Search results for: detecting of envelope modulation on noise

Authors: Keerthi S. Shetty, Annappa Basava

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In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

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Authors: Charn Loong Ng, Mamun Bin Ibne Reaz

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As elderly population grows globally, the percentage of people diagnosed with musculoskeletal disorder (MSD) increase proportionally. Electromyography (EMG) is an important biosignal that contributes to MSD’s clinical diagnose and recovery process. Conventional conductive electrode has many disadvantages in the continuous EMG measurement application. This research has design a new surface EMG biosensor based on the parallel-plate capacitive coupling principle. The biosensor is developed by using a double-sided PCB with having one side of the PCB use to construct high input impedance circuitry while the other side of the copper (CU) plate function as biosignal sensing metal plate. The metal plate is insulated using kapton tape for contactless application. The result implicates that capacitive biosensor is capable to constantly capture EMG signal without having galvanic contact to human skin surface. However, there are noticeable noise couple into the measured signal. Post signal processing is needed in order to present a clean and significant EMG signal. A complete design of single ended, non-contact, high input impedance, front end EMG biosensor is presented in this paper.

Keywords: contactless, capacitive, biosensor, electromyography

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Authors: Gurbir Singh Khaira, Anmoldeep Singh Kang

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Exposures to environmental pollution remain a major source of health risk throughout the world, though risks are generally higher in developing countries, where poverty, lack of investment in modern technology and weak environmental legislation combine to cause high pollution levels. This paper will tell us about the environment is threatened severely by so many problems, some of which are caused by the activities of Construction Projects. The research reveals major environmental impacts of building construction projects to include environmental pollution, resource depletion and habitat destruction causing Destruction of ecosystem, Desertification, Soil Erosion and increasing Material Wastage. Construction is considered as one of the main sources of environmental pollution in the world, the level of knowledge and awareness of project participants, especially project managers, with regards to environmental impacts of construction processes needs to be enhanced. It was found that ‘Transportation Resource’, ‘Noise Pollution’, and ‘Dust Generation with Construction Machinery’ are the greatest environmental impacts in INDIA respectively. The results of this study are useful for construction managers and other participants in construction sites to become aware of construction processes impacts on the environment.

Keywords: construction projects, environmental impacts, material waste age, awareness

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Authors: Kaushalendra K. Khadanga, Lee Hee Hyol

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Passenger comfort has been paramount in the design of suspension systems of high speed cars. To analyze the effect of vibration on vehicle ride quality, a vertical model of a six degree of freedom railway passenger vehicle, with front and rear suspension, is built. It includes car body flexible effects and vertical rigid modes. A second order linear shaping filter is constructed to model Gaussian white noise into random rail excitation. The temporal correlation between the front and rear wheels is given by a second order Pade approximation. The complete track and the vehicle model are then designed. An active secondary suspension system based on a Linear Quadratic Gaussian (LQG) optimal control method is designed. The results show that the LQG control method reduces the vertical acceleration, pitching acceleration and vertical bending vibration of the car body as compared to the passive system.

Keywords: active suspension, bending vibration, railway vehicle, vibration control

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Authors: Raef Cherif, Houssine Bakkali, Wafaa El Khatiri, Yacine Yaddaden

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The analysis of how vibrations are transmitted between components is required in many engineering applications. Transfer path analysis (TPA) has been a valuable engineering tool for solving Noise, Vibration, and Harshness (NVH problems using sub-structuring applications. The most challenging part of a TPA analysis is estimating the equivalent forces at the contact points between the active and the passive side. Component TPA in situ Method calculates these forces by inverting the frequency response functions (FRFs) measured at the passive subsystem, relating the motion at indicator points to forces at the interface. However, matrix inversion could pose problems due to the ill-conditioning of the matrices leading to inaccurate results. This paper establishes a TPA model for an academic system consisting of two plates linked by four springs. A numerical study has been performed to improve the interface forces identification. Several parameters are studied and discussed, such as the singular value rejection and the number and position of indicator points chosen and used in the inversion matrix.

