Search Results (18,605)

Industrial IoT (Internet of Things) systems increasingly depend on sensor reports for monitoring, automation, and operational decisions. However, sensor faults or Byzantine behavior can produce inconsistent or missing reports. PBFT (practical Byzantine fault tolerance) can maintain consistent processing among replicas despite a bounded number of Byzantine faults. Its overhead grows when many factory sensors participate in one expanding consensus group. Processing complete sensor-event records also increases local computation as record size grows. This paper proposes independent PBFT groups using fixed-length SHA-256 sensor-event digests. Groups are formed according to production processes or sensor characteristics. Each group limits consensus participation, while digests keep consensus-command size fixed. Raspberry Pi experiments separated grouping benefits from digest-processing benefits. Fixed-size groups moderated aggregate replica-local computation growth across 10–100 logical sensors. Digest processing became more beneficial as original sensor-event records increased in size. A four-device deployment also maintained consensus under evaluated Byzantine backup and primary faults. These results indicate that the scheme can reduce PBFT processing burden in resource-constrained IIoT deployments. Full article

The mining industry in Ghana, despite its economic significance, continues to grapple with occupational health and safety (OHS) issues, which include but are not limited to high accident rates and a largely reactive approach to safety. In this study, current practices in OHS management are assessed and a model is developed that reflects the interdependencies and associations between workplace hazards, accidents, health effects and preventative actions in a probabilistic fashion. A quantitative analytical design was employed and a sample of 298 workers, safety officers and supervisors from mines were surveyed. The data were analysed using Principal Component Analysis (PCA) to derive latent OHS factors, K-modes clustering and Hierarchical Clustering to classify worker safety profiles, and a Bayesian Network model was employed to investigate probabilistic dependencies. Three major OHS dimensions emerged from PCA: perceived adequacy of safety measures, formal training exposure, and safety resources. Three distinct worker profiles were identified, suggesting that the provision of physical safety equipment does not necessarily reflect perceived operational safety. Moreover, the Bayesian Network model indicated a conditional dependency between workers’ reported health issues and formal accident reporting, and that high hazard environments more than double the probability of an accident occurring (from 0.216 to 0.453). The results indicate that the industry is now operating in an incident-based manner. In order to mitigate the likelihood of accidents, management needs to move towards anticipatory safety management systems, which involve proactive health monitoring, equipment maintenance, and implementation of safety policies in practice. Full article

Real-time LiDAR semantic segmentation for industrial AGVs must distinguish repeated structures and weak glass returns while remaining robust to sensor-dependent intensity and tight edge computing budgets. We introduce DS-RangeNet, a lightweight range image network that processes geometry and material-sensitive intensity in separate streams. The geometry stream uses voxel-PCA descriptors, while the intensity stream uses normalized range, local intensity statistics, boundary strength, and intensity curvature. A lightweight convolutional attention block handles shallow fusion, whereas intensity–geometry cross-attention (IGCA) links deep features by estimating affinity within the guiding stream and routing values from the other stream. Centered kernel alignment (CKA) and normalized cross-covariance reveal weak similarity after separate encoding and progressively stronger alignment during fusion. On the site disjoint UBPC-9 test split, DS-RangeNet reaches 73.2% mIoU with 5.69 M parameters and 37 ms end-to-end latency on Jetson AGX Orin. A nine-fold leave-one-environment-out evaluation obtains 71.0% mIoU. The evaluation further spans SemanticPOSS and SemanticKITTI, five random seeds, 21 corruption conditions across seven families, standard cross-attention and convolution controls, a 60 min Jetson run, and cross-sensor transfer. Full article

