Search results for: threshold detecting

Authors: Reza Soleimanpour, Ching Tai Ng

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Fibre-composites are widely used in various structures due to their attractive properties such as higher stiffness to mass ratio and better corrosion resistance compared to metallic materials. However, one serious weakness of this composite material is delamination, which is a subsurface separation of laminae. A low level of this barely visible damage can cause a significant reduction in residual compressive strength. In the last decade, the application of guided waves for damage detection has been a topic of significant interest for many researches. Among all guided wave techniques, nonlinear guided wave has shown outstanding sensitivity and capability for detecting different types of damages, e.g. cracks and delaminations. So far, most of researches on applications of nonlinear guided wave have been dedicated to isotropic material, such as aluminium and steel, while only a few works have been done on applications of nonlinear characteristics of guided waves in anisotropic materials. This study investigates the nonlinear interactions of the fundamental antisymmetric lamb wave (A0) with delamination in composite laminates using three-dimensional (3D) explicit finite element (FE) simulations. The nonlinearity considered in this study arises from interactions of two interfaces of sub-laminates at the delamination region, which generates contact acoustic nonlinearity (CAN). The aim of this research is to investigate the phenomena of CAN in composite laminated beams by a series of numerical case studies. In this study interaction of fundamental antisymmetric lamb wave with delamination of different sizes are studied in detail. The results show that the A0 lamb wave interacts with the delaminations generating CAN in the form of higher harmonics, which is a good indicator for determining the existence of delaminations in composite laminates.

Keywords: contact acoustic nonlinearity, delamination, fibre reinforced composite beam, finite element, nonlinear guided waves

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Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Ndje Ndje Mireille

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For some years in Cameroon, there has been an increase in violence in schools. This violence has gone from verbal to physical, sometimes going as far as murder. At the centre of this violence, we find the student who is a teenager in the midst of both physical and psychological changes. The unpredictable transformations of his body, the unexpected emotions arrouse when he encouters someonelse, intrusion, shortcomings, boredom, loneliness and self-deception are the threats to which the teenager faces daily. From the psychopathological point of view, the greatest threat in adolesence is probably the depresive threat. During adolescence and for several resons, the subject is confronted with the self image. He displays certantity which sometimes hides great uncertaintity about what leads him to manifest some particular behaviours or undertake certain actions. Faced with aggressiveness twards those he confronts, he feels more or less guilt. This can lead a certain number of adolescents to feel heplessness faced to their vis-à-vis, faced to life. This helplessness is sometimes reinforced by the social, cultural and economic context in which they are. The teeneger then feels threatens by this depression which, when it reaches its extreme, it is manifested by the feeling that he can no longer do anything. Generally, the depressive threats manifest itself in defensive forms vis-à-vis with the depression itself. Reason why, it is indeed a threat and not a threshold already crossed. This threat often manifests itself in inappropriate forms of attack on one’s own body as seen in a number of repetitive risky behaviours. We also see teenegers confront peers and even adults through physical attacks and often go as far as murder. All these behaviours appears as an absurd way of attacking and at the same time confronting the feeling of remaining alive. This depressive threats can also be expressed in forms of attacks on an individual’s thinking abilities or more explicitely in the form of accademic downfall. The depressive threats does not sum up all the problems of adolescence, but, undoubtly represents currently, one of the deepest form of unease adolescents face.

Keywords: violence, school, depression threats, adolescent, behavior

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Authors: Taslim Khan, Ray Hua Horng, Rajendra Singh

