Transdisciplinary Journal of Engineering & Science

Systems Engineering Transformation: Transdisciplinary Endeavor

2024-01-01T19:23:45-07:00

System Engineering (SE) solves the most complex problems by bringing together societal issues, theoretical engineering, and the transformation of theory into products and services to better mankind and reduce suffering. The research of this paper utilizes transdisciplinary engineering to develop a SE methodology for the concept stage permitting proposal generation accuracy and expediency to provide solutions to counter regional aggressive threats.

Today’s world stage is witnessing superpower dominance over neighboring less military-capable nations. The societal impact goes beyond the borders of conflict and affects the world’s global market. In the deterrence of dominance, the United States’ posture is providing weapon systems to the victim in our increasingly unstable geopolitical environment. The US has many reasons to make this technology available to other nations; just as it has many reasons to constrain proliferation.

The United States Government is energizing the U.S. defense industry to provide the needed weapon systems following US prescribed acquisition methods. The sale of US weapon systems to International Customers is through a US defense contractor implementing a specialized acquisition model specific for international customers acquiring exportable defense articles. These constraints in the form of export regulations, critical to this paper, are essential to account for early in the concept development. The added complexity to concept development in international defense is that in addition to the usual, “what capabilities does the customer need to accomplish their mission?” and “what are the alternative solutions?” questions, we need to address the fact that US contracts may be required to limit capabilities and/or the technical solution space in opposition to the international customer’s desires. Whereas we normally turn to systems engineering processes to address such complex problems, we have found that the current SE methodology for the concept stage does not address the complexities associated with international sales.

The international customers’ need for complex solutions in an expedited time frame emphasizes the prime contractor’s complex and inadequate proposal concept stage. Under U.S. government procurement, the prime contractor shifts a portion of the design phase into the concept phase. This modality requires significant time and funding to fully develop the technical baseline, meanwhile, the international customer requires a solution to react to an immediate threat to their country’s safety. The main objective of this research is to develop a system engineering methodology for the concept stage to effectively understand the technical baseline maturity for proposals specific to International Customers.

Transdisciplinary Methodology for Refugee Resettlement Process

2024-01-03T16:26:46-07:00

This paper’s primary goal is to present a novel transdisciplinary (TD) methodology for the
resettlement of refugees. Numerous factors, such as budgetary and cost concerns, federal law and policy,
administrative difficulties, security screening protocols, education and training, housing and health, crime
rate, socioeconomic issues, and many more, can be considered that influence the refugee resettlement
process. Refugee resettlement is a complex matter with numerous factors to consider. Using the Interactive
Collective Intelligent Management (ICIM) Workshop, the working group developed transdisciplinary collective
intelligence to investigate the issue. The Nominal Group Technique (NGT) was used to develop and clarify
a list of issues about the complex issue through the survey. A condensed example of refugee resettlement
that has ten TD solutions and six complex issue challenges created with ICIM. Using TD integrated tools,
TD solutions to create effective administrative processes that will significantly affect the challenging issues
associated with refugee resettlement have been examined.

The Intervention to Incentive the Use of Systemic-Transdisciplinary Attitudes

2024-01-10T14:08:22-07:00

The objective is incentive the use of transdisciplinary systemic tools (TD-ST) for the improvement of TD-ST skills and attitudes by educational intervention in graduate students. The main findings were a) Attributes of the students that improved days after of the intervention (p≤ 0.05) with the highest percentage of variation were happiness (39%), union (41%) and integration (31%), knowledge and appropriation of the tools (50%) and several application potentials (38%). b) The improved attributes were classified as medium changes (attention capacity (0.47), cognitive process (0.46), perceptive function (0.46), linguistic function (0.52), creativity (0.5), use of TD-ST (0.68) and their appropriation (0.65), integration (0.52), joy (0.51) and interest in the use of TD-ST (0.52)), and high changes (union (0.70) and knowledge of the TD-ST (0.75)). c) Students prefer to be taught TD-ST in a creative way using didactic material and real-world, everyday applications. Trainers of transdisciplinary researchers have to develop in ourselves and in our students, skills and attitudes transdisciplinary systemic, basic to the sustainability.

