Search Results (335)

The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article

Lean manufacturing focuses on perfection, trying to eliminate all types of Muda (waste), Mura (inconsistency), Muri (overburden), defects, injuries, and accidents through a continuous improvement process. Assembly lines are the final stages of manufacturing before the product is delivered to customers. Kata methodology provides a practical approach to achieving perfection in assembly lines, but its effectiveness is often hindered by delays in data collection, analysis, and diagnostics. In this study, we address these challenges by leveraging industrial internet of things (IIoT) solutions in an industrial setting. The research question of this paper is as follows: “Why was the full potential of traditional Kata to achieve assembly line perfection not realized, and will IIoT-integrated Kata address the limitations of the traditional Kata?” We demonstrate the integration of IIoT and Kata methodology in a factory assembling automobile heating, ventilation, and air conditioning (HVAC) systems to enhance assembly line productivity. We observe that the integration of IIoT with Kata methodology not only addresses existing limitations but drives substantial gains in assembly line performance. We validate improvements in both productivity and efficiency through quantitative and qualitative outcomes. We underscore the pivotal role of real-time data for Kata’s effectiveness, discuss the process for digital transformation, and explain the need for data monetization. We recommend the development of an IIoT-savvy workforce, traceability of 4M (men, method, materials, and machine), and present the task scorecards and dashboards for real-time monitoring and decision-making. We highlight the positive impact of IIoT-enabled traceability on overall equipment effectiveness (OEE). The company reduced its workforce from 15 to 13 operators, increased OEE from 75% to 85%, and improved average throughput from 60 to 90 assemblies per hour. The time for traceability of 4M (men, machines, material, and method) was reduced from hours to minutes. The factory eliminated 350 paper documents to achieve a paperless shop floor. This real-world case study serves as a model for companies looking to transition from traditional continuous improvement processes to IIoT-supported lean manufacturing. Full article

In the assembly process of aerospace parts, drilling is essential for carbon fiber-reinforced materials. However, due to the extreme thermal sensitivity of these composites, continuous drilling often leads to irreparable defects such as hole wall burns and exit delamination caused by concentrated cutting heat, resulting in the scrapping of parts. To address this issue, this paper explores the impact of temperature characteristics on drilling quality, providing guidance for optimizing the composite drilling process. A simulation model for single and continuous drilling was established to analyze the temperature distribution on the tool surface during drilling. A drilling temperature measurement system based on thin-film thermocouple technology was developed, enabling real-time online temperature monitoring. Continuous drilling experiments were conducted, analyzing the correlation between maximum drilling temperature and hole quality. Results show that temperatures from −25.75 °C to −9.75 °C and from 182 °C to 200.75 °C cause significant exit damage, while optimal hole quality is achieved between −1.25 °C and 168 °C. Full article

Activation of the ubiquitin ligase APC/C by the protein Cdc20 is an essential requirement for proper cell division in higher organisms, including humans. APC/C is the ultimate effector of the Spindle Assembly Checkpoint (SAC), the signalling system that monitors the proper attachment of chromosomes to microtubules during cell division. Defects in this process result in genome instability and cancer. Interfering with APC/C substrate ubiquitylation in cancer cells delays mitotic exit, which induces cell death. Therefore, impairing APC/C function represents an opportunity for the treatment of cancer and malignancies associated with SAC dysregulation. In this study, we report a new class of pyrimidinethylcarbamate apcin analogues that interfere with APC/C activity in 2D and 3D breast cancer cells. The new pyrimidinethylcarbamate apcin analogues exhibited higher cytotoxicity than apcin in all breast cancer cell subtypes investigated, with much lower cytotoxicity observed in fibroblasts and RPE-1 cells. Further molecular rationalisation of apcin and its derivatives was conducted using molecular docking studies. These structural modifications selected from the in silico studies provide a rational basis for the development of more potent chemotypes to treat highly aggressive breast cancer and possibly other aggressive tumour types of diverse tissue origins. Full article