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This study presents a comprehensive analysis of the design, fabrication, and performance evaluation of a solar-blind Schottky photodetector based on ZnGa₂O₄ grown via MOCVD, utilizing Ni/Au as the Schottky electrode. ZnGa₂O₄, with its wide bandgap of 5.2 eV, is well-suited for high-performance solar-blind photodetection applications. The photodetector demonstrates an impressive responsivity of 280 A/W, indicating its exceptional sensitivity within the solar-blind ultraviolet band. One of the device's notable attributes is its high rejection ratio of 10⁵, which effectively filters out unwanted background signals, enhancing its reliability in various environments. The photodetector also boasts a photodetector responsivity contrast ratio (PDCR) of 10⁷, showcasing its ability to detect even minor changes in incident UV light. Additionally, the device features an outstanding detective of 10¹⁸ Jones, underscoring its capability to precisely detect faint UV signals. It exhibits a fast response time of 80 ms and an ON/OFF ratio of 10⁵, making it suitable for real-time UV sensing applications. The noise-equivalent power (NEP) of 10^-17 W/Hz further highlights its efficiency in detecting low-intensity UV signals. The photodetector also achieves a high forward-to-backward current rejection ratio of 10⁶, ensuring high selectivity. Furthermore, the device maintains an extremely low dark current of approximately 0.1 pA. These findings position the ZnGa₂O₄-based Schottky photodetector as a leading candidate for solar-blind UV detection applications. It offers a compelling combination of sensitivity, selectivity, and operational efficiency, making it a highly promising tool for environments requiring precise and reliable UV detection.

Keywords: wideband gap, solar blind photodetector, MOCVD, zinc gallate

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Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: David Karasek, Veronika Kubickova, Ondrej Krystynik, Dominika Goldmannova, Lubica Cibickova, Jan Schovanek

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Introduction: Adiponectin, adipocyte-fatty acid-binding protein (A-FABP), and Wnt1 inducible signaling pathway protein-1 (WISP-1) are adipokines particularly associated with insulin resistance. The aim of the study was to compare their levels in women with gestational diabetes (GDM), type 2 diabetes mellitus (T2DM) and healthy controls and determine their relation with metabolic parameters. Methods: Fifty women with GDM, 50 women with T2DM, and 35 healthy women were included in the study. In addition to adipokines, anthropometric, lipid parameters, and markers, insulin resistance, and glucose control were assessed in all participants. Results: Compared to healthy controls only significantly lower levels of adiponectin were detected in women with GDM, whereas lower levels of adiponectin, higher levels of A-FABP and of WISP-1 were present in women with T2DM. Women with T2DM had also lower levels of adiponectin and higher levels of A-FABP compared to women with GDM. In women with GDM or T2DM adiponectin correlated negatively with body mass index (BMI), triglycerides (TG), C-peptide and positively with HDL-cholesterol; A-FABP positively correlated with BMI, TG, waist, and C-peptide. Moreover, there was a positive correlation between WISP-1 and C-peptide in women with T2DM. Conclusion: Adverse adipokines production detecting dysfunctional fat tissue is in women with GDM less presented than in women with T2DM, but more expressed compared to healthy women. Acknowledgment: Supported by AZV NV18-01-00139 and MH CZ DRO (FNOl, 00098892).

Keywords: adiponectin, adipocyte-fatty acid binding protein, wnt1 inducible signaling pathway protein-1, gestational diabetes, type 2 diabetes mellitus

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Authors: D. S. Gomes, A. T. Silva

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Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

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Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

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Authors: Tae Bo Jeon

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Korean air force is undertaking a big project to replace prevailing hundreds of old air fighters such as F-4, F-5, KF-16 etc. The task is to develop and produce domestic fighters equipped with 2 complete-type engines each. A large number of engines, however, will be purchased as products from a foreign engine maker. In addition to the fighters themselves, secure the proper number of spare engines serves a significant role in maintaining combat readiness and effectively managing the national defense budget due to high cost. In this paper, we presented a model dynamically updating spare engine requirements. Currently, the military administration purchases all the fighters, engines, and spare engines at acquisition stage and does not have additional procurement processes during the life cycle, 30-40 years. With the assumption that procurement procedure during the operational stage is established, our model starts from the initial estimate of spare engine requirements based on limited information. The model then performs military missions and repair/maintenance works when necessary. During operation, detailed field information - aircraft repair and test, engine repair, planned maintenance, administration time, transportation pipeline between base, field, and depot etc., - should be considered for actual engine requirements. At the end of each year, the performance measure is recorded and proceeds to next year when it shows higher the threshold set. Otherwise, additional engine(s) will be bought and added to the current system. We repeat the process for the life cycle period and compare the results. The proposed model is seen to generate far better results appropriately adding spare engines thus avoiding possible undesirable situations. Our model may well be applied to future air force military operations.

