Search Results (340)

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS problem. The IBCOA integrates a local search strategy and a periodic mode boundary handling technique, significantly improving its ability to search and exploit the feature space. By doing so, the IBCOA effectively reduces dimensionality, while improving classification accuracy. The algorithm’s performance was evaluated using support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers on eighteen multi-scale benchmark datasets. The findings showed that the IBCOA performed better than nine recent binary optimizers, attaining 100% accuracy and decreasing the feature set size by as much as 0.8. Statistical evidence supports that the proposed IBCOA is highly competitive according to the Wilcoxon rank sum test (alpha = 0.05). This study underscores the IBCOA’s potential for enhancing FS processes, providing a robust solution for high-dimensional data challenges. Full article

The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher’s exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the “256 Angular Variance of Energy” texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes. Full article

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems. Full article

This study aimed to investigate the effects of antiepileptic drugs on salience network regions in patients with epilepsy with generalized tonic-clonic seizures alone (EGTCSa). A retrospective observational case-control study was performed on 40 patients diagnosed with epilepsy with EGTCSa and 40 healthy age-matched controls. In LORETA, a voxel-by-voxel analysis between regions from the salience network was performed for both hemispheres, specifically between the anterior cingulate (BA 32 and BA 24) and the sublobar insula (BA 13). Subsequently, a Wilcoxon rank-sum test (the Mann-Whitney U test) was conducted for the equality of medians in the transformation matrix. A comparison was then made between each region of interest as defined by the salience network and the controls. Marked differences were found in the brain regions assessed in patients with EGTCSa treated with valproic acid and carbamazepine compared to the control group; few differences in patients treated with levetiracetam; and no difference was found in the group without treatment compared with those in the control group. These results suggest that ASMs can influence cognitive processes, which provide novel insights toward understanding the neural mechanisms underlying the effects of ASMs administration. Full article

Fungi are an important part of the atmospheric ecosystem yet an underexplored group. Airborne pathogenic fungi are the root cause of hypersensitive and allergenic states highly prevalent in Kuwait. Frequent dust storms in the region carry them further into the urban areas, posing an occupational health hazard. The fungal population associated with the respirable (more than 2.5 µm) and inhalable (2.5 µm and less) fractions of aerosols is negligibly explored and warrants comprehensive profiling to pinpoint tAhe health implications. For the present investigation, aerosol was collected using a high-volume air sampler coupled with a six-stage cascade impactor (Tisch Environmental, Inc) at a rate of 566 L min⁻¹. The samples were lysed, DNA was extracted, and the internal transcribed regions were sequenced through targeted amplicon sequencing. Aspergillus, Penicillium, Alternaria, Cladosporium, Fusarium, Gleotinia and Cryptococcus were recorded in all the size fractions with mean relative abundances (RA%) of 17.5%, 12.9%, 12.9%, 4.85%, 4.08%, 2.77%, and 2.51%, respectively. A weak community structure was associated with each size fraction (ANOSIM r² = 0.11; p > 0.05). The Shannon and Simpson indices also varied among the respirable and inhalable aerosols. About 24 genera were significantly differentially abundant, as described through the Wilcoxon rank sum test (p < 0.05). The fungal microbiome existed as a complex lattice of networks exhibiting both positive and negative correlations and were involved in 428 functions. All the predominant genera were pathogenic, hence, their presence in inhalable fractions raises concerns and poses an occupational exposure risk to both human and non-human biota. Moreover, long-range transport of these fungi to urban locations is undesirable yet plausible. Full article

Galectins are innate immune system regulators associated with disease progression in cancer. This paper aims to investigate the correlation between mutated cancer-critical genes and galectin levels in breast cancer patients to determine whether galectins and genetic profiles can be used as biomarkers for disease and potential therapy targets. Prisma Health Cancer Institute’s Biorepository provided seventy-one breast cancer samples, including all four stages spanning the major molecular subtypes and histologies. Hotspot mutation statuses of cancer-critical genes were determined using multiplex PCR in tumor samples from the same patients by Precision Genetics and the University of South Carolina Functional Genomics Core Facility. The galectin-1, -3, and -9 levels in patients’ sera were analyzed using Enzyme-linked Immunosorbent Assay (ELISA). An analysis was performed using JMP software to compare mean and median serum galectin levels between samples with and without specific cancer-critical genes, including pooled t-test, Wilcoxon Rank Sum Test, ANOVA, and Steel Dwass Test ($\alpha =0.05$). Our analysis indicates that KIT mutations correlate with elevated serum levels of galectin-9 in patients with breast cancer. In patients with Luminal A subtype, FLT3 mutation correlates with lower serum galectin-1 and -9 levels and TP53 mutations correlate with higher serum galectin-3 levels. Patients with invasive ductal carcinoma had significantly higher serum galectin-3 levels than patients with ductal carcinoma in situ. Patients with both TP53 and PIK3CA mutations exhibit elevated serum galectin-3 levels, while patients with one or neither mutation show no significant difference in serum galectin-3 levels. In addition, metastatic breast cancer samples were more likely to have a KIT or PIK3CA mutation compared to primary breast cancer samples. The relationship between genetic mutations and galectin levels has the potential to identify appropriate candidates for combined therapy, targeting genetic mutations and galectins. Further understanding of the effect of genetic mutations and galectin levels on cancer progression and metastasis could aid in the search for biomarkers for breast cancer diagnosis, disease progression, and prognosis. Full article

