Analysis Model Research Articles

A generation of synthetic samples and artificial outliers via principal component analysis and evaluation of predictive capability in binary classification models

Unbalanced sample groups tend to yield models with a higher prevalence of predominant classes. A sample group with balanced classes contributes to the development of more robust models with improved predictive capability to classify classes equally. In the literature, two methodologies for sample balancing can be found: elimination (undersampling) and synthetic sample generation (oversampling). Undersampling methodologies result in the loss of real samples, while oversampling methods may introduce issues related to adding non-real signals to the original spectra. To overcome these challenges, this paper aimed to utilize Principal Component Analysis (PCA) for the generation of virtual samples (synthetic samples and artificial outliers) to balance data in multivariate classification models. The proposed methodology was applied to data from mid-infrared spectroscopy (MIR) and high-resolution mass spectrometry (HRMS) with Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) models. The constructed models demonstrate that the addition of virtual samples enhances performance parameters (e.g., false negative rate, false positive rate, accuracy, sensitivity, specificity, among others) compared to unbalanced models, while also mitigating overfitting (a problem found in unbalanced models). Performance parameters exhibited a more significant improvement percentage using the non-linear model (SVM) compared to the linear model (PLS-DA). Furthermore, the created virtual spectra do not introduce new signals, i.e., original, and virtual spectra exhibit a similar spectral profile, differing only in the intensity levels. Finally, all models demonstrated good predictive capability according to permutation testing for the binary model developed in this work, limiting the rate of class permutation retention (between 40 % and 60 % of the y-vector remained in the original class). All created models exhibited accuracy values higher than the accuracy distribution of models with permuted classes for the test group.

Neoadjuvant chemotherapy as an indication to preserve nipple areolar complex in advanced breast cancer.

e12655 Background: Nipple areolar skin sparing mastectomy (NASSM) has similar oncologic and surgical safety compared to skin sparing mastectomy (SSM) and conventional mastectomy. It provides a superior cosmetic outcome, patient satisfaction and quality of life. With the increasing rate of pathological response rate with neoadjuvant chemotherapy (NAT), nipple preservation can be achieved. The aim of our study is to evaluate whether NASSM is an appropriate surgical procedure for patients in whom there is radiological evidence of extension or involvement of nipple areolar complex (NAC) prior to NAT. Methods: Patients with advanced breast cancer received neoadjuvant chemotherapy and NASSM or SSM between June 2006 and December 2021 at Asan Medical Center were retrospectively reviewed. We excluded patients with metastatic breast cancer on presentation, bilateral cancer and inflammatory breast cancer. Kaplan-Meier survival analysis and Cox proportional hazard models were applied to determine clinicopathological and radiological factors to determine NAC preservation. Our primary objective to assess tumor cells persistence in the NAC after radiological complete response or resolution of extension to NAC. Results: Total of 1,105 patients included. 71.7% of patients underwent NASSM and 17.2% SSM as a final operation. 144 patients (13%) had a change in the pre- NAT type of surgery decision. 83(7.5%) patients had nipple involvement on final pathology. Out of 312 patients who underwent SSM, only 76 patients had nipple involvement on final pathology When comparing patients who had nipple involvement to those who didn’t on final pathology, clinical T & N stage, histologic & nuclear grade, breast cancer subtype, presence of multifocality/multicentric disease on MRI pre or post NAT, distance of mass to NAC and mass size pre and post NAT, the presence of non mass extension to NAC post NAT, NAC thickening on any image pre and post NAT and presentation symptoms of nipple retraction and skin changes were statistically significant. A multivariate regression analysis was performed to compare all the above factors and showed that breast cancer subtype, tumor nipple distance on MRI pre NAT, nipple areolar thickening on MRI pre NAT, clinical response and nipple retraction on MMG post NAT were statistically significant and indicators of nipple involvement on final pathology. Only 8 patients developed NAC recurrence in the NASSM group. Conclusions: NASSM is a safe procedure in patients who had tumor extension to NAC prior to NAT and showed improvement post NAT as evident on MRI. Further prospective studies are needed to establish and criteria to preserve NAC in this subgroup of patients.

Prognosis of deep margin excisions within or beyond subcutaneous fat for invasive acral melanoma of the sole: A multi-institutional retrospective study.

