Search Results (21,344)

The appropriate selection of renovation plaster properties is essential for ensuring the durability and effectiveness of conservation works. This study focused on the design and characterization of cement-based renovation mortars modified with synthetic zeolites with different Si/Al ratios. It was assumed that high-silica zeolites would provide more favorable mechanical and hygric performance than low-silica types. Owing to their porous structure and pozzolanic reactivity, zeolites proved to be effective additives, enhancing both the microstructure and functionality of the mortars. The modified mixtures exhibited increased total porosity, higher capillary absorption, and improved moisture transport compared with the reference mortar based on CEM I 52.5R. Dynamic vapor sorption tests confirmed that the zeolite-containing mortars achieved Moisture Buffer Values (MBV) above 2.0 g/m², which corresponds to the “excellent” moisture buffering class. Electrical resistivity measurements further demonstrated the relationship between denser microstructure and enhanced durability. At the frequency of 10 kHz, the electrical resistivity of the reference mortar reached 43,858 Ω·m, while mortars with 15% ZSM-5 and 15% Na-A achieved 62,110 Ω·m and 21,737 Ω·m. These results show that the addition of high-silica zeolite promotes the formation of a denser and more insulating matrix, highlighting the potential of this method for non-destructive quality assessment. The best overall performance was observed in mortars containing the high-silica zeolite ZSM-5. A 35% replacement of cement with ZSM-5 increased compressive strength by 10.5% compared with the reference mortar R (4.3 MPa). Frost resistance tests showed minimal mass loss (0.03% at 15% and 1.79% at 35% replacement), and ZSM-5 mortars also maintained integrity under salt crystallization. These improvements were attributed to the reaction of reactive SiO₂ and Al₂O₃ from the zeolites with Ca(OH)₂, leading to the formation of additional C-S-H. A higher Si/Al ratio promoted a denser, fibrous C-S-H morphology, as confirmed by SEM, which explains the improved strength and durability of mortars modified with ZSM-5. Full article

In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future yield prediction models. Six hybrids were grown at three planting densities (S1, S4, S7). Grain yield and yield components were estimated at four developmental points during grain filling (V1 to V4). These regression models and machine learning (ML) were applied to predict maize production under variable weather conditions. The factor year was the main source of variability, with less favourable conditions in the second year (G2) reducing yield by approximately 1–2%. Lower planting density (S1) improved individual plant development and yield components, while maximum density (S7) resulted in higher grain yield despite reduced individual performance. Hybrid H5 showed strong tolerance to high density, producing the highest yield under S7 conditions. Machine learning models accurately predicted key seed quality traits—moisture, oil, and protein—with performance metrics exceeding 80% accuracy. Specifically, R² values reached 0.82 for moisture content and 0.77 for oil concentration, indicating strong predictive capability. These findings support careful selection of hybrids and optimal planting density strategies in future cropping systems to increase yield and maintain seed quality in different environments. Full article

Aluminum–lithium alloys, as promising next-generation aerospace materials, exhibit outstanding properties, such as high strength, low density, excellent cryogenic performance, and superior corrosion resistance. In this study, aluminum–lithium alloy powders were processed via selective laser melting to systematically investigate the effects of processing parameters on manufacturing quality, microstructure, microhardness, residual stress, and tensile properties, with a particular emphasis on crack initiation and evolution. The results demonstrate that increasing laser power significantly improves specimen densification and reduces surface roughness. Moreover, the number of cracks decreases while their average length increases with elevated laser power. The maximum microhardness of 106.8 HV was achieved at the highest laser power, which also corresponded to the optimal tensile performance. These findings provide valuable insights into the relationship between laser parameters, microstructural evolution, and mechanical behavior, offering practical guidance for optimizing process parameters in the SLM fabrication of Al-Li alloy components for aerospace applications. Full article

Graphene Nano-Carbons (GNCs) have a huge potential, but current production methods limit their exploration and use. Many GNCs will be explored here with a focus on CNTs (Carbon NanoTubes) (which have some of the highest strengths of any known material, conductivity, EMF absorptivity, and many other useful properties. Manufacturing them abundantly, inexpensively, and in eco-friendly ways remains a significant challenge. Two CNT/GNCs production methods are compared and reviewed. Traditional Chemical Vapor Deposition (CVD) production heats organic reactants with metal catalysts to form GNC/CNTs. As of now, the CVD CNT production has been limited by the high-energy costs, costs per weight comparable to precious metals, and a high CO₂-footprint. C2CNT is an electrochemical methodology that overcomes the constraints of CVD, while producing high-quality CNT/GNCs. C2CNT is a molten carbonate CO₂-electrolysis that makes GNCs. The C2CNT process also selectively produces a wider variety of CNTs (including helical, magnetic, and doped) and GNCs with higher product specificity than CVD by fine-tuning electrolysis parameters. The wide variety of CNTs/GNCs that can be produced by each of these methods is reviewed and discussed. The goal of this perspective is to compare GNC production methods. Full article

