Search results for: successful intelligence

Authors: Pattaka Sa-Ngimnet

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That the University as we have known it is no longer serving the needs of the vast majority of students and potential students has been a topic of much discussion. Institutions of higher education, in this age of global culture, are in a process of metamorphosis. Technology is being used to allow more students, older students, working students and disabled students, who cannot attend conventional classes, to have greater access to higher education through the internet. But change must come about only after much evaluation and experimentation or education will simply become a commodity as, in some cases, it already has. This paper will be concerned with the meaning and methods of change and evaluation as they are applied to institutions of higher education. Organization’s generally have different goals and different approaches in order to be successful. However, the means of reaching those goals requires rational and effective planning. Any plans for successful change in any institution must take into account both effectiveness and efficiency and the differences between them. “Effectiveness” refers to an adequate means of achieving an objective. “Efficiency” refers to the ability to achieve an objective without waste of time or resources (The Free Dictionary). So an effective means may not be efficient and an efficient means may not be effective. The goal is to reach a synthesis of effectiveness and efficiency that will maximize both to the extent each is limited by the other. This focus of this paper then is to determine how an educational institution can become either successful or oppressive depending on the kinds of planning, evaluating and changes that operate by and on the administration. If the plan is concerned only with efficiency, the institution can easily become oppressive and lose sight of its purpose of educating students. If it is overly concentrated on effectiveness, the students may receive a superior education in the short run but the institution will face operating difficulties. In becoming only goal oriented, institutions also face problems. Simply stated, if the institution reaches its goals, the stake holders may become satisfied and fail to change and keep up with the needs of the times. So goals should be seen only as benchmarks in a process of becoming even better in providing quality education. Constant and consistent evaluation is the key to making all these factors come together in a successful process of planning, testing and changing the plans as needed. The focus of the evaluation has to be considered. Evaluations must take into account progress and needs of students, methods and skills of instructors, resources available from the institution and the styles and objectives of administrators. Thus the role of evaluation is pivotal in providing for the maximum of both effective and efficient change in higher education institutions.

Keywords: change, effectiveness, efficiency, education

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Authors: Muhammad Abu Bakar Tariq

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Construction industry plays a crucial role in the progress of societies and economies. Furthermore, construction projects have social as well as economic implications, thus, their success/failure have wider impacts. However, the industry is lagging behind in terms of efficiency and productivity. Building Information Modeling (BIM) is recognized as a revolutionary development in Architecture, Engineering and Construction (AEC) industry. There are numerous interest groups around the world providing definitions of BIM, proponents describing its advantages and opponents identifying challenges/barriers regarding adoption of BIM. This research is aimed at to determine what actually BIM is, along with its potential role in delivering successful construction projects. The methodology is critical analysis of secondary data sources i.e. information present in public domain, which include peer reviewed journal articles, industry and government reports, conference papers, books, case studies etc. It is discovered that clash detection and visualization are two major advantages of BIM. Clash detection option identifies clashes among structural, architectural and MEP designs before construction actually commences, which subsequently saves time as well as cost and ensures quality during execution phase of a project. Visualization is a powerful tool that facilitates in rapid decision-making in addition to communication and coordination among stakeholders throughout project’s life cycle. By eliminating inconsistencies that consume time besides cost during actual construction, improving collaboration among stakeholders throughout project’s life cycle, BIM can play a positive role to achieve efficiency and productivity that consequently deliver successful construction projects.

Keywords: building information modeling, clash detection, construction project success, visualization

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Li Yong

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Using the data of 135 financial institutions in China from 2018 to 2022, this paper deeply analyzes the underlying theoretical mechanism of artificial intelligence (AI) technology promoting financial development and examines the impact of AI technology on the digital transformation performance of financial enterprises. It is found that the level of AI technology has a significant positive impact on the development of finance. Compared with the impact on the expansion of financial scale, AI technology plays a greater role in improving the performance of financial institutions, reflecting the trend characteristics of the current AI technology to promote the evolution of financial structure. By investigating the intermediary transmission effects, we found that AI technology plays a positive role in promoting the performance of financial institutions by reducing operating costs and improving customer satisfaction, but its function in innovating financial products and mitigating financial risks is relatively limited. In addition, the promotion of AI technology in financial development has significant heterogeneity in terms of the type, scale, and attributes of financial institutions.

