Hybrid Strategy Research Articles

Experimental investigation of hybrid energy sharing strategy for multi-source electric vehicle

This paper deals with a current topic addressing the issue of energy management. Adequate energy management consists of finding the best harmony between different sources intended to supply the vehicle to satisfy the power required by the traction motor while ensuring good performances. Energy management strategy makes it possible to find the adequate distribution of the energy flow between power source elements by generating power references to be given by each source to satisfy the required power while respecting the constraints on these sources. In this context, a new energy management strategy based on the combination of flatness theory and the frequency separation principle is developed. This strategy determines the powers that the energy sources must supply and provides power source references based on their dynamics, imposing constraints to be taken into account. This novel hybrid strategy is applied to a hybrid vehicle configuration that uses a fuel cell as the primary source with a hybrid storage device (supercapacitor and battery). The implementation using dSPACE DS1005 will be presented to demonstrate the effectiveness of the proposed strategy.

Curved-crease origami hybrid structures with tailorable buckling and energy absorption

Origami-inspired structures (OIS), renowned for their lightweight design, encounter energy absorption challenges attributed to global buckling. This paper presents a design and hybridization strategy that integrates origami-inspired structures with existing state-of-the-art cellular lattices to create origami-inspired hybrid structures (OIHS), aimed at addressing buckling concerns and customizing the crushing behavior of thin-walled structures. The investigation explores the compressive response of additively-manufactured curved crease OIS and prismatic structures (PS) with diverse cross-sections, including circular origami structure (COS), triangular origami structure (TOS), square origami structure (SOS), and hexagon origami structure (HOS). The experimental results indicate that the COS design offered the highest specific energy absorption (SEA) of 11 kJ/kg, due to controlled deformation associated with the crease lines. The COS hybridized structure, with cellular lattices, exhibited a yielding-dominated behavior, resulting in a lower peak force, a sustained plateau force and a well-controlled deformation response. Furthermore, the COS hybridized with a plate lattice, exceeded the SEA of the auxetic and BCC OIHS structures by 62 % and 71 %, respectively. The circular origami plate hybrid (COPH) design was selected to investigate the effect of varying the top edge angle (α) and the relative density on the mechanical properties and the SEA. It was found that increasing the value of alpha resulted in a higher peak stress and an increased buckling load. Moreover, with its higher relative density, the vertical plate within the OIS contributed to a greater level of structural stability in the plateau region, resulting in an increase in mechanical properties and SEA. These findings advance the understanding of OIS by presenting effective hybridization strategies to mitigate buckling and achieve stable plateau stresses and higher crushing force efficiencies, particularly at lower relative densities, surpassing those reported in the literature. This contributes significantly to the broader field of lightweight structural design.

Characterization of genetic diversity in Eragrostis tef (zucc.) Trotter through microsatellite and morphological markers

Eragrostis tef (Zucc.) Trotter is Ethiopia's most significantly important grain crop; however, its productivity is quite low (1.7 tons per hectare) due to a variety of issues, including the poor yield potential of varieties produced thus far. To achieve future production improvements in tef, comprehensive study of the crop's genetic variability and variety, utilizing both genetic and morphological markers, necessary. The study was designed to assess the genetic diversity of 64 tef genotypes utilizing 10 Simple Sequence Repeats (SSRs) markers. This investigation took place at the Agricultural Biotechnology Research Center in Holeta, Ethiopia. Field phenotypic diversity evaluation was conducted at two distinct locations: Debre Zeit and Worabe, applied a simple lattice design during 2020/21 season. Molecular analysis of variance demonstrated a substantial proportion of variation among individuals (46 %), followed closely variation among individuals (43 %), with the least variation observed within population (11 %). Concurrently, analysis of morphological data, encompassing twenty phenotypic characteristics, revealed significant variation (P ≤ 0.01) among almost all the tested genotypes for recorded parameters, as indicated by combined analysis of variance across sites. These findings underscore the high diversity among the studied genotypes, suggesting a considerable potential for crop improvement through direct selection and intra-specific hybridization strategies. From the perspectives of both conservation and utilization of tef genetic materials, there is a pressing need for more extensive and systematic molecular level research. Promoting field trials and genotype-by-environment interaction research is crucial. These efforts will enhance our understanding of tef genetic and contribute to its effective conservation and utilization.

