Exploratory Phase Research Articles

The integration of entrepreneurial marketing processes and controls

Purpose This study aims to explore the evolving relationship between entrepreneurial marketing (EM) processes and control mechanisms across three phases of opportunity development: exploration, evaluation and exploitation. By examining how control mechanisms respond to the uncertainties inherent in each phase, this research illustrates how controls emerge and adapt, enabling entrepreneurs to effectively navigate the challenges specific to each stage of their venture’s growth. Design/methodology/approach Using a qualitative, inductive approach, this study uses semistructured interviews with founders of nine high-growth, early-stage startups based in California. The selected ventures vary by industry and business model but share early market traction. The Gioia methodology, a structured inductive approach for theme development, guides data analysis, revealing how control mechanisms evolve to address the challenges of each EM phase. Findings Controls adapt across the EM phases, becoming progressively structured to manage uncertainties. In the exploration phase, controls are belief-driven and flexible, guiding resource allocation and framing opportunity spaces. During evaluation, controls become more structured, aligning market feedback with product-market fit. In the exploitation phase, controls focus on scalability and efficiency, supporting alignment between strategic objectives and operational capabilities. Originality/value This research provides novel insights by demonstrating that controls do not stifle opportunity development but rather evolve with it. The findings contribute to theory and practice at the marketing/entrepreneurship interface, offering a nuanced understanding of the role of controls in supporting the EM process.

An IoT Approach Enhanced with AI Capabilities for Predictive Neurology in Smart Homes

<p dir="ltr"><span>The integration of Artificial Intelligence (AI) and Internet of Things (IoT) offers remarkable chances for improving healthcare in smart home environments. In order to anticipate and observe neurological disorders, the study explores the use of IoT devices along with AI analytics. The goal is to identify early signs of any decline in cognitive functions or neurodegenerative conditions such as Alzheimer’s or Parkinson’s. The research emphasises on examining a framework for predictive neurology while it faces challenges associated with real-time data processing and prediction precision. The outcomes should result in a smart home ecosystem development that focuses on healthcare interventions and enhances life quality. This paper explores a multi-layered architecture, including an exploratory phase to analyse user needs, a developmental phase to design and integrate IoT devices, Machine Learning algorithms, and cloud architectures, as well as a validation phase to undergo rigorous testing in simulated environments to evaluate performance metrics.</span></p><div><span><br /></span></div>

Improved Whale Optimization Algorithm for Dynamic Optimal Power Flow with Renewable Energy Penetration

This paper proposes the Improved Whale Optimization Algorithm (IWOA) method to solve the Dynamic Optimal Power Flow (DOPF) problem in Renewable Energy Systems (RES), taking into account the influence of Energy Storage (ES). The convergence performance of the Whale Optimization Algorithm (WOA) algorithm is enhanced by incorporating Fuzzy Logic Control (FLC) in the exploration phase. The FLC assists the IWOA agents in finding the optimal weight values more quickly. The weights generated by the IWOA method are dynamic, continuously adjusting based on the deviation or error in the agents' movement during each iteration, which is essential for locating the global optimum. This paper primarily uses FLC to determine the weights, accelerating the IWOA optimization process. The effectiveness of this method is tested on the IEEE 30-bus system with the integration of PV and ES. The findings of this study demonstrate the superiority of the IWOA method over the WOA algorithm, resulting in a lower generation cost of $25/day and a faster convergence time.

An exploratory phase II trial of low-dose decitabine and sintilimab combined with chemotherapy in HER2-negative gastric or gastroesophageal junction adenocarcinoma.

