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Engineering Proceedings

Engineering Proceedings is an open access journal dedicated to publishing findings resulting from conferences, workshops, and similar events, in all areas of engineering. The conference organizers and proceedings editors are responsible for managing the peer-review process and selecting papers for conference proceedings.

Imprint Information Journal Flyer Open Access ISSN: 2673-4591

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Vol. 75

ICAME 2024

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Vol. 73

IECB 2024

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Vol. 72

IECMA 2024

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Vol. 71

ASWEC 2024

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Vol. 70

EEPES 2024

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Vol. 69

WDSA/CCWI 2024

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Latest Articles

4 pages, 743 KiB

Open AccessProceeding Paper

Inclusion of Water Age in Conjunctive Optimal Operation of Water and Power Grids

by Tomer Shmaya and Avi Ostfeld

Eng. Proc. 2024, 69(1), 196; https://doi.org/10.3390/engproc2024069196 (registering DOI) - 15 Oct 2024

Water distribution systems (WDSs) are critical infrastructure systems designed to safely supply water to consumers. As complex systems, they require constant operational decision-making, which is often the result of an optimization process. WDSs require power for pumping and the operation of water treatment [...] Read more.

Water distribution systems (WDSs) are critical infrastructure systems designed to safely supply water to consumers. As complex systems, they require constant operational decision-making, which is often the result of an optimization process. WDSs require power for pumping and the operation of water treatment facilities. Power is supplied through power grids (PGs)—essential infrastructure which must be strategically operated as well, under constraints. This work is focused on the effects of PG operation on water quality, which is a major operational challenge of WDSs. The inclusion of the PG as part of the WDS optimal operation problem has the potential of influencing flow directions in the WDS, which in turn affects water quality. In this work, a model for the optimal operation of water and power networks is constructed, including water age considerations. The model is applied to a simple case study, containing a small WDS connected to a small PG. The results demonstrate the effect of PG operation on water quality. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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5 pages, 1521 KiB

Open AccessProceeding Paper

Raw Water Main Flow Conditioning to Manage Material Load and Treatment Capacity

by Stewart Husband, Neil Walkington-Mayo and Joby Boxall

Eng. Proc. 2024, 69(1), 193; https://doi.org/10.3390/engproc2024069193 (registering DOI) - 15 Oct 2024

A water treatment works in the UK endured elevated inlet turbidity and iron concentrations following increased demands in the raw water supply main, reducing its capacity by blocking filters that required costly extra cleaning. Adding flow and turbidity monitoring allowed novel raw water [...] Read more.

A water treatment works in the UK endured elevated inlet turbidity and iron concentrations following increased demands in the raw water supply main, reducing its capacity by blocking filters that required costly extra cleaning. Adding flow and turbidity monitoring allowed novel raw water main variable condition discolouration model (VCDM) simulations to track the accumulation and mobilisation behaviour, showing the full 18.7 km contributing material and risk returning in only 2 months, helping explain the multiple annual events. The utility is now applying operational efficient flow conditioning, developed here using the VCDM, to manage risks and capacity. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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5 pages, 1006 KiB

Open AccessProceeding Paper

Identifying Hydraulic Conditions for Discolouration Material Accumulation

by Reinar Lokk, Joby Boxall and Stewart Husband

Eng. Proc. 2024, 69(1), 190; https://doi.org/10.3390/engproc2024069190 (registering DOI) - 15 Oct 2024

Understanding the interactions between hydraulic conditions and the accumulation of discolouration material in drinking water distribution systems is crucial to help identify risk locations and inform effective maintenance. With two accumulation processes now acknowledged and the known range in size and characteristics of [...] Read more.