To improve the energy efficiency and indoor environmental quality of rural dwellings in China’s cold regions, this study selected a typical rural dwelling in Linyi, Shandong Province, as a case study. Integrating field measurements with parametric simulations, the Orthogonal Experimental Design method was employed to systematically evaluate the impacts of 12 envelope design parameters on building energy demand (ED_tot, ED_H, ED_C), thermal comfort (PNT_ave), daylight performance (UDI_ave), and economic outcomes (retrofit cost and return on investment, ROI). Three sets of orthogonal experiments with varying value ranges (Case 1–3) were conducted. The results revealed that U-Window and SHGC are the most critical factors influencing energy demand and thermal comfort, while light transmittance (Trans) exerts the greatest influence on daylighting. The economic analysis demonstrated that window material is the primary determinant of retrofit costs, whereas building depth and the south window-to-wall ratio (WWR-South) significantly affect ROI. Additional range and variance analyses quantified the significance of each parameter and revealed nonlinear influence patterns. This research provides data support and decision-making references for the energy-efficient retrofit and multi-objective optimization of rural dwellings in cold regions, offering strong practical implications. Full article

In the current economic context, organizations providing automotive repair services face significant challenges in ensuring service quality, operational efficiency, and long-term sustainability. This paper examines the importance of implementing process monitoring systems through the integration of European quality frameworks and Japanese operational principles such as Kaizen, Lean Manufacturing, and Poka-Yoke, to improve the quality of services and increase performance within automotive repair organizations. The research is grounded in Sustainable Development Goals (SDG 9—Industry, Innovation and Infrastructure, and SDG 12—Responsible Consumption and Production), demonstrating how structured quality practices contribute to reducing waste, optimizing processes, and delivering responsible services. The main objectives of the study are to identify the elements that influence the performance of service-specific processes, to improve the quality management practices related to these processes, to eliminate non-conformities, and to enhance profitability and competitive differentiation through service quality assurance. A mixed-methods research design was applied, including direct participatory observation, performance monitoring, and correlational statistical analysis over a six-month period in two Romanian automotive service centers. Key performance indicators (KPIs) such as technician efficiency, rework rate, and order throughput time were collected and analyzed before and after the implementation of selected tools. Findings demonstrate measurable improvements: rework rates decreased from 7.8% to 2.6%, technician efficiency improved from 89% to 105%, and average service completion time was reduced by 1.6 days. Correlation analysis confirmed strong relationships between visual management adoption and rework reduction (r = −0.75), as well as between Lean implementation and technician efficiency (r = +0.89). The study’s novelty lies in its integration of cross-cultural quality management practices into a replicable and sustainable operational model for post-sale service environments. The results validate that implementing monitoring systems, combined with Kaizen, Lean, and Poka-Yoke, supported by visual management and active employee engagement, can lead to superior service quality management, increased customer satisfaction, and long-term organizational success in the automotive repair industry. Full article

Background and Objectives: Ultra-hypofractionated radiotherapy (uhRT) is increasingly used for low- and intermediate-risk localized prostate cancer, necessitating exceptional precision compared to conventional fractionation. CBCT-based online-adaptive uhRT may help mitigate pelvic organ motion but has not yet been established in clinical routine. We report initial clinical experiences focusing on the feasibility and technical aspects of treatment delivery. Materials and Methods: Seven patients (35 fractions) with low- or intermediate-risk prostate cancer were treated with online-adaptive uhRT on the Varian Ethos^® system within routine clinical care. The target included the prostate and proximal seminal vesicles (CTV1, 5 × 7.25 Gy), with an integrated boost to the prostate (CTV2, 5 × 8.00 Gy). For each fraction, dose–volume histogram (DVH) parameters for targets and organs at risk (OARs) were recorded retrospectively for both scheduled and adaptive plans, along with the plan selection decision. Plan quality was evaluated per clinical DVH constraints and target coverage. The treatment time was recorded. Results: Online-adaptive uhRT was successfully delivered every day in 5 patients and on alternate days in 2 patients. Mean treatment time was 30:17 (±05:49 SD) minutes per fraction. The median recorded change in target and OAR volumes was <10%. Adaptive plans resulted in a statistically significantly improved target coverage for CTV1 (V100%, p = 0.01), PTV1 (D98%, p < 0.001), PTV2 boost (D98%, p < 0.001) in Wilcoxon signed-rank tests. OAR dose reduction was limited, with a small improvement in bladder V40Gy (p = 0.02). Adaptive plans were applied in 32/35 fractions (91.4%). To encompass intra-fractional motion in 95% of fractions, positional adjustments up to 0.77 cm (longitudinal), 0.37 cm (lateral), and 0.59 cm (sagittal) were required. Conclusions: Online-adaptive uhRT appears feasible, leading to optimized target volume coverage. Considerable treatment times must be taken into account. A second CBCT is recommended to compensate for intra-fractional motion. Further research regarding patient-related endpoints and cost-effectiveness is highly needed. Full article

