Search Results (291)

Concrete is an essential material in the construction industry due to its strength and versatility. However, its quality can be compromised by environmental factors during its fresh and early-age states. To address this vulnerability, various sensors have been implemented to monitor critical parameters. While high-precision sensors (e.g., piezoelectric and fiber optic) offer accurate measurements, their cost and fragility limit their widespread use in construction environments. In response, this study proposes a cost-effective, Arduino-based wireless monitoring system to track temperature and humidity in fresh and early-age concrete elements. The system was validated through laboratory tests on cylindrical specimens and industrial applications on self-compacting concrete New Jersey barriers. The sensors recorded temperature variations between 15 °C and 35 °C and relative humidity from 100% down to 45%, depending on environmental exposure. In situ monitoring confirmed the system’s ability to detect thermal gradients and evaporation dynamics during curing. Additionally, the presence of embedded sensors caused a tensile strength reduction of up to 37.5% in small specimens, highlighting the importance of sensor placement. The proposed solution demonstrates potential for improving quality control and curing management in precast concrete production with low-cost devices. Full article

Since 2014, the Paleoanthropology Group of the National Museum of Natural Sciences (CSIC), in collaboration with Equatoguinean researchers, has been conducting archeo-paleontological fieldwork in Equatorial Guinea, continuing a longstanding Spanish naturalist tradition in this region of West Central Africa. These multidisciplinary investigations, framed within an archeo-paleo-anthropological approach, aim primarily to identify early human occupation in the Central African rainforests. To date, robust evidence of Pleistocene human presence has been documented, particularly through lithic assemblages. Although the scarcity and fragmentation of well-dated sites in Central Africa complicate chronological placement, technological traits observed in the lithic industries recorded in Equatorial Guinea show clear affinities with the African Middle Stone Age (MSA). Complementary taphonomic analyses of faunal remains have been undertaken to better understand bone preservation and fossilization processes under tropical rainforest conditions, thereby contributing to the interpretation of archeological contexts. In parallel, ongoing primatological research within the project—focused on extant primates in their natural habitats—seeks to provide ethological models relevant to the study of hominin locomotor evolution. Notably, the project has led to the ecogeographic characterization of the Engong chimpanzee group in Monte Alén National Park, one of the country’s most pristine protected areas. Full article

Automatic techniques for analog integrated circuit layout design have been proposed in the literature for over four decades. However, as analog design moves into deep nanometer integration nodes, the increasing number of design rules, the influence of layout-dependent effects, congestion, and the impact of parasitic structures constantly challenges existing automatic layout generation techniques and keeps the pressure on for further improvement. At the time of writing, no automatic tool or flow has been established in the industrial environment, resulting in a time-consuming and difficult-to-reuse design process. However, very recently, machine and deep learning techniques started to offer solutions for problems not dealt with in the previous generation of automatic layout tools and are reshaping analog design automation. Therefore, this paper conducts a review of the most recent analog integrated circuit automatic layout techniques powered by machine and deep learning methods, covering placement, routing, and trends on post-layout performance estimation, as well as providing an actual, complete, and comprehensive guide for circuit designers and design automation developers. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

Successful dental implant therapy relies on accurate planning and placement, e.g., through static, computer-aided implant surgery using CAD/CAM-fabricated surgical guides. This study examined production methods’ influence on surgical guide sleeve housing geometry. A model with two edentulous spaces was digitized using intraoral scanning and CBCT, and two virtually positioned implants were planned. Ten guides per group were produced using milling (MCX5), DLP printing (ASIGA and SHERA), and SLA printing (FORM), printing with 50 µm and 100 µm layers each. Each guide (n = 70) was then digitized using an industrial scanner before and after sterilization. Superimposition of the actual guide data with the reference data allowed for evaluation of deviations at the drill sleeve housing along the x-, y-, z-, and dxyz-axes. Descriptive and statistical evaluation was performed (significance level: p ≤ 0.0125). Significant differences existed among the production methods: Milling and SLA showed higher deviations than the DLP group (p < 0.001). Milled guides post-sterilization showed the highest deviations (0.352 ± 0.08 mm), while one DLP printer at 50 μm layer thickness showed lowest deviations (0.091 ± 0.04 mm). The layer thickness was insignificant, whereas sterilization increased deviation (p < 0.001). DLP produced the most precise implant surgical guides. All 3D printers were suitable for fabricating clinically acceptable surgical guides. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

