Search results for: Uputaua therapeutic approach

Authors: Jack Johnstone

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This research presents postcolonial translation as an approach to eradicate traces of colonialism in former colonies. An example of demonstrating postcolonial translation in the Indonesian context is in Hasan Aspahani's Chairil, a biographical narrative and history book based on the personal life of a well-known Indonesian poet and writer, Chairil Anwar (1922-1949) in Dutch occupied Indonesia. This postcolonial translation approach has been applied in the first five chapters on his early years under Dutch colonization, in an attempt to show a postcolonialised TT. This approach aims to demonstrate the postcolonial refutation of the Dutch colonial language to convey the Indonesian setting to target readers. It is also designed to explicate the summary of the book as well as my attempt to apply postcolonial translation as a strategy to reject the Dutch colonial terms in this book. The data conveys 26 important examples of the ST and TT, in consideration of the chosen three factors of culture, forced-Europeanisation, and cross-genre between a biographical narrative and history under categories of Cultural Bound Objects, Politics and Place. However, the 10 selected examples will be analyzed in the Analysis Chapter, which are discussed at word, sentence, and paragraph level. As well, the translation strategies used, namely retention, substitution and specification on four main examples, on the methods utilized to achieve a postcolonialised translation that attempts to 1) examine the way the alteration of the TT can affect the message portrayed within the ST, 2) show the notion of disagreement between the Dutch colonizers and colonized Indonesians on their views on the way Indonesia should be governed and 3) present a translation that reverses the inequality between the superior colonials and inferior Indigenous Indonesians during the Dutch colonial era.

Keywords: Chairil, Dutch colonialism, Indonesia, postcolonial translation

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Authors: Salamah Ansari

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In absence of a comprehensive international legal regime for sovereign debt restructuring, majority of the complications arising from sovereign debt restructuring are frequently left to the uncertain market forces. The resort to market forces for sovereign debt restructuring has led to a phenomenal increase in litigations targeting assets of defaulting sovereign nations, internationally across jurisdictions with the first major wave of lawsuits against sovereigns in the 1980s with the Latin American crisis. Recent experiences substantiate that majority of obstacles faced during sovereign debt restructuring process are caused by inefficient creditor coordination and collective action problems. Collective action problems manifest as grab race, rush to exits, holdouts, the free rider problem and the rush to the courthouse. On defaulting, for a nation to successfully restructure its debt, all the creditors involved must accept some reduction in the value of their claims. As a single holdout creditor has the potential to undermine the restructuring process, hold-out creditors are snowballing with the increasing probability of earning high returns through litigations. This necessitates a mechanism to avoid holdout litigations and reinforce collective action on the part of the creditor. This can be done either through a statutory reform or through market-based contractual approach. In absence of an international sovereign bankruptcy regime, the impetus is mostly on inclusion of collective action clauses in debt contracts. The preference to contractual mechanisms vis- a vis a statutory approach can be explained with numerous reasons, but that's only part of the puzzle in trying to understand the economics of the underlying system. The contractual approach proposals advocate the inclusion of certain clauses in the debt contract for an orderly debt restructuring. These include clauses such as majority voting clauses, sharing clauses, non- acceleration clauses, initiation clauses, aggregation clauses, temporary stay on litigation clauses, priority financing clauses, and complete revelation of relevant information. However, voluntary market based contractual approach to debt workouts has its own complexities. It is a herculean task to enshrine clauses in debt contracts that are detailed enough to create an orderly debt restructuring mechanism while remaining attractive enough for creditors. Introduction of collective action clauses into debt contracts can reduce the barriers in efficient debt restructuring and also have the potential to improve the terms on which sovereigns are able to borrow. However, it should be borne in mind that such clauses are not a panacea to the huge institutional inadequacy that persists and may lead to worse restructuring outcomes.

Keywords: sovereign debt restructuring, collective action clauses, hold out creditors, litigations

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Authors: Wisdom Dordudnu, George M. Y. Owusu, Samuel N. Y. Simpson

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The recent failures of many corporate giants have generated intense research interest in the factors that influence accountants’ job performance. Against the backdrop that these factors also create an enabling environment for success at the work place, this study contributes to literature on job performance of accountants by exploring the impact of two psycho-spiritual factors: religiosity and spiritual intelligence on job performance of accountants in Ghana. The study employs a survey approach using questionnaires as the principal means of data collection to elicit responses from accountants working in the 222 certified firms of Institute of Chartered Accountants Ghana (ICAG). A structural equation modeling-based approach is employed to examine the relationship among the study constructs. Results of this study indicate that there is a positive relationship between these factors and accountants’ performance. It is expected that this study provides strong evidence and highlight the need for specific action from managers to look critically at the non-material aspect of accountants in accounting firms.

