BACKGROUND: Randomised clinical trials from over 20 years ago demonstrated that an implantable cardioverter defibrillator (ICD) improved survival for patients with severely reduced left ventricular ejection fraction (LVEF) after myocardial infarction (MI) compared with optimal medical therapy (OMT) alone. Since then advances in therapy have led to the reduction in the incidence of sudden cardiac death (SCD) in this population, whilst complication rates from ICD implantation are still substantial. OBJECTIVES: To determine whether OMT without ICD implantation is not inferior to OMT with ICD implantation with respect to all-cause mortality. DESIGN: The PROFID EHRA trial is an investigator-driven, prospective, parallel-group, randomised, open-label, blinded outcome assessment (PROBE), multi-centre, non-inferiority trial without dedicated investigational medical device (Proof of Strategy Trial) with two groups with 1:1 randomisation. PROFID-EHRA will recruit approximately 3,595 patients with documented history of MI at least three months prior, LVEF ≤35%, on OMT for at least 3 months, and with New York Heart Association class II or III, who will be randomised to OMT or OMT plus ICD, to collect 374 first primary outcome events within a median observation period of around 28 months from about 180 clinical sites in an estimated 13 countries. The primary outcome is time from randomisation to the occurrence of all-cause death. Secondary outcomes include time from randomisation to death from cardiovascular causes, to SCD, to first hospital readmission for cardiovascular causes after date of randomisation, the average length of hospital stay during follow-up, and quality of life trajectories. SUMMARY: The PROFID-EHRA trial will provide contemporary evidence for the use of ICD implantation in patients with MI and severely reduced LVEF. CLINICAL: Trials.gov NCT05665608.
- Keywords
- Heart failure, Implantable cardioverter defibrillator, Mortality, Myocardial infarction, PROFID EHRA, Randomised clinical trial, Sudden cardiac death,
- Publication type
- Journal Article MeSH
OBJECTIVES: INCORPORATE trial was designed to evaluate whether default coronary-angiography (CA) and ischemia-targeted revascularization is superior compared to a conservative approach for patients with treated critical limb ischemia (CLI). Registered at clinicaltrials.gov (NCT03712644) on October 19, 2018. BACKGROUND: Severe peripheral artery disease is associated with increased cardiovascular risk and poor outcomes. METHODS: INCORPORATE was an open-label, prospective 1:1 randomized multicentric trial that recruited patients who had undergone successful CLI treatment. Patients were randomized to either a conservative or invasive approach regarding potential coronary artery disease (CAD). The conservative group received optimal medical therapy alone, while the invasive group had routine CA and fractional flow reserve-guided revascularization. The primary endpoint was myocardial infarction (MI) and 12-month mortality. RESULTS: Due to COVID-19 pandemic burdens, recruitment was halted prematurely. One hundred eighty-five patients were enrolled. Baseline cardiac symptoms were scarce with 92% being asymptomatic. Eighty-nine patients were randomized to the invasive approach of whom 73 underwent CA. Thirty-four percent had functional single-vessel disease, 26% had functional multi-vessel disease, and 90% achieved complete revascularization. Conservative and invasive groups had similar incidences of death and MI at 1 year (11% vs 10%; hazard ratio 1.21 [0.49-2.98]). Major adverse cardiac and cerebrovascular events (MACCE) trended for hazard in the Conservative group (20 vs 10%; hazard ratio 1.94 [0.90-4.19]). In the per-protocol analysis, the primary endpoint remained insignificantly different (11% vs 7%; hazard ratio 2.01 [0.72-5.57]), but the conservative approach had a higher MACCE risk (20% vs 7%; hazard ratio 2.88 [1.24-6.68]). CONCLUSION: This trial found no significant difference in the primary endpoint but observed a trend of higher MACCE in the conservative arm.