Artificial Intelligence and the Transdisciplinary Human Mediation of HPTD-M

2024-01-17T08:05:54-07:00

This article studies the scope of Artificial Intelligence (AI) through the HPTD-M theory, i.e., the Holopraxis Transdisicplinary Management. It aims at collaborating to the debate on the limits of AI, including ChatGPT simulations, comparing the four types of intelligence in the HPTD-M Theory, namely empirical, emotional, rational, and intuitive, with the nine types of Gardner´s multiple intelligences theory (MI Theory). The types of intelligence are also compared with psychosomatics, the emotional shadow of the Western culture, the levels of the collective unconscious, and soft skills. The concept of mediating manager has an essential role in showing the limits of AI, which is an exceptional instrument for KNOWING but not UNDERSTANDING. An example of adequate use of AI through ChatGPT is demonstrated through a discussion on Plato's four virtues. A table considering the HPTD-M quaternary structure of intelligence shows how there cannot be consciousness awakening in AI, since it is limited to rationality. Besides, the HPTD-M’s three types of logic, i.e., Binary, Feedback, and Included Third, are another way to demonstrate that AI is based merely on the Binary logic. Management tools need to be used a priori with awareness of the limits of their applications. AI is no different, a disruptive technology that every professional will have to learn to deal with, like the personal computer in the late 1980s, an excellent rational tool, but not an empirical, emotional, or intuitive resource for problem solving. The AI binary logic does not apply to the complexity of human phenomena. Furthermore, AI can function as a consultant or assistant in terms of an efficient source of information, but not as an effective manager or decision maker. Roughly, through the managerial theory, effectiveness is to do the right thing, which is more than efficiency (to do things right). Then, in this author´s opinion constructed through simulations in the ChatGPT to obtain efficient results, the questions to AI need to be objective and precise in the concepts. There cannot be complex issues involving human phenomena: This is for the effective human decision maker, not for AI to answer since there can’t be consciousness awakening in AI.

Epistemology in AI (Transdisciplinary AI)

2024-01-09T09:43:27-07:00

A critical look at the evolution of AI strongly shows a sustained but stealth race to replace humans with AI. Early scientific literature and discourse on AI for some reasons (either to allow AI gain entry and acceptability in mainstream scientific and technological arena) vehemently deny this "human replacement agenda". This thinking pattern unknowingly shaped current scientific literature, discourse, general understanding of what AI is and its development and applicability (a reductionist thinking). This limits our understanding in both the beneficial and destructive capabilities of AI. But when considering a TD assessment on the developmental dynamic of AI, one would comfortably say and must be bold to admit, that indeed AI intends replacing humans and is on course for fulfilling this.

We see this AI human replacement agenda in intensified R&D efforts dedicated to developing powerful AI system of systems which massively augment human reasoning, most times far better. The inexhaustible list includes the AI replacement of formerly considered human-centric jobs, advanced autonomous weapon systems, killer robots and AI in warfare, intelligent facial recognition, biometric monitoring, integrating AI on biological, nuclear and space-based weapons systems, etc. If this is the direction AI is taking, then a secondary aim would surely arrive at integrating epistemology in AI or "grant spirits" for AI systems. This is because a distinctive characteristic of a human is his spirit and one cannot replace humans with AI without creating proportionate or appropriate spirits for the AI systems. Sooner or later our AI systems would have epistemological functions and possess spirits. The place of the soul for such AI systems would be attained as well. If human knowledge, beliefs, voices, clips and laws can be preserved long after they are gone as is possible in smart digital technologies, then spirit-based AI would indeed cause these humans to live forever. If the feat of a spirit enamored AI is near, then why worry? Indeed when considering that humans possess good and bad spirits (from the epistemology of rational and irrational inertia) then these AI systems would of course have good or bad spirits and be bad or good AI.

