Search Results (1,845)

The performance upgrading of stratospheric airships hinges on breakthroughs in the mechanical properties of envelope materials. As a multi-layer composite, the envelope’s load-bearing layer exhibits orthotropic and nonlinear mechanical behaviors owing to its unique structure and manufacturing process. To overcome the limitations of traditional testing methods and classical strength criteria in characterizing envelope materials, this paper presents a systematic investigation of typical airship envelope materials. The classical cruciform biaxial specimen was modified with a double-layer heat-sealed loading arm design to ensure preferential failure of the core region. Combined with digital image correlation (DIC) equipment, tensile tests were conducted under seven warp–weft stress ratios to acquire full-range stress–strain data. A three-dimensional stress–strain response surface was fitted based on the experimental results, and biaxial tensile constitutive models with varying precisions were established. Furthermore, a five-parameter implicit quadratic strength criterion was adopted to characterize the failure envelope of the envelope material. The model was calibrated using five biaxial failure points and independently validated against uniaxial tensile strengths, achieving a prediction error of less than 4%. The criterion’s generalization capability was enhanced through systematic parameterization based on the present test data. This work provides experimental evidence and reliable support for the engineering design and strength prediction of envelope materials. Full article

This review highlights the rapidly evolving role of artificial intelligence (AI) in transforming lung cancer care, with a specific focus on its integrated applications across diagnosis, biomarker discovery, and drug development. The novelty of this work lies in its holistic examination of how AI bridges these traditionally separate domains, from radiology and pathology to genomics and clinical trials, to create a more cohesive and personalized oncology pipeline. We detail how AI algorithms significantly enhance early detection by improving the accuracy and efficiency of pulmonary nodule characterization on computed tomography scans and enable precise cancer subtyping via computational pathology. In biomarker discovery, AI-driven analysis of radiomic features and genomic data facilitates the non-invasive prediction of tumor genotype, PD-L1 expression, and immunotherapy response, moving beyond invasive tissue biopsies. Furthermore, AI is accelerating the drug development lifecycle by identifying novel therapeutic targets and optimizing patient selection for clinical trials. The review also explores AI’s critical role in personalizing treatment regimens, including predicting outcomes for radiotherapy and immunotherapy, thereby tailoring therapy to individual patient profiles. We critically address the challenges of clinical translation, including model interpretability, data standardization, and ethical considerations, which are pivotal for real-world implementation. Finally, we contend that the future of lung cancer management hinges on robust, multi-institutional validation of AI tools and the development of trustworthy, explainable systems. Full article

We present a one-parameter family of smooth generalized pinball loss functions to overcome the challenges of non-differentiability, noise sensitivity, and resampling instability inherent in traditional loss functions such as hinge loss. These functions make the objective function in the formulation of the support vector machine (SVM) model twice continuously differentiable and improve model performance by reducing noise sensitivity and preserving the sparsity of the solution. Similarly, a novel twin-bounded support vector machine (TBSVM) model with a smooth generalized pinball loss function is obtained. Furthermore, we compare the performance of the TBSVM with the novel type of smooth loss function against other contemporary approaches, offering a comprehensive assessment of its strengths and limitations by conducting an evaluation with UCI datasets. The experimental results show that the proposed model has the best performance in the TBSVM with RBFSampler. Additionally, we prove that the generalized pinball loss function can be approximated by a novel smooth generalized pinball loss function in the uniform norm with arbitrary precision. We further show that the solutions of the proposed SVM and TBSVM models are unique and that they converge to the solutions of the models with non-smooth generalized pinball loss as the parameter approaches zero. Full article

