Engineering Expertise Research Articles

Optimizing solar powered electrokinetic remediation of chromium contaminated groundwater: One step closer to a sustainable solution

Electrokinetic remediation (EKR) presents a promising solution for addressing soil and groundwater contamination challenges. However, practical viability is hindered by significant energy consumption and related expenses, necessitating a comprehensive sustainability approach beyond solely adopting renewable energy and low-carbon sources. This study aims to merge innovative solar-powered systems with a risk-based evaluation strategy for tackling chromium-contaminated groundwater, providing valuable insights into EKR economically viable and applicable approaches. Through the integration of environmental and electrical engineering expertise, this study introduces the Continuous Solar-powered Electrokinetic Remediation (CSER) system and risk-based energy efficiency (REF) evaluation approach, addressing intermittency issues while optimizing energy generation and minimizing costs. Experimental results demonstrate removal efficiencies ranging from 29.85% to 84.26%, highlighting the impact of intermittent current, voltage boosters, and electrolyte selection on risk reduction and energy consumption. While intermittent current intensifies the focusing phenomenon and reduces contaminant migration, it may offer promising results in specific cases, such as observed in the EK2 experiment. Conversely, the EK3 results reveal that the integration of conventional solar power with a voltage booster may enhance removal rates but often leads to unfavorable outcomes in risk reduction considering related higher energy consumption. However, CSER, with its utilization of constant current, proves more efficient in power generation, risk reduction, and energy usage compared to other systems. Integration of such a system with cost-effective and environmentally friendly chemical agents could significantly increase the REF index while minimizing related costs, thereby shifting EKR remediation techniques towards sustainable solutions for contaminated groundwater worldwide.

Formal Inconsistencies of Expertise Aggregation Techniques Commonly Employed in Engineering Teams

Engineering managers leverage the expertise of engineers in their teams to inform decisions. Engineers may convey their expertise in the form of opinions and/or judgements. Given a decision, it is common to elicit and aggregate the expertise from various engineers to capture a broader set of experiences and knowledge. Establishing an internally and externally consistent aggregation framework is therefore paramount to yield a meaningful aggregation, that is, to make sure that the expertise of each engineer is accounted for reasonably. However, we contend that most de facto aggregation techniques lack such consistency and lead to the inadequate use and aggregation of engineering expertise. In this paper, we investigate the consistency or lack thereof of various expertise aggregation techniques. We derive implications of such inconsistencies and provide recommendations about how they may be overcome. We illustrate our discussion using safety decisions in engineering as a notional case.

Development of the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course.

Manual wheelchairs (MWC) users have limited mobility during winter months as they encounter many environmental barriers that restrict their community participation. This paper outlines the creation and standardization of an outdoor environment designed to simulate the real-life conditions and obstacles experienced by MWC users in winter. This study consisted of four phases. In Phase 1, researchers used a qualitative ethnographic approach to document the specific challenges and adaptive strategies used by MWC users in winter conditions. In Phase 2, key informants with expertise in MWC winter mobility were invited to co-design the Standardized Navigation Of Winter Mobility & Accessibility Network (SNOWMAN) course. Participants reviewed draft design solutions and offered their input and suggestions to expand upon the initial design. A second co-design workshop included additional key informants, including service providers, policymakers, and professionals with expertise in landscape architecture and engineering, to validate the design solution. The workshops resulted in a detailed illustration of the SNOWMAN course, including five sections: platforms with side slopes, a miniature ice rink, curbs and curb cuts, a path with uneven winter surfaces, and modular ramps at various slopes. Phases 3 and 4 marked the conclusion of the study and involved fabrication of the SNOWMAN course and establishment of a standardized protocol for course setup and maintenance. The project aimed to offer several additional potential benefits, supported by the various stakeholders across the study phases, that extend beyond creation of a controlled and safe environment for wheelchair users to develop their winter mobility skills. Practicing wheelchair skills in this area may assist wheelchair users in gaining confidence which may ultimately translate to increased participation in the community.

