Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to trigger maintenance before a possible breakdown; however, much less focus has been devoted to the use of such PM predictions as feedback in automated process control mechanisms. They usually integrate preventive solutions to protect the machines, usually causing downtimes. The premise of this study is to develop a holistic adaptive process scheduling mechanism that incorporates PM analysis as a safety component to optimize the operation mode of an industrial process toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. As PM is largely a data-driven approach; hence, relies on the setup, we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time adaptive process scheduling mechanism. It schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, according to the abnormal readings. To demonstrate the pipeline on action, we implement our approach on a small-scale conveyor belt system utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control provides an efficient process with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

Predictive maintenance (PdM) represents a transformative approach to ensuring the security and reliability of critical infrastructure systems. This proactive maintenance strategy leverages advanced data analytics, machine learning, and sensor technology to anticipate and address potential failures before they occur. By continuously monitoring the health of infrastructure components and analyzing performance data, predictive maintenance enables timely interventions, minimizes unplanned downtimes, and enhances overall system resilience. This paper explores the application of predictive maintenance in safeguarding critical infrastructure, focusing on its impact on security, operational efficiency, and cost-effectiveness. Case studies demonstrate how PdM has been successfully implemented across various sectors, including transportation, energy, and utilities, highlighting its role in mitigating risks and extending the lifespan of essential assets. The findings underscore the importance of integrating predictive maintenance into security strategies to protect vital infrastructure from emerging threats and ensure uninterrupted service delivery.

Using data-driven models to solve predictive maintenance problems has been prevalent for original equipment manufacturers (OEMs). However, such models fail to solve two tasks that OEMs are interested in: (1) Making the well-built failure prediction models working on existing scenarios (vehicles, working conditions) adaptive to target scenarios. (2) Finding out the failure causes, furthermore, determining whether a model generates failure predictions based on reasonable causes. This paper investigates a comprehensive architecture towards making the predictive maintenance system adaptive and interpretable by proposing (1) an ensemble model dealing with time-series data consisting of a long short-term memory (LSTM) neural network and Gaussian threshold to achieve failure prediction one week in advance and (2) an online transfer learning algorithm and a meta learning algorithm, which render existing models adaptive to new vehicles with limited data volumes. (3) Furthermore, the Local Interpretable Model-agnostic Explanations (LIME) interpretation tool and super-feature methods are applied to interpret individual and general failure causes. Vehicle data from Isuzu Motors, Ltd., are adopted to validate our method, which include time-series data and histogram data. The proposed ensemble model yields predictions with 100% accuracy for our test data on engine stalling problem and is more rapidly adaptive to new vehicles with smaller error following application of either online transfer learning or the meta learning method. The interpretation methods help elucidate the global and individual failure causes, confirming the model credibility.

In the manufacturing industry, orders are typically scheduled and delivered through batches, and the probability of machine failure under high-load operation is high. On this basis, we focus on a single machine batch scheduling problem with a maintenance interval (SMBSP-MI). The studied problem is expressed by three-field representation as 1|B,MI|\sum{F_j+\mu}m, and the optimization objective is to minimize total flow time and delivery costs. Firstly, 1|B,MI|\sum{F_j+\mu}m is proved to be NP-hard by Turing reduction. Secondly, shortest processing time (SPT) order is shown the optimal scheduling of SMBSP-MI, and a dynamic programming algorithm based on SPT (DPA-SPT) with the time complexity of O(n^3T_1) is proposed. A small-scale example is designed to verify the feasibility of DPA-SPT. Finally, DPA-SPT is approximated to a fully-polynomial dynamic programming approximation algorithm based on SPT (FDPAA-SPT) by intervals partitioning technique. The proposed FDPAA-SPT runs in O(\frac{n^5}{\varepsilon^2})\ time with the approximation (1+\varepsilon).

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Refractory materials are used worldwide in process equipment. However, gaseous and liquid process products penetrate the surface layer, and deep into the volume of refractories destructing rather expensive constructions complicated to repair. To address this challenge, there is a need to develop protective coatings for refractory that can limit the penetration of working media and extend their operational lifespan. In this work, the application of gas-dynamic cold spraying (CGDS) to produce a coating on the refractory using fine titanium dioxide (TiO2) particles is explored. These particles are accelerated within a nitrogen flow, passing through a Laval nozzle, and then sprayed onto a fireclay surface. The mechanisms of particle deposition and layer formation on porous surfaces through experiments and numerical simulations were investigated. The geometry of a typical refractory pore was determined, which was then incorporated into Computational Fluid Dynamics (CFD) simulations to model the cold spraying process of porous substrates. As a result, the influence of the particle size on its velocity and angle of penetration into pores was established. Experimental findings demonstrate the effective closure of pores and the formation of a particle layer on the refractory surface. Furthermore, the nanoindentation tests for the refractory samples showcase capabilities for checking coating thickness for porous materials.

Artificial Intelligence (AI) models are expected to have a great impact in the manufacturing industry, optimizing time and resource cost by enabling applications such as predictive maintenance (PM) of production machines. A necessary condition for this is the availability of high quality data collected as close as possible to the process in question. With the advent of 5G equipped multi sensor platforms (MSPs), high sampling rate data can be collected and transmitted for processing in real time. This poses a data security challenge, since this data may give valuable insight into confidential business information of companies. Federated learning (FL) enables the training of AI models with data from multiple sources without it leaving the shop floor, by utilizing distributed computing resources available on premise. This paper introduces an architecture of FL based on data collected from 5G MSPs for enabling PM in industrial environments and discusses its potential benefits and challenges.

