Search results for: bio-inspired search algorithms

Authors: Masoud Safdari, Jacob Fish

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Despite vast amount of existing theoretical knowledge, the implementation of a universal multiscale modeling, analysis, and simulation software framework remains challenging. Existing multiscale software and solutions are often domain-specific, closed-source and mandate a high-level of experience and skills in both multiscale analysis and programming. Furthermore, tools currently existing for Atomistic-to-Continuum (AtC) multiscaling are developed with the assumptions such as accessibility of high-performance computing facilities to the users. These issues mentioned plus many other challenges have reduced the adoption of multiscale in academia and especially industry. In the current work, we introduce Multiscale Hub (MsHub), an effort towards making AtC more accessible through cloud services. As a joint effort between academia and industry, MsHub provides a universal web-enabled framework for practical multiscaling. Developed on top of universally acclaimed scientific programming language Python, the package currently provides an open-source, comprehensive, easy-to-use framework for AtC coupling. MsHub offers an easy to use interface to prominent molecular dynamics and multiphysics continuum mechanics packages such as LAMMPS and MFEM (a free, lightweight, scalable C++ library for finite element methods). In this work, we first report on the design philosophy of MsHub, challenges identified and issues faced regarding its implementation. MsHub takes the advantage of a comprehensive set of tools and algorithms developed for AtC that can be used for a variety of governing physics. We then briefly report key AtC algorithms implemented in MsHub. Finally, we conclude with a few examples illustrating the capabilities of the package and its future directions.

Keywords: atomistic, continuum, coupling, multiscale

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Authors: Kahlouche Amal, Touzene F. Zohra, Betatache Fatihaet Nouani Abdelouahab

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The development of the world production of the cheese these last decades, as well as agents' greater request cheap coagulants, accentuated the search for new surrogates of the rennet. What about the interest to explore the vegetable biodiversity, the source well cheap of many naturals metabolites that the scientists today praise it (thistle, latex of fig tree, Cardoon, seeds of melon). Indeed, a big interest is concerned the search for surrogates of vegetable origin. The objective of the study is to show the possibility of extracting a protease coagulant the milk from the cake of Sunflower, available raw material and the potential source of surrogates of rennet. so, the determination of the proteolytic activity of raw extracts, the purification, the elimination of the pigments of tint of the enzymatic preparations, a better knowledge of the coagulative properties through study of the effect of certain factors (temperature, pH, concentration in CaCl2) are so many factors which contribute to value milk particularly those produced by the small ruminants of the Algerian dairy exploitations. Otherwise, extracts coagulants of vegetable origin allowed today to value traditional, in addition, although the extract coagulants of vegetable origin made it possible today to develop traditional cheeses whose Iberian peninsula is the promoter, but the test of 'pressed paste not cooked' cheese manufacturing led to the semi-scale pilot; and that, by using the enzymatic extract of sunflower (Helianthus annus) which gave satisfactory results as well to the level of outputs as on the sensory level,which, statistically,did not give any significant difference between studied cheeses. These results confirm the possibility of use of this coagulase as a substitute of rennet commercial on an industrial scale.

Keywords: characterization, cheese, Rennet, sunflower

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Stephen L. Roberts

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This article offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The article traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This article demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This article argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the article also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this article demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice of global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance.

Keywords: algorithms, global health, pandemic, surveillance

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Authors: Sally Kafelghazal

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Introduction: Arab Americans, who include immigrants, refugees, or U.S. born persons of Middle Eastern or North African descent, may experience significant difficulties during acculturation to Western society. Influential stressors include relocation, loss of social support, language barriers, and economic factors, all of which can impact mental health. There is limited research investigating the effects of acculturation on the mental health of the Arab American population. Objectives: The purpose of this study is to identify ways in which acculturation impacts the mental health of Arab Americans, specifically the development of depression and anxiety. Method: A literature search was conducted using PubMed and PsycArticles (ProQuest), utilizing the following search terms: “Arab Americans,” “Arabs,” “mental health,” “depression,” “anxiety,” “acculturation.” Thirty-nine articles were identified and of those, nine specifically investigated the relationship between acculturation and mental health in Arab Americans. Three of the nine focused exclusively on depression. Results: Several risk factors were identified that contribute to poor mental health associated with acculturation, which include immigrant or refugee status, facing discrimination, and religious ideology. Protective factors include greater levels of acculturation, being U.S. born, and greater heritage identity. Greater mental health disorders were identified in Arab Americans compared to normative samples, perhaps particularly depression; none of the articles specifically addressed anxiety. Conclusion: The current research findings support the potential association between the process of acculturation and greater levels of mental health disorders in Arab Americans. However, the diversity of the Arab American population makes it difficult to draw consistent conclusions. Further research needs to be conducted in order to assess which subgroups in the Arab American population are at highest risk for developing new or exacerbating existing mental health disorders in order to devise more effective interventions.