Chemical leakage accidents in chemical industrial parks pose significant threats to personnel safety, particularly during evacuation processes, where individual behavior and evacuation strategies have a considerable impact on overall efficiency. This study takes a leakage incident at an alkylation unit as a case study. First, ALOHA5.4.7 software was used to simulate the influence of meteorological conditions across different seasons on the dispersion range of toxic gases, thereby generating an annual comprehensive risk zone distribution map. Subsequently, different evacuation scenarios were constructed in Pathfinder2024.1.0605, with the integration of trigger mechanisms to simulate individual behaviors during evacuation, such as variations in risk perception and peer influence. Furthermore, this study expanded the conventional application scope of Pathfinder—typically limited to small-scale building evacuations—by successfully adapting it for large-scale evacuation simulations in chemical industrial parks. The feasibility of such simulations was thereby demonstrated, highlighting the software’s potential. According to the simulation results, exit configuration, shelter placement, and individual behavior modeling significantly affect the total evacuation time. This study provides both theoretical insights and practical guidance for emergency response planning in chemical industrial parks. Full article

Carbon Capture and Storage (CCS) is a key technology for reducing anthropogenic greenhouse gas emissions, in which pipelines play a vital role in transporting CO₂ captured from industrial emitters to geological storage sites. To aid the efficient and safe operation of the CO₂ transport infrastructure, robust, accurate, and reliable solutions for monitoring pipelines transporting industrial CO₂ streams are urgently needed. This literature review study summarizes the monitoring objectives and identifies the problems and relevant mathematical algorithms developed for optimization of monitoring systems for pipeline transportation of water, oil, and natural gas, which can be relevant to the future CO₂ pipelines and pipeline networks for CCS. The impacts of the physical properties of CO₂ and complex designs and operation scenarios of CO₂ transport on the pipeline monitoring systems design are discussed. It is shown that the most relevant to liquid- and dense-phase CO₂ transport are the sensor placement optimization methods developed in the context of detecting leaks and flow anomalies for water distribution systems and pipelines transporting oil and petroleum liquids. The monitoring solutions relevant to flow assurance and monitoring impurities in CO₂ pipelines are also identified. Optimizing the CO₂ pipeline monitoring systems against several objectives, including the accuracy of measurements, the number and type of sensors, and the safety and environmental risks, is discussed. Full article

This study presents a novel multi-material additive manufacturing (MMAM) strategy by combining virgin polylactic acid (vPLA) with recycled polylactic acid (rPLA) in a layered configuration to improve both performance and sustainability. Specimens were produced using fused deposition modelling (FDM) with various vPLA: rPLA ratios (33:67, 50:50, and 67:33) and two distinct layering approaches: one with vPLA forming the external layers and rPLA as the core, and a second using the reversed arrangement. Mechanical testing revealed that when vPLA is used as the exterior, printed components exhibit tensile strength and elongation improvements of 10–25% over conventional single-material prints, while the tensile modulus is largely influenced by the distribution of the two materials. Thermal analysis shows that both vPLA and rPLA begin to degrade at approximately 330 °C; however, rPLA demonstrates a higher end-of-degradation temperature (461.7 °C) and increased residue at elevated temperatures, suggesting improved thermal stability due to enhanced crystallinity. Full-field strain mapping, corroborated by digital microscopy (DM) and scanning electron microscopy (SEM), revealed that vPLA-rich regions display more uniform interlayer adhesion with minimal voids or microcracks, whereas rPLA-dominated areas exhibit greater porosity and a higher propensity for brittle failure. These findings highlight the role of optimal material placement in mitigating the inherent deficiencies of recycled polymers. The integrated approach of combining microstructural assessments with full-field strain mapping provides a comprehensive view of interlayer bonding and underlying failure mechanisms. Statistical analysis using analysis of variance (ANOVA) confirmed that both layer placement and material ratio have a significant influence on performance, with high effect sizes highlighting the sensitivity of mechanical properties to these parameters. In addition to demonstrating improvements in mechanical and thermal properties, this work addresses a significant gap in the literature by evaluating the combined effect of vPLA and rPLA in a multi-material configuration. The results emphasise that strategic material distribution can effectively counteract some of the limitations typically associated with recycled polymers, while also contributing to reduced dependence on virgin materials. These outcomes support broader sustainability objectives by enhancing energy efficiency and promoting a circular economy within additive manufacturing (AM). Overall, the study establishes a robust foundation for industrial-scale implementations, paving the way for future innovations in eco-efficient FDM processes. Full article

