Search Results (2,402)

With the widespread application of artificial intelligence in real estate price prediction, model explainability has become a critical factor influencing its acceptability and trustworthiness. The Shapley value, as a classic cooperative game theory method, quantifies the average marginal contribution of each feature, ensuring global fairness in the explanation allocation. However, its focus on average fairness lacks robustness under data perturbations, model changes, and adversarial attacks. To address this limitation, this paper proposes a hybrid explainability framework that integrates the Shapley value and Least Core attribution. The framework leverages the Least Core theory by formulating a linear programming problem to minimize the maximum dissatisfaction of feature subsets, providing bottom-line fairness. Furthermore, the attributions from the Shapley value and Least Core are combined through a weighted fusion approach, where the weight acts as a tunable hyperparameter to balance the global fairness and worst-case robustness. The proposed framework is seamlessly integrated into mainstream machine learning models such as XGBoost. Empirical evaluations on real-world real estate rental data demonstrate that this hybrid attribution method not only preserves the global fairness of the Shapley value but also significantly enhances the explanation consistency and trustworthiness under various data perturbations. This study provides a new perspective for robust explainable AI in high-risk decision-making scenarios and holds promising potential for practical applications. Full article

The evaluation of pressure drops across the length of production wells is a crucial task, as it influences both the cost-effective selection of tubing and the development of an efficient production strategy, both of which are vital for maximizing oil recovery while minimizing operational expenses. To address this, our study proposes an innovative hybrid intelligent system designed to predict bottom-hole flowing pressure in vertical multiphase conditions with superior accuracy compared to existing methods using a data set of 150 field measurements amassed from Algerian fields. In this work, the applied hybrid framework is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which integrates artificial neural networks (ANN) with fuzzy logic (FL). The ANFIS model was constructed using a subtractive clustering technique after data filtering, and then its outcomes were evaluated against the most widely utilized correlations and mechanistic models. Graphical inspection and error statistics confirmed that ANFIS consistently outperformed all other approaches in terms of precision, reliability, and effectiveness. For further improvement of the ANFIS performance, a particle swarm optimization (PSO) algorithm is employed to refine the model and optimize the design of the antecedent Gaussian memberships along with the consequent linear coefficient vector. The results achieved by the hybrid ANFIS-PSO model demonstrated greater accuracy in bottom-hole pressure estimation than the conventional hybrid approach. Full article

In order to solve the problem of low thermal efficiency of a 130 t/h industrial circulating fluidized bed boiler, a computational particle fluid dynamic approach was used in this work to study two-phase gas–solid flow, heat transfer, and combustion. The factors influencing coal particle size distributions, air distribution strategies, and operational loads are addressed. The results showed that particle distribution exhibits “core–annulus” flow with a dense-phase bottom region and dilute-phase upper zone. A higher primary air ratio (0.8–1.5) enhances axial gas velocity and bed temperature but reduces secondary air zone (2.5–5.8 m) temperature. A higher primary air ratio also decreases outlet O₂ mole fraction and increases fly ash carbon content, with optimal thermal efficiency at a ratio of 1.0. In addition, as the coal PSD decreases and the load increases, the overall temperature of the furnace increases and the outlet O₂ mole fraction decreases. Full article

Background/Objectives In this study we approach the problem of medical image segmentation by introducing a new loss function envelope that is derived from the Top-k loss strategy. We exploit the fact that, for semantic segmentation, the training loss is computed at two levels, more specifically at pixel level and at image level. Quite often, the envisaged problem has particularities that include noisy annotation at pixel level and limited data, but with accurate annotations at image level. Methods To address the mentioned issues, the Top-k strategy at image level and respectively the “Bottom all but $\sigma$” strategy at pixel level are assumed. To deal with the discontinuities of the differentials faced in the automatic learning, a derivative smoothing procedure is introduced. Results The method is thoroughly and successfully tested (in conjunction with a variety of backbone models) for several medical image segmentation tasks performed onto a variety of image acquisition types and human body regions. We present the burned skin area segmentation in standard color images, the segmentation of fetal abdominal structures in ultrasound images and ventricles and myocardium segmentation in cardiac MRI images, in all cases yielding performance improvements. Conclusions The proposed novel mechanism enhances model training by selectively emphasizing certain loss values by the use of two complementary strategies. The major benefits of the approach are clear in challenging scenarios, where the segmentation problem is inherently difficult or where the quality of pixel-level annotations is degraded by noise or inconsistencies. The proposed approach performs equally well in both convolutional neural networks (CNNs) and vision transformer (ViT) architectures. Full article