Keywords: transfer path analysis, matrix inverse method, indicator points, SVD decomposition

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Lingling Shui, Shuting Xie

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As alternatives to electronic devices, optically active structures from responsive nanomaterials offer great opportunity buildup smart functional sensors. Hereby, we report on droplet microfluidics enabled construction and application of photonic microcapsules (PMCs) for colorimetric temperature microsensors, enabling miniaturization for injectable local micro-area sensing and integration for large-area sensing. Monodispersed PMCs are produced by in-situ photopolymerization of hydrogel shells of cholesteric liquid crystal (CLC)-in-water-in-oil double emulsion droplets prepared using microfluidic devices, with controllable physical structures and chemical compositions. Constructed PMCs exhibit thermal responsive structural color according to the selective Bragg reflection of CLC’s periodical helical structures within the microdroplet’s spherical confinement. Constructed PMCs with tunable size and composition have been successfully applied for monitoring the living cell extracellular temperature via co-incubation with cell suspension, and for detecting human body temperature via a flexible device from assembled PMCs. These PMCs could be flexibly applied in either micro-environment or large-area surface, enabling wide applications for precision temperature monitoring biological activities (e.g. cells or organs), optoelectronic devices working conditions (e.g. temperature indicators under extreme conditions), and etc.

Keywords: droplet, microfluidics, assembly, soft materials, microsensor

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Authors: Gehan Moubarz, Atef M. F. Mohammed, Inas A. Saleh, Heba Mahdy-Abdallah, Amal Saad-Hussein

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This study estimates the association between respiratory outcomes among employees of a secondary aluminum plant and airborne pollutants. Additionally, it looks into the relationship between pulmonary dysfunction in workers and XRCC1 gene polymorphisms. 110 exposed workers and 58 non-exposed workers participated in the study. Measurements have been conducted on SO₂, NO₂, and particulate particles. Pulmonary function was tested. Eosinophil cationic protein (ECP), C-reactive protein (CRP), matrix metalloproteinase-1 (MMP-1), interleukin 6 (IL6), GM-CSF, X-Ray Repair Cross Complementing 1 (XRCC1) protein, and genotyping of XRCC1 gene polymorphisms were examined. Results: The annual average concentrations of (PM₂.₅, PM₁₀, TSP, SO₂, and NO₂) were lower than the permissible limit. The areas around ovens, evaporators, and cold rolling mills exhibited the highest amounts. The majority of employees in these departments had impaired lung function. With longer exposure times, the exposed group's FEV1% and FVC% considerably reduced. The exposed workers had considerably higher XRCC1 levels. The evaluated inflammatory biomarkers showed no statistically significant difference. Conclusion: Aluminum workers are at risk of developing respiratory disorders. The level of serum XRCC1 may act as a biomarker that might be very useful for detecting susceptible workers.

Keywords: aluminum industry, particulate matter, SO₂, NO₂, lung function, XRCC1 gene polymorphism, XRCC1 protein, inflammatory biomarkers

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Authors: Varshith Kotagiri, Lei Li

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Fluorescent sensors are organic fluorophores that detect specific analytes with quantitative fluorescence intensity changes. They have offered impressive benefits compared with instrumental techniques, such as low cost, high selectivity, and rapid responses. One issue that limits the fluorescent sensors for further application is their poor solubility in the aqueous medium, where most targeted analytes, including metal ions, inorganic anions, and neutral biomolecules, are readily soluble. When fluorescent sensors are utilized to detect these analytes, a heterogeneous phase is formed. In most cases, an extra water-miscible organic solvent is needed as an additive to facilitate the sensing process, which complicates the measurement operations and produces more organic waste. We aim to resolve this issue by skillful molecular design to introduce a hydrophilic side chain to the fluorescent sensor, increasing its water solubility and facilitating its sensing process to analytes, like various protons, fluoride ions, and copper ions, in an aqueous medium. Simultaneously, its sensitivity and selectivity will be retained. This work will simplify the sensing operations and reduce the amount of organic waste produced during the measurement. This strategy will additionally be of broad interest to the chemistry community, as it introduces the idea of modifying the molecular structure to apply an initial hydrophobic compound under hydrophilic conditions in a feasible way.

