Search Results (6,121)

The Industrial Internet of Things (IIoT) has revolutionized smart manufacturing by enhancing automation, operational efficiency, and data-driven decision making. However, the interconnected nature of IIoT devices raises significant concerns about security and system integrity. This paper examines the application of blockchain technology to address these challenges, with a focus on data integrity, access control, and traceability. This paper proposes a blockchain-based framework that leverages decentralized security, smart contracts, and edge computing to mitigate vulnerabilities, including unauthorized access and data manipulation. The framework is evaluated for practicality, scalability, and constraints within IIoT environments. Additionally, this paper discusses the integration of complementary security mechanisms, such as Zero Trust architecture and AI-driven anomaly detection, to provide a comprehensive cybersecurity solution for the Industrial Internet of Things (IIoT). Full article

The cosmetic industry in the Ecuadorian Amazon region faces the challenge of competitively integrating locally sourced plant-based raw materials into efficient and sustainable production processes. This study proposes the design of a pilot plant for the production of a cosmetic emulsion (CE), using oils extracted from Morete (Mauritia flexuosa) and Ungurahua (Oenocarpus bataua), with a focus on process intensification to reduce both capital investment and resource consumption. Process design methodologies and computational simulation (SuperPro Designer V10) were applied, along with Systematic Layout Planning (SLP) principles to optimize spatial configuration. The intensified scheme enabled the integration of extraction lines, reducing the number of major equipment units from 12 to 9 and lowering the investment from USD 1,016,000 to USD 719,000. Energy and environmental indicators showed consumption levels of 5.86 kWh and 48.4 kg of water per kg of cream, which are lower than those reported for other natural cosmetics plants. The intensified design achieved a Net Present Value (NPV) of USD 577,000 and a payback period of 3.93 years. Furthermore, solid by-products were valorized through circular economy principles. This approach offers a feasible, viable, and sustainable solution for the utilization of these Amazonian oils in the cosmetic industry. Full article

Diagnosing Alzheimer’s disease (AD) remains a significant challenge due to its multifactorial nature and the limitations of traditional diagnostic methods, such as clinical assessments and neuroimaging, which often lack the specificity and sensitivity required for early detection. The urgent need for innovative diagnostic tools is further underscored by the potential of early intervention to improve treatment outcomes and slow disease progression. Recent advancements in biosensing technologies offer promising solutions for precise and non-invasive AD detection. Electrochemical and optical biosensors, in particular, provide high sensitivity, specificity, and real-time detection capabilities, making them valuable for identifying key biomarkers, including amyloid-β (Aβ) peptides and tau proteins. Additionally, integrating these biosensors with nanomaterials enhances their performance, stability, and detection limits, enabling improved diagnostic accuracy. Beyond nanomaterial-based sensors, emerging innovations in microfluidics, surface plasmon resonance (SPR), and artificial intelligence-assisted biosensing further contribute to the development of next-generation AD diagnostics. This review provides a comprehensive analysis of the latest advancements in biosensing technologies for AD, highlighting their mechanisms, advantages, and future perspectives in detecting biomarkers from biological fluids. Full article

Resource-exhausted cities face dual crises of economic stagnation and ecological degradation, which is primarily attributable to the inefficient use of industrial land. The redevelopment of industrial land has emerged as a crucial solution to the “resource depletion-urban decline” dilemma. The issue of inefficient industrial land use in resource-exhausted cities is of great significance as it directly impacts both economic development and ecological protection. Therefore, finding effective ways to redevelop this land is essential for the sustainable development of these cities. This research takes Hegang, a representative resource-exhausted city in China, as a case study. A multi-dimensional evaluation framework and an adaptive redevelopment strategy system are constructed in this research. By integrating data related to land use status, land use efficiency, policy constraints, and development potential, a parcel-scale assessment model is established. This model consists of 4 primary indicators and 13 secondary indicators. Through this model, 11.01 km² of inefficient industrial land in the main urban area of Hegang is identified. Standard deviation ellipse and kernel density analysis are employed to reveal the spatial pattern of inefficient land. The results show that the inefficient industrial land in Hegang exhibits a pattern of “overall dispersion with localized agglomeration”. It is found that idle and abandoned land are the dominant types of inefficient industrial land in Hegang’s main urban area, accounting for 69.7% of the total. This finding provides a clear understanding of the nature of the inefficient land use problem in resource-exhausted cities. A strategic framework is proposed, which incorporates classified governance, dynamic restoration, and multi-stakeholder collaboration. This framework offers a governance toolkit with both theoretical depth and practical value for resource-exhausted cities. Breaking the locked relationship between industrial land and resource dependence promotes the deep integration of spatial restructuring and sustainable transformation. The findings of this research provide significant scientific insights for similar cities worldwide to address the challenges they face and achieve harmony between human activities and land use. Future research could focus on further refining the evaluation framework and redevelopment strategies based on different regional characteristics and resource endowments. Full article