Background: Digital twins (DTs) are a key technology of Industry 4.0 and play a crucial role in the digital transformation of industry. The education of experts for the needs of Industry 4.0 and the emerging 5.0 must align with the requirements of practice. However, the availability of these technologies for educational institutions is problematic due to their complexity and limited resources. Methods: This study develops and implements DT and a virtual monitoring system for a specialised 6-DOF laboratory robotic manipulator. The architecture uses a multi-layered communication via middleware KEPServerEX. AI-assisted “Vibe Coding” supports development of a custom Python control application and integration with multiple APIs. Next, the application of a virtual monitoring system enables tracking of selected metrics via a web interface. Results: Our work provides three primary contributions: (1) consolidation of multiple devices through middleware (KEPServerEX) to link robots and PLCs; (2) accelerated development through AI-assisted “Vibe Coding”; and (3) remote accessibility through a custom web-based monitoring platform. Conclusions: The study demonstrates that a fully functional digital twin, integrated with a virtual monitoring system, can be implemented in a university laboratory environment using a combination of cost-effective technologies. Full article

Robots are now expected to execute increasingly complex long-horizon tasks in unstructured environments. Despite the strong potential of pretrained Vision-Language Models (VLMs) in task planning, their direct application to robotic manipulation is hindered by logical reasoning deviations and inadequate geometric scene perception. This work proposes an adaptive task planning method based on video priors and dynamic scene graphs (ATP-VPDSG). It leverages the VLM to extract manipulation logic from video demonstrations, thus supplementing manipulation priors. Meanwhile, scene graphs were integrated to convert unstructured environments into structured representations with spatial topological relations, compensating for perceptual deficiencies. A dual-track feedback mechanism based on visual expectations was further incorporated to enable failure diagnosis and adaptive replanning in complex environments. Extensive long-horizon robotic manipulation experiments were conducted on the LIBERO-10 benchmark with Qwen3-VL as the core VLM. Results showed that ATP-VPDSG achieved an average task planning accuracy of 91.2% and a task execution success rate of 74.67%, outperforming the selected task planning baselines. Ablation studies verified that video priors and dynamic scene graphs exerted complementary effects on logical constraints and physical feasibility. Furthermore, a real-robot experiment on an industrial slider–rail assembly task demonstrated successful sim-to-real transfer, achieving an 82.0% success rate without task-specific fine-tuning. Full article

Urban green spaces are an integral part of urban ecosystems and play a crucial role in improving the urban ecological environment and promoting sustainable urban development. This study aimed to quantify the spatiotemporal evolution of urban green spaces in central Shenyang and to assess how their relationships with selected urban development and socioeconomic drivers varied across space and time. Urban green space information for 2010, 2015, and 2020 was extracted from remote sensing imagery using ENVI and ArcGIS. Eight indicators—impervious surface intensity, population density, nighttime light intensity, economic density, industrial structure, road density, traffic accessibility, and land use intensity—were examined using spatial-pattern and contribution analyses, ordinary least squares regression (OLS), and geographically weighted regression (GWR). Urban green space areas were 330.56 km² (23.55%), 344.55 km² (24.55%), and 337.32 km² (24.03%) in 2010, 2015, and 2020, respectively, representing a net increase of 6.76 km² (2.05%) and an annual dynamic degree of 0.20% over the study period. Spatially, green spaces were concentrated in peripheral areas, fragmented in the central area, and prominently distributed along river corridors. At α = 0.05, coefficient-level OLS results showed negative associations with impervious surface intensity in 2010 (standardized β = −0.184, p < 0.001) and 2015 (β = −0.098, p = 0.005). In 2020, impervious surface intensity showed a small positive coefficient (β = 0.056, p = 0.043), whereas traffic accessibility showed a negative coefficient (β = −0.0556, p = 0.033). The OLS models had low explanatory power, with R² values of 0.034, 0.008, and 0.007 and adjusted R² values of 0.029, 0.003, and 0.002 for 2010, 2015, and 2020, respectively. The GWR local R² values ranged from 0.000 to 0.310 and showed spatially shifting high-value areas, indicating marked spatial variation in model explanatory power. These findings provide case-specific evidence for Shenyang and a transferable analytical framework for spatially differentiated green space planning in comparable old industrial and rapidly restructuring cities. Full article