Keywords: arab americans, arabs, mental health, anxiety, depression, acculturation

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

Procedia PDF Downloads 448

Authors: Daniela V. Bavaresco, Tamy Colonetti, Antonio Jose Grande, Francesc Colom, Joao Quevedo, Samira S. Valvassori, Maria Ines da Rosa

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Objective: Performed a systematic review and meta-analysis to evaluated the potential effect of the cyclo-oxygenases (Cox)-2 inhibitor Celecoxib adjunct treatment in Bipolar Disorder (BD), through of randomized controlled trials. Method: A search of the electronic databases was proceeded, on MEDLINE, EMBASE, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Biomed Central, Web of Science, IBECS, LILACS, PsycINFO (American Psychological Association), Congress Abstracts, and Grey literature (Google Scholar and the British Library) for studies published from January 1990 to February 2018. A search strategy was developed using the terms: 'Bipolar disorder' or 'Bipolar mania' or 'Bipolar depression' or 'Bipolar mixed' or 'Bipolar euthymic' and 'Celecoxib' or 'Cyclooxygenase-2 inhibitors' or 'Cox-2 inhibitors' as text words and Medical Subject Headings (i.e., MeSH and EMTREE) and searched. The therapeutic effects of adjunctive treatment with Celecoxib were analyzed, it was possible to carry out a meta-analysis of three studies included in the systematic review. The meta-analysis was performed including the final results of the Young Mania Rating Scale (YMRS) at the end of randomized controlled trials (RCT). Results: Three primary studies were included in the systematic review, with a total of 121 patients. The meta-analysis had significant effect in the YMRS scores from patients with BD who used Celecoxib adjuvant treatment in comparison to placebo. The weighted mean difference was 5.54 (95%CI=3.26-7.82); p < 0.001; I2 =0%). Conclusion: The systematic review suggests that adjuvant treatment with Celecoxib improves the response of major treatments in patients with BD when compared with adjuvant placebo treatment.

Keywords: bipolar disorder, Cox-2 inhibitors, Celecoxib, systematic review, meta-analysis

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Authors: Elisabetta Cianfanelli, Paolo Pupparo, Maria Claudia Coppola

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The recent evolutions in the innovation processes and in the intrinsic tendencies of the product development process, lead to new considerations on the design flow. The instability and complexity that contemporary life describes, defines new problems in the production of products, stimulating at the same time the adoption of new solutions across the entire design process. The advent of Additive Manufacturing, but also of IOT and AI technologies, continuously puts us in front of new paradigms regarding design as a social activity. The totality of these technologies from the point of view of application describes a whole series of problems and considerations immanent to design thinking. Addressing these problems may require some initial intuition and the use of some provisional set of rules or plausible strategies, i.e., heuristic reasoning. At the same time, however, the evolution of digital technology and the computational speed of new design tools describe a new and contrary design framework in which to operate. It is therefore interesting to understand the opportunities and boundaries of the new man-algorithm relationship. The contribution investigates the man-algorithm relationship starting from the state of the art of the Made in Italy model, the most known fields of application are described and then focus on specific cases in which the mutual relationship between man and AI becomes a new driving force of innovation for entire production chains. On the other hand, the use of algorithms could engulf many design phases, such as the definition of shape, dimensions, proportions, materials, static verifications, and simulations. Operating in this context, therefore, becomes a strategic action, capable of defining fundamental choices for the design of product systems in the near future. If there is a human-algorithm combination within a new integrated system, quantitative values can be controlled in relation to qualitative and material values. The trajectory that is described therefore becomes a new design horizon in which to operate, where it is interesting to highlight the good practices that already exist. In this context, the designer developing new forms can experiment with ways still unexpressed in the project and can define a new synthesis and simplification of algorithms, so that each artifact has a signature in order to define in all its parts, emotional and structural. This signature of the designer, a combination of values and design culture, will be internal to the algorithms and able to relate to digital technologies, creating a generative dialogue for design purposes. The result that is envisaged indicates a new vision of digital technologies, no longer understood only as of the custodians of vast quantities of information, but also as a valid integrated tool in close relationship with the design culture.

