Multiple visual items can be simultaneously compared with target templates in memory.

When we search for something, we often rely on both what we see and what we remember. This process can be divided into three stages: selecting items, identifying those items, and comparing them with what we are trying to find in our memory. It has been suggested that we select items one by one, and we can identify several items at once. In the present study, we tested whether we need to finish comparing a selected item in the visual display with one or more target templates in memory before we can move on to the next selected item. In Experiment 1, observers looked for either one or two target types in a rapid serially presented stimuli stream. The time interval between the presentation onset of successive items in the stream was varied to get a threshold. For search for one target, the threshold was 89 ms. When look for either of two targets, it was 192 ms. This threshold difference offered a baseline. In Experiment 2, observers looked for one or two types of target in a search array. If they compared each identified item separately, we should expect a jump in the slope of the RT × Set Size function, on the order of the baseline obtained in Experiment 1. However, the slope difference was only 13 ms/item, suggesting that several identified items can be compared at once with target templates in memory. Experiment 3 showed that this slope difference was not just a memory-load cost.

Peripheral vision contributes to implicit attentional learning: Findings from the "mouse-eye" paradigm.

The central visual field is essential for activities like reading and face recognition. However, the impact of peripheral vision loss on daily activities is profound. While the importance of central vision is well established, the contribution of peripheral vision to spatial attention is less clear. In this study, we introduced a "mouse-eye" method as an alternative to traditional gaze-contingent eye tracking. We found that even in tasks requiring central vision, peripheral vision contributes to implicit attentional learning. Participants searched for a T among Ls, with the T appearing more often in one visual quadrant. Earlier studies showed that participants' awareness of the T location probability was not essential for their ability to learn. When we limited the visible area around the mouse cursor, only participants aware of the target's location probability showed learning; those unaware did not. Adding placeholders in the periphery did not restore implicit attentional learning. A control experiment showed that when participants were allowed to see all items while searching and moving the mouse to reveal the target's color, both aware and unaware participants acquired location probability learning. Our results underscore the importance of peripheral vision in implicitly guided attention. Without peripheral vision, only explicit, but not implicit, attentional learning prevails.

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space.

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search.

Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.

Visual search for real-world scenes in patients with Alzheimer's disease and amnestic mild cognitive impairment.

BACKGROUND: Visual attention-related processes that underlie visual search behavior are impaired in both the early stages of Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), which is considered a risk factor for AD. Although traditional computer-based array tasks have been used to investigate visual search, information on the visual search patterns of AD and MCI patients in real-world environments is limited. AIM: The objective of this study was to evaluate the differences in visual search behaviors among individuals with AD, aMCI, and healthy controls (HCs) in real-world scenes. MATERIALS AND METHODS: A total of 92 participants were enrolled, including 28 with AD, 32 with aMCI, and 32 HCs. During the visual search task, participants were instructed to look at a single target object amid distractors, and their eye movements were recorded. RESULTS: The results indicate that patients with AD made more fixations on distractors and fewer fixations on the target, compared to patients with aMCI and HC groups. Additionally, AD patients had longer fixation durations on distractors and spent less time looking at the target than both patients with aMCI and HCs. DISCUSSION: These findings suggest that visual search behavior is impaired in patients with AD and can be distinguished from aMCI and healthy individuals. For future studies, it is important to longitudinally monitor visual search behavior in the progression from aMCI to AD. CONCLUSION: Our study holds significance in elucidating the interplay between impairments in attention, visual processes, and other underlying cognitive processes, which contribute to the functional decline observed in individuals with AD and aMCI.

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

A national virtual job search series for neonatal-perinatal medicine fellows.

BACKGROUND: A standardized approach to prepare trainees for the job search has not been described. The objective of this study was to describe and evaluate an educational series on the job search for Neonatal-Perinatal Medicine (NPM) fellows and identify participants' job search knowledge gaps. METHODS: During the 2020-2021 academic year, we created a virtual, seven-part job search series for NPM fellows that required no funding. The series has been repeated annually. We use REDCap surveys to register participants, collect baseline/demographic information, and evaluate the series' impact at the beginning and end of the job search timeline. RESULTS: In the 2021-2022 academic year, 290 individuals registered for the series, and 89% completed the baseline/demographic survey. The majority were NPM fellows (89%). Early career neonatologists, NPM hospitalists, and pediatric residents also utilized the series (11%). Less than 25% reported being "knowledgeable" or "very knowledgeable" of core job search components, including the timeline of the job search, contract negotiation, and the general roles and responsibilities of junior faculty. Of those who completed the final job search survey and underwent a job search (60%, 97 of 162), the majority (86%) felt that career planning during training was stressful and believed that job search preparation should be structured into the NPM fellowship curriculum (81%). Many felt that the Job Search Series was helpful in elucidating components of the job search. CONCLUSIONS: We identified several knowledge gaps in NPM fellows' understanding of how to find, prepare for, and negotiate their first post-training job. We strongly believe these knowledge gaps are not unique to NPM fellows and that all graduate medical education trainees would benefit from a similar, easy-to-implement, no-cost series.