Keywords: job performance, psycho-spiritual, religiosity, spiritual intelligence

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Authors: Hye Kyung Kim, Myung-Gyou Kim, Kang-Hyun Leem

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The deer velvet antler is well known for its traditional medicinal value and is widely used in the clinic. It has been considered to possess bone-strengthening activity. The goal of this study was to investigate the anti-osteoporotic effect of deer antler velvet on ovariectomized rats (OVX), and their possible mechanism of the action. In the first step, the in vitro effects of DAE on bone loss were determined. The proliferation, collagen content and alkaline phosphatase (ALP) activity of human osteoblastic MG-63 cells and osteoclastogenesis from bone marrow-derived precursor cells were measured. The in vivo experiment confirmed the positive effect of DAE on bone tissue. 3-month old female Sparague-Dawley rats were either sham operated or OVX, and administered DAE (20 and 100 mg/kg) for 4 weeks. DAE increased MG-63 cell proliferation and ALP activity in a dose-dependent manner. Collagen content was also increased by DAE treatment. However, the effect of DAE on bone resorption was not observed. OVX rats supplemented with DAE showed osteoprotective effects as the bone ALP level was increased and c-terminal telopeptide level was decreased by 100 mg/kg DAE treatment compared with OVX controls. Moreover, the tartrate-resistant acid phosphatase-5b level was also decreased by DAE treatment. The present study suggests that DAE is effective in preventing bone loss in OVX rats, and may be potential therapeutic agents for the treatment of postmenopausal osteoporosis.

Keywords: bone ALP, c-terminal telopeptide, deer antler, osteoporosis, ovariectomy, tartrate-resistant acid phosphatase-5b

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Authors: Bijan Nahavandi, Navid Jafarinejad, Somayeh Mehrafzad

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Strategy maps represent the way of value creation in in each organization. Nowadays, implementation of strategy is the main concern for all organizations. Strategy map establishment is the start-up point of strategy implementation and this shows the critical importance of this concept. After some years past since emergence of strategy map, there are some shortcomings in its methodology that frequently quoted by many of researchers. One of these shortcomings is the shortage of a mechanism for refinement of objectives candidate for entrance to map. Organizations in practice have obsession and avidity to determine more number of objectives in strategy map. This study wants to represent a step by step approach to help obviate this problem using quality function deployment (QFD) as a helpful tool and fuzzy screening method. Finally, represented approach applies in a practical case and conclusions have been explained.

Keywords: balanced scorecard, fuzzy screening, house of strategic objectives (HoSO), quality function deployment, strategy map

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Authors: Jukka Torronen

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Introduction: Alcohol consumption and health are areas where gender binaries have persisted. By intoxication, men have displayed their masculinity as strong, while matters of health have formed a feminine undertaking. However, in recent years young people’s alcohol consumption has declined and been challenged by competing activities, including the rising health trend. This makes the comparison of young people’s masculinities and femininities in health and heavy drinking an important case to study. Methods: The data consists of semi-structured interviews about alcohol, health, and leisure activities among young people aged between 15 and 19 (N=56). By drawing on Butler’s work on “gender as performative” and Connell’s understanding of gendered identities as “configurations of practices,” the paper analyzes how the interviewees are doing masculinities and femininities in relation to health and heavy drinking, and how their gender performances are dichotomous, naturalized and contested. Results: The interviewees approach health from two perspectives, which are called “social health” and “physical health” approaches. They are both gendered. Especially in the “social health” approach, in which intoxication and risk-taking are used to increase well-being, the interviewees perform stereotypical gender binaries. The interviewees’ gendered performances in the “physical health” approach show more variability and are more reflective and critical. In contrast to intoxication, in relation to which the interviewees perform biologically driven gender binaries, they perform culturally driven genders in relation to health. Conclusions: Health seems to provide for the interviewees a field in which they feel more liberated to perform flexible and alternative genders.