Keywords: decision making, design euristics, product design, product design process, design paradigms

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Authors: Ramin Mansouri

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Due to the contrast of rivers discharge regime with water demands, one of the best ways to use water resources is to regulate the natural flow of the rivers and supplying water needs to construct dams. Optimal utilization of reservoirs, consideration of multiple important goals together at the same is of very high importance. To study about analyzing this method, statistical data of Bakhtiari and Roudbar dam over 46 years (1955 until 2001) is used. Initially an appropriate objective function was specified and using DE algorithm, the rule curve was developed. In continue, operation policy using rule curves was compared to standard comparative operation policy. The proposed method distributed the lack to the whole year and lowest damage was inflicted to the system. The standard deviation of monthly shortfall of each year with the proposed algorithm was less deviated than the other two methods. The Results show that median values for the coefficients of F and Cr provide the optimum situation and cause DE algorithm not to be trapped in local optimum. The most optimal answer for coefficients are 0.6 and 0.5 for F and Cr coefficients, respectively. After finding the best combination of coefficients values F and CR, algorithms for solving the independent populations were examined. For this purpose, the population of 4, 25, 50, 100, 500 and 1000 members were studied in two generations (G=50 and 100). result indicates that the generation number 200 is suitable for optimizing. The increase in time per the number of population has almost a linear trend, which indicates the effect of population in the runtime algorithm. Hence specifying suitable population to obtain an optimal results is very important. Standard operation policy had better reversibility percentage, but inflicts severe vulnerability to the system. The results obtained in years of low rainfall had very good results compared to other comparative methods.

Keywords: reservoirs, differential evolution, dam, Optimal operation

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Authors: Anda-veronica Gherman, Dimitrios Varthalitis

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Neurosurgery, like many surgical specialties, undoubtedly exhibits a significant gender gap, particularly in leadership positions. While increasing women representation in neurosurgery is important, it is crucial to increase their presence in leadership positions. Across the globe and Europe there are concerning trends of only 4% of all neurosurgical departments being chaired by women. This study aims to explore the situation regarding gender disparities in leadership in the United Kingdom and to identify possible contributing factors as well as discussing future strategies to bridge this gap. Methods: A literature review was conducted utilising PubMed as main database with search keywords including ‘female neurosurgeon’, ‘women neurosurgeon’, ‘gender disparity’, ‘leadership’ and ‘UK’. Additionally, a manual search of all neurosurgical departments in the UK was performed to identify the current female department leads and training director leads. Results: The literature search identified a paucity of literature addressing specifically leadership in female neurosurgeons within the UK, with very few published papers specifically on this topic. Despite more than half of medical students in the UK being female, only a small proportion pursue a surgical career, with neurosurgery being one of the least represented specialties. Only 27% of trainee neurosurgeons are female, and numbers are even lower at a consultant level, where women represent just 8%.Findings from published studies indicated that only 6.6% of leadership positions in neurosurgery are occupied by women in the UK. Furthermore, our manual searches across UK neurosurgical departments revealed that around 5% of department lead positions are currently held by women. While this figure is slightly higher than the European average of 4%, it remains lower compared to figures of 10% in other North-West European countries. The situation is slightly more positive looking at the training directors, with 15% being female. Discussion: The findings of this study highlight a significant gender disparity in leadership positions within neurosurgery in the UK, which may have important implications, perpetuating the lack of diversity on the decision-making process, limiting the career advancement opportunities of women and depriving the neurosurgical field from the voices, opinions and talents of women. With women representing half of the population, there is an undeniable need for more female leaders at the policy-making level. There are many barriers that can contribute to these numbers, including bias, stereotypes, lack of mentorship and work-like balance. A few solutions to overcome these barriers can be training programs addressing bias and impostor syndrome, leadership workshops tailored for female needs, better workplace policies, increased in formal mentorship and increasing the visibility of women in neurosurgery leadership positions through media, speaking opportunities, conferences, awards etc. And lastly, more research efforts should focus on the leadership and mentorship of women in neurosurgery, with an increased number of published papers discussing these issues.

