Operational Downtime Research Articles

Integration of Internet of Things (IoT) Technology in the Management of Educational Facilities and Infrastructure

Internet of Things (IoT) technology has developed rapidly and has had a significant impact on various sectors, including education. The integration of IoT in the management of educational facilities and infrastructure offers the potential to improve the efficiency, monitoring, and maintenance of facilities in real time. In this digital era, the need for a smarter and more responsive management system is very important to support an effective and sustainable teaching and learning process. This research aims to analyze the potential and implementation of IoT technology in the management of educational facilities and infrastructure. The main focus is to identify the benefits, challenges, and impact of implementing IoT on operational efficiency, rationality, and quality of educational services. This research uses a qualitative approach with a case study method. The research results show that the integration of IoT in the management of educational facilities and infrastructure can increase operational efficiency through automating facility monitoring and maintenance. For example, IoT sensors enable early detection of damage and maintenance needs, which can reduce repair costs and operational downtime. Apart from that, IoT also improves security through sensor-based surveillance systems and sophisticated data analytics. However, this research also identified several challenges, such as high initial costs, the need for reliable network infrastructure, and the need for special training for staff to operate this technology. The conclusion of this research explains that the integration of IoT technology in the management of educational facilities and infrastructure shows great potential to increase the efficiency and quality of educational services. Although there are challenges to overcome, the benefits gained from implementing IoT are much more effective. A mature implementation strategy and support from various parties is needed to ensure the success of this integration.

Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings.

Role of metaheuristic algorithms in healthcare: A comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health

Abstract Metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems in many areas, including healthcare. These algorithms can efficiently search through large solution spaces and locate optimal or near-optimal responses to complex issues. Although metaheuristic algorithms are crucial, previous review studies have not thoroughly investigated their applications in key healthcare areas such as clinical diagnosis and monitoring, medical imaging and processing, healthcare operations and management, as well as public health and emergency response. Numerous studies also failed to highlight the common challenges faced by metaheuristics in these areas. This review thus offers a comprehensive understanding of metaheuristic algorithms in these domains, along with their challenges and future development. It focuses on specific challenges associated with data quality and quantity, privacy and security, the complexity of high-dimensional spaces, and interpretability. We also investigate the capacity of metaheuristics to tackle and mitigate these challenges efficiently. Metaheuristic algorithms have significantly contributed to clinical decision-making by optimizing treatment plans and resource allocation and improving patient outcomes, as demonstrated in the literature. Nevertheless, the improper utilization of metaheuristic algorithms may give rise to various complications within medicine and healthcare despite their numerous benefits. Primary concerns comprise the complexity of the algorithms employed, the challenge in understanding the outcomes, and ethical considerations concerning data confidentiality and the well-being of patients. Advanced metaheuristic algorithms can optimize the scheduling of maintenance for medical equipment, minimizing operational downtime and ensuring continuous access to critical resources.

Sustainable multi-biofuel production with stochastic lead time and optimum energy utilization under flexible manufacturing

Many nations obtain their energy from fossil fuels. The benefit of renewable energy is that it decreases the dependence on conventional fuel, and minimizes carbon emissions. However, the commercial feasibility of multi-biofuel production poses a major challenge. A resilient and cost-efficient biofuel supply chain network is essential for commercialization. Furthermore, disruption risks arise from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with the aforementioned requirements, a multi-biofuel supply chain network model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. The lead time crashing cost is utilized here to reduce the lead time, which increases because of transportation disruptions. To decrease energy expenses and carbon emissions, a variable production rate is applied for creating an economic way to advance the manufacturing industry. The model aims to show bioenergy’s effect on making a sustainable multi-biofuel supply chain network. Two numerical examples are used to illustrate the effectiveness of the proposed model. It is seen that by utilizing the presented method, the production cost of biofuels is significantly reduced through minimized energy consumption and carbon emissions. Moreover, the impact of critical parameters on the total system cost is explained through sensitivity analysis and graphical representations. The effort provides a systematic guideline for designing various environmental, economic, and social aspects of a sustainable supply chain network for bioenergy under minimized consumption. Multi-biofuel producers, distributors, investors, and the regulators surely be benefitted from the proposed model.