Understanding the interactions between hydraulic conditions and the accumulation of discolouration material in drinking water distribution systems is crucial to help identify risk locations and inform effective maintenance. With two accumulation processes now acknowledged and the known range in size and characteristics of discolouration material, this is not a trivial challenge. A full-scale pipe loop system, adapted for precise flow control and with multiple turbidity monitors, was dosed with discolouration material collected from operational networks. By tracking changes in bulk water material loading, this study indicates that at above 1.25 L/s (0.25 m/s, Re 15,000, 0.213 N/m²), a change in accumulation behaviour occurs. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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9 pages, 1346 KiB

Open AccessProceeding Paper

From Grinding to Green Energy: Pursuit of Net-Zero Emissions in Cement Production

by Md. Shahariar Ahmed, Anica Tasnim and Golam Kabir

Eng. Proc. 2024, 76(1), 8; https://doi.org/10.3390/engproc2024076008 (registering DOI) - 15 Oct 2024

In an age of heightened environmental awareness and the pressing need for net-zero emissions, concerns over rising energy consumption in cement production, responsible for 5–8% of global CO₂ emissions, have intensified. This paper proposes a novel pioneering framework that integrates Shannon’s entropy [...] Read more.

In an age of heightened environmental awareness and the pressing need for net-zero emissions, concerns over rising energy consumption in cement production, responsible for 5–8% of global CO₂ emissions, have intensified. This paper proposes a novel pioneering framework that integrates Shannon’s entropy and Multi-Criteria Decision Making (MCDM) methods to steer the cement industry towards sustainability and net-zero emissions. Utilizing Shannon’s entropy, the research impartially determines the significance of multiple criteria, reducing biases in decision-making for energy efficiency in cement production. Four MCDM methods (TOPSIS, VIKOR, ELECTRE, WSM) are applied to rank energy efficiency alternatives, providing a nuanced analysis of options for the cement industry. The study integrates sensitivity analysis to evaluate the robustness of MCDM methods under varying conditions, assessing the impact of changes in criteria weights on the ranking of energy efficiency alternatives and showcasing the adaptability of the proposed framework. Examining six diverse scenarios reveals the framework’s adaptability and the versatility of the Horizontal Roller Mill (HRM), with the Vertical Roller Mill (VRM) emerging as a cost-effective emission reduction alternative. This interdisciplinary approach, integrating information theory, decision science, and environmental engineering, extends beyond industry relevance, providing valuable insights aligned with global sustainability goals. Harmonizing economic viability with ecological responsibility, this report offers an instructive guide, propelling the cement industry toward a more sustainable future. Full article

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8 pages, 1822 KiB

Open AccessProceeding Paper

Improving Internal Combustion Engine Performance through Inlet Valve Geometry and Spray Angle Optimization: Computational Fluid Dynamics Study

by Muhammad Ahsan and Mian Noman

Eng. Proc. 2024, 72(1), 6; https://doi.org/10.3390/engproc2024072006 (registering DOI) - 15 Oct 2024

This study aimed to calculate the impact of inlet valve geometry and spray angle on the performance of internal combustion engines using computational fluid dynamics (CFD) analysis. CFD analysis was performed to explore the fuel flow dynamics within a combustion chamber at critical [...] Read more.

This study aimed to calculate the impact of inlet valve geometry and spray angle on the performance of internal combustion engines using computational fluid dynamics (CFD) analysis. CFD analysis was performed to explore the fuel flow dynamics within a combustion chamber at critical stages, considering factors such as swirl and tumble. This study investigated the role of the intake port’s geometry and spray angles in creating squish and swirl, which is crucial for enhancing combustion efficiency and overall engine performance. The analysis employed the Finite Volume Method (FVM), solved within ANSYS Fluent 2021 software, utilizing the standard k-ε turbulence model. Design Modeler was used for the geometry design and ANSYS Fluent facilitated the CFD analysis of the injection. Four distinct cases were explored to assess engine performance across various designs, examining parameters such as pressure, temperature, and velocity. These performance parameters were evaluated against the existing literature, enabling the identification of optimal configurations. This study identified optimal performance parameters based on the existing literature. The best design was further validated against existing designs under identical boundary conditions. This research demonstrates improved engine performance across all parameters compared to existing values in the literature. This suggests the efficacy of the proposed inlet valve geometry and spray angle configurations in increasing internal combustion engine efficiency. Full article

(This article belongs to the Proceedings of The 2nd International Electronic Conference on Machines and Applications)

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8 pages, 208 KiB

Open AccessProceeding Paper

A Meta-Analysis of Adopters and Non-Adopters of Rooftop Photovoltaics in Indonesian Households

by Bertha Maya Sopha and Sholeh Ma’mun

Eng. Proc. 2024, 76(1), 5; https://doi.org/10.3390/engproc2024076005 (registering DOI) - 15 Oct 2024

Although the Indonesian government has conducted various interventions to escalate the uptake of rooftop PV in Indonesian households, adoption has still been sluggish. Few studies have been conducted to explore the issue, and these studies are scattered. The paper aims to assess generalizations [...] Read more.

