Industrial Solutions Research Articles

Sustainable Approach to Achieve New Green Solutions for the Construction Industry

In recent years, noise levels in administrative buildings have been considered one of the main causes of the stress and lack of productivity of employees. Based on the responses collected from a questionnaire survey distributed among companies with an open space configuration and studying the office’s acoustic conditions, from the specialized literature and through the authors’ experience, this study focused on the development of a new green dividing panel with superior sound absorption and acoustic insulation performances. In the experimental part, it was noticed that the presence of plants can influence the acoustic absorption values through their leaves’ conformation and distribution. Additionally, it was observed that the introduction of a coconut fiber layer in the panel led to higher values of the sound absorption coefficients in most of the studied plants. Through the conducted measurements, Tradescantia pallida registered superior values, with sound absorption coefficients with constantly increased values that varied in the range of 0.72–0.98 for the frequency range of 250–3150 Hz. Also, the weighted sound reduction index recorded a superior value of Rw (C; Ctr) = 27 (−1; −4) dB, comparable to other existing solutions.

Development of Objective Test Functions for Modeling and Sensitivity Analysis of the Mechanical Behaviour of Aluminum-Agro-Marine Waste Composites.

Abstract Aluminum-based composites reinforced with agro-marine waste materials present an eco-friendly, cost-effective solution for industries needing lightweight and durable materials. This study develops and evaluates the mechanical properties of aluminum (AA6061) composites reinforced with plantain stem ash, eucalyptus wood ash, and periwinkle shell powder for potential aerospace, automotive, and marine applications. Composite samples were prepared according to ASTM standards for tensile, compressive, flexural, wear, and fatigue tests. Objective Test Functions (OTFs) were derived using physical modeling and sensitivity analysis to filter impactful variables. Testing was conducted using universal testing machines, and wear/friction tests followed ASTM G99 guidelines. The physical models showed strong correlations (adjusted R²: 0.9033–1.0000). Measured properties included tensile strength (1907.46–2000.05 Pa), compressive strength (30.33–215.14 Pa), flexural strength (412.72–556.42 N-m²), wear rate (55.01–63.27 m³/m), coefficient of friction (0.102), and fatigue cycle (13.81–27.63 cycles). Except for the coefficient of friction, all results were consistent with the developed OTFs, confirming the material's structural integrity. The composites displayed favourable properties for use in aerospace, automotive, and marine industries, offering strength, lightweight characteristics, and corrosion resistance. The OTFs validated the material's performance, making it a viable alternative to conventional materials. Consequently, aluminum-agro-marine waste composites demonstrate excellent mechanical properties, with high potential for industrial applications.

Integrating Textile Waste in Interior Design for Sustainable Furnishings: A Case Study of a Cafe Library

This paper explores the integration of textile waste in interior design, focusing on sustainable furniture and decorative elements. With growing environmental concerns, the demand for eco-friendly practices in the industry is increasing. This research investigates methods for repurposing textile waste and the role of eco-friendly resins in creating durable, aesthetic furniture pieces. A cafe library is the case study for developing design prototypes incorporating textile waste, presenting innovative, sustainable solutions for the interior design industry.

Assessment of active back-support exoskeleton for carpentry task: muscle activity and range of motion

Purpose This study aims to investigate the impact of active back-support exoskeletons on muscle activity and range of motion during carpentry tasks, focusing on addressing back disorders in the construction sector. The purpose is to understand how exoskeletons can serve as ergonomic solutions in the construction industry. Design/methodology/approach Sixteen participants engaged in simulated carpentry framing tasks under “no-exoskeleton” and “active-exoskeleton”. The study measured muscle activity and range of motion during tasks such as measuring, assembly, moving, lifting, installing and nailing. This experimental design was chosen to assess the effectiveness of the exoskeleton in different carpentry scenarios. Findings The results indicate that the active back-support exoskeleton reduced the back’s range of motion by 3%–26% during various tasks, suggesting its impact on movement limitations. Additionally, the exoskeleton led to reduced muscle activity in most muscles, with task-specific variations. There was an increase in muscle activity by 1–35%, during measuring and assembly tasks, revealing nuanced effects. Research limitations/implications The findings may be task-specific, however, the absence of a consistent correlation between muscle activity and range of motion suggests potential complexities that warrant further investigation. Originality/value This research contributes to the understanding of exoskeletons in construction, emphasizing that task-specific designs are crucial for addressing unique work requirements. The study provides valuable data on the diverse effects of exoskeletons in different carpentry tasks.

CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquiring such labelled data is often challenging in industrial environments due to the rarity of faults and the labour-intensive nature of the labelling process. To address this, we propose the chroma-augmented semi-supervised anomaly detection (CASSAD) method, designed to perform effectively with limited labelled data. At the core of CASSAD is the one-class SVM (OC-SVM), a model specifically developed for anomaly detection in cases where labelled anomalies are scarce. We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. CASSAD introduces chroma features, such as chroma energy normalised statistics (CENS), the constant-Q transform (CQT), and the chroma short-time Fourier transform (STFT), enhanced through filtering to capture rich harmonic information from idler sounds. To reduce feature complexity, we utilize the mean and standard deviation (std) across chroma features. The dataset is further augmented using additive white Gaussian noise (AWGN). Testing on an industrial dataset of idler sounds, CASSAD achieved an AUC of 96% and an accuracy of 91%, surpassing a baseline autoencoder and other traditional models. These results demonstrate the model’s robustness in detecting anomalies with minimal dependence on labelled data, offering a practical solution for industries with limited labelled datasets.

A Comprehensive Risk Based IT Governance Framework for SaaS Adoption in Capital Market

ABSTRACT Software as-a-service (SaaS) adoption is essential in regulated industry such as capital market to bring in scalability, required to deal with the fluctuations in trading volumes, and agility, required to stay responsive to changing market conditions. In this context, the study examines the factors that the capital market organization look for in adopting SaaS. Based on the perceived risk theory and prospect theory, we propose a perceived risk based IT governance framework in SaaS adoption decision in capital market. Following a case study-based research methodology, we examined the factors that influence the risk perception of the decision makers about SaaS-based solution in a capital market industry. We found that the decision to opt for SaaS adoption in organization depends on (i) the top management’s consideration for risk preference, problem framing, decision domain, and data security, and (ii) organizational processes in practice such as IT governance archetype and risk management processes. Based on this, we generate a risk score for a capital market firm to weigh potential benefits and risks in adopting SaaS. In this study, we assume that the top management provides guidance toward firm’s risk appetite which is then compared to the generated risk score to decide for SaaS adoption. This study contributes to the technology adoption literature by emphasizing the role of individual risk preferences and problem framing in influencing technology adoption decisions particularly for technologies that pose certain risks. Finally, the study contributes by bringing forth the role of an IT governance framework in assessing SaaS-related risks.

Development of high-performance biocomposites through lignin modification and fiber reinforcement

This study aimed to develop and characterize novel biocomposites incorporating tall oil fatty acid (TOFA)-modified lignin (TeL), citric acid-esterified polyvinyl alcohol (CeP), and unbleached fibers (UNB) to enhance thermal and mechanical properties while utilizing renewable resources. The research addressed the growing demand for sustainable high-performance materials in various industrial applications. Kraft lignin was modified through esterification with TOFA, while polyvinyl alcohol (PVA) was crosslinked using citric acid. These modified components were combined with UNB to create biocomposites with varying compositions. The materials were characterized using Fourier-transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and tensile strength testing. FTIR analysis confirmed successful esterification of lignin and PVA, evidenced by a strong ester carbonyl peak at 1730 cm⁻1. DMA results revealed significant improvements in viscoelastic properties, with the highest glass transition temperature (Tg) of 179.47 °C observed in the sample containing maximum TeL and CeP content. TGA demonstrated enhanced thermal stability, with samples containing higher TeL and CeP content exhibiting increased char formation and residual masses up to 47% at 500 °C. Mechanical testing showed a strong correlation between composition and performance, with the optimal formulation (TeL12-CeP4-UNB4) achieving a tensile strength of 8.7 MPa and a tensile modulus of 59.3 MPa. Potential applications of these high-performance biocomposites include sustainable alternatives for packaging, automotive components, building materials or insulation, electronic devices and other industries where enhanced thermal and mechanical properties are required. These materials present a viable option for replacing conventional petroleum-based polymers, contributing to the advancement of eco-friendly industrial solutions.