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Population interaction network in representative gravitational search algorithms: Logistic distribution leads to worse performance.

In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way.

Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

10 Hz rhythmic stimulation modulates electrophysiological, but not behavioral markers of suppression.

We investigated the role of alpha in the suppression of attention capture by salient but to-be-suppressed (negative and nonpredictive) color cues, expecting a potential boosting effect of alpha-rhythmic entrainment on feature-specific cue suppression. We did so by presenting a rhythmically flickering visual bar of 10 Hz before the cue - either on the cue's side or opposite the cue -while an arrhythmically flickering visual bar was presented on the respective other side. We hypothesized that rhythmic entrainment at cue location could enhance the suppression of the cue. Testing 27 participants ranging from 18 to 39 years of age, we found both behavioral and electrophysiological evidence of suppression: Search times for a target at a negatively cued location were delayed relative to a target away from the cued location (inverse validity effects). In addition, an event-related potential indicative for suppression (the Distractor Positivity, Pd) was observed following rhythmic but not arrhythmic stimulation, indicating that suppression was boosted by the stimulation. This was also echoed in higher spectral power and intertrial phase coherence of EEG at rhythmically versus arrhythmically stimulated electrode sites, albeit only at the second harmonic (20 Hz), but not at the stimulation frequency. In addition, inverse validity effects were not modulated by rhythmic entrainment congruent with the cue side. Hence, we propose that rhythmic visual stimulation in the alpha range could support suppression, though behavioral evidence remains elusive, in contrast to electrophysiological findings.

The roles of phishing knowledge, cue utilization, and decision styles in phishing email detection.

This study investigated the roles of phishing knowledge, cue utilization, and decision styles in contributing to phishing email detection. Participants (N = 145) completed an online email sorting task, and measures of phishing knowledge, email decision styles, cue utilization, and email security awareness. Cue utilization was the only factor that uniquely predicted the capacity to discriminate phishing from genuine emails. Phishing knowledge was associated with greater phishing detection and a bias towards classifying all emails as phishing. A preference for intuitive decision making predicted lower detection of phishing emails, driven by a greater tendency to classify emails as genuine. These findings support the proposition that cue utilization is a distinct cognitive process that enables expert performance. The outcomes indicate that, in addition to increasing phishing knowledge and developing safe behavioral patterns, anti-phishing training needs to provide opportunities for trainees to develop meaningful cue associations.

Geographic Barriers to Indexing: Examining Appalachia.

The objective of this study was to examine the accuracy of indexing for "Appalachian Region"[Mesh]. Researchers performed a search in PubMed for articles published in 2019 using "Appalachian Region"[Mesh] or "Appalachia" or "Appalachian" in the title or abstract. Only 17.88% of the articles retrieved by the search were about Appalachia according to the ARC definition. Most articles retrieved appeared because they were indexed with state terms that were included as part of the mesh term. Database indexing and searching transparency is of growing importance as indexers rely increasingly on automated systems to catalog information and publications.

Comparison of the effectiveness and efficiency of the grid and link search methods to recover scattered skeletal remains.

The grid and link search methods are used to recover scattered skeletal remains. Neither have not been compared robustly and clear guidelines for the link method have not been sufficiently developed. The study aimed to compare the effectiveness and efficiency of both methods and propose guidelines for the link method. The scattering patterns of two scavengers of forensic relevance-slender mongooses (Galerella sanguinea) and black-backed jackals (Canis mesomelas)-were recreated using four pig skeletons (Sus scrofa domesticus). Two groups (n = 6 each) were assigned a different method to recover the scattered remains. The length of the search and when each bone was located for each scatter pattern was recorded for each group and scatter pattern. A Likert scale questionnaire assessed participants' perceptions of their assigned method. A paired t-test (p = 0.005) compared the efficiency of each method and the questionnaire answers. Both methods were effective, recovering 100% of all remains. The link method was more efficient for both scatter patterns, despite there being no statistical significance (jackal: p = 0.089; mongoose: p = 0.464). Participants indicated favorable views for both methods; however, the link method scored significantly more favorably (p = 0.01) for efficiency. Specific guidelines were developed for the use of the link method. The link method is suggested for the recovery of scattered remains in forensic contexts, especially when the scavenger, its behavior, and scattering pattern is known or suspected.

A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene.

Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.

Bumblebees attend to both the properties of the string and the target in string-pulling tasks, but prioritize the features of the string.

Previous studies have demonstrated that associative learning and experience play important roles in the string-pulling of bumblebees (Bombus terrestris). However, the features of the target (artificial flower with sugar reward) and the string that bees learn in such tasks remain unknown. This study aimed to explore the specific aspects of the string-flower arrangement that bumblebees learn and how they prioritize these features. We show that bumblebees trained with string-pulling are sensitive to the flower stimuli; they exhibit a preference for pulling strings connected to flowers over strings that are not attached to a target. Additionally, they chose to pull strings attached to flowers of the same color and shape as experienced during training. The string feature also plays a crucial role for bumblebees when the flower features are identical. Furthermore, bees prioritized the features of the strings rather than the flowers when both cues were in conflict. Our results show that bumblebees solve string-pulling tasks by acquiring knowledge about the characteristics of both targets and strings, and contribute to a deeper understanding of the cognitive processes employed by bees when tackling non-natural skills.