Predictive maintenance technologies have emerged as a transformative force in retail supply chain management, offering the potential to significantly enhance operational efficiency, reduce costs, and improve customer satisfaction. This study explores the implementation and impact of predictive maintenance within the retail sector, focusing on its benefits, challenges, technological tools, and future directions. The findings indicate that predictive maintenance enables a proactive approach to equipment management, leading to substantial reductions in downtime and maintenance costs, extended equipment lifespan, and enhanced sustainability through reduced energy consumption and waste. However, the adoption of these technologies is not without challenges. High initial costs, the need for specialized skills in data analytics and IoT, issues with data quality and reliability, organizational resistance, and cybersecurity concerns present significant barriers to successful implementation. Technological tools such as IoT sensors, data analytics platforms, machine learning algorithms, and digital twins are crucial for the effective functioning of predictive maintenance systems, providing real-time data monitoring and analysis that improve decision-making and maintenance planning. The study also highlights promising future trends, including advancements in AI, machine learning, IoT, and blockchain technologies, which are expected to further enhance predictive maintenance capabilities. To fully realize the potential of predictive maintenance, companies must address the identified challenges, invest in workforce development, and implement robust data security measures. Future research should focus on exploring these advancements, understanding the broader environmental impacts, and conducting cross-industry analyses to identify best practices. Overall, while predictive maintenance offers substantial benefits, its successful integration requires a nuanced approach that balances technological innovation with strategic and operational considerations.

One way to reduce the negative impact of human activity on the natural environment is to use natural, easily available and relatively cheap to produce compounds in industrial processes. One such compound is naturally occurring calcium carbonate (CaCO3). This compound has adsorption properties so that it can be an alternative to commonly used adsorbents. The aim of this work is to determine the possibility of using CaCO3 to remove pharmaceutical substances such as sulfadiazine and tetracycline from water. The CaCO3 used in this work was synthesised using our own method, which allows the production of CaCO3 particles with nanometric size. In the conducted research, calcium carbonate was used in the form of a suspension in purified solutions and as an inorganic filling of the developed membranes. The mass of pharmaceutical substances removed from their aqueous solutions was determined in the tests carried out. Based on the results obtained, it can be concluded that CaCO3 has the ability to adsorb both tetracycline and sulfadiazine. In suspension tests, the mass of the removed substances per unit mass of adsorbent was 1.52 mg/g and 6.85 mg/g, respectively. In turn, in the case of the process using the developed membranes, the mass of the removed substances per unit mass of adsorbent was 232 mg/m2 and 524 mg/g, which is a several times greater increase in the mass of the removed substance compared to the process using an unmodified membrane.

The main goal was to investigate the ability of non-thermal plasma reactor with gliding arc discharge to remove the diesel exhaust particulates (DEPs). A conventional knifed-shaped LTP GA reactor was utilized. Three cases were studied: 140 L/min, 70 L/min and 14 L/min of air drawn through the reactor and diesel exhaust fumes were sampled continuously. They were assayed in terms of concentration and number particle size distribution. The higher the residence time’s, the higher the energy input that may be utilized for DEPs’ removal. Reactor’s performance definitely lowered the concentration of DEPs (250 - 580 nm) and altered their number size distribution. There was no effect on the number concentration, nor on the particle size distribution, of DEP particulates of 10 – 250 nm in size. The effectiveness of DEPs’ removal: Decreasing flow rate from 140 L/min to 70 L/min somehow altered the values. Achieving the airflow of 14 L/min led to a substantial improvement (even to a fourfold increase for 300 – 480 nm particles). Non-thermal plasma reactors with gliding arc discharge may be successfully adapted in the process of DEPs’ treatment. Their performance may be optimized by adjusting the airflow at the inlet of reactor to guarantee the longest aerosol residence times and the highest removal efficiency.

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in a dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature etc). Simulation results exhibit the benefits of proposed HEMS by showing the reduction of up to 47% in electricity cost and up to 48% in peak power consumption.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

Waste cooking oil (WCO) recycled asphalt is facing issues regarding insufficient thermal oxidation stability and aging resistance. In this research, glycerol esterification was adopted to pretreat WCO, and the consequences of this treatment on the aging resistance and thermal stability of WCO were analyzed. The impacts of varying levels of esterification of WCO on the high-temperature, low-temperature performances, fatigue properties, as well as aging resistance of recycled asphalt were investigated. Furthermore, the mechanisms of regeneration and anti-aging of deeply esterified WCO recycled asphalt were revealed by Fourier transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC) tests. The results indicated that variations in the physical properties of WCO during the aging process were reduced, and its aging resistance was improved following glycerol esterification therapy. The initial thermal decomposition temperature was increased by approximately 115°C, which resulted in the enhancement of thermal stability significantly. Recycled asphalt obtained from deeply esterified WCO exhibited superior high-temperature, low-temperature performances and fatigue properties. Moreover, the thermal oxidation stability and aging resistance of recycled asphalt with deep-esterified WCO could be promoted by reducing the oxidation and volatilization of light components during the aging process.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in a harsh environment with excess temperature and humidity, vibration, fatigue and load. A breakdown or malfunction in one of these machineries can significantly impact the vessel’s operation and safety and consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This research presents the economic and technical benefits of predictive maintenance over to traditional preventive maintenance, and repair by replacement approaches in the maritime domain. By leveraging modern technology and Artificial Intelligence, we can analyze real-time operating conditions of machinery, enabling early detection of potential damages and allowing for effective planning of future maintenance and repair activities. In this paper, we propose and develop a Convolutional Neural Network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.

Asymmetric lithium salts, such as Lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li+ conductivity and the capacity to generate stable solid electrolyte interphase (SEI) while maintaining compatibility with aluminum (Al0) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, the detailed SEI reactions of LiDFTFSI-based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of SEI. Our results show that asymmetric DFTFSI– weakens the interaction between carbonate solvents and Li+, and substantially alter the solvation structure, exhibiting a well-balanced coordination capacity to bis(trifluoromethanesulfonyl)imide (TFSI–). Nanoseconds hybrid molecular dynamics simulation further reveals that the preferential decomposition of LiDFTFSI produces sufficient LiF and Li2O to facilitate a robust SEI. Moreover, the abundant F– generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al3+ from the current collector to form AlF3, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collector.