Keywords: DMSMS, operational availability, METRIC, PRS

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Authors: Aya Gamal Khattab

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Chronic infection with the hepatitis C virus (HCV) is now the leading cause of liver-related morbidity and mortality; Currently, alanine aminotransferase ALT measurement is not only widely used in detecting the incidence, development, and prognosis of liver disease with obvious clinical symptoms, but also provides reference on screening the overall health status during health check-ups. Exercise is a low-cost, reliable and sustainable therapy for many chronic diseases. Low-volume high intensity interval training HIT is time efficient while also having wider application to different populations including people at risk for chronic inflammatory diseases. Purpose of this study was to investigate the effect of low volume high intensity interval training on ALT, AST in HCV patients. All practical work was done in outpatient physiotherapy clinic of Suez Canal Authority Hospitals. Forty patients both gender (27 male, 13 female), age ranged (40-60) years old submitted to low volume high intensity interval training on treadmill for two months three sessions per week. Each session consisting of five min warming up, two bouts for 10 min each bout consisting of 30 sec - 1 min of high intensity (75%-85%) HRmax then two to four min active recovery at intensity (40%-60%) HRmax, so the sum of high intensity intervals was one to two min for each session and four to eight min active recovery, and ends with five min cooling down. ALT and AST were measured before starting exercise session and 2 months later after finishing the total exercise sessions through blood samples. Results showed significant decrease in ALT, AST with improvement percentage (18.85%), (23.87%) in the study, so the study concluded that low volume high intensity interval training had a significant effect in lowering the level of circulating liver enzymes (ALT, AST) which means protection of hepatic cells and restoration of its function.

Keywords: alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis C (HCV), low volume high intensity interval training

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Authors: Ansah Iris Baffour, Dong-Ho Kim, Sung-Gyu Park

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The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus and is gradually shifting to an endemic phase which implies the outbreak is far from over and will be difficult to eradicate. Global cooperation has led to unified precautions that aim to suppress epidemiological spread (e.g., through travel restrictions) and reach herd immunity (through vaccinations); however, the primary strategy to restrain the spread of the virus in mass populations relies on screening protocols that enable rapid on-site diagnosis of infections. Herein, we employed surface enhanced Raman spectroscopy (SERS) for the rapid detection of SARS-CoV-2 lysate on an Au-modified Au nanodimple(AuND)electrode. Through in situone-pot Au electrodeposition on the AuND electrode, Au-lysate nanocomposites were synthesized, generating3D internal hotspots for large SERS signal enhancements within 30 s of the deposition. The capture of lysate into newly generated plasmonic nanogaps within the nanocomposite structures enhanced metal-spike protein contact in 3D spaces and served as hotspots for sensitive detection. The limit of detection of SARS-CoV-2 lysate was 5 x 10-2 PFU/mL. Interestingly, ultrasensitive detection of the lysates of influenza A/H1N1 and respiratory syncytial virus (RSV) was possible, but the method showed ultimate selectivity for SARS-CoV-2 in lysate solution mixtures. We investigated the practical application of the approach for rapid on-site diagnosis by detecting SARS-CoV-2 lysate spiked in normal human saliva at ultralow concentrations. The results presented demonstrate the reliability and sensitivity of the assay for rapid diagnosis of COVID-19.