390 Background: Combining epigenetic modulator with PD-1 inhibitor may enhance treatment outcomes in cancer patients. This study aimed to evaluate the efficacy and safety of epigenetic drug decitabine, sintilimab (a PD-1 inhibitor), and chemotherapy in gastric cancer. Methods: In this single-arm, exploratory phase II trial, patients with HER2-negative, locally advanced or metastatic gastric or gastroesophageal junction (G/GEJ) adenocarcinoma were enrolled between August 12, 2021, and October 22, 2022. Treatment included low-dose decitabine (10 mg/m² for 5 days), sintilimab (a PD1 inhibitor, 200 mg every 21 days), and XELOX. The primary endpoint was objective response rate (ORR) and second endpoints included disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and safety. The protocol was approved by the ethics committee of the Changzhi Medical College Affiliated Changzhi People's Hospital (No. 2021K002). All patients provided written informed consent form before study participation. The trial is registered with Chinese Clinical Trial, number ChiCTR2100043312. Results: Sixteen patients were initially enrolled, with a median follow-up of 14.8 months. Eleven patients exhibited a PD-L1 combined positive score (CPS) < 5. In the per-protocol sets, 11 patients remained eligible for efficacy analysis. In the intention-to-treat analysis of all 16 patients, 9 achieved partial response and 2 had stable disease, resulting in an ORR of 69% and a DCR of 56%. As of the data cutoff on November 4, 2023, the median PFS was 11.1 months (95% CI, 5.1-not reached) and the median OS was 13.2 months (95% CI, 5.1-not reached). Treatment-related adverse events were generally well-tolerated, with most being grade 1-2. Three patients experienced severe AEs, including one fatal case of grade 4 immune-related pneumonia. Conclusions: The combination of low-dose decitabine, sintilimab, and chemotherapy appears promising strategy for locally advanced or metastatic G/GEJ adenocarcinoma, especially in patients with negative HER2 and low PD-L1 expression. These preliminary findings support further investigations to confirm the benefits and explore the underlying mechanisms. Clinical trial information: ChiCTR2100043312. Objective response and disease response. Full analysis set (n=16) Efficacy analysis set (n=11) Complete response 0 0 Partial response 9 9 Stable disease 2 2 Progressive disease 0 0 Non-evaluable* 5 NA Objective response 9 (56%) 9 (81.8%) Disease control 11 (69%) 11 (100%) NA=not applicable. *Included patients without any post-baseline response assessment.

P-962. Designing ID Elective Goal and Learning Objectives Based On Need Assessment From Learners

Abstract Background Comprehensive Infectious disease (ID) education is indispensable for clinicians, given challenges posed by antimicrobial resistance, pandemics, expanding immunocompromised population, and the effect of climate change. ID fellowship applications have failed to match the demand for training programs. While numerous factors contribute to the prevailing challenges in ID workforce, enhancing clinical education in ID holds promise for bridging this gap. Need Assessment Survey Results Methods An anonymous survey was conducted to understand the reasons learners in undergraduate (UME) and graduate medical education (GME) choose ID electives, what and how they want to learn during the rotation. In the academic year 2023, there were 59 GME and 10 UME learners enrolled in ID elective at our institution. 38 responded to the survey (55% response rate). Results The survey showed 93% of respondents opted for electives to augment their knowledge. 24% sought networking and mentorship from faculty members, and 21% wanted to explore career options in ID. However, only 8% plan to apply to ID fellowship programs. Learners expressed interest in exploring outpatient ID clinics (63%), antimicrobial stewardship initiatives (81%), and infection control activities (42%), alongside inpatient consultation services. Moreover, a majority (87%) expressed a desire for structured didactics. Conclusion While most pursue ID electives to address knowledge gaps, 21% of learners seem to be in an exploratory phase, seeking mentorship and contemplating a career in ID. ID electives have potential to serve as a platform for early trainees and students to explore their interests in the field. To optimize their experience, we could provide interactive didactics and diversifying clinical activities such as antimicrobial stewardship initiatives, and subspecialty clinics. Such initiatives not only provide learners with comprehensive insights into the field but also foster a deeper appreciation for the multifaceted nature of infectious diseases. Limitations from this study include small sample size and low response rate which impact generalizability of this finding. Conducting longitudinal surveys and evaluations with elective learners could provide further insights into how to improve the curriculum and better meet the needs of participants. Disclosures All Authors: No reported disclosures

Global hotspots and trends of nutritional supplements for sick populations from 2000 to 2024

