Search results for: Signal Processing

Authors: Zenah Gheblawi, Susan Armijo-Olivo, Elisa B. Pelai, Vaishali Sharma, Musa Tashfeen, Angela Fung, Francisca Claveria

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Due to physiological differences between men and women, pain is experienced differently between the two sexes. Chronic pain disorders, notably temporomandibular disorders (TMDs), disproportionately affect women in diagnosis, and pain severity in opposition of their male counterparts. TMDs are a type of musculoskeletal disorder that target the masticatory muscles, temporalis muscle, and temporomandibular joints, causing considerable orofacial pain which can usually be referred to the neck and back. Therapeutic methods are scarce, and are not TMD-centered, with the latest research suggesting that subjects with chronic musculoskeletal pain disorders have abnormal alterations in the grey matter of their brains which can be remedied with exercise, and thus, decreasing the pain experienced. The aim of the study is to investigate the effects of exercise therapy in TMD female patients experiencing chronic jaw pain and to assess the consequential effects on brain activity. In a randomized controlled trial, the effectiveness of an exercise program to improve brain alterations and clinical outcomes in women with TMD pain will be tested. Women with chronic TMD pain will be randomized to either an intervention arm or a placebo control group. Women in the intervention arm will receive 8 weeks of progressive exercise of motor control training using visual feedback (MCTF) of the cervical muscles, twice per week. Women in the placebo arm will receive innocuous transcutaneous electrical nerve stimulation during 8 weeks as well. The primary outcomes will be changes in 1) pain, measured with the Visual Analogue Scale, 2) brain structure and networks, measured by fractional anisotropy (brain structure) and the blood-oxygen level dependent signal (brain networks). Outcomes will be measured at baseline, after 8 weeks of treatment, and 4 months after treatment ends and will determine effectiveness of MCTF in managing TMD, through improved clinical outcomes. Results will directly inform and guide clinicians in prescribing more effective interventions for women with TMD. This study is underway, and no results are available at this point. The results of this study will have substantial implications on the advancement in understanding the scope of plasticity the brain has in regards with pain, and how it can be used to improve the treatment and pain of women with TMD, and more generally, other musculoskeletal disorders.

Keywords: exercise therapy, musculoskeletal disorders, physical therapy, rehabilitation, tempomandibular disorders

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Sunday Olufemi Adesogan

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The supply of potable water at least is a very important index in national development. Water tariffs depend on the treatment cost which carries the highest percentage of the total operation cost in any water supply scheme. In order to keep water tariffs as low as possible, treatment costs have to be minimized. The University of Ibadan, Nigeria, water supply scheme consists of a treatment plant with three distribution stations (Amina way, Kurumi and Lander) and two raw water supply sources (Awba dam and Eleyele dam). An operational study of the scheme was carried out to ascertain the efficiency of the supply of potable water on the campus to justify the need for water supply schemes in tertiary institutions. The study involved regular collection, processing and analysis of periodic operational data. Data collected include supply reading (water production on daily basis) and consumers metered reading for a period of 22 months (October 2013 - July 2015), and also collected, were the operating hours of both plants and human beings. Applying the required mathematical equations, total loss was determined for the distribution system, which was translated into monetary terms. Adequacies of the operational functions were also determined. The study revealed that water supply scheme is justified in tertiary institutions. It was also found that approximately 10.7 million Nigerian naira (N) is lost to leakages during the 22-month study period; the system’s storage capacity is no longer adequate, especially for peak water production. The capacity of the system as a whole is insufficient for the present university population and that the existing water supply system is not being operated in an optimal manner especially due to personnel, power and system ageing constraints.

Keywords: development, panacea, supply, water

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Authors: Othman Maklouf, Abdunnaser Tresh

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Land vehicle navigation is subject of great interest today. Global Positioning System (GPS) is the main navigation system for positioning in such systems. GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation (INS) is the implementation of inertial sensors to determine the position and orientation of a vehicle. The availability of low-cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop INS using an inertial measurement unit (IMU). INS has unbounded error growth since the error accumulates at each step. Usually, GPS and INS are integrated with a loosely coupled scheme. With the development of low-cost, MEMS inertial sensors and GPS technology, integrated INS/GPS systems are beginning to meet the growing demands of lower cost, smaller size, and seamless navigation solutions for land vehicles. Although MEMS inertial sensors are very inexpensive compared to conventional sensors, their cost (especially MEMS gyros) is still not acceptable for many low-end civilian applications (for example, commercial car navigation or personal location systems). An efficient way to reduce the expense of these systems is to reduce the number of gyros and accelerometers, therefore, to use a partial IMU (ParIMU) configuration. For land vehicular use, the most important gyroscope is the vertical gyro that senses the heading of the vehicle and two horizontal accelerometers for determining the velocity of the vehicle. This paper presents a field experiment for a low-cost strap down (ParIMU)\GPS combination, with data post processing for the determination of 2-D components of position (trajectory), velocity and heading. In the present approach, we have neglected earth rotation and gravity variations, because of the poor gyroscope sensitivities of our low-cost IMU (Inertial Measurement Unit) and because of the relatively small area of the trajectory.