Discovery of Novel Nonpeptidic Proteasome Inhibitors Using Covalent Virtual Screening and Biological Evaluation.

Many reported proteasome inhibitors, including the three clinically approved inhibitors, bortezomib, carfilzomib, and ixazomib, have peptidic structures. In this study, using a hybrid and versatile strategy for covalent virtual screening by combining warhead screening and preprocessing with GOLD and CovDock software that were applied to the ZINC virtual library, we identified multiple proteasome inhibitors with new nonpeptidic structural scaffolds. Proteasome inhibition assays confirmed the inhibitory activities of these new compounds. These results demonstrate the effectiveness of our computational strategy for large-scale covalent virtual screening. Furthermore, these identified proteasome inhibitors may serve as starting points for the development of a new class of nonpeptidic therapeutic agents.

Advances and Challenges for the Malaria Vaccine: A Review

Malaria remains a leading cause of morbidity and mortality worldwide, particularly in sub-Saharan Africa and Southeast Asia, where transmission rates are highest. Traditional control measures, such as insecticide-treated bed nets and antimalarial drugs, have helped reduce the disease’s impact, but challenges such as drug and insecticide resistance continue to undermine these efforts. A highly effective malaria vaccine represents a crucial step towards achieving long-term control and eventual eradication. This review explores recent advances in malaria vaccine development, including the RTS, S/AS01 and R21/Matrix-M vaccines, while highlighting their limitations related to efficacy, immunity duration, and accessibility in resource-poor regions. Additionally, it addresses the challenges in developing multivalent vaccines, targeting different strains of Plasmodium, and the need for integrated approaches combining vaccination with existing control strategies. Looking ahead, the review discusses the potential of next-generation vaccines, mRNA platforms, and hybrid strategies that could enhance global malaria control efforts. Keywords: Malaria vaccine, RTS, S/AS01, R21/Matrix-M, transmission-blocking vaccines, malaria control.

Intelligent Health Assessment Based on Communication and AI Technologies for Public Health

Automating EEG pattern categorization in public health helps in the early detection of epilepsy and other brain illnesses. However, present methods have difficulties in reaching excellent accuracy and adaptability. This study utilizes a hybrid strategy that incorporates Spider wasp-tuned Convolutional neural network (SW-CNN). Convolutional neural networks (CNNs) use complex feature extraction from EEG data, and Spider wasp optimization (SWO) improves network parameters by comparing them to the pursuing and developing behaviors of spider wasps. By enhancing classification accuracy, precision, recall, and F1-score measures, the combined effect seeks to achieve significant improvements over conventional techniques. The research concludes that the SW-CNN hybrid model performs better at differentiating between non-seizure activity and epileptic seizures. This strategy improves diagnosis accuracy and efficiency in public health, resulting in better patient outcomes and treatment planning. Effective transmission of such results is critical for furthering clinical diagnosis and research in neurological diseases.

Efficient Intrusion Detection in Patient Health Records Based on LSTM-Based RNN

Advancements in Information and Communication Technology applications play an important role in the field of healthcare and bio-medical engineering. The process of image processing and analysis starts from receiving visual information to giving out the description of the scene for improving the visual appearance of the human viewer and extract the features of the data. Moreover, IDS can be deployed along with other security mechanisms as a line of defence to ensure security of system and network resources. There have been various efforts in designing IDS using Machine Learning (ML) techniques. Additionally, by developing a hybrid strategy for intrusion detection and classification and using feature engineering approaches to extract significant features for learning, among other things, efforts have been made to improve the classification performance of ML-based IDS. However, as networking technology have advanced, attack types have evolved as well. For this reason, an efficient and successful intrusion detection and classification system must be created. To address the issue and achieve good generalization ability for intrusion detection and classification, the paper presents empirical analysis of LSTM based RNN classifiers. The suggested methods' effectiveness is assessed using a range of performance metrics, such as recall, f-score, accuracy, precision, and False Positive Rate (FPR). The LSTM based RNN method recognize attacks with 99.2% accuracy with minimum time complexity (5 s).

