight Association studies have highlighted the influence of SNPs in obesity, with particular focus on FTO rs9939609. Thus far, no data on the possible association of this SNP to obesity in the adult Lithuanian population has been reported. Here, for the first time, we demonstrate an association between the FTO rs9939609 homozygous AA genotype and increased BMI when compared to homozygous TT. Furthermore, a positive association was determined between the FTO rs9939609 variant and risk of metabolic syndrome. Background: This study aimed to examine the associations between the fat mass and obesity associated (FTO) gene rs9939609 variant with obesity and metabolic syndrome in Lithuanian adult population. Materials and Methods: A cross-sectional health survey was carried out in randomly selected municipalities of Lithuania. The random sample was obtained from lists of 25–64 year-old inhabitants. The data from 1020 subjects were analysed. The rs9939609 SNP of the FTO gene was assessed using TaqMan assays (Applied Biosystems, Foster City, CA, USA). The Applied Biosystems 7900HT Real-Time Polymerase Chain Reaction System was used for detecting the SNPs. Results: The carriers of the AA genotype had the highest mean values of BMI and waist circumference (WC) and the highest risk of obesity. Interactions ‘genotype x age’ and ‘genotype x physical activity’ in determining BMI and WC were shown. Neither lipid and glucose levels, nor blood pressure were associated with the rs9939609 independently of BMI. In the age group of 25-44 years, association between the FTO genotypes and metabolic syndrome was found. Conclusion: The FTO rs9939609 variant was significantly associated with BMI and WC, and with the risk of obesity in Lithuanian population. The FTO polymorphism might have a greater influence on weight status in younger individuals and in subjects with a low level of physical activity.

Keywords: obesity metabolic syndrome, FTO gene, polymorphism, Lithuania

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Authors: Petcharat Kerdonfag, Surasak Taneepanichskul, Winai Wadwongtham

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Background: Although there have not been effective treatments for Noise Induced Hearing Loss (NIHL), it can be definitely preventable with promoting the use of Hearing Protection devices (HPDs) among workers who have been exposed to excessive noise for a long period. Objectives: The objectives of this study were to explore the use of HPDs among steel industrial workers in the high noise level zone in Samut Prakan province, Thailand and to examine the relationships of the HPDs use and hearing loss. Materials and Methods: In this cross-sectional study, eligible ninety-three participants were recruited in the designated zone of higher noise (> 85dBA) of two factories, using simple random sampling. The use of HPDs was gathered by the self-record form, examined and confirmed by the researcher team. Hearing loss was assessed by the audiometric screening at the regional Samut Prakan hospital. If an average threshold level exceeds 25 dBA at high frequency (4 and 6 Hz) in each ear, participants would be lost of hearing. Data were collected from October to December, 2016. All participants were examined by the same examiners for the validity. An Audiometric testing was performed with the participants who have been exposed to high noise levels at least 14 hours from workplace. Results: Sixty participants (64.5%) had secondary level of education. The average mean score of percent time of using HPDs was 60.5% (SD = 25.34). Sixty-seven participants (72.0%) had abnormal hearing which they have still needed to increase lower percent time of using HPDs (Mean = 37.01, SD = 23.81) than those having normal hearing (Mean = 45.77, SD = 28.44). However, there was no difference in the mean average of percent time of using HPDs between these two groups.Conclusion: The findings of this study have confirmed that the steel industrial workers still need to be motivated to use HPDs regularly. Future research should pay more attentions for creating a meaningful innovation to steel industrial workers.

Keywords: hearing protection devices, noise induced hearing loss, audiometric testing, steel industry

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Sayyed-Javad Ziaolhagh, Mojtaba Hokmabadi

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Introduction: Childhood obesity is a serious medical condition that affects children and adolescents even in the central nervous system. Semaphorins also play a role in the inflammatory process of the nervous system. On the other hand, it has been stated that garlic and stevia extracts following aerobic exercise are effective on immune system inflammation in addition to aerobic activity. Materials and Methods: For 15 weeks, 50 3-week-old male Wistar rats were fed with conventional rodent chow for control and a high-fat diet to induce obesity. Obese rats then were randomly assigned into 7 groups (n=5) based on the Lee index: healthy control (C), obese (OBS), obese + garlic (OBS+GAR), obese + Stevia (OBS+STV), obese + aerobic exercise (OBS+EXE), obese + garlic + aerobic exercise (OBS+GAR+EXE), and obese + stevia + aerobic exercise (OBS+STV+EXE). Training groups completed a progressive aerobic running program (at 8-15 m/min, 5-20 min/day, 5 days/week), and Stevia and garlic extract group (250 mg/kg/day, 5 days/week) were given orally once a day. Real-time PCR was used to determine the levels of Semaphorin 4A, and Plexin D1 gene expressions in the hypothalamus. Fold change analysis with ANOVA was performed for statistical analysis, with a significance threshold of P<0.05. Results: Body weight increased significantly in OBS compared to C (p= 0.013), but was not significantly changed in all treatment rats. Moreover, Semaphorin 4A was significantly increased in obese compared to control group (p= 0.041) and after 8 weeks, stevia extract (p=0.006), aerobic exercise (p=0.012) and garlic extract + aerobic exercise (p=0.008) significantly decreased compared to obese rats. In addition, Plexin D1 genes were also found in the hypothalamus of both obese and control rats but were insignificantly up-regulated when compared with the obese group (p=0.950). Conclusion: High-fat diet caused neuroinflammation by elevation of sema4A in obese rats and stevia, stevia with aerobic and garlic with aerobic could reduce this inflammation in rats. Also, none of them could alter Plexin D1.