Keywords: GPS, IMU, Kalman filter, materials engineering

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Authors: Arijit Saha, Hassan Kassem, Leo Hoening

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Ongoing climate change demands the increasing use of renewable energies. Wind energy plays an important role in this context since it can be applied almost everywhere in the world. To reduce the costs of wind turbines and to make them more competitive, simulations are very important since experiments are often too costly if at all possible. The wind turbine on a vast open area experiences the turbulence generated due to the atmosphere, so it was of utmost interest from this research point of view to generate the turbulence through various Inlet Turbulence Generation methods like Precursor cyclic and Kaimal Spectrum Exponential Coherence (KSEC) in the computational simulation domain. To be able to validate computational fluid dynamic simulations of wind turbines with the experimental data, it is crucial to set up the conditions in the simulation as close to reality as possible. This present work, therefore, aims at investigating the turbulent inflow strategy and boundary conditions of KSEC and providing a comparative analysis alongside the Precursor cyclic method for Large Eddy Simulation within the context of wind energy applications. For the generation of the turbulent box through KSEC method, firstly, the constrained data were collected from an auxiliary channel flow, and later processing was performed with the open-source tool PyconTurb, whereas for the precursor cyclic, only the data from the auxiliary channel were sufficient. The functionality of these methods was studied through various statistical properties such as variance, turbulent intensity, etc with respect to different Bulk Reynolds numbers, and a conclusion was drawn on the feasibility of KSEC method. Furthermore, it was found necessary to verify the obtained data with DNS case setup for its applicability to use it as a real field CFD simulation.

Keywords: Inlet Turbulence Generation, CFD, precursor cyclic, KSEC, large Eddy simulation, PyconTurb

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Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

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Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

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Authors: Farideh Halakou, Emel Sen, Attila Gursoy, Ozlem Keskin

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Protein–Protein Interactions (PPIs) mediate major biological processes in living cells. The study of PPIs as networks and analyze the network properties contribute to the identification of genes and proteins associated with diseases. In this study, we have created the sub-networks of brain and lung metastasis from primary tumor in breast cancer. To do so, we used seed genes known to cause metastasis, and produced their interactions through a network-topology based prioritization method named GUILDify. In order to have the experimental support for the sub-networks, we further curated them using STRING database. We proceeded by modeling structures for the interactions lacking complex forms in Protein Data Bank (PDB). The functional enrichment analysis shows that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing are more involved in brain metastasis. The structural analyses of the sub-networks vividly demonstrated their difference in terms of using specific interfaces in lung and brain metastasis. Furthermore, the topological analysis identified genes such as RPL5, MMP2, CCR5 and DPP4, which are already known to be associated with lung or brain metastasis. Additionally, we found 6 and 9 putative genes that are specific for lung and brain metastasis, respectively. Our analysis suggests that variations in genes and pathways contributing to these different breast metastasis types may arise due to change in tissue microenvironment. To show the benefits of using structural PPI networks instead of traditional node and edge presentation, we inspect two case studies showing the mutual exclusiveness of interactions and effects of mutations on protein conformation which lead to different signaling.

Keywords: breast cancer, metastasis, PPI networks, protein conformational changes

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Authors: Jonathan Martino, Kristof Harri

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In some cases, the increase in computing resources makes simulation methods more affordable. The increase in processing speed also allows real time analysis or even more rapid tests analysis offering a real tool for test prediction and design process optimization. Vibration tests are no exception to this trend. The so called ‘Virtual Vibration Testing’ offers solution among others to study the influence of specific loads, to better anticipate the boundary conditions between the exciter and the structure under test, to study the influence of small changes in the structure under test, etc. This article will first present a virtual vibration test modeling with a main focus on the shaker model and will afterwards present the experimental parameters determination. The classical way of modeling a shaker is to consider the shaker as a simple mechanical structure augmented by an electrical circuit that makes the shaker move. The shaker is modeled as a two or three degrees of freedom lumped parameters model while the electrical circuit takes the coil impedance and the dynamic back-electromagnetic force into account. The establishment of the equations of this model, describing the dynamics of the shaker, is presented in this article and is strongly related to the internal physical quantities of the shaker. Those quantities will be reduced into global parameters which will be estimated through experiments. Different experiments will be carried out in order to design an easy and practical method for the identification of the shaker parameters leading to a fully functional shaker model. An experimental modal analysis will also be carried out to extract the modal parameters of the shaker and to combine them with the electrical measurements. Finally, this article will conclude with an experimental validation of the model.

