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Technologies, Volume 12, Issue 3 (March 2024) – 15 articles

Cover Story (view full-size image): Light-duty vehicle emission regulations set limits for carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is limited by fleet targets. The instrument principles of operation are non-dispersive infrared (NDIR) analysis for CO and CO2, flame ionization detection (FID) for hydrocarbons, and chemiluminescence (CLA) or non-dispersive ultraviolet detection (NDUV) for NOX. Alternative principles that can measure some or all of these pollutants include Fourier transform infrared (FTIR) and laser absorption spectroscopy (LAS). The second category includes quantum cascade laser (QCL) spectroscopy in the mid-infrared region or laser diode spectroscopy (LDS) in the near-infrared region, such as tunable diode laser absorption spectroscopy (TDLAS). View this paper
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21 pages, 5583 KiB  
Article
Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps
by Satyabrata Dash, Sujata Chakravarty, Nimay Chandra Giri, Umashankar Ghugar and Georgios Fotis
Technologies 2024, 12(3), 42; https://doi.org/10.3390/technologies12030042 - 19 Mar 2024
Viewed by 1295
Abstract
The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power [...] Read more.
The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure. Full article
(This article belongs to the Collection Electrical Technologies)
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14 pages, 6383 KiB  
Article
Implementation of a Wireless Sensor Network for Environmental Measurements
by Rosa M. Woo-García, José M. Pérez-Vista, Adrián Sánchez-Vidal, Agustín L. Herrera-May, Edith Osorio-de-la-Rosa, Felipe Caballero-Briones and Francisco López-Huerta
Technologies 2024, 12(3), 41; https://doi.org/10.3390/technologies12030041 - 16 Mar 2024
Viewed by 1474
Abstract
Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable [...] Read more.
Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management. Full article
(This article belongs to the Special Issue Perpetual Sensor Nodes for Sustainable Wireless Network Applications)
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27 pages, 4248 KiB  
Review
Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry
by Alireza Kamran-Pishhesari, Amin Moniri-Morad and Javad Sattarvand
Technologies 2024, 12(3), 40; https://doi.org/10.3390/technologies12030040 - 15 Mar 2024
Cited by 1 | Viewed by 1599
Abstract
Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of [...] Read more.
Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D model of the environment. This study investigates the existing gap in fully immersive teleoperation systems within the mining industry, aiming to identify the most optimal methods for their development and ensure operator’s safety. To achieve this purpose, a literature search is employed to identify and extract information from the most relevant sources. The most advanced teleoperation systems are examined by focusing on their visualization types. Then, various 3D reconstruction techniques applicable to mining VR teleoperation are investigated, and their data acquisition methods, sensor technologies, and algorithms are analyzed. Ultimately, the study discusses challenges associated with 3D reconstruction techniques for mining teleoperation. The findings demonstrated that the real-time 3D reconstruction of underground mining environments primarily involves depth-based techniques. In contrast, point cloud generation techniques can mostly be employed for 3D reconstruction in open-pit mining operations. Full article
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25 pages, 5080 KiB  
Article
Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids
by Mohamed A. Afifi, Mostafa I. Marei and Ahmed M. I. Mohamad
Technologies 2024, 12(3), 39; https://doi.org/10.3390/technologies12030039 - 15 Mar 2024
Viewed by 1499
Abstract
As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low [...] Read more.
As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers. Full article
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25 pages, 378 KiB  
Review
Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey
by Enamul Karim, Hamza Reza Pavel, Sama Nikanfar, Aref Hebri, Ayon Roy, Harish Ram Nambiappan, Ashish Jaiswal, Glenn R. Wylie and Fillia Makedon
Technologies 2024, 12(3), 38; https://doi.org/10.3390/technologies12030038 - 11 Mar 2024
Viewed by 2237
Abstract
Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper [...] Read more.
Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper presents a comprehensive survey of the current landscape in cognitive fatigue detection. We systematically review various approaches, encompassing physiological, behavioral, and performance-based measures, for robust and objective fatigue detection. The paper further analyzes different challenges, including the lack of standardized ground truth and the need for context-aware fatigue assessment. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the multifaceted challenge of cognitive fatigue detection. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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15 pages, 3321 KiB  
Article
Pioneering a Framework for Robust Telemedicine Technology Assessment (Telemechron Study)
by Sandra Morelli, Carla Daniele, Giuseppe D’Avenio, Mauro Grigioni and Daniele Giansanti
Technologies 2024, 12(3), 37; https://doi.org/10.3390/technologies12030037 - 8 Mar 2024
Viewed by 1380
Abstract
The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies [...] Read more.