In recent years, the AHRS has become an important research area. In order to improve the orientation measurement accuracy of AHRS, this paper first expounds the algorithm principle of the AHRS. Then, aiming at the problem of poor stability of the traditional gradient descent method under high dynamics or large magnetic field interference, a gain-scheduled anti-interference AHRS orientation algorithm is designed. By using the switching architecture designed with this algorithm for gain scheduling, it can ensure stable performance while being subjected to apparent acceleration and ferromagnetic interference. Finally, experimental verification is conducted, and the experimental results show that: in the presence of interference, the roll angle error is 0.0350°, the pitch angle error is 0.0277°, and the yaw angle error is 0.9611°, thereby verifying the strong anti-interference performance of the gain-scheduled AHRS orientation algorithm proposed in this paper.

This investigation explores the potential of enhancing aqueous zinc-ion batteries (AZIBs) through the introduction of a novel cathode material, NH4V4O10 (NVO), combined with reduced graphene oxide (rGO). Utilizing Density Functional Theory (DFT), it was hypothesized that the incorpora-tion of rGO would increase the interlayer spacing of NVO and diminish the charge transfer in-teractions, thus promoting enhanced diffusion of Zn2+ ions. These theoretical predictions were substantiated by experimental data acquired from hydrothermal synthesis, which indicated a marked increase in interlayer spacing. Significantly, the NVO-rGO composite exhibits remarkable cyclic durability, maintaining 95% of its initial specific capacity of 507 mAh g−1 after 600 cycles at a current density of 5 A g−1. The electrochemical performance of NVO-rGO not only surpasses that of pristine NVO but also outperforms the majority of existing vanadium oxide cathode materials reported in the literature. This study underscores the effective integration of theoretical insights and experimental validation, contributing to the advancement of high-performance energy storage technologies.

This study explores the enhancement of aqueous zinc-ion batteries (AZIBs) using ammonium-enhanced vanadium oxide cathodes. Density Functional Theory (DFT) calculations reveal that NH₄⁺ incorporation into V₆O₁₆ lattices significantly facilitates Zn²⁺ ion diffusion by reducing electrostatic interactions, acting as a structural lubricant. Subsequent experimental validation using (NH₄)₂V₆O₁₆ cathodes synthesized via a hydrothermal method corroborates the DFT findings, demonstrating remarkable electrochemical stability with a capacity retention of 90% after 2000 cycles at 5 A g⁻¹. These results underscore the potential of NH₄⁺ in improving the performance and longevity of AZIBs, providing a pathway for sustainable energy storage solutions.

This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also per-formed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. The challenge is to apply it to a surface with high temperatures, owing to the greater probe contact and the limitation of ultrasonic properties. This literature review provides a more precise direction for future research to evaluate PACM on piping that can be utilized in inspection during plant operation at elevated temperatures. Detecting and monitoring corrosion growth without shutdown.

The goal of this contribution is an application of the Linear General Model Predictive Control (LGMPC). In this paper, stability of the LGMPC is proven by means of a demonstration of a Theorem stating a sufficient and constructive condition. This condition can be applied for calculating the weight matrices of the cost function in the optimisation problem in LGMPC. Lower bounds conditions are found for one of these matrices and then a system with saturation is taken into consideration. The conditions could be interpreted and discussions through physical aspects. The obtained results were tested by means of computer simulations and an example with a recover water process is considered.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

The aim of this study was to investigate and effect of application of different conditions of freeze-drying on the kinetics of process and the sorption properties of dried apples. Slices of apple were frozen and subjected to freezing-drying process with different combination of a shelf temperature (-20, 10, 20, 30°C) and pressure (37, 63, 103, 165 Pa). During the freeze-drying, the temperature in the centre of material was recorded. The moisture content in dried material and changes of water content dried apples stored at humidity of 75.3% were obtained. The Midilli et al. model was used to describe drying kinetics of the freeze-drying with good fit. Drying time decreased from 660 (variant with a constant shelf temperature of 30°C, pressure 63 Pa) to 1305 minutes (variant with temperatures −20: 10: 20: 30°C, pressure 63 Pa). For this reason, the most favourable experimental parameters were: temperature of 30°C and pressure of 63 Pa. However, application of these parameters caused higher absorption of water vapour during storage. Therefore, the selection of freeze-drying parameters should also be related to the expected properties of the final product.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

Linear alkylbenzene sulfonate (LAS) anionic surfactants are widely used, present in wastewater, and can be discharged causing environmental damage. Without considering biodegradation, sorption and desorption reactions play an important role and depend on the characteristics of the aquifer medium such as the content of organic matter and clay. These processes are modelled with PHREEQC using laboratory-obtained data on columns with an input LAS pulse. LAS adsorption and desorption step experiments carried out in sand columns were also modelled with this software and distribution constants were similar to those obtained for pulse. The values of the starting distribution constant provided by batch tests with sand and agricultural soil mixtures establish the maximum value that must be obtained for column modelling. Distribution constants from column experiments obtained by Freundlich and linear isotherms provided very similar results, the latter with more consistent achievement. The modelling results showed longer elution times in the case of the heavier homologues and with a higher percentage of agricultural soil, which would translate into the great persistence of these compounds in the environment. In the case of soil contamination with wastewater-containing surfactants, these results would allow the development of mitigation strategies for these environmental impacts.

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

The development of Artificial Intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are deeply changing both from a theoretical and a practical perspective, even in the field of ship propulsion systems. Namely, conventional preventive maintenance schedules audits and checks at fixed times by using statistics on component failures, but it can be improved by a predictive maintenance based on the real component health status, which is inspected by proper sensors. In this way, time and costs are saved. More in details, data-driven models, through Machine Learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements coming from the asset, hence for a diagnosis based on the difference between expectations and observations. Then, a ML-based fault detection starts with the choice of the ML algorithm. This process is often not the consequence of an in-depth deep analysis of the different algorithms available in the literature. For that reason, the authors propose a simple procedure to support a first implementation stage of the so-called Condition Based Maintenance (CBM): a quick benchmarking of the most suitable ML algorithms useful for fault detection. The algorithms are compared by taking into account not only the algorithm output error, shown by the Mean Squared Error between predictions and data, but also the response time, which is a crucial variable for maintenance purpose.