The problem of quantifying prestress loss in the external tendons of in-service bridges is of immense practical importance, and the development of reliable, cost-effective methods is a commendable goal. Based on the principle of static equilibrium, this paper proposes a direct method for determining the effective stress in external prestressed tendons using the variation principle, whose calculation accuracy was validated by conducting experimental and theoretical analysis considering the prestressed tendon arrangement form. A transverse tensioning experiment of the prestressed tendons was carried out under four tension conditions of 50 kN, 80 kN, 110 kN and 170 kN at the anchorage end, and the theoretically calculated internal force of the prestressed tendons gradually approached the measured value as the transverse tension increased. Once the appropriate level of transverse tension was reached, stable and reliable results could be obtained. Ultimately, the error between them will stabilize below 5%. This method was used to detect stress loss in the external prestressed tendons of 20 m, 40 m and 50 m T-beams affected by both internal and external uncertain factors simultaneously, and the probability distribution hypothesis test of the stress loss rate was carried out, the results of which reveal that they all follow normal distribution. The ratio of stress at the bottom edge of the T-beam under self-weight and prestressed load to that under vehicle load is defined as the compressive stress reserve coefficient, which is a verified and reliable index for evaluating the external prestressed stress loss on the reinforcement effect of the bridge. Full article

Borophene, a two-dimensional (2D) allotrope of boron, has emerged as a highly promising material owing to its exceptional mechanical strength, electronic conductivity, and diverse structural phases. Unlike graphene and other 2D materials, borophene exhibits inherent anisotropy, flexibility, and metallicity, offering unique opportunities for advanced nanotechnological applications. This review presents a comprehensive summary of recent progress in borophene synthesis methods, highlighting both bottom–up strategies such as chemical vapor deposition (CVD) and molecular beam epitaxy (MBE), and top–down approaches, including liquid-phase exfoliation and sonochemical techniques. A key challenge discussed is the stabilization of borophene’s polymorphs, as bulk boron’s non-layered structure complicates exfoliation. The influence of substrates and doping strategies on structural stability and phase control is also explored. Moreover, the intrinsic physicochemical properties of borophene, including its high flexibility, oxidation resistance, and anisotropic charge transport, were examined in relation to their implications for electronic, catalytic, and sensing devices. Particular attention was given to borophene’s performance in hydrogen storage and hydrogen evolution reactions (HERs), where functionalization with alkali and transition metals significantly enhances H₂ adsorption energy and storage capacity. Studies demonstrate that certain borophene–metal composites, such as Ti- or Li-decorated borophene, can achieve hydrogen storage capacities exceeding 10 wt.%, surpassing the U.S. Department of Energy targets for hydrogen storage materials. Despite these promising characteristics, large-scale synthesis, long-term stability, and integration into practical systems remain open challenges. This review identifies current research gaps and proposes future directions to facilitate the development of borophene-based energy solutions. The findings support borophene’s strong potential as a next-generation material for clean energy applications, particularly in hydrogen production and storage systems. Full article

The management of groundwater depth (GWD) in alluvial soils under irrigation in arid climates is critical for soil and water conservation, given its influence on salt dynamics and water availability for crops. GWD is influenced by the interaction of irrigation water supply and drainage system design and operation. Controlling GWD is a significant issue in the Hetao Irrigation District due to continuous irrigation, arid climate, and high risks of soil salinization, which concerns farmers and water management authorities. To address this issue, a study was conducted based on open-air laboratory experimentation to rigorously assess the effects of GWD on soil salt dynamics and capillary rise contribution to maize cultivation under level basin irrigation. Data collected served as the basis for parameterizing and calibrating the HYDRUS-1D model, facilitating simulation of soil water and salt dynamics to enhance understanding of GWD effects ranging from 1.25 m to 2.25 m. It was concluded that during calibration and validation, the model demonstrated strong performance; SWC simulations achieved R² > 0.69, RMSE < 0.03 cm³ cm⁻³, and NSE approaching 1; and EC simulations yielded R² ≥ 0.74 with RMSE < 0.22 S cm⁻¹. Additionally, the simulated bottom boundary moisture flux closely matched the measured values. The most favorable GWD range should be between 1.75 m and 2.0 m, minimizing the negative impacts of irrigation-induced soil salinity while maximizing water use efficiency and crop productivity. A higher GWD causes crop water stress, while a lower value results in a greater risk of soil salinity. This study anticipates future field application in Hetao to assess drainage system effectiveness and variability in salinity and productivity effects. Full article

