Search Results (2,014)

The Personal Care Products (PCP) industry, encompassing cosmetics, hygiene, and personal care items, serves millions of consumers daily and operates under constant pressure for innovation, agility, and sustainability. Within this context, supply chains are viewed as complex and integrated systems, composed of interrelated elements whose interactions determine overall performance and are influenced by external factors. Disruptions—particularly those involving indirect suppliers—can propagate throughout the network, affecting operations, reputation, and business outcomes. Despite the importance of the topic, empirical studies that systematically identify and prioritize these risks in the PCP sector remain scarce, which motivated the conduct of this study. Thus, the aim of this research is to identify, analyze, and evaluate the main supply risks faced by the PCP industry, considering severity, occurrence, and detection capability. Methodologically, the research employed an exploratory multi-case design, carried out in three steps: a literature review to identify key supply chain risks; structured interviews with industry experts to analyze and evaluate these risks; and the application of Gray Relational Analysis (GRA) to aggregate expert judgments and construct a prioritized risk ranking. This combination of qualitative and quantitative techniques provided a detailed foundation for analyzing and interpreting the main risks in the Brazilian PCP sector. The results indicate that indirect supplier failure is the most critical risk, prioritized by 70% of the companies studied. Other significant risks include the inability to meet changes in demand, import issues, lack of supply chain visibility, natural and social disasters, and sustainability or reputational concerns. Consequently, this study contributes to a systemic understanding of risk management in the PCP industry supply chain, providing managers with a practical mapping of critical points and highlighting concrete opportunities to strengthen integration, anticipate disruptions, and enhance operational resilience and performance across the sector. Full article

In Hungary, on-site mixed stabilization of cohesive soil is considered only as soil improvement not a proper pavement layer, therefore its bearing capacity is not taken into account when designing pavement. It was our hypothesis that on low-volume roads built on cohesive soil, lime or lime–cement stabilization can be an alternative to granular base layers. A case study was conducted to obtain initial results and to verify the research methodology. The efficacy of lime stabilization was evaluated across eight experimental road sections, with a view of assessing its structural and economic performance in comparison with crushed stone base layers reinforced with geo-synthetics. The results of the testing demonstrated elastic moduli of 120–180 MPa for the lime-stabilized layers, which closely matched the 200–280 MPa range observed for the crushed stone bases. The results demonstrated that lime stabilization offers a comparable load-bearing capacity while being the most cost-effective solution. Furthermore, this approach enhances sustainability by enabling the utilization of local soils, reducing reliance on imported materials, minimizing transport-related costs, and lowering carbon emissions. Lime stabilization provides a durable, environmentally friendly alternative for road construction, effectively addressing the challenges of material scarcity and rising construction costs while supporting infrastructure resilience. The findings highlight its potential to replace traditional base layers without compromising structural performance or economic viability. Full article

Against the backdrop of the rapid proliferation of electric vehicles (EVs), gender-oriented behavioral mechanisms remain underexplored, particularly the unique pathways of female users in usage experience, value assessment, and purchase decision-making. This study constructs an integrated framework based on the Stimulus–Organism–Response (SOR) model, leveraging social media big data to analyze in depth how gender differences influence EV users’ purchase intentions. By integrating natural language processing techniques, grounded theory coding, and structural equation modeling (SEM), this study models and analyzes 272,083 pieces of user-generated content (UGC) from Chinese social media platforms, identifying key functional and emotional factors shaping female users’ perceptions and attitudes. The results reveal that esthetic value, safety, and intelligent features more strongly drive emotional responses among female users’ decisions through functional cognition, with gender significantly moderating the pathways from perceived attributes to emotional resonance and cognitive evaluation. This study further confirms the dual mediating roles of functional cognition and emotional experience and identifies a masking (suppression) effect for the ‘intelligent perception’ variable. Methodologically, it develops a novel hybrid paradigm that integrates data-driven semantic mining with psychological behavioral modeling, enhancing the ecological validity of consumer behavior research. Practically, the findings provide empirical support for gender-sensitive EV product design, personalized marketing strategies, and community-based service innovations, while also discussing research limitations and proposing future directions for cross-cultural validation and multimodal analysis. Full article

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article

This paper addresses the problem of observer-based control (OBC) for nonlinear systems with time delay (TD). A novel hybrid modeling framework for nonlinear TD systems is first introduced by synergistically combining TD Takagi–Sugeno (TDTS) fuzzy and Lipschitz approaches. The proposed methodology broadens the range of representable systems by enabling Lipschitz nonlinearities to fulfill dual functions: they may describe essential dynamic behaviors of the system or represent aggregated uncertainties, depending on the specific application. The proposed TDTS–Lipschitz (TDTSL) model class features measurable premise variables while accommodating Lipschitz nonlinearities that may depend on unmeasurable system states. Then, through the construction of an appropriate Lyapunov–Krasovskii (L-K) functional, we derive sufficient conditions to ensure exponential stability of the augmented closed-loop model. Subsequently, through a decoupling methodology, these stability conditions are reformulated as a set of linear matrix inequalities (LMIs). Finally, the proposed OBC design is validated through application to a continuous stirred tank reactor (CSTR) with lumped uncertainties. Full article