Keywords: young people, decline in drinking, qualitative interviews, gender, health, risk-taking

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Authors: Suhas Pednekar, Prashant Chavan, Ramesh Chaughule, Deepak Patkar

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Iron oxide (Fe3O4) magnetic nanoparticles (MNPs) are one of the most attractive nanomaterials for various biomedical applications. An important potential medical application of polymer-coated iron oxide nanoparticles (NPs) is as imaging agents. Composition, size, morphology and surface chemistry of these nanoparticles can now be tailored by various processes to not only improve magnetic properties but also affect the behavior of nanoparticles in vivo. MNPs are being actively investigated as the next generation of magnetic resonance imaging (MRI) contrast agents. Also, there is considerable interest in developing magnetic nanoparticles and their surface modifications with therapeutic agents. Our study involves the synthesis of biocompatible cancer drug coated with iron oxide nanoparticles and to evaluate their efficacy as MRI contrast agents. A simple and rapid microwave method to prepare Fe3O4 nanoparticles has been developed. The drug was successfully conjugated to the Fe3O4 nanoparticles which can be used for various applications. The relaxivity R2 (reciprocal of the spin-spin relaxation time T2) is an important factor to determine the efficacy of Fe nanoparticles as contrast agents for MRI experiments. R2 values of the coated magnetic nanoparticles were also measured using MRI technique and the results showed that R2 of the Fe complex consisting of Fe3O4, polymer and drug was higher than that of bare Fe nanoparticles and polymer coated nanoparticles. This is due to the increase in hydrodynamic sizes of Fe NPs. The results with various amounts of iron molar concentrations are also discussed. Using MRI, it is seen that the R2 relaxivity increases linearly with increase in concentration of Fe NPs in water.

Keywords: cancer drug, hydrodynamic size, magnetic nanoparticles, MRI

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Authors: Khodamorad Nabaki, Jianhu Shen, Xiaodong Huang

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This paper develops a method for considering the critical fatigue stress as a constraint in the Bi-directional Evolutionary Structural Optimization (BESO) method. Our aim is to reach an optimal design in which high cycle fatigue failure does not occur for a specific life time. The critical fatigue stress is calculated based on modified Goodman criteria and used as a stress constraint in our topology optimization problem. Since fatigue generally does not occur for compressive stresses, we use the p-norm approach of the stress measurement that considers the highest tensile principal stress in each point as stress measure to calculate the sensitivity numbers. The BESO method has been extended to minimize volume an object subjected to the critical fatigue stress constraint. The optimization results are compared with the results from the compliance minimization problem which shows clearly the merits of our newly developed approach.

Keywords: topology optimization, BESO method, p-norm, fatigue constraint

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Authors: Rashmi Uchil, A. H. Sequeira

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Post-liberalization era in India has seen an unprecedented growth of mergers, both domestic as well as cross-border deals. Indian organizations have slowly begun appreciating this inorganic method of growth. However, all is not well as is evidenced in the lowering value creation of organizations after mergers. Several studies have identified that organizational culture is one of the key factors that affects the success of mergers. But very few studies have been attempted in this realm in India. The current study attempts to identify the factors in the organizational culture variable that may be unique to India. It also focuses on the difference in the impact of organizational culture on merger of organizations in the manufacturing and service sectors in India. The study uses a mixed research approach. An exploratory research approach is adopted to identify the variables that constitute organizational culture specifically in the Indian scenario. A few hypotheses were developed from the identified variables and tested to arrive at the Grounded Theory. The Grounded Theory approach used in the study, attempts to integrate the variables related to organizational culture. Descriptive approach is used to validate the developed grounded theory with a new empirical data set and thus test the relationship between the organizational culture variables and the success of mergers. Empirical data is captured from merged organizations situated in major cities of India. These organizations represent significant proportions of the total number of organizations which have adopted mergers. The mix of industries included software, banking, manufacturing, pharmaceutical and financial services. Mixed sampling approach was adopted for this study. The first phase of sampling was conducted using the probability method of stratified random sampling. The study further used the non-probability method of judgmental sampling. Adequate sample size was identified for the study which represents the top, middle and junior management levels of the organizations that had adopted mergers. Validity and reliability of the research instrument was ensured with appropriate tests. Statistical tools like regression analysis, correlation analysis and factor analysis were used for data analysis. The results of the study revealed a strong relationship between organizational culture and its impact on the success of mergers. The study also revealed that the results were unique to the extent that they highlighted a marked difference in the manner of internalization of change of organizational culture after merger by the organizations in the manufacturing sector. Further, the study reveals that the organizations in the service sector internalized the changes at a slower rate. The study also portrays the industries in the manufacturing sector as more proactive and can contribute to a change in the perception of the said organizations.