Keywords: label-free detection, nanocomposites, SARS-CoV-2, surface-enhanced raman spectroscopy

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Kidane workelul, Li xu, Xiaoyang Pang, Jiaping Lv

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Dairy products are exceptionally ideal media for the growth of microorganisms because of their high nutritional content. There are several ways that milk might get contaminated throughout the milking process, including how the raw milk is transported and stored, as well as how long it is kept before being processed. Psychrotrophic bacteria are among the one which can deteriorate the quality of milk mainly their heat resistance proteas and lipase enzyme. For this research purpose 8 selected strains of Psychrotrophic bacteria (Entrococcus hirae, Pseudomonas fluorescens, Pseudomonas azotoformans, Pseudomonas putida, Exiguobacterium indicum, Pseudomonas paralactice, Acinetobacter indicum, Serratia liquefacients)are chosen and try to determine their characteristics based on the research methodology protocol. Thus, the 8 selected strains are cultured, plated incubate, extracted their genomic DNA and genome DNA was amplified, the purpose of the study was to identify their Psychrotrophic properties, lipase hydrolysis positive test, their optimal incubation temperature, designed primer using the noble strain P,flourescens conserved region area in target with lipA gene, optimized primer specificity as well as sensitivity and PCR detection for lipase positive strains using the design primers. Based on the findings both the selected 8 strains isolated from stored raw milk are Psychrotrophic bacteria, 6 of the selected strains except the 2 strains are positive for lipase hydrolysis, their optimal temperature is 20 to 30 OC, the designed primer specificity is very accurate and amplifies for those strains only with lipase positive but could not amplify for the others. Thus, the result is promising and could help in detecting the Psychrotrophic bacteria producing heat resistance enzymes (lipase) at early stage before the milk is processed and this will safe production loss for the dairy industry.

Keywords: dairy industry, heat-resistant, lipA, milk, primer and psychrotrophic

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Authors: Xiaoxian Zhang

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Guidelines of ecological risk assessment (ERA) and food safety assessment (FSA) used nowadays, based on an S-shaped threshold dose-response curve (SDR), fail to consider hormesis, a reproducible biphasic dose-response model represented as a J-shaped or an inverted U-shaped curve, that occurs in the real-life environment across multitudinous compounds on cells, organisms, populations, and even the ecosystem. Specifically, in SDR-based ERA and FSA practice, predicted no effect concentration (PNEC) is calculated separately for individual substances from no observed effect concentration (NOEC, usually equivalent to 10% effect concentration (EC10) of a contaminant or food condiment) over an assessment coefficient that is bigger than 1. Experienced researchers doubted that hormesis in the real-life environment might lead to a waste of limited human and material resources in ERA and FSA practice, but related data are scarce. In this study, hormetic effects on bioluminescence of Aliivibrio fischeri (A. f) induced by sulfachloropyridazine (SCP) under 40 conditions to simulate the real-life scenario were investigated, and hormetic effects on growth of human MCF-7 cells caused by brown sugar and mascavado sugar were found likewise. After comparison of related parameters, it has for the first time been proved that there is a 50% probability for safe concentration (SC) of contaminants and food condiments to fall within the hormetic-stimulatory range (HSR) or left to HSR, revealing the unreliability of traditional parameters in standardized (eco)toxicological studies, and supporting qualitatively and quantitatively the over-strictness of ERA and FSA resulted from misuse of SDR. This study provides a novel perspective for ERA and FSA practitioners that hormesis should dominate and conditions where SDR works should only be singled out on a specific basis.

Keywords: dose-response relationship, food safety, ecological risk assessment, hormesis

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Masoud Irannezhad, Sirpa Rasmus, Saghar Ahmadian, Deliang Chen, Bjorn Klove

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Variability and changes in thermal conditions play a crucial role in functioning of human society, particularly over cold climate regions like Finland. Accordingly, the frost-free season (FFS) parameters in terms of start (FFSS), end (FFSE) and length (FFSL) have substantial effects not only on natural environment (e.g. flora and fauna), but also on human requirements (e.g. agriculture, forestry and energy generation). Applying the 0°C threshold of minimum temperature (Tmin), the FFS was defined as the period between the last spring frost as FFSS and the first fall frost as FFSE. For this study, gridded (10 x 10 km2) daily minimum temperature datasets throughout Finland during 1961-2011 was used to investigate recent spatio-temporal variations and trends in frost-free season (FFS) parameters and their relationships with the well-known large-scale climate teleconnections (CTs). The FFS in Finland naturally increases from north (~60 days) to south (~190 days), in association with earlier FFSS (~24 April) and later FFSE (~30 October). Statistically significant (p<0.05) trends in FFSL were all positive (increasing) ranged between 0 and 13.5 (days/decade) and mainly observed in the east, upper west, centre and upper north of Finland. Such lengthening trends in FFS were attributable to both earlier FFSS and later FFSE mostly over central and upper northern Finland, while only to later FFSE in eastern and upper western parts. Variations in both FFSL and FFSS were significantly associated with the Polar (POL) pattern over northern Finland, while with the East Atlantic (EA) pattern over eastern and upper western areas. However, the POL and Scandinavia (SCA) patterns were most influential CTs for FFSE variability over northern Finland.