Keywords: artificial intelligence technology, financial development, China, heterogeneity

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Authors: Han-Yu Cheng

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This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment.

Keywords: generative AI, anime aesthetics, Japanese anime illustration, art consumption

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Authors: Ying Zhu

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Recent years have witnessed a boom of foreign investments in the field of artificial intelligence (AI). Foreign investments provide critical capital for AI development but also trigger national security concerns of host states. A notable example is an increasing number of cases in which the Committee on Foreign Investment in the United States (CFIUS) has denied Chinese acquisitions of US technology companies on national security grounds. On July 19, 2018, the Congress has reached a deal on the final draft of a new provision to strengthen CFIUS’s authority to review overseas transactions involving sensitive US technology. The question is: how to reconcile the emerging tension between, on the one hand, foreign AI investors’ expectations of a predictable investment environment, and on the other hand, host states’ regulatory power on national security? This paper provides a methodology to reconcile this tension under international investment law. Based on an examination, the national security exception clauses in international investment treaties and the application of national security justification in investor-state arbitration jurisprudence, the paper argues that a traditional interpretation of the national security exception, based on the necessity concept in customary international law, fails to take into account new risks faced by countries, including security concerns over strategic industries such as AI. To overcome this shortage, the paper proposes to incorporate an integrated national security clause in international investment treaties, which includes a two-tier test: a ‘self-judging’ test in the pre-establishment period and a ‘proportionality’ test in the post-establishment period. At the end, the paper drafts a model national security clause for future treaty-drafting practice.

Keywords: foreign investment, artificial intelligence, international investment law, national security exception

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Authors: J. Junk, M. Eickermann

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The Small Hive Beetle, Aethina tumida (Coleoptera: Nitidulidae) is a parasite for honey bee colonies, Apis mellifera, and was recently introduced to the European continent, accidentally. Based on the literature, a model was developed by using regional meteorological variables (daily values of minimum, maximum and mean air temperature as well as mean soil temperature at 50 mm depth) to calculate the time-point of hive invasion by A. tumida in springtime, the development duration of pupae as well as the number of generations of A. tumida per year. Luxembourg was used as a test region for our model for 2005 to 2013. The model output indicates a successful surviving of the Small Hive Beetle in Luxembourg with two up to three generations per year. Additionally, based on our meteorological data sets a first migration of SHB to apiaries can be expected from mid of March up to April. Our approach can be transferred easily to other countries to estimate the risk potential for a successful introduction and spreading of A. tumida in Western Europe.

Keywords: Aethina tumida, air temperature, larval development, soil temperature

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Joy Winston James, Abdul Kadher Jilani

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This review article provides a comprehensive overview of social engineering attacks, specifically those executed through the Kali Linux operating system. It aims to present an in-depth analysis of the background and importance of social engineering in cybersecurity, the tools, and techniques used in these attacks, real-world case studies that demonstrate their effectiveness, and ethical considerations that need to be taken into account while using them. The article highlights the Kali Linux tools that are commonly used in social engineering attacks, including SET, Metasploit, and BeEF, and discusses techniques such as phishing, pretexting, and baiting that are crucial in conducting successful social engineering attacks. It further explores real-world case studies that demonstrate the effectiveness of these techniques, emphasizing the importance of implementing effective countermeasures to reduce the risk of successful social engineering attacks. Moreover, the article sheds light on ethical considerations that need to be taken into account while using social engineering tools, emphasizing the importance of using them ethically and legally. Finally, the article provides potential countermeasures such as two-factor authentication, strong password policies, and regular security audits to help individuals and organizations better protect themselves against this growing threat. By understanding the tools and techniques used in social engineering attacks and implementing appropriate countermeasures, individuals and organizations can minimize the risk of successful social engineering attacks and improve their cybersecurity posture. To illustrate the effectiveness of social engineering attacks, we present real-world case studies that demonstrate how easily individuals and organizations can fall prey to these attacks. We also discuss ethical considerations that must be taken into account while using social engineering tools, emphasizing the need for responsible and legal use of these tools.