Hybrid toughening effect of flax fiber and thermoplastic polyurethane elastomer in 3D‐printed polylactic acid composites

AbstractFlax fiber has emerged as a promising, eco‐friendly alternative to traditional synthetic reinforcement in polymer composites. However, manufacturing biocomposites using three‐dimensional (3D) printing technology is typically accompanied by significant processing challenges and weak product performance under dynamic loading conditions. This study aims to unlock the potential of 3D‐printed polylactic acid (PLA) by incorporating chemically modified chopped flax fibers and thermoplastic polyurethane elastomer to improve impact strength and processability. To achieve this, we employed the fused deposition modeling (FDM) technique to prepare composite specimens for the study. The crystallization behavior, tensile and impact properties, as well as the fracture behavior of the composites were investigated. The findings suggest that our approach stands out because it not only facilitates the challenging task of 3D printing PLA with fiber additives of high weight fraction and high aspect ratio but also results in a remarkable 120% enhancement in impact strength and an around 31.2% increase in tensile elongation compared to neat PLA, without compromising the elastic modulus.Highlights Flax fibers were modified through alkalization and silanization. Alkalization significantly enhanced printing quality. Silanization reduced fiber attrition and doubled the fiber aspect ratio. TPU particles facilitated the 3D printing of biocomposites. For the first time, the hybrid strategy doubled the impact strength of PLA.

Hybrid strategy of drug-eluting stent and drug-coated balloon in the treatment of de novo coronary artery disease: 1-year clinical outcomes.

The hybrid strategy of drug-eluting stent (DES) and drug-coated balloon (DCB) has been increasingly accepted for the treatment of de novo coronary artery disease. However, data regarding the clinical outcome of this practice in a Southeast Asian population are limited. We aimed to investigate the safety and clinical outcome of this hybrid strategy (DES and DCB) in the treatment of de novo coronary artery disease. The primary endpoint was target lesion failure (TLF) in the DES/DCB-treated segment at 12 months. TLF is defined as the composite of cardiac death, target vessel myocardial infarction (TVMI) and ischaemia-driven target lesion revascularisation (ID-TLR) in the DES- and/or DCB-treated segment. A total of 401 patients with 458 lesions were treated with the hybrid strategy at the National Heart Institute (IJN), Kuala Lumpur, Malaysia, from 1 July 2021 to 30 June 2022, were retrospectively enrolled in the study. A total of 38 patients (9.5%) were lost to subsequent follow-up, and the remaining 363 patients (90.5%) were included in the outcome analysis. Clinical outcomes at 1 year were analysed. In all, 219 lesions (47.8%) involved the left anterior descending artery, 146 lesions (31.9%) involved the right coronary artery, and 57 lesions (12.4%) involved the left circumflex artery. In all, 87 lesions (19%) were bifurcation lesions. A total of 8 patients (2.2%) had TLF, of whom 3 patients (0.83%) had TVMI, 3 patients (0.83%) had ID-TLR, and 2 patients (0.6%) experienced cardiac death. Four patients died of a non-cardiac cause at 1-year follow-up. A hybrid strategy of DES and DCB for the treatment of de novo coronary artery lesions appears to be feasible and clinically safe according to the 1-year outcomes.

Night cooling by hybrid ventilation – analytical predictions of the purge time

Many low-energy buildings rely on a night purge strategy during periods of excessively warm weather in order to flush out excess heat and thereby help prevent overheating on the following day. A typical purge utilises either a mechanical or a natural displacement ventilation strategy. However, we investigate purging by the simultaneous application of natural and mechanical ventilation, i.e. using a hybrid strategy. Hybrid ventilation is widely promoted as a means of overcoming the inherent limitations of both natural and mechanical ventilation, although relatively little is understood about its operation on a fundamental level. Such was the lack of knowledge on hybrid ventilation that even the time taken to complete a purge was not known at the outset of this work. Through the development of a mathematical model, supported by a series of laboratory experiments, we show that the time taken to purge a room using hybrid ventilation can be established analytically, and is controlled by two ratios: (i) the flow rate of the mechanical supply (the ‘forcing’) relative to the initial flow rate through the vents in the absence of mechanical forcing, and (ii) the ratio of the floor-level and ceiling-level vent areas. The analytical predictions show close agreement with our measurements. Supplementing a natural displacement ventilation strategy with even a modest mechanical supply can reduce significantly the time taken to purge warm air from a room. Some of the implications of our findings for preventing overheating in buildings by increasing the efficacy of a night cooling strategy are discussed. Practical Application This research is directly applicable to practitioners involved in the first-order design of hybrid ventilation systems – particularly designs which incorporate a ‘night purge’ regime. Governing equations are derived and the key parameters controlling the rate of purging of warm air are revealed, allowing informed first-order design decisions to be made without the need for expensive and time-consuming computational simulations.