Keywords: lumped parameters model, shaker modeling, shaker parameters, state-space, virtual vibration

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Authors: Mohannad Mayyas, Farshid Pahlevani, Veena Sahajwalla

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Automotive shredder residue (ASR) is an industrial waste, generated during the recycling process of End-of-life vehicles. The large increasing production volumes of ASR and its hazardous content have raised concerns worldwide, leading some countries to impose more restrictions on ASR waste disposal and encouraging researchers to find efficient solutions for ASR processing. Although a great deal of research work has been carried out, all proposed solutions, to our knowledge, remain commercially and technically unproven. While the volume of waste materials continues to increase, the production of materials from new sustainable sources has become of great importance. Advanced ceramic materials such as nitrides, carbides and borides are widely used in a variety of applications. Among these ceramics, a great deal of attention has been recently paid to Titanium nitride (TiN) owing to its unique characteristics. In our study, we propose a new sustainable approach for ASR management where TiN nanoparticles with ideal particle size ranging from 200 to 315 nm can be synthesized as a by-product. In this approach, TiN is thermally synthesized by nitriding pressed mixture of automotive shredder residue (ASR) incorporated with titanium oxide (TiO2). Results indicated that TiO2 influences and catalyses degradation reactions of ASR and helps to achieve fast and full decomposition. In addition, the process resulted in titanium nitride (TiN) ceramic with several unique structures (porous nanostructured, polycrystalline, micro-spherical and nano-sized structures) that were simply obtained by tuning the ratio of TiO2 to ASR, and a product with appreciable TiN content of around 85% was achieved after only one hour nitridation at 1550 °C.

Keywords: automotive shredder residue, nano-ceramics, waste treatment, titanium nitride, thermal conversion

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Bernardo C. P. Albuquerque, Darym J. F. Campos

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Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management.

Keywords: statistical slope stability analysis, skew distributions, probability of failure, functions of random variables

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Authors: Roopa Singh, Anurag Singh, Ajay

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Agriculture contributes a major share in the national economy of India. A major portion of Indian economy (about 70%) depends upon agriculture as it forms the main source of income. About 43% of India’s geographical area is used for agricultural activity which involves 65-75% of total population of India. The given work deals with the Fast moving Consumer Goods (FMCG) industries and their inventories which use agricultural produce as their raw material or input for their final product. Since the beginning of inventory practices, many developments took place which can be categorised into three phases, based on the review of various works. The first phase is related with development and utilization of Economic Order Quantity (EOQ) model and methods for optimizing costs and profits. Second phase deals with inventory optimization method, with the purpose of balancing capital investment constraints and service level goals. The third and recent phase has merged inventory control with electrical control theory. Maintenance of inventory is considered negative, as a large amount of capital is blocked especially in mechanical and electrical industries. But the case is different in food processing and agro-based industries and their inventories due to cyclic variation in the cost of raw materials of such industries which is the reason for selection of these industries in the mentioned work. The application of electrical control theory in inventory control makes the decision-making highly instantaneous for FMCG industries without loss in their proposed profits, which happened earlier during first and second phases, mainly due to late implementation of decision. The work also replaces various inventories and work-in-progress (WIP) related errors with their monetary values, so that the decision-making is fully target-oriented.

Keywords: control theory, inventory control, manufacturing sector, EOQ, feedback, FMCG sector

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Authors: Yehjune Heo

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As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

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Authors: Fatima Majeed Almonajim

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Panucum miliaceum L is a plant from the genus Gramineae, In the world, millets are regarded as a significant grain, however, they are very little exploited. Millet grain is abundant in nutrients and health-beneficial phenolic compounds, making it suitable as food and feed. The plant has received considerable attention for its high content of phenolic compounds, low glycemic index, the presence of unsaturated fats and lack of gluten which are beneficial to human health, and thus, have made the plant being effective in treating celiac disease, diabetes, lowering blood lipids (cholesterol) and preventing tumors. Moreover, the plant requires little water to grow, a property that is worth considering. This study provides an overview of the nutritional and health benefits provided by millet types grown in 2 areas Iraq and Iran, aiming to compare the effect of climate on the components of millet. In this research, millet samples collected from the both Babylon (Iraqi) and Isfahan (Iranian) types were extracted and after HPTLC, the resulted pattern of the two samples were compared. As a result, the Iranian millet showed more terpenoid compounds than Iraqi millet, and therefore, Iranian millet has a higher priority than Iraqi millet in increasing the human body's immunity. On the other hand, in view of the number of essential amino acids, the Iraqi millet contains more nutritional value compared to the Iranian millet. Also, due to the higher amount of histidine in the Iranian millet, compiled to the lack of gluten found from previous studies, we came to the conclusion that the addition of millet in the diet of children, more specifically those children with irritable bowel syndrome, can be considered beneficial. Therefore, as a component of dairy products, millet can be used in preparing food for children such as dry milk.

