Using the parametrically designed battery pack in Grasshopper, a Voronoi grain-based discrete element model was established in the three-dimensional distinct element code, incorporating progressively increasing grain equivalent diameters. A grain-based stress corrosion model, enhanced with an improved stress corrosion theory, was further proposed to simulate both the direct shear characteristics and shear creep behaviour of unsaturated sandstone under varying normal stresses. The mesoscopic parameters, including the contact properties between grains and the stress corrosion parameters, were then calibrated. This ensured that the model could accurately reproduce the mechanical properties of sandstone observed in laboratory tests. The modelling results indicated that tensile cracks were the dominant cracks generated during the shear process under various saturations and normal stresses, along with a few shear cracks. A significant negative correlation was observed between the joint roughness coefficient (JRC) and the ratio of long-term to peak shear strength. Additionally, increased normal stress or decreased saturation were both found to accelerate the time-dependent failure process, leading to a shorter time-to-failure under constant shear loading. We summarize that the proposed model effectively characterizes the direct shear and creep behaviour of unsaturated sandstone at varying roughnesses and normal stresses.
Se presenta un análisis cuantitativo de las distribuciones de palabras en mensajes anuales al congreso durante más de cincuenta años, abarcando ocho períodos presidenciales en Chile y un período de liderazgo militar. Hemos evaluado los rankings de palabras y las distribuciones de su frecuencia de repetición o espaciamiento inter-palabra, así como el cumulante de cuarto orden o de Binder. Suponemos que el espaciamiento entre palabras implica un orden estructural similar a los marcadores de ADN que organizan el texto a través del trabajo del autor quien elige, pero aun así dentro del lenguaje que le permite. Encontramos evidencia de cambios en las distribuciones de las palabras más frecuentes que pueden ser responsables de la desviación de la ley de Zipf, previamente reportada para el idioma español. Este trabajo es una restauración de los textos y compilación más extensa de los recursos disponibles como mensajes al congreso y al pueblo chileno cada año desde 1973, y su aplicación tiene el potencial de optimizar el corpus de palabras, tomando distribuciones intrínsecas extraídas desde cantidades estadísticas más frecuentes basadas en el propio corpus según ranking.
Mechanical engineering and machinery, Industrial engineering. Management engineering
Flooding is a recurrent and devastating issue in Bangladesh, largely due to its geographical and climatic conditions. This study examined the performance of four deep learning architectures Feed-forward Neural Network (FNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) in predicting floods in Bangladesh. Utilizing a binary classification dataset of historical meteorological and hydrological data, the findings revealed that GRU outperformed the other models, achieving an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1-score of 99%. In contrast, LSTM attained an accuracy of 96%, a precision of 99%, a recall of 95%, and an F1-score of 97%. These results underscored the effectiveness of GRU for operational flood forecasting, which was critical for enhancing disaster preparedness in the region.
Information technology, Electronic computers. Computer science
Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining methodologies. However existing works mainly focus on conventional ML models without provide systems design real world applications systems and do not include any dimension related to network security dimension, cost and benefit analyzing dimension utility dimension and light weight A.I model for edge computing. In this paper, we contribute with a systematic literature review of state-of-the-art data-mining techniques for predictive maintenance with emphasis on hybrid AI frameworks, deep learning and online data processing approaches, as well as, privacy-aware methods. We contribute by providing a number of real-world industrial use case which differentiate us from previous researched; we discuss details of cybersecurity issues in IoT-enabled PdM; and we discuss use of XAI (Explainable AI) to build interpretable models. Moreover, this survey introduces marginal AI applications in edge computing, predictive maintenance frameworks with scalability, and AI-powered anomaly identification for enhancing predictions in industrial-scale production. It also covers a review of predictive maintenance methodologies in addition to a future research agenda, highlighting emerging patterns such as digital twins, Industry 5.0, and reinforcement learning in predictive maintenance. The current study aims to bridge critical gaps in the literature and support valuable direction for researchers, industry practitioners and policymakers for effective predictive maintenance strategies and task performance.
