Search Results (3,779)

Objective: The aim of this study was to develop and evaluate non-antibiotic strategies against Helicobacter pylori by establishing a bovine immune milk platform and designing a synergistic multi-antigen immunogen to enhance humoral immune responses. Methods: Inactivated Helicobacter pylori (H. pylori) was used to immunize dairy cows, and the resulting immune milk was characterized for antibody specificity, acid stability, and target antigens via ELISA, Western blot, agglutination assays, and mass spectrometry. Key identified antigens (UreA, UreB, UreE, UreG, HypA, FlaA, and FlaB) were produced as recombinant proteins. Their immunogenicity was evaluated in a murine model, comparing single antigens with various protein combinations. Immune responses were assessed by antigen-specific IgG ELISA, bacterial agglutination titers, flow cytometry for T-cell activation, and histopathology for safety. Results: Immune milk contained high-titer, acid-stable IgG antibodies targeting multiple H. pylori virulence factors. In mice, while single proteins induced specific IgG, a multi-antigen cocktail (FlaA + FlaB + HypA + UreA + UreB + UreE + UreG) elicited significantly higher serum agglutination titers (~7 × 10³) than single antigens or inactivated whole-cell vaccine, alongside robust CD4⁺ T-cell activation. No formulations showed any hepatorenal or splenic toxicity. Conclusion: Bovine immune milk is a viable platform for acid-stable antibody delivery. A rationally designed multi-antigen cocktail synergistically enhances functional humoral immunity in vivo, providing a promising foundation for developing antibody-based or subunit vaccine strategies against H. pylori. Full article

Selecting a sustainable healthcare waste treatment method is a complex multi-criteria problem influenced by environmental, economic, social and technological factors. This study addresses key gaps in the literature by proposing an intuitionistic fuzzy AHP–TOPSIS framework that explicitly models cognitive uncertainty and expert hesitation, while demonstrating its application through a real-world case study in Adana, Türkiye. In contrast to prior studies utilizing fewer criteria, our framework evaluates four treatment alternatives—incineration, steam sterilization, microwave, and landfill—across 17 comprehensive criteria that directly integrate circular economy principles such as resource recovery and energy efficiency. The results indicate that steam sterilization is the most sustainable option, demonstrating superior performance across environmental, economic, social, and technological dimensions. A 15-scenario sensitivity analysis ensures ranking resilience across varying decision contexts. Furthermore, a systematic comparative analysis highlights the methodological advantages of the proposed framework in terms of analytical granularity and robustness compared to existing models. The study also offers step-by-step operational guidance, creating a transparent and policy-responsive decision-support tool for healthcare waste management authorities to advance sustainable practices. Full article

The optimization of historic district form, given the coordinated relationship between global urbanization and sustainable development, faces the core contradiction between preservation and development. Taking Shenyang’s Nanshi area as a case study, this study aimed to construct a sustainable urban form evaluation system comprising 7 dimensions and 23 indicators by integrating multi-source geographic Big Data. A combination of a weighting approach in rank-order analysis and the entropy weight method was adopted, followed by spatial quantitative analysis conducted based on ArcGIS. The results showed that the sustainability of the area exhibited significant spatial differentiation: historic blocks became high-value areas due to their “small blocks, dense road network” fabric and high functional mix. However, newly built residential areas were low-value zones, constrained by factors such as fragmented green spaces, single-functional land use, and other limitations. Road network density and functional mixing were identified as the primary driving factors, while green coverage rate served as a secondary factor. Based on these findings, a three-tier “element–structure–system” optimization strategy was proposed, providing quantitative decision support for the low-carbon renewal of high-density historic urban districts. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