Keywords: female neurosurgeons, female leadership, female mentorship, gender disparities

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Authors: Fahad Hanash Alzahrani

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This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

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Authors: David Blaine, George Raschbaum

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The evaluation of DHCP has synthesized SCSI disks, and current trends suggest that the exploration of e-business that would allow for further study into robots will soon emerge. Given the current status of embedded algorithms, hackers worldwide obviously desire the exploration of replication, which embodies the confusing principles of programming languages. In our research we concentrate our efforts on arguing that erasure coding can be made "fuzzy", encrypted, and game-theoretic.

Keywords: SCHI disks, robot, algorithm, hacking, programming language

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Soumi Banerjee

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In this research paper we will go deep into understanding Benedict Anderson’s definition of the nation as an imagined community and we will analyze why and how national identities were created through long and complex processes, and how there can exist strong emotional bonds between people within an imagined community, given the fact that these people have never known each other personally, but will still feel some form of imagined unity. Such identity construction on the part of an individual or within societies are always in some sense in a state of flux as imagined communities are ever changing, which provides us with the ontological foundation for reaching on this paper. This sort of identity crisis among individuals living in the Western world, who are in search for psychological comfort and security, illustrates a possible need for spatially dislocated, ontologically insecure and vulnerable individuals to have a secure identity. To create such an identity there has to be something to build upon, which could be achieved through what may be termed as ‘homesteading’. This could in short, and in my interpretation of Kinnvall and Nesbitt’s concept, be described as a search for security that involves a search for ‘home’, where home acts as a secure place, which one can build an identity around. The next half of the paper will then look into how populism and identity have played an increasingly important role in the political elections in the so-called western democracies of the world, using the U.S. as an example. Notions of ‘us and them’, the people and the elites will be looked into and analyzed through a social constructivist theoretical lens. Here we will analyze how such narratives about identity and the nation state affects people, their personality development and identity in different ways by studying the U.S. President Donald Trump’s speeches and analyze if and how he used different identity creating narratives for gaining political and popular support. The reason to choose narrative analysis as a method in this research paper is to use the narratives as a device to understand how the perceived notions of 'us and them' can initiate huge identity crisis with a community or a nation-state. This is a relevant subject as results and developments such as rising populist rightwing movements are being felt in a number of European states, with the so-called Brexit vote in the U.K. and the election of Donald Trump as president are two of the prime examples. This paper will then attempt to argue that these mechanisms are strengthened and gaining significance in situations when humans in an economic, social or ontologically vulnerable position, imagined or otherwise, in a general and broad meaning perceive themselves to be under pressure, and a sense of insecurity is rising. These insecurities and sense of being under threat have been on the rise in many of the Western states that are otherwise usually perceived to be some of the safest, democratically stable and prosperous states in the world, which makes it of interest to study what has changed, and help provide some part of the explanation as to how creating a ‘them’ in the discourse of national identity can cause massive security crisis.

Keywords: identity crisis, migration, ontological security(in), nation-states

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Authors: Enes Gunaltili, Burak Dam

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The airplane history started with the Wright brothers' aircraft and improved day by day. With the help of this advancements, big aircrafts replace with small and unmanned air vehicles, so in this study we design this type of air vehicles. First of all, aircrafts mainly divided into two main parts in our day as a rotary and fixed wing aircrafts. The fixed wing aircraft generally use for transport, cargo, military and etc. The rotary wing aircrafts use for same area but there are some superiorities from each other. The rotary wing aircraft can take off vertically from the ground, and it can use restricted area. On the other hand, rotary wing aircrafts generally can fly lower range than fixed wing aircraft. There are one kind of aircraft consist of this two types specifications. It is named as VTOL (vertical take-off and landing) type aircraft. VTOLs are able to takeoff and land vertically and fly horizontally. The VTOL aircrafts generally can fly higher range from the rotary wings but can fly lower range from the fixed wing aircraft but it gives beneficial range between them. There are many other advantages of VTOL aircraft from the rotary and fixed wing aircraft. Because of that, VTOLs began to use for generally military, cargo, search, rescue and mapping areas. Within this framework, this study answers the question that how can we design VTOL as a small unmanned aircraft systems for search and rescue application for benefiting the advantages of fixed wing and rotary wing aircrafts by eliminating the disadvantages of them. To answer that question and design VTOL aircraft, multidisciplinary design optimizations (MDO), some theoretical terminologies, formulations, simulations and modelling systems based on CFD (Computational Fluid Dynamics) is used in same time as design methodology to determine design parameters and steps. As a conclusion, based on tests and simulations depend on design steps, suggestions on how the VTOL aircraft designed and advantages, disadvantages, and observations for design parameters are listed, then VTOL is designed and presented with the design parameters, advantages, and usage areas.