Keywords: organic fluorescent sensor, analytes, sensing, aqueous medium, phenanthroimidazole, hydrophilic side chain

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Authors: Haiqin Zhou, Robin Irons

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The introduction of hydrogen into local networks may, in many cases, require the initial operation of those systems on natural gas/hydrogen blends, either because of a lack of sufficient hydrogen to allow a 100% conversion or because existing infrastructure imposes limitations on the % hydrogen that can be burned before the end-use technologies are replaced. In many systems, the largest number of end-use technologies are small-scale but numerous appliances used for domestic and industrial heating and cooking. In such a scenario, it is important to understand exactly how much hydrogen can be introduced into these appliances before their performance becomes unacceptable and what imposes that limitation. This study seeks to explore a range of significantly higher hydrogen blends and a broad range of factors that might limit operability or environmental acceptability. We will present tests from a burner designed for space heating and optimized for natural gas as an increasing % of hydrogen blends (increasing from 25%) were burned and explore the range of parameters that might govern the acceptability of operation. These include gaseous emissions (particularly NOx and unburned carbon), temperature, flame length, stability and general operational acceptability. Results will show emissions, Temperature, and flame length as a function of thermal load and percentage of hydrogen in the blend. The relevant application and regulation will ultimately determine the acceptability of these values, so it is important to understand the full operational envelope of the burners in question through the sort of extensive parametric testing we have carried out. The present dataset should represent a useful data source for designers interested in exploring appliance operability. In addition to this, we present data on two factors that may be absolutes in determining allowable hydrogen percentages. The first of these is flame blowback. Our results show that, for our system, the threshold between acceptable and unacceptable performance lies between 60 and 65% mol% hydrogen. Another factor that may limit operation, and which would be important in domestic applications, is the acoustic performance of these burners. We will describe a range of operational conditions in which hydrogen blend burners produce a loud and invasive ‘screech’. It will be important for equipment designers and users to find ways to avoid this or mitigate it if performance is to be deemed acceptable.

Keywords: blends, operational, domestic appliances, future system operation.

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: Siamak Ghorbani, Nikolay Ivanovich Polushin

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Chatter vibrations, occurring during cutting process, cause vibration between the cutting tool and workpiece, which deteriorates surface roughness and reduces tool life. The purpose of this study is to investigate the influence of cutting parameters and tool construction on surface roughness and vibration in turning of aluminum alloy AA2024. A new design of cutting tool is proposed, which is filled up with epoxy granite in order to improve damping capacity of the tool. Experiments were performed at the lathe using carbide cutting insert coated with TiC and two different cutting tools made of AISI 5140 steel. Taguchi L9 orthogonal array was applied to design of experiment and to optimize cutting conditions. By the help of signal-to-noise ratio and analysis of variance the optimal cutting condition and the effect of the cutting parameters on surface roughness and vibration were determined. Effectiveness of Taguchi method was verified by confirmation test. It was revealed that new cutting tool with epoxy granite has reduced vibration and surface roughness due to high damping properties of epoxy granite in toolholder.

Keywords: ANOVA, damping capacity, surface roughness, Taguchi method, vibration

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Authors: Tarik Sipahi

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Photonics research continues to push the boundaries of optical science, and the development of metasurface technology has emerged as a transformative force in this domain. The work presents the intricacies of a unified metasurface design tailored for efficient polarization and color control in optical systems. The proposed unified metasurface serves as a singular, nanoengineered optical element capable of simultaneous polarization modulation and color encoding. Leveraging principles from metamaterials and nanophotonics, this design allows for unprecedented control over the behavior of light at the subwavelength scale. The metasurface's spatially varying architecture enables seamless manipulation of both polarization states and color wavelengths, paving the way for a paradigm shift in optical system design. The advantages of this unified metasurface are diverse and impactful. By consolidating functions that traditionally require multiple optical components, the design streamlines optical systems, reducing complexity and enhancing overall efficiency. This approach is particularly promising for applications where compactness, weight considerations, and multifunctionality are crucial. Furthermore, the proposed unified metasurface design not only enhances multifunctionality but also addresses key challenges in optical system design, offering a versatile solution for applications demanding compactness and lightweight structures. The metasurface's capability to simultaneously manipulate polarization and color opens new possibilities in diverse technological fields. The research contributes to the evolution of optical science by showcasing the transformative potential of metasurface technology, emphasizing its role in reshaping the landscape of optical system architectures. This work represents a significant step forward in the ongoing pursuit of pushing the boundaries of photonics, providing a foundation for future innovations in compact and efficient optical devices.