This paper analyzes different approaches for the mathematical modeling and optimization of process parameters in the hard turning process of 42CrMo4 steel using a hybrid approach combining response surface methodology (RSM), multi-criteria decision making (MCDM), and machine learning through, support vector regression (SVR) with one-factor-at-a-time (OFAT) sensitivity analysis. Controlled process parameters such as cutting speed, depth of cut, feed, and insert radius are applied to conduct the experiments based on a full factorial experimental design. RSM was used to develop models that describe the effect of controlled parameters on surface roughness and cutting forces. Special emphasis was placed on the analysis of standardized residuals to evaluate the predictive capabilities of the RSM-developed model on an unseen data set. For all four outputs considered, analysis of the standardized residuals shows that over 97% of the points lie within ±3 standard deviations. A multi-criteria optimization technique was applied to establish an optimal combination of input parameters. The SVR model had high performance for all outputs, with coefficient of determination values between 89.91% and 99.39%, except for surface roughness on the test set, with a value of 9.92%. While the SVR model achieved high predictive accuracy for cutting forces, its limited generalization capability for surface roughness highlights the higher complexity and stochastic nature of surface formation mechanisms in the turning process. OFAT analysis showed that feed rate and depth of cut have been shown to be the most important input variables for all analyzed outputs. Full article

The black soil region of Northeast China (NEC) is China’s most important food production base. Long-term inefficient land use has made its ecosystem vulnerable to widespread degradation, prompting the implementation of ecological restoration projects (ERPs) to enhance ecosystem service (ES) resilience. Yet, the complex interactions among key ESs, including grain production (GP), water yield (WY), soil conservation (SC), and carbon storage (CS), as well as the spatial non-stationarity of their driving factors post-ERPs, have caused spatially heterogeneous, scale-dependent ES relationships. To address these gaps, this study aims to analyze temporal changes in ESs across multiple scales in NEC from 2000 to 2020. By mapping the interactions and quantifying their intensities, we revealed spatial variations in driving factors under different ERPs. The results show that the Natural Wetland Conservation Project (NWCP) and Three-North Shelterbelt Program (TNSP) have led to overall improvements in all ESs. In contrast, the Grain for Green Program (GFGP), the Land Salinity/Sodicity Amelioration Project (LASP), and the Natural Forests Conservation Program (NFCP) are associated with trade-offs between ESs. Interactions between ESs exhibited clear spatial scale dependence, and the dominant drivers varied across scales and restoration contexts. These findings highlight the importance of considering spatial scale and non-stationarity when evaluating ecological restoration outcomes. This study provides a scientific basis for the development and management of ecological restoration programs in intensively managed agricultural regions worldwide, particularly those undergoing multiple, overlapping restoration interventions, from a multi-scale spatial perspective. Full article

The integration of Software-Defined Networking (SDN), blockchain (BC), and machine learning (ML) has emerged as a promising approach to securing Internet of Things (IoT) and Industrial IoT (IIoT) networks. This paper conducted a comprehensive review of recent studies focusing on multi-layered security across device, control, network, and application layers. The analysis reveals that BC technology ensures decentralised trust, immutability, and secure access validation, while SDN enables programmability, load balancing, and real-time monitoring. In addition, ML/deep learning (DL) techniques, including federated and hybrid learning, strengthen anomaly detection, predictive security, and adaptive mitigation. Reported evaluations show similar gains in detection accuracy, latency, throughput, and energy efficiency, with effective defence against threats, though differing experimental contexts limit direct comparison. It also shows that the solutions’ effectiveness depends on ecosystem factors such as SDN controllers, BC platforms, cryptographic protocols, and ML frameworks. However, most studies rely on simulations or small-scale testbeds, leaving large-scale and heterogeneous deployments unverified. Significant challenges include scalability, computational and energy overhead, dataset dependency, limited adversarial resilience, and the explainability of ML-driven decisions. Based on the findings, future research should focus on lightweight consensus mechanisms for constrained devices, privacy-preserving ML/DL, and cross-layer adversarial-resilient frameworks. Advancing these directions will be important in achieving scalable, interoperable, and trustworthy SDN-IoT/IIoT security solutions. Full article