Keywords: HPTLC, phytochemicals, specialty foods, Panucum miliaceum L, nutrition

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Authors: Nowbuth Manta Devi, Rasmally Mohammed Hussein

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Flood is one of the most devastating climate impacts that many countries are facing. Many different causes have been associated with the intensity of floods being recorded over time. Unplanned development, low carrying capacity of drains, clogged drains, construction in flood plains or increasing intensity of rainfall events. While a combination of these causes can certainly aggravate the flood conditions, in many cases, increasing drainage capacity has not reduced flood risk to the level that was expected. The present study analyzed the extent to which land use is contributing to aggravating impacts of flooding in a city. Satellite images have been analyzed over a period of 20 years at intervals of 5 years. Both unsupervised and supervised classification methods have been used with the image processing module of ArcGIS. The unsupervised classification was first compared to the basemap available in ArcGIS to get a first overview of the results. These results also aided in guiding data collection on-site for the supervised classification. The island of Mauritius is small, and there are large variations in land use over small areas, both within the built areas and in agricultural zones involving food crops. Larger plots of agricultural land under sugar cane plantations are relatively more easily identified. However, the growth stage and health of plants vary and this had to be verified during ground truthing. The results show that although there have been changes in land use as expected over a span of 20 years, this was not significant enough to cause a major increase in flood risk levels. A digital elevation model was analyzed for further understanding. It could not be noted that overtime, development tampered with natural flow paths in addition to increasing the impermeable areas. This situation results in backwater flows, hence increasing flood risks.

Keywords: climate change, flood, natural flow paths, small islands

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Authors: I. Wardana Ketut, I. Suparwa Nyoman

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This study investigated the form and characteristic of speech sound produced by three Balinese subjects who have recovered from aphasia as well as intervened their language impairment on side of linguistic and neuronal aspects of views. The failure of judging the speech sound was caused by impairment of motor cortex that indicated there were lesions in left hemispheric language zone. Sound articulation phenomena were in the forms of phonemes deletion, replacement or assimilation in individual words and meaning building for anomic aphasia. Therefore, the Balinese sound patterns were stimulated by showing pictures to the subjects and recorded to recognize what individual consonants or vowels they unclearly produced and to find out how the sound disorder occurred. The physiology of sound production by subject’s speech organs could not only show the accuracy of articulation but also any level of severity the lesion they suffered from. The subjects’ speech sounds were investigated, classified and analyzed to know how poor the lingual units were and observed to clarify weaknesses of sound characters occurred either for place or manner of articulation. Many fricative and stopped consonants were replaced by glottal or palatal sounds because the cranial nerve, such as facial, trigeminal, and hypoglossal underwent impairment after the stroke. The phonological intervention was applied through a technique called phonemic articulation drill and the examination was conducted to know any change has been obtained. The finding informed that some weak articulation turned into clearer sound and simple meaning of language has been conveyed. The hierarchy of functional parts of brain played important role of language formulation and processing. From this finding, it can be clearly emphasized that this study supports the role of right hemisphere in recovery from aphasia is associated with functional brain reorganization.

Keywords: aphasia, intervention, phonology, stroke

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Authors: Milton Manyangadze, Joseph Govha, T. Bala Narsaiah, Ch. Shilpa Chakra

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The availability and access to fresh water is today a serious global challenge. This has been a direct result of factors such as the current rapid industrialization and industrial growth, persistent droughts in some parts of the world, especially in the sub-Saharan Africa as well as population growth. Growth of the chemical processing industry has also seen an increase in the levels of pollutants in our water bodies which include heavy metals among others. Heavy metals are known to be dangerous to both human and aquatic life. As such, they have been linked to several diseases. This is mainly because they are highly toxic. They are also known to be bio accumulative and non-biodegradable. Lead for example, has been linked to a number of health problems which include damage of vital internal body systems like the nervous and reproductive system as well as the kidneys. From this background therefore, the removal of the toxic heavy metal, Pb2+ from waste water was investigated using nano silica hollow spheres (NSHS) as the adsorbent. Synthesis of NSHS was done using a three-stage process in which CaCO3 nanoparticles were initially prepared as a template. This was followed by treatment of the formed oxide particles with NaSiO3 to give a nanocomposite. Finally, the template was destroyed using 2.0M HCl to give NSHS. Characterization of the nanoparticles was done using analytical techniques like XRD, SEM, and TGA. For the adsorption process, both thermodynamic and equilibrium studies were carried out. Thermodynamic studies were carried out and the Gibbs free energy, Enthalpy and Entropy of the adsorption process were determined. The results revealed that the adsorption process was both endothermic and spontaneous. Equilibrium studies were also carried out in which the Langmuir and Freundlich isotherms were tested. The results showed that the Langmuir model best described the adsorption equilibrium.

Keywords: characterization, endothermic, equilibrium studies, Freundlich, Langmuir, nanoparticles, thermodynamic studies

