In the retail industry, demand forecasting is absolutely crucial for guaranteeing efficient inventory and supply chain control. Different artificial intelligence (AI) techniques have been used lately to improve forecasting performance. Demand fluctuation, seasonal patterns, and outside influences continue to create difficulties, though. Using several machine-learning techniques Linear Regression, XGBoost, Random Forest, Decision Tree, Prophet, and LSTM this paper offers a comparative study to forecast product demand. A retail dataset obtained from Kaggle served as the basis for training and testing the forecasting models. The experimental results demonstrate that the LSTM model outperforms all others with accuracy, precision, recall, and F1-score of 92.31%, 92.31%, 100.00%, and 96.00%, respectively, followed by Prophet with 85.71%, 92.31%, 92.31%, and 92.31%, respectively, Decision Tree with 93.05%, 75.76%, 76.13%, and 75.94%, respectively, Random Forest with 91.99%, 66.86%, 88.08%, and 76.02%, respectively, XGBoost with 83.21%, 45.70%, 87.84%, and 60.12%, respectively, and Linear Regression with 60.67%, 25.46%, 89.75%, and 39.67%, respectively. These results verify that ensemble and deep learning models can greatly help retailers in raising operational efficiency and notably improve forecasting accuracy.
Muhammad A. P. Putra, Neny Rosmawarni, Muhammad Adrezo
et al.
Fire is a phenomenon that causes physical and material losses to humans. Fires are difficult to predict based on causes and location. Therefore, early detection of fires is necessary to reduce the impact. With these issues, this research aims to detect fires based on CCTV images. So far, there has been no research on fire detection based on CCTV images using a 1D CNN. The detection of fires based on CCTV images will be carried out by creating an algorithm model with a 1D convolutional neural network. This research uses a dataset of fire images based on CCTV that is already pre-processed. An interface display is created for inputting data using the Tkinter library to show a graphical user interface (GUI). The result of the algorithm model process using the 1D convolutional neural network based on accuracy, precision, and recall is 88.43%. The understanding of the actual input data is still low in terms of detecting fires based on CCTV images and requires further processing of CCTV image data.
To enhance the accessibility and efficiency of airports, the concept of airport connectivity is extended to High-Speed Rail (HSR), as major hub airports now have direct access to an HSR station. The traditional hub connectivity index is supplemented by the number and quality of connections between train and flight departures/arrivals (or timetables). The methodology is tested at the Paris-Charles de Gaulle airport. The results highlight that air–rail and rail–air connections can represent up to 72% of the total hub connectivity. A disaggregated analysis of connectivity across origin–destination pairs was conducted, revealing potential synchronization gaps. These findings demonstrate that this tool can assist transportation service providers in synchronizing their timetables, by measuring the degree to which it contributes to improve connectivity. Moreover, the findings offer new insights into air–rail timetable coordination and provide policy implications regarding the replacement of feeder flights by HSR.
In this article, the application of virtual reality technology for the realistic and immersive visualization of various tasks and scenarios in fields such as power engineering and fire safety has been examined in order to help prepare students and professional electrical engineers with electrical safety, the operation of electrical substations, potential emergencies, injury prevention, fire safety, and others. Additionally, the use of machine learning algorithms to guide evacuations from hazardous environments, fault prevention, fire prediction, and discovery of conductive materials has been examined. The most frequently used algorithms in these areas have also been described and summarized, and conclusions have been made about the combined advantages of using VR and ML algorithms. Finally, the needs, contributions, and challenges of using machine learning in virtual reality projects have been examined.
Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.
Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.
Alexandros Gazis, Ioannis Papadongonas, Athanasios Andriopoulos
et al.
This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with integrated processing capabilities. The article also explores the growing importance of mini-computing devices in educational environments. These devices provide cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, this article examines the most widely used sensors, detailing their properties and modes of operation to help readers understand how sensor systems function. The aim of this study is to provide an overview of the most suitable sensors for various applications by explaining their uses and operations in simple terms. This clarity will assist researchers in selecting the appropriate sensors for educational and research purposes or understanding why specific sensors were chosen, along with their capabilities and possible limitations. Ultimately, this research seeks to equip future engineers with the knowledge and tools needed to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.
