This article addresses lenges in enterprise financial management, including difficulties in processing multi-source data, limited adaptability to dynamic environments, and a lack of systematic integration in the decision-making process. To tackle these issues, a new intelligent optimization framework, named genetic algorithm-fuzzy logic-Transformer (GA-FL-Transformer), is proposed. First, the framework employs the Transformer architecture to achieve unified encoding and feature fusion across multiple sources of financial data, high-dimensional features with strong discriminative power. Subsequently, an attention-weight-guided co-evolutionary mechanism integrating genetic algorithm (GA) and fuzzy logic (FL) is designed. This mechanism incorporates the features and attention weights into chromosome encoding, fitness function formulation, and genetic operations, thereby enabling dynamic optimization of fuzzy rules and membership functions. Finally, an intelligent optimization framework that integrates perception, optimization, and decision-making is constructed, achieving closed-loop optimization from data to decision-making via a bidirectional flow mechanism and supporting continuous learning and system-wide self-adjustment. Results on financial datasets from Compustat and CRSP show that the proposed method outperforms competing models in financial optimization. Ablation experiments further validate the contributions of the Transformer-based feature extraction, genetic algorithm optimization, and fuzzy reasoning mechanism to the system’s performance. This study provides a crucial theoretical foundation for enterprises to construct intelligent financial decision-making systems.
Brent L. Winner, Theodore S. Switzer, Sean F. Keenan
et al.
Abstract Recognized limitations of fishery-dependent data under rapidly changing management regimes have resulted in significant effort during recent years to improve the availability of fishery-independent data in the southeastern United States. These fishery-independent surveys target numerous species and habitats with various sampling methods, including the use of trawls, longlines, gill nets, traps, and visual surveys. Although passively fished hooked gear (e.g., longlines) are often used to assess the abundance and life history of managed reef fishes, such types of gear are often limited by the habitats they can fish effectively and are species selective. To address these shortcomings, we developed and implemented an actively fished approach to provide fishery-independent data: the repetitive timed-drop hooked-gear method (RTD method). Despite the high degree of standardization applied to the RTD method, important questions remain as to whether active fishing imparts strong angler variability that may reduce the utility of survey data. Accordingly, we analyzed data from 2014 to 2018 to evaluate potential angler bias and how angler-associated variability compares to other factors often thought to be important predictors of reef fish abundance and community structure. During this study, 962 stations were sampled, representing a variety of artificial and natural reef habitats. In total, 5,770 fish were caught, representing 92 taxa. Sampling was conducted by 103 unique anglers, including 42 commercial or charter fishers and 61 scientists. Results from both population- and assemblage-level analyses found that most of the variability in the catch could be explained by hook size, habitat, water depth, and year. Angler type was rarely correlated with reef fish abundance or assemblages. Our analyses suggest that the RTD method is effective in gathering fishery-independent abundance and life history data for reef fishes in the eastern Gulf of Mexico and that the resulting data are not strongly biased by an angler effect.
We consider nondeterministic higher-order recursion schemes as recognizers of languages of finite words or finite trees. We propose a type system that allows to solve the simultaneous-unboundedness problem (SUP) for schemes, which asks, given a set of letters A and a scheme G, whether it is the case that for every number n the scheme accepts a word (a tree) in which every letter from A appears at least n times. Using this type system we prove that SUP is (m-1)-EXPTIME-complete for word-recognizing schemes of order m, and m-EXPTIME-complete for tree-recognizing schemes of order m. Moreover, we establish the reflection property for SUP: out of an input scheme G one can create its enhanced version that recognizes the same language but is aware of the answer to SUP.
RB Domingues, GW Kuster, FL Onuki de Castro
et al.
The aim of this study was to describe the frequency and features of headache among patients with confirmed dengue virus infection and to compare the headache features in patients with dengue fever and dengue haemorrhagic fever, primary and secondary dengue infection, and patients with and without neurological involvement. Patients with classic dengue fever had a more intense headache than those with the more severe form of the disease, dengue haemorrhagic fever.
The system FT< of ordering constraints over feature trees has been introduced as an extension of the system FT of equality constraints over feature trees. We investigate the first-order theory of FT< and its fragments in detail, both over finite trees and over possibly infinite trees. We prove that the first-order theory of FT< is undecidable, in contrast to the first-order theory of FT which is well-known to be decidable. We show that the entailment problem of FT< with existential quantification is PSPACE-complete. So far, this problem has been shown decidable, coNP-hard in case of finite trees, PSPACE-hard in case of arbitrary trees, and cubic time when restricted to quantifier-free entailment judgments. To show PSPACE-completeness, we show that the entailment problem of FT< with existential quantification is equivalent to the inclusion problem of non-deterministic finite automata. Available at http://www.ps.uni-saarland.de/Publications/documents/FTSubTheory_98.pdf
Miami Beach, FL, US · Miami, FL, US · Aventura, FL, US · North Miami Beach, FL, US · Doral, FL, US · Kendall, FL, US, Carlos de la Hoz, Phillippe Wuilleumier
et al.
Stem cells are undifferentiated cells that can divide and differentiate into various types of cells. This special ability makes stem cells a hopeful treatment for regenerating damaged tissue and restoring diminished or completely lost function. In this case study, we discuss a 19-year-old soccer player who presented with a Grade III MCL tear on the right knee. Treatment for a Grade III MCL tear is controversial being that both surgical and non-surgical approaches have been effective. However, due to the patient’s sports background, the patient came to our clinic seeking a faster recovery using a non-surgical treatment. After extensive medical examinations, the patient began a treatment based on adipose tissue-derived stem cells injection. Adipose tissue-derived stem cells were harvested and injected into the distal insertion of the superficial MCL and on the deep MCL origin. After 122 days the patient was able to practice full contact sports which is a significant decrease compared to a recent study that averaged surgical recovery time to 181 days. The recovery was supported by an MRI which demonstrated full regeneration of the previously damaged MCL. This case study demonstrates the potential stem cells have in both complementing and replacing surgical treatments for tissue repair. In addition, this case study demonstrates the efficacy of accelerating recovery of MCL tears using stem cells which inspires hope of using stem cells in similar pathologies. Therefore, more clinical trials should be conducted to further expand research on stem cell treatment in regenerative medicine.