BackgroundNutritional supplements (NS) can help patients by providing various nutrients such as essential vitamins and minerals, helping to prevent and recover from diseases. This study provides a broad overview of the field of NS for sick people through bibliometrics and visualization analysis, to analyze the status and development dynamics, explore the popular research questions and directions, and reveal the development trends and research frontiers.MethodsWe searched the Web of Science Core Collection databases for literature related to NS for diseased populations from 2000 to 2024. A total of 1,550 articles were included in the analysis after screening. Analyses performed using CiteSpace and VOSviewer software.ResultsThe field of NS for the sick population has witnessed an overall rapid growth in the number of publications, which is divided into three phases: 2000–2008 was the exploratory phase, 2009–2017 was the sustained development phase, and 2018 to date is in the rapid development phase. Research focuses on dietary supplementation, oxidative stress, in vitro injections, development, antioxidant activity, double-blind trials, lipid supplements, functional foods, the health of diseased populations, and the risks of NS.ConclusionDifferent supplements each possess unique benefits and should be chosen according to the type of disease to ensure they contain the corresponding nutrients. Vitamin supplements are widely mentioned among patient populations across the globe. Future trends may focus on applying nutritional supplements in gut microbiota and bioactive compounds. Researchers frequently mention the application of NS in women, infants, and children. It should continue to be monitored and optimized in the future to enhance its therapeutic effects, thereby accelerating patients’ recovery and improving their quality of life.

MutaSwarmClus: enhancing data clustering efficiency with mutation-enhanced swarm algorithm

Data clustering is a fundamental technique in data mining, pivotal for various applications such as statistical analysis and data compression. Traditional clustering algorithms often struggle with noisy or high-dimensional datasets, hindering their efficacy in addressing real-world challenges. In response, this research introduces MutaSwarmClus, a novel hybrid metaheuristic algorithm that combines Mouth Brooding Fish (MBF), Ant Colony Optimization (ACO), and mutation operators to enhance clustering quality. MutaSwarmClus intends to adaptively control the exploration and exploitation phases of the solution space, solve issues with local optima and changes in the distribution of available data. Moreover, it incorporates an Iterated Local Search (ILS) to refine solutions and avoiding getting stuck in local optima. MutaSwarmClus therefore increases the robustness of the clustering process by incorporating controlled randomness through mutation operators in order to handle noisy and outlier data points well. According to the contributions analysis, the proposed algorithm improves the clustering solution with the combined system of MBF, ACO, and mutation operators, which enables the mechanism of exploration and exploitation in the process of information search. As shown through the results of experimental studies, MutaSwarmClus has high performance when used with various benchmarks, and outperforms or performs as well as or better than compared to other clustering algorithms such as K-means, ALO, Hybrid ALO, and MBF. It achieves an average error rate of only 10%, underscoring its accuracy in clustering tasks. The utilization of MutaSwarmClus offers a solution to the existing problems in clustering large datasets in terms of scalability, efficiency and accuracy. Possible directions for future work can continue to optimize the model parameters of the algorithm and study its adaptability in dynamic conditions and with large amounts of data.

Adaptive anomaly detection disruption prediction starting from first discharge on tokamak

Abstract Plasma disruption presents a significant challenge in tokamak fusion, especially in large-size devices like ITER, where it causes severe damage. While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. However, future tokamaks will begin operations without any prior data, making it difficult to train data-driven disruption predictors and select appropriate hyperparameters during the early operation period. In this period disruption prediction also aims to support safe exploration of operation range and accumulate necessary data to develop advanced prediction models. Thus, predictors must adapt to evolving plasma states during this exploration phase. To address these challenges, this study further develops the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor and proposes a cross-tokamak adaptive transfer method based on E-CAAD. By training the E-CAAD model on data from existing devices, the predictor can effectively distinguish between disruption precursor and non-disruption samples in new device, enabling disruption prediction from the first shot on the new device. Additionally, adaptive learning from scratch and alarm threshold adaptive adjustment strategies are proposed to enable model automatically adapt to changes in the discharge scenario. The adaptive learning strategy enables the predictor to fully use scarce data during the early operation of the new device while rapidly adapting to changes in the discharge scenario. The threshold adaptive adjustment strategy addresses the challenge of selecting alarm thresholds on new devices where the validation set is lacking, ensuring that the alarm thresholds adapt to changes in the discharge scenario. Finally, the experiment transferring the model from J-TEXT to EAST exhibit that this method enables disruption prediction from the first shot on EAST, allowing the predictor to adapt to changes in the discharge scenario and maintain high prediction performance.