Manajemen praktek kerja lapangan pada program keahlian teknik komputer dan jaringan di SMK Prima Sejahtera Kotamobagu

The purpose of this study was to determine and analyze the management of Field Work Practice (PKL) Computer and Network Engineering Expertise Program at SMK Prima Sejahtera Kotamobagu, in terms of Planning, Implementation and Evaluation. This research is a type of qualitative research with research procedures that produce descriptive data. The methods used by researchers to collect data are observation, interviews and documentation studies. The subjects of this research are the Principal, Deputy for Public Relations, Head of the Computer and Network Engineering Department (TKJ), Supervising Teacher. After the data is collected, the data is analyzed including data reduction, and conclusion drawing. Based on the results of the study, it shows that the PKL planning stage includes: 1) PKL committee formation and coordination, 2) Industry mapping, 3) Administrative preparation (such as: making submission letters and submission procedures, making submission and withdrawal letters, making PKL activity schedules), 4) Determination of PKL place, 5) Debriefing. The stages of implementation activities: 1) Delivery of PKL students, 2) PKL implementation process in the Business World / Industrial World (DUDI), 3) PKL program monitoring, 4) Journal preparation of daily PKL activities, and 5) Withdrawal of PKL students. The PKL evaluation stage includes: 1) Completion of the journal which is carried out after the pick-up of students, 2) PKL assessment based on the results of coordination by industry and school supervisors and based on the results of reports/journals.

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

Pelatihan Penggunaan Trainer Solar Panel Untuk Guru Sekolah Menengah Kejuruan Teknik Ketenagalistrikan Sumatera Barat Menuju Era Energi Baru Terbarukan

Background: Teachers in vocational schools play an essential role in integrating renewable energy education into the curriculum, but limited tools and knowledge in assembling solar panels hinder practical learning. The program aims to provide teachers with practical knowledge and skills in assembling solar panels. Method: This activity was attended by productive teachers of SMK in the field of expertise in Electrical Power Installation Engineering throughout the province of West Sumatra. Activities are carried out in 4 stages, Preparation, Implementation, Monitoring, Evaluation, and Follow-up. Written tests and practical tests are conducted to find out the extent of the teacher's ability to use solar panel trainers. Result: The result of this program is an increase in knowledge and skills, teachers in solar panel assembly. This program contributes to trained teachers, to provide an interactive and interesting learning approach for students in the use of renewable energy. Conclusion: This training program not only meets the needs of education in facing the development of renewable energy technology, but also makes a real contribution in creating human resources who are ready to work in the renewable energy sector.

Enhancing large-span structure design and maintenance through the synergy of CAD, BIM, and IoT-AI technologies

The integration of Computer-Aided Design (CAD), Building Information Modeling (BIM), and Internet of Things (IoT) with Artificial Intelligence (AI) represents a transformative shift in the architectural and engineering practices for large-span structures. This paper delves into the quantitative and qualitative enhancements brought about by these technologies, focusing on their pivotal roles in design precision, structural analysis, project management, and maintenance strategies. CAD's evolution from simple drafting to complex 3D modeling has significantly reduced design time and errors, while its integration with structural analysis software has improved load distribution accuracy and material cost efficiency. BIM's contribution to design, construction, and sustainability emphasizes optimized resource allocation and energy-efficient practices. Furthermore, IoT and AI's role in real-time monitoring and predictive maintenance underpins a proactive approach to structural health, ensuring longevity and safety. Through statistical analysis, mathematical modeling, and case studies, this paper highlights the synergistic impact of these technologies on reducing project timelines, enhancing collaboration, and fostering innovation in large-span structure engineering. The findings advocate for a multidisciplinary approach, combining engineering expertise with advanced computational tools to achieve superior outcomes in the construction and maintenance of large-span structures.