Although the Indonesian government has conducted various interventions to escalate the uptake of rooftop PV in Indonesian households, adoption has still been sluggish. Few studies have been conducted to explore the issue, and these studies are scattered. The paper aims to assess generalizations of the previous studies regarding adopters’ and non-adopters’ characteristics of Indonesian households and their perceptions of rooftop PV attributes using meta-analysis. The findings show that statistically significant differences between the two studies in terms of socio-demographic factors, problem awareness, innovativeness, and perceived qualities of rooftop photovoltaics exist. Despite the differences, the adopters of both studies perceived equally that using renewable energy was important, that rooftop photovoltaics were environmentally friendly, and that they were generally aware of environmental problems. It appears that the non-adopters sample drawn from stratified random sampling demonstrates a similar distribution specified by Diffusion of Innovation. Furthermore, the non-adopters in the two research show a comparable belief regarding the significance of putting renewable energy into practice. Due to inconclusive patterns, an empirical investigation that sufficiently represents both the rooftop PV adopters and non-adopters in Indonesian households is suggested. Other potential future research are also discussed. Full article

8 pages, 1012 KiB

Open AccessProceeding Paper

Fuzzy Logic Approach to Circular Economy Maturity Assessment of Manufacturing Companies

by Dennis Kreutzer, Esther Borowski and Ingrid Isenhardt

Eng. Proc. 2024, 76(1), 4; https://doi.org/10.3390/engproc2024076004 (registering DOI) - 15 Oct 2024

The transition from linear to circular value creation is leading to a fundamental transformation in all areas of manufacturing organisations. Maturity models are used to analyse and support the transformation, but these have deficiencies regarding holism and the ability to process fuzziness. To [...] Read more.

The transition from linear to circular value creation is leading to a fundamental transformation in all areas of manufacturing organisations. Maturity models are used to analyse and support the transformation, but these have deficiencies regarding holism and the ability to process fuzziness. To address these deficiencies, a holistic Fuzzy Logic approach to Circular Economy maturity assessment is proposed. Circular Economy maturity indicators are processed in a multi-stage fuzzy system. This allows for the identification of potential for change in all areas of the organisation to derive actions to improve the organisation’s circularity. Full article

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8 pages, 212 KiB

Open AccessProceeding Paper

Constructing Cyber Resilience: A Focus on Cybersecurity Measures in the South African Construction Sector

by Seyi Stephen, Clinton Aigbavboa, Ayodeji Oke, Opeoluwa Akinradewo and Ayobami Idowu

Eng. Proc. 2024, 76(1), 3; https://doi.org/10.3390/engproc2024076003 (registering DOI) - 15 Oct 2024

In addressing the challenges of cyber threats in the South African construction sector, the study employed a quantitative methodology involving a questionnaire retrieved from 86 of the study’s respondents. It employed tools like mean item score (MIS), standard deviation (SD), and the pattern [...] Read more.

In addressing the challenges of cyber threats in the South African construction sector, the study employed a quantitative methodology involving a questionnaire retrieved from 86 of the study’s respondents. It employed tools like mean item score (MIS), standard deviation (SD), and the pattern matrix of exploratory factor analysis (EFA). The findings revealed critical cybersecurity measures, including adherence to international information security standards such as the General Data Protection Regulation (GDPR), ISO 27001, or the Cybersecurity Framework by NIST, two-factor authentication, and strategic planning. The implications of these findings underscore the importance of robust cybersecurity frameworks and heightened awareness. This research contributes insights for enhancing cyber resilience in the construction industry, urging stakeholders to prioritize protective measures against cyber risks. Full article

8 pages, 222 KiB

Open AccessProceeding Paper

Diverse Approaches to Construction and Demolition Waste Reuse: A Case of South Africa

by Kenneth Otasowie, Clinton Aigbavboa, Ayodeji Oke, Peter Adekunle and Emmanuel Ayorinde

Eng. Proc. 2024, 76(1), 2; https://doi.org/10.3390/engproc2024076002 (registering DOI) - 15 Oct 2024

Construction and demolition waste management through the reuse of materials has drawn a considerable amount of attention in recent years. Hence, this study examines the diverse approaches to construction and demolition waste reuse in South Africa. Surveying 122 construction professionals, 87 responses were [...] Read more.