9582 Background: There is scant evidence concerning the optimal deep margins for wide excision (WE) of primary tumors in melanoma surgery. Despite the importance of preserving plantar subcutaneous fat in the sole for cushioning function, no study data exists on the optimal depth margins for acral melanoma of the sole (sole AM). Thus, we aimed to compare the prognosis associated with different deep margin excisions for sole AM. Methods: We conducted a retrospective review of clinical records from patients with invasive resectable sole AM who underwent WE of the primary tumor across 41 Japanese institutions. The prognosis was compared between two groups based on different deep margin excisions: those with tumors excised within (S-group) or beyond (D-group) the subcutaneous fat. Kaplan—Meier analysis and multivariable Cox proportional hazard models were used to estimate the survival probabilities. Propensity-score matching (PSM) was used to adjust for numerical differences in baseline characteristics. Results: This study included a total of 425 patients (median age 73 years) with invasive sole AM, who underwent WE based on the peripheral surgical margins recommended in the National Comprehensive Cancer Network guidelines. The median follow-up period was 48 months. The S-group included 263 patients and the D-group 162 patients. The baseline characteristics significantly differed between the two groups in terms of Breslow thickness ( P < 0.01), presence of ulcer ( P < 0.01), and presence of in-transit, satellite, and regional lymph node metastasis ( P < 0.01). The other baseline characteristics were similar. Although local recurrence-free survival (LRFS) did not significantly differ between groups, the S-group showed significantly longer disease-free (DFS) and overall survival (OS) than the D-group (5-year LRFS, 96% vs. 90%; P = 0.08; 5-year DFS, 70% vs. 41%; P < 0.01; 5-year OS, 84% vs. 69%; P < 0.01). In contrast, multivariable Cox proportional hazard models showed no significant differences in LRFS, DFS, and OS between the two groups (LRFS, hazard ratio [HR], 1.87, P = 0.18; DFS, HR, 1.05, P = 0.77; OS, HR, 1.08, P = 0.72). After PSM, the groups were balanced at a 1:1 ratio (128 patients in each group) and no statistical significance was found in LRFS, DFS, or OS between the adjusted groups (5-year LRFS, 97 % vs. 91 %; P = 0.40; 5-year DFS, 65 % vs. 48 %; P = 0.053; 5-year OS, 80 % vs. 73%; P = 0.18). Subgroup analysis of stage I, II, and III also indicated no statistical significance in LRFS, DFS, and OS between the two groups (stage I: LRFS, P = 0.11; DFS, P = 0.65; OS, P = 0.26; stage II: LRFS, P = 0.37; DFS, P = 0.06; OS, P = 0.85; stage III: LRFS, P = 0.29; DFS, P = 0.67; OS, P = 0.85). Conclusions: In sole AM, WE with excessive deep margins beyond the subcutaneous fat did not improve prognosis. Excision of the primary tumor within the subcutaneous fat may be an optimal deep margin for sole AM.

Determination of Growth Centers in Regional Development in Wonogiri Regency

Abstract One of the reasons for the social inequality that occurs in various regions is the difference in the pace of the economy and the uneven development across regions. In general, the problems that exist in areas experiencing inequality are high unemployment rates, lack of community facilities and infrastructure, and even inequality in the quality of natural resources and human resources. This problem causes underdeveloped areas to experience poverty. For this reason, it is necessary to determine distributed growth centers in Wonogiri Regency to minimize the gaps that occur. The aim of this research is to determine which sub-districts are suitable as locations for growth centers in Wonogiri Regency. The analysis used is scalogram analysis and gravity model analysis. Scalogram analysis is used to analyze the location of growth centers in terms of the number of facilities and infrastructure available, while gravity model analysis is used to analyze the determination of the hinterland of each growth center. The results of the analysis show that of the 25 sub-districts in Wonogiri Regency, 5 sub-districts have been identified as centers of growth, namely Wonogiri Sub-district, Jatisrono Sub-district, Baturetno Sub-district, Purwantoro Sub-district and Pracimantoro Sub-district.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity.

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Impact of CYP2D6 polymorphisms on adverse events in patients with breast cancer treated with tamoxifen: Insights from an early dose escalation trial in CYP2D6 poor metabolizers.

