Search Results (17,974)

To address the challenges of high-dimensional complexity and increasing heterogeneity in expert opinions, this study proposes a novel numerical aggregation model for multi-expert decision making based on triangular intuitionistic fuzzy numbers (TIFNs) and the Plant Growth Simulation Algorithm (PGSA). The proposed framework transforms experts’ fuzzy preference information into five-dimensional geometric vectors and employs the PGSA to perform global optimization, thereby yielding an optimized collective decision matrix. To comprehensively evaluate the aggregation performance, several quantitative indicators—such as weighted Hamming distance, correlation sum, information intuition energy, and weighted correlation coefficient—are introduced to assess the results from the perspectives of consensus, stability, and informational strength. Extensive numerical experiments and comparative analyses demonstrate that the proposed method significantly improves expert consensus reliability and aggregation robustness, achieving higher decision accuracy than conventional approaches. This framework provides a scalable and generalizable tool for high-dimensional fuzzy group decision making, offering promising potential for complex real-world applications. Full article

A general notion of a weakly symmetric continuous linear functional on a Banach space, in the case where the space is ${\ell }_1$ (i.e., the space of all absolutely summable sequences of complex numbers), reduces to a continuous linear functional whose Riesz representation is a periodic sequence. We consider the completion of the space of all such linear continuous functionals on ${\ell }_1$ with periods of Riesz representations equal to powers of $2.$ It is known that this completion is a Banach space with a Schauder basis. In this work, we construct a sequence Banach space with the standard Schauder basis ${\{e_m=({\underbrace{0,\dots ,0}}_{m-1},1,0,\dots )\}}_{m=1}^{\infty }$ that is isometrically isomorphic to this completion. Results of the work can be used to describe spectra of topological algebras of analytic functions on ${\ell }_1$ that can be approximated by weakly symmetric functions. Full article

Bovine respiratory disease is a significant health concern in dairy calves, impacting short-term growth and potentially long-term productivity. While previous studies have linked early-life lung consolidation, often subclinical and diagnosed by lung ultrasonography, to reduced preweaning average daily gain, its extended effects on production and reproductive parameters remain less studied, particularly in specific geographical contexts. This study presents a follow-up analysis on a cohort of dairy calves originally monitored weekly from birth to weaning for lung consolidation via TUS in an Iranian dairy herd. Two years post-weaning, comprehensive production and reproduction data were collected and analyzed for these same animals. Our objective was to investigate the long-term associations between early-life lung consolidation and subsequent outcomes, including mature equivalent milk yield, corrected milk yield, culling risk before and during lactation, age at first breeding, age at first calving, and reproduction parameters such as services per conception and conception rate (the percentage of cows or heifers that become pregnant after a single insemination or breeding attempt). Data analysis, employing descriptive statistics, survival analysis, and non-parametric tests, revealed that while early-life lung consolidation (defined by ≥1 cm or ≥3 cm depth, and number of episodes) did not show significant associations with culling probability or mature equivalent milk yield, interesting numerical trends were observed. The study highlights the complexities of establishing clear long-term links, suggesting that while subclinical BRD can have immediate growth impacts, its chronic effects on later production and reproduction may be subtle and require larger cohorts or more targeted analysis to achieve statistical significance. The findings reinforce the need for cautious interpretation of p-values in the context of multiple comparisons and underscore the challenges in quantifying long-term economic consequences of early-life respiratory health. Full article

Fuzzy edge coloring has proven to be a powerful tool for modeling and optimizing complex network systems, owing to its ability to effectively represent and manage the uncertainty in relational strengths and conflicts. It focuses on defining the fuzzy edge chromatic number, optimizing its computation, and exploring practical applications. For join graphs derived from fuzzy graphs, we propose an efficient fuzzy edge coloring algorithm and analyze the associated properties. Building on this, fuzzy edge coloring offers effective strategies for software promotion and traffic signal optimization. This work addresses fundamental theoretical challenges related to algorithm design, complexity analysis, and structural properties in fuzzy graph edge coloring, while also demonstrating its practical utility in complex scenarios such as software promotion and traffic signal optimization. Full article