Health Monitoring System for CNC Machine

This paper introduces a novel Health Monitoring System (HMS) tailored for CNC machines, addressing the critical need for maintaining their optimal performance and preventing unexpected breakdowns in modern manufacturing settings. The system integrates various sensors and data acquisition methods to continuously monitor key parameters like temperature, vibration, and tool wear. By employing advanced data analytics and machine learning algorithms, the HMS can swiftly identify anomalies in real-time, facilitating proactive maintenance and minimizing operational downtime. Additionally, the system features a user- friendly interface for visualizing machine health status and creating predictive maintenance schedules. Experimental validation conducted on a CNC machining center validates the efficacy and reliability of the developed HMS in enhancing machine efficiency, prolonging equipment lifespan, and curbing maintenance expenses. In summary, this Health Monitoring System offers a robust solution for ensuring the seamless operation and longevity of CNC machines in modern manufacturing environments.

A cloud-based infrastructure to deploy supervisory forecast models for predictive coagulant dosing control

Abstract Advanced optimal dosing control based on multiple online sensor data is operational in several treatment facilities in Norway. The benefits of the dosing control system in maintaining stable phosphate/solids removal and saving coagulant usage are documented in the literature. The dosing algorithm is currently implemented in a programmable logic controller (PLC) connected to the treatment plant's Supervisory Control and Data Acquisition System (SCADA) system. The PLC receives online sensor data from the plant's SCADA, calculates the optimal dosing values, and transmits optimal dosage values back to the SCADA system. The dosing algorithm is frequently updated to keep in sync with the process and equipment upgrades of the treatment plant and advances in control algorithm schemes. The upgrades include new regulatory feedback loops structural changes to the dose equation, and the addition of conditional setpoints. Each maintenance and upgrade routine entails operational downtime where the dosing algorithm is set to a sub-optimal flow-proportional dose. This paper presents a non-intrusive Internet of Things (IoT) infrastructure to implement a predictive/forecast component to an existing dosing control algorithm. The benefits of the new cloud-based system in improving nutrient removal, increasing operational flexibility, and reducing maintenance downtime are presented in this work.

Enhancing Wear Resistance of Drilling Motor Components: A Tribological and Materials Application Study

The oil and gas industry faces significant challenges due to wear on drilling motor components, such as thrust pins and inserts. These components are critical to the efficiency and reliability of drilling operations, yet are susceptible to wear, leading to significant economic losses, operational downtime, and safety risks. Despite previous research on wear-resistant materials and surface treatments, gaps exist in understanding the unique properties of thrust pins and inserts. The aim of this study is to enhance mechanical system performance by characterizing the wear resistance of these components. Through chemical analysis, hardness assessments, and metallographic examinations, the study seeks to identify specific alloys and microstructures conducive to wear resistance. Key findings reveal that AISI 9314 thrust pins exhibit superior wear resistance with a tempered martensite microstructure and a hardness of 41 HRc, whereas AISI 9310 inserts are less resistant, with a hardness of 35 HRc. The research employs advanced techniques, including a pin-on-disc tribometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and profilometry, to evaluate wear behavior, visualize wear patterns, analyze elemental composition, and quantify material loss and surface roughness. Our findings demonstrate that optimizing the material selection can significantly enhance the durability and efficiency of drilling motors. This has profound implications for the oil and gas industry, offering pathways to reduce maintenance costs, improve operational efficiency, and contribute to environmental sustainability by optimizing energy consumption and minimizing the carbon footprint of drilling operations.

