Search Results (46,145)

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

Many organizations execute multiple projects simultaneously, competing for limited resources, including specialized and expensive equipment. Managing such multi-project environments requires advanced planning and decision-making. An additional difficulty is taking into account the possibility and profitability of using internal and external resources. The construction industry is a particularly demanding example of this scenario, where simultaneously executed projects must share high-value equipment with limited availability. Project management planning with resource allocation is supported by various types of IT tools. ERP (enterprise resource planning) systems are particularly useful in this regard, as they use the organization’s transaction data directly, but only offer basic project support. Therefore, it is necessary to extend their functionality in order to fulfill the expected functional requirements of business users, with particular emphasis on the provision of a consistent, graphically supported interface. This article proposes an algorithm to support decision-making on equipment allocation in a multi-project environment, taking into account the use of own and third-party equipment. A case study is presented demonstrating the practical implementation of the proposed solution in the IFS Applications ERP system. The developed extension supports users through graphical and numerical presentation of machine workloads across multiple projects. Full article

This study analyzed subjective theories on school violence and convivencia escolar expressed in the public discourse of the Chilean Ministry of Education in 2022. This research focused on the return to in-person learning, a time when concerns about violence in schools increased and public policies aimed at addressing it were launched. Inductive content analysis and grounded theory techniques were used to examine 66 tweets issued by official ministry accounts during 2022. The analysis identified three interpretative sets. The first suggests that although violence has external structural causes, it must be eradicated from schools. The second links convivencia escolar with well-being and socioemotional skills, but without an explicit association with violence. The third locates the origin of psychological distress in external factors but assigns its management to the school system. A predominance of expert knowledge existed in the promoted solutions. These findings are discussed based on the idea that the Ministry of Education’s discourse on Twitter not only informs but also seeks to shape educational common sense and validate public policies. This raises questions about its impact on the interpretive autonomy of school communities. Full article

Background: Polybasic peptides are being developed as components of reagents for diagnosing and treating patients with systemic amyloidosis. In addition to fibrils, amyloid deposits ubiquitously contain heparan sulfate proteoglycans. We have hypothesized that pan amyloid-targeting peptides can specifically engage, in addition to fibrils, a subset of glycosaminoglycans (GAGs) with high negative charge density. In this study, we characterized the binding of peptides p5+14 (a PET imaging agent for amyloid [¹²⁴I-evuzamitide]) and p5R (a fusion protein used in the therapeutic AT-02) to GAGs. Methods: The peptide structure was evaluated in the presence of low molecular weight heparin using circular dichroism, and their interaction with synthetic GAGs of varying length and charge was interrogated. The binding patterns of p5+14 and p5R were compared using correlation analyses. Results: The peptides exist as mixed structural-fractions in solution but adopt an α-helical structure in the presence of heparin. Both peptides preferentially recognize heparin and heparan sulfate GAGs with a linear positive correlation between binding and the total charge and charge density. Conclusions: These peptides have previously been shown to specifically target amyloid deposits in vivo. A component of this specificity is their preferential interaction with a subset of heparan sulfate GAGs that have high charge density, potentially related to the degree of 6-O-sulfation. These data support the hypotheses that amyloid-associated GAGs have unique sulfation patterns, thereby explaining why these peptides do not bind GAGs found on the plasma membrane and extracellular matrix of healthy tissues. Full article

A bioresorbable cannulated bone screw was developed using PLA/PCL-based composites reinforced with hydroxyapatite (HA) and titanium dioxide (TiO₂), two additives previously reported to enhance mechanical compliance, biocompatibility, and molding feasibility in biodegradable polymer systems. The design incorporated a crest-trimmed thread and a strategically positioned gate in the thin-wall zone opposite the hexagonal socket to preserve torque-transmitting geometry during micromolding. To investigate shrinkage behavior, a Taguchi orthogonal array was employed to systematically vary micromolding parameters, generating a structured dataset for training a back-propagation neural network (BPNN). Analysis of variance (ANOVA) identified melt temperature as the most influential factor affecting shrinkage quality, defined by a combination of shrinkage rate and dimensional variation. A hybrid AI framework integrating the BPNN with genetic algorithms and particle swarm optimization (GA–PSO) was applied to predict the optimal shrinkage conditions. This is the first use of BPNN–GA–PSO for cannulated bone screw molding, with the shrinkage rate as a targeted output. The AI-predicted solution, interpolated within the Taguchi design space, achieved improved shrinkage quality over all nine experimental groups. Beyond the specific PLA/PCL-based systems studied, the modeling framework—which combines geometry-specific gate design and normalized shrinkage prediction—offers broader applicability to other bioresorbable polymers and hollow implant geometries requiring high-dimensional fidelity. This study integrates composite formulation, geometric design, and data-driven modeling to advance the precision micromolding of biodegradable orthopedic devices. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