Alzheimer’s disease (AD) is a complex neurodegenerative disorder influenced by various genetic factors. In addition to the well-established amyloid precursor protein (APP), Presenilin-1 (PSEN1), Presenilin-2 (PSEN2), and apolipoprotein E (APOE), several other genes such as Sortilin-related receptor 1 (SORL1), Phospholipid-transporting ATPase ABCA7 (ABCA7), Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), Phosphatidylinositol-binding clathrin assembly protein (PICALM), and clusterin (CLU) were implicated. These genes contribute to neurodegeneration through both gain-of-function and loss-of-function mechanisms. While it was traditionally thought that heterozygosity in autosomal recessive mutations does not lead to disease, haploinsufficiency was linked to several conditions, including cancer, autism, and intellectual disabilities, indicating that a single functional gene copy may be insufficient for normal cellular functions. In AD, the haploinsufficiency of genes such as ABCA7 and SORL1 may play significant yet under-explored roles. Paradoxically, heterozygous knockouts of PSEN1 or PSEN2 can impair synaptic plasticity and alter the expression of genes involved in oxidative phosphorylation and cell adhesion. Animal studies examining haploinsufficient AD risk genes, such as vacuolar protein sorting-associated protein 35 (VPS35), sirtuin-3 (SIRT3), and PICALM, have shown that their knockout can exacerbate neurodegenerative processes by promoting amyloid production, accumulation, and inflammation. Conversely, haploinsufficiency in APOE, beta-secretase 1 (BACE1), and transmembrane protein 59 (TMEM59) was reported to confer neuroprotection by potentially slowing amyloid deposition and reducing microglial activation. Given its implications for other neurodegenerative diseases, the role of haploinsufficiency in AD requires further exploration. Modeling the mechanisms of gene knockout and monitoring their expression patterns is a promising approach to uncover AD-related pathways. However, challenges such as identifying susceptible genes, gene–environment interactions, phenotypic variability, and biomarker analysis must be addressed. Enhancing model systems through humanized animal or cell models, utilizing advanced research technologies, and integrating multi-omics data will be crucial for understanding disease pathways and developing new therapeutic strategies. Full article

Functionalized nanomaterials with surface-active groups have garnered significant research interest due to their wide-ranging applications, particularly in water treatment for removing various contaminants. This study focuses on developing a novel, multi-functional nanobiosorbent by synthesizing nanosized biochar from artichoke leaves (NBAL) and molybdic acid (MA). The resulting nanobiosorbent, MA@NBAL, is produced through a microwave-irradiation process, offering a promising material for enhanced environmental remediation. The characteristics of assembled MA@NBAL were evaluated from SEM-EDX, XPS, TGA, FT-IR, and zeta potential detection. The size of particles ranged from 18.7 to 23.7 nm. At the same time, the EDX analysis denoted the existence of several major elements with related percentage values of carbon (52.9%), oxygen (27.6%), molybdenum (8.8%), and nitrogen (4.5%) in the assembled MA@NBAL nanobiosorbent. The effectiveness of MA@NBAL in removing Hg(II) ions was monitored via the batch study method. The optimized maximum removal capacity of Hg(II) ions onto MA@NBAL was established at pH 6.0, 30.0 min equilibrium time, and 20 mg of nanobiosorbent, providing 1444.25 mg/g with a 10.0 mmol/L concentration of Hg(II). Kinetic studies revealed that the adsorption process followed a pseudo-second-order model, with R² values ranging from 0.993 to 0.999 for the two tested Hg(II) concentrations, indicating excellent alignment with the experimental data. This suggests that the chemisorption mechanism involves cation exchange and complex formation. Isotherm model evaluation further confirmed the adsorption mechanism, with the Freundlich model providing the best fit, yielding an R² of 0.962. This result indicates that Hg(II) adsorption onto the surface of MA@NBAL nanobiosorbent occurs on a heterogeneous surface with multilayer formation characteristics. The results of the temperature factor and computation of the thermodynamic parameters referred to endothermic behavior via a nonspontaneous process. Finally, the valid applicability of MA@NBAL nanobiosorbent in the adsorptive recovery of 2.0 and 5.0 µg/mL Hg(II) from contaminated real aquatic matrices was explored in this study, providing 91.2–98.6% removal efficiency. Full article