Would integrating a spirit into a rule-based or machine learning algorithmic structure of an AI system have benefits? Yes!, profound benefits too. A spirit-based AI would of course make possible the "possession of feelings" by AI systems, a feat unattainable in both algorithmic, operational and inferential basis of AI systems today. This inability of AI systems to have feelings has continued to remain a major setback in the acceptability (indeed trust) and utilization of AI. As we agree that the spirit in AI is possible, then overlooking efforts aimed at making this possible or allowing AI to attain this level unhindered (admitting dangers of human involvement in AI) could pose a dangerous threat which can become highly destructive to mankind. This calls for critical supervision (TD-based ethical policing) and the accompanying of the evolution, development and applicability of AI hence venerating the need for human mediation in AI both as a major TD research subject and applicable function.

A discussion would be made on my approach which considers the synergy of critical systems heuristics (CSH) and systems engineering (Transdisciplinary Systems Engineering) to create "Transdisciplinary AI" which would formulate methods of integrating "human" epistemology in expert systems. Human epistemology is emphasized because by the maturity of this future nature of AI, there would be terms known as "AI or machine epistemology" or "AI or machine spirits". The investigation begins with creating expert systems (knowledge-based systems) with these functions with plans of moving into robotics and other machine learning arena. Finally, to move the needle on what is considered permissible epistemology or permissible spirit of/in AI is a critical component of the study of human mediation and AI which must be given critical attention. This would be discussed as well.

Navigating "Wicked Problems" in Public Policy: The Power and Promise of Using SHAMROQ's Transdisciplinary Approach to Find Regulatory Text Patterns – A Mixed Method Study

2024-01-10T14:06:46-07:00

In an era marked by significant public policy challenges, including climate change, public health, and economic inequality, traditional bureaucratic or technocratic methods prove increasingly inadequate. These so-called "wicked problems," characterized by their complexity and resistance to simple solutions, require more dynamic, inclusive approaches, that transcend jurisdictional boundaries. Embracing these approaches, we extend SHAMROQ, a novel transdisciplinary approach that combines Software Engineering, Artificial Intelligence, Linguistics, and Logic to extract, classify, and model deontic expressions from regulatory texts. This mixed-method study examines three CFR titles: Title 16 Commercial Practices, Title 45 Public Welfare, and Title 48 Federal Acquisition Regulations Systems, with an emphasis on the distribution of deontic expressions and identifying challenges in regulatory text. The results, supported by a chi-square test of independence with a highly significant p-value < .001 , show a strong association between deontic expression types and CFR Titles. This research also provides deeper insights into regulatory language complexity by broadening the analysis across CFR Titles 16, 45, and 48. Title 16 features diverse permissions and obligations, reflecting commercial law's complexity. Title 45 uses strict directives like 'must' and 'shall' for public welfare compliance, while Title 48 focuses on obligatory terms for federal acquisition, aligning with procurement demands. Such insights can inform future research and practical applications in regulatory analytics, compliance, and public policy.

Artificial Intelligence and Our Secret Mind: Human Mediation in Grey Zones

2024-02-09T09:56:16-07:00

CIRET has set up an AI research group to highlight the need for human ethical mediation in the
age of digital technology and binary logic. Francisco Varela has observed that cognition can take place in
the computer field without appealing to consciousness. Yet human decision-making cannot be the result
of cognition alone and requires the interaction between cognition and consciousness. In fact, rationalist
and reductionist models borrowed from the hard sciences have only shown a mechanistic vision of AI or
a biological-environmental vision, which cannot be applied to complex human phenomena occurring in a
grey zone. In this grey or fuzzy zone of mediation, conciliation, and repair, we need the dialectical process
or dialogue between consciousness and cognition. In this context, the proposal of mediating leaders and
managers appears as a possible ethical alternative to demonstrate that consciousness is beyond the logic
of the computer. Humans must remain responsible for all the effective decisions that will help us solve
problems theoretically and concretely. We therefore need an emerging global wisdom that flows from our
conversations about AI and appeals to human consciousness at all its levels of reality. This group produced
a Symposium on November 21 and 22, 2023, in which we imagine that AI may be at the service of human
evolution and resiliency in “learning to be societies” instead of contributing to block our evolutions: if AI
remains a work tool. . . Certainly it will because the creation sparkles still a mystery for men themselves.