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates conflict-free, efficient schedules using evolutionary algorithms and multi-objective optimization. Resource usage, scheduling prob-lems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

Background: This retrospective study evaluates the survival effects of metronomic maintenance therapy with fluoropyrimidine in patients with stage III colorectal cancer (CRC) according to epidermal growth factor receptor (EGFR) expression. Methods: We enrolled 197 patients with stage III CRC who had undergone radical resection and FOLFOX regimen adjuvant chemotherapy. The clinicopathological features and effects of metronomic maintenance therapy on survival according to treatment group and EGFR expression were analyzed. By conducting an in vitro cell line study and in vivo study through knockdout of EGFR gene, we analyzed the capacities of cell proliferation and migration. Results: Postoperative relapse and mortality were significantly more common in the FOLFOX group. Metronomic maintenance therapy was a significantly independent predictive factor of postoperative relapse and mortality, as well as a prognostic factor of disease-free survival and overall survival. The significant differences of survival between the two groups were only observed in patients with positive EGFR expression. Conclusions: The present study suggested EGFR expression as the prognostic factor in patients with stage III CRC receiving metronomic maintenance therapy. By analyzing EGFR expression, we can identify the potential candidates with optimal survival benefit from metronomic maintenance therapy in patients with stage III CRC.

Process parameters influencing mechanical properties have always been a critical area of research for quite some time. We investigated the effect of laser power and laser scan velocity on the impact strength and maximum stress of 3D-printed metallic specimens of steel 1.2709 and 1.4542. Laser power significantly affected the impact strength and maximum stress of both the steels. With the increase in laser power, the impact strength increased for 1.2709 steel. However, there was a little increase in the impact strength with increasing laser power in the case of 1.4542 steel. Laser power had a decreasing impact on the maximum stress for 1.2709 steel. For 1.4542, maximum stress increased with increasing laser power, but then gradually declined. We observed that using laser power beyond 350W led to printing defect in the specimens, as maximum stress for some specimens reached close to zero in the case of 1.4542 steel. Laser scan velocity had a strong declining effect on the impact strength of 1.2709 steel.

High-density olive groves, despite their interesting production potential, have several limitations, also due to the high fruit load and the irrigation requirements.The study aimed to evaluate the effects of fruit load and deficit irrigation on oil yield and fruit quality, and on olive chemical composition in a high-density olive grove (cv Sikitita). Our main hypothesis was that the primary metabolism, as influenced by crop load and stress, could modify the accumulation of different phenolic classes.Different fruit loads were generated through flower thinning (66%, 50%, 33%, 0%) and two deficit irrigation treatments (-60%, -75%) were compared to the well-watered control (920 m3/ha).Thinning treatments had a limited effect on oil yield, on the other hand, deficit irrigation caused considerably less oil accumulation in the fruit on all sampling dates.Thinning 66% and deficit irrigation 75% were considered with the control for untargeted metabolomic analysis, considering three sampling dates. 233 different phenolic compounds were annotated. Multivariate HCA results indicated the impact of harvest time on the phenolic profile of olive fruits, yielding two separate clusters grouping t1 and t2 together and apart from t3. Regarding agronomic techniques, they played a differential role in the phenolic profile (supervised OPLS – DA). Fruit load mostly affected flavonoid glycosides. In contrast, the phenolic response to deficit irrigation is more heterogeneous, with phenolic acids (35%), flavonoids (25%), LMW, and other phenols (25%).

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

As a member of smart materials, electrorheological elastomers (EREs) formed by pre-treating active electrorheological particles are attracting more and more attention. In this work, four Mg-doped strontium titanate particles with spherical, dendritic, flake-like and pinecone-like morphorlogies were obtained by hydrothermal and low-temperature co-precipitation. XRD, SEM, Raman and FT-IR were used to characterize the obtained products. The results showed that the sizes of Mg-doped strontium titanates are about 1.5‒2.0 μm, and the phase structure are dominated by cubic crystal. These Mg-doped strontium titanates were dispersed in a hydrogel composite elastic medium. Then, Mg-doped strontium titanate/glycerol/gelatin electrorheological composite hydrophilic elastomers were obtained with or without electric field. The electric field response properties of Mg-doped strontium titanate composite elastomers were investigated. It concluded that dendritic Mg-doped strontium titanate composite elastomers is high-performance EREs. And the maximum value of energy storage modulus of Mg-doped strontium titanate composite elastomers has achieved 8.70 MPa. This high electrorheological performance of the products is helpful for their applications in vibration control, force transducers, smart structures, dampers, and other fields.

This study investigates the impact of thin flame combustor design on hydrogen flame characteristics and combustion performance through numerical simulations. Variations in flame shape and combustibility between 100% methane and 100% hydrogen combustion are analyzed. Three combustor header shapes (flat, concave, and convex) are modeled to assess their influence on flame behavior. Results show distinct flow patterns, with concave headers promoting strong central flows and convex headers dispersing the flow outward. Temperature field analysis indicates that hydrogen flames have higher temperatures and shorter quenching distances compared to methane flames. A comparative analysis of combustion products is conducted to evaluate combustion performance and NOx emissions. Consequently, the concave header has high combustibility and increases temperatures and NOx fraction in hydrogen combustion.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. In the next step, container workloads based on Docker and Podman as underlying container technologies were orchestrated/automated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in that process, as there’s still much work to figure out how to apply containerization platforms for HPC in real-life scenarios. Kubernetes and OpenShift have many advantages – generally speaking, container technologies use quite a bit less overhead from the computing perspective while providing many benefits in flexibility, modularity, and maintenance. Therefore, they are ideal for tasks requiring a lot of computing power. There are also some tradeoffs regarding the complexity of these two platforms - they’re just not that user-friendly when used by people without years of experience managing them. In this paper, we propose a different architecture based on seamless hardware integration and user-friendly, dynamic workload placement based on real-time performance analysis and prediction coupled with Machine Learning-based scheduling.