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Authors: Ibrahim Hakeem

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Biosolids are stabilised sewage sludge produced from wastewater treatment processes. Biosolids contain valuable plant nutrient which facilitates their beneficial reuse in agricultural land. However, the increasing levels of legacy and emerging contaminants such as heavy metals (HMs), PFAS, microplastics, pharmaceuticals, microbial pathogens etc., are restraining the direct land application of biosolids. Pyrolysis of biosolids can effectively degrade microbial and organic contaminants; however, HMs remain a persistent problem with biosolids and their pyrolysis-derived biochar. In this work, we demonstrated the integrated processing of biosolids involving the acid pre-treatment for HMs removal and selective reduction of ash-forming elements followed by the bench-scale pyrolysis of the treated biosolids to produce quality biochar and bio-oil enriched with valuable platform chemicals. The pre-treatment of biosolids using 3% v/v H₂SO₄ at room conditions for 30 min reduced the ash content from 30 wt% in raw biosolids to 15 wt% in the treated sample while removing about 80% of limiting HMs without degrading the organic matter. The preservation of nutrients and reduction of HMs concentration and mobility via the developed hydrometallurgical process improved the grade of the treated biosolids for beneficial land reuse. The co-removal of ash-forming elements from biosolids positively enhanced the fluidised bed pyrolysis of the acid-treated biosolids at 700 ℃. Organic matter devolatilisation was improved by 40%, and the produced biochar had higher surface area (107 m²/g), heating value (15 MJ/kg), fixed carbon (35 wt%), organic carbon retention (66% dry-ash free) compared to the raw biosolids biochar with surface area (56 m²/g), heating value (9 MJ/kg), fixed carbon (20 wt%) and organic carbon retention (50%). Pre-treatment also improved microporous structure development of the biochar and substantially decreased the HMs concentration and bioavailability by at least 50% relative to the raw biosolids biochar. The integrated process is a viable approach to enhancing value recovery from biosolids.

Keywords: biosolids, pyrolysis, biochar, heavy metals

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Authors: Maria Antonietta Marongiu

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Propositional information in discourse is made coherent, intelligible, and persuasive through metadiscourse. The linguistic and rhetorical choices that writers/speakers make to organize and negotiate content matter are intended to help relate a text to its context. Besides, they help the audience to connect to and interpret a text according to the values of a specific discourse community. Based on these assumptions, this work aims to analyse the use of metadiscourse in the spoken performance of teachers in online EFL, ESP, and subject-teacher courses taught in English to non-native learners in higher education. In point of fact, the global spread of Covid 19 has forced universities to transition their in-class courses to online delivery. This has inevitably placed on the instructor a heavier interactional responsibility compared to in-class courses. Accordingly, online delivery needs greater structuring as regards establishing the reader/listener’s resources for text understanding and negotiating. Indeed, in online as well as in in-class courses, lessons are social acts which take place in contexts where interlocutors, as members of a community, affect the ways ideas are presented and understood. Following Hyland’s Interactional Model of Metadiscourse (2005), this study intends to investigate Teacher Talk in online academic courses during the Covid 19 lock-down in Italy. The selected corpus includes the transcripts of online EFL and ESP courses and subject-teachers online courses taught in English. The objective of the investigation is, firstly, to ascertain the presence of metadiscourse in the form of interactive devices (to guide the listener through the text) and interactional features (to involve the listener in the subject). Previous research on metadiscourse in academic discourse, in college students' presentations in EAP (English for Academic Purposes) lessons, as well as in online teaching methodology courses and MOOC (Massive Open Online Courses) has shown that instructors use a vast array of metadiscoursal features intended to express the speakers’ intentions and standing with respect to discourse. Besides, they tend to use directions to orient their listeners and logical connectors referring to the structure of the text. Accordingly, the purpose of the investigation is also to find out whether metadiscourse is used as a rhetorical strategy by instructors to control, evaluate and negotiate the impact of the ongoing talk, and eventually to signal their attitudes towards the content and the audience. Thus, the use of metadiscourse can contribute to the informative and persuasive impact of discourse, and to the effectiveness of online communication, especially in learning contexts.

Keywords: discourse analysis, metadiscourse, online EFL and ESP teaching, rhetoric

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Authors: Andre S. de Lima, Alfredo R. de Faria, Jose J. R. Faria

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Accurate prediction of thermal stresses is particularly important for laminated composite structures, as large temperature changes may occur during fabrication and field application. The normal transverse deformation plays an important role in the prediction of such stresses, especially for problems involving thick laminated plates subjected to uniform temperature loads. Bearing this in mind, the present study aims to investigate the thermo-mechanical behavior of laminated composite structures using a new beam element based on global-local superposition, accounting for through-the-thickness effects. The element formulation is based on a global-local superposition in the thickness direction, utilizing a cubic global displacement field in combination with a linear layerwise local displacement distribution, which assures zig-zag behavior of the stresses and displacements. By enforcing interlaminar stress (normal and shear) and displacement continuity, as well as free conditions at the upper and lower surfaces, the number of degrees of freedom in the model is maintained independently of the number of layers. Moreover, the proposed formulation allows for the determination of transverse shear and normal stresses directly from the constitutive equations, without the need of post-processing. Numerical results obtained with the beam element were compared to analytical solutions, as well as results obtained with commercial finite elements, rendering satisfactory results for a range of length-to-thickness ratios. The results confirm the need for an element with through-the-thickness capabilities and indicate that the present formulation is a promising alternative to such analysis.