Driving Towards Digitalization and Industry 4.0 in the Coal Mining Sector

This research provides an in-depth analysis of the impact of digitalization on the coal mining sector, with a special focus on PT X. The study employs a combination of content analysis, narrative analysis, and business solution mapping to extract insights from interviews conducted with five key stakeholders at PT. X. The objective is to assess how technological advancements can elevate operational efficiency and competitiveness in the era of Industry 4.0, and to identify strategies for effectively reducing reliance on human resources in site monitoring, while ensuring employee satisfaction and smooth transition. Through the methodological triangulation of content and narrative analysis, the study distils crucial themes and perspectives from the interviews. It identifies that the integration of advanced technologies like IoT, data analytics, and automation is vital for enhancing operational efficiency and reducing operational downtime. The research highlights the importance of transitioning from manual to automated site monitoring, emphasizing the adoption of drones and automated systems to bolster operational accuracy and efficiency. A key finding of the study is the identification of potential obstacles in digital technology investment, such as cost, system integration challenges, and employee resistance to new technologies. The study suggests that overcoming these barriers requires effective communication strategies and comprehensive training programs for employees. Drawing on the successful digital transformation strategies of companies like Adaro, the research recommends that PT. X should adapt similar strategies, customized to its unique challenges and context. The study contributes valuable insights into the digital transformation process in the coal mining sector, offering practical recommendations for companies like PT. X navigating the complex landscape of Industry 4.0.

Strategies to Boost Boiler Efficiency and Reduce Operational Downtime

This study examines the decline in boiler performance, highlighting the imperative of efficient maintenance to prevent operational disruptions and financial losses. The focus is on the analysis of boiler functionality, identifying factors that contribute to reduced efficiency and increased downtime. Utilizing performance data from August 2023, the research documents a significant downtime of 4610 minutes, primarily attributed to mechanical issues and extended recovery periods totaling 1920 minutes. The study also reports a high fuel usage efficiency ratio of 35 Kg/cm3, underscoring the challenges in initial recovery efforts. Despite these setbacks, machine quality metrics indicated no defects, suggesting effective quality control measures. The findings underscore the critical relationship between maintenance strategies and boiler efficiency, proposing that proactive maintenance could mitigate downtime and enhance productivity, thereby supporting sustained operational efficiency. Highlights: Maintenance crucial for boiler efficiency and cost savings. Downtime leads to production delays and financial losses. Performance metrics indicate operational reliability and effectiveness. Keywords : Boiler, Maintenance, Operating Time

Inducing Deep Sweeps and Vortex Ejections on Patterned Membrane Surfaces to Mitigate Surface Fouling.

Patterned membrane surfaces offer a hydrodynamic approach to mitigating concentration polarization and subsequent surface fouling. However, when subjected to steady crossflow conditions, surface patterns promote particle accumulation in the recirculation zones of cavity-like spaces. In order to resolve this issue, we numerically subject a two-dimensional, patterned membrane surface to a rapidly pulsed crossflow. When combined with cavity-like spaces, such as the valleys of membrane surface patterns, a rapidly pulsed flow generates mixing mechanisms (i.e., the deep sweep and the vortex ejection) and disrupts recirculation zones. In only four pulses, we demonstrate the ability of these mechanisms to remove over half of the particles trapped in recirculation zones via massless particle tracking studies (i.e., numerical integration of the simulated velocity field). The results of this work suggest that when combined with a rapidly pulsed inlet flow, patterned membrane surfaces can not only alleviate concentration polarization and the surface fouling that follows but also reduce the need for traditional cleaning methods that require operational downtime and often involve the use of abrasive chemical agents.

Automatizacija praćenja stanja reduktora vetroturbina - uporedna studija tehnike mašinskog učenja zasnovane na analizi vibracija

Wind turbines play a role in the adoption of renewable energy production, but they are susceptible to shutdowns that require thorough monitoring. Gearbox failures are an issue leading to maintenance and operational downtime. This study investigates the application of machine learning methods to enhance the diagnosis of gearbox problems using vibration analysis. Through the application of fault scenarios that impact bearings and gears, the researchers successfully extracted time domain features from vibration data of a 750 kW turbine testbed in order to detect indications of damage. Support Vector Machine (SVM), Naive Bayes, and K Nearest Neighbour (KNN) machine learning models were used to classify gearbox faults. Among these models, Naive Bayes achieved an accuracy rate of 95.7%, which exceeded the established benchmarks. The probabilistic approach was able to successfully associate symptom characteristics with fault patterns. Intelligent monitoring systems could improve maintenance efficiency. This data-driven approach highlights the potential of machine learning in supporting wind power development by eliminating gearbox inefficiencies and improving turbine reliability, and further research is being conducted to ensure that this approach works in concert with diversity and in the real world. This shows how machine learning is contributing to advances in renewable energy by helping to analyze predictive problems and prevent costly gearbox failures.