Keywords: pen testing, hacking, Kali Linux, social engineering

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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Authors: Chastiti Mediafira Wulolo, Tri Legionosuko, Suhirwan, Yusuf

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Terrorism and radicalization have become a common threat to every nation in this world. As a part of the asymmetric warfare threat, terrorism and radicalization need a complex strategy as the problem solver. One such way is by collaborating with the international community. The Our Eyes Initiative (OEI), for example, is a cooperation pact in the field of intelligence information exchanges related to terrorism and radicalization initiated by the Indonesian Ministry of Defence. The pact has been signed by Indonesia, Philippines, Malaysia, Brunei Darussalam, Thailand, and Singapore. This cooperation mostly engages military acts as a central role, but it still requires the involvement of various parties such as the police, intelligence agencies and other government institutions. This paper will use a qualitative content analysis method to address the opportunity and enhance the optimization of OEI. As the result, it will explain how OEI takes the opportunities as the strategy for counter-terrorism by building it up as the regional cooperation, building the legitimacy of government and creating the legal framework of the information sharing system.

Keywords: our eyes initiative, terrorism, counter-terrorism, ASEAN, cooperation, strategy

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Authors: Ferry Syarifuddin

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The potential for growth in the Japanese halal industry is promising, especially for the export of processed food products, due to the significant increase in the Muslim population over the past decade. Japan is also the second largest destination for processed food export from developing countries. However, there has been a decline in the export of processed food from Indonesia, a Muslim-majority developing country, to Japan, dropping from $350 million in 2019 to $119 million in 2023. To address this issue, this study aims to assess the strengths, weaknesses, opportunities, and threats (SWOT) of Indonesian halal processed food products export to the Japanese market, investigate successful strategies employed by other countries and recommend the most prioritized strategy for exporting Indonesian halal processed food products to the Japan market. Our findings identify collaborating with Japan's food industry associations and trade organizations as the key strategy for successful export to the Japanese market.

Keywords: ANP-SWOT, export strategy, halal product, Japan market

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Authors: Ali Riza Perçin, Eser Bingül

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Internet technologies have created opportunities with which people share their ideologies, thoughts and products. This virtual world, named social media has given the chance of gathering individual users and people from the world's remote locations and establishing an interaction between them. However, to an increasingly higher degree terrorist organizations today use the internet and most notably social-network media to create the effects they desire through a series of on-line activities. These activities, designed to support their activities, include information collection (intelligence), target selection, propaganda, fundraising and recruitment to name a few. Meanwhile, these have been used as the most important tool for recruitment especially from the different region of the world, especially disenfranchised youth, in the West in order to mobilize support and recruit “foreign fighters.” The recruits have obtained the statue, which is not accessible in their society and have preferred the style of life that is offered by the terrorist organizations instead of their current life. Like other terrorist groups, for a while now the terrorist organization Islamic State (IS) in Iraq and Syria has employed a social-media strategy in order to advance their strategic objectives. At the moment, however, IS seems to be more successful in their on-line activities than other similar organizations. IS uses social media strategically as part of its armed activities and for the sustainability of their military presence in Syria and Iraq. In this context, “Generation Y”, which could exist at the critical position and undertake active role, has been examined. Additionally, the explained characteristics of “Generation Y” have been put forward and the duties of families and society have been stated as well.

Keywords: social media, "generation Y", terrorist organization, islamic state IS

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences. Humanity has never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Günther Schuh, Christina König, Toni Drescher

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One of the key factors for the competitiveness and market success of technology-driven companies is the timely provision of information about emerging technologies, changes in existing technologies, as well as relevant related changes in the market's structures and participants. Therefore, many companies conduct technology intelligence (TI) activities to ensure an early identification of appropriate technologies and other (weak) signals. One base activity of TI is technology monitoring, which is defined as the systematic tracking of developments within a specified topic of interest as well as related trends over a long period of time. Due to the very large number of dynamically changing parameters within the technological and the market environment of a company as well as their possible interdependencies, it is necessary to focus technology monitoring on specific indicators or other criteria, which are able to point out technological developments and market changes. In addition to the execution of a literature review on existing approaches, which mainly propose patent-based indicators, it is examined in this paper whether indicator systems from other branches such as risk management or economic research could be transferred to technology monitoring in order to enable an efficient and focused technology monitoring for companies.