High-precision indoor positioning technology is increasingly prominent in its application value in emerging fields such as the Industrial Internet of Things, smart cities, and autonomous driving. 5G networks can transmit large-bandwidth signals and have the capability to transmit and receive signals with multiple antennas, enabling the simultaneous acquisition of angle and distance observation information, providing a solution for high-precision positioning. Differences in the types and quantities of observation information in complex environments lead to positioning scenarios having a multimodal nature; how to propose an effective observation model that covers multimodal scenarios for high-precision robust positioning is an urgent problem to be solved. This paper proposes a three-stage time–frequency synchronization method based on group peak time sequence tracing. Timing coarse synchronization is performed through a group peak accumulation timing coarse synchronization algorithm for multi-window joint estimation, frequency offset estimation is based on cyclic prefixes, and finally, fine timing synchronization based on the primary synchronization signal (PSS) sliding cross-correlation is used to synchronize 5G signals to chip-level accuracy. Then, a tracking loop is used to track the Positioning Reference Signal (PRS) to within-chip accuracy, obtaining accurate distance information. After obtaining distance and angle information, a high-precision positioning method for multimodal scenarios based on 5G heterogeneous measurement combination is proposed. Using high-precision angle observation values as intermediate variables, this algorithm can still solve a closed-form positioning solution under sparse observation conditions, enabling the positioning system to achieve good positioning performance even with limited redundant observation information. Full article

Soil erosion and surface runoff on forest road slopes are major environmental concerns, especially in harvested areas, making effective mitigation strategies essential for sustainable forest management. The study compared the effectiveness of three selected woody species on forest road slopes as a possible mitigating action for runoff and soil erosion in harvested sites. Plots measuring 2 m × 3 m were set up with three species—alder (Alnus glutinosa (L.) Gaertn.), medlar (Mespilus germanica L.) and hawthorn (Crataegus monogyna Jacq.)—on the slopes of forest roads. Within each plot, root abundance, root density, canopy percentage, canopy height, herbaceous cover percentage, and selected soil characteristics were measured and analyzed. Root frequency and Root Area Ratio (the ratio between the area occupied by roots in a unit area of soil) measurements were conducted by excavating 50 × 50 cm soil profiles at a 10-cm distance from the base of each plant in the four cardinal directions. The highest root abundance and RAR values were found in hawthorn, followed by alder and medlar in both cases. The same order of magnitude was evidenced in runoff (255.42 mL m⁻² in hawthorn followed by 176.81 mL m⁻² in alder and 67.36 mL m⁻² in medlar) and the reverse order in terms of soil erosion (8.23 g m⁻² in hawthorn compared to 22.5 g m⁻² in alder and 50.24 g m⁻² in medlar). The results of the study confirm that using plant species with dense and deep roots, especially hawthorn, significantly reduces runoff and erosion, offering a nature-based solution for sustainable forest road management. These results highlight the need for further research under diverse ecological and soil conditions to optimize species selection and improve erosion mitigation strategies. Full article

Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. To this aim, the city of Granada, Spain, was used as a case study. The city is classified into different Local Climate Zones (LCZs) to determine the area available for retrofitting GRs in built-up areas. A total of 14 Surface Temperature Collection 2 Level-2 images were acquired through Landsat 8–9, while 14 images for spectral indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Building Index (NDBI), and Proportion Vegetation (PV) were calculated from Sentinel-2 in dates coinciding or close to LST images. Additional factors were considered including the sky view factor (SVF) and water distance (WD). The results suggest that Granada has limited suitable areas for retrofitting GRs, and available areas can reduce LST with a moderate impact, at an average of 1.45 °C; however, vegetation plays an important role in decreasing LST. This study provides a methodological example to identify the benefits of implementing GRs in reducing LST in semi-arid cities and recommends a combination of strategies for LST mitigation. Full article

Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. This study investigates the feasibility and benefits of embedding three prominent metaheuristic algorithms, the Genetic Algorithm (GA), the Firefly Algorithm (FA), and the Group Search Optimizer (GSO) embedded into the ANSYS Parametric Design Language (APDL). The performance of each optimizer was assessed in three case studies. The first two are spatial truss structures, one comprising 22 bars and the other 25 bars, commonly used in structural optimization research. The third is a planar 15-bar truss in which member sizing and internal topology were simultaneously refined using a Discrete Topology (DT) variable method. For both the FA and the GSO, enhanced ranger-movement strategies were implemented to improve exploration–exploitation balance. Comparative analyses were conducted to assess convergence behavior, solution quality, and computational efficiency across the different metaheuristics. The results underscore the practical advantages of a fully integrated APDL approach, highlighting improvements in execution speed, workflow automation, and overall robustness. This work not only provides a comprehensive performance comparison of GA, FA, and GSO in structural optimization tasks, but it can also be considered a novelty in employing native APDL routines for metaheuristic-based finite element analysis. Full article

In recent years, wireless sensor networks (WSNs) have been employed across various domains, including military services, healthcare, disaster response, industrial automation, and smart infrastructure. Due to the absence of fixed communication infrastructure, WSNs rely on ad hoc connections between sensor nodes to transmit sensed data to target nodes. Within a WSN, a sensor node whose failure partitions the network into disconnected segments is referred to as a critical node or cut vertex. Identifying such nodes is a fundamental step toward ensuring the reliability of WSNs. The critical node detection problem (CNDP) focuses on determining the set of nodes whose removal most significantly affects the network’s connectivity, stability, functionality, robustness, and resilience. CNDP is a significant challenge in network analysis that involves identifying the nodes that have a significant influence on connectivity or centrality measures within a network. However, achieving an optimal solution for the CNDP is often hindered by its time-consuming and computationally intensive nature, especially when dealing with large-scale networks. In response to this challenge, we present a method based on particle swarm optimization (PSO) for the detection of critical nodes. We employ discrete PSO (DPSO) along with the modified position equation (MPE) to effectively solve the CNDP, making it applicable to various k-vertex variations of the problem. We examine the impact of population size on both execution time and result quality. Experimental analysisusing different neighborhood topologies—namely, the star topology and the dynamic topology—was conducted to analyze their impact on solution effectiveness and adaptability to diverse network configurations. We consistently observed better result quality with the dynamic topology compared to the star topology for the same population size, while the star topology exhibited better execution time. Our findings reveal the promising efficacy of the proposed solution in addressing the CNDP, achieving high-quality solutions compared to existing methods. Full article

Arsenic contamination in water poses a significant global health risk, necessitating efficient and sustainable remediation strategies. Arsenic contamination affects groundwater in at least 106 countries, potentially exposing over 200 million people to elevated levels, primarily through contaminated drinking water. Among the most affected regions, Bangladesh remains a critical case study, where widespread reliance on shallow tubewells has resulted in one of the largest mass poisonings in history. Bio-based nanomaterials have emerged as promising solutions due to their eco-friendly nature, cost-effectiveness, and high adsorption capabilities. These nanomaterials offer a sustainable approach to arsenic remediation, utilizing materials like biochar, modified biopolymers, and bio-based aerogels, which can effectively adsorb arsenic and other pollutants. The use of environmentally friendly nanostructures provides a potential option for improving the efficiency and sustainability of arsenic remediation from groundwater. This review explores the mechanisms underlying arsenic remediation using such nanomaterials, including adsorption, filtration/membrane technology, photocatalysis, redox reactions, complexation, ion exchange, and coagulation–flocculation. Despite their potential, challenges such as scalability, stability, and regeneration hinder widespread application. We discuss recent advancements in material design, surface modifications, and hybrid systems that enhance performance. Finally, future perspectives are highlighted, including the integration of these bio-derived systems with smart sensing technologies, sustainable water-treatment frameworks, smart design, and life-cycle integration strategies, particularly for use in resource-constrained regions like Bangladesh and other globally impacted areas. Full article

Reinforced concrete structures often require retrofitting due to damage caused by natural disasters such as earthquakes, floods, or hurricanes; deterioration from aging; or exposure to harsh environmental conditions. Retrofitting strategies may involve adding new structural elements like shear walls, dampers, or base isolators, as well as strengthening the existing components using methods such as reinforced concrete, steel, or fiber-reinforced polymer jacketing. Selecting the most appropriate retrofit method can be complex and is influenced by various factors, including initial cost, long-term maintenance, environmental impact, and overall sustainability. This study proposes utilizing an artificial neural network (ANN) to predict sustainable and cost-effective seismic retrofit solutions. By training the ANN with a comprehensive dataset that includes jacket thickness, material specifications, reinforcement details, and key sustainability indicators (economic and environmental factors), the model was able to recommend optimized retrofit designs. These designs include ideal values for jacket thickness, concrete strength, and the configuration of reinforcement bars, aiming to minimize both costs and environmental footprint. A major focus of this research was identifying the optimal number of neurons in the hidden layers of the ANN. While the number of input and output neurons is defined by the dataset, determining the right configuration for hidden layers is critical for performance. The study found that networks with one or two hidden layers provided more reliable and efficient results compared to more complex architectures, achieving a total regression value of 0.911. These findings demonstrate that a well-tuned ANN can serve as a powerful tool for designing sustainable seismic retrofit strategies, helping engineers make smarter decisions more quickly and efficiently. Full article