Keywords: airplane, rotary, fixed, VTOL, CFD

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Authors: Kofi Nkonkonya Mpuangnan, Samantha Govender, Hlengiwe Romualda Mhlongo

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Educational leaders are skilled architects crafting a vibrant environment where growth, creativity, and adaptability can flourish within schools. Their journey is one of transformation, urging them to explore administrative strategies that align seamlessly with evolving educational models and cater to the specific needs of students, educators, and stakeholders. Through this committed effort to innovate, they seek to enhance the effectiveness and influence of educational systems, paving the way for a more inclusive and forward-thinking educational environment. In this context, the authors explored the concept of transforming educational leadership with administrative strategies in alignment with the following research objectives. To find the strategies that can be adopted by transformation leaders to promote effective administrative practices in an educational setting and to explore the roles of educational leaders in promoting collaboration in education. To find answers to these questions, a systematic literature review underpinned by the transformational leadership model was adopted. Therefore, concepts integrated from a variety of outlets, including academic journals, conference proceedings, and reports found within SCOPUS, WoS, and IBSS databases. A search was aided using specific themes like innovative administrative practices, the roles of educational leaders, and interdisciplinary approaches to administrative practices. The process of conducting the search adhered to the five-step framework, which was subjected to inclusion and exclusion of studies. It was found that transformational leadership, agile methodologies, employee wellbeing, seminars and workshops could foster a culture of innovation and creativity among teachers and staff to transform administrative practices in education settings. It was recommended that professional development programs be organized periodically for educational leaders in educational institutions to help them revitalize their knowledge and skills in educational administration.

Keywords: educational leadership, innovative strategies, administrative practices, professional development, stakeholder engaement, student outcome

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Authors: M. L. Mangena, N. Mokoena, K. Rashamuse, M. G. Tlou

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Tertiary alcohol ester (TAE) hydrolases are a group of esterases (EC 3.1.1.-) that catalyze the kinetic resolution of TAEs and as a result, they are sought-after for the production of optically pure tertiary alcohols (TAs) which are useful as building blocks for number biologically active compounds. What sets these enzymes apart is, the presence of a GGG(A)X-motif in the active site which appears to be the main reason behind their activity towards the sterically demanding TAEs. The genome of Pseudomonas syringae pv. maculicola (Psm) comprises a multitude of genes that encode esterases. We therefore, hypothesize that some of these genes encode TAE hydrolases. In this study, Psm was screened for TAE hydrolase activity using the linalyl acetate (LA) plate assay and a positive reaction was observed. As a result, the genome of Psm was screened for esterases with a GGG(A)X-motif using the motif search tool and two potential TAE hydrolase genes (PsmEST1 and 2, 1100 and 1000bp, respectively) were identified, PsmEST1 was amplified by PCR and the gene sequenced for confirmation. Analysis of the sequence data with the SingnalP 4.1 server revealed that the protein comprises a signal peptide (22 amino acid residues) on the N-terminus. Primers specific for the gene encoding the mature protein (without the signal peptide) were designed such that they contain NdeI and XhoI restriction sites for directional cloning of the PCR products into pET28a. The gene was expressed in E. coli JM109 (DE3) and the clones screened for TAE hydrolase activity using the LA plate assay. A positive clone was selected, overexpressed and the protein purified using nickel affinity chromatography. The activity of the esterase towards LA was confirmed using thin layer chromatography.