Surfactants are chemical compounds present in a large number of products that people use on a daily basis, starting with detergents for washing clothes, dishes, personal hygiene products, etc. Some products also contain certain heavy metals. Their uses cause heavy contamination of wastewater that must be purified before discharge into receivers. Given that some types of surfactants are very persistent and heavy metals are non-biodegradable and toxic even in small concentrations, the purification process requires a complex approach and a combination of different methods. Bioremediation, as an environmentally acceptable and economically clean technology, has great potential. It is based on the use of indigenous microorganisms that have developed different mechanisms for breaking down and removing or detoxifying a large number of pollutants and are excellent candidates for bioremediation of wastewater. Bacteria can degrade surfactants as sole carbon sources and exhibit tolerance to various heavy metals. This paper summarizes the most significant results, highlighting the potential of bacteria for the biodegradation of surfactants and heavy metals, with the aim of drawing attention to their insufficient practical application in wastewater treatment. Bioreactors and microbial fuel cells are described as currently relevant strategies for bioremediation. Full article

Oligometastatic breast cancer represents an intermediate state between localized and disseminated disease with reasonable potential for clinical cure. Advancements in surgery, radiotherapy, and systemic therapy have improved prognosis. Due to the high prevalence of bone metastases, an increasing number of studies are evaluating new treatment strategies for oligometastatic bone disease. The decision to perform skeletal surgery is complex and depends on optimal patient selection. Major criteria include impending or pathologic long bone fractures, severe neurologic compromise, and an expected survival of over 3 months. Factors associated with improved survival include solitary bone metastases, preserved performance status, adequate surgical margins, absence of pathologic fracture, metachronous metastases, and ER-positivity status. Radiotherapy, especially SBRT, offers effective local control and palliation. Interventional radiology techniques such as percutaneous thermal ablation have also been described as potential treatment alternatives, particularly for fragile patients. Systemic treatment varies according to the tumor subtype. For HR+ and HER2 subtypes, a combination of endocrine therapy with CDK4/6 inhibitors may be considered. HER2+ patients are often treated with HER2-targeted therapies combined with chemotherapy. For triple-negative breast cancer, chemotherapy is the primary treatment. Bone-modifying agents are also recommended to maintain bone strength, prevent skeletal-related events, and reduce the need for additional interventions. Skeletal muscle metastases in breast cancer patients are rare and typically indicate advanced disease with poor prognosis. Treatment options include chemotherapy, radiotherapy, and surgical excision, but should be tailored to the patient’s clinical condition and prognosis. Full article

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems. Full article

Atomic effects determining the adsorption of hydrogen and oxygen atoms on (111), (100), (110), and (211) surfaces of Cu and Pd have been studied using quantum chemical simulations. The deformation of the (111) and (100) surfaces during adatom bonding enhances the bond strength at active sites with high coordination numbers. In contrast, the deformation of the (110) and (211) surfaces does not exhibit a strong tendency. The atomic contribution of the nearest-neighbor environment depends on the square magnitude of the interaction matrix element, $V_{ad}^2$. A high $V_{ad}^2$ value increases the proportion of repulsive interactions within the metal adsorption complexes, leading to a decrease in the coordination number of the most stable adsorption site. Full article