This study aims to design a qualitative model of the safety and health culture of human resources in the Iranian Tobacco Company. The research strategy is qualitative and fundamental in terms of its objective. Furthermore, based on data collection methods, it is classified as field research. The participant population consists of senior managers of the Iranian Tobacco Company with over 15 years of work experience, totaling 18 individuals. Purposeful sampling was used in this study, and the data collection tool in the qualitative phase was semi-structured interviews. The data were analyzed using the Colaizzi and Diekelmann approaches, resulting in the development of a phenomenological model. Based on the results of the phenomenological analysis, the final identified components of the safety and health culture include: (1) participation in safety, (2) safety knowledge, (3) commitment to safety, (4) attitude toward safety, (5) perceived desirability of safety, (6) implementation of safety standards, (7) evaluation of process and non-process risks, (8) documentation of risk assessment and analysis, (9) development and equipping of the company with new machinery and devices, (10) transfer of new technologies, (11) placement and location of equipment according to ergonomic conditions, (12) planning and policy-making in the field of safety and health, (13) updating safety laws based on circumstances, (14) rewards and punishments, (15) work-life imbalance, (16) salary and wage conditions, (17) excessive workload, (18) senior management support, (19) training and empowerment, (20) organizational environment, (21) priority for safety and health, (22) reduction of musculoskeletal injuries, (23) improvement of safety behavior, (24) job motivation and satisfaction, (25) organizational productivity, (26) application of engineering and mechanical principles, (27) development and improvement of production rate, (28) performance evaluation, (29) reduction of organizational costs, (30) safety knowledge documentation, (31) sharing of safety knowledge, and (32) application of safety knowledge in the future. According to the findings, it was determined that human resources maturity, safety and health risk management, production equipment and technologies, safety and health laws and regulations, and job stressors are the most significant factors influencing the safety and health culture of human resources. The outcomes of the safety culture include individual and organizational consequences as well as safety knowledge management.
The modern business context is so cut-throat, therefore, organizations should place emphasis on process leadership in the quest to provide the best quality products to their clients. Quality management practices that incorporate FMEA are a significant measure that can help in finding and solving issues with high impacts. This study deals the technique called (FMEA) and that its character is forward-looking, which means that it could identify, prioritize and eliminate slots leading to different sort of failures, that result in optimal performance and customer satisfaction. Study makes use of FMEA as an important component of the quality management system by interconnecting it with other approaches like Six Sigma, TQM and ISO 9001, which could bring these paradigms to even higher level, if implemented properly. From this case studies and good practices from real organizations, we will discuss strategic benefits of applying FMEA into management practices of quality as well as affecting versatility for different scenarious. A FMEA method is an engineering methodology designed to detect and eliminate problems in systems, designs, processes and solution that may happen and thus prevent loss of resources due to mistakes made by users. The study researches the application of FMEA tool in the area of quality improvement. Indeed, with FMEA aiming to improve efficiency through the prioritization of these types of errors and the focus on the errors of highest risk priority. It is also provided with the high tech machinery required for industrial grade cables producing for automotive and electronic industries. Via FMEA methodology, the study reviewed error situations, which had a chance of happening after the product has been used by the customer. The study, additionally, used MCDM (Multi-Criteria Decision-Making) techniques to upgraded decision-making available at the FMEA analysis at the same time. What could be pointed out as its main feature is the key role of FMEA as a strategic tool. It could allow organization to reach world-class level in different areas by simply grasping its theoretical and practical fundamentals.
Alejandro Camino, Andrés Villegas, Esteban Pérez
et al.
An innovative cascade control strategy is presented in this work, based on sliding mode control (SMC) for trajectory tracking of the formation of mobile robots. The proposed strategy was compared with five alternative control approaches: PID control, inverse dynamics, and other SMC-based structures. The objective was to evaluate the most effective control technique by analyzing the integral of squared error (ISE) index. Additionally, robustness tests were carried out by varying the parameters of the dynamic model of the mobile robot and analyzing the response of the controllers to perturbations in the modeling. The results show that the PD-SMCV controller provides the best performance in trajectory tracking and robustness against disturbances, demonstrating significant superiority over the evaluated methods for maintaining a stable mobile robot formation under dynamic conditions.
Vibration related to several performances such as durability and noise performance is the one of the important performances on automotive product. The technique of reshaping eigenmode is a practical way to improve the performance on the product design phase. However, it is difficult to design desired mode shapes efficiently because the mode shapes are determined by the balance between mass and stiffness distribution on a whole structure. In this paper, a method for designing desired eigenmodes through changing internal forces between subsystems is proposed. Firstly, according to frequency based substructuring, the dynamic stiffness matrix of a whole structure could be divided into the matrices of subsystems and internal forces occurred from coupling components. Then, on a specified eigenfrequency as a design target, it can be derived that the mode shapes are controllable by changing to desired internal forces. Secondly, desired internal forces are calculated through designing spring constants between subsystems based on the kernel compliance analysis method which can analyze vibration when several subsystems are coupled on multiple degrees of freedom. Furthermore, when desired internal forces are calculated, a method to change a component of the internal force vector one by one is also proposed. This method can visualize the range which can be designed all spring constants as positive values in advance and can avoid selecting negative spring constants. Finally, this proposed method is applied to a numerical case study to reduce vibration responses with allocating modal strain energy to subsystems through reshaping an eigenmode.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
A simulation technique adaptable to calculating a laser-machined profile was proposed to develop microfabrication techniques for a cemented carbide using a focused picosecond pulsed laser. An experiment to obtain a defocus diagram, which shows the relationship between defocus and removal depth, was conducted by shifting the focal point upward to the work surface. The concept of zero cut, in which the laser beam was scanned repeatedly in the same path, was proposed, and it was verified using the proposed simulation technique that zero cut is useful for residual stock removal. In addition, a simulation technique adaptable to calculating both the laser-machined profile and the machining process conditions was developed using the defocus diagram as a machining condition. It was verified through a machining test by fabricating half-cut columns with radii of 30 and 50 μm formed on the upper half of a large base column that the dimensional error of the columns was less than 1% and the surface roughness was less than 0.1 μmRz. This means that the simulation technique using the defocus diagram as a machining condition is useful for the microfabrication of the cemented carbide using the focused picosecond pulsed laser.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
Itumeleng Kohitlhetse, Hilary Rutto, Kentse Motsetse
et al.