Keywords: climate teleconnections, Finland, frost-free season, trend analysis

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Archibong Eso

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Gas turbine operators have been faced with the dramatic financial setback resulting from compressor fouling. In a highly deregulated power industry where there is stiffness in the market competition, has made it imperative to improvise means of reducing maintenance cost in other to yield maximum profit. Compressor fouling results from the deposition of contaminants in the presence of oil and moisture on the compressor blade or annulus surfaces, which leads to a loss in flow capacity and compressor efficiency. These combined effects reduce power output, increase heat rate and cause creep life reduction. This paper also contains a model of two gas turbine engines via Cranfield University software known as TURBOMATCH, which is simulation software for detecting engine fouling rate. The model engines are of different configurations and capacities, and are operating in two different modes of constant output power and turbine inlet temperature for a two and three stage filter system. The idea is to investigate the more economically viable filtration systems by gas turbine users based on performance only. It has been demonstrated in the results that the two spool engine is a little more beneficial compared to the single spool. This is as a result of a higher pressure ratio of the two spools as well as the deceleration of the high-pressure compressor and high-pressure turbine speed in a constant TET. Meanwhile, the inlet filtration system was properly designed and balanced with a well-timed and economical compressor washing regime/scheme to control compressor fouling. The different technologies of inlet air filtration and compressor washing are considered and an attempt at optimization with respect to the cost of a combination of both control measures are made.

Keywords: inlet filtration, pressure loss, single spool, two spool

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Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

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In this work, we have described a new 3-dimensional (3D) network of cross-linked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: redox enzyme, nanomaterials, biosensors, electrical communication

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

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In this work, we have described a new 3-dimensional (3D) network of crosslinked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: biosensor, nanomaterials, redox enzyme, thiol-modified Au surface

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Authors: Javier Romera, Alberto Justo, Ignacio Fidalgo, Joshue Perez, Javier Araluce

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Nowadays, individuals with mobility needs face a significant challenge when docking vehicles. In many cases, after parking, they encounter insufficient space to exit, leading to two undesired outcomes: either avoiding parking in that spot or settling for improperly placed vehicles. To address this issue, the following paper presents a parking control system employing gestural teleoperation. The system comprises three main phases: capturing body markers, interpreting gestures, and transmitting orders to the vehicle. The initial phase is centered around the MediaPipe framework, a versatile tool optimized for real-time gesture recognition. MediaPipe excels at detecting and tracing body markers, with a special emphasis on hand gestures. Hands detection is done by generating 21 reference points for each hand. Subsequently, after data capture, the project employs the MultiPerceptron Layer (MPL) for indepth gesture classification. This tandem of MediaPipe's extraction prowess and MPL's analytical capability ensures that human gestures are translated into actionable commands with high precision. Furthermore, the system has been trained and validated within a built-in dataset. To prove the domain adaptation, a framework based on the Robot Operating System (ROS), as a communication backbone, alongside CARLA Simulator, is used. Following successful simulations, the system is transitioned to a real-world platform, marking a significant milestone in the project. This real vehicle implementation verifies the practicality and efficiency of the system beyond theoretical constructs.