Keywords: hydrolases, tertiary alcohol esters, tertiary alcohols, screening, Pseudomonas syringae pv., maculicola genome, esterase activity, linalyl acetate

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Longxiang Guo, Haoyan Hao, Xueli Sheng, Hanjun Yu, Jingwei Yin

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In order to prevent target splitting and ensure the accuracy of fusion, system error registration is an important step in multi-static sonar fusion detection system. To eliminate the inherent system errors including distance error and angle error of each sonar in detection, this paper uses offline estimation method for error registration. Suppose several sonars from different platforms work together to detect a target. The target position detected by each sonar is based on each sonar’s own reference coordinate system. Based on the two-dimensional stereo projection method, this paper uses real-time quality control (RTQC) method and least squares (LS) method to estimate sensor biases. The RTQC method takes the average value of each sonar’s data as the observation value and the LS method makes the least square processing of each sonar’s data to get the observation value. In the underwater acoustic environment, matlab simulation is carried out and the simulation results show that both algorithms can estimate the distance and angle error of sonar system. The performance of the two algorithms is also compared through the root mean square error and the influence of measurement noise on registration accuracy is explored by simulation. The system error convergence of RTQC method is rapid, but the distribution of targets has a serious impact on its performance. LS method can not be affected by target distribution, but the increase of random noise will slow down the convergence rate. LS method is an improvement of RTQC method, which is widely used in two-dimensional registration. The improved method can be used for underwater multi-target detection registration.

Keywords: data fusion, multi-static sonar detection, offline estimation, sensor registration problem

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Authors: Carlos M. Goncalves, Luisa Duarte, Teresa Cartaxo

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The aim of this work was to understand the reasons behind the underutilization of electroconvulsive therapy (ECT) in the younger population and raise possible solutions. We conducted a non-systematic review of literature throughout a search on PubMed, using the terms ‘children’, ‘adolescents’ and ‘electroconvulsive’, ‘therapy’. Candidate articles written in languages other than English were excluded. Articles were selected according to title and/or abstract’s content relevance, resulting in a total of 5 articles. ECT is a recognized effective treatment in adults for several psychiatric conditions. As in adults, ECT in children and adolescents is proven most beneficial in the treatment of severe mood disorders, catatonia, and, to a lesser extent, schizophrenia. ECT in adults has also been used to treat autism’s self-injurious behaviours, Tourette’s syndrome and resistant first-episode schizophrenia disorder. Despite growing evidence on its safety and effectiveness in children and adolescents, like those found in adults, ECT remains a controversial and underused treatment in patients this age, even when it is clearly indicated. There are various possible reasons to this; limited awareness among professionals (lack of knowledge and experience among child psychiatrists), stigmatic public opinion (despite positive feedback from patients and families, there is an unfavourable and inaccurate representation in the media, contributing to a negative public opinion), legal restrictions and ethical controversies (restrictive regulations such as a minimum age for administration), lack of randomized trials (the currently available studies are retrospective, with small size samples, and most of the publications are either case reports or case series). This shows the need to raise awareness and knowledge, not only for mental health professionals, but also to the general population, through the media, regarding indications, methods and safety of ECT in order to provide reliable information to the patient and families. Large-scale longitudinal studies are also useful to further demonstrate the efficacy and safety of ECT and can aid in the formulation of algorithms and guidelines as without these changes, the availability of ECT to the younger population will remain restricted by regulations and social stigma. In conclusion, these results highlight that lack of adequate knowledge and accurate information are the most important factors behind the underutilization of ECT in younger population. Mental healthcare professionals occupy a cornerstone position; if data is given by a well-informed healthcare professional instead of the media, general population (including patients and their families) will probably regard the procedure in a more favourable way. So, the starting point should be to improve health care professional’s knowledge and experience on this choice of treatment.

Keywords: adolescents, children, electroconvulsive, therapy

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Cyril Kaplan

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26 participants played 4 EEG neurofeedback (NF) games encouraged to find their strategies to control the specific NF parameter. Mixed method analysis of performance in the games and post-session interviews led to the identification of states of consciousness that correlated with success in the game. We found that increase in left frontal beta activity was facilitated by evoking interest in observed surroundings, by wondering what is happening behind the window or what lies in a drawer in front.

