Search Results (7,795)

In this paper, we propose a formulation of the permutation flow and job shop scheduling problems using special recursive functions and show its equivalence to the existing classical formulation. Equivalence is understood in the sense that both ways of defining the problem describe the same set of feasible schedules for each pair of jobs and machine numbers. In this paper, the apparatus of recursive functions is used to describe and solve three problems: permutation flow shop; permutation flow shop with the addition of the ’and’ predicate extending the machine chain to an acyclic graph; and permutation job shop. The predicate ’and’ allows the description of the flow shop with assembly operation tasks. Recursive functions have a common domain and range. To calculate an optimal schedule for each of these three problems, a branch and bound method is considered based on a recursive function that implements a job swapping algorithm. The complexity of the optimization algorithm does not increase compared to the non-recursive description of the PFSP. This article presents some results for the calculation of optimal schedules on several test instances. It is expected that the new method, based on the description of recursive functions and their superposition, will be productive for formulating and solving some extensions of scheduling problems that have practical significance. Full article

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

This work presents a comprehensive study of weakly B-symmetric warped product manifolds ${\left(WBS\right)}_n$, a natural extension of several classical curvature-restricted geometries including B-flat, B-parallel, and B-recurrent manifolds. We begin by formulating the fundamental properties of the B-tensor $B(X,Y)=aS(X,Y)+brg(X,Y)$, where S is the Ricci tensor, r the scalar curvature, and $a,b$ are smooth non-vanishing functions. The warped product structure is then exploited to obtain explicit curvature identities for base and fiber manifolds under various geometric constraints. Detailed characterizations are established for Einstein conditions, Codazzi-type tensors, cyclic parallel tensors, and the behavior of geodesic vector fields. The weakly B-symmetric condition is analyzed through all possible projections of vector fields, leading to sharp criteria describing the interaction between the warping function and curvature. Several applications are discussed in the context of Lorentzian geometry, including perfect fluid and generalized Robertson–Walker spacetimes in general relativity. These results not only unify different curvature-restricted frameworks but also reveal new geometric and physical implications of warped product manifolds endowed with weak B-symmetry. Full article

Cancer has remained a major global health challenge, with around 20 million new cases and 9.7 million fatalities recorded each year. Even though there has been recent progress in therapies such as radiotherapy, chemotherapy, immunotherapy, and gene therapy, cancer remains a major treatment challenge due to late diagnosis and difficulties in therapeutic effectiveness. Flavonoids, a substantial category of naturally occurring polyphenols, have received considerable interest in recent years for their potential involvement in cancer management and prevention, especially concerning breast cancer. These bioactive compounds, abundant in vegetables, fruits, and herbs, exhibit various therapeutic actions, including antioxidant, anti-inflammatory, and antimutagenic effects. The advanced therapeutic potential of flavonoids, when combined with FDA-approved medicines, offers synergistic effects and enhanced clinical results. Additionally, flavonoid-loaded nano-formulations, involving co-delivery systems, are being explored to increase solubility, stability, and bioavailability, enabling targeted delivery to cancer cells while reducing off-target adverse effects. This review examines the role of flavonoids in the prevention and management of breast cancer, focusing on their dietary sources, metabolism, and pharmacokinetic properties. Furthermore, we explore novel strategies, such as combination therapies with FDA-approved drugs and the application of flavonoid-based nanoformulations, which have the potential to enhance therapeutic outcomes. The clinical application of these strategies has the potential to improve breast cancer treatment and create new opportunities for the advancement of flavonoid-based therapies. Full article

Cancer, a global health burden, is characterized by uncontrolled cell growth and metastasis, often resulting in debilitating treatments and mortality. While conventional therapeutic strategies have improved survival rates, they are limited by challenges such as off-target toxicity and drug resistance. With their design to enable targeted drug delivery, nanoparticles have presented a promising avenue to overcome these limitations. Protein-based nanoparticles, particularly those based on albumin, are notable for their biocompatibility, stability, and ease of modification. The approval of Abraxane, an albumin-based nanoparticle formulation of paclitaxel, for metastatic breast cancer marked a significant milestone. However, further approvals have been slow to materialize until the recent approval of Fyarro^® in 2021. This focused review highlights the potential of albumin-based nanoparticles, emphasizing their advantages, current state, and progress in clinical use as anticancer therapeutics. We also discuss challenges impeding new approvals and future directions for unlocking the full potential of this technology. Full article

