Reliable Framework Research Articles

Economic Sustainability of the Music Industry through Copyright Fiduciary Guarantees with a Legal Economic Analysis Approach

This study investigates the economic sustainability of the music industry through the implementation of copyright fiduciary guarantees, employing a legal economic analysis approach. The primary objective is to explore how copyright as a financial asset can enhance the industry's economic resilience by providing a reliable framework for securing loans and investments. The research examines the intersection of intellectual property law and economic theory to evaluate the potential of copyright fiduciary guarantees in mitigating financial risks and promoting sustainable growth in the music industry. The methodology includes a qualitative analysis of existing legal frameworks, case studies of jurisdictions that have successfully integrated copyright fiduciary guarantees, and interviews with industry stakeholders such as artists, producers, legal experts, and financial institutions. Data were collected from a range of sources including legislative documents, industry reports, and academic literature. The analysis focuses on the legal mechanisms for establishing copyrights as collateral and the economic implications for stakeholders in the music industry. The findings indicate that the use of copyright as collateral can significantly improve access to financing for music professionals, thereby enhancing economic stability and growth potential within the industry. Case studies reveal that countries with robust legal frameworks for copyright fiduciary guarantees have experienced increased investment in the creative sector and reduced financial volatility. The study also identifies key challenges, such as the need for clear legal definitions and enforcement mechanisms, and the importance of raising awareness among industry participants about the benefits and procedures of copyright collateralization.

Constraining the age of the Pleistocene sedimentary infill of Cueva Mayor (Atapuerca, N Spain) through a multi-technique dating approach

Establishing chronologies of archaeological sites by using a single dating method may not always reliably constrain the age of a deposit or a fossil, as potential biases may naturally arise, particularly in complex sedimentary settings such as caves. In this study, we performed a multi-technique dating approach that targets different materials in two caves from the Cueva Mayor-Cueva del Silo karst system, Sierra de Atapuerca, Spain. Two new fossil teeth from lithostratigraphic unit LU4 (GE-I pit) at Galería de las Estatuas have been dated by combined U-series/ESR and Amino Acid Racemisation (AAR) methods. The former provides ages of 117 ± 13 and 87 ± 9 ka that agree with previous dating studies of this Neandertal site, confirming the assignment of level LU4 to Marine Isotope Stage (MIS) 5. In contrast, AAR analyses produce very scattered ages, pointing to significant post-burial chemical modifications in dentine. A first set of ages is presented for the Cata Litario pit at Sala de los Cíclopes, a palaeontological site formed exclusively of cave bear fossils belonging to the Middle Pleistocene species Ursus deningeri. We provide U-series/ESR and AAR ages for two bear teeth from lithostratigraphic unit LU5, and extended-range luminescence ages for the sediment deposits that host, and immediately underlie and overlie, the bear accumulation. Depositional ages of the sediment were obtained using single-grain thermally-transferred optically stimulated luminescence (TT-OSL) dating of individual quartz grains and post-infrared-infrared stimulated luminescence (pIR-IR) dating of K-feldspars. Several speleothems, both in situ and reworked fragments embedded in the different lithostratigraphic units, have additionally been dated by U-series to constrain the timing of the sequence formation. The results indicate that the sediment originally entered the cave between MIS-12 and MIS-10 (444 ± 28 ka to 367 ± 32 ka). Nevertheless, the AAR and U-series/ESR fossil ages, as well as some of the speleothem fragment ages from LUs 5 and 6 are significantly younger. Leaving aside methodologically questionable results for one of the bear tooth samples, the most reliable AAR and U-Series/ESR ages range from 287 ± 23 ka to 256 ± 24, while the speleothem fragments indicate several formation episodes between 444 ± 19 and 284 ± 8 ka. The apparent difference between the U-Series/ESR ages on the fossils and the luminescence ages on the host sediment are not likely to be wholly explained by uncertainties on uranium uptake modelling or gamma dose rate evaluation, and thus is most likely related to taphonomic processes or a complex site formation history.The presence of fossils and eroded speleothem clasts of younger age within these levels is consistent with either an event of prior erosion, transport and resedimentation of LUs 4, 5 and 6, and/or a complex post-depositional mixing history for the stratigraphic sequence at Cata Litario. This study highlights the importance of using different dating methods to reconstruct reliable chronological frameworks, and to understand the geological factors that can affect the dated materials. This includes the accumulation and taphonomic histories of fossils, post-depositional mixing or reworking processes affecting sediments during the residence time within the karst system.