Using iron and steel slags as feedstock for a mineral carbonation reaction using carbon dioxide gas is an excellent technique because they are easily accessible, contribute to land pollution, and have a reasonable quantity of lime and magnesia. The rate at which ironmaking blast furnace slag dissolves in an aqueous solution of ammonium acetate was investigated in relation to pH, stirring speed, solvent concentration, and temperature. A one-factor-at-a-time experiment was conducted, pH was monitored to the maximum value of 11, stirring speed ranged from 100 to 200 rpm, solvent concentration was adjusted from 0.01 to 1 M, whereas the reaction temperature was maintained between 25 and 80 °C. The dissolution kinetics of ironmaking slag were calculated by fitting experimental data to a model of a diminishing core. Using Atomic Absorption Spectroscopy, the leach liquor was characterized under various experimental conditions. The results of the trial revealed that this reaction is driven by a chemical reaction model equation. A semi-empirical model was also developed from the experimental data to better describe the dissolution kinetics.
Allen S. Credo, Mckenneth G. Pascual, Mark Jerome C. Villagracia
et al.
Farmers widely use malathion, even in households, and significant amounts seep through groundwater and effluent wastewater. It is toxic to animal and human life. Hence, its removal from wastewater is necessary. Here, we report the applicability of hydroxyapatite as a catalyst in the UV-light-assisted degradation of malathion. The hydroxyapatite was synthesized via calcination from milkfish (MF1000) and cream dory (CD1000) bones. FTIR and PXRD results proved the successful synthesis of hydroxyapatite from the fish bones. SEM images revealed that the synthesized hydroxyapatite varies in size from 19 to 52 nm with a pseudo-spherical morphology. Degradation efficiency increases when catalyst dosage or irradiation time are increased. Degradation efficiencies range from 8.18% to 67.80% using MF1000 and from 20.50% to 67.90% using CD1000. Malathion obeys first-order kinetics with a kinetic constant up to 7.0289 × 10<sup>−3</sup> min<sup>−1</sup> for 0.6 g catalyst loading. Meanwhile, malathion obeys second-order kinetics with a kinetic constant up to 1.1946 × 10<sup>−3</sup> L min<sup>−1</sup> mg<sup>−1</sup> for 0.6 g loading. Across all catalyst loadings, CD1000 has faster degradation kinetics compared to MF1000. The results of this study validate that the calcined fish bones are effective in removing malathion in an aqueous solution, which significantly lessens the detrimental effects of pesticides in groundwater and wastewater.
The increasing environmental concerns associated with conventional composites, made using glass-fiber-reinforced polymers (GFRP) and carbon-fiber-reinforced polymers (CFRP), have shifted attention to bio-based composites. These environmentally responsible alternatives offer performance without sacrificing biodegradability. The present study examines the biodegradability of a novel bio-based epoxy composite reinforced with Musa acuminata (banana) fibers. Two composite variants were compared: one with 2.5% Montmorillonite (MMT) nanoclay and one without. While previous research has demonstrated an enhancement in mechanical and physical properties of polymer matrix composites with the addition of MMT nanoclay, it was hypothesized in this study that nanoclay addition would not significantly impact the composites’ biodegradability. To confirm this, we conducted standard biodegradability tests and an SEM analysis. The SEM results revealed a uniform distribution of MMT nanoclay within the bio-based polymer matrix, in addition to strong interfacial adhesion and decreased void crater sizes. The inclusion of nanoclay did not significantly impact the composites’ biodegradability, according to the statistical analysis provided in the present study. The present study also developed regression models to predict biodegradability over time to facilitate the determination of the timespan required for 100 percent biodegradability of the tested bio-based composite. Thus, this study is a significant benchmark for advancing eco-friendly composite materials.
Sérgio F. Chevtchenko, Elisson da Silva Rocha, Monalisa Cristina Moura Dos Santos
et al.
Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.
Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.
In this paper, the adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed using a mixed-methods approach for an extensive comprehension of AI adoption. An initial structured interview was conducted with 100 software engineers, employing the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theories. A theoretical model named the Human-AI Collaboration and Adaptation Framework (HACAF) was deduced using the Gioia Methodology, characterizing AI adoption in software engineering. This model's validity was subsequently tested through Partial Least Squares - Structural Equation Modeling (PLS-SEM), using data collected from 183 software professionals. The results indicate that the adoption of AI tools in these early integration stages is primarily driven by their compatibility with existing development workflows. This finding counters the traditional theories of technology acceptance. Contrary to expectations, the influence of perceived usefulness, social aspects, and personal innovativeness on adoption appeared to be less significant. This paper yields significant insights for the design of future AI tools and supplies a structure for devising effective strategies for organizational implementation.