Despite declining water availability in semi-arid environments, demand for high-quality forages by the livestock and dairy industries continues to grow. Researching alternative crops for high quality forage and water use efficiency remains a priority for these regions to meet this growing demand. Cereal grass-legume intercropping system may provide such a viable alternative to traditional sole cropping to improve nutritive value, while maintaining the current yield levels with limited supply of water. A study was conducted near West Texas A&M University, Canyon, TX, USA. during 2020 and 2021 to evaluate forage sorghum [Sorghum bicolor (L.) Moench]-cowpea [Vigna unguiculata (L.) Walp] and pearl millet [Pennisetum glaucum (L.) Leeke]-cowpea intercrops for forage production, nutritive value (crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVTDMD)), and water use efficiency (WUE) under limited irrigation. Treatments were sole pearl millet (PM), sole forage sorghum (FS), sole cowpea (C), or mixtures of either pearl millet-cowpea or forage sorghum-cowpea planted in the same row (PM-C or FS-C), alternating rows (millet-cowpea 1:1 (PM-C 1:1) or forage sorghum-cowpea 1:1 (FS-C 1:1), or two rows alternating (millet-cowpea 2:2 (PM-C 2:2) or sorghum-cowpea 2:2 (FS-C 2:2). Results from both study years indicated that FS-C 1:1 intercrop showed either greater or equal forage dry matter (DM) yield compared to sole FS or sole PM (14.5 Mg ha⁻¹ (FS-C 1:1) vs. 13.8 Mg ha⁻¹ (FS) vs. 11.9 Mg ha⁻¹ (PM)). Similarly, the relative advantage of FS-C 1:1 intercrop in improving WUE was also observed in the study compared to sole FS and sole PM (44 kg ha⁻¹ mm⁻¹ (FS-C 1:1) vs. 41.6 kg ha⁻¹ mm⁻¹ (FS) vs. 35.9 kg ha⁻¹ mm⁻¹ (PM). CP yield per hectare (estimate based on DM yield and percent CP content) was also greater for the forage sorghum-cowpea planted in 1:1 alternate rows. This suggests that producers in the semi-arid USA, especially in the Southern High Plains, can be significantly benefited from the utilization of cereal grass-legume intercropping production systems in lieu of sole cropping as the basis for livestock rations without compromising yield and water productivity. Full article

Firms operating in import-dependent and information-intensive supply environments require organizational capabilities that support continuity and adaptation under disruption. This study examines the relationships of knowledge management and innovation ambidexterity with business resilience among cosmetic raw-material supplier firms in Indonesia. A cross-sectional survey of 189 senior representatives, each representing one firm, was analyzed using partial least squares structural equation modeling (PLS-SEM). The focal structural model showed that knowledge management was positively associated with business resilience (beta = 0.472, t = 5.850, p < 0.001), while innovation ambidexterity was also positively associated with business resilience (beta = 0.443, t = 5.876, p < 0.001). Together, the two organizational capabilities accounted for 67.50% of the variance in business resilience (R2 = 0.675). The measurement results supported the hierarchical representation of knowledge management through knowledge acquisition and knowledge transfer capability, innovation ambidexterity through exploratory and exploitative innovation, and business resilience through reengineering and collaboration, although construct separation should be interpreted cautiously. Drawing on the knowledge-based view and dynamic capabilities perspective, the findings provide context-specific evidence that knowledge-related and innovation-related capabilities are associated with organizational resilience in an upstream, import-dependent industry in an emerging economy. The study contributes primarily through its empirical and contextual positioning rather than through the development of a new theoretical mechanism. Environmental and social sustainability outcomes were not directly measured. Full article