Long-term enlargement of the neo-aortic root and aortic arch following arch reconstruction in hypoplastic left heart syndrome

BackgroundLong-term enlargement of the aortic arch after aortic arch reconstruction in hypoplastic left heart syndrome is not well described. MethodsAortic arch measurements for 50 patients with hypoplastic left heart syndrome who achieved Fontan completion were converted to Pediatric Heart Network z-scores. Dimensions were assessed using linear mixed models, and differences among time points were evaluated with F-tests. Sub-analysis was conducted comparing Norwood (n = 36) with hybrid (n = 14) strategies. ResultsMedian time to last imaging was 6.4 (interquartile range, 3.5-11.3) years. Before intervention, the main pulmonary artery was dilated, whereas the ascending aorta, transverse arch, and isthmus were hypoplastic. With aortic arch reconstruction, there were expected increases in all arch z-scores. The aortic arch continued to dilate after aortic arch reconstruction, reaching peak values at 7 months (neo-aortic complex: z = 6.9 [5.6-8.0]) or 12 months after stage I (ascending aorta: z = 6.1 [2.9-8.3]; transverse arch: z = 4.7 [3.0-5.9]). After peak values, there was a gradual decline in z-scores with most components still at least mildly dilated at 16 years (neo-aortic complex: z = 3.2 [3.1-3.9], ascending aorta: z = 3.9 [3.3-4.2]; transverse arch: z = 3.1 [2.5-3.7]) with abrupt caliber change at the isthmus: z = −0.8 (−1.1 to −0.3). Norwood and hybrid strategies showed similar enlargement profiles after 7 months of age. ConclusionsNeo-aortic root and aortic arch in hypoplastic left heart syndrome are enlarged early after aortic arch reconstruction and continue to enlarge out of proportion to normal controls until 12 months of age, with gradual decline in enlargement up to adolescence. Further work should focus on modifiable surgical factors that may prove important to optimize arch growth and geometry.

Artificial immune systems (GA-AIS) enabled power loss mitigation in distributed generation: X3PAIS optimization approaches

The distribution network is a crucial component of the power system, with industrialization driving increased energy demand. Traditional power-generating techniques, such as thermal and hydroelectric are not enough to meet this demand, leading to the development of Distributed Generation (DG). DG requires an extensive re-evaluation of the current power system, as it modifies energy losses and line flows. Inadequate integration of DG can cause power outages, disruption of protection coordination, and lead to islanding. AI can help overcome this issue by determining the best system architecture. Researchers have been interested in the Artificial Immune System (AIS) algorithm, which has room for development and lacks a fixed template. In order to improve AIS, X3PAIS, a hybridization strategy that combines clonal selection with a three-parent crossover has been developed within the scope of the study. X3PAIS was pre-tested using applications in a planetary gear train, a wastewater treatment facility, and mathematical calculations, showcasing its robustness and versatility. In the context of power distribution, X3PAIS is used in the multiple DG architecture of the power distribution system, reducing power losses by placing DG units in the best locations and sizing them to match load profiles. The four DGs' experiment results show that X3PAIS can minimize power losses by more than 89 %. To optimize power losses in the power distribution system, X3PAIS may be improved with a three-parent multiple-point crossover operation.

Energy management in a grid-connected microgrid using hybrid Golden Jackal optimization and gradient descent optimization and the concept of loadability