With the rapid expansion of urban green spaces and the increasing amount of domestic waste, efficient and sustainable treatment of green waste (GW) and kitchen waste (KW) has become a pressing issue. Co-composting offers a green and low-carbon solution, yet a systematic understanding of its greenhouse gas (GHG) emission dynamics remains lacking. This study aims to investigate the impact of varying GW:KW ratios on GHG emissions during composting, in order to identify optimal mixing strategies and sup-port the development of low-carbon urban waste management systems. Six treatments with different GW:KW ratios (10:0, 9:1, 8:2, 7:3, 6:4, and 5:5) were evaluated under continuous aeration for 42 days. Results showed: (1) All treatments exhibited a typical composting temperature profile (mesophilic, thermophilic, cooling, maturation), with final seed germination index (GI) >95% and significantly reduced E4/E6 ratios, indicating maturity. (2) When kitchen waste (KW) was ≤20%, cumulative GHG emissions slightly increased; KW ≥30% led to net reductions, with the 6:4 treatment (A4) achieving the highest decrease (17.44%) in total CO₂-equivalent emissions. In conclusion, maintaining KW at 40–50% optimally balances compost maturity and emission reduction, providing a viable strategy for the high-value utilization of urban organic waste and carbon mitigation. Full article

To address the challenges of complex task matching, limited semantic representation, and low recommendation efficiency in remote sensing data acquisition for natural disasters, this study proposes a semantic path-guided recommendation method based on a knowledge graph framework. A disaster-oriented remote sensing knowledge graph is constructed by integrating entities such as disaster types, remote sensing tasks, observation requirements, sensors, and satellite platforms. High-order meta-paths with semantic closure are designed to model task–resource relationships structurally. A Meta-Path2Vec embedding mechanism is employed to learn vector representations of nodes through path-constrained random walks and Skip-Gram training, capturing implicit semantic correlations between tasks and sensors. Cosine similarity and a Top-K ranking strategy are then applied to perform intelligent task-driven sensor recommendation. Experiments on multiple disaster scenarios—such as floods, landslides, and wildfires—demonstrate the model’s high accuracy and robust stability. An interactive recommendation system is also developed, integrating data querying, model inference, and visual feedback, validating the method’s practicality and effectiveness in real-world applications. This work provides a theoretical foundation and practical solution for intelligent remote sensing data matching in disaster contexts. Full article

Trajectory prediction is a critical component of autonomous driving decision-making systems, directly impacting driving safety and traffic efficiency. Despite advancements, existing reviews exhibit limitations in timeliness, classification frameworks, and challenge analysis. This paper systematically reviews multi-agent trajectory prediction technologies, focusing on generating future position sequences from historical trajectories, high-precision maps, and scene context. We propose a multi-dimensional classification framework integrating input representation, output forms, method paradigms, and interaction modeling. The review comprehensively compares conventional methods and deep learning architectures, including diffusion models and large language models. We further analyze five core challenges: complex interactions, rule and map dependence, long-term prediction errors, extreme-scene generalization, and real-time constraints. Finally, interdisciplinary solutions are prospectively explored. Full article

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

The efficient resource utilization of industrial solid wastes, such as ground granulated blast-furnace slag (GGBS) and coal gangue (CG), is essential for sustainable development. However, their activation commonly depends on expensive and corrosive chemical alkalis. This study proposes a solution by developing a fully waste-based cementitious material using calcium carbide slag (CS), another industrial residue, as an eco-friendly alkaline activator for the GGBS-CG system. The influence of CS dosage (0–20 wt%) on hydration evolution and mechanical properties was examined using uniaxial compression testing, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The results indicated that a CS dosage of 10 wt% yielded the highest compressive strength, reaching 10.13 MPa—a 16.5% improvement compared to the 20 wt% group. This enhancement is ascribed to the formation of hydrotalcite (HT) and calcium silicate hydrate (C-(A)-S-H) gel, which densify the microstructure. In contrast, higher CS contents led to a passivation effect that restrained further reaction. This work offers a practical and theoretical basis for the development of low-carbon, multi-waste cementitious materials and presents a promising strategy for large-scale valorization of industrial solid wastes. Full article