In order to establish a general design methodology for water-based electromagnetic metamaterial absorber microstructures, a topology optimization method for water-based metamaterial absorber microstructures design was proposed in this paper. According to Mie resonance and impedance matching theory, the realization mechanism and physical model of the broadband water-based metamaterial absorber were constructed. The highest average in-band absorption rate was taken as the design object; the topological optimization model for water-based metamaterial absorber design was established. A metamaterial absorber microstructure with 16 discretized water columns inside the unit cell was designed as an example. The obtained structure exhibited a very high average in band absorption rate in the specific frequency band. The proposed method was a collaborative optimization approach that employed a single type of design variable, namely water column height, to simultaneously adjust surface impedance matching and specific resonant modes. It provided a feasible method for achieving the highest average absorption rate within a specific band. Full article

Reproducibility is a cornerstone of scientific progress, yet in X-ray computed tomography (CT) reconstruction, it remains a critical and unresolved challenge. Current benchmarking practices in CT are hampered by the scarcity of openly available datasets, the incomplete or task-specific nature of existing resources, and the lack of transparent implementations of widely used methods and evaluation metrics. As a result, even the fundamental property of reproducibility is frequently violated, undermining objective comparison and slowing methodological progress. In this work, we analyze the systemic limitations of current CT benchmarking, drawing parallels with broader reproducibility issues across scientific domains. We propose an extended data model and formalized schemes for data preparation and quality assessment, designed to improve reproducibility and broaden the applicability of CT datasets across multiple tasks. Building on these schemes, we introduce checklists for dataset construction and quality assessment, offering a foundation for reliable and reproducible benchmarking pipelines. A key aspect of our recommendations is the integration of virtual CT (vCT), which provides highly realistic data and analytically computable phantoms, yet remains underutilized despite its potential to overcome many current barriers. Our work represents a first step toward a methodological framework for reproducible benchmarking in CT. This framework aims to enable transparent, rigorous, and comparable evaluation of reconstruction methods, ultimately supporting their reliable adoption in clinical and industrial applications. Full article

Because of the increased prevalence of chronic diseases, older adults frequently take many medications. However, adhering to a medication treatment tends to be difficult. The lack of medication adherence can cause health problems or even patient death. This paper describes the methodology used in developing a mobile augmented reality (MAR) pill box. The proposal supports patients in adhering to their medication treatment. First, we explain the design and construction of the automatic pill box, which includes alarms and uses QR codes recognized by the MAR system to provide medication information. Then, we explain the development of the MAR system. We conducted a preliminary survey with 30 participants to assess the feasibility of the MAR app. One hundred older adults participated in the survey. After one week of using the proposal, each patient answered a survey regarding the proposal functionality. The results revealed that 88% of the participants strongly agree and 11% agree that the app is a support in adhering to medical treatment. Finally, we conducted a study to compare the time elapsed between the scheduled time for taking the medication and the time it was actually consumed. The results from 189 records showed that using the proposal, 63.5% of the patients take medication with a maximum delay of 4.5 min. The results also showed that the alarm always sounded at the scheduled time and that the QR code displayed always corresponded to the medication that had to be consumed. Full article

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

The aim of this study is to investigate how the positioning of outrigger systems affects the seismic performance of high-rise buildings with either reinforced concrete (RC) shear walls or buckling-restrained braces (BRBs) in the core. Two important questions emerge as the focus and direction of the study: (1) How does the structural performance change when outriggers are placed at various positions? (2) How do outrigger systems affect structural behavior under sequential earthquake scenarios? Nonlinear time history analyses were employed as the primary methodology to evaluate the seismic response of the two reinforced concrete buildings with 24 and 48 stories, respectively. Each building type was developed for two different core configurations: one with a reinforced concrete shear wall core and the other with a BRB core system. Each analysis model also includes outrigger systems constructed with BRBs positioned at different floor levels. Five sequential ground motion records were used to assess the effects of main- and aftershocks. The analysis results were evaluated not only based on displacement and force demands but also using a damage measure called the Park-Ang Damage Index. In addition, displacement-based metrics, particularly the maximum inter-story drift ratio (MISD), were also utilized to quantify lateral displacement demands under consecutive seismic loading. With the results obtained from this study, it is aimed to provide design-oriented insights into the most effective use of outrigger systems formed with BRB in high-rise RC buildings and their functions in increasing seismic resistance, especially in areas likely to experience consecutive seismic events. Full article