Charrettes for Developing a Transdisciplinary Approach in Urban Housing

2024-01-19T09:31:03-07:00

This article is based on the importance of coexistence in urban housing and the search for methods that are capable of increasing the forms and tendencies of dwellers’ participation. Today, with shifting roles in architecture, an approach derived from the interaction of architects, designers, planners, experts, and dwellers is needed to weave an overlapping fusion of different housing approaches. Therefore, today, how can a seamless collaboration be established in cities such as İstanbul, where participation is relatively low? One possible approach to rethinking coexistence as a design process is to apply transdisciplinary (TR) approaches through the architecture and design charrettes that enable active participation through the open and interactive dialogues they provide. Therefore, the role of architectural charrettes in the application of the participatory nature of the TR approach can ensure the development of an open and interactive atmosphere.

Transdisciplinary approach to reduce electricity consumption using system dynamics

2024-03-06T10:30:58-07:00

The electricity sector holds a paramount position in Iran's energy landscape and plays a pivotal role in economic decision-making. Data indicates that Iran's average annual energy consumption has surged by approximately 10% each year over the past decade. The transdisciplinary approach to reducing electricity consumption involves an integrated method that goes beyond traditional disciplinary boundaries. Consequently, to regulate electricity usage through macro and national strategies, it is imperative to contemplate soft and qualitative methodologies employed in the analysis of socio-economic systems, particularly through the lens of system dynamics. This study aims to scrutinize the influence of diverse socio-cultural policies on diminishing electricity consumption utilizing system dynamics as a transdisciplinary approach. The primary objective of this model is to assess the repercussions of various socio-cultural policies on the forthcoming trajectory. Findings from research conducted in Kerman, a province in Iran, underscore that if the current scenario persists, electricity consumption will persistently escalate over the next decade. However, the results indicate that the implementation of pertinent educational and promotional policies can mitigate the growth rate of electricity consumption.

A Statistical Review of the Impact of COVID-19 on the World

2024-04-09T13:05:10-06:00

The COVID-19 epidemic has caused an unparalleled era of disruption, significantly impacting public health, the environment, the economy, and civilizations globally. This data analysis offers a thorough examination of COVID-19's worldwide effects. Through statistical methods, it explores the epidemic's spread, mechanics of transmission, and social repercussions. In addition, it carries out quantitative research to comprehend how the pandemic affects society's behavior, healthcare systems, and the environment. To understand the substantial repercussions of the pandemic, the review combines many datasets and presents data from a reliable source. Through the analysis of both quantitative and qualitative data, this review focuses on the pandemic's effects from several perspectives, such as public health measurements, societal shifts, and environmental changes. The findings demonstrate the horrifying cost of human deaths and describe how society has adapted to new issues and routines. The study additionally examines the data's graphical and tabular format, which comprises the total number of COVID-19 cases and fatalities that have been confirmed in nine different nations.