The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies in Public Health at the Italian National Institute of Health played the role of promoting and utilizing technology assessment tools within partnership projects. This study aims to outline the design, development, and application of assessment methodologies within the telemedicine project proposed by the ISS team, utilizing a specific framework developed within the project. The sub-objectives include evaluating the proposed methodology’s effectiveness and feasibility, gathering feedback for improvement, and assessing its impact on various project components. The study emphasizes the multifaceted nature of action domains and underscores the crucial role of technology assessments in telemedicine, highlighting its impact across diverse realms through iterative interaction cycles with project partners. Both the impact and the acceptance of the methodology have been assessed by means of specific computer-aided web interviewing (CAWI) tools. The proposed methodology received significant acceptance, providing valuable insights for refining future frameworks. The impact assessment revealed a consistent quality improvement trend in the project’s products, evident in methodological consolidations. The overall message encourages similar initiatives in this domain, shedding light on the intricacies of technology assessment implementation. In conclusion, the study serves as a comprehensive outcome of the national telemedicine project, witnessing the success and adaptability of the technology assessment methodology and advocating for further exploration and implementation in analogous contexts. Full article
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17 pages, 1256 KiB  
Article
Energy Sustainability Indicators for the Use of Biomass as Fuel for the Sugar Industry
by Reinier Jiménez Borges, Luis Angel Iturralde Carrera, Eduardo Julio Lopez Bastida, José R. García-Martínez, Roberto V. Carrillo-Serrano and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(3), 36; https://doi.org/10.3390/technologies12030036 - 8 Mar 2024
Viewed by 1461
Abstract
There are numerous analytical and/or computational tools for evaluating the energetic sustainability of biomass in the sugar industry. However, the simultaneous integration of the energetic–exergetic and emergetic criteria for such evaluation is still insufficient. The objective of the present work is to propose [...] Read more.
There are numerous analytical and/or computational tools for evaluating the energetic sustainability of biomass in the sugar industry. However, the simultaneous integration of the energetic–exergetic and emergetic criteria for such evaluation is still insufficient. The objective of the present work is to propose a range of indicators to evaluate the sustainability of the use of biomass as fuel in the sugar industry. For this purpose, energy, exergy, and emergy evaluation tools were theoretically used as sustainability indicators. They were validated in five variants of different biomass and their mixtures in two studies of technologies used in Cuba for the sugar industry. As a result, the energy method showed, for all variants, an increase in efficiency of about 5% in the VU-40 technology compared to the Retal technology. There is an increase in energy efficiency when considering AHRs of 2.8% or Marabu (Dichrostachys cinerea) (5.3%) compared to the V1 variant. Through the study of the exergetic efficiency, an increase of 2% was determined in both technologies for the case of the V1 variant, and an increase in efficiency is observed in the V2 variant of 5% and the V3 variant (5.6%) over the V1 variant. The emergetic method showed superior results for the VU-40 technology over the Retal technology due to higher fuel utilization. In the case of the V1 variant, there was a 7% increase in the renewability ratio and an 11.07% increase in the sustainability index. This is because more energy is produced per unit of environmental load. Full article
(This article belongs to the Section Environmental Technology)
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9 pages, 6879 KiB  
Communication
A 28 GHz Highly Linear Up-Conversion Mixer for 5G Cellular Communications
by Chul-Woo Byeon
Technologies 2024, 12(3), 35; https://doi.org/10.3390/technologies12030035 - 7 Mar 2024
Viewed by 1213
Abstract
In this paper, we present a highly linear direct in-phase/quadrature (I/Q) up-conversion mixer for 5G millimeter-wave applications. To enhance the linearity of the mixer, we propose a complementary derivative superposition technique with pre-distortion. The proposed up-conversion mixer consists of a quadrature generator, LO [...] Read more.
In this paper, we present a highly linear direct in-phase/quadrature (I/Q) up-conversion mixer for 5G millimeter-wave applications. To enhance the linearity of the mixer, we propose a complementary derivative superposition technique with pre-distortion. The proposed up-conversion mixer consists of a quadrature generator, LO buffer amplifiers, and an I/Q up-conversion mixer core and achieves an output third-order intercept point of 15.7 dBm and an output 1 dB compression point of 2 dBm at 27.6 GHz, while it consumes 15 mW at a supply voltage of 1 V. The conversion gain is 11.4 dB and the LO leakage and image rejection ratio are −56 dBc and 61 dB, respectively, in the measurement. The proposed I/Q up-conversion mixer is suitable for 5G cellular communication systems. Full article
(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G and Beyond Volume II)
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20 pages, 3425 KiB  
Article
Reinforcement Learning as an Approach to Train Multiplayer First-Person Shooter Game Agents
by Pedro Almeida, Vítor Carvalho and Alberto Simões
Technologies 2024, 12(3), 34; https://doi.org/10.3390/technologies12030034 - 5 Mar 2024
Viewed by 1578
Abstract
Artificial Intelligence bots are extensively used in multiplayer First-Person Shooter (FPS) games. By using Machine Learning techniques, we can improve their performance and bring them to human skill levels. In this work, we focused on comparing and combining two Reinforcement Learning training architectures, [...] Read more.