Keywords: EEG neurofeedback, states of consciousness, frontal beta activity, mixed methods

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Authors: Zhipeng Yan, Janice Wing-Tung Kwong, Ching-Lung Lai

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Background: Advanced melanoma accounts for the majority of skin cancer death due to its poor prognosis. Nivolumab and ipilimumab are monoclonal antibodies targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocytes antigen 4 (CTLA-4). Nivolumab and ipilimumab combination therapy has been proven to be effective for advanced melanoma. This systematic review and meta-analysis are to evaluate its clinical efficacy and adverse events. Method: A systematic search was done on databases (Pubmed, Embase, Medline, Cochrane) on 21 June 2020. Search keywords were nivolumab, ipilimumab, melanoma, and randomised controlled trials. Clinical trials fulfilling the inclusion criteria were selected to evaluate the efficacy of combination therapy in terms of prolongation of progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). The odd ratios and distributions of grade 3 or above adverse events were documented. Subgroup analysis was performed based on PD-L1 expression-status and BRAF-mutation status. Results: Compared with nivolumab monotherapy, the hazard ratios of PFS, OS and odd ratio of ORR in combination therapy were 0.64 (95% CI, 0.48-0.85; p=0.002), 0.84 (95% CI, 0.74-0.95; p=0.007) and 1.76 (95% CI, 1.51-2.06; p < 0.001), respectively. Compared with ipilimumab monotherapy, the hazard ratios of PFS, OS and odd ratio of ORR were 0.46 (95% CI, 0.37-0.57; p < 0.001), 0.54 (95% CI, 0.48-0.61; p < 0.001) and 6.18 (95% CI, 5.19-7.36; p < 0.001), respectively. In combination therapy, the odds ratios of grade 3 or above adverse events were 4.71 (95% CI, 3.57-6.22; p < 0.001) compared with nivolumab monotherapy, and 3.44 (95% CI, 2.49-4.74; p < 0.001) compared with ipilimumab monotherapy, respectively. High PD-L1 expression level and BRAF mutation were associated with better clinical outcomes in patients receiving combination therapy. Conclusion: Combination therapy is effective for the treatment of advanced melanoma. Adverse events were common but manageable. Better clinical outcomes were observed in patients with high PD-L1 expression levels and positive BRAF-mutation.

Keywords: nivolumab, ipilimumab, advanced melanoma, systematic review, meta-analysis

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Authors: Felipe M. de Freitas, Icaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

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The SPICE-based simulators are quite robust and widely used for simulation of electronic circuits, their algorithms support linear and non-linear lumped components and they can manipulate an expressive amount of encapsulated elements. Despite the great potential of these simulators based on SPICE in the analysis of quasi-static electromagnetic field interaction, that is, at low frequency, these simulators are limited when applied to microwave hybrid circuits in which there are both lumped and distributed elements. Usually the spatial discretization of the FDTD (Finite-Difference Time-Domain) method is done according to the actual size of the element under analysis. After spatial discretization, the Courant Stability Criterion calculates the maximum temporal discretization accepted for such spatial discretization and for the propagation velocity of the wave. This criterion guarantees the stability conditions for the leapfrogging of the Yee algorithm; however, it is known that for the field update, the stability of the complete FDTD procedure depends on factors other than just the stability of the Yee algorithm, because the FDTD program needs other algorithms in order to be useful in engineering problems. Examples of these algorithms are Absorbent Boundary Conditions (ABCs), excitation sources, subcellular techniques, grouped elements, and non-uniform or non-orthogonal meshes. In this work, the influence of the stability of the FDTD method in the modeling of concentrated elements such as resistive sources, resistors, capacitors, inductors and diode will be evaluated. In this paper is proposed, therefore, the electromagnetic modeling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-wide frequencies. The models of the resistive source, the resistor, the capacitor, the inductor, and the diode will be evaluated, among the mathematical models for lumped components in the LE-FDTD method (Lumped-Element Finite-Difference Time-Domain), through the parametric analysis of Yee cells size which discretizes the lumped components. In this way, it is sought to find an ideal cell size so that the analysis in FDTD environment is in greater agreement with the expected circuit behavior, maintaining the stability conditions of this method. Based on the mathematical models and the theoretical basis of the required extensions of the FDTD method, the computational implementation of the models in Matlab® environment is carried out. The boundary condition Mur is used as the absorbing boundary of the FDTD method. The validation of the model is done through the comparison between the obtained results by the FDTD method through the electric field values and the currents in the components, and the analytical results using circuit parameters.