Enhanced Euler–Lagrange Formulation for Analyzing Human Gait With Moving Base Reference

Abstract Euler–Lagrange's formulation is known for its systematic and simplified approach to deriving dynamics of complex systems. In order to apply the existing formulation to human gait dynamics, the base reference frame must be assumed as an inertial reference frame. Conventionally, the ankle joints or the hip joints are regarded as base reference frames during the stance and swing phases of human walking. As these joints are non-inertial in nature during actual locomotion, this assumption could result in inaccurate calculation of lower-limb joint torques and forces. Therefore, in this paper, an existing Euler–Lagrange-based formulation originally developed for fixed-base robotic manipulators is considered and modified to accommodate the movement of the base reference frame with respect to an inertial frame of reference defined outside the human body. The applicability of the modified formulation is studied, implemented, and validated using three standard and publicly available gait datasets covering the phases of walking and running. The joint torques obtained using the proposed dynamic model are compared with reference torques by calculating the mean absolute error values and visually through Bland–Altman plots. The obtained joint torque values and plots indicate a close agreement with published torques, thereby validating the accuracy of the proposed dynamic model. The robust formulation implementation makes it a valuable resource for researchers in this field, offering a reliable framework for gait analysis and the design of lower-limb prosthetics or exoskeletons.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.

Predicting the compressive strength of advanced concrete structures by developed African vulture optimization algorithm-Elman neural networks

ABSTRACT: Concrete plate structures’ compressive strength and natural frequency are connected by the mechanical characteristics of the material and structural dynamics. Concrete’s elastic modulus rises along with its compressive strength, making the structure stiffer. Since the natural frequency is proportional to the square root of the stiffness over the mass, the increased stiffness raises the natural frequency. In this work, the relationship between natural frequency and compressive strength in advanced concrete constructions reinforced with graphene oxide particles is examined. Also, the structure is under applied pressure. By using a trigonometric shear deformation plate theory, we hope to improve compressive strength prediction accuracy. Navier’s technique is used as the analytical solution process in the analysis. This study contributes to the design and assessment of more durable and effective structural systems by offering a reliable framework for estimating the compressive strength of reinforced concrete structures. This paper presents a novel method by combining Elman Neural Networks (ENNs) with the developed African Vulture Optimization Algorithm (DAVOA) to estimate the frequency of the structure. With the help of this integration, researchers and engineers can now more precisely estimate the compressive strength of sophisticated concrete structures, leading to the creation of stronger and more robust structural solutions.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks

MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks

Prism-shaped rock-physics template

Despite the emergence of statistical and intelligent approaches for quantitative seismic interpretation in recent years, rock-physics templates (RPTs) are still preferred because of their simplicity and easy implementation. RPTs have been introduced as a fundamental tool for lithology and fluid discrimination based on well-log and seismic data, which have already been proven in terms of accuracy and reliability. Considering the demand for comprehensive RPTs and improving their efficiency, I develop a novel 3D prism-shaped template for lithology and fluid discrimination called a prism-shaped RPT (P-RPT). For this, a theoretical methodology for designing conventional RPT is introduced with the aim of bounds and hybrid rock-physics models. Next, some novel triangular and rectangular ternary and binary templates are designed and connected to one another to build a prism for fluid discrimination and lithology identification. Two ternary charts are used for fluid and lithology, incorporating the P- and S-wave velocity ratios and lambda-mu parameters. Furthermore, binary charts, including acoustic impedance, Lamé parameters, and porosity, are designed and modified from the literature for fluid and lithology identification. Next, the obtained templates are successfully validated on blind data sets (ultrasonic, well logging, and seismic data) in different reservoirs with various lithologies and fluid types. The results indicate that the P-RPT could integrate the available RPTs into a 3D diagram and give a reliable framework for applications in seismic interpretation. User friendliness and generalizability are the most prominent advantages of this template for detecting fluid type and lithology based on well logs and seismic inversion, the results of which can be interpreted on a single chart rather than analyzing the data with different templates. The methodology and framework for implementing and generating templates are thoroughly explained in theory and practice to localize P-RPT or update the new RPT for another region.

MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images

Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have become prominent in the field of medical image segmentation, they suffer from certain limitations. In this study, we present a reliable framework for producing performant outcomes for the segmentation of pathological structures of 2D medical images. Our framework consists of a novel deep learning architecture, called deep multi-level attention dilated residual neural network (MADR-Net), designed to improve the performance of medical image segmentation. MADR-Net uses a U-Net encoder/decoder backbone in combination with multi-level residual blocks and atrous pyramid scene parsing pooling. To improve the segmentation results, channel-spatial attention blocks were added in the skip connection to capture both the global and local features and superseded the bottleneck layer with an ASPP block. Furthermore, we introduce a hybrid loss function that has an excellent convergence property and enhances the performance of the medical image segmentation task. We extensively validated the proposed MADR-Net on four typical yet challenging medical image segmentation tasks: (1) Left ventricle, left atrium, and myocardial wall segmentation from Echocardiogram images in the CAMUS dataset, (2) Skin cancer segmentation from dermoscopy images in ISIC 2017 dataset, (3) Electron microscopy in FIB-SEM dataset, and (4) Fluid attenuated inversion recovery abnormality from MR images in LGG segmentation dataset. The proposed algorithm yielded significant results when compared to state-of-the-art architectures such as U-Net, Residual U-Net, and Attention U-Net. The proposed MADR-Net consistently outperformed the classical U-Net by 5.43%, 3.43%, and 3.92% relative improvement in terms of dice coefficient, respectively, for electron microscopy, dermoscopy, and MRI. The experimental results demonstrate superior performance on single and multi-class datasets and that the proposed MADR-Net can be utilized as a baseline for the assessment of cross-dataset and segmentation tasks.

Exploring the chemical space of ionic liquids for CO2 dissolution through generative machine learning models

For discovering uncharted chemical space of ionic liquids (ILs) for CO2 dissolution, a reliable generative framework combining re-balanced variational autoencoder (VAE), artificial neural network (ANN), and particle swarm optimization (PSO) is developed based on a comprehensive experimental solubility database from literature. The re-balanced VAE transforms the chemical space of ILs into continuous latent space, which is demonstrated by t-distributed stochastic neighbor embedding (t-SNE) visualization and sampled ions of the latent space. ANN is connected with the re-balanced VAE to predict the CO2 solubility and the resultant VAE-ANN model achieves a low mean absolute error (MAE) of 0.022 on the test set. Lastly, the PSO algorithm is employed to search the latent space for optimal IL structures with the highest predicted solubility. A total of 5120 ILs are generated and optimized through 10 parallel runs of PSO. Their CO2 solubilities are predicted and compared to those of the 3735 ILs combined with the already-known cations and anions in the CO2 solubility database under 298.15 K and 100 kPa. The results demonstrate a notably larger distribution of higher CO2 solubility in optimized ILs after PSO, which effectively points out the significance and directions for exploring the wide IL chemical space.

Universal Machine Learning Kohn–Sham Hamiltonian for Materials

Abstract While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn–Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate machine learning models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moiré twisted bilayer heterostructure, and metal-organic frameworks. Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GNoME datasets, identifying 3940 crystals with direct band gaps and 5109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.

Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions

In the materials science domain, the accurate prediction of the yield strength of metallic compositions has often resulted in extensive experimental endeavors, leading to inefficiencies in both time and resources. Here, we introduce an innovative approach to predict yield strength, which can be applied to a variety of metallic substances ranging from the simplest pure metals to the most intricate alloys under varying temperatures and strain rates. The fusion of grain boundary sliding mechanism and cutting-edge machine-learning algorithm forges an expansive framework, which can help realize the critical factors influencing yield strength. The validity and wide applicability of the proposed framework were rigorously confirmed through experimental evaluations conducted on selected Fe-based alloys, such as Fe60Ni25Cr15, Fe60Ni30Cr10, and Fe64Ni15Co8Mn8Cu5. This breakthrough study significantly streamlines experimental design processes, optimizes resource utilization, and marks a significant leap forward in creating a reliable predictive framework for realizing material properties.