Construction and demolition waste management through the reuse of materials has drawn a considerable amount of attention in recent years. Hence, this study examines the diverse approaches to construction and demolition waste reuse in South Africa. Surveying 122 construction professionals, 87 responses were analysed using descriptive statistics. The results show that manufacturing the road base pavement layer from reclaimed asphalt, manufacturing furniture from used timber, using recycled plastic to manufacture plastic strips for soil embankments, and manufacturing fibreglass insulation from recycled glass are the most adopted and significant approaches to construction and demolition waste reuse in South Africa. By embracing these approaches, the construction industry can transition towards more sustainable and resource-efficient practices, thereby minimising waste generation, conserving natural resources, and mitigating environmental impacts. Full article

5 pages, 874 KiB

Open AccessProceeding Paper

Simulation of a Case-Study Intermittent Water Distribution Network by Using the Storm Water Management Model

by Aurora Gullotta and Alberto Campisano

Eng. Proc. 2024, 69(1), 192; https://doi.org/10.3390/engproc2024069192 - 14 Oct 2024

An EPA-SWMM model was used for the simulation of the intermittent water distribution system (WDS) of a small municipality in southern Italy. The model was compared with field data collected during an experimental campaign carried out in the intermittent WDS. The whole cycle [...] Read more.

An EPA-SWMM model was used for the simulation of the intermittent water distribution system (WDS) of a small municipality in southern Italy. The model was compared with field data collected during an experimental campaign carried out in the intermittent WDS. The whole cycle of operation of the WDS was simulated, including the filling, distribution and emptying phases of the intermittent network. The modelling also included water leakages and private tanks that are normally interposed between network pipes and end users. Comparison of model results and experimental observations concerned water levels at the reservoirs and pressures at specific nodes of the WDS during some days of the experiments. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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790 KiB

Open AccessProceeding Paper

Monolithic and Decomposition Methods for Optimal Scheduling of Dynamically Adaptive Water Networks

by Bradley Jenks, Aly-Joy Ulusoy and Ivan Stoianov

Eng. Proc. 2024, 69(1), 191; https://doi.org/10.3390/engproc2024069191 (registering DOI) - 14 Oct 2024

This paper presents an optimal scheduling problem for coordinating pressure and self-cleaning operations in dynamically adaptive water networks. Our problem imposes a set of time-coupling constraints to manage pressure variations during the transition between operational modes. Solving this time-coupled, nonlinear optimization problem poses [...] Read more.

This paper presents an optimal scheduling problem for coordinating pressure and self-cleaning operations in dynamically adaptive water networks. Our problem imposes a set of time-coupling constraints to manage pressure variations during the transition between operational modes. Solving this time-coupled, nonlinear optimization problem poses challenges for off-the-shelf nonlinear solvers due to its high memory demands. We compare the performance of a decomposition method using the alternating direction method of multipliers (ADMM) with a gradient-based sequential convex programming (SCP) monolithic solver. Solution quality and computational efficiency are evaluated using a model of a large-scale network in the UK. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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4 pages, 746 KiB

Open AccessProceeding Paper

Multi-Model Demand Forecasting in Water Distribution Network Districts

by Enrico Creaco, Carlo Giudicianni and Manuel Herrera

Eng. Proc. 2024, 69(1), 188; https://doi.org/10.3390/engproc2024069188 - 14 Oct 2024

A multi-model including three modelling elements is developed to solve the Battle of Water Demand Forecasting problem. The first two modelling elements working in parallel are a pattern-based algorithm and a Random Forest model. By varying the algorithm setting and predictors, 42 algorithms [...] Read more.