The authors of this paper highlight the creation of an experimental system for the implementation and testing of active low-voltage electronic power filters of the parallel type, with applicability in a wide range of electrical parameters. In this paper, the authors present the results obtained on an experimental test bench for power quality purposes. The experimental test bench is one of the results of a technology transfer project. One of the specific objectives of the project was to carry out industrial research and experimental development activities in order to develop a competitive, technical and economic solution for an intelligent power system, Active Power Filter (APF). Thus, this paper presents the experimental test bench for the design, implementation and testing of algorithms for current harmonics mitigation. The conceptual theoretical frame bases of both direct and indirect control have been presented by the authors. As a case study, both the simulation and experimental results of the indirect control strategy implemented on the test bench are provided. The indirect control method is chosen due to simplicity, no complex calculus requirements, and the use of a minimum number of transducers. By features comparison with modern control strategies, this study underlines the supremacy of the indirect control in active harmonics control. Full article

Sediment erosion is a persistent problem that leads to the deterioration of hydro-turbines over time, ultimately causing blade failure. This paper analyzes the dynamics of sediment in water and its effects on a small Kaplan turbine. Flow data is obtained independently and transferred to a separate Lagrangian-based finite element code, which tracks particles throughout the computational domain to determine local impacts and erosion rates. This solver uses a random walk approach, along with statistical descriptions of particle sizes, numbers, and release positions. The turbine runner features significantly twisted blades with rounded corners, and complex three-dimensional (3-d) flow related to leakage and secondary flows. The results indicate that flow quality, particle size, concentration, and the relative position of the blades against the vanes significantly influence the distribution of impacts and erosion intensity, subsequently the local eroded mass is cumulated for each element face and averaged across one pitch of blades. At the highest concentration of 2500 mg/m³, the results show a substantial erosion rate from the rotor blades, quantified at 4.6784 × 10⁻³ mg/hr and 9.4269 × 10⁻³ mg/hr for the nominal and maximum power operating points, respectively. Extreme erosion is observed at the leading edge (LE) of the blades and along the front part of the pressure side (PS), as well as at the trailing edge (TE) near the hub corner. The distributor vanes also experience erosion, particularly at the LE on both sides, although the erosion rates in these areas are less pronounced. These findings provide essential insights into the specific regions where protective coatings should be applied, thereby extending the operational lifespan and enhancing overall resilience against sediment-induced wear. Full article

An intelligent safety risk assessment model is proposed by integrating the λ-fuzzy measure, Choquet integral, and triangular fuzzy numbers. It addresses the limitations of conventional methods like AHP that neglect nonlinear interactions among risk factors. The framework quantifies expert linguistic judgments to capture synergistic and substitutive relationships. Validation using two Sinopec seismic projects shows a 23.3% reduction in assessment time and an 18.1% accuracy improvement. Computed λ values (HB: −0.999997; SC: −0.999821) confirm strong substitutive interactions. Sensitivity analysis demonstrates robustness, with ±10% fuzzy measure variation causing <±3% output change. The model provides a computationally efficient, reliable tool for seismic acquisition and other complex industrial systems. Full article

Biological clusters, encompassing proteins, nucleic acids, and lipids, represent functional assemblies that underpin cellular physiology and contribute to disease pathogenesis. Their detection and characterization remain technically challenging due to their multistep, heterogeneous, and often transient nature. Fluorescence correlation spectroscopy (FCS) has become a powerful tool for quantifying particle numbers, diffusion states, and brightness changes, thereby providing direct insights into finite molecular assemblies. Applications include diverse oligomers and complexes of proteins, lipids, and nucleic acids, underscoring both physiological and pathological relevance. Recent methodological extensions—including multi-color cross-correlation FCS, image- and super-resolution-based approaches, and brightness analyses—have expanded the capacity to resolve complex molecular interactions. Transient state (TRAST) monitoring provides additional sensitivity to photophysical state transitions of fluorophores and to their physicochemical environments. Looking ahead, integration with AI promises to lower technical barriers and accelerate broader adoption. This review highlights the conceptual framework, recent advances, and future opportunities of FCS in probing biological clusters and aggregates. Full article