- Keywords
- Coronary angiography, Coronary artery disease, Critical limb ischemia, Fractional flow reserve,
- MeSH
- Ischemia * therapy diagnosis MeSH
- Conservative Treatment * methods MeSH
- Coronary Angiography methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Coronary Artery Disease * complications MeSH
- Peripheral Arterial Disease * therapy complications diagnosis MeSH
- Prospective Studies MeSH
- Aged MeSH
- Treatment Outcome MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Randomized Controlled Trial MeSH
- Comparative Study MeSH
This paper introduces a novel two-step generalized parametric approach for addressing Fuzzy Multi-Objective Transportation Problems (FMOTPs), commonly encountered in logistics and transportation systems when essential parameters-such as supply, demand, and transportation costs-are uncertain. Driven by the necessity for resilient and flexible decision-making amidst uncertainty, the method employs Triangular Fuzzy Numbers (TFNs) and an accuracy parameter μ ∈ [0,1] to turn fuzzy data into precise equivalents through parametric transformation. Initially, imprecise input data are methodically converted into a sequence of Crisp Multi-Objective Transportation Problems (CMOTPs). In the subsequent phase, these CMOTPs are addressed by Fuzzy Linear Programming (FLP), and the most equitable solution at each μ-level is determined by its Euclidean distance from the fuzzy ideal solution. The suggested method is tested by numerical case studies and compared with current models-such as Nomani's approach, fuzzy DEA, and Grey Relational Analysis (GRA)-showing enhanced performance in optimality proximity, solution stability, and ranking accuracy. This research has practical applications, including improved managerial capacity to manage uncertainty, reconcile trade-offs among cost, time, and service quality, and execute robust transportation strategies in fluctuating environments. The model's scalability and openness make it suited for integration into enterprise logistics systems across industries such as manufacturing, retail, distribution, and e-commerce. The study offers a systematic and computationally efficient framework that enhances both theoretical comprehension and practical implementation of fuzzy optimization in multi-objective transportation planning.
In contemporary times, the environment is being progressively polluted by non-eco-friendly products from manufacturing sectors. Therefore, it is vital for individuals to be aware of the necessity of employing environmentally friendly items as a means to mitigate pollution. This consciousness, in return, drives an instant increase in the desire for environmentally friendly products, greatly improving their ecological sustainability. In this context, this study proposes a novel perishable inventory model that incorporates environmental attributes into demand and cost functions, which contributes to sustainable inventory management research. The maximum potential lifespan of a product is a crucial aspect of inventory management, especially when considering its suitability for reuse. One notable challenge in the connection between suppliers/manufacturers and merchants for products accessible during seasonal periods with high demand pertains to the issue of payment in advance. Integrating these multifaceted elements results in a perishable commodity inventory model characterized by a customer demand rate depending on the product's green level and price, an interval-valued holding cost, and a linearly time-dependent holding cost. A partial backlog of shortages with interval values is incorporated in this model. The associated optimization problem is characterized as a maximization problem, wherein the objective function exhibits values throughout an interval. To assess the accuracy and reliability of the proposed model, the Arctic Puffin Optimization (APO) algorithm is employed to analyze and solve a specific numerical illustration. Furthermore, seven other algorithms (Dandelion Optimizer (DO), Grey wolf optimizer (GWO), The whale optimization algorithm (WOA), Artificial electric field algorithm (AEFA), Harris hawks optimization (HHO), Multi-verse optimizer (MVO) and Slime mould algorithm (SMA)) are used to compare the obtained solution from APO. Quantitatively, the APO and DO algorithms provid the same solution for the given example. However, during the statistical test for review the performance of the algorithms, it is observed that APO is outperformed among all other algorithms. Subsequently, a post-optimality analysis examines the quantitative effects of changes made to different inventory parameters, which results in an insightful conclusion. This study not only contributes to the theoretical framework of perishable commodity inventory modeling but also provides practical implications for sustainable inventory management in response to environmental concerns.
This work introduces the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET), which is a novel optimization framework designed to enhance the efficiency and robustness of multi-robot exploration. AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. To validate its effectiveness, AMET is compared to both multi-objective and single-objective exploration strategies, including CME combined with Multi-Objective Grey Wolf Optimizer (CME-MGWO), Multi-Objective Ant Colony Optimization (CME-MACO), Multi-Objective Dragonfly Algorithm (CME-MODA), and the single-objective CME with traditional Salp Swarm Algorithm (CME-SSA). The evaluation focuses on four critical performance metrics: runtime efficiency, exploration area coverage, mission completion resilience, and the reduction of redundant exploration. Experimental results across multiple case studies demonstrate that AMET consistently outperforms both single-objective and multi-objective counterparts, achieving superior area coverage, reduced computational overhead, and enhanced exploration coordination. These findings highlight the potential of AMET as a scalable and efficient approach for robotic exploration, providing a foundation for future advancements in multi-robot systems. The proposed method opens new possibilities for applications in search-and-rescue operations, planetary surface exploration, and large-scale environmental monitoring.