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

Inactivation of dried cells and biofilms of Listeria monocytogenes by exposure to blue light at different wavelengths and the influence of surface materials.

Antimicrobial blue light (aBL) in the 400-470 nm wavelength range has been reported to kill multiple bacteria. This study assessed its potential for mitigating an important foodborne pathogen, Listeria monocytogenes (Lm), focusing on surface decontamination. Three wavelengths were tested, with gallic acid as a photosensitizing agent (Ps), against dried cells obtained from bacterial suspensions, and biofilms on stainless-steel (SS) coupons. Following aBL exposure, standard microbiological analysis of inoculated coupons was conducted to measure viability. Statistical analysis of variance was performed. Confocal laser scanning microscopy was used to observe the biofilm structures. Within 16 h of exposure at 405 nm, viable Lm dried cells and biofilms were reduced by approx. 3 log CFU/cm2 with doses of 2,672 J/cm2. Application of Ps resulted in an additional 1 log CFU/cm2 at 668 J/cm2, but its effect was not consistent. The highest dose (960 J/cm2) at 420 nm reduced viable counts on the biofilms by 1.9 log CFU/cm2. At 460 nm, after 800 J/cm2, biofilm counts were reduced by 1.6 log CFU/cm2. The effect of material composition on Lm viability was also investigated. Irradiation at 405 nm (668 J/cm2) of cells dried on polystyrene resulted in one of the largest viability reductions (4.0 log CFU/cm2), followed by high-density polyethylene (3.5 log CFU/cm2). Increasing the dose to 4,008 J/cm2 from 405 nm (24 h), improved its efficacy only on SS and polyvinyl chloride. Biofilm micrographs displayed a decrease in biofilm biomass due to the removal of biofilm portions from the surface and a shift from live to dead cells suggesting damage to biofilm cell membranes. These results suggest that aBL is a potential intervention to treat Lm contamination on typical material surfaces used in food production. IMPORTANCE Current cleaning and sanitation programs are often not capable of controlling pathogen biofilms on equipment surfaces, which transmit the bacteria to ready-to-eat foods. The presence of native plant microbiota and organic matter can protect pathogenic bacteria by reducing the efficacy of sanitizers as well as promoting biofilm formation. Post-operation washing and sanitizing of produce contact surfaces might not be adequate in eliminating the presence of pathogens and commensal bacteria. The use of a dynamic and harmless light technology during downtime and close of operation could serve as a useful tool in preventing biofilm formation and persistence. Antimicrobial blue light (aBL) technology has been explored for hospital disinfection with very promising results, but its application to control foodborne pathogens remains relatively limited. The use of aBL could be a complementary strategy to inactivate surfaces in restaurant or supermarket deli settings.

Forecast of Operational Downtime of the Generating Units for Sediment Cleaning in the Water Intakes: A Case of the Jirau Hydropower Plant

Hydropower plants (HPP) in the Amazon basin suffer from issues caused by trees and sediments carried by the river. The Jirau HPP, located in the occidental Amazon basin, is directly affected by high sediment transportation. These materials accumulate in the water intakes and obstruct the trash racks installed in the intake system to prevent the entry of materials. As a result, head losses negatively impact the efficiency of the generating units and the power production capacity. The HPP operation team must monitor these losses and take action timely to clear the intakes. One of the possible actions is to stop the GU to let the sediment settle down. Therefore, intelligent methods are required to predict the downtime for sediment settling and restoring operational functionality. Thus, this work proposes a technique that utilizes hidden Markov models and Bayesian networks to predict the fifty Jirau generation units’ downtime, thereby reducing their inactive time and providing methodologies for establishing operating rules. The model is based on accurate operational data extracted from the hydropower plant, which ensures greater fidelity to the daily operational reality of the plant. The results demonstrate the model’s effectiveness and indicate the extent of the impact on downtime under varying sediment levels and when neighboring units are generating or inactive.