Keywords: composite beam element, global-local superposition, laminated composite structures, thermal stresses

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Authors: José Alberto García Fernández, Zhimin Du, Xinqiao Jin

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Nowadays, data center industry faces strong challenges for increasing the speed and data processing capacities while at the same time is trying to keep their devices a suitable working temperature without penalizing that capacity. Consequently, the cooling systems of this kind of facilities use a large amount of energy to dissipate the heat generated inside the servers, and developing new cooling techniques or perfecting those already existing would be a great advance in this type of industry. The installation of a temperature sensor matrix distributed in the structure of each server would provide the necessary information for collecting the required data for obtaining a temperature profile instantly inside them. However, the number of temperature probes required to obtain the temperature profiles with sufficient accuracy is very high and expensive. Therefore, other less intrusive techniques are employed where each point that characterizes the server temperature profile is obtained by solving differential equations through simulation methods, simplifying data collection techniques but increasing the time to obtain results. In order to reduce these calculation times, complicated and slow computational fluid dynamics simulations are replaced by simpler and faster finite element method simulations which solve the Burgers‘ equations by backward, forward and central discretization techniques after simplifying the energy and enthalpy conservation differential equations. The discretization methods employed for solving the first and second order derivatives of the obtained Burgers‘ equation after these simplifications are the key for obtaining results with greater or lesser accuracy regardless of the characteristic truncation error.

Keywords: Burgers' equations, CFD simulation, data center, discretization methods, FEM simulation, temperature profile

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Authors: Eun Young Park, Jongwon Park

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A physical touch of a product can provide ample diagnostic information about the product attributes and quality. However, consumers’ product judgments and purchases can be erroneously influenced by non-diagnostic haptic information. For example, consumers’ evaluations of the coffee they drink could be affected by the heaviness of a cup that is used for just serving the coffee. This important issue has received little attention in prior research. The present research contributes to the literature by identifying when and how non-diagnostic haptic information can have an influence and why such influence occurs. Specifically, five studies experimentally varied the content of non-diagnostic haptic information, such as the weight of a cup (heavy vs. light) and the texture of a cup holder (smooth vs. rough), and then assessed the impact of the manipulation on product judgments and decisions. Results show that non-diagnostic haptic information has a biasing impact on consumer judgments. For example, the heavy (vs. light) cup increases consumers’ perception of the richness of coffee in it, and the rough (vs. smooth) texture of a cup holder increases the perception of the healthfulness of fruit juice in it, which in turn increases consumers’ purchase intentions of the product. When consumers are cognitively distracted during the touch experience, the impact of the content of haptic information is no longer evident, but the valence (positive vs. negative) of the haptic experience influences product judgments. However, consumers are able to avoid the impact of non-diagnostic haptic information, if and only if they are both knowledgeable about the product category and undistracted from processing the touch experience. In sum, the nature of the influence by non-diagnostic haptic information (i.e., assimilation effect vs. contrast effect vs. null effect) is determined by the content and valence of haptic information, the relative impact of which depends on whether consumers can identify the content and source of the haptic information. Theoretically, to our best knowledge, this research is the first to document the empirical evidence of the interplay between cognitive and affective processes that determines the impact of non-diagnostic haptic information. Managerial implications are discussed.

Keywords: consumer behavior, haptic information, product judgments, touch effect

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Authors: Yuta Kurashina, Chikahiro Imashiro, Kiyoshi Ohnuma, Kenjiro Takemura

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Research objectives and goals: To realize clinical applications of regenerative medicine, a mass cell culture is highly required. In a conventional cell culture, trypsinization was employed for cell detachment. However, trypsinization causes proliferation decrease due to injury of cell membrane. In order to detach cells using an enzyme-free method, therefore, this study proposes a novel cell detachment method capable of detaching adherent cells using acoustic radiation pressure exposed to the dish by the assistance of serum-free medium with ITS liquid medium supplement. Methods used In order to generate acoustic radiation pressure, a piezoelectric ceramic plate was glued on a glass plate to configure an ultrasonic transducer. The glass plate and a chamber wall compose a chamber in which a culture dish is placed in glycerol. Glycerol transmits acoustic radiation pressure to adhered cells on the culture dish. To excite a resonance vibration of transducer, AC signal with 29-31 kHz (swept) and 150, 300, and 450 V was input to the transducer for 5 min. As a pretreatment to reduce cell adhesivity, serum-free medium with ITS liquid medium supplement was spread to the culture dish before exposed to acoustic radiation pressure. To evaluate the proposed cell detachment method, C2C12 myoblast cells (8.0 × 104 cells) were cultured on a ø35 culture dish for 48 hr, and then the medium was replaced with the serum-free medium with ITS liquid medium supplement for 24 hr. We replaced the medium with phosphate buffered saline and incubated cells for 10 min. After that, cells were exposed to the acoustic radiation pressure for 5 min. We also collected cells by using trypsinization as control. Cells collected by the proposed method and trypsinization were respectively reseeded in ø60 culture dishes and cultured for 24 hr. Then, the number of proliferated cells was counted. Results achieved: By a phase contrast microscope imaging, shrink of lamellipodia was observed before exposed to acoustic radiation pressure, and no cells remained on the culture dish after the exposed of acoustic radiation pressure. This result suggests that serum-free medium with ITS liquid inhibits adhesivity of cells and acoustic radiation pressure detaches cells from the dish. Moreover, the number of proliferated cells 24 hr after collected by the proposed method with 150 and 300 V is the same or more than that by trypsinization, i.e., cells were proliferated 15% higher with the proposed method using acoustic radiation pressure than with the traditional cell collecting method of trypsinization. These results proved that cells were able to be collected by using the appropriate exposure of acoustic radiation pressure. Conclusions: This study proposed a cell detachment method using acoustic radiation pressure by the assistance of serum-free medium. The proposed method provides an enzyme-free cell detachment method so that it may be used in future clinical applications instead of trypsinization.