High-rise industrial buildings are particularly susceptible to vibration-induced comfort issues, which can negatively impact both the health and productivity of workers and office staff. Unlike most existing studies that focus on local structural components, this study proposes and validates a wave propagation analysis (WPA) method to predict peak accelerations of the floor caused by excitations located on different floors. The method is validated through on-site vibration tests conducted on a high-rise industrial building with shared factory and office space. A simplified regression-based propagation equation is further developed to facilitate practical design applications. The regression parameters are fitted using theoretical calculation results, enabling rapid prediction of peak acceleration responses on the same or different floors. To enhance vibration control, tuned mass dampers (TMDs) are installed on selected floors, and additional tests are conducted with the TMDs activated. An insertion loss-based correction is introduced into the WPA framework to account for the TMD’s frequency-dependent attenuation effects. The extended method supports both accurate prediction of vibration reduction and optimisation of TMD placement across multiple floors in high-rise industrial buildings. Full article

This review explores sensor selection and placement strategies for electric motor monitoring in industrial settings. A wide range of sensor types including temperature, vibration, current, and position sensors—are evaluated in terms of their technical features and application constraints. Preliminary experimental data on vibration sensors highlight how signal amplitude varies with sensor placement, reinforcing the importance of correct positioning. However, this study stops short of applying AI/ML techniques to optimize placement. Accordingly, this paper serves as a foundational step toward developing intelligent sensor deployment frameworks. Future work will build on this review by integrating supervised learning, dimensionality reduction, and reinforcement learning techniques to automate sensor placement and improve condition monitoring in electric motors. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article

Despite its proven effectiveness in inventory management across various industries, Demand-Driven Material Requirements Planning (DDMRP) remains largely a manual process, with few studies investigating its numerical integration. This research proposes a novel multi-stage production control framework grounded in DDMRP principles, enabling effective scheduling of production orders based on either demand forecasts or actual demand, when available. A mixed-integer programming (MIP) model is developed to capture the dynamic interactions between demand, buffer positioning, and replenishment policies, supporting reactive production planning in smart, reconfigurable manufacturing environments. To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. The MIP model provides the GA with production planning outputs used to evaluate the fitness of decisions regarding buffer placement. To demonstrate the effectiveness of this hybrid GA–MIP approach, simulations are conducted on three representative production configurations. The results show that the proposed method significantly improves the theoretical performance of each configuration by determining optimal buffer locations and planning replenishments, achieving a better balance between inventory levels and demand fulfillment. Full article

Currently, intelligent technologies are becoming both a topical subject for theoretical discussions and a proper tool for transforming traditional industries, including the construction industry. The construction industry intensively uses innovative methods based on intelligent algorithms of various natures. As practice shows, modern intelligent technologies based on AI surpass traditional ones in accuracy and speed of information processing. This study implements methods using convolutional neural networks, which solve an important problem in the construction industry—to classify crushed stone grains by their shape. Rapid determination of the crushed stone grain class will allow determining the content of lamellar and acicular grains, which in turn is a characteristic that affects the strength, adhesion, and filler placement. The classification algorithms were based on the ResNet50, MobileNetV3 Small, and DenseNet121 architectures. Three-dimensional images of acicular, lamellar, and cuboid grains were converted into single-channel digital tensor format. During the laboratory experiment, the proposed intelligent algorithms demonstrated high stability and efficiency. The total processing time for 200 grains, including the photo recording stage, averaged 16 min 41 s, with the accuracy reaching 92%, which is comparable to the results of manual classification by specialists. These models provide for the complete automation of crushed stone grain typing, leading to reduced labor costs and a decreased likelihood of human error. Full article