Keywords: hybrid circuits, LE-FDTD, lumped element, parametric analysis

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: M. Ahmadi, Sh. Damanabi, F. Sadoughi

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National Health Information System plays an important role in ensuring timely and reliable access to Health information which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, by using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system for better planning and management influential factors of performance seems necessary, therefore, in this study, different attitudes towards components of this system are explored comparatively. Methods: This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process, and output. In this context, search for information using library resources and internet search were conducted and data analysis was expressed using comparative tables and qualitative data. Results: The findings showed that there are three different perspectives presenting the components of national health information system, Lippeveld, Sauerborn, and Bodart Model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008 and Gattini’s 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities, and equipment. In addition, in the ‘process’ section from three models, we pointed up the actions ensuring the quality of health information system and in output section, except Lippeveld Model, two other models consider information products, usage and distribution of information as components of the national health information system. Conclusion: The results showed that all the three models have had a brief discussion about the components of health information in input section. However, Lippeveld model has overlooked the components of national health information in process and output sections. Therefore, it seems that the health measurement model of network has a comprehensive presentation for the components of health system in all three sections-input, process, and output.

Keywords: National Health Information System, components of the NHIS, Lippeveld Model

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Authors: Ana Mafalda Silva, Rebeca Fontes, Ana Paula Vaz, Carla Carreira, Ana Corte-Real

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Decades of research on the topic of interpersonal violence against minors highlight five main conclusions: 1) it causes harmful effects on children's development and health; 2) it is prevalent; 3) it violates children's rights; 4) it can be prevented and 5) parents are the main aggressors. The child abuse scenario is identified through clinical observation, administrative data and self-reports. The most used instruments are self-reports; however, there are no valid and reliable self-report instruments for minors, which consist of a retrospective interpretation of the situation by the victim already in her adult phase and/or by her parents. Clinical observation and collection of information, namely from the orofacial region, are essential in the early identification of these situations. The management of medical data, such as personal data, must comply with the General Data Protection Regulation (GDPR), in Europe, and with the General Law of Data Protection (LGPD), in Brazil. This review aims to answer the question: In a situation of medical assistance to minors, in the suspicion of interpersonal violence, due to mistreatment, is it necessary for the guardians to provide consent in the registration and sharing of personal data, namely medical ones. A scoping review was carried out based on a search by the Web of Science and Pubmed search engines. Four papers and two documents from the grey literature were selected. As found, the process of identifying and signaling child abuse by the health professional, and the necessary early intervention in defense of the minor as a victim of abuse, comply with the guidelines expressed in the GDPR and LGPD. This way, the notification in maltreatment scenarios by health professionals should be a priority and there shouldn’t be the fear or anxiety of legal repercussions that stands in the way of collecting and treating the data necessary for the signaling procedure that safeguards and promotes the welfare of children living with abuse.

Keywords: child abuse, disease notifications, ethics, healthcare assistance

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Authors: Sheikh Omar Sillah

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Introduction: Monitoring is an important means of ensuring the smooth implementation and quality of clinical trials. For many years, traditional site monitoring approaches have been critical in detecting data errors but not optimal in identifying fabricated and implanted data as well as non-random data distributions that may significantly invalidate study results. The objective of this paper was to provide recommendations based on best statistical monitoring practices for detecting data-integrity issues suggestive of fabrication and implantation early in the study conduct to allow implementation of meaningful corrective and preventive actions. Methodology: Electronic bibliographic databases (Medline, Embase, PubMed, Scopus, and Web of Science) were used for the literature search, and both qualitative and quantitative studies were sought. Search results were uploaded into Eppi-Reviewer Software, and only publications written in the English language from 2012 were included in the review. Gray literature not considered to present reproducible methods was excluded. Results: A total of 18 peer-reviewed publications were included in the review. The publications demonstrated that traditional site monitoring techniques are not efficient in detecting data anomalies. By specifying project-specific parameters such as laboratory reference range values, visit schedules, etc., with appropriate interactive data monitoring, statistical monitoring can offer early signals of data anomalies to study teams. The review further revealed that statistical monitoring is useful to identify unusual data patterns that might be revealing issues that could impact data integrity or may potentially impact study participants' safety. However, subjective measures may not be good candidates for statistical monitoring. Conclusion: The statistical monitoring approach requires a combination of education, training, and experience sufficient to implement its principles in detecting data anomalies for the statistical aspects of a clinical trial.

Keywords: statistical monitoring, data anomalies, clinical trials, traditional monitoring

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