Colon Cancer Detection of Colonoscopy Images Using CNN

Abstract: Colon cancer requires an early and precise diagnosis in order to be effectively treated because it is a common and potentially fatal condition. This study proposes a reliable framework for colon cancer detection through image preprocessing and transfer learning approaches. The research compares the performance of three popular convolutional neural networks (CNNs) for this task: VGG-15, ResNet-50, and AlexNet.The image preprocessing phase entails a number of critical operations, including image conversion, resizing, and contrast enhancement using histogram equalization. In order to guarantee that the neural networks receive high-quality inputs, these preprocessing techniques strive to improve the quality and consistency of the colon cancer dataset. Pre-trained models extract and classify features from big datasets using transfer learning. The preprocessed colon cancer dataset is used to fine-tune the chosen CNN architectures VGG-15, ResNet-50, and AlexNet and adapt them for this particular medical imaging purpose.The results of this study show that transfer learning has the potential for colon cancer detection and provides helpful direction for researchers and practitioners looking for the best deep-learning architectures for early cancer diagnosis. This study adds to continuing efforts to improve the precision and effectiveness of colon cancer detection techniques, ultimately leading to better patient outcomes.

Accurate and efficient evaluation of the ionization potentials of extreme ultraviolet photoresists using density functionals and semi-empirical methods

Extreme ultraviolet (EUV) photoresists have become the core materials in lithography with nanometer-sized patterns and are actively explored on the path to realizing smaller critical dimensions. These photoresists can be small molecule-, polymer-, or organic–inorganic hybrid-based, with the full molecular working mechanism under investigation. For the rational design of EUV photoresists, theoretical guidance using tools like first-principle calculations and multi-scale simulations can be of great help. Considering the extremely high standard of accuracy in EUV lithography, it is critical to ensure the adoption of the appropriate methodologies in the theoretical evaluation of EUV photoresists. However, it is known that density functionals and semi-empirical methods differ in accuracy and efficiency, without a universal rule across materials. This poses a challenge in developing a reliable theoretical framework for calculating EUV photoresists. Here, we present a benchmark investigation of density functionals and semi-empirical methods on the three main types of EUV photoresists, focusing on the ionization potential, a key parameter in their microscopic molecular reactions. The vertical detachment energies (VDE) and adiabatic detachment energies (ADE) were calculated using 12 functionals, including pure functionals, hybrid functionals, Minnesota functionals, and the recently developed optimally tuned range-separated (OTRS) functionals. Several efficient semi-empirical methods were also chosen, including AM1, PM6, PM7, and GFN1-xTB in the extended tight-binding theoretical framework. These results guide the accurate and efficient calculation of EUV photoresists and are valuable for the development of multi-scale lithography protocols.Graphical

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

Reliability evaluation of a multi-state system (MSS) with dependent components makes much practical sense because the independent identical assumption (i.i.d.) assumption between different components is sometimes impractical in the context of real engineering cases. The task becomes more challenging if imprecision gets involved due to the pervasive uncertainty. The loss of monotony resulting from the introduction of imprecise parameters makes many analytical reliability methods not applied. To address this challenge, in this paper, we develop a survival signature-based reliability framework for an MSS taking into account both dependence and uncertainty. In our framework, the survival function is derived through some unique structural reliability treatments. Vine copula and imprecise probability are integrated and embedded within the framework to address the case that dependence and imprecision simultaneously appear. Implementation-wise, two numerical simulation algorithms are developed to address some complicated cases in which the analytical solution is not available. For demonstration and validation, both the numerical case and application examples are presented. The results show the superiority of the proposed method and its potential in real engineering use.

Optimal Positioning of Small Wind Turbines Into a Building Using On-Site Measurements and Computational Fluid Dynamic Simulation

Abstract Renewable energy solutions are essential for addressing several pressing issues, including climate change, the fossil fuels supply chain fragility and fuel price fluctuations. One promising technological solution is rooftop-mounted turbines into buildings. This study presents an evaluation of the potential for wind energy utilization on the rooftop of a 29 m tall building. The primary objective of this research is to develop a methodology that can effectively investigate the integration of small wind turbines (SWTs) into urban buildings, intending to promote energy sufficiency in urban areas. A robust framework has been developed that consists of seven steps. These steps include site selection, evaluating urban wind energy with computational fluid dynamics (CFD) simulation and on-site measurements, selecting an appropriate SWT, estimating the annual energy production (AEP), conducting an evaluation of the environmental impact, resilience, and economic analysis, and finally, installing the system. This straightforward yet reliable framework provides a comprehensive approach to assessing the viability of wind energy utilization in urban areas. The findings revealed that the most suitable location for installation had an estimated AEP of around 1030 kWh, leading to a reduction in emissions of 0.64 tCO2/y. Additionally, it was observed that the building's geometry and orientation significantly affected the wind flow, causing a substantial decrease in wind speed downstream. Selecting optimal sites and considering wind patterns are essential for maximizing energy generation in wind energy projects.