This paper numerically analyses numerous parameters with the most sensitive impact on the in-plane lateral behaviour of light timber-framed (LTF) wall elements. Different types of sheathing material (fibre-plaster boards, OSB) are studied according to the parametrically chosen distance between the fasteners, using three different calculation procedures: (a) a previously developed semi-analytical procedure using the Modified Gamma Method (MGM) accounts for bending, shear, and timber-to-framing connection flexibility simultaneously; (b) a previously developed FEM Spring Model as the most accurate approach; and (c) in this study, a specially developed innovative FEM 2D Hinge Model using the two-dimensional hinge layer to simulate the deformability between the sheathing boards and the timber frame, which enables significantly faster FEM analysis compared to the already developed FEM Spring Model. This, in turn, realistically allows for much faster analysis of real multi-storey timber structures. In order to only judge the influence of the sheathing material and fastener disposition, in all cases, the tensile and compressive vertical supports are considered to be stiff-supported wall elements as prescribed by the valid Eurocode 5 standard; however, it is possible to additionally include all three possible supporting flexibilities. The study places particular emphasis on the deformation of sliding fasteners between the sheathing boards and the timber frame, which arises from fastener flexibility and can significantly reduce the overall in-plane stiffness of LTF wall elements. For specially selected parametric values of fastener spacing (s = 20, 37.5, 75, and 150 mm), parametric FEM analysis using a special 2D hinge layer is additionally developed and performed to validate the previously developed semi-analytical expressions by the MGM for the in-plane wall stiffness, which seems to be the most appropriate for designing engineering implementation. All applied approaches to modelling wall elements considered the same parameters for evaluating the stiffness of an individual wall element, which represents a fundamental input parameter in the modelling of frame wall elements within the overall structure. The aim of the study is to determine the most suitable and accurate model, as the response of the entire structure to horizontal loading depends on the design of the individual wall element. Among these, it has been demonstrated that the thickness of the load-bearing timber frame and the type of resisting LTF walls (internal or external) have practically no significant effect on the in-plane stiffness of such wall elements. Consequently, the type of sheathing material (FPB or OSB) and especially the spacing between the fasteners are much more sensitive parameters, which would probably need to be given further consideration in future FEM studies. Full article

Since a significant part of the Italian territory was not seismically classified until 2003, most existing bridges have been designed—for decades—disregarding earthquake-induced excitations. In fact, this means that load-bearing devices and shear keys of presently in-service infrastructures may not be up to current codes, both in terms of resistance and displacement capacity. Robust investigations are hence required for verifications and possible retrofit. In this study, the seismic behaviour of a case study post-tensioned concrete bridge built in the 1980s is numerically analysed. The examined structure is 440 m long and composed of nine spans, built with precast segments using the balance cantilever construction method. The deck is divided into two parts connected by a hinged joint in the middle of the central span, obtained with three shear keys and originally designed to allow for thermal expansion only. Most importantly, the mid-span hinge, the end joints and the bearing devices were originally designed without considering the effects of seismic action. In order to preliminarily investigate the performance of devices and joints, the case study bridge is analysed by means of non-linear dynamic time history simulations, formulating different hypotheses about the non-linear behaviour of the load bearings. Forces and displacements over time are obtained for a set of seven accelerograms, and maximum values are compared to the capacity of the bridge devices. Results are then critically discussed. Full article

CO₂ injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO₂ injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. The framework is applied to the Entrada Formation in the San Juan Basin, a laterally extensive sandstone unit with limited structural complexity across most of the basin, except for localized uplift in the Hogback region. A numerical model was calibrated to perform sensitivity analysis to identify the dominant parameters influencing injectivity. A dataset of these parameters generated through experimental design informs the development of several ML-based proxies and the best model is selected based on error metrics. These metrics include coefficient of determination (R²), mean absolute error (MAE), and mean squared error (MSE). The effective permeability-thickness product was obtained by the Peaceman’s well model, fractional flow slope, and Dykstra–Parsons coefficient were identified as the most influential parameters impacting the objective function. Train–test and blind test validation identified the Ridge model as the best, achieving an R² ≈ 0.994. The Ridge model which was used to map the Entrada Formation closely matches field-based correlations in the literature, confirming both its physical validity and the Entrada Formation’s strong injectivity potential, with slight deviations explained by the inclusion of additional parameters. This study reduces dependence on computationally intensive simulations while improving prediction accuracy. By benchmarking against established correlations, it enhances model reliability across diverse reservoir conditions. The proposed framework enables rapid, data-driven well placement and feasibility evaluations, streamlining decision-making for CO₂ storage projects. Full article