Keywords: sema 4A, plexin D1, garlic, stevia

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Authors: Nino Paresashvili, Nino Abesadze

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The modern world faces huge challenges. Globalization changed the socio-economic conditions of many countries. The current processes in the global environment have a different impact on countries with different cultures. However, an alleviation of poverty and improvement of living conditions is still the basic challenge for the majority of countries, because much of the population still lives under the official threshold of poverty. It is very important to stimulate youth employment. In order to prepare young people for the labour market, it is essential to provide them with the appropriate professional skills and knowledge. It is necessary to plan efficient activities for decreasing an unemployment rate and for developing the perfect mechanisms for regulation of a labour market. Such planning requires thorough study and analysis of existing reality, as well as development of corresponding mechanisms. Statistical analysis of unemployment is one of the main platforms for regulation of the labour market key mechanisms. The corresponding statistical methods should be used in the study process. Such methods are observation, gathering, grouping, and calculation of the generalized indicators. Unemployment is one of the most severe socioeconomic problems in Georgia. According to the past as well as the current statistics, unemployment rates always have been the most problematic issue to resolve for policy makers. Analytical works towards to the above-mentioned problem will be the basis for the next sustainable steps to solve the main problem. The results of the study showed that the choice of young people is not often due to their inclinations, their interests and the labour market demand. That is why the wrong professional orientation of young people in most cases leads to their unemployment. At the same time, it was shown that there are a number of professions in the labour market with a high demand because of the deficit the appropriate specialties. To achieve healthy competitiveness in youth employment, it is necessary to formulate regional employment programs with taking into account the regional infrastructure specifications.

Keywords: unemployment, analysis, methods, tendencies, regulation mechanisms

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Authors: Hassan Alhoujeiri, Angele Matrat, Sandra Beaufort, Claire joaniss Cassan, Jerome Silvester

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Aquaponics is a sustainable food production technology, and its transition to industrial-scale systems has created several challenges that require further investigation in order to make it a robust process. One of the critical concerns is the potential accumulation of compounds from veterinary treatments, phytosanitary agents, fish feed, or simply from contaminated water sources. The accumulation of these substances could negatively impact fish health, microbial biofilters, and plant growth, thereby disrupting the system’s overall balance and functionality. The lack of legislation and knowledge regarding the presence of such compounds in aquaponic systems raises concerns about their potential impact on both system balance and food safety. In this study, we focused on the effects of oxytetracycline (OTC), an antibiotic commonly used in aquaculture, on both the microbial biofilter and plant growth. Although OTC is rarely applied in aquaponics today, the fish compartment may need to be isolated from the system during treatment, as it inhibits specific bacterial populations, which could affect the microbial biofilter's efficiency. However, questions remain about the aquaponic system's tolerance threshold, particularly in cases of treatment or residual OTC traces post-treatment. This study results indicated a decline in microbial biofilter activity to 20% compared to the control, potentially corresponding to treatments of 41 mg/L of OTC. Analysis of microbial populations in the biofilter, using flow cytometry and microscopy (confocal and scanning electron microscopy), revealed an increase in bacterial mortality without disrupting the microbial biofilm. Additionally, OTC exposure led to noticeable changes in plant morphology (e.g., color) and growth, though it did not fully inhibit development. However, no significant effects were observed on seed germination at the tested concentrations despite a measurable impact on subsequent plant growth.