Alkali-activated binders (AAB) are a suitable and sustainable alternative to ordinary Portland cement (OPC), with reductions in natural resource usage and environmental emissions in regions where the necessary industrial residues are available. Despite its potential, the lack of mix design methods still limits its applications. This paper proposes a systematic parametric validation for AAB mix design applied to pastes and concretes, valorizing steel slag as precursors. The composed binders are based on coal fly ash (FA) and Basic Oxygen Furnace (BOF) steel slag. These precursors were activated with sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH) alkaline solutions. A parametric investigation was performed on the mix design parameters, sweeping the (i) alkali content from 6% to 10%, (ii) silica modulus (SiO₂/Na₂O) from 0.75 to 1.75, and (iii) ash-to-slag ratios in the proportions of 75:25 and 50:50, using parametric intervals retrieved from the literature. These variations were analyzed using response surface methodology (RSM) to develop a mechanical model of the compressive strength of the hardened paste. Flowability, yield stress, and setting time were evaluated. Statistical analyses, ANOVA and the Duncan test, validated the model and identified interactions between variables. The concrete formulation design was based on aggregates packing analysis with different paste contents (from 32% up to 38.4%), aiming at self-compacting concrete (SCC) with slump flow class 1 (SF1). The influence of the curing condition was evaluated, varying with ambient and thermal conditions, at 25 °C and 65 °C, respectively, for the initial 24 h. The results showed that lower silica modulus (0.75) achieved the highest compressive strength at 80.1 MPa (28 d) for pastes compressive strength, densifying the composite matrix. The concrete application of the binder achieved SF1 fluidity, with 575 mm spread, 64.1 MPa of compressive strength, and 26.2 GPa of Young’s modulus in thermal cure conditions. These findings demonstrate the potential for developing sustainable high-performance materials based on parametric design of AAB formulations and mix design. Full article

Urban stressors intensified by climate change affect plants in terms of growth, vitality, and ornamental value. This study examines how different light availability (full sun, partial shade, and shade) affect the development, fruit morphology, and planting suitability of Rosa rugosa Thunb. in urban environments. A total of 360 shrub individuals were analyzed in a linear formation along a riverbank in Novi Sad, Serbia, linking climatic parameters with the bioecological characteristics of the investigated plants. Comparison of the groups was performed using the multivariate methods and Principal Component Analysis (PCA). Furthermore, 13 morphological parameters were analyzed on a sample of 100 fruits per group. There were no significant deviations in fruiting patterns, but the fruit parameters, even though showing high yield and favorable fruit size, indicated that light variation affects morphology. These findings confirm the species’ resilience and adaptability to urban environments, capable of withstanding various challenges, including proximity to paved surfaces, heavy traffic, and diverse light conditions. R. rugosa proves to be an ideal choice for urban planting and nature-based solutions that enhance human well-being. Full article

A novel nanocomposite based on electrochemically reduced graphene oxide (ERGO) and electropolymerized polyproflavine (PPFL) was obtained within a “one-pot” synthesis from natural deep eutectic solvent (NADES). NADES consisted of citric acid, glucose, and water in a molar ratio of 1:1:6. The synthesis was carried out in potentiostatic mode by consequent potential application in cathodic and anodic areas. The composite was applied to develop the impedimetric DNA sensor for anthracycline determination. The sensor has provided linear range from 10 nM to 0.1 mM for doxorubicin, from 1 pM to 10 nM for epirubicin, and from 10 pM to 10 nM for idarubicin, with the limit of detection 3 nM, 1 pM, and 5 pM, respectively. The concentrations of doxorubicin below 10 nM did not have any other influence on epirubicin and idarubicin determination despite their molecular structure similarity. The sensor developed was used for the determination of anticancer medications, such as doxorubicin, epirubicin, and idarubicin, in their standard solutions, pharmaceuticals, artificial, and human urine samples. It is worth noting that the additions of mannitol and lactose, which are the stabilizers of the pharmaceuticals, exhibited an interfering effect on the sensor response. Full article

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method. Full article