Accurate electricity carbon emission factors are crucial for assessing overall social carbon emissions and achieving China’s “dual carbon” goals. This paper proposes a dynamic correction model that integrates lifecycle extension, power exchange networks, and multi-time-scale decomposition to address the limitations of static carbon emission factors. The model considers factors such as power generation structure, cross-regional transmission, clean energy proportion, line losses, and non-CO₂ greenhouse gas emissions, and achieves dynamic correction at quarterly and monthly scales, enhancing timeliness and regional adaptability. Results show that transmission losses, energy structure, and inter-provincial electricity exchange significantly impact carbon emission factors. For instance, in 2022, line losses in Xinjiang and Gansu raised the electricity carbon emission factor by over 0.06 kgCO₂/kWh. Monthly factors exhibit significant seasonal fluctuations, with some regions showing variations of up to 105% of the annual average. Areas rich in hydropower, such as Yunnan, Sichuan, and Qinghai, experience pronounced fluctuations, highly sensitive to changes in water volume, offering more accurate reflections of carbon emission changes during electricity consumption. This study presents a refined dynamic correction method for electricity carbon emission accounting, providing theoretical support for carbon emission policy development and performance evaluation. Full article

Sustainable development is crucial worldwide. Under the Paris Agreement, countries commit to Nationally Determined Contributions (NDCs) assessed every five years. China, a major contributor to global warming, has made significant efforts to reduce carbon emissions and achieve carbon neutrality, a key strategy for sustainable development. However, there is a lack of adequate attention to embodied emission reduction in rural residential construction, despite a surge in building to improve living standards. This paper evaluated the feasibility of applying a multi-ribbed composite wall structure (MRCWS) in rural China through a village service project. A full-scale shaking table test was conducted to study its seismic performance. Carbon emissions were analyzed using process-based life cycle assessment (P-LCA) and the emission-factor approach (EFA), while costs were estimated using life cycle costing (LCC) and the direct cost method (DCM). These analyses focused on sub-projects and specific structural members to validate the superiority of this prefabricated structure over common brick masonry. MRCWS blocks were prefabricated by mixing wheat straw with aerocrete, utilizing agricultural by-products from local farmlands, thus reducing both construction-related carbon emissions and agricultural waste treatment costs. Results show that this novel precast masonry structure exhibits strong seismic resistance, complying with fortification limitations. Its application can reduce embodied carbon emissions and costs by approximately 6% and 10%, respectively, during materialization phases compared to common brick masonry. This new prefabricated building product has significant potential for reducing carbon emissions and costs in rural housing construction while meeting seismic requirements. The recycling of agricultural waste highlights its adaptability, especially in rural areas. Full article

Fibrin clots with strain-hardening characteristics exhibit pronounced material nonlinearity and acoustic dispersion under ultrasound, leading to waveform distortion and shock formation during finite-amplitude wave propagation. However, peak-shock stress is limited by viscoelastic dissipation and dispersion, constraining the efficiency of ultrasound in applications such as thrombus ablation. To overcome this limitation, a shock wave amplification method using designed multi-wave-packet sequences is proposed. Based on a power-law model from quasi-static compression tests, shock generation under a single sinusoidal pulse was first simulated. The dual-wave-packet chasing strategy was then developed, in which the amplitude, frequency, and time delay of the second packet were tuned to achieve effective superposition with the precursor. The waveform superposition factor (WSF) was introduced for quantitative evaluation. Numerical results demonstrate that this strategy can significantly increase the peak-shock-wave stress, with a maximum gain of 22.7%. Parametric analysis further identified amplitude as the dominant factor influencing wavefront steepness and amplification effectiveness. This study provides a novel method and theoretical support for developing efficient and controllable ultrasonic sequences for thrombolysis. Full article