Keywords: technology forecasting, technology indicator, technology intelligence, technology management, technology monitoring

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Authors: Prakash Singh

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Employees who are satisfied at their place of work rate their leaders’ intrapersonal competencies as being high. They also believe that a leader’s intrapersonal competencies influence their sense of job satisfaction. Employees who indicate that they are unhappy at their place of work rate their leaders’ intrapersonal competencies as being low. They also believe that a leader’s intrapersonal intelligence influence their feeling of job satisfaction. The leader’s appropriate intrapersonal competencies are crucial to the creation of a motivated and satisfied employee team. In this study, the quantitative research method was used to determine the employees’ perceptions of their leaders’ intrapersonal competencies and their influence on their job satisfaction; the six competencies being self-awareness, self-confidence, self-expression, self-control, adaptability, and optimism. All the competencies of leaders identified in this quantitative study can therefore be described as intervening variables that influence an employee’s sense of job satisfaction. The number of responses that indicate that each of the intrapersonal competencies of a leader that will have an influence on an employee’s sense of job satisfaction, ranges from 93% (a leader’s sense of self-awareness) to 99% (a leader’s ability to be adaptable). As the responses are significantly similar, it can be stated that the respondents indicate that all the intrapersonal competencies of a leader can influence an employee’s sense of job satisfaction. The findings of this study strongly suggest that in order to be satisfied at work, employees prefer to be led by leaders who are confident in their leadership roles; who send out clear, unambiguous messages; who maintain self-control; who are adaptable and flexible;, who face the future with optimism and who support the establishment of a collegial working environment. Evidently, the findings corroborate the hypothesis that employees believe that the intrapersonal competencies of leaders have a positive influence on the employees’ sense of job satisfaction. This study’s findings, therefore, confirm that the key to the leaders’ self-knowledge is access to their own feelings and the ability to discriminate among them and draw upon them to guide behaviour in their organisations. This exploratory study makes a contribution to the emerging research being accomplished on leaders’ intrapersonal intelligence with more research still needing to be attempted to determine to what extent these competencies of leaders can reshape the organizational climate and culture.

Keywords: emotional intelligence, employees’ job satisfaction, leaders’ intrapersonal competencies, leaders’ self-knowledge

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Authors: Anita K. Kovacs, Peter Hegyes, Zsolt Bozso, Gabor Toth

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Fluorination has been used extensively by the pharmaceutical industry as a strategy to improve the pharmacokinetics of drugs due to its effectiveness in increasing the potency of antimicrobial peptides (AMPs). Multiple-fluorinated indole-ring-containing tryptophan derivatives have the potential of having better antimicrobial activity than the widely used mono-fluorinated indole-ring containing tryptophan derivatives, but they are not available commercially. Therefore, our goal is to synthesize multiple-fluorinated indole-ring containing tryptophan derivatives to incorporate them into AMPs to enhance their antimicrobial activity. During our work, we are trying several methods (classical organic synthesis, enzymic synthesis, and solid phase peptide synthesis) for the synthesis of the said compounds, with mixed results. With classical organic synthesis (four different routes), we did not get the desired results. The reaction of serin with substituted indole in the presence of acetic anhydride led to racemic tryptophane; with the reaction of protected serin with indole in the presence of nickel complex was unsuccessful; the reaction of serin containing protected dipeptide with disuccinimidyl carbonate we achieved a tryptophane containing dipeptide, its chiral purity is being examined; the reaction of alcohol with substituted indole in the presence of copper complex was successful, but it was only a test reaction, we could not reproduce the same result with serine. The undergoing tryptophan-synthase method has shown some potential, but our work has not been finished yet. The successful synthesis of the desired multiple-fluorinated indole-ring-containing tryptophan will be followed by solid phase peptide synthesis in order to incorporate it into AMPs to enhance their antimicrobial activity. The successful completion of these phases will mean the possibility of manufacturing new, effective AMPs.

Keywords: halogenation, fluorination, tryptophan, enhancement of antimicrobial activity

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Laura Brennan, Ram Balakumar

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ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

Procedia PDF Downloads 131

Authors: Sepinoud Hamedi

Abstract:

Over the past few decades, companies have appeared developing concerns in connection to the natural affect of their fabricating exercises. Green supply chain administration has been considered by the producers as a attainable choice to decrease the natural affect of operations whereas at the same time moving forward their operational execution. Contemporaneously the coming of digitalization and globalization within the supply chain space has driven to a developing acknowledgment of the importance of data preparing methodologies, such as enormous information analytics and fake insights innovations, in improving and optimizing supply chain execution. Also, supply chain collaboration in part intervenes the relationship between manufactured innovation and supply chain execution Ponders appear that the use of BDA-AI advances includes a significant impact on natural handle integration and green supply chain collaboration conjointly underlines that both natural handle integration and green supply chain collaboration have a critical affect on natural execution. Correspondingly savvy supply chain contributes to green execution through overseeing green connections and setting up green operations.