Background/Objectives: Children who have received hematopoietic cell transplants (HCTs) often face complex clinical courses and complications that increase their risk of functional impairments. Because of this, pediatric HCT recipients may benefit from early mobilization efforts to reduce long-term functional issues. However, early ambulation can be limited by clinical complexity and concerns about infectious transmission in HCT patients. Some patients are under contact precautions due to colonization with bacteria that produce extended-spectrum beta-lactamase (ESBL) enzymes. Our goal was to significantly increase ambulation in pediatric HCT recipients at our institution within three months of the intervention. We aimed to raise the number of ambulation events per day, the number of physical therapy (PT) visits per week, and the distance patients walked with PT per session. Methods: From January to October 2022, data on mobilization, demographics, and clinical characteristics were retrospectively collected from electronic health records. Starting in June 2022, we permitted ESBL-colonized patients to leave their rooms while wearing personal protective equipment (PPE), and we trained clinical staff about this in our QI initiative. Results: In Group 1, the ambulation rate was 1.36 times higher before the intervention than after, with an effect size of 0.3042 (p = 0.004 *). The ambulation rate in Group 2, admitted before the intervention, was 1.33 times higher than in Group 3, admitted after the intervention, with an effect size of 0.2856 (p = 0.016 *). Conclusions: The initiative did not increase ambulation among the targeted group. Patients ambulated more before the intervention, though these results lack statistical power. The lack of success of the intervention may be due to various factors, including the short monitoring period, retrospective data collection, difficulties with PPE use among young patients, and uncollected confounding variables related to clinical status. Full article

Herb-induced liver injury (HILI) is an increasingly recognized cause of liver damage, associated with the widespread global use of herbal products. Despite its rising incidence, HILI remains underrecognized and underreported due to the absence of specific biomarkers, limited regulatory oversight, and the complexity of multi-ingredient formulations. Diagnostic efforts rely heavily on the Roussel Uclaf Causality Assessment Method (RUCAM), with clinical presentations often nonspecific and dominated by hepatocellular patterns of injury. Epidemiological data demonstrate regional variation, with notably higher case numbers in Asia and the Americas. Mechanistically, HILI may result from either intrinsic (predictable, dose-dependent) or idiosyncratic (unpredictable, immune-mediated) reactions. Genetic predispositions, including certain HLA alleles, have been identified as risk factors. Hepatotoxicity is often linked to specific phytochemicals such as pyrrolizidine alkaloids, catechins, anthraquinones, and diterpenoids, which may contribute to oxidative stress, mitochondrial damage, or immune activation. Additionally, product inconsistencies and contamination complicate risk assessment and safety evaluation. Current management focuses on immediate discontinuation of the suspected product and supportive care, though severe cases may require liver transplantation. Future directions include the development of specific diagnostic tools, implementation of globally harmonized regulatory standards, improved pharmacovigilance systems, and enhanced public and professional education. Addressing these priorities is crucial for reducing HILI-related morbidity while supporting the safe use of herbal therapies. Full article

Anomaly detection is a critical approach for ensuring the security of microservice systems. In recent years, deep sequence models have been widely applied to transform sequence modeling into a language modeling problem. However, the objective of training sequence models with language modeling loss is not directly aligned with anomaly detection. Moreover, the diverse data types in microservice systems—namely metrics, logs, and traces—exhibit asynchrony and complex interdependencies. Existing methods based on deep sequence models, such as LogBERT and TranAD, can only account for a limited number of data modalities, failing to fully utilize multi-source data and effectively handle the interrelationships among multiple modalities. To address this, we propose a multimodal anomaly detection framework based on a generative pre-trained language model (GPT), named GSTGPT. GSTGPT represents multi-source data as a feature graph, with metrics and logs as node features and traces as edge features. Additionally, we model feature interactions and dependencies within sequences using spatio-temporal attention and enhance the model’s focus on critical features through feature augmentation. Experimental results on two real-world datasets demonstrate that GSTGPT achieves an F1 score of 0.967, an 8.3% improvement over baseline methods, significantly outperforming them. Full article