Golden Jackal optimization (GJO) maintains diversity and can simultaneously explore multiple regions of the search space since it operates on a population of solutions. However, the GJO has the demerit of deprived exploitation capability and it struck in local optima easily. For enhanced optimization efficacy, the suggested approach integrates a hybrid optimization algorithm that combines gradient descent and the GJO algorithm. The goal of this hybrid strategy is to influence the complimentary advantages of both algorithms: the local exploitation potential of gradient descent and the global exploration capabilities of GJO. The suggested hybrid method uses GJO to initialize a population of possible results. Then, iteratively exploring the search space, it notifies solutions based on GJO's mechanisms for exploration and exploitation. Furthermore, a portion of the population undergoes refinement using gradient descent in order to effectively converge towards local minima by utilizing local knowledge. The hybrid algorithm demonstrates improved convergence speed, robustness to diverse search space characteristics, and effectiveness in handling large-scale optimization problems compared to various optimization techniques including particle swarm optimization, pelican optimization, artificial bee colony, whale optimization algorithm and grey wolf optimization, standalone GJO and gradient descent algorithms. The total cost of the MG with the proposed methodology has reduced to 12017 rupees from 12350 by gradient descent and 12332 using GJO. Detailed comparison of various optimization techniques has been presented in the results section. Further, this paper also addressed the concept of loadability, which refers to the MG's ability to meet electricity demand under varying conditions, relying solely on its internal resources. Achieving optimal loadability in isolated MGs involves diversifying generation sources, leveraging energy storage systems, implementing demand-side management measures, utilizing predictive analytics, and designing resilient grid infrastructure. By implementing Bayesian sparse polynomial chaos expansion, the concept of loadability has been assessed. Due to which the MG operators can enhance energy management capabilities and maximize loadability, ensuring reliable and sustainable electricity supply to connected loads.

New Quinoline Derivatives and their Antimicrobial Potential Against Candida Albicans and Staphylococcus Aureus

AbstractAntimicrobial resistance is a pressing global health concern, demanding the development of novel antimicrobial agents. Quinoline derivatives emerge as promising candidates with their diverse chemical structures and recognized bioactive properties. This study aimed to investigate the antimicrobial potency of newly synthesized quinoline derivatives against Candida albicans and five bacterial strains, which are responsible for various infections and have exhibited varying degrees of antibiotic resistance. A series of quinoline derivatives were synthesized through simple structural modifications, such as the inclusion of nitro, amino, and hydrazino groups, or using the molecular hybridization strategy, involving the combination of quinoline and 1,2,3‐triazoles. None of the compounds showed promising activity against the bacterial strains. However, a promising fungistatic effect was observed against C. albicans. In this test, two of the compounds showed lower MIC values than fluconazole (25 μg/mL). In addition, the most promising compounds showed high affinity for the enzyme lanosterol 14 alpha demethylase, due to the low energy values found (−7.6 and −8.2 kcal/mol). Our study offers new avenues for future drug development in the fight against antimicrobial resistance.

Deep-UEO: Empowering medical image registration with hybrid strategy based on deep learning and united equilibrium optimizer

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

Application of a Multi-Layer Perceptron and Markov Chain Analysis-Based Hybrid Approach for Predicting and Monitoring LULCC Patterns Using Random Forest Classification in Jhelum District, Punjab, Pakistan.

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998-2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km2 as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies.

Design, synthesis, and biological evaluation of imidazolylacetophenone oxime derivatives as novel brain-penetrant agents for Alzheimer's disease treatment

Alzheimer's disease (AD, also known as dementia) has become a serious global health problem along with population aging, and neuroinflammation is the underlying cause of cognitive impairment in the brain. Nowadays, the development of multitarget anti-AD drugs is considered to be one effective approach. Imidazolylacetophenone oxime ethers or esters (IOEs) were multifunctional agents with neuroinflammation inhibition, metal chelation, antioxidant and neuroprotection properties against Alzheimer's disease. In this study, IOEs derivatives 1−8 were obtained by structural modifications of the oxime and imidazole groups, and the SARs showed that (Z)-oxime ether (derivative 2) had stronger anti-neuroinflammatory and neuroprotective ability than (E)-congener. Then, IOEs derivatives 9−30 were synthesized based on target-directed ligands and activity-based groups hybridization strategy. In vitro anti-AD activity screening revealed that some derivatives exhibited potentially multifunctional effects, among which derivative 28 exhibited the strongest inhibitory activity on NO production with EC50 value of 0.49 μM, and had neuroprotective effects on 6-OHDA-induced cell damage and RSL3-induced ferroptosis. The anti-neuroinflammatory mechanism showed that 28 could inhibit the release of pro-inflammatory factors PGE2 and TNF-α, down-regulate the expression of iNOS and COX-2 proteins, and promote the polarization of BV-2 cells from pro-inflammatory M1 phenotype to anti-inflammatory M2 phenotype. In addition, 28 can dose-dependently inhibit acetylcholinesterase (AChE) and Aβ42 aggregation. Moreover, the selected nuclide [18F]-labeled 28 was synthesized to explore its biodistribution by micro-PET/CT, of which 28 can penetrate the blood-brain barrier (BBB). These results shed light on the potential of 28 as a new multifunctional candidate for AD treatment.