Keywords: acoustic radiation pressure, cell detachment, enzyme free, ultrasonic transducer

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Authors: Shamim Ahsan

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Abstract Global digital advances associated with consumer’s strong inclination for the state of art digital technologies is causing overwhelming social and environmental challenges for global community. During recent years not only economic advances of electronic industries has taken place at steadfast rate, also the generation of e-waste outshined the growth of any other types of wastes. The estimated global e-waste volume is expected to reach 65.4 million tons annually by 2017. Formal recycling practices in developed countries are stemming economic liability, opening paths for illegal trafficking to developing countries. Informal crude management of large volume of e-waste is transforming into an emergent environmental and health challenge in. Contrariwise, in several studies formal and informal recycling of e-waste has also exhibited potentials for economic returns both in developed and developing countries. Some research on China illustrated that from large volume of e-wastes generation there are recycling potential in evolving from ∼16 (10−22) billion US$ in 2010, to an anticipated ∼73.4 (44.5−103.4) billion US$ by 2030. While in another study, researcher found from an economic analysis of 14 common categories of waste electric and electronic equipment (WEEE) the overall worth is calculated as €2.15 billion to European markets, with a potential rise to €3.67 billion as volumes increase. These economic returns and environmental protection approaches are feasible only when sustainable policy options are embraced with stricter regulatory mechanism. This study will critically review current researches to stipulate how global e-waste generation and sustainable e-waste recycling practices demonstrate future economic development potential in terms of both quantity and processing capacity, also triggering complex some environmental challenges.

Keywords: E-Waste, , Generation, , Economic Potential, Recycling

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Hayat Omar

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Languages provide speakers with a wide range of linguistic units to organize and deliver information. There are several ways to verbally express the mental representations of events. According to the linguistic tools they have acquired, speakers select the one that brings out the most communicative effect to convey their message. Our study focuses on two markers, -mm and dämmo, in Amharic (Ethiopian Semitic language). Our aim is to examine, from a developmental perspective, how they are used by speakers. We seek to distinguish the communicative and pragmatic functions indicated by means of these markers. To do so, we created a corpus of sixty narrative productions of children from 5-6, 7-8 to 10-12 years old and adult Amharic speakers. The experimental material we used to collect our data is a series of pictures without text 'Frog, Where are you?'. Although -mm and dämmo are each used in specific contexts, they are sometimes analyzed as being interchangeable. The suffix -mm is complex and multifunctional. It marks the end of the negative verbal structure, it is found in the relative structure of the imperfect, it creates new words such as adverbials or pronouns, it also serves to coordinate words, sentences and to mark the link between macro-propositions within a larger textual unit. -mm was analyzed as marker of insistence, topic shift marker, element of concatenation, contrastive focus marker, 'bisyndetic' coordinator. On the other hand, dämmo has limited function and did not attract the attention of many authors. The only approach we could find analyzes it in terms of 'monosyndetic' coordinator. The paralleling of these two elements made it possible to understand their distinctive functions and refine their description. When it comes to marking a referent, the choice of -mm or dämmo is not neutral, depending on whether the tagged argument is newly introduced, maintained, promoted or reintroduced. The presence of these morphemes explains the inter-phrastic link. The information is seized by anaphora or presupposition: -mm goes upstream while dämmo arrows downstream, the latter requires new information. The speaker uses -mm or dämmo according to what he assumes to be known to his interlocutors. The results show that -mm and dämmo, although all the speakers use them both, do not always have the same scope according to the speaker and vary according to the age. dämmo is mainly used to mark a contrastive topic to signal the concomitance of events. It is more commonly used in young children’s narratives (F(3,56) = 3,82, p < .01). Some values of -mm (additive) are acquired very early while others are rather late and increase with age (F(3,56) = 3,2, p < .03). The difficulty is due not only because of its synthetic structure but primarily because it is multi-purpose and requires a memory work. It highlights the constituent on which it operates to clarify how the message should be interpreted.