Artificial Intelligence bots are extensively used in multiplayer First-Person Shooter (FPS) games. By using Machine Learning techniques, we can improve their performance and bring them to human skill levels. In this work, we focused on comparing and combining two Reinforcement Learning training architectures, Curriculum Learning and Behaviour Cloning, applied to an FPS developed in the Unity Engine. We have created four teams of three agents each: one team for Curriculum Learning, one for Behaviour Cloning, and another two for two different methods of combining Curriculum Learning and Behaviour Cloning. After completing the training, each agent was matched to battle against another agent of a different team until each pairing had five wins or ten time-outs. In the end, results showed that the agents trained with Curriculum Learning achieved better performance than the ones trained with Behaviour Cloning by a matter of 23.67% more average victories in one case. In terms of the combination attempts, not only did the agents trained with both devised methods had problems during training, but they also achieved insufficient results in the battle, with an average of 0 wins. Full article
(This article belongs to the Section Information and Communication Technologies)
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14 pages, 2856 KiB  
Article
A Comparison between Kinematic Models for Robotic Needle Insertion with Application into Transperineal Prostate Biopsy
by Chiara Zandonà, Andrea Roberti, Davide Costanzi, Burçin Gül, Özge Akbulut, Paolo Fiorini and Andrea Calanca
Technologies 2024, 12(3), 33; https://doi.org/10.3390/technologies12030033 - 1 Mar 2024
Viewed by 1546
Abstract
Transperineal prostate biopsy is the most reliable technique for detecting prostate cancer, and robot-assisted needle insertion has the potential to improve the accuracy of this procedure. Modeling the interaction between a bevel-tip needle and the tissue, considering tissue heterogeneity, needle bending, and tissue/organ [...] Read more.
Transperineal prostate biopsy is the most reliable technique for detecting prostate cancer, and robot-assisted needle insertion has the potential to improve the accuracy of this procedure. Modeling the interaction between a bevel-tip needle and the tissue, considering tissue heterogeneity, needle bending, and tissue/organ deformation and movement is a required step to enable robotic needle insertion. Even if several models exist, they have never been compared on experimental grounds. Based on this motivation, this paper proposes an experimental comparison for kinematic models of needle insertion, considering different needle insertion speeds and different degrees of tissue stiffness. The experimental comparison considers automated insertions of needles into transparent silicone phantoms under stereo-image guidance. The comparison evaluates the accuracy of existing models in predicting needle deformation. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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21 pages, 4650 KiB  
Article
Measurement of Light-Duty Vehicle Exhaust Emissions with Light Absorption Spectrometers
by Barouch Giechaskiel, Anastasios Melas, Jacopo Franzetti, Victor Valverde, Michaël Clairotte and Ricardo Suarez-Bertoa
Technologies 2024, 12(3), 32; https://doi.org/10.3390/technologies12030032 - 28 Feb 2024
Viewed by 1362
Abstract
Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements [...] Read more.
Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements are carried out at the dilution tunnel with “standard” laboratory-grade instruments following well-defined principles of operation: non-dispersive infrared (NDIR) analyzers for CO and CO2, flame ionization detectors (FIDs) for hydrocarbons, and chemiluminescence analyzers (CLAs) or non-dispersive ultraviolet detectors (NDUVs) for NOX. In the United States in 2012 and in China in 2020, with Stage 6, nitrous oxide (N2O) was also included. Brazil is phasing in NH3 in its regulation. Alternative instruments that can measure some or all these pollutants include Fourier transform infrared (FTIR)- and laser absorption spectroscopy (LAS)-based instruments. In the second category, quantum cascade laser (QCL) spectroscopy in the mid-infrared area or laser diode spectroscopy (LDS) in the near-infrared area, such as tunable diode laser absorption spectroscopy (TDLAS), are included. According to current regulations and technical specifications, NH3 is the only component that has to be measured at the tailpipe to avoid ammonia losses due to its hydrophilic properties and adsorption on the transfer lines. There are not many studies that have evaluated such instruments, in particular those for “non-regulated” worldwide pollutants. For this reason, we compared laboratory-grade “standard” analyzers with FTIR- and TDLAS-based instruments measuring NH3. One diesel and two gasoline vehicles at different ambient temperatures and with different test cycles produced emissions in a wide range. In general, the agreement among the instruments was very good (in most cases, within ±10%), confirming their suitability for the measurement of pollutants. Full article
(This article belongs to the Section Environmental Technology)
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29 pages, 7839 KiB  
Review
Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review
by Denisse Kim, Bernardo Cánovas-Segura, Manuel Campos and Jose M. Juarez
Technologies 2024, 12(3), 31; https://doi.org/10.3390/technologies12030031 - 28 Feb 2024
Viewed by 1925
Abstract
In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. [...] Read more.