Keywords: metasurface, nanophotonics, optical system design, polarization control

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Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Kim Jin Sup

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This paper presents a vertical hall effect device using current-mode. Among different geometries that have been studied and simulated using COMSOL Multiphysics, optimized cross-shaped model displayed the best sensitivity. The cross-shaped model emerged as the optimum plate to fit the lowest noise and residual offset and the best sensitivity. The symmetrical cross-shaped hall plate is widely used because of its high sensitivity and immunity to alignment tolerances resulting from the fabrication process. The hall effect device has been designed using a 0.18-μm CMOS technology. The simulation uses the nominal bias current of 12μA. The applied magnetic field is from 0 mT to 20 mT. Simulation results achieved in COMSOL and validated with respect to the electrical behavior of equivalent circuit for Cadence. Simulation results of the one structure over the 13 available samples shows for the best geometry a current-mode sensitivity of 6.6 %/T at 20mT. Acknowledgment: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R7117-16-0165, Development of Hall Effect Semiconductor for Smart Car and Device).

Keywords: vertical hall device, current-mode, crossed-shaped model, CMOS technology

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Authors: Naydelis Brito Suárez, Deni Librado Torres Román, Fernando Hermosillo Reynoso

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Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time.

Keywords: convex hull, dynamic ROI detection, pixel entropy, time series, moving objects

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Authors: A. Watson, G. Telford, D. I. Pritchard

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Objective: We investigated the immunomodulatory ability of licorice (Glycyrrhiza glabra). Such activities could have value for the management of common immunological diseases in dogs, such as environmental allergy. This study investigated the potential of a Licorice root extract (LRE) to influence the relative expression of Th-1, Th-2, and Th-17 cytokines in canine peripheral blood mononuclear cells (PBMC). Methods: A LRE was prepared using an alcoholic-aqueous-based solvent method. The extract was tested in three in vitro assays using canine leukocytes to determine its toxicity and immunoregulatory profile. Extract toxicity was assessed using the human T-lymphocyte cell line, Jurkat E6.1. The impact of the extract on the proliferation of concanavalin-activated canine PBMC was also determined. Finally, the extract was assessed for its ability to influence cytokine release in activated PBMC, measuring culture medium concentrations of interleukin-17, interferon gamma, and interleukin-4. One-way ANOVA followed by Dunnett’s post-test was used for statistics using concanavalin positive control as reference (p ≤ 0.05). Results: There was evidence that the LRE had specific immunomodulatory properties, causing significant inhibition of IL4 expression over a non-toxic/non-cytostatic concentration range (p < 0.001). In the same cell incubations, there was no significant impact on IL17 nor IFNg over the same non-toxic/non-cytostatic concentration range. Conclusion: The study provides in vitro evidence that LRE preferentially reduces the expression of a Th-2-type cytokine, IL4. The dog population, as with humans, is prone to conditions associated with a Th-2 bias of the immune system, such as environmental allergy. Based on these results, licorice merits further evaluation as a useful immune modulator for such allergic diseases.

Keywords: cytokine, Glycyrrhiza glabra, peripheral blood mononuclear cells, T-cell activation

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Raj Raghupathy

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Recurrent Spontaneous Miscarriage (RSM) is a common form of pregnancy loss, 50% of which are due to ‘unexplained’ causes. Evidence exists to suggest that RSM may be caused by immunologic factors such as cytokines which are critical molecules of the immune system, with an impressive array of capabilities. An association appears to exist between Th2-type reactivity (mediated by Th2 or anti-inflammatory cytokines) and normal, successful pregnancy, and between unexplained RSM and Th1 cytokine dominance. If pro-inflammatory cytokines are indeed associated with pregnancy loss, the suppression of these cytokines, and thus the ‘redirection’ of maternal reactivity, may help prevent cytokine-mediated pregnancy loss. The objective of this study was to explore the possibility of modulating cytokine production using Dydrogesterone (Duphaston®), an orally-administered progestogen. Peripheral blood mononuclear cells from 34 women with a history of at least 3 unexplained recurrent miscarriages were stimulated in vitro with a mitogen (to elicit cytokine production) in the presence and absence of dydrogesterone. Levels of selected pro- and anti-inflammatory cytokines produced by peripheral blood mononuclear cells were measured after exposure to these progestogens. Dydrogesterone down-regulates the production of pro-inflammatory cytokines and up-regulates the production of anti-inflammatory cytokines. The ratios of Th2 to Th1 cytokines are markedly elevated in the presence of dydrogesterone, indicating a shift from potentially harmful maternal Th1 reactivity to a more pregnancy-conducive Th2 profile. We used a progesterone receptor antagonist to show that this cytokine-modulating effect of dydrogesterone is mediated via the progesterone receptor. Dydrogesterone also induces the production of the Progesterone-Induced Blocking Factor (PIBF); lymphocytes exposed to PIBF produce higher levels of Th2 cytokines, affecting a Th1 → Th2 cytokine shift which could be favourable to the success of pregnancy. We conclude that modulation of maternal cytokine production profiles is possible with dydrogesterone which has the merits that it can be administered orally and that it is safe.