Endocervical adenocarcinoma is now classified within an etiologic framework based on the presence or absence of high-risk human papillomavirus (HPV) infection. Gastric-type endocervical adenocarcinoma (GAS) is the prototypical HPV-independent subtype, accounting for up to 25% of endocervical adenocarcinomas and showing a particularly high frequency in East Asia. GAS is typically diagnosed at a more advanced stage than usual-type HPV-associated endocervical adenocarcinoma (UEA); exhibits deep stromal and parametrial invasion, lymphovascular space invasion, and a strong propensity for ovarian and peritoneal metastasis; and is associated with markedly worse survival, even in stage I disease. Radiological evaluation is challenging because of diffuse infiltrative growth, prominent mucin production, and frequent underestimation of extra-cervical spread. Histologically, GAS shows gastric-type (pyloric) differentiation, ranging from minimal deviation adenocarcinoma to poorly differentiated forms, and often overlaps with precursor lesions such as atypical lobular endocervical glandular hyperplasia and gastric-type adenocarcinoma in situ. Immunophenotypically, GAS is typically p16-negative, ER/PR-negative, and frequently exhibits mutant-type p53 and expression of gastric markers including MUC6, HIK1083, and claudin 18.2. Recent next-generation sequencing and multi-omics studies have revealed recurrent alterations in TP53, CDKN2A, STK11, KRAS, ARID1A, KMT2D, and homologous recombination-related genes, together with the activation of PI3K/AKT, WNT/β-catenin, TGF-β, and EMT pathways and characteristic metabolic reprogramming. GAS is highly resistant to conventional chemotherapy and radiotherapy, and its current management follows guidelines for squamous and usual-type adenocarcinoma. Emerging data support precision-medicine approaches targeting HER2/HER3, PD-1/PD-L1, and claudin 18.2, and suggest a role for PARP inhibition and other genotype-directed therapies in selected subsets. Given its aggressive biology and rising relative incidence in the HPV-vaccination era, GAS represents a critical unmet need in gynecologic oncology. Future progress hinges on developing reliable diagnostic biomarkers, refining imaging protocols, and validating targeted therapies through international clinical trials. Full article

Temporary ground anchors are widely used to provide anchorage for winches, tensioners, and guy wires during power transmission construction. In mountainous terrain, the drilling efficiency is limited, and conventional cement-grouted rock anchors are typically abandoned after use, causing resource waste and local environmental disturbances. This study proposes a plate–umbrella composite recyclable rock anchor in which a hinged umbrella head can unfold and retract within an end-plate sleeve to mobilize slab-bearing resistance under pull-out. A composite grouting scheme (epoxy mortar plus hot-melt adhesive) combined with resistive heating enables component recovery after service. Field pull-out/recovery trials and ABAQUS simulations were conducted to evaluate load–displacement behavior, recovery feasibility, and key influencing factors (embedment length and drilling/tension angle combinations). Compared with a conventional end-plate anchor of the same short embedment length (1 m), the proposed anchor achieved a markedly higher ultimate capacity and smaller displacement. Angle mismatch between the drilling and tension directions caused substantial capacity loss, highlighting the need for alignment control in practice. Parametric simulations further indicate stable performance across representative weathered granite conditions. The proposed system provides a promising approach for efficient and reusable temporary anchorage in mountainous transmission projects. Full article

District heating systems are central to Europe’s decarbonisation strategy and its 2050 climate-neutrality objective. However, district heating is deeply embedded in the socio-economic system and the built environment. This makes compliance with policy targets at the local level particularly challenging. The issues are attributable to two factors. Firstly, the process is characterised by a high degree of complexity and multidimensionality. Secondly, there is a scarcity of local resources (e.g., land, surface waters, waste heat, etc.). In Bucharest, Romania, the largest district heating system in the European Union, the process of decarbonisation represents a particularly complex challenge. The system is characterised by large physical dimensions, high technical wear, heavy dependence on natural gas, significant heat losses and complex governance structures. This paper presents a strategic planning exercise for aligning the Bucharest system with the Energy Efficiency Directive 2023/1791. Drawing on system data, investment modelling, and local resource mapping from the LIFE22-CET-SET_HEAT project, the study evaluates scenarios for 2028 and 2035 that shift heat generation from natural gas to renewable, waste heat, and high-efficiency sources. The central objective is the identification of opportunities and issues. Options include large-scale heat pumps, waste-to-energy, geothermal and solar heat. Heat demand profiles and electricity price dynamics are used to evaluate economic feasibility and operational flexibility. The findings show that the decarbonisation heat supply in Bucharest is technically possible, but financial viability hinges on phased investments, interinstitutional coordination, regulatory reforms and access to EU funding. The study concludes with recommendations for staged implementation, coordinated governance and socio-economic measures to safeguard heat affordability and system reliability. Full article