Microchip electrophoresis (ME) has been recognized as a promising analytical technique in life sciences, disease diagnostics, and environmental monitoring due to advantages such as minimal reagent consumption, rapid analysis, and compact size. While extensive efforts have been made to enhance ion analysis performance, the influence of solution properties—such as zeta potential, diffusion coefficient, ionic charge, and dynamic viscosity—has not been fully explored. In this study, the influence of solution properties on the performance of ion separation via ME was systematically evaluated through numerical simulations. A finite element method (FEM) model was established, in which multiple physical fields were considered. To verify the model, ion analysis experiments were conducted under corresponding conditions. Based on the validated model, a series of simulations were carried out to evaluate the effects of solution properties on separation performance. It was demonstrated that solution properties significantly affect the separation behavior, including ion arrival time, concentration-peak height, and separation resolution. These findings suggest that solution properties should not be overlooked in the design and optimization of ME systems. The simulation approach presented in this work is expected to provide valuable insights into the improvement of ion analysis using ME. Full article

CRISPR/Cas systems have made significant progress in the field of molecular diagnostics in recent years. To overcome the aerosol contamination problem brought on by amplicon transfer in the common two-step procedure, the “one-pot method” has become a major research hotspot in this field. However, these methods usually rely on specially designed devices or additional chemical modifications. In this study, a novel “one-pot” strategy was developed to detect the foodborne pathogen Cronobacter sakazakii (C. sakazakii). A specific sequence was screened out from the virulence gene ompA of C. sakazakii as the detection target. Combining with the recombinase polymerase amplification (RPA), a rapid detection platform for C. sakazakii based on the CRISPR/Cas12a system was established for the first time. The sensitivity of this method was determined from three different levels, which are 10⁻⁴ ng/μL for genomic DNA (gDNA), 1.43 copies/μL for target DNA, and 6 CFU/mL for pure bacterial culture. Without any microbial enrichment, the detection limits for artificially contaminated cow and goat milk powder samples were 4.65 CFU/mL and 4.35 CFU/mL, respectively. To address the problem brought on by aerosol contamination in the common RPA-CRISPR/Cas12a two-step method, a novel pipette tip-in-tube (PTIT) method for simple and sensitive one-pot nucleic acid detection was further developed under the inspiration of the capillary principle. The RPA and CRISPR/Cas systems were isolated from each other by the force balance of the solution in a pipette tip before amplification. The detection limits of the PTIT method in pure bacterial culture and the spiked samples were exactly the same as that of the two-step method, but with no false positive cases caused by aerosol contamination at all. Compared with other existing one-pot methods, the PTIT method requires no additional or specially designed devices, or any chemical modifications on crRNA and nucleic acid probes. Therefore, the PTIT method developed in this study provides a novel strategy for realizing one-pot CRISPR/Cas detection easily and holds significant potential for the rapid point-on-care testing (POCT) application. Full article

As global urbanization accelerates in ecologically fragile regions, Nature-Based Solutions (NBS) have emerged as a critical paradigm for integrating environmental sustainability with urban resilience. Particularly in hyper-arid environments, the deployment of NBS must navigate unique climatic, hydrological, and socio-political complexities. This paper advances a conceptual framework that synthesizes the International Union for Conservation of Nature’s (IUCN) tripartite typology—protection, sustainable management, and restoration/creation—within a broader systems-oriented governance lens. By engaging with international precedents and context-specific urban dynamics, the study explores how adaptive, multiscale strategies can translate ecological principles into actionable urban design and planning practices. Through a comparative lens and grounded regional inquiry, the research identifies critical leverage points and institutional enablers necessary to operationalize NBS under desert constraints. While highlighting both the structural potential and the contextual limitations of existing initiatives in the Eastern Province of Saudi Arabia, the analysis underscores the necessity of coupling typological coherence with flexible regulatory and participatory mechanisms. Empirical findings from the Saudi case reveal persistent institutional fragmentation, heavy reliance on top-down implementation, and limited hydrological monitoring as key constraints, while also pointing to emerging governance mechanisms under Vision 2030—such as cross-sectoral coordination and pilot participatory frameworks—that can support the long-term viability of NBS in hyper-arid cities. Building on these insights, the study distills a set of strategic lessons that provide clear guidance on hydrological integration, adaptive governance, and socio-cultural legitimacy, offering a practical roadmap for operationalizing NBS in desert urban contexts. Full article