Development of Teaching Factory Learning Model in Industrial Automation in Vocational High Schools

Vocational High Schools, as schools that aim to prepare students for work in a particular field, need effectiveness in the learning process. Its effectiveness must follow the real conditions required by the industry. With the rapid devel-opment of industry, industry-based learning, often called "Teaching Factory," is one of the right role models of learning. The type of research to be carried out is development research. The purpose of this study is to determine (1) the devel-opment of the teaching factory learning model, (2) the application of the teach-ing factory learning model, (3) user response to the product, and (4) the effec-tiveness of using the developed product. The development model used in this study is the Plomp development model. The Plomp development model includes (1) the initial investigation phase, (2) the design phase, (3) the realiza-tion/construction phase, (4) the testing, evaluation, and revision phase, and (5) the field test/implementation phase. Validity by model experts about the teach-ing factory learning model gets an average score of 95.45 and is included in the very decent category. The material expert gave it a score of 93.41, and it be-longs to the very decent category. The media expert gave it a score of 95.84, which belongs to the decent category. The effectiveness test result shows a sig-nificant difference between the learning outcomes in the Industrial Automation Engineering Expertise Concentration subjects before and after applying the Teaching Factory learning model. This shows the positive influence and effectiveness of using the Teaching Factory learning model on the learning out-comes of the Industrial Automation Engineering Expertise Concentration in In-dustrial Automation Engineering Vocational School students.

The role of the NPT in preventing further proliferation of nuclear weapons

The entire atmosphere created by the Treaty on the Non-Proliferation of Nuclear Weapons and its presence in the world played one of the responsible roles, since after the US bombing of Hiroshima and Nagasaki, nuclear weapons were never used in a military situation (unless, of course, you take into account that the nuclear powers conducted more than 2,000 nuclear explosions at their test sites).
 It can be said that this is the result of a broad consensus reached by the international community on the prevention of the proliferation of nuclear weapons and other types of weapons of mass destruction, as well as the result of the actual operation of the Treaty on the Non-Proliferation of Nuclear Weapons during its more than 50 years of existence. Currently, no bilateral or multilateral meeting on international security issues, including at the highest level, is held without a mention of nuclear non-proliferation, and it is often at the top of the agenda.
 The Treaty on the Non-Proliferation of Nuclear Weapons is an international legal and ethical norm that has entered the minds of millions of people. Many countries have developed and adopted national legislation establishing export control rules for materials used in the production of weapons of mass destruction, as well as other laws (customs, etc.) to help ensure the non-proliferation of weapons of mass destruction. Extensive training of young experts in basic knowledge to support the international non-proliferation regime of nuclear weapons is ongoing.
 In the field of technology, scientists and experts in reactor engineering are working in many countries to build nuclear power plants that will not produce materials used to create weapons of mass destruction. Many scientists are working to prevent the construction of reactors that threaten the proliferation of technologies and nuclear materials capable of producing nuclear weapons and other weapons of mass destruction.
 The conclusion of the Treaty on the Non-Proliferation of Nuclear Weapons and the establishment of an international non-proliferation regime contributed to a fundamental change in the world’s attitude to nuclear weapons. From the first impression in the eyes of the world public that nuclear weapons are a «genius» discovery, the latter is increasingly perceived as a mortal danger that threatens human existence, and this idea penetrates deeper and deeper into the consciousness of millions of people.

Addressing Post-Disaster Challenges and Fostering Social Mobility through Origami Infrastructure and Construction Trade Education

Natural disasters devastate property and infrastructure systems, impeding sustainable development. Low-income communities, due to economic, physical, and social disparities, face heightened exposure and vulnerability. These communities endure severe and long-lasting infrastructure damage, experiencing a fourfold increase in deaths per disaster and delayed recovery efforts. Consequently, they resort to constructing informal housing and infrastructure, worsening post-disaster challenges and vulnerabilities. This study aims to address post-disaster challenges in low-income communities by proposing two novel approaches that remain understudied despite their significant potential: (1) a short-term solution of origami temporary emergency housing for swift shelter post-disaster, enabling a return to routine activities while homes and infrastructure systems are being repaired or rebuilt; and (2) a long-term solution, including effective pedagogy, such as teaching methods and instructional tools, to educate and train low-income individuals to aid in sustainable post-disaster reconstruction while providing the added benefit of social mobility. To validate the feasibility of origami TEH and the need and effectiveness of the pedagogy, a survey among architecture, engineering, and construction experts in Puerto Rico, a region prone to natural disasters, was conducted. The results, analyzed using statistical measures including descriptive statistics and ordered probit regression analysis, emphasize the urgent need for sustainable TEH that can be quickly assembled and education for low-income individuals in construction trades. Implementing these solutions will significantly impact communities by addressing post-disaster challenges and promoting social mobility and job equity.