Identification of dynamic properties of a flooded ballast layer using impact excitation technique and minimization algorithm

AbstractIn recent times, the railway sector has faced a pressing need to expand its railway network, resulting in the widespread adoption of ballasted tracks. These tracks are favored for their ease of construction, making them a cost-effective solution for the railway industry. However, with global warming causing more volatile weather conditions leading to multiple occurrences of flooding in Thai railway structures. Ballast dynamic properties play a crucial role in the analysis of the performance and safety of the railway system. In response to these challenges, this study investigates how flooding affects the dynamic behavior of ballasted track structures. The impact hammering method is frequently used to evaluate the dynamic properties of ballasted tracks. However, previous studies have often simplified the analysis by treating the sleeper-ballast system as a single-degree-of-freedom (SDOF) system. This simplification overlooks the spatial variability in the conditions of the ballast layer. To achieve a more accurate representation, our study employs a multi-degree-of-freedom (MDOF) model, which considers the variability in the stiffness properties of the sleepers. The model for the sleeper-ballast system is constructed based on Timoshenko beam theory resting on an elastic foundation. Simulation of flooding on the ballast layer is conducted using a self-built wooden and acrylic box containing a full-scale track structure. This paper utilizes an instrumented impact hammering method, allowing for a comprehensive assessment of the dynamic properties of the ballast layer. To identify the dynamic characteristics of the ballast, a minimization algorithm is applied. The findings of this research indicate that as the water level rises, the stiffness decreases. This study provides crucial insights into how flooding impacts the dynamic properties of ballasted tracks, offering insights that can be applied to enhance the safety analysis of ballasted tracks in the future.

Mechanical and tribology performance of nanostructured ZrN-Cu coatings obtained by hybrid HiPIMS-DCMS technology

One of the biggest players in the world economy is the naval industry, which mainly controls the merchandise transportation sector. Any issue with ships could represent millions of USD of loss and increases in the cost of goods for the population worldwide. Two main problems which this industry has fought are corrosion and biofouling. Lastly, the pollution of the sea has gained importance, and more strict policies have been applied regarding the use of certain products by this industry. One of these is paintings, which represented this industry's definitive solution to avoid the mentioned problems for a long time. This situation allowed to explore other solutions like PVD coatings through multifunctional coatings. Zirconium nitride has been demonstrated to be useful in resisting corrosion with reliable mechanical properties. However, this material does not possess antimicrobial action. The present study presents a nanostructured coating combining ZrN with Cu, which works as a biocide, contributing to the desired multifunctionality. The developed coating was obtained using a hybrid magnetron co-sputtering employing High-power impulse (HiPIMS) and direct current (DCMS) power sources under a reactive atmosphere. SEM, EDX, XRD and Raman spectroscopy were used to assess the physico-chemical properties of the coatings. Besides, depth-sensing nano-indentation explored the mechanical properties. The tribological performance was tested by a reciprocating tribometer under dry and wet (with 3.5 % w/w NaCl solution) contact conditions and employing a soda lime glass ball as a counterbody. The results showed that adding Cu to ZrN through this technology resulted in a limited hardness reduction from 19 (pure ZrN) to 14 GPa. Also, the chemical activation with NaOCl solution softens the obtained coating and, together with the saline solution, influences the wear resistance. However, the nanostructured coating has been demonstrated to be suitable for use under real conditions, without loss of its protection over the used substrate. It opens a new possibility of a solution for the naval industry.

Polysaccharide-Based Composite Films: Promising Biodegradable Food Packaging Materials.

With growing concerns about environmental protection and sustainable development, the development of new biodegradable food packaging materials has become a significant focus for the future of food packaging. Polysaccharides, such as cellulose, chitosan, and starch, are considered ideal biodegradable packaging materials due to their wide availability, good biocompatibility, and biodegradability. These materials have garnered extensive attention from researchers in food packaging, leading to considerable advancements in the application of polysaccharide-based food packaging films, coatings, aerogels, and other forms. Therefore, this review focuses on the application of polysaccharide-based packaging films in food storage and preservation and discusses their preparation methods, application progress, challenges, and future development directions. Through an in-depth analysis of the existing literature, this review aims to provide sustainable and environmentally friendly solutions for the food packaging industry.

Editorial on Wireless Power Transfer (WPT): Present Advancements, Applications, and Future Outlooks

The Evolution and Significance of Wireless Power Transfer (WPT): Wireless power transfer (WPT) technologies, which enable the transmission of electrical energy without the need for physical connectors, have emerged as a transformative solution in various industries [...]

Modeling of the Nanofiltration Process Based on Convective Diffusion Theory

The article formulates the state of the problem of improving the theoretical calculation of the nanofiltration kinetic characteristics in the time cycle of separation of industrial solutions containing copper(II), iron(III), trisodium phosphate and OP-10 (a wetting agent used in electroplating, a product of treating a mixture of mono- and dialkylphenols with ethylene oxide) using the equations of convective diffusion, hydrodynamics and mass transfer. To calculate the kinetic characteristics of the nanofiltration process, the mathematical model was improved by numerically solving the equations of convective diffusion, the Navier–Stokes equation and the flow continuity equation in a polar coordinate system with initial and boundary conditions. The theoretical results obtained in the process of an analytical solution of the system of equations allow calculating changes in concentrations in the permeate and retentate tracts and the permeate volume during nanofiltration separation. The acceptability of the developed nanofiltration method for separating solutions is assessed by comparing the calculated data according to the mathematical model with the experimental data obtained on the nanofiltration unit during separation of solutions containing copper(II), iron(III), trisodium phosphate and OP-10.