This paper presents an original approach through Transfer-Matrix Method applied for the calculus of the continuous circular plate embedded at the exterior circumference, charged with asymmetrical uniform load on the entire upper surface of the plate. Continuous circular plates are elements often found in practice, in the machine building, aeronautics, chemical industries (the bottoms of chemical reactors), and in petrochemical, mechanical, robotic, medical, military, nuclear, and aerospace industries. The calculus of continuous circular plates is a special problem both from the point of view of the theory of elasticity and from the point of view of the mathematical approach. The results obtained with Transfer-Matrix Method were compared and validated with those obtained from classical analytical calculation, the Theory of Elasticity. Transfer-Matrix Method is an elegant method and relatively easy to program. In future research, we want to validate our results with those given by the Finite Elements Method and those measured experimentally. Full article

Combating antibiotic resistance is critically significant for global public health. The development of new antibacterial nanomaterial is a promising way to do this. In this study, a bottom-up approach was employed to fabricate antibacterial carbon dots (ACDs). During the synthesis, quaternary ammonium function groups with long alkyl chains were successfully grafted on ACDs’ surfaces. The obtained ACDs exhibited potent inhibitory against methicillin-resistant Staphylococcus aureus (MRSA) bacteria with minimum inhibitory concentrations of 2.5 µg/mL. Crucially, 2.5 µg/mL of ACDs could inhibit the growth of MRSA for as long as 72 h, which highlighted their long-term activity. Mechanistic investigations revealed that ACDs exerted bactericidal effects for MRSA bacteria primarily through disrupting the cell wall/membrane, destroying cell membrane potential, inducing the generation of excessive ROS, and triggering the leakage of nucleic acids and intracellular components. In sum, this work provided a kind of ACD with high efficiency and long-term antibacterial activity, offering promising potential for combating drug-resistant bacterial infections. Full article

Solid Oxide Fuel Cells (SOFCs) offer high-efficiency electrochemical conversion of fuels like natural gas, yet detailed modeling is crucial for optimization. This paper presents a simulation study of a natural gas-fueled SOFC system, developed using Aspen Plus with Fortran integration. Distinct from prevalent paradigms assuming rated power output, this work adopts rated current density as the primary input, enabling a more direct investigation of the cell’s electrochemical behavior. We conducted a comprehensive sensitivity analysis of key parameters, including fuel utilization, water-carbon ratio, and current density, and further investigated the impact of different interconnection configurations on overall module performance. Results demonstrate that a single unit operating at a current density of 180 mA/cm², a fuel utilization of 0.75, and a water-carbon ratio of 1.5 can achieve a maximum net stack-level electrical efficiency of 54%. Furthermore, optimizing the interconnection of a 400 kW module by combining series and parallel units boosts the overall net system-level electrical efficiency to 59%, a 5-percentage-point increase over traditional parallel setups. This is achieved by utilizing a bottoming cycle for exhaust heat recovery. This research validates the rated current density approach for SOFC modeling, offering novel insights into performance optimization and modular design for integrated energy systems. Full article

Sustainability within the rice and wheat supply chain is integral to attaining the UN’s Sustainable Development Goals (SDGs), as they are the two most consumed grains as food. Rice and wheat cultivation significantly impacts the environment, with the agricultural sector employing 27% of the global workforce and contributing 4% to the world’s GDP, thereby affecting social and economic sustainability. Developing a sustainability index for the wheat and rice supply chain is a complex endeavor, as it depends on various factors such as the location of growers, farming methods, the target audience, and the stakeholders involved. This index must be derived from an optimal selection of indicators to avoid information overload while covering all essential sustainability aspects. There are different methods, such as life cycle assessment, energy analysis, ecological footprint, and carbon footprint, being used to assess sustainability, with indicator-based assessment emerging as a comprehensive approach. This study utilised the Triple Bottom Line (TBL) to identify optimal sustainability indicators in the wheat and rice supply chain. A systematic literature review was initially conducted, followed by an expert opinion survey to determine the required indicators. The literature review unveiled a wide array of indicators used across studies, often contingent on each study’s specific objectives. While some consistency existed in environmental indicators, discussions on social and economic dimensions within the wheat and rice supply chain were limited. Analysis of the expert opinion survey revealed a consensus on most selected indicators, albeit with variations based on experts’ geographical locations. The final set of optimal indicators identified can serve as a foundation for developing a sustainability index, implementing a sustainability information management system, and formulating policy initiatives in the rice and wheat supply chain. Full article

Graphene, a two-dimensional material with remarkable electrical, thermal, and mechanical properties, has revolutionized the fields of electronics, energy storage, and nanotechnology. This review presents a comprehensive analysis of graphene synthesis techniques, which can be classified into two primary approaches: top-down and bottom-up. Top-down methods, such as mechanical exfoliation, oxidation-reduction, unzipping carbon nanotubes, and liquid-phase exfoliation, are highlighted for their scalability and cost-effectiveness, albeit with challenges in controlling defects and uniformity. In contrast, bottom-up methods, including chemical vapor deposition (CVD), arc discharge, and epitaxial growth on silicon carbide, offer superior structural control and quality but are often constrained by high costs and limited scalability. The interplay between synthesis parameters, material properties, and application requirements is critically examined to provide insights into optimizing graphene production. This review also emphasizes the growing demand for sustainable and environmentally friendly approaches, aligning with the global push for green nanotechnology. By synthesizing current advancements and identifying critical research gaps, this work offers a roadmap for selecting the most suitable synthesis techniques and fostering innovations in scalable and high-quality graphene production. The findings serve as a valuable resource for researchers and industries aiming to harness graphene’s full potential in diverse technological applications. Full article