Intelligent Risk-Based Cybersecurity Protection for Industrial Systems Control—A Feasibility Study

Summary Intelligent automated industrial process control, enabled by IR 4.0 technology, requires increased system integration and connectedness. With such development comes a heightened risk of cyberattack for operational technology (OT) systems, including industrial control systems (ICS) and industrial automation and control systems (IACS), historically shielded from cyberspace. Cyberattacks on OT systems can also impact the physical world. The recent attack on the US Colonial Pipeline interrupted delivery of oil along the US West coast for several days, resulting in a declaration of a state of emergency, while the Ukraine power grid attacks interrupted electrical power supply in Kyiv, impacting thousands of consumers. In 2021, an Equinor-operated platform on the Norwegian Continental shelf was hit by malware. The incident had the potential to affect the drilling control system (DCS). For ICS, the concern is ensuring a safe process, as opposed to the more traditional concern with data protection for information technology (IT) system management. To ensure safe implementation of advanced industrial systems control, while avoiding unnecessary operational downtime, there is a need for cybersecurity solutions that account for risk for the whole cyberphysical system (CPS), including the ICS, the work environment, the product, and the physical surroundings. Diagnostic models and methods must therefore be developed that cover the functionality of the whole CPS. Further, to account for process dynamics and evolving cyberattack threats, adaptability is required with respect to both process state and new types of attacks. For this purpose, knowledge-based awareness monitoring combined with existing cyberattack detection tools for ICS, known as ICS intrusion detection systems (IDS), is proposed, providing an element of artificial intelligence. Results from studies in a DCS environment indicate that existing monitoring applications can be used to detect and discern between different types of cyberattacks on CPSs. This indicates feasibility with respect to monitoring the control and IT components of the ICS system for building risk-based cybersecurity decision support solutions. The key challenge and novelty of the proposed approach is to extend the capability of cybermonitoring in systems control to automated evaluation of process risk, including risk to the physical environment. Such capability will enable appropriate decision support with respect to both process risk and operational downtime.

Raman Spectroscopic Analysis of the Reaction between Al-Si Coatings and Steel.

Hot-stamped ultrahigh strength steel components are pivotal to automotive light-weighting. Steel blanks, often coated with an aluminum-silicon (Al-Si) layer to protect them from oxidation and decarburization, are austenitized within a furnace and then simultaneously quenched and formed into shape. The Al-Si coating melts within the furnace and reacts with iron from the steel to yield an intermetallic phase that provides some long-term corrosion protection. During the intermediate liquid phase, some of the coating may transfer to the furnace components, leading to maintenance costs and operational downtime. A detailed understanding of the coating transformation mechanism is needed to avoid such production issues while ensuring that final intermetallic coatings conform to specifications. We introduce cross-sectional Raman microscopic mapping as a method to rapidly elucidate the coating transformation mechanism. Raman spectroscopic fingerprints for relevant intermetallic compounds were determined using synthesized Al-Fe-Si ternary and Al-Fe binary compounds. These fingerprints were used to map the spatial distribution of intermetallic compounds through cross sections of Al-Si-coated 22MnB5 specimens that were heated at temperatures between 570 and 900 °C. These chemical maps show that the intermetallic fraction of the coating does not grow significantly until formation of η (Al5Fe2) at the steel interface, suggesting that η facilitates extraction of iron from the steel and subsequent diffusion through the coating. Under the heating conditions used here, a series of reactions ultimately lead to a silicon-rich τ2 (Al3FeSi) phase on top of the binary η phase. The technique presented here simplifies structural analysis of intermetallic compounds, which will facilitate prototyping of strategies to optimize hot stamping.

Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator

The development of the fast protection system (FPS) was driven by the critical need to safeguard internal components of the accelerator from beam damage and minimize operational downtime. During accelerator operation, various faults can occur, posing a significant risk. The FPS acts as a rapid response system, initiating a shutdown signal to a reliable chopper system to prevent beam damage and ensure the operational availability of the accelerator. To meet the stringent shut off time requirements specific to critical faults, the FPS was designed to respond within 50 µs, while the total FPS time, including acquisition, redundancy, and processing, needed to be less than 20 µs. In order to achieve these goals, a customized FPS was developed for the RAON heavy-ion accelerator, utilizing the Xilinx ZYNQ system-on-chip (SoC). The FPS system comprised seven acquisition modules, one mitigation module with an embedded SoC, and employed optical fiber connections for efficient data transmission. This article provides a comprehensive account of the design, development, and testing of the FPS system. Experimental tests were conducted to validate its performance. These tests included verifying the accuracy of cyclic redundancy checks, acquiring interlock signals in short pulses, and measuring the delay time during abnormal signal occurrences. Of particular significance is the measurement of the total signal processing time for a 1 km optical cable in the RAON system, which was determined to be 9.8 µs. This result successfully met the stringent requirement of 20 µs for the FPS time. The ability of the FPS to operate within the desired time frame demonstrates its effectiveness in protecting the accelerator’s components from beam damage and minimizing downtime. Consequently, the FPS ensures the operational availability of the accelerator while maintaining the safety and integrity of its internal systems. By providing a detailed account of the FPS’s design, development, and testing, this article contributes valuable insights into the capabilities of the FPS in real-world accelerator scenarios. The successful implementation of the RAON-optimized FPS with the Xilinx ZYNQ SoC reaffirms its effectiveness as a fast and reliable protection system, thus enhancing the overall operational performance of the accelerator.

Lines of Efforts in Order to Improve the Military Supply Chains Performance in Military Operations

Abstract Supply chain resilience plays a critical role in maintaining operational readiness and effectiveness in military operations. However, the unique challenges faced by military supply chains necessitate tailored approaches to enhance resilience. This paper presents a comprehensive framework for optimizing supply chain resilience in military operations. The proposed framework incorporates key elements such as risk assessment, contingency planning, information sharing, and resource allocation. It leverages advanced analytics techniques, including simulation modeling, optimization algorithms, and real-time data integration, to support decision-making and improve resilience. Results demonstrate that the optimized supply chain resilience strategy significantly enhances the military's ability to respond to disruptions, reduce recovery time, and minimize operational downtime. Moreover, the framework identifies critical vulnerabilities and recommends proactive measures to enhance system robustness. By adopting this framework, military organizations can better prepare for and respond to a wide range of supply chain disruptions, ultimately improving operational efficiency and mission success. The findings of this study contribute to the emerging field of military logistics by providing a systematic approach to optimize supply chain resilience and offering valuable insights for military decision-makers.

Paradigm shift to resilience based design for improved seismic behaviour – State-of-the-Art

Seismic design codes have evolved over time, with a significant shift from the existing force-based design (FBD) to the performance-based design (PBD) approach. This shift was predicated on the improved understanding from recent research and observation of building damage after earthquakes. Currently, code requirements and design recommendations are set considering life safety criteria. Consequently, they do not limit damage to the structure and its components (structural/non-structural parts). This may lead to higher repair costs in the event of strong shaking. Experiences from past earthquakes demonstrate that buildings built using the existing FBD sustain severe damage, although most buildings did not collapse, thereby ensuring life safety. The substantial economic loss following earthquakes led to the development of a new design philosophy based on target performances called PBD. Like the FBD, the PBD ensures life safety (based on strength and ductility). In addition, the PBD can address the need for immediate damage assessment and limiting economic losses. However, PBD may not prevent damage to primary structural/non-structural parts (serviceability). This, in turn, leads to higher repair costs and operational downtime and may lead to abandoning the building. Several attempts were made to minimize the functionality loss of structural and non-structural elements following earthquakes. These efforts culminated in the emergence of a resilience-based design (RBD), which aims to mitigate the economic damages due to (a) severe building damage, (b) functionality loss, and (c) business downtime. RBD is a holistic design approach that defines building’s performance points considering immediate occupancy criteria and building functionality. Moreover, the RBD considers the total cost of the building as one of the design parameters. The RBD is expected to surpass code-intended performance targets and typical performance-based design practices in the near future. Given the importance of RBD, the paper explores the following: (a) implications of damage and the importance of the built environment, (b) evolution of seismic design of buildings, (c) the need to shift from PBD to RBD, (d) quantification of resilience, and (d) the necessity for resilient systems based on the real-time scenario.