Earthwork Network Architecture (ENA): Research for Earthwork Quantity Estimation Method Improvement with Large Language Model

Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects.

Navigating the Challenges and Seizing Opportunities in Implementing Closed-Loop Supply Chains

Closed-Loop Supply Chains (CLSCs) present a transformative solution for industries seeking sustainability by reintegrating returned products through recycling, refurbishment, and remanufacturing, reducing waste and dependency on added resources. While offering substantial benefits, CLSC adoption faces challenges, including high infrastructure costs, complex reverse coordination, and inconsistent regulatory support, which hinder widespread application. Through a mixed-method approach using surveys and case studies, this study reveals barriers such as financial constraints and operational restructuring needs, while highlighting enablers like digital tools (e.g., blockchain, IoT) that enhance efficiency and transparency. Furthermore, it emphasizes the critical role of regulatory incentives (subsidies, tax breaks, and extended producer responsibility mandates) and strategic partnerships that pool resources and share risks, essential for small and medium-sized enterprises (SMEs) aiming to implement CLSCs effectively. By proposing a practical CLSC framework, informed by empirical data, this research provides actionable insights for businesses to embrace circular economy principles, aligning environmental responsibility with corporate resilience, profitability, and consumer expectations. CLSCs, therefore, are not only an environmental solution but also a strategic opportunity that addresses sustainability imperatives while meeting regulatory requirements and advancing competitive advantage.

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

Constructing organic-inorganic woven hybrid membrane for highly-selective catalytic conversion of lignin-based phenolic molecule to value-added products

Addressing the global need for sustainable chemical processes, this study introduces an innovative Woven Hybrid Membrane (WHM) specifically designed for the efficient oxidation of vanillyl alcohol (VAA) to vanillin (VLN). This transformation is crucial for converting lignocellulosic biomass into valuable chemical products, aligning with sustainable development goals. Utilizing a novel catalytic system that combines 2,2,6,6-tetramethylpiperidine 1-oxyl (TMP) and copper on a unique mesh-like structure, the WHM enhances both the economic and environmental viability of the process. This design not only overcomes the limitations of powdered catalysts, which are challenging to recover and reuse, but also promotes high catalytic efficiency and selectivity. By employing oxygen as the oxidant, our system maintains near 100% selectivity for VLN, demonstrating high conversion rates across multiple cycles without significant degradation in performance. Advanced analytical techniques, including electrochemical analysis and Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), were used to explore the catalyst's functionality and the reaction mechanism. The results confirm the stability and effectiveness of the WHM, showcasing its potential as a reusable, highly efficient, and environmentally friendly catalyst. This research represents a significant step forward in the field of sustainable industrial chemistry, offering a robust solution for the bio-refinery industry's push towards greener processes.

Innovative heat sink design for enhanced cooling efficiency in Dual Active Bridge (DAB) topology

Abstract After in-depth research and analysis, aiming at the heat generation mechanism and limitations of the current heat dissipation method, this paper proposes an innovative heat dissipation design scheme. By optimizing the structural design of the heat sink, the heat dissipation surface area is increased, thus achieving more efficient heat emission and maintaining the optimal operating temperature of the equipment. Experimental data show that this new heat dissipation design significantly improves the cooling efficiency, an increase of about 60%, and provides support for the performance improvement of high-power power products, which brings a reliable and efficient cooling solution for related industries.

Economic Feasibility of 5G-Based Autonomous Mobile Robots Solutions for Industry 4.0

Economic Feasibility of 5G-Based Autonomous Mobile Robots Solutions for Industry 4.0

SH17: A dataset for human safety and personal protective equipment detection in manufacturing industry

Workplace accidents continue to pose significant human safety risks, particularly in the construction and manufacturing industries. The necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. We focus on developing non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN). The aim is to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The validation of the model across cross-domain datasets indicates that integrating these technologies can substantially enhance safety management systems. This approach offers a scalable and efficient solution for industries seeking to comply with human safety regulations while safeguarding their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.

Machine learning techniques for enhancing security in financial technology systems

The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.