Keywords: cytokines, dydrogesterone, progesterone, recurrent spontaneous miscarriage

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Authors: Kumari Bandana Bhatt, Navin Bhatt

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Background: Orphans and vulnerable children (OVC) are high risk of physical, mental, sexual and emotional abuse and face social stigma and discrimination which significantly increase the risk of mental and behavioral disorders such as anxiety, depression or emotional problems even they stay in well run child care homes. The objective of this study was to estimate the prevalence of depression and determine the determinants among OVC in child care homes in Nepal. Methods: An institutional-based analytical cross-sectional study was conducted in twenty orphanages of five districts of Nepal. Six hundred two children were recruited into the study. After the informed consent form obtaining, the guardian and assent were interviewed by a semi-structured questionnaire and Beck Depression Inventory-II (BDI-II). Logistic regression was used for detecting the association between variables at the significant level of =0.05. Results: The study revealed that 33.20% of OVC had depression. Among them 66.80% of children experienced minimal depression, 17.40% had mild depression, 11.30% had moderate depression 4.50% had severe depression. Sex, alcohol drinking, congenital problem, social support and bully were the main variables associated with depression among OVC of the child care homes in Nepal. Conclusion: Prevalence of depression was high among the orphans and vulnerable children living in child care homes especially among the female children in Nepal. Therefore, early identification and instituting of preventive measures of depression are essential to reduce this problem in this special group of children living in child care homes.

Keywords: Mental health, Depression, Orphans and vulnerable children, child care homes

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Mercedes Del Rio Merino, Jaime Santacruz Astorqui, Paola Villoria Saez, Carmen Viñas Arrebola

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The lack of treatment of the waste from construction and demolition waste (CDW) is a problem that must be solved immediately. It is estimated that in the world not to use CDW generates an increase in the use of new materials close to 20% of the total value of the materials used. The problem is even greater in case these wastes are considered hazardous because the final deposition of them may also generate significant contamination. Therefore, the possibility of including CDW in the manufacturing of building materials, represents an interesting alternative to ensure their use and to reduce their possible risk. In this context and in the last years, many researches are being carried out in order to analyze the viability of using CDW as a substitute for the traditional raw material of high environmental impact. Even though it is true, much remains to be done, because these works generally characterize materials but not specific applications that allow the agents of the construction to have the guarantees required by the projects. Therefore, it is necessary the involvement of all the actors included in the life cycle of these new construction materials, and also to promote its use for, for example, definition of standards, tax advantages or market intervention is necessary. This paper presents the main findings reached in "Waste to resources (W2R)" project since it began in October 2014. The main goal of the project is to develop new materials, elements and construction systems, manufactured from CDW, to be used in improving the energy efficiency of buildings. Other objectives of the project are: to quantify the CDW generated in the energy rehabilitation works, specifically wastes from the building envelope; and to study the traceability of CDW generated and promote CDW reuse and recycle in order to get close to the life cycle of buildings, generating zero waste and reducing the ecological footprint of the construction sector. This paper determines the most important aspects to consider during the design of new constructive solutions, which improve the energy efficiency of buildings and what materials made with CDW would be the most suitable for that. Also, a survey to select best practices for reducing "close to zero waste" in refurbishment was done. Finally, several pilot rehabilitation works conform the parameters analyzed in the project were selected, in order to apply the results and thus compare the theoretical with reality. Acknowledgements: This research was supported by the Spanish State Secretariat for Research, Development and Innovation of the Ministry of Economy and Competitiveness under "Waste 2 Resources" Project (BIA2013-43061-R).