In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics. Full article
(This article belongs to the Section Information and Communication Technologies)
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17 pages, 6549 KiB  
Article
Mapping Acoustic Frictional Properties of Self-Lubricating Epoxy-Coated Bearing Steel with Acoustic Emissions during Friction Test
by Venkatasubramanian Krishnamoorthy, Ashvita Anitha John, Shubrajit Bhaumik and Viorel Paleu
Technologies 2024, 12(3), 30; https://doi.org/10.3390/technologies12030030 - 24 Feb 2024
Viewed by 1458
Abstract
This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings [...] Read more.
This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings were observed and scrutinized. The RMS values of the acoustic sensor were correlated with the frictional coefficient to develop a standard based on the acoustic sensor, leading to the detection of the stick–slip phenomenon. As per the findings, the acoustic waveform remained relatively similar to the friction coefficient observed during the study and can be used effectively in detecting the stick–slip phenomenon between steel and polymer interaction. This work will be highly beneficial in industrial and automotive applications with a significant interaction of polymer and steel surfaces. Full article
(This article belongs to the Section Manufacturing Technology)
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14 pages, 2244 KiB  
Article
A Kinetic Study of a Photo-Oxidation Reaction between α-Terpinene and Singlet Oxygen in a Novel Oscillatory Baffled Photo Reactor
by Jianhan Chen, Rohen Prinsloo and Xiongwei Ni
Technologies 2024, 12(3), 29; https://doi.org/10.3390/technologies12030029 - 21 Feb 2024
Viewed by 1377
Abstract
By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are here used to investigate the reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O [...] Read more.
By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are here used to investigate the reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O2). In the mode of NMR data analysis that is widely used for this reaction, α-terpinene and ascaridole are treated as a reaction pair, assuming kinetically singlet oxygen is in excess or constant. We have, for the first time, here examined the validity of the method, discovered that increasing α-terpinene initially leads to an increase in ascaridole, indicating that the supply of singlet oxygen is in excess. Applying a kinetic analysis, a pseudo-first-order reaction kinetics is confirmed, supporting this assumption. We have subsequently initiated a methodology of estimating the 1O2 concentrations based on the proportionality of ascaridole concentrations with respect to its maximum under these conditions. With the help of the estimated singlet oxygen data, the efficiency of 1O2 utilization and the photo efficiency of converting molecular oxygen to 1O2 are further proposed and evaluated. We have also identified conditions under which a further increase in α-terpinene has caused decreases in ascaridole, implying kinetically that 1O2 has now become a limiting reagent, and the method of treating α-terpinene and ascaridole as a reaction pair in the data analysis would no longer be valid under those conditions. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
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13 pages, 3774 KiB  
Article
Nested Contrastive Boundary Learning: Point Transformer Self-Attention Regularization for 3D Intracranial Aneurysm Segmentation
by Luis Felipe Estrella-Ibarra, Alejandro de León-Cuevas and Saul Tovar-Arriaga
Technologies 2024, 12(3), 28; https://doi.org/10.3390/technologies12030028 - 21 Feb 2024
Viewed by 1454
Abstract
In 3D segmentation, point-based models excel but face difficulties in precise class delineation at class intersections, an inherent challenge in segmentation models. This is particularly critical in medical applications, influencing patient care and surgical planning, where accurate 3D boundary identification is essential for [...] Read more.
In 3D segmentation, point-based models excel but face difficulties in precise class delineation at class intersections, an inherent challenge in segmentation models. This is particularly critical in medical applications, influencing patient care and surgical planning, where accurate 3D boundary identification is essential for assisting surgery and enhancing medical training through advanced simulations. This study introduces the Nested Contrastive Boundary Learning Point Transformer (NCBL-PT), specially designed for 3D point cloud segmentation. NCBL-PT employs contrastive learning to improve boundary point representation by enhancing feature similarity within the same class. NCBL-PT incorporates a border-aware distinction within the same class points, allowing the model to distinctly learn from both points in proximity to the class intersection and from those beyond. This reduces semantic confusion among the points of different classes in the ambiguous class intersection zone, where similarity in features due to proximity could lead to incorrect associations. The model operates within subsampled point clouds at each encoder block stage of the point transformer architecture. It applies self-attention with k = 16 nearest neighbors to local neighborhoods, aligning with NCBL calculations for consistent self-attention regularization in local contexts. NCBL-PT improves 3D segmentation at class intersections, as evidenced by a 3.31% increase in Intersection over Union (IOU) for aneurysm segmentation compared to the base point transformer model. Full article
(This article belongs to the Section Assistive Technologies)
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