Keywords: aquaponic, oxytetracycline, nitrifying biofilter, plant, micropollutants, sustainability

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Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

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Authors: S. S. Sree Sanker, K. N. Madhusoodanan

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Optical biosensors are dominant alternative for traditional analytical methods, because of their small size, simple design and high sensitivity. Photonic sensing method is one of the recent advancing technology for biosensors. It measures the change in refractive index which is induced by the difference in molecular interactions due to the change in concentration of the analyte. Glucose is an aldosic monosaccharide, which is a metabolic source in many of the organisms. The terahertz waves occupies the space between infrared and microwaves in the electromagnetic spectrum. Terahertz waves are expected to be applied to various types of sensors for detecting harmful substances in blood, cancer cells in skin and micro bacteria in vegetables. We have designed glucose sensors using silicon based 1D and 2D photonic crystal pillar arrays in terahertz frequency range. 1D photonic crystal has rectangular pillars with height 100 µm, length 1600 µm and width 50 µm. The array period of the crystal is 500 µm. 2D photonic crystal has 5×5 cylindrical pillar array with an array period of 75 µm. Height and diameter of the pillar array are 160 µm and 100 µm respectively. Two samples considered in the work are blood and glucose solution, which are labelled as sample 1 and sample 2 respectively. The proposed sensor detects the concentration of glucose in the samples from 0 to 100 mg/dL. For this, the crystal was irradiated with 0.3 to 3 THz waves. By analyzing the obtained S parameter, the refractive index of the crystal corresponding to the particular concentration of glucose was measured using the parameter retrieval method. Refractive indices of the two crystals decreased gradually with the increase in concentration of glucose in the sample. For 1D photonic crystals, a gradual decrease in refractive index was observed at 1 THz. 2D photonic crystal showed this behavior at 2 THz. The proposed sensor was simulated using CST Microwave studio. This will enable us to develop a model which can be used to characterize a glucose sensor. The present study is expected to contribute to blood glucose monitoring.

Keywords: CST microwave studio, glucose sensor, photonic crystal, terahertz waves

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Authors: Mrinal Kanti Bhowmik, Usha Rani Gogoi Jr., Anjan Kumar Ghosh, Debotosh Bhattacharjee

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In the last few decades, breast thermography has achieved an average sensitivity and specificity of 90% for breast tumor detection. Breast thermography is a non-invasive, cost-effective, painless and radiation-free breast imaging modality which makes a significant contribution to the evaluation and diagnosis of patients, suspected of having breast cancer. An abnormal breast thermogram may indicate significant biological risk for the existence or the development of breast tumors. Breast thermography can detect a breast tumor, when the tumor is in its early stage or when the tumor is in a dense breast. The infrared breast thermography is very sensitive to environmental changes for which acquisition of breast thermography should be performed under strictly controlled conditions by undergoing some standard protocols. Several factors like air, temperature, humidity, etc. are there to be considered for characterizing thermal images as an imperative tool for detecting breast cancer. A detailed study of various breast thermogram acquisition protocols adopted by different researchers in their research work is provided here in this paper. After going through a rigorous study of different breast thermogram acquisition protocols, a new standard breast thermography acquisition setup is proposed here in this paper for proper and accurate capturing of the breast thermograms. The proposed breast thermogram acquisition setup is being built in the Radiology Department, Agartala Government Medical College (AGMC), Govt. of Tripura, Tripura, India. The breast thermograms are captured using FLIR T650sc thermal camera with the thermal sensitivity of 20 mK at 30 degree C. The paper is an attempt to highlight the importance of different critical parameters of breast thermography like different thermography views, patient preparation protocols, acquisition room requirements, acquisition system requirements, etc. This paper makes an important contribution by providing a detailed survey and a new efficient approach on breast thermogram capturing.

Keywords: acquisition protocol, breast cancer, breast thermography, infrared thermography

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Authors: Aparna Lal, Ashwin Swaminathan, Teisa Holani

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Background: Clostridium difficile infections (CDIs) have been on the rise globally. In Australia, rates of CDI in all States and Territories have increased significantly since mid-2011. Identifying risk factors for CDI in the community can help inform targeted interventions to reduce infection. Methods: We examine the role of neighbourhood socio-economic status, demography, testing practices and the number of residential aged care facilities on spatial patterns in CDI incidence in the Australian Capital Territory. Data on all tests conducted for CDI were obtained from ACT Pathology by postcode for the period 1st January 2004 through 31 December 2014. Distribution of age groups and the neighbourhood Index of Relative Socio-economic Advantage Disadvantage (IRSAD) were obtained from the Australian Bureau of Statistics 2011 National Census data. A Bayesian spatial conditional autoregressive model was fitted at the postcode level to quantify the relationship between CDI and socio-demographic factors. To identify CDI hotspots, exceedance probabilities were set at a threshold of twice the estimated relative risk. Results: CDI showed a positive spatial association with the number of tests (RR=1.01, 95% CI 1.00, 1.02) and the resident population over 65 years (RR=1.00, 95% CI 1.00, 1.01). The standardized index of relative socio-economic advantage disadvantage (IRSAD) was significantly negatively associated with CDI (RR=0.74, 95% CI 0.56, 0.94). We identified three postcodes with high probability (0.8-1.0) of excess risk. Conclusions: Here, we demonstrate geographic variations in CDI in the ACT with a positive association of CDI with socioeconomic disadvantage and identify areas with a high probability of elevated risk compared with surrounding communities. These findings highlight community-based risk factors for CDI.