Water quality and aquatic ecosystems along lakeshores are vital for ecological balance and human well-being. However, research has primarily focused on plankton, with benthic niches being largely overlooked. To enhance understanding of benthic microbial communities, we utilized 16S and 18S rRNA sequencing alongside multivariate statistical methods to analyze samples from the shoreline of Lake Taihu in Huzhou City, Zhejiang Province. Our results reveal a marked difference in species composition between benthic and planktonic microorganisms, with benthic cyanobacteria predominantly comprising filamentous genera like Tychonema, while 95% of planktonic cyanobacteria were Cyanobium. The β-diversity of benthic microorganisms was notably higher than that of planktonic counterparts. The neutral community model indicated that stochastic processes dominated planktonic microbial assembly, while deterministic processes prevailed in benthic communities. Null models showed that homogeneous selection influenced benthic community assembly, whereas planktonic communities were affected by undominated processes and dispersal limitation. Network analysis indicated that planktonic networks were more stable than benthic networks. Importantly, dominant benthic cyanobacterial genera posed potential toxin risks, highlighting the need for enhanced monitoring and ecological risk assessment. Overall, these findings enhance our understanding of benthic and planktonic microbial communities in lakeshores and offer valuable insights for aquatic assessment and management in eutrophicated environments. Full article

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors. Full article

For monitoring capillary moisture conduction, the most important parameter is the moisture conductivity coefficient, which is a material characteristic; however, its use in practical calculations is not very common. For further development in the field of liquid moisture propagation, an automated measuring apparatus has been developed and granted a European patent. Its essence lies in detecting the liquid water content based on a well-known physical phenomenon: electromagnetic radiation in the microwave range. The determination of the spatiotemporal moisture field is the first and fundamental step for describing transportation phenomena. The moisture field thus created allows for the viewing of the moisture conductivity coefficient, which is one of the most important parameters in describing transportation phenomena as a function of moisture. The presence of water in building materials can significantly affect their physical properties, such as mechanical or thermal–technical characteristics. This may lead to unacceptable consequences, which might only manifest after a certain period of time. In the case of multi-layered structures, moisture can transfer from one material to another. Therefore, it is essential to address this process. The advantage of the software solution described by the methodology is the use of an open communication protocol in the form of a synchronized array, which is not common in typical applications of this type. The principle of separating hardware modules is also unusual for devices of this type, as it requires the independent communication of each module with the control software. Mutual communication is handled exclusively at the software level, making it possible to modify, optimize, or parameterize the procedures as needed. Upon closer examination of the wetting curves of various materials, anomalies were revealed in some of their structures. This can be advantageously utilized in the research of newly developed composite materials. The assembled system of measuring instruments, their software integration, and control provide a foundation for the practical application of the described procedures and methods for determining the moisture field of building materials. The parameterization of individual processes, as well as the open access to data, allows for the optimization of the methodology, as materials of entirely different characteristics may require an individual approach, which will certainly contribute to the advancement of science and research in this area. Currently, this work is being followed by further extensive studies, not yet published by the authors, focusing on the application of the described moisture field to evaluate the moisture conductivity coefficient as a function dependent on the material’s mass moisture content. Their application requires specific mathematical and programming approaches due to the significant volume of data involved. Full article

The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. Traditional industries facing capacity constraints require noninvasive methods for in-depth operations analysis to optimize processes and increase revenue. In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. This pipeline employs a Convolutional Neural Network (CNN)-based OD model to accurately identify potential objects on the production floor, followed by a CNN-based tracker to monitor their lifecycle in each workstation. The extracted metadata are further processed through the proposed framework. Our analysis of a real-world manufacturing facility over six months revealed that the bottleneck station operated at only 73.1% productivity, falling to less than 40% on certain days; additionally, the processing time of each item increased by 53% during certain weeks due to critical labor and materials shortages. These findings highlight significant opportunities for process optimization and efficiency improvements. The proposed pipeline can be extended to other production facilities where manual labor is used to assemble parts, and can be used to analyze and manage labor and materials over time as well as to conduct audits and improve overall yields, potentially transforming capacity management in smart manufacturing environments. Full article