Foreign direct investment (FDI) has often been cast as a straightforward engine of growth, yet its record across Central and Eastern Europe tells a more tangled story where outcomes hinge on the interplay of education, governance, and the timing of external shocks. This study embeds fixed effects panel econometrics within a systems framework, treating FDI as a subsystem of socio-economic dynamics. Using a long-run panel of eleven economies from 2000 to 2023, the analysis models path dependence and regime shifts through interaction terms and period-specific dummies set against a systems-thinking backdrop. The analysis shows that for the average CEE economy, FDI’s contribution has waxed and waned: it dragged on growth during the early transition years (2000–2007), settled into a neutral role after the global financial crisis, and proved unpredictable in the pandemic era. Romania stands out, however, with a marked “FDI premium” quantified as approximately 0.7 pp of growth per pp of FDI that seems to stem from reinforcing loops between rising tertiary enrolment and productivity spillovers. Mapping these feedbacks brings to light virtuous circles where human capital and resilience make or break the benefits of foreign capital. The policy message is plain: nurture the positive loops through investment in skills and firm linkages, keep institutions nimble enough to adapt, and watch for early warning signs of systemic strain. Full article

The catalytic hydrogenation of carbon dioxide (CO₂) represents a transformative approach for reducing greenhouse gas emissions while producing sustainable fuels and chemicals, with ethanol being particularly promising due to its compatibility with existing energy infrastructure. Despite significant progress in converting CO₂ to C₁ products (e.g., methane, methanol), selective synthesis of C₂₊ compounds like ethanol remains challenging because of competing reaction pathways and byproduct formation. Recent advances in thermo-catalytic CO₂ hydrogenation have explored diverse catalyst systems including noble metals (Rh, Pd, Au, Ir, Pt) and non-noble metals (Co, Cu, Fe), supported on zeolites, metal oxides, perovskites, silica, metal–organic frameworks, and carbon-based materials. These studies reveal that catalytic performance hinges on the synergistic effects of multimetallic sites, tailored support properties and controlled reaction micro-environments to optimize CO₂ activation, controlled hydrogenation and C−C coupling. Mechanistic insights highlight the critical balance between CO₂ reduction steps and selective C−C bond formation, supported by thermodynamic analysis, advanced characterization techniques and theoretical calculations. However, challenges persist, such as low ethanol yields and undesired byproducts, necessitating innovative catalyst designs and optimized reactor configurations. Future efforts must integrate computational modeling, in situ/operando studies, and renewable hydrogen sources to advance scalable and economically viable processes. This review consolidates key findings, proposes potential reaction mechanisms, and outlines strategies for designing high-efficiency catalysts, ultimately providing reference for industrial application of CO₂-to-ethanol technologies. Full article

The precise identification of pollution sources constitutes a cornerstone for effective water environment management in mountainous watersheds. This study employed principal component analysis–absolute principal component scores–multiple linear regression (PCA-APCS-MLR) receptor modeling to analyze monthly water quality indicators across the Longxi River Basin. Results revealed comparable water quality between the main stream and its tributaries, with no statistically significant differences identified. Water quality exhibited a distinct spatial pattern, with superior conditions in the upstream and downstream segments compared to the middle reaches. Water quality parameters exhibited significant seasonal variations. During the wet period, the degradation of water quality was primarily driven by diffuse agricultural sources, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For Permanganate index (COD_Mn) exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For ammonium nitrogen (NH₃-N), wet season agricultural non-point sources (44.4%) and dry season rural domestic pollution (71.8%) were key contributors. Agricultural non-point sources were the dominant pollution source for total nitrogen (TN) in the wet season (84.2%). Effective water quality improvement in the Longxi River Basin hinges on targeted strategies—to mitigate diffuse agricultural sources through optimized fertilization, and to enhance the collection and treatment of rural domestic sewage. This study not only enhances the understanding of pollution source distribution and quantification in mountainous watersheds, but also serves as a vital reference for formulating targeted water environment management strategies. Full article