IT-Enabled WGCNA for Critical Gene Module Mapping and Therapy Optimization: Advancing Leukemia Care

2024-04-09T15:54:44-06:00

Acquiring a profound comprehension of the complex correlation between genes is essential for progressing treatment and diagnostics in acute leukemia research. This work utilizes WGCNA to analyze the gene expression data obtained from 72 patients diagnosed with acute leukemia. This approach has not been widely employed in this particular setting previously. Our first objective was to identify essential gene modules and core genes that could offer a novel insight into the fundamental causes of the disease. To ensure the precision of the gene expression data, a thorough pre-processing is carried out, which includes normalization and quality control techniques. The WGCNA technique produces a gene co-expression network that targets explicitly differentially expressed genes (DEGs). This network is designed primarily to exploit pairwise correlations, as the underlying data is meant to create such a network. The modules identified via hierarchical clustering are assigned distinct colors to aid in recognizing gene expression patterns and associations that may not be readily apparent when examining individual genes in isolation. An essential component of our research involved identifying pivotal genes inside these modules using several centrality metrics, including degree, closeness, and betweenness centralities. These genes are suspected to have a crucial role in starting and progressing acute leukemia. We must mention that genes such as M91438_at and S82362_s_at came out as significant, in line with their centrality in the network analysis. However, it is privileged that these results hold a promise for further validation. The study throws open trails for future research, especially in experimental validation of these genes. These findings indeed have the potential to contribute to the development of targeted therapies and improvement in diagnostic methods, resulting in better patient outcomes in acute leukemia. This study also highlights the applicability of WGCNA towards unraveling leukemia's genomics, underscoring the continued exploration in this critical area of medical research.

Deriving manageable Transdisciplinary research models for complicated problematics associated with next-generation cyber-physical systems: Part 1 - Theoretical and Methodological Foundation

2024-05-20T20:14:20-06:00

There are many large-scale, transdisciplinary research problematics associated with next-generation cyber-physical systems, which are difficult to capture, analyze, and transfer into sharable research models. This two-part paper is intended to contribute to a better understanding and to provide a systematic approach to describing, scoping, and specifying manageable contents for transdisciplinary research models. Part 1 of the paper analyzes the essence and the interplays of the most important current trends, and creates a robust theoretical and methodological foundation for capturing and scoping research problematics associated with the evolving paradigm of cyber-physical systems. The elaboration and deployment of the proposed approach are discussed in detail in Part 2. Besides the overall procedural framework of the proposed holistic systematic combinational breakdown, all steps are explained and exemplified in an illustrative real-life example. The discussion in both Parts concludes with a number of propositions and further research opportunities concerning the theoretical and methodological foundations.

Deriving Manageable Transdisciplinary Research Models for Complicated Problematics Associated with Next-Generation Cyber-Physical Systems: Part 2 - Elaboration and Deployment

2024-05-04T10:57:16-06:00

This second Part of the paper (i) develops a detailed procedural model for handling complicated research problematics, (ii) transfers the procedural framework into a scenario of processing steps, (iii) demonstrates the deployment of the procedural scenario in a sample case, and (iv) addresses some important issues of research model development for supradisciplinary research. First, the framework called Holistic Systematic Combinatorial Scoping (HSCS) is introduced that comprises 21 subsequent activity steps. To illustrate the practical application of the HSCS framework, a demonstrative case study of preventing frequently occurring accidents at an uncontrolled intersection is presented. This case-study explains how the HSCS framework can facilitate (i) semantic capturing and rendering of complicated research problematics, (ii) scoping the overall research problematics and constructing definitive research problematics, and (iii) deriving a manageable research model of definitive research problematics. Further research intends to test the methodology by transdisciplinary research collectives and other CPSs related problematics.

Transdisciplinary Research in Marine Science: What’s the added value of involving stakeholders?