Precast concrete frames integrating ultra-high-performance concrete (UHPC) beams and high-strength concrete (HSC) columns offer exceptional seismic resilience and construction efficiency. However, a performance-based seismic design methodology tailored for this hybrid structural system remains underdeveloped. This study aims to develop and validate a direct displacement-based design (DDBD) procedure specifically for precast UHPC-HSC frames. A novel six-tier performance classification scheme (from no damage to severe damage) was established, with quantitative limit values of interstory drift ratio proposed based on experimental data and code calibration. The DDBD methodology incorporates determining the target displacement profile, converting the multi-degree-of-freedom system to an equivalent single-degree-of-freedom system, and utilizing a displacement response spectrum. A ten-story case study frame was designed using this procedure and rigorously evaluated through pushover analysis. The results demonstrate that the designed frame consistently met the predefined performance objectives under various seismic intensity levels, confirming the effectiveness and reliability of the proposed DDBD method. This work contributes a performance oriented seismic design framework that enhances the applicability and reliability of UHPC-HSC structures in earthquake regions, offering both theoretical insight and procedural guidance for engineering practice. Full article

The main objective of this work is to provide a comprehensive explanation of the Register Transfer Level (RTL) to Graphic Data System II (GDSII) flow for designing custom Field-Programmable Gate Array (FPGA) architectures at the 130 nm technology node using the SKY130 Process Design Kit (PDK). By leveraging open-source tools—specifically OpenLane and OpenFPGA—this study details the methodology and implementation steps required to generate a GDSII layout of a custom FPGA. OpenLane offers an integrated RTL-to-GDSII flow by combining multiple Electronic Design Automation (EDA) tools, while OpenFPGA enables the construction of flexible and customizable FPGA architectures. The article covers key aspects of the RTL-to-GDSII workflow, including RTL file configuration, the utilization of configuration variables for physical design, hierarchical chip design, macro and core implementation, chip-level integration, and gate-level simulation. Experimental results validate the proposed workflow, showcasing the successful transformation from RTL to GDSII. The findings of this research provide valuable insights for researchers and engineers in the FPGA design field, advancing the state of the art in FPGA architecture development. Full article

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems. Full article

Nature-Based Solutions (NBS) represent an alternative for achieving environmental and resilience goals in diverse global contexts with varying needs. As such, NBS can be understood as processes involving actions that promote circular economy (CE) strategies within their function. Therefore, this research aims to conduct a systematic literature review to identify and analyze the main NBS applied and explore how they are associated with CE strategies. This study performs a systematic literature review of NBS and their relationship with the CE using the PRISMA methodology, analyzing a total of 32 articles retrieved from the SCOPUS database. The main NBS include constructed wetlands, green infrastructure, and soil restoration and enrichment solutions. Constructed wetlands are linked to strategies such as recycling and reuse due to their role in treating urban and domestic wastewater for reuse, thereby increasing water availability. Green infrastructure is associated with strategies like redesign and reduction, as it involves the use of lower-impact materials and designs for rainwater harvesting and thermal comfort improvement. Soil enrichment and remediation solutions are connected to reuse and recycling strategies, as most derive from organic waste composting or microorganisms. NBS and CE strategies highlight how these solutions not only provide direct environmental benefits but also, when analyzed from a sustainability perspective, can offer social and economic benefits. Furthermore, understanding their relationship will facilitate their integration into regulations for transitioning toward circularity in industries and cities. The contribution of this article lies in synthesizing and systematizing the evidence on how NBS operationalizes CE strategies, identifying the main mechanisms and gaps, and proposing a conceptual model that can guide future research and policy design. Full article

Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. This study explores how game-based learning, supported by situated learning theory and game design elements, can boost learner motivation and knowledge construction. Using 20,293 user comments from the Chinese video platform Bilibili, the study applies sentiment analysis and LDA to uncover users’ sentimental tendencies and cognitive themes. The analysis identifies four core themes: (1) The application of contextual strategies in language learning, (2) Autonomous exploration and active participation in gamified learning, (3) Progressive enhancement of logical thinking in gamified environments, and (4) Teaching innovation in promoting knowledge construction and deepening. Building on these findings, the study further develops a cognitive scaffolding model integrating “contextualization–procedural logic–active construction” to explain the mechanisms of motivation–cognition interaction in gamified learning. Methodologically, this study innovatively combines LDA topic modeling with sentiment analysis, offering a new approach for multidimensional measurement of learner attitudes in gamified education. Theoretically, it extends the application of situated learning theory to digital education, providing systematic support for instructional design and meaning-making. Findings enrich empirical research on gamified learning and offer practical insights for optimizing educational platforms and personalized learning support. Full article