Fostering strategic synergy: Empirical insights on aligning innovation activities with competitive strategies

An effective innovation strategy is crucial for firm performance and national economic growth, yet many firms still face the challenge of misalignment between their innovation activities and strategies. While previous studies focused on the mediation role of innovation activities on strategy and performance, research is needed to examine the complex interdependencies between strategies and innovation activities to better understand their impact on firm performance.From a configurational perspective, this study investigates how different combinations of innovation activities and strategies influence firm performance. Twenty combinative effects of four categories of innovation activities and five strategic types on two firm performance measures (growth and profit) were explored, resulting in the creation of an innovation-strategy alignment map. This study contributes to innovation strategy literature by providing the alignment map, where four configurational clusters with equifinality were identified. It also contributes to competitive strategy literature by offering empirical verification for the “pure” versus “hybrid” debate and identifying two new types of hybrids with more and different strategic dimensions by using an empirical method, thus advancing the call for more detailed hybrid strategy research in this area. In addition, the innovation-strategy alignment map provides options to managers with different resources and capacities for effective planning.

Anatomical Flow Diversion by Hybrid Strategy for Intractable Large Cerebral Aneurysms.

Flow diverters (FDs) provide curative endovascular treatment for wide-necked sidewall aneurysms. The efficacy of FDs for bifurcation or branching sidewall aneurysms is probably limited. We used anatomical flow diversion (AFD) for intractable large cerebral aneurysms. We report our experiences with AFD. The concept of AFD is the transformation from the bifurcation or branching sidewall type to the nonbranching sidewall type. Linearization of the parent artery by stenting, intentional branch occlusion, and aneurysmal coil embolization were performed. Furthermore, bypass surgery is performed for patients intolerant to branch occlusions. We evaluated the clinical outcomes of intractable aneurysms treated with AFD. AFD was performed in seven unruptured large aneurysms. Aneurysmal locations were the top of the basilar artery (BA), BA-superior cerebellar artery (SCA), internal carotid artery (IC)-posterior communicating artery (PcomA), and IC terminal. The mean dome diameter was 17.0 ± 4.6 mm. Six patients underwent bypass surgery. The occluded branches were the PCA + SCA, PcomA, and anterior cerebral artery (ACA) A1. An FD was used in three patients and a neck bridge stent in four patients. No intraprocedural complications occurred. Two postprocedural ischemic complications occurred in one patient. Six (86%) patients demonstrated a modified Rankin Scale (mRS) 0 at the 3-month follow-up, and one with an ischemic complication showed an mRS 5. Complete occlusion of all aneurysms was maintained with a median follow-up duration of 60 months. AFD is useful for intractable large cerebral aneurysms with high curability, although safety verification is required.

Combining A Hybrid Genetic Algorithm with A Fuzzy Logic Classifier Enhances Heart Disease Diagnosis

Because cardio illness has a significant impact on death rates worldwide, diagnosing heart disease is an essential field of healthcare. The study proposes a unique method, the HDD-GA-FL model, to enhance Heart Disease Diagnosis (HDD) that combines a hybrid genetic algorithm (GA) along with a fuzzy logic classifier (FL). The suggested hybrid system aims to overcome the difficulties brought on by the intricacy and ambiguity involved in diagnosing cardiac disease. Fuzzy logic classifiers are used to analyze ambiguous medical data, while genetic algorithms are used for choosing features and optimization. Combining these two methods provides a strong foundation for precise and effective diagnosis. Experiments on an extensive dataset with different clinical factors and cases of heart disease are conducted to assess the efficacy of the hybrid strategy. Compared to conventional diagnostic techniques, there have been significant improvements in diagnostic reliability and accuracy. When navigating the complex feature space involved in diagnosing heart illness, the combination of GA and FLC performs better than alone. It can capture minute associations and trends in the data. The suggested hybrid system has great potential for real-world application in clinical settings, providing doctors with an invaluable instrument for accurately diagnosing and detecting cardiac problems early on. This method advances the latest developments in cardiac healthcare by utilizing the complementary strengths of fuzzy logic classifiers and genetic algorithms, eventually improving patient outcomes and lowering healthcare costs.