A multi-model including three modelling elements is developed to solve the Battle of Water Demand Forecasting problem. The first two modelling elements working in parallel are a pattern-based algorithm and a Random Forest model. By varying the algorithm setting and predictors, 42 algorithms are constructed and calibrated using demand and weather data in the previous weeks to the generic n-th week, when the objective is the prediction of the hourly demand pattern in the n + 1-th week. Then, a third modelling element is used, which consists of an optimizer aimed at combining the results yielded by the 42 algorithms by analyzing algorithm performance in the n-th week. The same algorithm combination is used to forecast demand at the n + 1-th week. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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4 pages, 526 KiB

Open AccessProceeding Paper

Microwave-Assisted Condensation of Two Potential Antibacterial Pharmacophores (Sulfonamide and Oxazolidinone)

by Radia Bouasla and Malika Berredjem

Eng. Proc. 2024, 67(1), 64; https://doi.org/10.3390/engproc2024067064 - 14 Oct 2024

In recent years, microwave heating has become a widely used technique in organic synthesis. The reactions take place within a very short time, under mild conditions with high yields, and produce pure and selective compounds with fewer side reactions. In this context and [...] Read more.

In recent years, microwave heating has become a widely used technique in organic synthesis. The reactions take place within a very short time, under mild conditions with high yields, and produce pure and selective compounds with fewer side reactions. In this context and under green chemistry conditions, we synthesized an organic compound containing two pharmacophore groups, oxazolidinone and sulfonamide, with a good yield. Full article

(This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes)

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701 KiB

Open AccessProceeding Paper

AI-Driven Improvements in Electrochemical Biosensors for Effective Pathogen Detection at Point-of-Care

by Inderpreet Singh, Asmita Gupta, Chansi Gupta, Ashish Mani and Tinku Basu

Eng. Proc. 2024, 73(1), 5; https://doi.org/10.3390/engproc2024073005 (registering DOI) - 14 Oct 2024

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel framework for integrating artificial intelligence (AI) into electrochemical biosensors for pathogen detection. Real-world samples often present unwanted noise in the signal, particularly when [...] Read more.

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel framework for integrating artificial intelligence (AI) into electrochemical biosensors for pathogen detection. Real-world samples often present unwanted noise in the signal, particularly when utilizing portable point-of-care devices. To overcome this challenge, a framework using AI for noise reduction from a portable potentiostat is proposed in this work. This approach involves employing a denoising autoencoder (DAE) to effectively remove noise from the electrochemical signals generated from a portable potentiostat by utilizing training datasets generated from benchtop potentiostat for training the DAE, bringing the performance of portable devices on par with their benchtop counterparts. This enhancement is crucial for point-of-care applications where environmental and operational factors often compromise data quality. Smartphones are often used as interfaces for portable electrochemical devices, the proposed framework can leverage the computational capabilities of smartphones to run the DAE model for processing electrochemical signals in real-time, thus making it compatible with fully point-of-care solution. The proposed system has been validated using COVID-19 and dengue DPV data, demonstrating its potential as a powerful tool in the rapid and accurate detection of SARS-CoV-2 and other pathogens. The integration of AI into electrochemical biosensing offers a more reliable and accessible option for healthcare professionals and researchers. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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8 pages, 685 KiB

Open AccessProceeding Paper

The Analysis of the Effect of a Mental Workload and Burnout on Employees’ Safety Behavior in the Oil and Gas Industry Using Roster Systems

by Denby Truman and Ratna Sari Dewi

Eng. Proc. 2024, 76(1), 1; https://doi.org/10.3390/engproc2024076001 - 14 Oct 2024

The oil and gas industry is a high-risk industry because of the work, meaning that employees in this industry to be very prone to work accidents. Employees who work in isolated locations such as offshore platforms in the long term can experience mental [...] Read more.