Keywords: acquisition, cohesion, connection, contrastive topic, contrastive focus, discourse marker, pragmatics

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Authors: Alireza Vahid, Santu Rana, Sunil Gupta, Pratibha Vellanki, Svetha Venkatesh, Thomas Dorin

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Aluminum is the preferred material for lightweight applications and its alloys are constantly improving. The high strength 7xxx alloys have been extensively used for structural components in aerospace and automobile industries for the past 50 years. In the next decade, a great number of airplanes will be retired, providing an obvious source of valuable used metals and great demand for cost-effective methods to re-use these alloys. The design of proper aerospace alloys is primarily based on optimizing strength and ductility, both of which can be improved by controlling the additional alloying elements as well as heat treatment conditions. In this project, we explore the design of high-performance alloys with 7xxx as a base material. These designed alloys have to be optimized and improved to compare with modern 7xxx-series alloys and to remain competitive for aircraft manufacturing. Aerospace alloys are extremely complex with multiple alloying elements and numerous processing steps making optimization often intensive and costly. In the present study, we used Bayesian optimization algorithm, a well-known adaptive design strategy, to optimize this multi-variable system. An Al alloy was proposed and the relevant heat treatment schedules were optimized, using the tensile yield strength as the output to maximize. The designed alloy has a maximum yield strength and ultimate tensile strength of more than 730 and 760 MPa, respectively, and is thus comparable to the modern high strength 7xxx-series alloys. The microstructure of this alloy is characterized by electron microscopy, indicating that the increased strength of the alloy is due to the presence of a high number density of refined precipitates.

Keywords: aluminum alloys, Bayesian optimization, heat treatment, tensile properties

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Authors: Gilberto Goracci, Fabio Curti

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This work presents a robust, light, and inexpensive algorithm to perform autonomous orbit determination using onboard magnetometer data in real-time. Magnetometers are low-cost and reliable sensors typically available on a spacecraft for attitude determination purposes, thus representing an interesting choice to perform real-time orbit determination without the need to add additional sensors to the spacecraft itself. Magnetic field measurements can be exploited by Extended/Unscented Kalman Filters (EKF/UKF) for orbit determination purposes to make up for GPS outages, yielding errors of a few kilometers and tens of meters per second in the position and velocity of a spacecraft, respectively. While this level of accuracy shows that Kalman filtering represents a solid baseline for autonomous orbit determination, it is not enough to provide a reliable state estimation in the absence of GPS signals. This work combines the solidity and reliability of the EKF with the versatility of a Recurrent Neural Network (RNN) architecture to further increase the precision of the state estimation. Deep learning models, in fact, can grasp nonlinear relations between the inputs, in this case, the magnetometer data and the EKF state estimations, and the targets, namely the true position, and velocity of the spacecraft. The model has been pre-trained on Sun-Synchronous orbits (SSO) up to 2126 kilometers of altitude with different initial conditions and levels of noise to cover a wide range of possible real-case scenarios. The orbits have been propagated considering J2-level dynamics, and the geomagnetic field has been modeled using the International Geomagnetic Reference Field (IGRF) coefficients up to the 13th order. The training of the module can be completed offline using the expected orbit of the spacecraft to heavily reduce the onboard computational burden. Once the spacecraft is launched, the model can use the GPS signal, if available, to fine-tune the parameters on the actual orbit onboard in real-time and work autonomously during GPS outages. In this way, the provided module shows versatility, as it can be applied to any mission operating in SSO, but at the same time, the training is completed and eventually fine-tuned, on the specific orbit, increasing performances and reliability. The results provided by this study show an increase of one order of magnitude in the precision of state estimate with respect to the use of the EKF alone. Tests on simulated and real data will be shown.

Keywords: artificial intelligence, extended Kalman filter, orbit determination, magnetic field

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Authors: Punam Kumari Mandal, Namita Yangden, Bhumika Rai, Achala Niraula, Sabitra Subedi

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introduction: Family planning not only improves women’s health but also promotes gender equality, better child health, and improved education outcomes, including poverty reduction. The objective of this study is to assess the utilization of family planning methods and associated factors in Sunsari, Nepal. methodology: A cross-sectional analytical study was conducted among women of the reproductive age group (15-49 years) in Sunsari in 2020. Nonprobability purposive sampling was used to collect information from 212 respondents through face-to-face interviews using a Semi-structured interview schedule from ward no 1 of Barju rural municipality. Data processing was done by using SPSS “statistics for windows, version 17.0(SPSS Inc., Chicago, III.USA”). Descriptive analysis and inferential analysis (binary logistic regression) were used to find the association of the utilization of family planning methods with selected demographic variables. All the variables with P-value <0.1 in bivariate analysis were included in multivariate analysis. A P-value of <0.05 was considered to indicate statistical significance at a level of significance of 5%. results: This study showed that the mean age and standard deviation of the respondents were 26±7.03, and 91.5 % of respondent’s age at marriage was less than 20 years. Likewise, 67.5% of respondents use any methods of family planning, and 55.2% of respondents use family planning services from the government health facility. Furthermore, education (AOR 1.579, CI 1.013-2.462)., husband’s occupation (AOR 1.095, CI 0.744-1.610)., type of family (AOR 2.741, CI 1.210-6.210)., and no of living son (AOR 0.259 CI 0.077-0.872)are the factors associated with the utilization of family planning methods. conclusion: This study concludes that two-thirds of reproductive-age women utilize family planning methods. Furthermore, education, the husband’s occupation, the type of family, and no of living sons are the factors associated with the utilization of family planning methods. This reflects that awareness through mass media, including behavioral communication, is needed to increase the utilization of family planning methods.

Keywords: family planning methods, utilization. factors, women, community

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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