Apple cultivation faces soil acidification and pollution due to excessive fertilization, compounded by a scarcity of potassium (K) fertilizers. Polyhalite, a natural multi-nutrient mineral, offers a potential sustainable alternative. Therefore, a three-year field experiment was conducted, comprising a no-potassium control (CK), two conventional potassium fertilizers (sulfate of potash-based and muriate of potash-based), and six polyhalite compound fertilizer treatments (with different basal and topdressing strategies), to evaluate their effects on apple growth and soil fertility. Results showed that the single topdressing application of potassium chloride-type polyhalite compound fertilizer (T6) achieved the highest yield in the final year, which was 10.11–28.03% higher than the other potassium-applied treatments. It also achieved the highest fruit vitamin C and soluble solids content (9.53 mg 100 g⁻¹ and 13.27%, respectively). The T6 treatment demonstrated the best performance in terms of agronomic efficiency and partial factor productivity of potassium fertilizer, reducing fertilizer waste and loss. Furthermore, the T6 treatment effectively increased soil pH, available potassium, and exchangeable calcium levels, thereby improving soil fertility. Thus, polyhalite proves effective in replacing conventional K fertilizers, with the single topdressing of MOP-type polyhalite compound fertilizer (T6) offering the most comprehensive agronomic and environmental benefits. Full article

Milling force is a parameter affecting wood processing quality, tool life, and energy consumption, and its variation is influenced by the multi-factor coupling of cutting parameters and tool geometric factors. This study systematically investigates milling forces during the processing of pine wood (Pinus sylvestris var. mongholica Litv.) using a hybrid modeling approach combining principal component analysis (PCA) and multiple linear regression (MLR). Firstly, PCA was employed to reduce the dimensionality of the tool rake angle (γ), helix angle (λ), cutting depth (h), feed per tooth (U_z), and triaxial milling forces (F_x, F_y, F_z); this eliminated the multicollinearity among variables and extracted the integrated features. Subsequently, an MLR model was constructed using the principal components as independent variables to quantitatively evaluate the contribution of each factor to milling forces. The results support the conclusion that PCA successfully extracted the first four principal components (cumulative variance contribution rate: 92.78%), with PC1 (49.16%) characterizing the comprehensive milling force effect and PC2 (15.03%) primarily reflecting the characteristics of the tool geometric parameters. The established MLR model demonstrated a high significance (R²: F_x = 0.915, F_y = 0.907, F_z = 0.852). The cutting depth exerted a significant positive driving effect on the triaxial milling forces via PC1 (each 1 mm increase in depth increased the PC1 score by 0.64 units, resulting in increases of 27.2%, 26.6%, and 21.8% for F_x, F_y, and F_z, respectively). The helix angle significantly suppressed F_y through PC2 (β = −0.090, p < 0.001), whereas the rake angle exhibited a weak negative effect on F_x via PC3 (β = −0.015). Parameter optimization identified the combination γ = 25°, λ = 30°, h = 0.5 mm, and U_z = 0.1 mm∙z⁻¹ as optimal, which reduced the triaxial milling forces by 62.3% compared to the experimental maximum. This study provides a theoretical foundation and novel parameter optimization strategy for the efficient, low-damage processing of wood materials. Full article