The rapid emergence and evolution of avian viral pathogens present a major challenge to global poultry health and food security. Traditional vaccine development is often slow, costly, and limited by antigenic diversity. In this study, we present a comprehensive artificial intelligence (AI)-driven pipeline for the rational design, modeling, and optimization of multi-epitope vaccines targeting economically important RNA and DNA viruses affecting poultry, including H5N1, NDV, IBV, IBDV, CAV, and FPV. We utilized advanced machine learning and deep learning tools for epitope prediction, antigenicity assessment, and structural modeling (via AlphaFold2), and codon optimization. B-cell and T-cell epitopes were selected based on binding affinity, conservation, and immunogenicity, while adjuvants and linker sequences enhanced construct stability and immune response. In silico immune simulations forecasted robust humoral and cellular responses, including cytokine production and memory cell activation. The study also highlights challenges such as data quality, model interpretability, and ethical considerations. Our work demonstrates the transformative potential of AI in veterinary vaccinology and offers a scalable model for rapid, data-driven vaccine development against avian diseases. Full article

The growing diversity of cloud services has made evaluating their relative merits in terms of price, functionality, and availability increasingly complex, particularly given the wide range of deployment alternatives and service capabilities. Cloud manufacturing often requires the integration of multiple services to accomplish user tasks, where the effectiveness of resource utilization and capacity sharing is closely tied to the adopted service composition strategy. This complexity, intensified by competition among providers, renders cloud service selection and composition an NP-hard problem involving multiple challenges, such as identifying suitable services from large pools, handling composition constraints, assessing the importance of quality-of-service (QoS) parameters, adapting to dynamic conditions, and managing abrupt changes in service and network characteristics. To address these issues, this study applies the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in conjunction with Multi-Criteria Decision Making (MCDM) to evaluate and rank cloud services, while the Analytic Hierarchy Process (AHP) combined with the entropy weight method is employed to mitigate subjective bias and improve evaluation accuracy. Building on these techniques, a novel Adaptive Multi-Level Linked-Priority-based Best Method Selection with Multistage User-Feedback-driven Cloud Service Composition (MLLP-BMS-MUFCSC) framework is proposed, demonstrating enhanced service selection efficiency and superior quality of service compared to existing approaches. Full article

In the global agricultural production system, maintaining and improving soil quality are core elements for ensuring food security and sustainable agricultural development. As a key indicator of soil quality, the content and dynamic change in soil organic carbon have a profound impact on the physical, chemical and biological properties of soil, and play a decisive role in soil fertility, structural stability, water and fertilizer conservation capacity and microbial activity. However, its decomposition is slow, and a large number of straws returning to the field will impact crop growth; its combination with irrigation is a more reasonable solution, as it can significantly improve the soil environment, increase soil moisture and promote straw decomposition. Therefore, in order to further study the effects of different irrigation methods and straw-returning combinations on soil active-carbon content, an experiment was carried out in long-term arid and semi-arid areas under in-field corn cultivation during 2019–2020. Three irrigation modes were designed—flood irrigation (BI), shallow drip irrigation (SD) and drip irrigation under film (DP)—and straw returning (CS) and no straw returning (CK) were set up, with irrigation applied at critical corn growth stages (internode elongation, heading, bell mouth stage) to support plant growth. The results are as follows: (1) The content of soil organic carbon in different treatments had a gradual upward trend with the advance of growth period; the content of soil organic carbon in DP treatment was significantly higher than that in SD and BI treatment under the same straw returning mode, indicating that drip irrigation under film and straw-returning mode can synergistically improve soil fertility and organic carbon content. (2) Different irrigation methods and straw-returning methods have significant effects on the content of soil active organic carbon components. Different drip irrigation modes can significantly improve the content of soil POC and MBC compared with flood irrigation. The Kos of SD treatment is significantly higher than that of other irrigation treatments, and the CPMI is lower than that of the other two irrigation methods, indicating that the soil organic carbon of SD treatment is more stable. Therefore, under straw-returning conditions, drip irrigation can significantly improve the carbon content of soil components and the management index of soil carbon pool, thus significantly increasing the accumulation of soil organic matter. This study discussed the effects of straw returning on soil organic carbon composition and soil carbon pool index under different irrigation methods to provide theoretical and practical bases for the selection and promotion of straw-returning methods and rational irrigation methods in semi-arid areas. Full article