Transoral endoscopic thyroid lobectomy and ipsilateral central neck lymph node dissection vestibular approach: analysis of the learning curve and clinical outcomes evaluation.

The transoral endoscopic thyroidectomy vestibular approach (TOETVA) is distinguished by its ability to leave no visible scars on the body's surface. Currently, there is still a lack of single-center large sample size analysis on the learning curve of TOETVA, especially for the treatment of thyroid cancer. This study aims to fill this void by presenting a comprehensive analysis of the learning curve and assessing the procedure's feasibility in managing thyroid cancer. Between June 2020 and June 2023, a retrospective analysis was conducted on 195 patients who had undergone the transoral endoscopic thyroidectomy vestibular approach (TOETVA) at the First Affiliated Hospital of Zhengzhou University. We employed the cumulative sum method (CUSUM) to delineate the learning curve of TOETVA. Additionally, clinical parameters across different stages of the learning process were meticulously compared and analyzed. All patients successfully completed endoscopic surgery without conversion to open surgery. Utilizing the CUSUM algorithm, two distinct learning phases were delineated: the exploration phase, comprising 58 cases, and the maturation phase, encompassing 137 cases. Analysis revealed that the maturation phase demonstrated significantly reduced operative times (189.7 ± 237.27 vs. 138.15 ± 26.62 minutes, p<0.001), decreased blood loss (15.49 ± 15.05 vs. 9.67 ± 4.12 ml, p=0.005), and a lower incidence of complications (7 vs. 4, p=0.028) when compared to the exploration phase. Additionally, in the maturation phase, we achieved successful surgical outcomes in a subset of obese patients and those with nodular goiter. TOETVA has been demonstrated to be safe and feasible, with the capability to effectively address complex cases once the learning curve has been surmounted.

Gaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics.

Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. This improvement is achieved by incorporating a Gaussian barebone mechanism, a wormhole strategy, and an elimination strategy into the MFO. To assess the effectiveness of BWEMFO, a series of comparison experiments is conducted, comparing it against conventional metaheuristic algorithms, advanced metaheuristic algorithms, and various MFO variants. The experimental results reveal a significant enhancement in both the convergence speed and the capability to escape local optima with the implementation of BWEMFO. The scalability of the algorithm is confirmed through benchmark functions. Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. Subsequently, BWEMFO-KELM undergoes diagnostic and predictive experimentation on three distinct medical datasets: the breast cancer dataset, colorectal cancer datasets, and mammographic dataset. Through comparative analysis against five alternative machine learning methodologies across four evaluation metrics, our experimental findings evince the superior diagnostic accuracy and reliability of the proposed BWEMFO-KELM model.

Optical molecular imaging in oral- and oropharyngeal squamous cell carcinoma using a novel uPAR-targeting near-infrared imaging agent FG001 (ICG-Glu-Glu-AE105): An explorative phase II clinical trial.

Background: In oral and oropharyngeal squamous cell carcinoma (OSCC, OPSCC), frequent inadequate surgical margins highlight the importance of precise intraoperative identification and delineation of cancerous tissue for improving patient outcomes. Methods: A prospective, open-label, single-center, single dose, exploratory phase II clinical trial (EudraCT 2022-001361-12) to assess the efficacy of the novel uPAR-targeting near-infrared imaging agent, FG001, for intraoperative detection of OSCC and OPSCC. Macroscopic tumor detection was quantified with sensitivity and intraoperative tumor-to-background ratio (TBR). Microscopic tumor-specificity was assessed by analysis of morphological co-localization between tumor tissue, uPAR-expression, and optical signal. Blood samples were collected up to 44 hours post-injection to further characterize the pharmacokinetic profile of the agent. The trial was conducted with close safety monitoring. Results: Sixteen patients undergoing primary surgical resection were systemically administered 36 mg (n = 4), 16 mg (n = 8), or 4 mg (n = 4) of FG001 the evening prior to surgery. Intraoperatively, using a near-infrared imaging system, real-time optical imaging successfully identified all 16 tumors (sensitivity: 100%, mean TBR: 2.99 range: 2.02 - 3.95), and tumor-specificity was confirmed by histology. Clinical neck metastasis was detected with optical imaging. The maximal plasma concentrations were measured after 1 hour, and the half-life of FG001 was 12 hours. No drug-related or serious adverse events were observed. Conclusions: FG001 holds great potential for optical molecular imaging of OSCC and OPSCC. Further trials are warranted to explore FG001 for intraoperative margin delineation and as a decision-making tool.