Abstract Coevolutionary spreading, the interdependent propagation of multiple-type information (or epidemics or social behaviors), has attracted both scientific and industrial attention due to its complex dynamics. While agent-based models (ABMs) are well-suited for modeling single-type contagion dynamics, they struggle to represent the microscopic interdependencies of co-evolving information types within different network topologies. This paper proposes a multi-information co-evolution propagation model based on self-organizing multi-agents, breaking through the limitations of traditional threshold spreading models and agent-based models. The model, which is validated through consistency with traditional SIR models under the circumstance of well-mixed agents, can be used to uncover the spreading mechanisms on different network topologies (such as ER, BA, WS) through a series of transmitting and recovering rules that act on each agent with social contagion behaviors and attributes. Furthermore, sophisticated spreading patterns, such as active counterattack and cooperative operation, are also explored based on this model to simulate the multi-information propagation process. These complex propagation simulations reveal some interesting phenomena: (1) When counterattacking the spread of a specific source information, blindly increasing the proportion of counterattackers or the information exclusion coefficient may not necessarily be the best choice, even without considering costs. (2) In networks with long-short loop structures, compared to the situation of single information dissemination, the coevolutionary spread of two types of information is more prone to avalanche phenomena, with the S (susceptible) state of information dropping sharply from a steady state of 60% to a steady state of 20% by the 10th generation. These findings provide actionable insights for controlling misinformation in social networks and optimizing public health interventions, emphasizing that "more intervention" does not always equate to "better control" in coevolutionary systems.
Electronic computers. Computer science, Information technology
The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.
In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive to collect. Data augmentation can alleviate the problem, generating new data from a smaller initial dataset. Geometric and color space image augmentation methods can increase accuracy of Deep Learning models but are often not enough. More advanced solutions are Domain Randomization methods or the use of simulation to artificially generate the missing data. Data augmentation algorithms are usually specifically designed for single images. Most recently, Deep Learning models have been applied to the analysis of video sequences. The aim of this paper is to perform an exhaustive study of the novel techniques of video data augmentation for Deep Learning models and to point out the future directions of the research on this topic.
Seán McGarraghy, Gudrun Olafsdottir, Rossen Kazakov
et al.
System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from the level of independent actors, each with their own motivations and agenda, but responding to externalities and to the behaviours of other actors. The focus is to show how simulation modelling can be applied to problems such as fairness and power asymmetries in European food value chains by evaluating the outcome of interventions in terms of relevant operational indicators of interorganisational fairness (e.g., profit distribution, market power, bargaining power). The main concepts of system dynamics and agent-based modelling are introduced and the applicability of a hybrid of these methods to food value chains is justified. This approach is outlined as a research agenda, and it is demonstrated how cognitive maps can help in the initial conceptual model building when implemented for specific food value chains studied in the EU Horizon 2020 VALUMICS project. The French wheat to bread chain has many characteristics of food value chains in general and is applied as an example to formulate a model that can be extended to capture the functioning of European FVCs. This work is to be further progressed in a subsequent stream of research for the other food value chain case studies with different governance modes and market organisation, in particular, farmed salmon to fillet, dairy cows to milk and raw tomato to processed tomato.
Mostafa Amini, Mohammad Hassanzadeh, Mostafa Morshedi
Objectives: The purpose of this research was to improve the five steps of Schallmo's methodology for digital transformation of business models, using Schumacher’s digital maturity assessment model, three approaches to business model transformation based on Heikkilä's recommendations and to improve digital innovation approach. The business model changes are based on Stampfl’s recommendations. Methods: This research has a combined methodology which consists of a validity review study method and action research method in a manufacturing SME. This improved methodology has been used in strategic planning for the digital transformation of the business model of one of Iran's manufacturing companies. Results: After reviewing the various documents of the Schallmo’s five-step framework, it was improved by Schumacher's maturity model for industry 4.0, approaches of business model transformation recommended by Heikkilä et al. and various types of business model innovation as described by Stampfl. Then the improved framework was used to map the target state of company's digital transformation. Conclusions: Since the proposed model of this research, in addition to improving the Schallmo model, has been implemented in a real company, so it seems that it can be used by Digital transformation planning and digital operating models as well as change managers and consultants.