Keywords: manufacturing industries, mergers, organizational culture, service industries

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Authors: Ruben Lopez-Rodriguez

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In a microgrid, diverse energy sources (both renewable and non-renewable) are combined with energy storage units to form a localized power system. Microgrids function as independent entities, capable of meeting the energy needs of specific areas or communities. This paper introduces a Model Predictive Control (MPC) approach tailored for grid-connected microgrids, aiming to optimize their operation. The formulation employs Mixed-Integer Programming (MIP) to find optimal trajectories. This entails the fulfillment of continuous and binary constraints, all while accounting for commutations between various operating conditions such as storage unit charge/discharge, import/export from/towards the main grid, as well as asset connection/disconnection. To validate the proposed approach, a microgrid case study is conducted, and the simulation results are compared with those obtained using a rule-based strategy.

Keywords: microgrids, mixed logical dynamical systems, mixed-integer optimization, model predictive control

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Authors: Ehsan Atefi, Michael Grigware

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Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

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Authors: Abdelrahman Taha, Niloofar Malekghaini, Hamed Ebrahimian, Ramin Motamed

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This paper compares the substructure and direct methods for soil-structure interaction (SSI) analysis in the time domain. In the substructure SSI method, the soil domain is replaced by a set of springs and dashpots, also referred to as the impedance function, derived through the study of the behavior of a massless rigid foundation. The impedance function is inherently frequency dependent, i.e., it varies as a function of the frequency content of the structural response. To use the frequency-dependent impedance function for time-domain SSI analysis, the impedance function is approximated at the fundamental frequency of the structure-soil system. To explore the potential limitations of the substructure modeling process, a two-dimensional reinforced concrete frame structure is modeled using substructure and direct methods in this study. The results show discrepancies between the simulated responses of the substructure and the direct approaches. To isolate the effects of higher modal responses, the same study is repeated using a harmonic input motion, in which a similar discrepancy is still observed between the substructure and direct approaches. It is concluded that the main source of discrepancy between the substructure and direct SSI approaches is likely attributed to the way the impedance functions are calculated, i.e., assuming a massless rigid foundation without considering the presence of the superstructure. Hence, a refined impedance function, considering the presence of the superstructure, shall be developed. This refined impedance function is expected to significantly improve the simulation accuracy of the substructure approach for structural systems whose behavior is dominated by the fundamental mode response.

Keywords: direct approach, impedance function, soil-structure interaction, substructure approach

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Authors: Narine Ghazaryan, Joana Catarina Macedo, Sara Vaz, Naira Ayvazyan, Elsa Logarinho

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Macrovipera lebetina obtusa (MLO) is a poisonous snake in Armenia. Obtustatin represents the shortest known monomeric disintegrin, isolated from the snake venom of MLO, and is known to specifically inhibit α1β1 integrin. Its oncostatic effect is due to the inhibition of angiogenesis, which likely arises from α1β1 integrin inhibition in the endothelial cells. To explore the therapeutic potential of the MLO snake venom and obtustatin, we studied activity of obtustatin and MLO venom in vitro, by testing their efficacy in human dermal microvascular endothelial cells (HMVEC-D) and in vivo, using chick embryo chorioallantoic membrane assay (CAM assay). Our in vitro results showed that obtustatin in comparison with MLO venom did not exhibit cytotoxic activity in HMVEC-D cells in comparison to MLO venom. But in vivo results have shown that 4µg /embryo (90 µM) of obtustatin inhibited angiogenesis induced by FGF2 by 17% while MLO snake venom induced 22% reduction of the angiogenic index. The concentration of obtustatin in the crude MLO venom was 0.3 nM, which is 300.000 times less than the concentration of the obtustatin itself. Given this enormous difference in concentration, it is likely that some components of the crude venom contribute to the observed anti-angiogenic effect. Hypotheses will be ascertained to justify this action: components in the MLO venom may increase obtustatin efficacy or have independent but synergic anti-angiogenic activities.