Material extrusion 3D printing has been widely used in industrial, educational and residential environments. However, the associated emissions and exposure health impacts need to be evaluated, especially for the new and emerging filament materials. This study characterized particle and chemical emissions from 3D printing using five different metal composite filaments, which contain over 90% (by weight) of metal powder blended with polymer binders. The emission characterization was conducted using an exposure chamber following a standard testing method. Real-time particle measurements showed that particle emission rates ranged from 8 × 10⁹ to 2 × 10¹¹ particles/h for particle number and 200 to 1400 µg/h for particle mass. Over 98% of the emitted particles were smaller than 1 µm, which poses an inhalation hazard. Inductively coupled plasma–mass spectrometry analysis detected manganese, copper, zinc, and selenium in emitted particles from all filaments. However, metal powder in raw filaments tended not to be transferred into particle emissions, resulting in the total metals (and metalloids) accounting for 0.07% to 0.95% of emitted particle mass. Sorbent tube sampling and analytical analyses showed various volatile organic compounds emitted during printing, including hydrocarbons, alcohols, aldehydes, and aromatic compounds. Overall, metal composite filaments generated lower levels of volatile organic compounds (VOCs) compared to thermoplastic polymer filaments; in addition, the emitted VOC compositions differed. Organic chemicals associated with metal composite filament emissions included formaldehyde, benzaldehyde, acetaldehyde, naphthalene, and trimethylbenzene, exposure to which may cause irritations or other adverse health impacts. This study estimated personal exposure to hazardous components assuming a person is close to the printer with low ventilation to represent an acute worst-case exposure scenario. Modeled personal exposure to emissions showed potential exceedances of exposure to fine (PM_2.5) and coarse (PM₁₀) particulate matter, arsenic, manganese, formaldehyde, caprolactam, acetaldehyde, and naphthalene compared to reference levels. A modeled office room with ventilation showed reduced exposure levels by up to two orders of magnitude, assuming the same emission source. Users can avoid close proximity to an operating printer and increase dilution through larger room volumes and higher air change rates to reduce potential inhalation exposures at given printing conditions. Full article

This study investigates the realization and verification of safety-related functions of the KZ-MPC-MA national modular microprocessor-based railway interlocking system for Kazakhstan. The work focuses on the realization stage of the IEC 61508 safety lifecycle and establishes traceability between station-specific safety requirements, interlocking algorithms, software implementation, hardware–software integration, and functional-safety assessment. The implemented interlocking logic incorporates 29 traffic safety conditions defined for the considered station configuration. A fail-safe control architecture based on central and distributed controller modules was implemented using certified safety-related industrial controllers and the SILworX development environment. A laboratory prototype was developed to verify route-setting functions, switch and signal control, and fail-safe system response under specified operational and failure conditions. Quantitative functional-safety assessment was performed using the probability of failure on demand ($PF{D}_{\mathrm{avg}}$) as a supplementary measure and the probability of dangerous failure per hour ($PFH$) as the governing criterion for continuous/high-demand operation within the defined assessment boundary and assumptions. The obtained $PFH$ demonstrates that the investigated safety-related controller subsystem satisfies the specified SIL4 quantitative criterion. The study demonstrates the feasibility of realizing safety-related interlocking functions on the selected hardware–software platform and provides a basis for further system-level verification and validation. Full article

The mining industry is facing increasing pressure to improve operational safety, environmental performance, and resource efficiency while adapting to rapid technological change. In this context, the concept of Mining Industry 5.0 is emerging as an extension of Industry 4.0, promoting the integration of human-centric approaches with advanced digital technologies in mining systems. This paper proposes a 6S framework—Safety, Security, Sustainability, Sensitivity, Service, and Smartness—conceptualized as a Cyber-Physical-Social Systems (CPSS)-based model that co-optimizes these six dimensions and supports adaptive and human-centric mining operations across the entire value chain. The study is based on a structured literature review and a comparative analysis of digital transformation practices in related sectors, including manufacturing, logistics, and energy, to identify solutions applicable to mining environments. The key enabling technologies, including artificial intelligence, digital twins, cyber-physical systems, and intelligent automation, are evaluated as particularly relevant to mining operations for improving workplace safety, process efficiency, and environmental management. The paper addresses challenges, including high investment costs, limited digital competencies, data interoperability issues, and cybersecurity concerns in mining practice. The results support the development of a roadmap for Mining Industry 5.0 by integrating the proposed 6S framework with technical and organizational operations in the mining sector. Full article