Hyperspectral imaging (HSI) has emerged as a powerful tool for non-destructive phenotyping, yet fruit crop applications remain underexplored. We propose a methodological framework to enhance the spectral characterization of apple fruits by identifying robust vegetation indices (VIs) and interpretable endmembers. We screened 284 Vis, which were evaluated using four feature selection algorithms (Boruta, MI+Lasso, RFE, and ensemble voting), generalizing across red, yellow, green, and purple apple cultivars. An ensemble criterion (≥2 algorithms) yielded 50 selected VIs from the NDSI/DSI/RSI families, preserving > 95% classification accuracy and capturing cultivar-specific variation. Pigment-sensitive wavelength bands were identified via PLS-DA VIP scores and one-vs-rest ANOVA. Using these bands, we formulated a new normalized-difference, ratio, and difference spectral indices tailored to cultivar-specific pigmentation. Several indices achieved >89% classification accuracy and showed patterns consistent with those of anthocyanin, carotenoid, and chlorophyll. A two-stage spectral unmixing pipeline (K-Means → N-FINDR) achieved the lowest reconstruction RMSE (0.043%). This multi-level strategy provides a scalable, interpretable framework for enhancing phenotypic resolution in apple hyperspectral data, contributing to fruit index development and generalized spectral analysis methods for horticultural applications. Full article

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies. Full article

Essential oils play important roles in the modern food industry as additives and spices. At the same time, most essential oils have broad-spectrum bacteriostatic properties and can be used as natural antimicrobial materials. However, the application of essential oils is limited due to their strong volatility and insolubility in aqueous substrates. In this study, we used ultrasonic emulsification, carboxymethyl chitosan, and Tween 80 to formulate tea tree essential oil (TTO) nanoemulsions with high stability. With a minimum diameter of about 51 nm (PDI = 0.236 ± 0.021) post-emulsification, the TTO nanoemulsions disperse effectively in the drainage system and exhibit good stability after 14 days of storage. In addition, the bioactivity (antibacterial and antioxidant) of TTO nanoemulsions was significantly enhanced following emulsification, as evidenced by MIC and DPPH assays, indicating that nano-emulsification is beneficial to the development of various essential oils. TTO nanoemulsions can be used as a new food preservative to control the growth of bacteria and prevent the deterioration of food via oxidation. Full article

This research is to assess the potential use of phosphate sludge from the Gafsa (Tunisia) phosphate laundries as an alternative raw material for the manufacture of ecological refractory bricks. Feasibility was evaluated through comprehensive physico-chemical and mineralogical characterizations of the raw materials using X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FTIR), and thermal analysis (TGA-DTA). Bricks were formulated by substituting phosphate sludge with clay and diatomite, then activated with potassium silicate solution to produce geopolymeric materials. Specific formulations exhibited mechanical performance ranging from 7 MPa to 26 MPa, highlighting the importance of composition and minimal water absorption values of approximately 17.8% and 7.7%. The thermal conductivity of the bricks was found to be dependent on the proportions of diatomite and clay, reflecting their insulating potential. XRD analysis indicated the formation of an amorphous aluminosilicate matrix, while FTIR spectra confirmed the development of new chemical bonds characteristic of geopolymerization. Thermal analysis revealed good stability of the materials, with mass losses mainly related to dehydration and dehydroxylation processes. Environmental assessments showed that most samples are inert or non-hazardous, though attention is required for those with elevated chromium content. Overall, these findings highlight the viability of incorporating phosphate sludge into fired brick production, offering a sustainable solution for waste valorization in accordance with the circular economy. Full article