The numerical algorithm used for the original multi-dimensional non-linear optimization analysis of the marine fixed pitch (screw) propulsion presented herewith, after its invention and publication has been widely used for the analysis and best fit (most probable / less uncertain) solution of over-determined non-linear mathematical problems, where the points of observations (measured data) are more than the unknown parameters in the mathematical formulation expressing the significant aspects of the observed phenomena. Any “local” attainable solution of this formidable and ingenious algorithm, may rely to the set of initial values of the unknown parameters, while not all possible solutions are expected to be physically significant for a specific problem, whereas judgment, bounds and testing are to be practiced for making sure that the attained solution is the best fit (most probable / less uncertain) one, and not necessarily the “global minimum” one. In the present paper the above algorithm is applied for the original analysis of standard ships propulsion, and particularly for the original combined application of thermo-fluid and frictional models of main engines, as well as of fixed pitch propulsion hydrodynamic models, necessary for resolving in an integrated manner the combined marine propulsion problem for standard ships under seagoing conditions.

It is an utmost importance to characterize drug candidates as early in the development pipeline as possible. Appropriate application of the emerging high throughput analytical techniques unlocks the opportunities to obtain experimental data for in vitro absorption, distribution, metabolism and elimination (ADME) profiling of large compound libraries which is crucial for selection of lead compounds. In an usual scenario, only one drug candidate will be selected from thousands of early compounds based on several stability and screening results. A high to medium throughput screening approaches could significantly accelerates the subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high throughput LC-MS/MS techniques to meet with the lead profiling demands. In this study presented an innovative experimental approach that can offer a refreshing alternative. Laser Assisted Rapid Evaporative Ionization Mass Spectrometry (LA-REIMS) is a quick and efficient method for characterizing complex biological samples without laborious sample preparation and chemical separation. Therefore, using an automated LA-REIMS well plate reader as fast as 8 seconds per sample, we assessed the oxidative metabolic stability of active agents using biomimetic metalloporphyrin-based oxidative model reactions at various pH for commercially available drug substances and compared stability measurements with traditional HPLC-DAD-MS results.

Abstract: As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

In this paper, carbon nanotubes modified screen-printed electrode (CNTs/SPE) was prepared and the CNTs/SPE was employed for the electrochemical determination of antioxidant substance Chlorogenic acids (CGAs). A pair of well-defined redox peak of CGA was observed at the CNTs/SPE in 0.10 mol∙L^-1 acetic acid-sodium acetate buffer (pH 6.2) and electrode process is adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) method for the determination of CGA were proposed based on the CNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 4.73×10^-7 to 4.45×10^-5 mol∙L^-1, the linear regression equation was Ipa(µA) = 4.1993 C (mol∙L^-1)+1.1039 (r = 0.9976) and the detection limit for CGA could reach 3.25×10^-6 mol∙L^-1. The recovery of matrine was 94.74~106.65% (RSD = 2.92%) in coffe beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA.

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using Land use transfer flow, and Land use transfer matrix. At the same time, based on the remote sensing Ecological index (RSEI) model, the ecological environment quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of ecological environment quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on Spatial autocorrelation and Geographic detector. The results show that from 2013 to 2023, the decrease of forest land (16.23km²) and the increase of construction land (13.25km²) were the main land use transfer in Longnan City. The ecological environment index of Longnan City in 2013, 2018 and 2023 was 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environment quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environment quality, while traffic land, construction land and other land have a negative impact on ecological environment quality. The response of ecological environment quality to different land use transfer modes is related to the change of the overall ecological environment quality. The interaction between LUCC and other factors had a great impact on the evolution of ecological environment quality in Longnan City.

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

Immortal-time bias (ITB) is known to be common in cohort studies and distorts the association estimates between treated and untreated groups. We used data from the last of two large studies in an Italian province on COVID-19 vaccines safety and effectiveness incurred this bias, and aligned the entire population on a single index date, to correct the ITB. We considered the "all-cause deaths" outcome to compare the survival curves between the unvaccinated group and the various vaccination statuses. The all-cause deaths Hazard Ratios in univariate analysis for unvaccinated (reference) versus vaccinated with 1, 2, 3/4 doses were 0.88 (CI95: 0.78 –1.00; p-value 0.044), 1.23 (1.16–1.32; p-value ≤0.001) and 1.21 (1.14–1.29; p-value ≤0.001), respectively. The multivariate values were 2.40 (2.00–2.88; p-value <0.0001), 1.98 (1.75–2.24; p-value <0.0001), 0.99 (0.90–1.09; ns). The possible explanations of the trend of the Hazard Ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting windows bias; a healthy-vaccinee bias; or some their combination. With two and even with 3/4 doses the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: small duct type and large duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes–inflammation and proliferation–have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

. Manufacturers are increasingly adopting mixed-flow manufacturing models to meet the growing customized, diverse, and dynamic customer demand. In such an environment, material distribution scheduling optimization is vital for smooth operations and is integral to production management. However, manufacturers frequently encounter problems like disordered and delayed material distribution. Traditional scheduling methods suffer from problems like inadequate transparency, delayed decision directives, and suboptimal results, impacting performance. To this end, this study proposes a dynamic material distribution scheduling optimization model and strategy based on digital twin (DT) to address these problems. Firstly, we introduce workstation satisfaction and establish a material distribution path optimization model minimizing total distribution cost while maximizing workstation satisfaction. Subsequently, we present a cloud-edge computing-based decision framework and explain the DT-based material distribution system's components and operation. Furthermore, a dynamic material distribution scheduling optimization mechanism based on DT is designed. By incorporating a savings method and incentive, penalty strategies, improvements are made to the path node selection probabilities and the information pheromone update rules of the traditional ant colony optimization (ACO) algorithm. Finally, a numerical case study, using real data from collaborating enterprises, validates the proposed algorithm and strategy. This research offers valuable insights into logistics management and algorithm design in smart manufacturing environments.