A modified Johnson–Cook model for the plastic behavior of metals in ultrasonic vibration-assisted upsetting processes

In this work, we present a constitutive description of the plastic behavior of the metals and alloys under Ultrasonic Vibration (UV). When UV is imposed, it induces a softening effect on the plastic deformation of metals and alloys, referred to as the acoustoplastic phenomenon. The volumetric softening mechanism of the acoustoplasticity must consider stress superposition and acoustic softening. Taking the Johnson–Cook (JC) constitutive model as the reference for metal thermo-plasticity, the flow stress reduction due to UV is introduced by three types of softening terms: subtractive, multiplicative, and coupled. Those different configurations will be quantified via physical and phenomenological assumptions. For the study and validation of the proposed constitutive models, we focus on a well-known material, 6063 aluminum alloy. The ultrasonic vibration is modeled by an assisted upsetting process of an aluminum billet and compared with experiments. The impact of variations of the strain rate, ultrasonic amplitude, and frequency on the softening behavior of the models are analyzed and compared with available experimental results. We find out that the subtractive and coupled models agreed well with the experimental results showing the errors ranging from 2.4–4.8% and 4.4%–8%, respectively. However, the predictive ability of the multiplicative model exhibits large discrepancies at the low stage of strains following the acceptable predictions at high stains. By understanding the impact of the UV on the acoustoplastic deformation of the metals and alloys we can establish a reliable theoretical framework for prospective numerical studies of the UV-assisted manufacturing processes which is the goal of the current research presented here.

Use of the Ahp Method in the Development of an Accident Prevention Program in the Chemical Industry In Brazil

Objective: The objective of this research was to investigate whether the decision-making for the prioritization of the implementation of the Accident Prevention Program (APP) elements in a chemical industry was consistent with the judgment of a group of professionals with different academic backgrounds and different positions. Theoretical Framework: From a PPA implemented in a chemical industry, the use of the multicriteria tool for decision-making, Analytic Hierarchy Process (AHP) and the Survey carried out with different professionals, a solid support for the result obtained was defined. Method: The methodology applied was qualitative, using bibliographic and documentary research, survey (data collection) and case study. Data collection was carried out through a questionnaire available on the Survey-Monkey platform for 100 professionals, with a respondent percentage of 44%. Results and Discussion: The results obtained demonstrate the great challenge for the corporate world on the need for a broad debate on the people and facilities safety, stimulating the use of multicriteria tools for decision-making in the case of implementing a program as complex as the APP. Implications of the Research: With investors' increasing concern about environmental, social and governance (ESG) issues, the research provides a reliable and solid framework for the implementation of an accident control program in the chemical industry. Originality: The research on the use of multicriteria analysis for the APP implementation is original, especially in the use of the Analytic Hierachy Process, a powerful tool for multicriteria decision-making.

Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

Alloy catalysts design faces the contradiction of small sample and huge screen space, especially in rational design with multi-objective and multi-constraints. Here, we conduct the active learning enabled innovative catalyst screening in alloy space composed of transition metal elements. First, the small dataset and large search space are constructed based on knowledge informed feature group. Then, the surrogate model based on Gaussian process regression are trained for predicting catalytic activity and N2 selectivity descriptors for selective catalytic oxidation of ammonia (NH3-SCO) process, merely based on the small dataset. Furthermore, coupling pareto solution and Bayesian optimization, the active learning driven material searching are performed in the huge alloy spaces with more than 1000 configurations. After 3 iterations with high throughput calculation on 30 alloyed configurations and further theoretical validation based on microkinetic and stability analysis, 9 ensemble configurations are considered as innovative alloy catalysts of NH3-SCO with reactivity and selectivity trade-off. Our study provides the reliable framework for catalyst design with multi-objective and multi-constraints when facing with small sample data and huge searching space and gives the specific guidance for rational design of NH3−SCO alloy catalysts.