Keywords: angiogenesis, alpa1 beta 1 integrin, Macrovipera lebetina obtusa, obtustatin

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Authors: Evy Rombaut, Marie-Anne Guerry

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Predicting voluntary turnover of employees is an important topic of study, both in academia and industry. Researchers try to uncover determinants for a broader understanding and possible prevention of turnover. In the current study, we use a data set based approach to reveal determinants for turnover, differing for blue and white collar workers. Our data set based approach made it possible to study actual turnover for more than 500000 employees in 15692 Belgian corporations. We use logistic regression to calculate individual turnover probabilities and test the goodness of our model with the AUC (area under the ROC-curve) method. The results of the study confirm the relationship of known determinants to employee turnover such as age, seniority, pay and work distance. In addition, the study unravels unknown and verifies known differences between blue and white collar workers. It shows opposite relationships to turnover for gender, marital status, the number of children, nationality, and pay.

Keywords: employee turnover, blue collar, white collar, dataset analysis

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Authors: Rui Yin, Haojie Wang, Xun Li

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Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data.

Keywords: dense retrieval, query generation, unsupervised training, text retrieval

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Authors: Mariana Mangas, Yaroslava Martins, Ana Matos Pires

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Introduction: Stigmatization is a common problem to overcome for people suffering from chronic diseases. It usually follows mental disorders and complicates the course of illness and reduces quality of life for people with mental illness. Objective: unsystematic review concerning stigma and mental illness, its impact on psychiatric disease and strategies to eradicate stigma. Methods: A search was conducted on PubMed, using keywords 'stigma' and 'mental illness'. Results and Discussion: Stigma is a psychosocial process that identifies individuals by the negative label of their differences. Stigma often brings a loss of occupational success and social support, reduced functioning and lower quality of life. The sense of stigma is common in individuals with mental illness and has considerable negative repercussions: delays treatment achievement, promotes social isolation, stress and maladaptive coping behaviors and it is associated with higher symptom levels, placing these individuals at higher risk for a poorer outcome and prognoses. Conclusion: Given the interrelation between stigma, symptoms, treatment seeking and disease management, stigma is a key construct in mental illness upon which anti-stigma initiatives may have considerable therapeutic potential. It will take multidisciplinary interventions to overcome mental illness stigma, including changes in social policy, attitudes and practices among mental health professionals, liaison between general public and people with a mental illness under conditions of equity and parity, family support, and easy access to evidence-based treatments.

Keywords: discrimination, stigma, mental illness, quality of life

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Authors: Omaima N. Ahmad AL-Allaf

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Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.

Keywords: image watermarking, genetic algorithm, particle swarm optimization, discrete wavelet transform

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Haruo Okabayashi

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The mutual understanding in conversation is very important for human relations. This study investigates the mental function of the formation of mutual understanding between two people in conversation using the embodied approach. Forty people participated in this study. They are divided into pairs randomly. Four conversation situations between two (make/listen to fun or pleasant talk, make/listen to regrettable talk) are set for four minutes each, and the finger plethysmogram (200 Hz) of each participant is measured. As a result, the attractors of the participants who reported “I did not understand my partner” show the collapsed shape, which means the fluctuation of their rhythm is too small to match their partner’s rhythm, and their cross correlation is low. The autonomic balance of both persons tends to resonate during conversation, and both LLEs tend to resonate, too. In human history, in order for human beings as weak mammals to live, they may have been with others; that is, they have brought about resonating characteristics, which is called self-organization. However, the resonant feature sometimes collapses, depending on the lifestyle that the person was formed by himself after birth. It is difficult for people who do not have a lifestyle of mutual gaze to resonate their biological signal waves with others’. These people have features such as anxiety, fatigue, and confusion tendency. Mutual understanding is thought to be formed as a result of cooperation between the features of self-organization of the persons who are talking and the lifestyle indicated by mutual gaze. Such an entanglement phenomenon is called a nonlinear relation. By this research, it is found that the formation of mutual understanding is expressed by the rhythm of a biological signal showing a nonlinear relationship.