Keywords: spatial, socio-demographic, infection, Clostridium difficile

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Authors: Franziska Mais, Till Gramberg

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The paper presents the development of a sustainable business model for a platform-based application tailored for sawing companies in small and medium-sized enterprises (SMEs). The focus is on the integration of sustainability principles into the design of the business model to ensure a technologically advanced, legally sound, and economically efficient solution. Easy2IoT is a research project that aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements, and potential solutions for smart services are derived. The structuring of the business ecosystem within the application plays a central role, whereby the roles of the partners, the management of the IT infrastructure and services, as well as the design of a sustainable operator model are considered. The business model is developed using the value proposition canvas, whereby a detailed analysis of the requirements for the business model is carried out, taking sustainability into account. This includes coordination with the business model patterns, according to Gassmann, and integration into a business model canvas for the Easy2IoT product. Potential obstacles and problems are identified and evaluated in order to formulate a comprehensive and sustainable business model. In addition, sustainable payment models and distribution channels are developed. In summary, the article offers a well-founded insight into the systematic development of a sustainable business model for platform-based applications in SME sawmills, with a particular focus on the synergy of ecological responsibility and economic efficiency.

Keywords: business model, sustainable business model, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Qin Zhu

Abstract:

Under the background of accelerating the construction of Beautiful and Livable Park City in Chengdu, the Tianfu greenway system, as an important support system for the construction of parks in the whole region, its accessibility is one of the key indicators to measure the effectiveness of the greenway construction. In recent years, cycling has become an important transportation mode for residents to go to the greenways because of its low-carbon, healthy and convenient characteristics, and the study of greenway accessibility under cycling mode can provide reference suggestions for the optimization and improvement of greenways. Taking Jincheng Greenway in Chengdu City as an example, the Baidu Map Application Programming Interface (API) and questionnaire survey was used to improve the two-step floating catchment area (2SFCA) method from the three dimensions of search threshold, supply side and demand side, to calculate the cycling accessibility of the greenway and to explore the spatial matching relationship with the population density, the number of entrances and the comprehensive attractiveness. The results show that: 1) the distribution of greenway accessibility in Jincheng shows a pattern of "high in the south and low in the north, high in the west and low in the east", 2) the spatial match between greenway accessibility and population density of the residential area is imbalanced, and there is a significant positive correlation between accessibility and the number of selectable greenway access points in residential areas, as well as the overall attractiveness of greenways, with a high degree of match. On this basis, it is proposed to give priority to the mismatch area to alleviate the contradiction between supply and demand, optimize the greenway access points to improve the traffic connection, enhance the comprehensive quality of the greenway and strengthen the service capacity, to further improve the cycling accessibility of the Jincheng Greenway and improve the spatial allocation of greenway resources.

Keywords: accessibility, Baidu maps API, cycling, greenway, 2SFCA

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Authors: Farzaneh Shayeganfar, Ali Ramazani

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Exciton (strong electronic interaction of electron-hole) and hot carriers created by surface plasmon polaritons has been demonstrated in nanoscale optoelectronic devices, enhancing the photoresponse of the system. Herein, we employ a quantum framework to consider coupled exciton- hot carriers effects on photovoltaiv energy distribution, scattering process, polarizability and light emission of 2D-semicnductor. We use density functional theory (DFT) to design computationally a semi-functionalized 2D honeycomb silicon boride (SiB) monolayer with H atoms, suitable for photovoltaics. The dynamical stability, electronic and optical properties of SiB and semi-hydrogenated SiB structures were investigated utilizing the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential. The calculated phonon dispersion shows that while an unhydrogenated SiB monolayer is dynamically unstable, surface semi-hydrogenation improves the stability of the structure and leads to a transition from metallic to semiconducting conductivity with a direct band gap of about 1.57 eV, appropriate for photovoltaic applications. The optical conductivity of this H-SiB structure, determined using the random phase approximation (RPA), shows that light adsorption should begin at the boundary of the visible range of light. Additionally, due to hydrogenation, the reflectivity spectrum declines sharply with respect to the unhydrogenated reflectivity spectrum in the IR and visible ranges of light. The energy band gap remains direct, increasing from 0.9 to 1.8 eV, upon increasing the strain from -6% (compressive) to +6% (tensile). Additionally, compressive and tensile strains lead, respectively, to red and blue shifts of optical the conductivity threshold around the visible range of light. Overall, this study suggests that H-SiB monolayers are suitable as two-dimensional solar cell materials.

Keywords: surface plasmon, hot carrier, strain engineering, valley polariton

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