Sulfur dioxide (SO₂) is commonly employed as an antioxidant and preservative in food processing, but excessive intake of SO₂ can pose significant health risks. Therefore, accurate detection of sulfite content in food is crucial for ensuring food quality and safety. A novel fluorescent probe, BODIPY-Y, composed of a BODIPY derivative and an ethyl cyanoacetate group linked by a carbon–carbon double bond, was synthesized for detecting sulfur dioxide derivatives. When the BODIPY-Y probe interacts with SO₃²⁻, the probe exhibits enhanced fluorescence at 514 nm. Spectrometric experiments show that the probe exhibits high sensitivity (LOD: 0.263 μmol/L), a fast response time (50 s) and excellent selectivity for SO₃²⁻. Mechanistic studies confirm that the BODIPY-Y probe operates via an intramolecular charge transfer (ICT) mechanism. The carbon–carbon double bond in BODIPY-Y undergoes nucleophilic addition with SO₃²⁻, blocking the ICT process and resulting in a blue shift in the fluorescence spectrum. In addition, the probe was applied to quantify SO₃²⁻ levels in real food samples. The measured concentrations of SO₂ in the white sugar and red wine were 15.93 μmol/L and 7.30 μmol/L, respectively, with recovery rates of 77.9–98.1%. This work presents a prospective chemical tool for monitoring sulfur dioxide derivatives in food products. Full article

This paper presents the concept and development of an autonomous DIY air quality sensor for citizen science. Large civil monitoring projects often rely on air quality calculations based on PM2.5 and PM10 dust readings in combination with some gases and do not cover the full list of air quality indicators. The authors have analyzed existing air quality calculation methodologies and attempted to conceptualize a universal AQI monitoring device for use in citizen science and by volunteers. This device is based on the available ESP32 DevKit v1 platform to which compatible sensors have been selected to monitor AQI indicators such as PM2.5 and PM10 dust particles, ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and ammonia. The SD card module was chosen for data storage, the NB-IoT module for data transmission, and a battery pack for autonomy. The housing, sensor design components, and fasteners were also selected. All components are available on the international market. Based on the selected element base, an electrical connection diagram was designed, the device’s design, presented in the form of 3D models, was developed, and the assembly process was described. The cost of the device was also evaluated and compared to the price level of existing DIY devices used in citizen science. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

Numerous industries have begun using cloud computing. Among other things, this presents a plethora of novel security and dependability concerns. Thoroughly verifying cloud solutions to guarantee their correctness is beneficial, just like with any other computer system that is security- and correctness-sensitive. While there has been much research on distributed system validation and verification, nobody has looked at whether verification methods used for distributed systems can be directly applied to cloud computing. To prove that cloud computing necessitates a unique verification model/architecture, this research compares and contrasts the verification needs of distributed and cloud computing. Distinct commercial, architectural, programming, and security models necessitate distinct approaches to verification in cloud and distributed systems. The importance of cloud-based Service Level Agreements (SLAs) in testing is growing. In order to ensure service integrity, users must upload their selected services and registered services to the cloud. Not only does the user fail to update the data when they should, but external issues, such as the cloud service provider’s data becoming corrupted, lost, or destroyed, also contribute to the data not becoming updated quickly enough. The data saved by the user on the cloud server must be complete and undamaged for integrity checking to be effective. Damaged data can be recovered if incomplete data is discovered after verification. A shared resource pool with network access and elastic extension is realized by optimizing resource allocation, which provides computer resources to consumers as services. The development and implementation of the cloud platform would be greatly facilitated by a verification mechanism that checks the data integrity in the cloud. This mechanism should be independent of storage services and compatible with the current basic service architecture. The user can easily see any discrepancies in the necessary data. While cloud storage does make data outsourcing easier, the security and integrity of the outsourced data are often at risk when using an untrusted cloud server. Consequently, there is a critical need to develop security measures that enable users to verify data integrity while maintaining reasonable computational and transmission overheads. A cryptography-based public data integrity verification technique is proposed in this research. In addition to protecting users’ data from harmful attacks like replay, replacement, and forgery, this approach enables third-party authorities to stand in for users while checking the integrity of outsourced data. This research proposes a Cloud Integrity Verification and Validation Model using the Double Token Key Distribution (CIVV-DTKD) model for enhancing cloud quality of service levels. The proposed model, when compared with the traditional methods, performs better in verification and validation accuracy levels. Full article

Australia consumes approximately 29 million m³ of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO_2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article