2024-07-02T12:24:39-06:00

The holistic solution of complex, global problems in marine topics requires innovative research formats - which can be served by transdisciplinarity. To assess the success of the transdisciplinary approach for marine research, several case studies from marine research at Kiel University (Germany) were analysed for this study. Interviews with both scientists and stakeholders were carried out to 1) clarify whether stakeholder involvement in scientific projects provides additional knowledge and to 2) identify the underlying success factors. Both groups see added value in transdisciplinary projects and name positive aspects of cooperation, mutual interaction and information exchange. Essential are the applicability of research results, and the formation and maintenance of networks enabling further joint activities. Stakeholders add practical contexts to scientific knowledge so that the results of this transdisciplinary research can be translated into practical actions. Resource availability sets clear limits within the project framework. Successful transdisciplinary approaches require standardized definitions of terms, the selection of suitable participation formats and continuous, clear communication. Transdisciplinary projects proved suitable as a tool for sensitizing the general public and raising awareness of complex marine challenges.

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

2024-07-02T12:09:30-06:00

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators can take proactive measures to maintain a stable and resilient power infrastructure. Monitoring smart grid data from various sources and analyzing how to control the stability of the grid are challenging tasks. A convolutional neural network (CNN) can effectively capture spatial dependencies and patterns from grid data and can help in enabling accurate prediction and classification of stability-related events in a power system. However, developing a CNN that has fewer learnable parameters and provides high accuracy is challenging. This paper presents a sequential CNN architecture to detect the stability of the Decentral Smart Grid Control (DSGC) system. A mathematical model of the 4-node start architecture of a smart grid was presented. Later, 12 parameter-based grid datasets from the UCI repository were used to validate the proposed network. The proposed CNN accepts sequential data to capture temporal dependencies in the data. The sequential process in a single dimension offers fewer learnable parameters, making the network more compact and computationally efficient. The proposed 11-layered CNN has a total of 12.7K learnable parameters. The detailed analysis of the proposed CNN using ambiguity and the t-SNE score suggested that the model can identify discriminative features for classifying data into stable and unstable classes. A comparison analysis of the quantitative parameters revealed that the model performed well, with 98.82%, 98.55%, 98.88%, and 98.77% accuracy, precision, recall, and F1 score, respectively.

Analysis of Acceptance of Simulated Practices in Respiratory Therapy Students

2024-09-16T12:26:21-06:00

Resumen: Las prácticas clínicas simuladas son una herramienta innovadora diseñada para mejorar la colaboración interdisciplinaria en educación para la salud, así como la comunicación asertiva y la toma de decisiones basadas en el pensamiento crítico, el razonamiento clínico y la ética. Estas prácticas ofrecen a los estudiantes controlados; Escenarios del mundo real dirigidos por instructores. Este estudio tuvo como objetivo evaluar la aceptación de prácticas simuladas entre estudiantes de Terapia Respiratoria en una universidad de Bogotá, Colombia. Se realizó un estudio cuantitativo transversal con 243 estudiantes que completaron un cuestionario del Modelo de Aceptación de Tecnología (TAM) a través de Google Forms. Los datos se recogieron de marzo a mayo de 2024, siguiendo las directrices éticas. Resultados: El cuestionario mostró una alta fiabilidad interna, con un Alfa de Cronbach de 0,97. Las puntuaciones medias superiores a 4 en todas las categorías de TAM indicaron fuertes interrelaciones.: El TAM fue eficaz para evaluar las condiciones de aprendizaje de los estudiantes e identificar estrategias para mejorar el uso de los laboratorios de simulación tanto por parte de los estudiantes como de los instructores.

Global conflict: Analysis of non-political factors and possible non-violent solutions in the context of sustainable development

2024-09-16T12:30:16-06:00

The paper presents a novel approach to the analysis of non-political factors and potential non-violent solutions to global conflicts. The distinctive feature of this approach is its focus on habitual thinking (conventional wisdom) as a research subject, rather than on the human himself. Using the systems transdisciplinarity models, the construct of individual conventional wisdom was investigated. The study identified the main types of conventional wisdom and described the types of knowledge that underlie each individual's worldview formation. Furthermore, the process of socialization through which individual conventional wisdom is integrated with the conventional wisdom of contemporary humanity was simulated. The findings of this study enabled elucidating the objective, non-political factors contributing to the global conflict associated with the completion of the first stage of socialization of the emerging liberal society. The forthcoming changes in the new model of the world order were delineated, as was the necessity to create deterrents that will soon replace nuclear weapons. A proposal will be made to interested organizations, suggesting a joint undertaking of large-scale transdisciplinary research. The objective of this research is to establish a foundation for the attainment of non-violent solutions to the global conflict and the resolution of the problems associated with the sustainable development of humanity.)