The oil and gas industry is a high-risk industry because of the work, meaning that employees in this industry to be very prone to work accidents. Employees who work in isolated locations such as offshore platforms in the long term can experience mental and emotional fatigue, resulting in burnout. Therefore, this research was conducted with the aim of analyzing and identifying the influence of mental workload and burnout on safety behavior with different roster systems of 2 weeks and 3 weeks in the oil and gas industry. The researchers used several approaches to analyzing mental workload, burnout, and safety behavior using the PLS-SEM method with SMART PLS software. Based on the results obtained from the comparative analysis of the roster systems, it was concluded that the 2-week roster system is better than the 3-week one. This research can also provide information to companies regarding the level of mental workload, burnout, and their relationship with safety behavior and recommendations programs for companies to minimize the risk of accidents by paying attention to the mental health aspects of their employees. Full article

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4 pages, 1855 KiB

Open AccessProceeding Paper

Transient Flow Dynamics in Tesla Valve Configurations: Insights from Computational Fluid Dynamics Simulations

by Mohamad Zeidan, Davaasuren Yondonjamts, Márton Németh, Gopinathan R. Abhijith, Richard Wéber and Avi Ostfeld

Eng. Proc. 2024, 69(1), 195; https://doi.org/10.3390/engproc2024069195 - 12 Oct 2024

This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive computational fluid dynamics (CFD) simulations. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended [...] Read more.

This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive computational fluid dynamics (CFD) simulations. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. The research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. The study reveals the emergence of distinctive “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to a non-uniform propagation of pressure throughout the system. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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8 pages, 2328 KiB

Open AccessProceeding Paper

Object Detection for Autonomous Logistics: A YOLOv4 Tiny Approach with ROS Integration and LOCO Dataset Evaluation

by Souhaila Khalfallah, Mohamed Bouallegue and Kais Bouallegue

Eng. Proc. 2024, 67(1), 65; https://doi.org/10.3390/engproc2024067065 - 12 Oct 2024

This paper presents an object detection model for logistics-centered objects deployed and used by autonomous warehouse robots. Using the Robot Operating System (ROS) infrastructure, our work leverages the set of provided models and a dataset to create a complex system that can meet [...] Read more.

This paper presents an object detection model for logistics-centered objects deployed and used by autonomous warehouse robots. Using the Robot Operating System (ROS) infrastructure, our work leverages the set of provided models and a dataset to create a complex system that can meet the guidelines of the Autonomous Mobile Robots (AMRs). We describe an innovative method, and the primary emphasis is placed on the Logistics Objects in Context (LOCO) dataset. The importance is on training the model and determining optimal performance and accuracy for the implemented object detection task. Using neural networks as pattern recognition tools, we took advantage of the one-stage detection architecture YOLO that prioritizes speed and accuracy. Focusing on a lightweight variant of this architecture, YOLOv4 Tiny, we were able to optimize for deployment on resource-constrained edge devices without compromising detection accuracy, resulting in a significant performance boost over previous benchmarks. The YOLOv4 Tiny model was implemented with Darknet, especially for its adaptability to ROS Melodic framework and capability to fit edge devices. Notably, our network achieved a mean average precision (mAP) of 46% and an intersection over union (IoU) of 50%, surpassing the baseline metrics established by the initial LOCO study. These results demonstrate a significant improvement in performance and accuracy for real-world logistics applications of AMRs. Our contribution lies in providing valuable insights into the capabilities of AMRs within the logistics environment, thus paving the way for further advancements in this field. Full article

(This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes)

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5 pages, 554 KiB

Open AccessProceeding Paper

Detection of Alzheimer’s and Parkinson’s Diseases Using Deep Learning-Based Various Transformers Models

by Mesut Güven

Eng. Proc. 2024, 73(1), 4; https://doi.org/10.3390/engproc2024073004 - 11 Oct 2024

Alzheimer’s disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson’s disease manifests as a neurological disorder featuring primary motor, secondary motor, [...] Read more.