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are taken using two approaches: i) an optimization and ii) a rule-based approach, followed by a comparative study. A MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions, using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the other hand, the complexity of the problem and the experience of production engineers and plant operators can significantly impact the quality of the obtained solutions for the rule-based approach.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

Nowadays, rotor-stator mixers are widely used in the chemical process industry to carry out the dispersion and de-agglomeration processes for manufacturing the homogenous electrode slurries. Development of the process simulation models for the turbulent mixing of these slurries helps to optimize the mixing parameters such as rotational speed and dispersion time. In this work, computational fluid dynamics (CFD) approach through ANSYS Fluent are explored to determine and analyze the flow patterns, velocity fields and viscosity distribution during the dispersion of anode slurries. It utilizes a non-homogeneous Eulerian multiphase approach and the typical k-ε model to create turbulence fields and compute the mixing rate. This work significantly improves our understanding of the relationship between rotor speed, dispersion time, viscosity distribution, and mixer geometry in rotor-stator mixers.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain the company's operational capacity at the required level. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology is a systematic literature review using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2024) and studies published in English. This resulted in the selection of 201 of the most relevant papers in the area of investigation. Finally, selected articles in this review were categorized into seven groups: a) air transportation, b) railway transportation, c) land transportation (road), d) in-house logistics, e) water and intermodal transportation, f) supply chain operation, and g) other applications. One of the main advantages of the study is that the results are obtained from different scientific sources/databases thanks to using the multi-search tool. Moreover, a bibliometric analysis of the selected papers was performed. The results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. In addition, based on the review results, a framework for DT in transportation system maintenance was developed. The study ends with conclusions and future research directions.

An algorithm is proposed for discriminating the fatigue state of air traffic controllers based on ap-plying multispeech feature fusion using an FSVM to voice data, and for extracting eye-fatigue-state discrimination features based on PERCLOS eye data. For the speech algorithm and an eye-fatigue index, a new controller fatigue-state evaluation index based on the entropy weight method is proposed based on decision-level fusion of fatigue discrimination results for speech and the eyes. Experimental results show that the fatigue-state recognition accuracy rate was 84.81% for the fatigue state evaluation index, which was 3.36% and 1.86% higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Further, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e. parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

As the demand for long-distance flights continues to increase, supersonic transport (SST) has once again become a hot research topic. However, due to its uniqueness, there was few corresponding evaluation methods to compare the supersonic civil aircraft with common subsonic aircraft. This paper proposed an evaluation method for the competitiveness of supersonic civil aircraft based on the Analytic Hierarchy Process (AHP). Key indicators were selected to form a competitiveness evaluation model from the perspective of four criterion factors of economy, comfort, adaptability, and environment. The evaluation method proposed in this paper could reflect characteristics of supersonic civil aircraft and other subsonic aircraft, and effectively compare their competitiveness. This work can be used to evaluate the social acceptance of supersonic civil aircraft and provide reliable decision-making support for airlines' design work.

The multi-stage, multi-level assembly job shop scheduling problem (MsMlAJSP) is commonly encountered in the manufacturing of complex customized products. Ensuring production efficiency while effectively improving energy utilization is a key focus in the industry. For the energy-efficient MsMlAJSP (EEMsMlAJSP), an improved imperialist competitive algorithm based on Q-learning (IICA-QL) is proposed to minimize the maximum completion time and total energy consumption. In IICA-QL, a decoding strategy with energy-efficient triggers based on problem characteristics is designed to ensure solution quality while effectively enhancing search efficiency. Additionally, an assimilation operation with operator parameter self-adaptation based on Q-learning is devised to overcome the challenge of balancing exploration and exploitation with fixed parameters, thus the convergence and diversity of the algorithmic search is enhanced. Finally, the effectiveness of the energy-efficient strategy decoding trigger mechanism and the operator parameter self-adaptation operation based on Q-learning is demonstrated through experimental results, and the effectiveness of IICA-QL for solving EEMsMlAJSP is verified by comparing with other algorithms.

Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.

This study investigates the use of geopolymer technology as an alternative for the management of mine tailings, which is a serious environmental problem in mining environments including that of Arequipa region of Peru. In this study, the mixture of stabilized mine tailings with different percentages of binders (metakaolin and pumice) and their impact on the mechanical, microstructural, and toxicological properties of the synthesized geopolymers were analyzed. The ratios of mine tailings to binder material varied between 100/0 and 0/100. The activation was carried out with an alkaline solution of sodium hydroxide (10M) and sodium silicate (modulus 2.5). 50 mm cubic specimens were fabricated, and 7 mixtures with 3 replicates each were evaluated. The evaluations included compressive strength at 7, 14, 28, and 56 days of curing, chemical analysis by X-Ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR), microstructural characterization by Scanning Electron Microscopy (SEM/EDS), thermal behavior by thermogravimetry and differential thermal analysis (TGA/DTA) between 40°C and 1000°C, and toxicological tests by the Toxicity Characteristic Leaching Procedure (TCLP, EPA 1311) to determine the efficiency of immobilization of toxic metals. The results showed good mechanical strength, reaching 25 MPa at 7 days of curing, high microstructural stability with an average pore size of 7.21 μm, and good efficiency in the immobilization of heavy metals in geopolymers composed of 60% - 40% and 70% - 30% of the mine tailings - binders ratio, as the concentrations of As, Cd, Pb, Hg were below the established thresholds, acquiring the classification of non-hazardous materials. Geopolymers with 30 to 50% binder showed good mechanical behavior, microstructural stability, and high efficiency in the immobilization of heavy metals, complying with current regulations. Therefore, these geopolymers are suitable for various applications and in different environmental conditions.

Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

Electrochemical methods are used for the treatment of both municipal and industrial wastewater, either independently or in conjunction with biological methods. This conjunction is used for pretreatment or posttreatment of biologically treated wastewater. In our work, we focused on the combination of these processes, where pre-electrolysis was used to produce dissolved iron before the activation process. Electrolysis was also directly introduced into the activation using either iron or carbon electrodes. The surface of one iron electrode was 32.2 cm2, the applied voltage was 21 V, and the supplied current was 270 mA. The surface of one carbon electrode was 7.54 cm2, the supplied current was 82.5 mA, and the applied voltage was 21 V. From laboratory research on the treatment of synthetic municipal wastewater using a combination of electrolysis and activation processes, it resulted that the use of iron electrodes increased the efficiency of phosphorus removal compared to its precipitation with iron salts. The electrolysis had a positive effect on the sedimentation properties of sludge and the destruction of filamentous microorganisms. Electrolysis negatively affected the respiration rates of activated sludge and the efficiency of denitrification. However, it did not have a negative impact on the nitrification activity of the sludge.

Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) and decision tree (DT, RPART; R2 accuracy CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.

In this paper, inspired by Jleli and Samet [journal of inequalities and applications 38 (2014) 2 1–8] we introduce two new classes of auxiliary functions and utilize the same to define (q, y)R-weak 3 contractions. Utilizing (q, y)R-weak contractions, we prove some fixed point theorems in the setting 4 of relational metric spaces. We employ some examples to substantiate the utility of our newly proved 5 results. Finally, we apply one of our newly proved results to ensure the existence and uniqueness of 6 solution of a Volterra-type integral equation.

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with the rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive-repetitive maintenance, within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

The significance of virtual reality (AR/VR/MR) technology stands out because it can be used in various construction fields such as urban design, construction review, maintenance and education, etc. As to prove this, conglomerates such as Facebook, Google, Sony, Microsoft, and Samsung are increasing their investments to preoccupy the virtual reality market and are competing to dominate the contents and platform market. Virtual reality technology has a concept that collectively refers to VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), but technically, the two technologies are strictly separated. While VR technology is an immersive virtual environment using computer graphic technology, AR technology is a more advanced technology that combines real data and VR data, and MR technology is a technology that combines both AR and VR. In Korea, Republic of, the use of BIM (Building Information Modeling), a 3D information model, becomes mandatory and based on this, the demand for a new market where VR/AR/MR technologies and advanced sensing equipment are combined increases. This study implemented a building maintenance platform using AR based on 5G, and developed matching technology between BIM of the building maintenance platform and AR equipment. Besides this study implemented the technology to enhance the matching rate of the matching technology and carried out the process to enhance the matching rate empirically through continuous tests. As a result, the initial target of 90% matching rate could be improved to 96%. Based on this, it is expected that effective EV (Optimum Life Cycle Cost) could be achieved at a lower cost compared to the previous one by using it for maintenance monitoring after construction and completion of the building, and can be used as an effective solution in the aspect of building maintenance as well.

The aim of this paper is to use deep learning tools to innovate pre-trained object detection models to improve the accuracy of non-destructive testing (NDT) of civil aviation maintenance. First, this thesis classifies object defects for NDT, such as cracks, undercut, etc. Nowadays, thesis surveys innovation deep-learning methods technology is used to improve the defect detection performance inferencing capability, increase the accuracy and efficiency of automatic identification which in enhanced the safety and reliability of aircraft fuselage in future, mark hidden cracks and solve the challenges that cannot be identified by manual inspection. Second, recent mainstream techniques the YOLOv4 neural network to the graphics card GPU core operator to speed up the recognition of defect images is being applied to the non-destructive inspection process of aircraft maintenance on A, C and D-Level, fully validating the deep learning model's powerful defect detection target capability. The attention-based YOLOv4 algorithm is improved by applying a one-stage attention mechanism to the YOLOv4, thereby improving the accuracy of the innovation model. Finally, thesis improved YOLOv4 based on an attention mechanism is proposed for object detection NDT via the deep learning method to effectively improve and shorten the inspection anomaly detection method for automatic detection sensor systems.

BACKGROUND AND AIMS: Cardiovascular disease and colorectal cancer (CRC) are significant health problems and share some risk factors. The aim of our study was to develop and validate a predictive score for advanced colorectal neoplasia (CRN) based on risk factors for cardiovascular disease and CRC. MATERIALS AND METHODS: A cross-sectional study comprising a derivation cohort and an external validation cohort of 1049 and 308 patients respectively. A prediction score for advanced CRN (CRNAS: Colorectal Neoplasia Advanced Score) was developed from a logistic regression model, comprising sex, age, first-degree family history for CRC, systolic and diastolic blood pressure, total cholesterol, HDL-cholesterol, body mass index, diabetes, smoking, and antihypertensive treatment. Other cardiovascular risk scores (Framingham-Wilson, REGICOR, SCORE and FRESCO) were also used to predict the risk of advanced CRN. The discriminatory capacity of each score was evaluated using the area under the curve (AUC). RESULTS: CRN were found in 379 subjects from the derivation cohort (36%) including 228 patients (22%) with an advanced CRN. Male sex, age, diabetes, and smoking were identified as independent risk factors for advanced CRN. The newly created score (CRNAS), showed an AUC of 0.68 (95% CI: 0.64-0.73) for advanced CRN, which was better than cardiovascular risk scores (p

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is con-structed based on state analysis, considering the operation status of the charging station and users' charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data is established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging station is conducted using the lack of power supply (ENC), evaluating the risk level of each station, and determining the operation and maintenance order. The optimal opera-tion and maintenance model for DC charging stations, aimed at achieving both economic and reliable goals, is constructed to determine the operation and maintenance schedule for each sta-tion. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall ben-efits of operation and maintenance while meeting the required standards.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2-2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

The cooling of server components has been continuously developed over the past few years in order to meet the increasing cooling requirements of server component manufacturers such as for the processor chips. The growth in performance and power density increases the cooling demand. These requirements directly influence the energy consumption and performance of the hardware. To gain a better understanding of the historical evolution and possible future technology development paths in the field of data center cooling, a patent analysis method was used. The focus is on previous and current development priorities and possible research gaps within the server and computer room levels. The European patent database was selected as data source. To determine relevant patents, the search terms for various cooling technologies were developed iteratively. After the patent extraction for the period 2000 - 2023 (earliest priority year), the search results were classified and analysed. Most of the patents deal with air or liquid cooling. In the area of liquid cooling, the focus was on direct liquid cooling, followed by total liquid cooling. Since 2015 a technological shift from air to liquid cooling can be identified on the level of patent activities. In this context, the patent groups with focus on “constructional details for different types of electric apparatus” and “electrical digital data processing” have the largest share of patents. In addition, the technology focus of the TOP 10 patent applicants was in the field of liquid cooling. In conclusion from the patent analysis, it can be derived that liquid cooling will continue to gain in importance in the future and could also be combined with other approaches in the form of hybrid cooling. However air cooling may still be relevant, even if the main cooling load is handled by liquid-based approaches. At the same time, the optimisation potential for air cooling seems to have been largely exploited in comparison to liquid cooling, as can be seen from the falling number of the patent applications.