This study explores the automation enhancement in the assembly process of wiring harnesses for automotive applications, focusing on manually inserting fuses and relays into boxes—a task known for quality and efficiency challenges. This research aimed to address these challenges by implementing a robotic arm integrated with API technology for digital twin. The methods used included the development of a digital twin model to simulate and monitor the assembly process, supported by real-time adjustments and optimizations. The results showed that the robotic system significantly improved the accuracy and speed of fuse insertion, reducing the insertion errors typically seen in manual operations. The conclusions drawn from the research confirm the feasibility of using robotic automation supported by digital twin technology to enhance assembly processes in automotive manufacturing, promising substantial improvements in production efficiency and quality control. Full article

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet. Full article

Bioassembly encompasses various techniques such as bioprinting, microfluidics, organoids, and self-assembly, enabling advances in tissue engineering and regenerative medicine. Advancements in bioassembly technologies have enabled the precise arrangement and integration of various cell types to more closely mimic the complexity functionality of the neurovascular unit (NVU) and that of other biodiverse multicellular tissue structures. In this context, bioprinting offers the ability to deposit cells in a spatially controlled manner, facilitating the construction of interconnected networks. Scaffold-based assembly strategies provide structural support and guidance cues for cell growth, enabling the formation of complex bio-constructs. Self-assembly approaches utilize the inherent properties of cells to drive the spontaneous organization and interaction of neuronal and vascular components. However, recreating the intricate microarchitecture and functional characteristics of a tissue/organ poses additional challenges. Advancements in bioassembly techniques and materials hold great promise for addressing these challenges. The further refinement of bioprinting technologies, such as improved resolution and the incorporation of multiple cell types, can enhance the accuracy and complexity of the biological constructs; however, developing bioinks that support the growth of cells, viability, and functionality while maintaining compatibility with the bioassembly process remains an unmet need in the field, and further advancements in the design of bioactive and biodegradable scaffolds will aid in controlling cell adhesion, differentiation, and vascularization within the engineered tissue. Additionally, integrating advanced imaging and analytical techniques can provide real-time monitoring and characterization of bioassembly, aiding in quality control and optimization. While challenges remain, ongoing research and technological advancements propel the field forward, paving the way for transformative developments in neurovascular research and tissue engineering. This work provides an overview of the advancements, challenges, and future perspectives in bioassembly for fabricating neurovascular constructs with an add-on focus on bioprinting technologies. Full article

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production. Full article

Policy interventions in the school food environment can improve dietary behaviors. However, the literature describing its development and implementation is scarce. This manuscript aims to describe the process of co-creation, implementation, monitoring, and evaluation of a Healthy Snacks Policy, in the scope of Sintra Grows Healthy intervention. Through a community-based participatory research methodology, the co-creation of the Healthy Snacks Policy comprises six stages: snacks evaluation, feedback sessions, class assemblies, school community assemblies, school cluster policy approval, and process evaluation. Within one school year, a Healthy Snacks Policy was co-created, approved, incorporated in the school regulations, implemented, continuously monitored, and evaluated. Regarding snacks evaluation, 1900 snacks were evaluated at the beginning of the school year and 1079 at the end of the school year. There were three feedback sessions, twenty-two class assemblies, and three school community assemblies. Most teachers perceived that children began to consume healthier snacks (72%); 66% of the children were considered to have started eating healthier; and most families said “yes or sometimes” when asked whether their children started requesting healthier snacks (70%), trying new foods (63%), and noticing improvements in their eating habits (74%). The co-creation of a Healthy Snacks Policy establishes an approach to effectively implement existing guidelines for school food supplies, complying with national priority implementation recommendations. Full article