Alzheimer’s disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson’s disease manifests as a neurological disorder featuring primary motor, secondary motor, and non-motor symptoms, accompanied by the rapid demise of cells in the brain’s dopamine-producing region. Utilizing brain images for accurate diagnosis and treatment is integral to addressing both conditions. This study harnessed the power of artificial intelligence for classification processes, employing state-of-the-art transformer models such as Swin transformer, vision transformer (ViT), and bidirectional encoder representation from image transformers (BEiT). The investigation utilized an open-source dataset comprising 450 images, evenly distributed among healthy, Alzheimer’s, and Parkinson’s classes. The dataset was meticulously divided, with 80% allocated to the training set (390 images) and 20% to the validation set (90 images). Impressively, the classification accuracy surpassed 80%, showcasing the efficacy of transformer-based models in disease detection. Looking ahead, this study recommends delving into hybrid and ensemble models and leveraging the strengths of multiple transformer-based deep learning architectures. Beyond contributing crucial insights at the intersection of artificial intelligence and neurology, this research emphasizes the transformative potential of advanced models for enhancing diagnostic precision and treatment strategies in Alzheimer’s and Parkinson’s diseases. It signifies a significant step towards integrating cutting-edge technology into mainstream medical practices for improved patient outcomes. Full article

(This article belongs to the Proceedings of The 4th International Electronic Conference on Biosensors)

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5 pages, 626 KiB

Open AccessProceeding Paper

Operating Water Distribution Systems for Equitable Access to Clean Water

by Brent Vizanko, Tomer Shmaya, Sriman Pankaj Boindala, Avi Ostfeld and Emily Berglund

Eng. Proc. 2024, 69(1), 194; https://doi.org/10.3390/engproc2024069194 - 10 Oct 2024

Water distribution systems (WDSs) are designed to deliver potable water across urban areas. Unpredicted changes in water demands and hydraulics can increase the residence time in pipes, leading to the growth of microbes and decreased water quality at some locations in a network. [...] Read more.

Water distribution systems (WDSs) are designed to deliver potable water across urban areas. Unpredicted changes in water demands and hydraulics can increase the residence time in pipes, leading to the growth of microbes and decreased water quality at some locations in a network. During the COVID-19 pandemic, large-scale reductions in demands, especially in industrial and commercial areas as individuals worked from home, led to hot-spots of increased water age. In response to reduced water quality, consumers may avoid using tap water for end uses including drinking, cooking, and cleaning. The lack of access to clean water can create high costs for some households due to the cost of buying bottled water. Inequitable access to safe, affordable water is explored in this research in the context of the COVID-19 pandemic through a coupled framework. This research extends an existing agent-based modeling (ABM) framework that simulated COVID-19 transmission, social distancing decision-making, reductions in water demands, and flows in a water distribution system. The ABM is extended in this work to simulate households that perceive water quality problems with tap water and choose to buy bottled water for cooking, cleaning, and hygienic purposes. Agents choose tap water avoidance behaviors based on water age, a surrogate for water quality. Equity is evaluated using the cost of water, both tap and bottled, as a percentage of income. An optimization approach is coupled with the ABM framework and applied to design operational strategies that improve equitable access to safe affordable water. A graph theory approach identifies valves that should be opened and closed to improve water quality at nodes and maximize equity. The results demonstrate an increase in water age due to social distancing behaviors, and water of high age is observed to be disproportionately located near industrial areas. Adjusted income demonstrates inequities in access to safe and affordable water. Operational strategies are developed to improve equity for a community through valve operations that improve the equitable delivery of safe water. This research develops an approach to assess equity of the quality of delivered water and can be used to facilitate WDS management that provides equitable access to safe water. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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4 pages, 476 KiB

Open AccessProceeding Paper

Full-Scale Water Supply System Pipe Burst Analysis Method and Application in Case Studies

by Markus I. Sunela, Janne Väyrynen and Lauri Rantala

Eng. Proc. 2024, 69(1), 186; https://doi.org/10.3390/engproc2024069186 - 10 Oct 2024

This paper presents an EPANET pressure-dependent analysis-based method for analyzing bursts in every pipe in a water supply system (WSS) and applies the method to large Finnish WSSs. EPANET is enhanced with the per-junction required and minimum pressures, a flow- and pressure-controlled pump [...] Read more.

This paper presents an EPANET pressure-dependent analysis-based method for analyzing bursts in every pipe in a water supply system (WSS) and applies the method to large Finnish WSSs. EPANET is enhanced with the per-junction required and minimum pressures, a flow- and pressure-controlled pump battery component and a full control system model to accurately capture the dynamic behavior of the whole system, including the effect of control system parameters and settings. The results are combined with population and income data, and the correlations of the various physical and hydraulic parameters affecting the burst effects are analyzed. Full article

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))

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