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant’s performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants’ consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts. Full article

In order to fully achieve energy saving goals, it is necessary to establish a comprehensive evaluation system for carbon reduction in transmission and transformation projects. Subsequently, weights were assigned to these indicators using a combination of the fuzzy analytical hierarchy process (FAHP) and the entropy weight method (EWM) through both subjective and objective methods. Finally, the ultimate weights were obtained by applying the principle of minimum information. During the construction of the evaluation model, the rank–sum ratio (RSR) method was introduced into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for approximating ideal solution ranking. And the Euclidean distance in TOPSIS was replaced with standardized Euclidean distance, effectively avoiding evaluation discrepancies caused by different dimensions. The modified TOPSIS-RSR method was utilized to evaluate and rank power transmission and transformation projects in four regions. By comparing the test values of the two models, the superiority of the enhanced model was confirmed. Furthermore, the GM (1,1) model is used to predict the electricity sales volume of the optimal ranking area. This evaluation model can also be applied to the benefit evaluation of carbon reduction benefits in power transmission and transformation projects in other regions. Full article

The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems. Full article

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran’s I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM_2.5 and PM₁₀ in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O₃. Seasonal peaks for O₃ occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies. Full article

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI’s ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model’s performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise. Full article

Introduction: Although virtual care (VC) has become an integral part of oncology care and healthcare delivery, clinicians’ perspectives on and satisfaction with this modality are not well understood. Methods: Using a National Network Forum framework and expert panel review, we developed a questionnaire to measure oncologists’ satisfaction with VC. The questionnaire was distributed to Canadian oncologists through medical society email lists (n = 1541). We used a 5-point Likert scale to capture their responses, which included strongly disagree (1), disagree (2), undecided (3), agree (4), and strongly agree (5). Results: A total of 61 oncologists and/or oncology trainees, of 768 (7.9%) who opened their email, completed questionnaires between October 2022 and January 2023. Every questionnaire item had a response rate greater than 98%. Seventy-two percent of the respondents were satisfied with VC. Oncologists who were less comfortable with technology were more likely to report lower levels of satisfaction (p < 0.001, Wilcoxon rank-sum). The questionnaire items that received the highest levels of agreement were related to VC reducing costs and improving access for patients and concerns about missing a diagnosis and assessing patients’ functional status. The questionnaire items that received the greatest disagreement were related to VC improving access for patients with language barriers, VC being associated with time-savings for clinicians, improvements in clinical efficacy, and more readily available lab tests. Conclusions: Most of the oncologists surveyed are satisfied with VC; however, there are some concerns with VC that need to be addressed. Future research on optimizing VC should address clinicians’ concerns, in addition to addressing the patient experience. Full article

Building façades represent one of the most critical elements affecting a city’s quality of life, and they impact the country’s economic income by attracting visitors. However, performance data on façades are limited or incomplete, making it challenging for designers to evaluate their effectiveness in energy efficiency, thermal performance, durability, and other key performance metrics. This paper presents a comprehensive framework for evaluating and prioritizing material selection criteria in building cladding, establishing the relationship with available alternatives, and integrating decision-making processes with Building Information Modeling (BIM) to automate the Value Engineering (VE) concept. The material selection criteria from the literature and international standard manual were identified, and their criteria weight was then evaluated using SWARA (stepwise weight assessment ratio analysis). Additionally, WASPAS (weighted aggregated sum product assessment) was utilized to evaluate the alternative cladding materials based on the defined criteria and their associated quality weight (QW). The life cycle cost (LCC) of the alternatives was computed. The VE was computed and then ranked based on the QW and LCC of the alternatives. The procedure was connected to the BIM model to automate the assessment, specifying the necessary parameters and the BIM computation. A case study of an office building façade was conducted to validate the proposed framework. In this study, the significant criteria were durability, wind load resistance, and thermal insulation. This approach enables executives to evaluate cladding selection, ensuring efficient decision-making processes. The proposed method and its results were subjected to expert testing, and the satisfaction rate exceeded 80%, confirming the framework’s reliability in evaluating alternatives. This paper enhances the understanding of material selection methodologies and provides a valuable contribution to the field of construction management. Full article

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO₂, light, pressure, and different types of gases, namely PM₁, PM_2.5, and PM₁₀. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94. Full article

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios. Full article