Aiming at the wind-blown snow disasters plaguing tunnel portals along the China-Tajikistan Highway Phase II Project, this study optimizes the protective parameters of wind deflectors through numerical simulation to improve the disaster prevention efficiency of tunnel portals in alpine mountainous areas. Three core control parameters of wind deflectors, namely horizontal distance from the tunnel portal (L), plate inclination angle (β), and top installation height (h), were selected as the research objects. Single-factor numerical simulation scenarios were designed for each parameter, and an L9(3³) orthogonal test was further adopted to formulate 9 groups of multi-parameter combination scenarios, with the snow phase volume fraction at 35 m on the leeward side of the tunnel portal set as the core evaluation index. A computational fluid dynamics (CFD) model was established to systematically investigate the influence laws of each parameter on the wind field structure and snow drift deposition characteristics at tunnel portals and clarify the flow field response rules under different parameter configurations. Single-factor simulation results show that the wind deflector exerts distinct regulatory effects on the wind-snow flow field with different parameter settings: when L = 6 m, the disturbance zone of the wind deflector precisely covers the main wind flow development area in front of the tunnel portal, which effectively lifts the main incoming flow path, compresses the recirculation zone (length reduced from 45.8 m to 22.3 m), and reduces the settlement of snow particles, achieving the optimal comprehensive prevention effect; when β = 60°, the leeward wind speed at the tunnel portal is significantly increased to 10–12 m/s (from below 10 m/s), which effectively promotes the transport of snow particles and mitigates the accumulation risk, being the optimal inclination angle; when h = 2 m, the wind speed on both the windward and leeward sides of the tunnel portal is significantly improved, and the snow accumulation risk at the portal reaches the minimum. Orthogonal test results further quantify the influence degree of each parameter on the snow prevention effect, revealing that the horizontal distance from the tunnel portal is the most significant influencing factor. The optimal parameter combination of the wind deflector is determined as L = 6 m, β = 60°, and h = 2 m. Under this optimal combination, the snow phase volume fraction at 35 m on the leeward side of the tunnel portal is 0.0505, a 12.3% reduction compared with the non-deflector condition; the high-concentration snow accumulation zone is shifted 25 m leeward, and the high-value snow phase volume fraction area (>0.06) disappears completely, which can effectively alleviate the adverse impact of wind-blown snow disasters on the normal operation of tunnel portals. The research results reveal the regulation mechanism of wind deflector parameters on the wind-snow flow field at alpine tunnel portals and determine the optimal protective parameter combination, which can provide important theoretical reference and technical support for the prevention and control of wind-blown snow disasters at tunnel portals in similar alpine mountainous areas. Full article

Despite the growing recognition of heritage risk reduction, a comprehensive framework for multi-risk assessment remains notably absent within the context of historic urban landscapes (HULs) across diverse global contexts. This paper aims to fill this gap by developing an assessment framework to address multiple emerging risks in HUL management, considering climate-related, human-induced, and mixed hazards in UNESCO World Heritage properties. A four-step process is established—hazard identification, exposure categorisation, adaptation capacity-building, and vulnerability monitoring and evaluation. Using content analysis, this framework is applied to official reports from 33 World Heritage HUL cases across 33 countries. The results show that, although various hazards have been acknowledged by state parties, local governments prioritise human-induced or natural hazards more often than mixed hazards, leading to a shortage of comprehensive risk management plans and practical actions in most cases. Regarding heritage adaptation, the factors of capacity and governance are widely addressed, demonstrating the commitment of state parties to formulate strategies and solve problems. However, public participation and education practices remain insufficiently implemented, resulting in a relatively low degree of adaptation capacity-building. The proposed multi-risk assessment framework offers a crucial reference for global urban heritage management and risk reduction. Full article

Ensuring the structural integrity of tower cranes is paramount for construction safety, yet jib lower chords—serving as trolley tracks—often undergo coupled wear–fatigue degradation that is rarely quantified in conventional service-life assessments. This study proposes a quantitative, maintenance-focused framework for integrity evaluation and life prediction of in-service tower cranes, validated through a decommissioned unit with 26 years of service in high-rise building construction. Through the integration of on-site construction operational statistics, ANSYS (Version 2022 R1, ANSYS, Inc., Canonsburg, PA, USA)—driven stress simulations, and rainflow counting, a multi-condition load spectrum was developed to quantify cumulative damage. Field measurements pinpointed Segment b03 as the critical damage zone, showcasing a maximum wear depth of 2.3 mm and roughly 30% thickness loss in the 20–30 m range, driven by stress concentration and high-frequency trolley movements during material handling. Theoretical fatigue life estimates of 42.1 years were revised to 24.1 years by incorporating wear geometry evolution and other degradation factors, resulting in a prediction error of approximately 7–8% relative to the actual service life. The proposed approach effectively bridges the gap between mechanical-based calculations and construction engineering practice, providing robust support for inspection scheduling, maintenance prioritization, and lifecycle management of aging tower cranes. Full article