Gellan gum (Gellan), a versatile polysaccharide applied in gel formation and prebiotic formulations, is often processed to tailor its molecular properties. Previous studies employed gamma irradiation and chemical hydrolysis, though without addressing systematic scaling behavior. This study investigates the structural and conformational modifications of Gellan in dilute aqueous salt solutions using a safer and eco-friendly approach: atmospheric low-dose electron beam (e-beam) degradation coupled with viscosity analysis. Native and E-beam-treated Gellan samples (0.05 g/cm³ in 0.1 M KCl) were examined by relative viscosity at varying temperatures, with intrinsic viscosity and molar mass determined via Solomon–Ciuta and Mark–Houwink relations. Molar mass degradation followed first-order kinetics, yielding rate constants and degradation lifetimes. Structural parameters, including radius of gyration and second virial coefficient, produced scaling coefficients of 0.62 and 0.15, consistent with perturbed coil conformations in a good solvent. The shape factor confirmed preservation of an ideal random coil structure despite irradiation. Conformational flexibility was further analyzed using theoretical models. Transition state theory (TST) revealed that e-beam radiation lowered molar mass and activation energy but raised activation entropy, implying reduced flexibility alongside enhanced solvent interactions. The freely rotating chain (FRC) model estimated end-to-end distance (R_θ) and characteristic ratio (C_∞), while the worm-like chain (WLC) model quantified persistence length (l_p). Results indicated decreased R_θ, increased l_p, and largely unchanged C_∞, suggesting diminished chain flexibility without significant deviation from ideal coil behavior. Overall, this work provides new insights into Gellan’s scaling laws and flexibility under aerobic low-dose E-beam irradiation, with relevance for bioactive polysaccharide applications. Full article

We design a phase rendering technique that exploits the link between the angular deviations of a beam and the optical implementation of cross-correlations. We employ two suitably coded gratings, which are incorporated as part of a new device here called a dark-field, Lau interferometer. To this end, we use a first grating whose unit cell is coded with the white and black versions of a Barker sequence. We employ a second grating that is coded as the Babinet’s complementary of the first grating. We describe the cross-correlation operation by using a compact matrix formulation, which is amenable to numerical evaluation. Full article

This paper proposes a Tibetan judicial event argument extraction method based on machine reading comprehension (MRC) to address the challenges of data scarcity and insufficient model generalization in low-resource language scenarios. Unlike traditional methods, this work models event argument extraction as an MRC task, progressively identifying and extracting various event arguments through a question-guided approach. First, a strategy for constructing event knowledge-enhanced questions tailored to the Tibetan judicial domain is designed. Specifically, interrogative words are formulated for different types of event arguments, and event semantic information is incorporated into questions to effectively disambiguate questions. Second, a deep semantic understanding architecture for Tibetan judicial events based on the CINO (Chinese Minority Pretrained Language Model) is proposed, incorporating a multi-head self-attention mechanism to enhance semantic alignment and global understanding between event sentences and questions. Finally, a two-stage training strategy is proposed for low-resource languages. Training is performed on a general Tibetan machine reading comprehension dataset, followed by task-adaptive fine-tuning on judicial domain data, effectively alleviating the data scarcity issue. Experimental results show that the proposed method achieved an F1-score of 76.59% in the Tibetan judicial event argument extraction task. This research offers new ideas for low-resource language event extraction and is of great significance for promoting intelligent information processing of minority languages. Full article

The concept of green cities promotes efficient utilisation of resources, with urban rooftop farms (URFs) being a key initiative involving a series of actions and decisions between stakeholders and the state. The new public governance discourse (NPGD) emphasises this interplay between the state, the market, and civil society to strengthen collaboration and network-driven social innovation and requires a comprehensive understanding of human/stakeholder behaviour. In this study, we explore the connection between organisational rational choice in URF policy development and social innovation. Through a review of the existing literature on URF policies and a case study of Dhaka, Bangladesh, we investigate the development of a comprehensive policy via participation and collaboration, considering the popularity of URFs and the absence of governing mechanisms in Dhaka. The results suggest that, despite the rising popularity of URFs in Dhaka, existing policies and strategies lack clarity. The review findings suggest that a participatory and co-productive approach is optimal for URF policy formulation. This would require active engagement from community members, local governments, and non-governmental organisations and gaining an enhanced understanding of stakeholder dynamics by testing stakeholder salience and co-production theories for successful URF governance. Full article