Axon polarization is a fundamental process in neuronal development, providing the structural basis for directional signaling in neural circuits. Precise control of axon specification is, thus, essential for the bottom-up construction of neuronal networks with defined architecture and connectivity. Although neurite length and elongation timing have both been implicated as determinants of axonal fate, their relative contributions have remained unresolved due to technical limitations in manipulating these factors independently in conventional culture systems. Here, we developed a constructive neuroengineering platform based on modifiable agarose gel microstructures that enables dynamic, in situ control of neurite outgrowth length and timing during neuronal cultivation. This approach allowed us to directly address whether axon polarization depends primarily on neurite length or the order of neurite extension. Using a single-neurite elongation paradigm, we quantitatively defined two length thresholds for axon specification: a critical length of 43.3 μm, corresponding to a 50% probability of axonal differentiation, and a definitive length of 95.4 μm, beyond which axonal fate was reliably established. In experiments involving simultaneous or sequential elongation of two neurites, we observed that neurite length—not elongation order—consistently predicted axonal identity, even when a second neurite was introduced after the first had already begun to grow. The presence of a competing neurite modestly elevated the effective critical length, suggesting inhibitory interactions that modulate length thresholds. These findings provide the first direct experimental confirmation that neurite length is the primary determinant of axon polarization and demonstrate the utility of constructive microfabrication approaches for dissecting fundamental principles of neuronal polarity. Our platform establishes a powerful experimental foundation for future efforts to achieve complete control over axon and dendrite orientation during the engineered construction of functional neuronal circuits. Full article

The Sanniang Bay (SNB) and Dafeng River Estuary (DFR) in the Northern Beibu Gulf, China, are critical habitats for the Indo-Pacific humpback dolphin (Sousa chinensis). However, whether and how the decreased dissolved oxygen (DO) has happened in bottom seawater remains poorly understood. This study investigated DO depletion and microbial community responses using a multidisciplinary approach. High-resolution spatiotemporal sampling (16 stations across four seasons) was combined with functional annotation of prokaryotic taxa (FAPROTAX) to characterize anaerobic metabolic pathways and quantitative PCR (qPCR) targeting dsrA and dsrB genes to quantify sulfate-reducing bacteria. Partial least-squares path modeling (PLS-PM) was employed to statistically link environmental variables (seawater properties and nutrients) to microbial community structure. Results revealed pronounced bottom DO declining to 5.44 and 7.09 mg L⁻¹, a level approaching sub-optimal state (4.0–4.8 mg L⁻¹) in September. Elevated chlorophyll-a (Chl-a) near the SDH coincided with anaerobic microbial enrichment, including sulfate reducers (dsrA/dsrB abundance: SNB > DFR). PLS-PM identified seawater properties (turbidity, DO, pH) and nitrogen as key drivers of anaerobic taxa distribution. Co-occurrence network analysis further demonstrated distinct microbial modules in SNB (phytoplankton-associated denitrifiers) and DFR (autotrophic sulfur oxidizers, nitrogen fixation, and denitrification). These findings highlight how environmental factors drive decreased DO, reshaping microbial networks and threatening coastal ecosystems. This work underscores the need for regulating aquaculture/agricultural runoff to limit eutrophication-driven hypoxia and temporarily restrict human activities in SNB during peak hypoxia (September–October). Full article

This study explores the application of modular design in the regeneration of community public spaces within rapidly transforming urban environments, using Haikou as a case study. The objective is to improve spatial quality and community sustainability while preserving cultural identity and community engagement. Through a mixed-methods approach involving questionnaires, GIS-based spatial analysis, and case studies, the research identifies key challenges such as fragmented layouts, limited accessibility, and insufficient green space. In response, a “policy–design–community” integration mechanism is proposed to guide bottom-up and top-down coordination. A multidimensional evaluation framework is developed to assess the effectiveness of modular interventions across functional, spatial, and cultural dimensions. The findings suggest that modular design—owing to its standardization and flexibility—enhances spatial adaptability and construction efficiency, and strengthens cultural identity and community engagement. This research provides a replicable and data-informed strategy for the renewal of public spaces in Chinese urban environments. Full article