Microgrid (MG) control is essential for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control a MG by finding global optima and handling complex, non-linear, and non-convex problems, but they can be computationally intensive, making real-time control challenging. Model Predictive Control (MPC)-based EMS predicts future behaviour over a defined horizon, ensuring optimal performance and constraint satisfaction but typically relies on linear models. This paper proposes a novel Genetic Predictive Control (GPC) method, combining GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GA with MPC significantly improves the handling of non-linearities and non-convexity in the system model, leading to more accurate and effective control. Comparative analysis shows that GPC significantly reduces excess power production—surplus power generated beyond load demand—improving resource allocation efficiency. For instance, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0W compared to 87.0W with GA. Additionally, GPC balances cost, emissions, and power efficiency. In the Crossover-Elitism scenario, GPC achieved a daily cost of $113.94 compared to GA's $127.80, and carbon emissions of 52.83 kg CO2e compared to GA's 69.71 kg CO2e, demonstrating GPC's cost efficiency and environmental benefits. While MPC optimizes a weighted sum of objectives, designing appropriate weights can be challenging and may result in non-convex problems. GA handles multiple, conflicting objectives through multi-objective optimization, providing Pareto-optimal solutions. GPC's dynamic adaptability maintains optimal performance by forecasting future load demands and adjusting control actions. GPC consistently achieves lower emissions than GA across various scenarios and, although it sometimes incurs higher costs, such as $113.94 compared to $131.90 in the Crossover–Random Selection scenario, its holistic approach balances these costs with other metrics, making it cost-effective in the long run. By reducing excess power production and emissions, GPC contributes to economic savings and promotes sustainable practices. These findings underscore GPC's potential as a versatile, efficient, and environmentally beneficial optimization tool for power generation systems.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

The present era of the Internet of Things (IoT) having intelligent functionalities in solving problems pertaining to realtime mission-critical systems has brought an immense revolution in diverse fields including healthcare and navigation systems. However, to the best of our knowledge, the potential of IoT has not been fully exploited yet in the field of the energy sector. We argue that there is an immense need to shift the traditional mission-critical electric power system architecture to IoT-based fully orchestrated architecture in order to increase efficiency, as billions of investment is reserved for the energy sector globally. Since network orchestration deals with auomating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the architecture. The mission-critical systems with urgent task execution often suffer from issues of missing task deadlines. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of nonrenewable energy resources and maximizing the use of renewable energy resources. Moreover, major components of IoT task orchestration such as task mapping and task scheduling are also enhanced using NLP and PSO optimization modules. The proposed task scheduling algorithm incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid mission-critical system. Furthermore, the comparison with state-of-the-art scheduling algorithms showed that the task starvation rate is reduced to 16% and 12% when compared with RR and FEF algorithms, respectively.

For years, effort has been devoted to establishing an effective bull breeding soundness evaluation procedure; usual research on the subject is based on BBSE (Bull Breeding Soundness Examination) methodologies, which have significant limitations in terms of their evaluation procedure, such as high cost, time-consuming and administratively difficult. This research focuses on the creation of a prediction model to supplement and/or improve the BBSE approach, through the study of two algorithms, Clustering and Artificial Neural Network (ANN), to find the optimum Machine Learning (ML) approach for our application, with an emphasis on data categorization accuracy. The tool is designed to assist veterinary medicine and farmers in identifying key factors and increasing certainty in their decision-making during the selection of bulls for breeding purposes, providing data from a limited number of factors generated from a deep pairing study of bulls. Zebu Bulls, European Bulls, and Crossbred Bulls were the general groupings. The data utilized in the model's creation (N =359) considers five variables that influence improvement decisions. This approach enhances decision-making by 12%. ANN obtained an accuracy of 90%; Precision was 97% for satisfying, 92% for unsatisfying, and 85% for Bad.

In order to reduce the influence of ground vibration generated by tractor during driving and operation on driving stability and driver ride comfort, this paper proposes a control method of tractor active seat suspension based on model predictive control. By establishing a 3-degree of freedom tractor active seat suspension model and a ground excitation input model, a new model predictive controller for the active seat suspension system is designed. The vertical acceleration of the seat center of mass, the seat suspension stroke and the tire dynamic displacement are optimized by limiting the dynamic deflection of the seat suspension and the active force output of the actuator. The control model of MPC tractor active seat suspension system is built and its performance is simulated and verified. The results show that compared with the passive seat suspension and the PID seat suspension, the tractor active seat suspension based on model predictive control (MPC) has better damping characteristics and improves ride comfort.

Motivated by the PEACOC project on metal recovery from solid wastes, an innovative magnetic density separation (MDS) process has been developed for granular material sorting. It has intrinsic advantages over the existing industrialized MDS by eliminating fluid turbulence and particle jamming problems. The new MDS applies an inclined planar magnet and a horizontal basin containing a static magnetic fluid as the separation medium. A particle sliding phenomenon is identified as a feature of the process which could help the separation. The MDS successfully sorted shredded PCBAs to concentrate valuable metals and sorted shredded wires to reduce metallic contaminants in the plastic fractions. A pilot scale facility is introduced to show the design to achieve continuous production and to reduce the consumption of ferrofluid.

As a consequence of applying advanced maintenance practices, the theoretical probability of failures is relatively low. However, observation of low market intelligence and maintenance management has been reported. The experimental investigation is supported by findings from a survey targeting asset-intensive companies applying hydraulic power systems. Next, the study incorporates multidimensional data analysis using CA-AHC (Correspondance Analysis with Agglomerative Hierarchical Clustering) approach. The non-parametric machine learning models are used from generated feature subspace to extract features affecting maintenance performance indicators. The results support empirical evidence that equipment age increases the probability of failures. However, the novel findings show that number of maintenance personnel, equipment size measured by nominal working energy consumption, and activities dedicated to maintaining fluid cleanliness impact regression results of companies utilising hydraulic applications.