A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems.

The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems.

Efficient parameter extraction in PV solar modules with the diligent crow search algorithm

In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed.

A Bibliometric Analysis of Research Examining How Space Radiation Affects Human and Rodent Cognition, 1990–2023

The pursuit of exploring the outer space environment for biological research has been a topic of interest for nearly 60 years. The success of the next phase of space exploration depends on the ability to increase crew safety by identifying ways to mitigate these threats. Using a universal scientific citation indexing tool, we extracted data on literature production in terms of the most prolific key terms, authors, countries, institutions, and journals for two distinct topic sets related to space radiation research published from 1 January 1990 to 31 December 2023. The focus of space radiation research in relation to its effects on human health has fluctuated over time, as reflected in the term maps that were generated for each decade. Our bibliometric analysis provides insight into the trends in the top producers in the space radiation research field over the years, as well as into how the focus of such studies has evolved throughout the decades.

A Novel Slime Mould Multiverse Algorithm for Global Optimization and Mechanical Engineering Design Problems

The slime mould optimization algorithm (SMA) is one of the well-established optimization algorithms with a superior performance in a variety of real-life optimization problems. The SMA has certain limitations that reduce the diversity and accuracy of solutions, raising the risk of premature convergence with an inadequate balance between its exploitation and exploration phases. In this study, a novel hybrid slime mould multi-verse algorithm (SMMVA) is proposed to improve the performance of SMA algorithm. The SMA and multi-verse optimization (MVO) algorithm hybrid is introduced while updating variation parameter through novel nonlinear convergence factor. The proposed algorithm balances the ability of the SMA algorithm to explore and exploit, boosts the global exploration capability and improves the accuracy, stability, and convergence speed. The performance of SMMVA algorithm is compared with 16 well-established and recently-published metaheuristic algorithms on 23 standard benchmark functions, CEC2017, CEC2022 test functions, five engineering design problems, and five UCI repository datasets. The statistical tests such as Friedman’s test, box plot comparison and Wilcoxon rank sum test are employed to verify the SMMVA’s stability and statistical superiority. The algorithm was tested on total 64 benchmark functions, achieving an overall success rate of 68.75% across 30 runs compared to the other counterparts. The results for the feature selection problem show that the proposed algorithm with k-nearest neighbour (KNN) classifier obtained more informative features with higher accuracy values. Thus, the proposed SMMVA algorithm is proven to perform excellent performance in solving optimization problems with better solution accuracy and promising prospect.

Efficacy and safety of the combination of envafolimab and lenvatinib in unresectable hepatocellular carcinoma: a single-arm, multicentre, exploratory phase II clinical study.