Keywords: embodied approach, finger plethysmogram, mutual understanding, nonlinear phenomenon

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Authors: Junie B. Billones, Maria Constancia O. Carrillo, Voltaire G. Organo, Stephani Joy Y. Macalino, Inno A. Emnacen, Jamie Bernadette A. Sy

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One of the major interferences in the Philippines’ tuberculosis control program is the widespread prevalence of Mtb strains that are resistant to known drugs, such as the MDR-TB (Multi Drug Resistant Tuberculosis) and XDR-TB (Extensively Drug Resistant Tuberculosis). Therefore, there is a pressing need to search for novel Mtb drug targets in order to be able to combat these drug resistant strains. The enzyme 7,8-diaminopelargonic acid aminotransferase enzyme, or more commonly known as BioA, is one such ideal target, as it is known that humans do not possess this enzyme. BioA primarily plays a key role in Mtb’s lipid biosynthesis pathway; more specifically in the synthesis of the enzyme cofactor biotin. In this study, structure-based pharmacophore screening, docking, and ADMET evaluation of compounds obtained from the DrugBank chemical database were performed against the MtbBioA enzyme. Results of the screening, docking, ADMET, and TOPKAT calculations revealed that out of the 6,516 compounds in the library, only 7 compounds indicated more favorable binding energies as compared to the enzyme’s known inhibitor, amiclenomycin (ACM), as well as good solubility and toxicity properties. Moreover, out of these 7 compounds, Molecule 6 exhibited the best solubility and toxicity properties. In the future, these lead compounds may then be subjected to bioactivity assays in vitro or in vivo for further evaluation of its therapeutic efficacy.

Keywords: 7, 8-diaminopelargonic acid aminotransferase, BioA, pharmacophore, molecular docking, ADMET, TOPKAT

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Authors: Mohammad Sadeghi, Mahdieh Saniee

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Nowadays, development laws have become a major branch of engineering science, laws help humankind achieve his/her basic needs, and it is attracted to the attention of the nations. Therefore, lawyers have been invited to contemplate legislator's approaches respecting legislating countries' economic, social and cultural development plans and to observe the reliance of approaches on two elements of distributive justice and transitional justice in light of legal rationality. Legal rationality in development planning has encountered us with this question that whether a rational approach and existing models in the Iran development planning system approximate us to the goal of development laws respecting the rationalist approach and also regarding wisely decision-making model. The present study will investigate processes, approaches, and damages of development planning in the legislation of country development plans to answer this question.

Keywords: rationality, decision-making process, policymaking, development

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Authors: Chaouch Souad, Abdou Latifa, Larbi Chrifi Alaoui

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This paper deals with sensorless backstepping control of induction motor using MRAS technique associated to sliding mode approach. A high order genetic algorithm structure is used to approximate a control law designed by the Backstepping technique, and to find the best parameters globally optimized. However, the Backstepping control approach is unsuitable for high performance applications because the need of a speed sensor for increased accuracy and the absence of any error decay mechanism. In this paper a nonlinear observer, obtained by combining sliding mode structure and model reference adaptive system (MRAS), is designed for the rotor flux and rotor speed estimations. To validate the proposed method, the results are presented for showing the improved drive characteristics and performances.

Keywords: Backstepping Control, Induction Motor, Genetic Algorithm, Sliding Mode observer

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

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Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (bi)digraphs, rough set theory, systems of interacting agents, complex systems

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Authors: Pradyumna R., Sridhar S., A. Satyanarayana, Alok S. Chauhan, Baig M. A. H.

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Modern turbine engines for aerospace applications need precision investment cast components such as integrally cast rotors and stators, for their hot end turbine stages. Traditionally, these turbines are used as starter engines. In recent times, such engines are also used for strategic missile applications. The rotor/stator castings consist of a central hub (shrouded in some designs) over which a number of aerofoil shaped blades are located. Since these components cannot be machined, investment casting is the only available route for manufacture and hence stringent dimensional aerospace quality has to be in-built in the casting process itself. In the process of investment casting, pattern generation by injection of wax into dedicated dies/moulds is the first critical step. Traditional approach deals in producing individual blades with hub/shroud features through wax injection and assembly of a set of such injected patterns onto a dedicated and precisely manufactured fixture to wax-weld and generate an integral wax pattern, a process known as the ‘segmental approach’. It is possible to design a single-injection die with retractable metallic inserts in the case of untwisted blades of stator patterns without the shroud. Such an approach is also possible for twisted blades of rotors with highly complex design of inter-blade inserts and retraction mechanisms. DMRL has for long established methods and procedures for the above to successfully supply precision castings for various defence related projects. In recent times, urea based soluble insert approach has also been successfully applied to overcome the need to design and manufacture a precision assembly fixture, leading to substantial reduction in component development times. Present paper deals in length various approaches tried and established at DMRL to generate precision wax patterns for aerospace quality turbine rotors and stators. In addition to this, the importance of simulation in solving issues related to wax injection is also touched upon.