Keywords: gesture detection, mediapipe, multiperceptron layer, robot operating system

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Authors: Karina Reiter, Stefan Dullinger, Christoph Plutzar, Dietmar Moser

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Due to population growth and changing patterns of production and consumption, the demand for natural resources and, as a result, the pressure on Earth’s ecosystems are growing. Biodiversity mapping can be a useful tool for assessing species endangerment or detecting hotspots of extinction risks. This paper explores the benefits of using the change in trophic energy flows as a consequence of the human alteration of the biosphere in biodiversity mapping. To this end, multiple linear regression models were developed to explain species richness in areas where there is no human influence (i.e. wilderness) for three taxonomic groups (birds, mammals, amphibians). The models were then applied to predict (I) potential global species richness using potential natural vegetation (NPPpot) and (II) global ‘actual’ species richness after biomass appropriation using NPP remaining in ecosystems after harvest (NPPeco). By calculating the difference between predicted potential and predicted actual species numbers, maps of estimated species richness loss were generated. Results show that biomass appropriation for human use can indeed be linked to biodiversity loss. Areas for which the models predicted high species loss coincide with areas where species endangerment and extinctions are recorded to be particularly high by the International Union for Conservation of Nature and Natural Resources (IUCN). Furthermore, the analysis revealed that while the species distribution maps of the IUCN Red List of Threatened Species used for this research can determine hotspots of biodiversity loss in large parts of the world, the classification system for threatened and extinct species needs to be revised to better reflect local risks of extinction.

Keywords: biodiversity loss, biomass harvest, human appropriation of net primary production, species richness

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Authors: Majid Farsadrouh Rashti, Parisa Jahani, Amir Shafiee, Mehrdad Mofidi

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The dihydroxybenzene isomers, hydroquinone (HQ), catechol (CC) and resorcinol (RS) have been widely recognized as important environmental pollutants due to their toxicity and low degradability in the ecological environment. Speciation of HQ, CC and RS is very important for environmental analysis because they co-exist of these isomers in environmental samples and are too difficult to degrade as an environmental contaminant with high toxicity. There are many analytical methods have been reported for detecting these isomers, such as spectrophotometry, fluorescence, High-performance liquid chromatography (HPLC) and electrochemical methods. These methods have attractive advantages such as simple and fast response, low maintenance costs, wide linear analysis range, high efficiency, excellent selectivity and high sensitivity. A novel modified glassy carbon electrode (GCE) with Pd@ Cu/CNTs core−shell nanowires for the simultaneous determination of hydroquinone (HQ), catechol (CC) and resorcinol (RS) is described. A detailed investigation by field emission scanning electron microscopy and electrochemistry was performed in order to elucidate the preparation process and properties of the GCE/ Pd/CuNWs-CNTs. The electrochemical response characteristic of the modified GPE/LFOR toward HQ, CC and RS were investigated by cyclic voltammetry, differential pulse voltammetry (DPV) and Chronoamperometry. Under optimum conditions, the calibrations curves were linear up to 228 µM for each with detection limits of 0.4, 0.6 and 0.8 µM for HQ, CC and RS, respectively. The diffusion coefficient for the oxidation of HQ, CC and RS at the modified electrode was calculated as 6.5×10⁻⁵, 1.6 ×10⁻⁵ and 8.5 ×10⁻⁵ cm² s⁻¹, respectively. DPV was used for the simultaneous determination of HQ, CC and RS at the modified electrode and the relative standard deviations were 2.1%, 1.9% and 1.7% for HQ, CC and RS, respectively. Moreover, GCE/Pd/CuNWs-CNTs was successfully used for determination of HQ, CC and RS in real samples.