Accuracy and Bias in Artificial Intelligence Chatbot Recommendations for Oculoplastic Surgeons.

Purpose The purpose of this study is to assess the accuracy of and bias in recommendations for oculoplastic surgeons from three artificial intelligence (AI) chatbot systems. Methods ChatGPT, Microsoft Bing Balanced, and Google Bard were asked for recommendations for oculoplastic surgeons practicing in 20 cities with the highest population in the United States. Three prompts were used: "can you help me find (an oculoplastic surgeon)/(a doctor who does eyelid lifts)/(an oculofacial plastic surgeon) in (city)." Results A total of 672 suggestions were made between (oculoplastic surgeon; doctor who does eyelid lifts; oculofacial plastic surgeon); 19.8% suggestions were excluded, leaving 539 suggested physicians. Of these, 64.1% were oculoplastics specialists (of which 70.1% were American Society of Ophthalmic Plastic and Reconstructive Surgery (ASOPRS) members); 16.1% were general plastic surgery trained, 9.0% were ENT trained, 8.8% were ophthalmology but not oculoplastics trained, and 1.9% were trained in another specialty. 27.7% of recommendations across all AI systems were female. Conclusions Among the chatbot systems tested, there were high rates of inaccuracy: up to 38% of recommended surgeons were nonexistent or not practicing in the city requested, and 35.9% of those recommended as oculoplastic/oculofacial plastic surgeons were not oculoplastics specialists. Choice of prompt affected the result, with requests for "a doctor who does eyelid lifts" resulting in more plastic surgeons and ENTs and fewer oculoplastic surgeons. It is important to identify inaccuracies and biases in recommendations provided by AI systems as more patients may start using them to choose a surgeon.

Tracking Chinese Online Activity and Interest in Osteoporosis Using the Baidu Index.

Introduction China's most widely used online search engine, Baidu (Baidu, Inc., Beijing, China), has developed a data collection and analysis tool called the Baidu Index for tracking Internet search trends. The purpose of this study was to examine the utility of the Baidu Index in tracking online osteoporosis information-seeking behavior and comprehending the traits and concerns of the Chinese population. Methods We used the search term "osteoporosis" for the Baidu Index for the years 2018-2022. The geographic and demographic distributions, search volumes, and demand maps were recorded. Results The popularity of the search term "osteoporosis" has increased over time. The search was mostly conducted among women aged 20-39 in northern China. The demand map revealed that the most significant concerns are related to the diagnosis, treatment, and etiology of osteoporosis. Conclusion The Baidu Index is a valuable tool for tracking online health information-seeking behavior among Chinese netizens. Online search trend data appears to reflect the geographic and demographic aspects of osteoporosis to a certain extent.

The representation of contextual cue is stimulus-specific yet its expression is flexible.

Background: Contextual cueing refers to the phenomenon in which individuals utilize frequently encountered environmental contexts, comprised of distractors, as cues to expedite a target search. Due to the conflict between the widespread occurrence of contextual cue transfer and the observed impact of changing the identity of distractors on contextual cue learning, the content of contextual cue representations remains contentious. Considering the independent nature of contextual cue learning and expression, our proposition is twofold: (1) Contextual cue representations are stimulus-specific, and (2) their expression is highly flexible. Methods: To validate the model, two experiments were conducted. Experiment 1 aimed to confirm the hypothesis that contextual cue representations are stimulus-specific. We manipulated the identity consistency of distractors within repeated scenes during contextual cue learning. Difficulty in contextual cue learning under the identity-changing condition would suggest the necessity of identity within contextual cue representation, indicating the stimulus-specific nature of these representations. Experiment 2 was designed to affirm the conclusion of Experiment 1 and explore the flexibility in the expression of contextual cue representations. This experiment comprised two phases: learning and testing. During the learning phase, participants were exposed to two sets of repeated scenes in different colors under two learning conditions: load and no-load. Working memory load was introduced to interfere with the expression to prevent it from becoming automatic. In the subsequent testing phase, the colors of the two scene sets were interchanged to impede retrieval based on identity. If both load and no-load conditions demonstrate similar levels of contextual cue effects during the testing phase, it implies the flexibility in the expression of contextual cue representations and confirms the conclusion of Experiment 1. Results: In Experiment 1, a notable contextual cue learning effect was observed under the identity-consistent condition (p = 0.001). However, this effect was not evident under the identity-changing condition (p = 0.286). This finding strongly supports the stimulus-specific nature of contextual cue representation. In Experiment 2, the contextual cueing effect appeared but did not show a significant difference between the two conditions (t(23) = 0.02, p = 0.987, BF10 = 0.215), indicating the cognitive system's ability to flexibly redefine retrieval cues. This adaptability aligns with our hypothesis and confirms the high flexibility in the expression process of contextual cue representations and confirms the conclusion of Experiment 1.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations.

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.