Air conditioning systems are essential for ensuring indoor thermal comfort in commercial buildings; however, they are also significant consumers of electrical energy, contributing to increased environmental impact. Optimizing the design of mechanical ventilation (MV) systems through multi-objective approaches can greatly improve both energy efficiency and cost-effectiveness. This study presents an advanced optimization strategy for MV in both a classical reference case and a real-world commercial installation. The methodology integrates principles of fluid mechanics with computational modeling to perform mass and pressure balances, combined with a simulated annealing algorithm for system optimization. The results demonstrate notable reductions in energy consumption, installation costs, and root mean square deviation of airflow rates from design targets. Furthermore, the proposed approach enables effective airflow distribution without the use of dampers. These findings highlight the potential of optimization techniques, particularly simulated annealing, in enhancing the performance, economic feasibility, and environmental sustainability of HVAC systems in commercial applications.
- Keywords
- Airflow distribution, Energy efficiency, HVAC, Mechanical ventilation, Simulated annealing, Ventilation demand,
- Publication type
- Journal Article MeSH
BACKGROUND: Aluminium based composites with hybrid reinforcement hold significant potential to replace Al-alloys in a variety of automotive sectors where cheap cost, a significant ratio of strength to weight, and better wear resistance are required. METHODS: Stir casting was utilized to make aluminium matrix composites (AMCs) with 3%, 6%, and 9% of B4C/Fly ash particles. The wear was examined with various Sliding Speed, S (1 m/s, 1.5 m/s and 2 m/s), Sliding Distance, D (500 m, 1000 m and 1500 m), applied load, L (15 N, 30N and 45 N) and reinforcement %, R (3, 6 and 9%). Grey Relational Analysis was used to optimise the wear variables. Taguchi's L27 Orthogonal array (OA) was selected for this statistical approach in order to analyse responses like Specific wear rate (SWR) and Coefficient of Friction (CoF). Furthermore, analysis of variance (ANOVA) was utilized to investigate the influence of input parameters on wear behavior by choosing "smaller is better" feature. RESULTS: Based on this study, the optimal values of S - 1.5 m/s, D - 500 m, L - 30 N, and R% - 9 wt% Hybrid (4.5% Fly ash and 4.5% B4C) are found to yield the lowest SWR and CoF. Wear rate of composite decreased with an increase in reinforcement particles. Increase in hardness was also the reason for decrease in wear rate. The responses have a narrow margin of error, according to confirmation studies. There exists a good agreement between them. DISCUSSION: The research on LM6/B4C/fly ash composite fabrication using Grey Relational Analysis (GRA) has significantly contributed to the development of high-performance materials for wear-related applications. Through the optimization of wear parameters, GRA allows for the improvement of wear resistance, strength, and sustainability.
- MeSH
- Aluminum * chemistry MeSH
- Coal Ash * chemistry MeSH
- Alloys chemistry MeSH
- Materials Testing * methods MeSH
- Friction MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Aluminum * MeSH
- Coal Ash * MeSH
- Alloys MeSH
OBJECTIVE: Pelvic exenteration is a radical surgery for advanced or recurrent pelvic tumors, requiring careful patient selection and a multi-disciplinary approach. Despite advancements, it remains high-risk, with limited data on outcomes. The present meta-analysis evaluates survival, mortality, and trends to clarify its role in gynecologic oncology. METHODS: A systematic search was conducted in January 2025 to identify studies on pelvic exenteration outcomes for gynecologic malignancies. Studies with at least 10 patients reporting 5-year overall survival or 30-day mortality were included. Data extracted included patient and surgical characteristics, and a scoring system based on study design, sample size, and center volume was used to include high-quality studies (score ≥3). Poisson regression models were used to analyze the associations between predictors and outcomes, with results expressed as incidence rate ratios and a 95% CI. RESULTS: A total of 46 studies involving 4417 patients met the inclusion criteria. Most patients underwent pelvic exenteration for cervical cancer (N = 3183). Positive pelvic and aortic nodal involvement were key predictors of reduced 5-year overall survival, decreasing by 3.9% and 5.9% per 1% increase in nodal positivity, respectively. Pelvic wall involvement also significantly reduced survival by 15.9%. The 30-day mortality rate was 5.1%, with sepsis (27.2%) being the leading cause of death. Peri-operative mortality decreased significantly over time, with each year of publication associated with a 2.6% decrease in incidence rate. However, pelvic sidewall involvement and total exenteration increased 30-day mortality by 11.5% and 0.7%, respectively. CONCLUSIONS: Pelvic exenteration remains a viable but high-risk option for select patients with advanced gynecologic malignancies. Pre-operative assessment and multi-disciplinary planning are essential for optimizing outcomes.