Autonomous driving relying on visual navigation plays a vital role in promoting automation within the jujube industry. Conventional visual navigation strategies fail to satisfy the demands of straddle-type jujube harvesters owing to their unique row-straddling configuration and complex orchard environments. Accordingly, this paper proposes a novel binocular vision-based path detection algorithm for autonomous jujube harvesters. In the proposed method, target trunks detected from binocular images are used to generate a navigation path that better aligns with the operating trajectory of harvester. A Single Shot MultiBox Detector (SSD) deep learning model is employed to detect trunk bounding boxes. To mitigate interference induced by false detections from the deep learning model, a curve-fitting-based path calibration strategy is implemented. Experimental results demonstrate that the proposed algorithm achieves a detection speed of 14.38 fps with a false detection rate of 3.64%, satisfying the operational demands for autonomous driving of jujube harvesters. Furthermore, this algorithm can be extended to other orchard mobile robots that execute row-straddling operations similar to jujube harvesters. Full article

Contemporary urban environments are characterized by intense and continuous activity, resulting in a significant transformation of the ambient acoustic environment. Noise pollution from sources such as traffic, construction, industry, and recreation propagate throughout these areas, although some residents may report a low perception of disturbance even under high exposure levels. This study investigates the moderating influence of housing conditions on the relationship between outdoor noise levels and perceived indoor acoustic environment in Coimbra, Portugal. The methodology combines primary data on housing characteristics and subjective indoor noise perception, obtained through a questionnaire survey, with secondary data from an environ-mental noise map. Logistic regression models, incorporating interaction terms, were employed to evaluate the moderating effect of housing conditions. The results demonstrate a statistically significant association between outdoor noise exposure and perceived indoor noise, with a limited moderating effect of housing conditions on this relationship. Full article

Particular attention is devoted to the characteristics of elastic couplings as essential connecting elements within machine drive systems. The operational characteristics of such couplings are determined under both static and dynamic loading conditions, while also taking into consideration the nature of the working environment and the specific type of driven machinery, such as crushing machines, mills, and related industrial equipment. The relationship between the transmitted torque load and the deformation of the coupling’s rubber working elements constitutes a significant factor contributing to the compensation of vibrations and shaft misalignments. Furthermore, the rubber elements are characterized by inherent damping properties and residual elastic deformation. The parameters governing damping behavior and deformation exhibit variations over time, which is of particular importance in deformation analysis and measurement. A series of experimental investigations was carried out to evaluate the influence of measurement time on the deformation state of the rubber element in a “SEGME”-type coupling. Four time intervals—5, 10, 20, and 30 s—are considered for recording the instantaneous deformation of the working element. Based on the obtained experimental data, mathematical relationships describing the energy dissipation and damping capacity of the rubber working elements are established as functions of the deformation measurement time. In addition, a coefficient characterizing the influence of deformation measurement duration was determined. Full article

Adaptive reuse of industrial heritage has become an important approach for extending building life cycles, preserving cultural identity, and promoting urban regeneration. However, limited research has examined how users’ perceptions of regenerated industrial spaces relate to broader evaluations of adaptive reuse outcomes. This study investigates ZhiYin 1953, a commercially reused former textile factory complex in Shijiazhuang, China. Using an extended Stimulus–Organism–Response (S-O-R) framework, we analyzed data from 396 valid on-site questionnaires using confirmatory factor analysis and structural equation modeling. The results show that industrial heritage perception, spatial environmental quality, and commercial vitality are positively associated with place attachment (PA). PA is positively related to both behavioral intention (BI) and perceived human settlement value (PHSV), while BI is also positively associated with PHSV. Mediation analysis suggests that PA and BI represent important indirect pathways linking site-related perceptions with users’ evaluations of adaptive reuse outcomes. By contrast, commercial–industrial culture coordination does not significantly predict PA, suggesting that symbolic coordination between commercial functions and industrial cultural elements may not be sufficient to strengthen users’ emotional connections. These findings highlight the importance of evaluating industrial heritage adaptive reuse beyond physical conservation and functional transformation by considering users’ perceptions of heritage meaning, spatial quality, public life, and continued engagement. Sustainable regeneration strategies should therefore integrate heritage-sensitive design, accessible public spaces, balanced commercial activation, and meaningful cultural interpretation. Full article