Currently, therapeutic combinations of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents have shown promising outcomes and have the potential to establish a new standard of care. The efficacy and safety of the first-line combination of envafolimab (an ICI) and lenvatinib (an anti-tumor angiogenesis drug) for the treatment of patients with inoperable hepatocellular carcinoma (HCC) have not been demonstrated. Unresectable HCC patients with an Eastern Cooperative Oncology Group (ECOG) physical status score ≤ 1 and a Child-Pugh score ≤ 7 who had not received systemic therapy were included in this single-arm, exploratory, multicentre phase II clinical study. All patients were required to meet the criteria of being at least 18 years of age, having no history of other malignancies, and existing at least one measurable lesion. The patients were treated with envafolimab (150mg, QW, subcutaneous) in combination with lenvatinib (12 mg for patients weighing over 60 kg, 8 mg for patients weighing under 60 kg). The co-primary endpoint of the study was overall survival (OS), while surrogate endpoints included progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and safety. Between March 2022 and April 2023, 36 patients were enrolled, 30 of whom were treated with envafolimab plus lenvatinib. At data cutoff, the median follow-up duration was 20 months (95% CI 18.9-21.1). Among the 30 assessable patients (patients treated according to the trial protocol), the median overall survival (mOS) and median progression-free survival (mPFS) for the therapy comprising envafolimab alongside lenvatinib were 18.5 months (95% CI 13.2-23.8) and 9.4 months (95% CI 1.6-15.6), respectively. The ORR and the DCR (evaluated according to mRECISTcriteria) reached 40% and 80%, respectively. In terms of safety, 23 patients (76.7%) experienced at least one treatment-related adverse event (TRAE), of which the most common was elevated aspartate aminotransferase (AST, 23.3%). Furthermore, grade 3 and higher TRAEs occurred in 30%. This study demonstrates that envafolimab in combination with lenvatinib exhibits favourable anti-cancer activity and a manageable safety profile for the first-line treatment of patients with unresectable HCC.

An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning.

This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of the SWO algorithm. By innovating the population iteration formula and integrating the advantages of Differential Evolution and the Crayfish Optimization Algorithm, along with introducing an opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) and crossover probability (Cr) are dynamically updated to balance the exploration and exploitation phases. In each generation, ISWO optimizes individual positions using Lévy flights, DE's mutation, and crossover operations, and COA's adaptive update mechanisms. The OBL strategy is applied every 10 generations to enhance population diversity. As the iterations progress, the population size gradually decreases, ultimately yielding the optimal solution and recording the convergence process. The algorithm's performance is tested using the 2017 test set, modeling a mountainous environment with a Gaussian function model. Under constraint conditions, the objective function is updated to establish a mathematical model for UAV flight. The minimal cost for obstacle-avoiding flight within the specified airspace is obtained using the fitness function, and the flight path is smoothed through cubic spline interpolation. Overall, ISWO generates high-quality, smooth paths with fewer iterations, overcoming premature convergence and the insufficient local search capabilities of traditional genetic algorithms, adapting to complex terrains, and providing an efficient and reliable solution.

Using the EPIS Framework to Implement a Physical Activity Promotion Intervention at a Large: Academic Healthcare System.

To describe the implementation of a workplace health promotion to address low levels of physical activity (PA). Using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework we implemented and evaluated a 10-week workplace step-count challenge to promote PA. All health system employees invited to participate. Data were collected on the exploration, preparation and implementation phases. During exploration, we recognized inadequate PA among employees. Meetings with key personnel were held to determine details of the health promotion and obtain support. We pursued a step-count PA intervention, capitalizing on employee ownership of smartphones with accelerometers. Vendors to host the intervention were evaluated. All employees were invited to participate. Participants received weekly messages about improving PA and notifications of weekly challenges. Exit interviews provided feedback and suggestions. A workplace health promotion focused on employee PA is feasible using EPIS.

Plant Disease Detection Using CNN with The Optimization Called Beluga Whale Optimization Mechanism

Plant disease detection is critical for ensuring agricultural productivity. Early and accurate identification of plant diseases can help in the timely application of remedies, reducing yield loss and improving crop quality. This paper presents a deep learning(DL) approach using Convolutional Neural Networks (CNN) for plant disease detection, combined with an advanced optimization technique named the Beluga Whale Optimization Mechanism (BWOM). The CNN is implemented to extract and features from plant images, providing a robust model capable of differentiating between healthy and diseased plants. The BWOM is utilized to optimize the CNN's hyper parameters and weights, enhancing model accuracy and efficiency by reducing overfitting and improving generalization. The BWOM mimics the social behaviour and echolocation techniques of beluga whales to navigate and optimize solution spaces effectively. By iterating through population-based exploration and exploitation phases, BWOM provides a balanced search mechanism to fine-tune CNN parameters. Further the results demonstrate the effectiveness of combining CNN with BWOM in achieving high accuracy rates for plant disease classification.

Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.