Keywords: dihydroxybenzene isomers, galvanized copper nanowires, electrochemical sensor, Palladium, speciation

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Authors: Meryem Zarri, Mohame Tahiri, Fouad Amraoui

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In the context of water resources shortage that Morocco is experiencing in recent years, the mobilization of non-conventional resources becomes a necessity. The reuse of treated water and the bioconversion of biological sewage sludge into value-added products is considered an environmentally friendly and economical approach to the management of this significant resource which represent at least 80 % of consumed fresh wate In this work, we compare the quality of treated water and sewage sludge from wastewater treatment plants in the peri-urban Casablanca by analyzing different physicochemical and bacteriological parameters. The choice was made for three wastewater plants installed in different regions and monitored either by LYDEC and Commune of Had Soualem and use different technologies. Recycling of treated water in agriculture and watering of green spaces is dependent on the compliance of the parameters with international standards (WHO, FAO, …etc.) The preliminary tests of the samples taken during the second half of the year 2021 showed that the advanced technologies put in place at the level of the Mediouna and the airport zone stations (membrane reactor and activated sludge, respectively) give water to the output of the stations more respectful of the standards required in terms of physicochemical parameters (pH, Conductivity, Tubidity, COD, BOD5, TNK, and TPK) and bacteriological (fecal germs, Escherichia Coli, streptococci, Helminthes eggs). The parameters relating to the Had Soualem natural lagoon station are generally at the tolerance’s threshold. The results of analyzes relating to the residual sludge collected at the end of the cycle are, on the whole satisfactory despite a fluctuating variability of the bacteriological parameters.

Keywords: urban wastewater treatment plants, purified wastewater, sewage sludge, physicochemical parameters, bacteriological parameters, peri-urban area of ​​casablanca, morocco

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Authors: T. D. Ibragimov, A. R. Imamaliyev, G. M. Bayramov

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The polymer network liquid crystals based on the liquid crystals Н37 and 5CB with polymethylvinilpirydine (PMVP) and polyethylene glycol (PEG) have been developed. Mesogene substance 4-n-heptyoxibenzoic acid (HOBA) is served for stabilization of obtaining composites. Kinetics of network formation is investigated by methods of polarization microscopy and integrated small-angle scattering. It is shown that gel-like states of the composite H-37 + PMVP + HOBA and 5CB+PEG+HOBA are formed at polymer concentration above 7 % and 9 %, correspondingly. At slow cooling, the system separates into a liquid crystal –rich phase and a liquid crystal-poor phase. At this case, transition of these phases in the H-37 + PMVP + HOBA (87 % + 12 % + 1 %) composite to an anisotropic state occurs at 49 оС and и 41 оС, accordingly, while the composite 5CB+PEG+HOBA (85% +13 % +2%) passes to anisotropic state at 36 оС corresponding to the isotropic-nematic transition of pure 5CB. The basic electro-optic parameters of the obtained composites are determined at room temperature. It is shown that the threshold voltage of the composite H-37 + PMVP + HOBA increase in comparison with pure H-37 and, accordingly, there is a shift of voltage dependence of rise times to the high voltage region. The contrast ratio worsens while decay time improves in comparison with the pure liquid crystal at all applied voltage. The switching times of the composite 5CB + PEG + HOBA (85% +13 % +2%) show anomalous behavior connected with incompleteness of the transition to an anisotropic state. Experimental results are explained by phase separation of the system, diminution of a working area of electro-optical effects and influence of areas with the high polymer concentration on areas with their low concentration.

Keywords: liquid crystals, polymers, small-angle scattering, optical properties

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Authors: Eric Sitorus, Septian Prahastudhi, Turgod Nainggolan, Erwin Riyanto

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Mining by block or panel caving is a mining method that takes advantage of fractures within an ore body, coupled with gravity, to extract material from a predetermined column of ore. The caving column is weakened from beneath through the use of undercutting, after which the ore breaks up and is extracted from below in a continuous cycle. The nature of this method induces cyclical stresses on the pillars of excavations as stress is built up and released over time, which has a detrimental effect on both the installed ground support and the rock mass itself. Ground support capacity, especially on the production where excavation void ratio is highest, is subjected to heavy loading. Strain above threshold of the elongation of support capacity can yield resulting in damage to excavations. Geotechnical engineers must evaluate not only the remnant capacity of ground support systems but also investigate depth of rock mass yield within pillars, backs and floors. Ground Penetrating Radar (GPR) is a geophysical method that has the ability to evaluate rock mass damage using electromagnetic waves. This paper illustrates a case study from the Grasberg mining complex where non-invasive information on the depth of damage and condition of the remaining rock mass was required. GPR with 100 MHz antenna resolution was used to obtain images of the subsurface to determine rehabilitation requirements prior to recommencing production activities. The GPR surveys were used to calibrate the reflection coefficient response of varying rock mass conditions to known Rock Quality Designation (RQD) parameters observed at the mine. The calibrated GPR survey allowed site engineers to map subsurface conditions and plan rehabilitation accordingly.