Keywords: die/mold and fixtures, integral rotor/stator, investment casting, wax patterns, simulation

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Authors: Tanwi Priya, Brijesh Kumar Mishra

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Occurrence of aromatic fractions of Natural Organic Matter (NOM) led to formation of carcinogenic disinfection by products such as trihalomethanes in chlorinated water. In the present study, the efficiency of aluminium based electrocoagulation on the removal of prominent aromatic groups such as phenol, hydrophobic auxochromes, and carboxyl groups from NOM enriched synthetic water has been evaluated using various spectral indices. The effect of electrocoagulation on turbidity has also been discussed. The variation in coagulation performance as a function of pH has been studied. Our result suggests that electrocoagulation can be considered as appropriate remediation approach to reduce trihalomethanes formation in water. It has effectively reduced hydrophobic fractions from NOM enriched low turbid water. The charge neutralization and enmeshment of dispersed colloidal particles inside metallic hydroxides is the possible mechanistic approach in electrocoagulation.

Keywords: aromatic fractions, electrocoagulation, natural organic matter, spectral indices

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Authors: Abbas Moradi, Mohsen Safajoy, Reza Yazdanparast

Abstract:

Analysis of blade-disc dovetail joints is one of the biggest challenges facing designers of aero-engines. To avoid comparatively expensive experimental full-scale tests, numerical methods can be used to simulate loaded disc-blades assembly. Mortar method provides a powerful and flexible tool for solving frictional contact problems. In this study, 2D frictional contact in dovetail has been analysed based on the mortar algorithm. In order to model the friction, the classical law of coulomb and moving friction cone algorithm is applied. The solution is then obtained by solving the resulting set of non-linear equations using an efficient numerical algorithm based on Newton–Raphson Method. The numerical results show that this approach has better convergence rate and accuracy than other proposed numerical methods.

Keywords: computational contact mechanics, dovetail joints, nonlinear FEM, mortar approach

Procedia PDF Downloads 334

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

Abstract:

The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 340

Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

Abstract:

Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: G. Ozturk, B. Demirci

Abstract:

It is well known that essential oils used for therapeutic purposes for many years. In this study, five different Pharmacopoeia grade essential oils (Achillea millefolium L., Pimpinella anisum L., Matricaria recutita L., Eucalyptus globulus L., Salvia officinalis L.) which obtained from commercial sources were evaluated for chemical compositions, synergistic antimicrobial activities, and lipoxygenase enzyme inhibitions. Volatile components were determined by gas chromatography/flame ionization detector and gas chromatography/mass spectrometer, simultaneously. The potential antimicrobial activity of essential oils was tested against oral pathogenic standard strains such as Streptococcus mutans, Streptococcus sanguinis, Staphylococcus aureus, Corynebacterium striatum, Candida albicans and Candida krusei by broth microdilution methods. Ciprofloxacin and ketoconazole were used positive controls. It has been observed that the essential oils tested have average inhibitory antimicrobial activity against oral pathogens with a Minimum Inhibition Concentration of 20-0.625 mg/mL. The active essential oils have been combined with antibiotics and synergistic effects have been evaluated by Checkerboard method. ƩFIC values were determined. In combination with antibiotics M. recutita essential oil has been shown to have a synergistic effect against S. aureus in combination with tetracycline (ƩFIC 0.46). In addition, 5-LOX inhibitory activity was measured by modifying the spectrophotometric method developed by Baylac and Racine. As a result, 5-LOX % inhibition of S. officinalis, E. globulus and M. recutita were calculated as 34.0 ± 6.66, 72.7 ± 2.78 and 27.7 ± 0.60, respectively.

Keywords: antimicrobial activity, essential oils, synergistic activity, 5-lipoxygenase inhibition

Procedia PDF Downloads 92

Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

Abstract:

In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

Procedia PDF Downloads 58