Current trends in digital twin development, maintenance, and operation: an interview study

Digital twins (DTs) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems, and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin’s lifecycle.

Assessing the attractiveness of chemical and process facilities to terrorism using a situational crime prevention approach

As intentional attacks to industry have been on the rise in the last years, researchers and institutions put effort in addressing risks of industrial assets exposed to acts of interference. One of the key steps of security risk assessment is the evaluation of the attractiveness, i.e. determining which installation or part of it could be of interest to a potential attacker. As security concerns are intrinsically tied to human intention, attractiveness does not only depend on technical characteristics of the facility, but also on the type of threat and its geographic, economic, and socio-political context. Therefore, interdisciplinarity is among the key features of a team tasked with assessing attractiveness. This work is the result of the joint effort of Physical Sciences and Engineering experts and Social Sciences and Humanities experts, and it presents a methodology for the assessment of attractiveness of process facilities to physical terroristic attacks developed using a “terrorist thinking” approach. Technical factors such as plant layout and physical protection systems were combined with modern criminology principles (namely Situational Crime Prevention, i.e. how actors take advantage of situated opportunities to commit a crime) to obtain a comprehensive picture of the possible motives behind a physical terroristic attack to a chemical and process facility (meant as facilities where chemical substances are present and where chemical and/or physical processes occurs). The methodology was applied to a case study featuring facilities located in a critical context; a ranking among facilities was obtained, and the robustness of the results was tested through a Monte Carlo-based uncertainty analysis. The results revealed that it is not possible to obtain a clear ranking of critical facilities accounting only for technical or non-technical factors, but both are necessary. The method developed was demonstrated easy to apply and could be used not only by plant security managers, but also by Institutions and Governments to prioritize critical assets in threatening contexts.

Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health.

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Infection prevention and control and related practices in African neonatal units: The Pan-African neonatal care assessment study (PANCAS)

BackgroundThe burden of neonatal mortality is primarily borne by low- and middle-income countries (LMICs), including deaths due to healthcare-associated infections (HAIs). Few studies have assessed infection prevention and control (IP&C) practices in African units caring for small and/or sick newborns aimed to reduce HAIs. MethodsWe performed a mixed-methods study composed of a survey and virtual tour to assess IP&C and related practices. We created a survey composed of multiple-choice and open-ended questions delivered to site respondents via Zoom or video equivalent. Respondents provided a virtual tour of their unit via video and the study team used a checklist to evaluate specific practices. ResultsWe recruited 45 units caring for small and sick newborns in 20 African countries. Opportunities to optimize hand hygiene, Water, Sanitation and Hygiene (WASH) practices, Kangaroo Mother Care, and IP&C training were noted. The virtual tour offered further understanding of IP&C challenges unique to individual sites. All respondents expressed the need for additional space, equipment, supplies, education, and IP&C staff and emphasized that attention to maternal comfort was important to IP&C success. DiscussionThis study identified opportunities to improve IP&C practices using low-cost measures including further education and peer support through learning collaboratives. Virtual tours can be used to provide site-specific assessment and feedback from peers, IP&C specialists and environmental engineering experts.

Determinan Pembentuk Karakter Peserta Didik Pada Kompetensi Keahlian TITL di Sekolah Menengah Kejuruan

This study aims to obtain in-depth information about the determinants of the character formation process of students on the competence of Electrical Power Installation Engineering expertise in Vocational High Schools. The type of research used is qualitative with a case study method at SMKN 1 Padarincang. Data collection techniques used in this study were participatory observation, in-depth interviews, and documentation. Data analysis techniques using interactive models Miles and Huberman help ATLAS TI 9 software. The results obtained in this study are (1) The process of forming the character of students in the competence of Electrical Power Installation Engineering expertise, which is integrated with learning, habituation, extracurricular, and school management (2). Supporting factors for the process of forming the character of students include teachers with character, community, willingness of students, schools, and families. (3). Factors inhibiting the process of forming the character of students are less active teachers, school environment, students' desires, families, and inadequate infrastructure.