Keywords: green supply chain, artificial intelligence, manufacturers, technology, environmental

Procedia PDF Downloads 45

Authors: Ewenam Gbormittah

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Because of the effects of the pandemic, companies are focusing on the future of work arrangements for their employees. This includes adapting to a remote or hybrid working model. It is important that employers provide those working remotely or in a hybrid mode a rewarding onboarding experience and opportunities for interaction. Although, Information & Communication Technologies (ICT) have transformed the ways organisations manage employees over the years, there is still a need for a platform where organisations can adjust their onboarding to suit the social and interactive aspects of their employees, to facilitate successful integration. This study aimed to explore this matter by investigating whether Virtual Reality (VR) technology contributes to new employees integration into the organisation during their job-onboarding (JOB) process. The research questions are as follows: (1) To what extent does VR have an impact on employees successful integration into the organisation, and (2) How does VR help elements of new employees Psychological Contract (PC) during the course of interactions. An exploratory case study approach, which consisted of a semi-structured interview was conducted on 20 employees, split from two different case organisations. The results of the data were analysed according to each case, and then a cross-case comparison was provided. The results have generated 8 themes, presenting in excess of 7 sub-themes for CS1 and presented 7 themes, in excess of 7 sub-themes for CS2. The cross-case analysis has revealed that VR does have the potential to support employees integration into the organisation. However, the effects were shown to be stronger for employees in CS2, compared to employees in CS1. The results highlight practical implications for onboarding psychology and strategic talent solutions within recruitment. Such strategy this research particularly outlines, involves providing insights on how to manage the PC of employees from the recruitment stage to creating successful employment relationships.

Keywords: job-onboarding, psychological contract, virtual reality, case study one, case study two

Procedia PDF Downloads 39

Authors: Shankaran Sitarama

Abstract:

New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

Procedia PDF Downloads 56

Authors: Muneeb Ahmad, Ali Raza

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A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR.

Keywords: artificial innate immune system, flocking swarm, immune system, swarm intelligence

Procedia PDF Downloads 82

Authors: Divine Agozie, Muesser Nat, Eric Afful-Dadzie

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This examination attempts an analysis of the effect of business intelligence and analytics (BI&A) capabilities on organizational role stressors and the implications of such an effect on performance. Two hundred twenty-eight responses gathered from seventy-six firms across Ghana were analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to validate the hypothesized relationships identified in the research model. Findings suggest both endogenous and exogenous dependencies of the sensing capability on the multiple role requirements of personnel. Further, transforming capability increases role conflict, whereas driving capability of BI&A systems impacts role conflict and role ambiguity. This study poses many practical insights to firms seeking to acquire analytics capabilities to drive performance and data-driven decision-making. It is important for firms to consider balancing role changes and task requirements before implementing and post-implementation stages of BI&A innovations.

Keywords: business intelligence and analytics, dynamic capabilities view, organizational stressors, structural equation modelling

Procedia PDF Downloads 91

Authors: Malte Brettel, David Bendig, Michael Keller, Marius Rosenberg

Abstract:

What are the core competences necessary in order to sustain manufacturing in high-wage countries? Aspiring countries all over the world gain market share in manufacturing and rapidly close the productivity and quality gap that has until now protected some parts of the industry in Europe and the United States from dislocation. However, causal production planning and manufacturing, the basis for productivity and quality, is challenged by the ever-greater need for flexibility and customized products in an uncertain business environment. This article uses a case-study-based approach to assess how production managers in high-wage countries can apply decision-making principals from successful entrepreneurs. 'Effectuation' instead of causal decision making can be applied to handle uncertainty of mass customization, to seek the right partners in alliances and to advance towards virtual production. The findings help managers to use their resources more efficiently and contribute to bridge the gap between production research and entrepreneurship.

Keywords: case studies, decision-making behavior, effectuation, production planning

Procedia PDF Downloads 325