The Adoption of E-ticketing Apps on Intercity Buses: A Transdisciplinary Approach

2024-09-16T13:21:51-06:00

The acceleration and development of toll road infrastructure have caused a rapid increase in bus intercity travel. Bus entrepreneurs further offset the number of passengers by increasing the number of fleets and bus departure hours. Furthermore, to facilitate the management and booking of travel tickets, bus companies use e-ticketing apps. This research investigates the factors that affect the adoption of e-ticketing apps with a transdisciplinary approach. Data was collected by providing questionnaires to respondents using random sampling techniques, with valid data from 243 respondents. Furthermore, data processing and analysis are carried out using SmartPLS 3 software. Based on hypothesis testing, the factors constructed in the model were perceived ease of use, subjective norm, information quality, online review, and price-saving orientation, which positively and significantly affect the intention to use e-ticketing apps.

Transdisciplinary education for reducing environmental impact of AEC industry

2024-11-11T15:23:10-07:00

The Architecture, Engineering, and Construction (AEC) industry plays a crucial role in shaping the built environment but contributes significantly to environmental impact, accounting for nearly 40% of global energy-related carbon dioxide emissions. An interdisciplinary systematic literature review (SLR) explores how transdisciplinary education (TDE) can be integrated into AEC curricula to address environmental sustainability challenges. TDE facilitates students’ understanding of the environmental impact (EI) of their design projects and equips them with the knowledge and skills to develop sustainable solutions. This review examines various pedagogical models, including problem-based learning, to evaluate their effectiveness in fostering TDE for reducing EI in the AEC sector. Findings from the literature are synthesized to present a framework aligned with sustainable development goals (SDGs) that promotes sustainability in AEC education. The review aims to inform future research and curriculum development, advancing the role of education in equipping future professionals to mitigate environmental impacts in the AEC industry.

Prioritizing project risks by a transdisciplinary approach using the grey ordinal priority approach: A case study of an electricity distribution company

2024-11-14T12:21:06-07:00

This paper addresses important concerns unique to network privacy in energy distribution systems by presenting a transdisciplinary framework that integrates transdisciplinary tools and transdisciplinary knowledge concepts from risk management, project management, electrical engineering and financial management. The study advances solutions that take into consideration both organizational and technical issues. This is achieved by combining effective decision-making approaches with real-world applications in electrical networks through the use of the Grey Ordinal Priority Approach (GOPA). Electricity distribution projects can immediately benefit from the comprehensive framework proposed by this study, which merges technical expertise with strategic risk management and offers useful insights that can be applied to a variety of domains. By emphasizing the importance of linking disciplines to effectively address complex, high-stakes project challenges, this transdisciplinary viewpoint enhances both the academic and practical dialogue. Results show that changes in project prioritization and announcement of new plans, pressure for early project delivery, improper selection of contractor and consultant, and lack of close supervision of project progress and scheduling are among the significant risks in the project. Electricity distribution companies can improve management of the complexity of their real-world projects by focusing on eliminating or reducing the severity of the aforementioned risks effects.

3D Printed Capacitive Ink Sensors for Sustainable Electronics

2024-04-09T10:49:52-06:00

The current revolution of Internet of Things (IoT) and smart homes cannot be possible if the cost of current sensors and circuitry is not reduced. In this work we present an effective conductive ink/paint by experimenting with various materials under different compositions and use this conductive paint for the fabrication of capacitive sensors in the electrical circuits. Additionally, the proposed capacitive ink used for printing the sensors offers a lot of flexibility and creativity in designing the circuits. The conductive paint can be used in wearables because of its unique advantage of being very thin, water resistant and versatile to all surfaces. The capacitive ink proposed offers high conductivity and has demonstrated improved switching capability over the traditional capacitive sensors.