- Keywords
- Cervical Cancer, Endometrial Cancer, Gynecologic, Pelvic Exenteration, Vulvar Cancer,
- MeSH
- Pelvic Exenteration * mortality methods MeSH
- Humans MeSH
- Genital Neoplasms, Female * surgery mortality MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
- Systematic Review MeSH
The rising energy demand, substantial transmission and distribution losses, and inconsistent power quality in remote regions highlight the urgent need for innovative solutions to ensure a stable electricity supply. Microgrids (MGs), integrated with distributed generation (DG), offer a promising approach to address these challenges by enabling localized power generation, improved grid flexibility, and enhanced reliability. This paper introduces the Improved Lyrebird Optimization Algorithm (ILOA) for optimal sectionalizing and scheduling of multi-microgrid systems, aiming to minimize generation costs and active power losses while ensuring system reliability. To enhance search efficiency, ILOA incorporates the Levy Flight technique for local search, which introduces adaptive step sizes with long-distance jumps, improving the exploration-exploitation balance. Unlike conventional local search strategies that rely on fixed step sizes, Levy Flight prevents premature convergence by allowing the algorithm to escape local optima and explore the solution space more effectively. Additionally, a chaotic sine map is integrated to enhance global search capability, ensuring better diversity and superior optimization performance compared to traditional algorithms. Simulation studies are conducted on a modified 33-bus distribution system segmented into three independent microgrids. The algorithm is evaluated under single-objective scenarios (cost and loss minimization) and a multi-objective optimization framework combining both objectives. In single-objective optimization, ILOA achieves a generation cost of $19,254.64/hr with 0.7118 kW of power loss, demonstrating marginal improvements over the standard Lyrebird Optimization Algorithm and significant gains over Genetic Algorithm (GA) and Jaya Algorithm (JAYA). In multi-objective optimization, ILOA surpasses competing methods by achieving a generation cost of $89,792.18/hr and 10.26 kW of power loss. The optimization results indicate that, for the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm achieves savings of approximately 0.0014%, 0.0041%, and 0.657% in operation costs compared to LOA, JAYA, and GA, respectively, when MG-1, MG-2, and MG-3 are operational. The analysis of real power loss reduction demonstrates that, in the IEEE-33 bus system without considering EIR, the proposed ILOA algorithm effectively minimizes power loss by approximately 0.692%, 1.696%, and 1.962% in comparison to LOA, JAYA, and GA, respectively, under the operational conditions of MG-1, MG-2, and MG-3. Additionally, reliability constraints based on the Energy Index of Reliability (EIR) are effectively incorporated, further validating the robustness of the proposed approach. Considering EIR, the real power loss analysis for the IEEE-33 bus system highlights that the proposed ILOA algorithm achieves a reduction of approximately 1.319%, 2.069%, and 2.134% in comparison to LOA, JAYA, and GA, respectively, under the operational scenario where MG-1, MG-2, and MG-3 are active. The results confirm that ILOA is a highly efficient and reliable solution for distributed generation scheduling and multi-microgrid sectionalizing, showcasing its potential for real-world applications such as dynamic economic dispatch and demand response integration in smart grid systems.
Image segmentation using bi-level thresholds works well for straightforward scenarios; however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved Reptile Search Algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which included COVID-19 scans along with standard color and grayscale images. A thorough evaluation was conducted using metrics such as the fitness function, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the Friedman ranking test. The results indicate that the proposed algorithm seems to surpass existing state-of-the-art methods, demonstrating its effectiveness and robustness in multi-level thresholding tasks.
- Keywords
- Image segmentation, Medical images, Multi-level threshold, Otsu method, Kapur method, Reptile search algorithm,
- MeSH
- Algorithms * MeSH
- COVID-19 * diagnostic imaging virology MeSH
- Humans MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Signal-To-Noise Ratio MeSH
- SARS-CoV-2 isolation & purification MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