Developing Shape Change-Based Fashion Prototyping Strategies: Enhancing Computational Thinking in Fashion Practice and Creativity

Emerging technologies enable fluid and versatile material forms of fashionable wearables and e-textiles, with experts in engineering and material science proposing numerous strategies for dynamic textile and garment structures to satisfy various needs. Nevertheless, a critical gap remains in developing practical fashion prototyping strategies that fuse with computational thinking to challenge current norms and envision the future of fashion. This study introduces shape change-based fashion prototyping as a design strategy for dynamic expressions and affordances to inspire fashion practitioners’ interdisciplinary endeavors. We present three studio-based practices as case studies to demonstrate how shape-changing mechanisms including servo motors, shape memory alloys, and pneumatics, spur new fashion construction skills and broaden the scope of potential applications. By doing so, this study contributes to material and conceptual innovation, creating pathways for the seamless integration of technologies from conceptualization, and implementation to envision. Our findings shed light on design possibilities and challenges and offer design recommendations that guide future endeavors. The implications of our research underscore the importance of adopting a relational approach to design variables, emphasize the value of fostering shared vocabulary between fashion and technical design, and highlight the transformative potential of shape-changing prototyping in reshaping the intricate body-material relationship.

The Impact of Indoor Environmental Quality on Occupant Satisfaction in Commercial Buildings: A Comparison of Building Expert Opinions and Residents’ Experiences

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience.

Data-set class-balancing and the Convolutional Vision Transformer: An analysis of chest radiographs for SARS-CoV-2 detection

Introduction:The COVID-19 pandemic has had impact on global public health and economies. Chest X-rays (CXRs) play a critical role in screening, especially in resource-constrained regions. This work focuses on the development of a deep learning model for detecting COVID-19 in CXRs images, aiming to address the challenges associated with manual interpretation. Methods:Our approach integrates Convolutional Vision Transformers with traditional Convolutional Neural Networks for CXRs analysis. Using a dataset comprising 5572 CXRs, including various COVID-19 severities and other pneumonia types, the model underwent training across multiple configurations and evaluation using various metrics. Each Dataset Configuration (DSC) involved 10 training iterations. We employed an enhanced attention visualization technique to identify key areas in X-rays. Results:This work presents the top five models based on training and validation outcomes across different radiographic classes. The model’s performance varied with dataset configurations, particularly in distinguishing COVID-19 from other pneumonia types. DSC 6 emerged as the most effective, demonstrating 97.1% sensitivity and a 2.9% false negative rate during training, and maintaining high performance during validation with 87.2% sensitivity and a 12.8% false negative rate. Conclusion:This fusion of engineering and medical expertise yields an efficient screening tool. The COVID-19 crisis underscores the importance of enhanced healthcare preparedness. Our proposed model achieves over 90% average recall, accuracy, and sensitivity, coupled with a low false negative rate. It reliably identifies COVID-19 across different severities and effectively distinguishes it from other pneumonia types, establishing its utility as a robust COVID-19 detection tool.

Development of Learning Models Computer and Network Engineering Expertise Concentration Based on Flipped - blended Learning

The objectives of this research are: (1) analyzing the need for learning models; (2) designing learning tools; (3) analyzing the effectiveness of the learning model. The research method used is the development research method or Research and Development. The process of developing this learning model refers to the ADDIE development model which includes: (1) Analysis: performance analysis and needs analysis; (2) the design stage composes teaching modules, model books, LMS designs, and manual books. Preparation of research products and tools based on literature reviews that have been carried out by researchers; (3) Development through individual trials and small group trials. Then produce a product prototype (4) Implementation conduct field tests at YPPP Wonomulyo Vocational School by implementing a learning model prototype, and collecting user responses to the learning model applied; (5) Evaluation in the form of a review of research products and tools. The research results showed that at the small group testing stage and user responses were in the very good category. The learning model for concentrating computer and network engineering skills based on flipped-blended learning is effective based on observer assessments and user responses. Therefore, it is recommended that the learning model for concentrating computer and network engineering skills based on flipped-blended learning can be used as a reference for managing learning that is fun, makes things easier, increases motivation and optimizes student learning outcomes.