Keywords: building waste, construction and demolition waste, recycling, resources

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Authors: Zoran Veršić, Josip Galić, Marin Binički, Lucija Stepinac

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With the current increase in CO₂ emissions and global warming, the sustainability of both existing and new solutions must be assessed on a wide scale. As the implementation of closed cavity facades (CCF) is on the rise, a variety of factors must be included in the analysis of new types of CCF. This paper aims to cover the relevant factors included in the sustainability assessment of new types of CCF. Several mathematical models are being used to describe the physical behavior of CCF. Depending on the type of CCF, they cover the main factors which affect the durability of the façade: thermal behavior of various elements in the façade, stress, and deflection of the glass panels, pressure inside a cavity, exchange rate, and the moisture buildup in the cavity. CCF itself represents a complex system in which all mentioned factors must be considered mutually. Still, the façade is only an envelope of a more complex system, the building. Choice of the façade dictates the heat loss and the heat gain, thermal comfort of inner space, natural lighting, and ventilation. Annual consumption of energy for heating, cooling, lighting, and maintenance costs will present the operational advantages or disadvantages of the chosen façade system in both the economic and environmental aspects. Still, the only operational viewpoint is not all-inclusive. As the building codes constantly demand higher energy efficiency as well as transfer to renewable energy sources, the ratio of embodied and lifetime operational energy footprint of buildings is changing. With the drop in operational energy CO₂ emissions, embodied energy emissions present a larger and larger share in the lifecycle emissions of the building. Taken all into account, the sustainability assessment of a façade, as well as other major building elements, should include all mentioned factors during the lifecycle of an element. The challenge of such an approach is a timescale. Depending on the climatic conditions on the building site, the expected lifetime of CCF can exceed 25 years. In such a time span, some of the factors can be estimated more precisely than others. The ones depending on the socio-economic conditions are more likely to be harder to predict than the natural ones like the climatic load. This work recognizes and summarizes the relevant factors needed for the assessment of new types of CCF, considering the entire lifetime of a façade element and economic and environmental aspects.

Keywords: assessment, closed cavity façade, life cycle, sustainability

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Authors: Emma K. Sales

Abstract:

Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments; 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit. Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR analysis, genotype, genetic diversity, cultivars

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Authors: S. Mohamed, D. Gonzalez, C. Sayada, P. Halfon

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Drug resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance (DR) interpretations has not yet been studied. Fifty HIV-1 patients who experienced virological failure were included in this retrospective study. The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, E, F, and G. DeepChek®-HIV simplified DR interpretation software was used to compare Sanger sequencing and UDS. The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of DR revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with > 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the DR interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences. A combination of UDS and DeepChek® software for the interpretation of DR results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterisation of the viral population by identifying additional resistance mutations and improving the DR interpretation.

Keywords: HIV-1, ultra-deep sequencing, Sanger sequencing, drug resistance

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Authors: Salma Krafessi

Abstract:

The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: Eva Nedeliakova, Jaroslav Masek, Juraj Camaj

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This article describes the results of research focused on quality of railway freight transport services. Improvement of these services has a crucial importance in customer considering on the future use of railway transport. Processes filling the customer demands and output quality assessment were defined as a part of the research. In this, contribution is introduced the map of quality planning and the algorithm of applied methodology. It characterises a model which takes into account characters of transportation with linking a perception services quality in ordinary and extraordinary operation. Despite the fact that rail freight transport has its solid position in the transport market, lots of carriers worldwide have been experiencing a stagnation for a couple of years. Therefore, specific results of the research have a significant importance and belong to numerous initiatives aimed to develop and support railway transport not only by creating a single railway area or reducing noise but also by promoting railway services. This contribution is focused also on the application of dynamic quality models which represent an innovative method of evaluation quality services. Through this conception, time factor, expected and perceived quality in each moment of the transportation process can be taken into account.

Keywords: quality, railway, transport, service

Procedia PDF Downloads 445