Keywords: block caving, ground penetrating radar, reflectivity, RQD

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Richard G. Boakye, Dara A Stanley, Mathavan Vickneswaran, Blanaid White

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The cultivation of cocoa (Theobroma cocoa), an important cash crop that contributes immensely towards the economic growth of several Western African countries, depends almost entirely on pesticide application owing to the plant’s vulnerability to pest and disease attacks. However, the extent to which pesticides inputted for cocoa cultivation impact bees and bee products has rarely received attention in research. Through this study, the effects of pesticides applied for cocoa cultivation on honey in Ghana were examined by evaluating honey samples from cocoa and forest ecosystems in Ghana. An analysis of five honey samples from each land use type confirmed pesticide contaminants from these land use types at measured concentrations for acetamiprid (0.051mg/kg); imidacloprid (0.004-0.02 mg/kg), thiamethoxam (0.013-0.017 mg/kg); indoxacarb (0.004-0.045 mg/kg) and sulfoxaflor (0.004-0.026 mg/kg). None of the observed pesticide concentrations exceeded EU maximum residue levels, indicating no compromise of the honey quality for human consumption. However, from the results, it could be inferred that toxic effects on bees may not be ruled out because observed concentrations largely exceeded the threshold of 0.001 mg/kg at which sublethal effects on bees have previously been reported. One of the most remarkable results to emerge from this study is the detection of imidacloprid in all honey samples analyzed, with sulfoxaflor and thiamethoxam also being detected in 93% and 73% of the honey samples, respectively. This suggests the probable prevalence of pesticide use in the landscape. However, the conclusions reached in this study should be interpreted within the scope of pesticide applications within Bia West District and not necessarily extended to other cocoa-producing districts in Ghana. Future studies should therefore include multiple cocoa-growing districts and other non-cocoa farming landscapes. Such an approach can give a broader outlook on pesticide residues in honey produced in Ghana.

Keywords: honey, cocoa, pesticides, bees, land use, landscape, residues, Ghana

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Authors: Bulcha Belay Etana, Benny Malengier, Janarthanan Krishnamoorthy, Timothy Kwa, Lieva VanLangenhove

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Electromyography measures the electrical activity of muscles using surface electrodes or needle electrodes to monitor various disease conditions. Recent developments in the signal acquisition of electromyograms using textile electrodes facilitate wearable devices, enabling patients to monitor and control their health status outside of healthcare facilities. Here, we have developed and tested wearable textile electrodes to acquire electromyography signals from patients suffering from Parkinson’s disease and incorporated a feedback-control system to relieve muscle cramping through thermal stimulus. In brief, the textile electrodes made of stainless steel was knitted into a textile fabric as a sleeve, and their electrical characteristic, such as signal-to-noise ratio, was compared with traditional electrodes. To relieve muscle cramping, a heating element made of stainless-steel conductive yarn sewn onto cotton fabric, coupled with a vibration system, was developed. The system integrated a microcontroller and a Myoware muscle sensor to activate the heating element as well as the vibration motor when cramping occurs, and at the same time, the element gets deactivated when the muscle cramping subsides. An optimum therapeutic temperature of 35.5 °C is regulated by continuous temperature monitoring to deactivate the heating system when this threshold value is reached. The textile electrode exhibited a signal-to-noise ratio of 6.38dB, comparable to that of the traditional electrode’s value of 7.05 dB. For a given 9 V power supply, the rise time was about 6 minutes for the developed heating element to reach an optimum temperature.

Keywords: smart textile system, wearable electronic textile, electromyography, heating textile, vibration therapy, Parkinson’s disease

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