An Improved CNN model for Identifying Tomato Leaf Diseases

2024-04-25T09:20:56-06:00

A major loss in gross domestic product, quantity and quality of products produced, as well as tomato production, is caused by diseases in tomato leaves due to which farmers have a difficult time in controlling and monitoring the health of tomato leaves, one of which is leaf disease. In our project, we developed an Enhanced CNN by using data augmentation techniques to identify the seven classes(blight ,leaf curl, leaf miner ,Alteneria, leaf spot, cutwork infected) of tomato leaf diseases. Using 27807 trainable parameters, the enhanced CNN obtains the maximum training accuracy of ninety nine point nine eight percent(99.98%) and validating accuracy of ninety eight point four percent(98.4%) .With fewer parameters, The Enhanced CNN can more accurately determine the type of illness of tomato leaf. Our Enhanced CNN model also determine the type of illness of tomato leaf when tested with the images of diseased tomato leaf collected from the internet sources.Using 152850 trainable parameters the enhanced CNN obtain the maximum training accuracy of 99.68% and validation accuracy of 89%.

Industrial-Based GSM Water Leakage Detection, Monitoring and Controlling System: A case of North Rift Valley Water Agency in Kenya

2024-05-03T11:34:20-06:00

The current system for detecting and monitoring water leaks in Kenyan industries is manual and costly. Despite emerging new technological trends, many industries lack automated systems to detect, monitor, and control water leakage due to the high cost of maintenance and installation. This study's objective was to develop an automatic, remote, and real-time detection, monitoring, and, control system for water leaks. The system is made up of two nodes, one at the source and one at the destination or tap. The two nodes are made up of an ESP microcontroller, which is used to control all the connected components. The use of the ESP 32 microcontroller was efficient due to its ability to provide WI-FI. Aside from the solenoid valve, which was used to turn the water flow on or off in the event of leaks, the system also includes the FY-201 water flow sensor, which was used to gauge the amount of water flowing through the pipe. Water leakage is detected when the volume of water passing through the two sensors differs in terms of volume, indicating that a water leakage has just occurred. Thing-Board, an IoT-based platform used to monitor and visualize data from various devices connected, was used for real-time monitoring, visualization, and control. The developed system was tested with different water service providers, and the results showed that the system responds positively to water leakage parameters with capability of monitoring water leakages in a real time.

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

2024-05-27T08:56:47-06:00

Abstract: Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture.

Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

Harnessing Transdisciplinary Knowledge: Integrated Deep Learning Techniques for Accurate Tomato Leaf Disease Classification

2024-11-14T07:27:49-07:00

The study proposes a transdisciplinary approach integrating knowledge from fields such as
computer science, botany, and data science to classifying leaf diseases. We integrated two deep-learning
models that combine the strengths of the Inception network and the ResNet architecture to address the
challenge of accurately classifying tomato leaf diseases. The Inception network’s ability to quickly pick up
visual features on multiple scales is used to pull out fine-grained details that are needed to tell the difference
between small changes in the shape of tomato leaves and disease symptoms. The ResNet architecture is
good at learning deep representations and getting around the vanishing gradient problem. This lets the
model learn the high-level concepts and complicated connections between different tomato leaf disease
patterns. The integration of these two powerful deep-learning techniques results in a robust and highly
performant tomato leaf classification model. Extensive tests on a 10-class dataset of tomato leaves, with 9
disease categories and 1 healthy class, show that the proposed model works better than others, with a test
set accuracy of 98.07%. The findings of this research contribute to the advancement of automated and
efficient tomato leaf disease detection systems, which can aid in the early identification and management of
tomato diseases, leading to improved crop yields and quality.