This study presents a hybrid approach integrating metaheuristic optimization and machine learning methods to quickly and reliably estimate the optimum design parameters of dome-shaped axially symmetric cylindrical reinforced concrete (RC) walls. A comprehensive dataset was created using the Jaya algorithm to minimize total material cost for hinged and fixed support conditions. For each optimized design case, total wall height (H), dome height (Hd), dome thickness (hd), and fluid unit weight (γ) were considered as input parameters; optimum wall thickness (hw) and total cost were determined as output parameters. Using the obtained dataset, a total of thirteen different regression-based machine learning algorithms, including linear regression-based models, tree-based ensemble methods, and neural network models, were trained and tested. Hyperparameter adjustments for all models were performed using the Optuna framework, and model performances were evaluated using a ten-fold cross-validation method and holdout dataset results. The results showed that machine learning models can learn the optimum design space obtained from metaheuristic optimization outputs with high accuracy. In optimum wall thickness estimation, Gradient Boosting-based models provided the highest accuracy under both hinged and fixed support conditions. In total cost estimation, the Gradient Boosting model stood out under hinged support conditions, while the XGBoost model yielded the most successful results for fixed support conditions. The findings clearly show that no single machine learning model exhibits the best performance for all output parameters and support conditions. The proposed approach offers significantly higher computational efficiency compared to traditional iterative optimization processes and allows for rapid estimation of optimum design parameters without the need for any iterations. In this respect, this study provides an effective decision support tool that can be used especially in the preliminary design phases and contributes to sustainable, cost-effective reinforced concrete structure design. Full article

This study addresses a critical gap in seismic design by quantifying how plan asymmetry and multiple earthquake sequences interact to affect the nonlinear reaction of reinforced concrete (RC)-framed models. While earthquake-resistant design provisions have evolved, most current codes are based on single-event assumptions and simplified symmetry considerations, overlooking the cumulative effects of repeated ground motions observed in recent international studies. In this research, symmetrical and asymmetrical low-rise RC buildings are analyzed through nonlinear dynamic simulations, with both single- and multiple-event ground excitations considered for comparison. The analyses incorporate three-dimensional ground motions in horizontal and vertical orientations, while explicitly modeling the nonlinear inelastic response of RC sections under severe seismic demands. The evaluation of elastoplastic findings relies on normalized indices, by considering a simple dimensionless parameter to quantify the physical symmetry or asymmetry of the RC models. Results show that increasing plan asymmetry amplifies inter-story drift, torsional rotations, and plastic hinge concentrations, particularly under successive earthquake sequences. These findings indicate that existing design provisions may underestimate the vulnerability of irregular RC buildings. This work is among the first to integrate plan asymmetry and multi-event seismic loading into a unified evaluation framework, offering a novel tool for refining earthquake-resistant design standards. Full article

The global consensus on sustainable development hinges on the coordinated advancement of economic, social, and environmental dimensions, with high-quality development serving as China’s pivotal pathway for practical implementation. As the primary implementers, cities are confronted with the dual challenge of defining the level of high-quality development and mapping out clear actionable pathways. Therefore, unraveling the driving mechanisms of high-quality urban development is significant. This study constructed a high-quality development evaluation index system, employing a sustainable development index to measure Lianyungang City’s development level from 2008 to 2023. The interrelationships among driving factors were revealed through the coupling coordination degree model, entropy weight method, and Pearson correlation coefficient. The study indicated that innovation stood out as the primary contributor, with contribution rising from 0.09 (2008–2017) to 0.10 (2017–2023). High-tech enterprises and valid invention patents were core drivers of the innovation index’s rise, with weights of 30.35% and 28.92%. Innovation investment promoted the transformation of cities toward technology-intensive development models while effectively supporting Sustainable Development Goals such as industrial upgrading, environmental improvement, and livelihood enhancement. Overall, advancing high-quality urban development required focusing on innovation-driven strategies while catalyzing other areas of development to achieve Sustainable Development Goals. Full article