Dalibor Karavci'c, Sanjin J. Guti'c, Borislav Vasi'c
et al.
Electrochemical reduction of thin graphene-oxide films in aqueous solutions: restoration of conductivity Dalibor Karačić1, Sanjin J. Gutić2, Borislav Vasić3, Vladimir M. Mirsky4, Natalia V. Skorodumova5, Slavko V. Mentus1,6, Igor A. Pašti1,5* 1 University of Belgrade – Faculty of Physical Chemistry, Belgrade, Serbia 2 University of Sarajevo, Faculty of Science, Department of Chemistry, Sarajevo, Bosnia and Herzegovina 3 Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia 4 Institute of Biotechnology, Department of Nanobiotechnology, Brandenburgische Technische Universität Cottbus-Senftenberg, Germany 5 Department of Materials Science and Engineering, School of Industrial Engineering and Management, KTH – Royal Institute of Technology, Stockholm, Sweden 6 Serbian Academy of Sciences and Arts, Belgrade, Serbia
Kseniia V. Zimenko, Maxim Ya. Afanasev, Mikhail V. Kolesnikov
Subject of Research. The paper proposes an approach to the development of a numerical control kernel. The approach implies creating software from separate modules interacting via a unified programming interface with a high level of granularity. Thus, a system with a required configuration can be developed in a relatively short time. The study also considers the possibility of using open source computer numerical control systems as a basis, which will further reduce the design time. The approach for computer numerical control development is considered in the context of its application on multipurpose modular equipment. Method. The proposed solution is based on a multi-protocol control
system and combines software and hardware components from different manufacturers. Platform independence is also provided. This method allows a prompt development of the numerical control system for any type of processing or other operations according to the requirements of hardware, and also gives wide opportunities for further modifications that increase the equipment efficiency. Main Results. The practical result obtained is a software trajectory-planning library, including geometry analysis, feed rate control and interpolation. Commands for controlling outputs and status of inputs are integrated into the cyclic data of the drive control and transmitted via the same interface. All developed modules are independently designed and can be embedded into other open source systems, as well as be further modified for processing efficiency increase. Practical Relevance. The work is aimed at increasing the economic independence of small design organizations and enterprises. The proposed modular approach allows the development of a required numerical control system for multipurpose modular equipment in a short time, and will significantly expand the capabilities of rapid prototyping and ensure the prompt production of pilot batches.
Pradeka Brilyan Purwandoko, Kudang Boro Seminar, Sutrisno
et al.
Rice is an essential food commodity in national and food security in Indonesia with a complex supply chain network. Various risks related to food quality and food safety occurs along the supply chain. Therefore, a tool is needed to monitor the rice production process from upstream to downstream (land-to-table) by implementing a traceability system to promote food transparency. In this system, all actors must be responsible for ensuring the quality and safety of products through various handling processes carried out from cultivation to product distribution. This paper aimed to develop a smart IT (Information Technology)-based traceability system in the rice supply chain using the System Development Life Cycle (SDLC). The actors involved in the rice supply chain consist of farmers, processing industries, distributors, bulogs, and retailers. Furthermore, this paper discussed the system architecture and the development of traceability system design using a data flow diagram (DFD). The developed prototype system shows the functional requirements of the system and can be used by stakeholders to monitor the production process and assist the decision-making process.
Similar to any other governments, Indonesia government has the role of protecting the security of its citizens via the established police unit. However, the executive unit is often unable to provide response in timely manner due to the huge data size. For the reason, an executive information system (EIS) is established in order to provide necessary information to leverage the decision making process. This work intends to establish and evaluate the executive information system and its support to facilitate the efforts to fight crimes in Indonesia territory. The EIS prototype is established and is evaluated on the basis of the six information system success factors where the required data are collected by means of questionnaire. The results suggest that the factors of system quality, information quality, easy-of-use, user satisfaction, and individual and organization impacts are very significant.