547 Background: Tamoxifen is widely used in treating hormone receptor-positive breast cancer. It is primarily metabolized by the CYP2D6 enzyme to yield active metabolites. However, CYP2D6 polymorphisms have been associated with a differential response to tamoxifen. In a clinical trial involving an early dose escalation of tamoxifen in poor metabolizers, we found endoxifen concentration levels similar to those of patients with extensive phenotype, and no long-term survival differences among CYP2D6 phenotypes. However, the impact of that dose escalation on side effect incidence was not addressed. Methods: In this study, we analyzed the incidence of specific side effects - osteoarticular pain, hot flashes, asthenia, and uterine alterations - in 86 breast cancer patients, differentiated by their CYP2D6 phenotype estimated according to the Clinical Pharmacogenetics Implementation Consortium. In poor metabolizers, the tamoxifen standard dose of 20 mg/day was escalated to 40 mg/day and 60 mg/day for 4 months each, and then treated with 20 mg/day until completing the treatment. Kaplan-Meier analysis and Cox proportional hazards regression model were employed to assess the relationship between the CYP2D6 phenotype and the incidence of these side effects. Results: The median follow-up time was 139.5 months. The analyses revealed that only uterine changes showed significant differences between CYP2D6 phenotypes, with a lower incidence in extensive metabolizers (P-value < 0.001 in Kaplan-Meier; HR in Cox of 0.195, 95% CI 0.073 - 0.521, P = 0.001). This finding persisted after adjustment for the covariates tumor grade, size, nodal status, chemotherapy, and radiotherapy in multivariate Cox regression (HR 0.098, 95% CI 0.020 - 0.466, P = 0.004). Selection of a subgroup through Propensity Score Matching (21 vs 21) corroborated these results. The spectrum of uterine changes and pathologies included endometrial hyperplasia, polyps, adenocarcinoma, myometrial or endometrial thickening, and the presence of multiple myomas. Conclusions: Our study indicates that an early increase in tamoxifen dose in poor metabolizer patients according to CYP2D6 polymorphisms may significantly affect the incidence of uterine changes, but not the incidence of osteoarticular pain, hot flashes, or asthenia. Clinical trial information: 2007-002942-40/ES .

Biomarker analysis and updated clinical outcomes: Neoadjuvant systemic treatment (NST) with nivolumab (nivo) and relatlimab (rela) in surgically resectable melanoma.

9587 Background: NST with immune checkpoint blockade (ICB) improves clinical outcomes over upfront surgery followed by adjuvant treatment (tx) in patients (pts) with stage III, surgically resectable melanoma. The NST platform also provides an opportunity for deep translational analysis to identify baseline (BL) biomarkers predicting pathologic response and to gain insights into resistance. BL biomarkers, including gene expression signatures (GES) such as IFN-γ/ tumor mutational burden, tumor infiltrating lymphocytes, and presence of tertiary lymphoid structures, have been associated with pathologic response to NST with anti-PD1 and/or anti-CTLA4 antibodies. These studies have not evaluated the newest ICB combination, nivo (anti-PD1) + rela (anti-LAG3), which achieved a major pathologic response rate (MPR) (≤10% viable tumor) of 63% in a Ph II study (NCT02519322). Here we report GES analysis from BL and longitudinal tumor tissues obtained on this study and correlations with outcomes. Methods: Longitudinal tissue was collected from 30 pts treated on the clinical trial and available RNA analyzed using the NanoString nCounter PanCancer IO360 panel. Clinical outcomes, including recurrence and event-free survival, were also updated with longer follow-up. Pts were grouped into MPR and non- major pathologic responders (non MPR). Normalization, differential expression / GES evaluation was performed using nSolver Advanced Analysis software. Differential expression is fit on a per signatures basis using a linear model for analysis. P-values were adjusted within each analysis and on the grouping variable level difference t-test using the Benjamini and Yekutieli False Discovery Rate adjustment to account for correlations amongst the tests. BL analysis was grouped based on pathologic response, MPR vs non-MPR. Results: Of the 30 pts (median follow up 44 months), 53 RNA samples from 26 patients (9 NR and 17 R) were included in the full analysis. Immune GES with significantly higher (adjusted p< 0.05) differences at BL in MPR vs non-MPR included: B cells, CD45, CD-8+ T cells, TIGIT, IDO1, and IFN-γ. Significantly higher B7-H3 GES at BL was associated with non MPR. Additional analyses (i.e., cell population changes over time) and association with event outcomes are underway. Conclusions: To our knowledge, this is the first longitudinal RNA analysis reported from pts treated with nivo+rela. BL immune features associated with major pathologic response include higher GES of B cells, CD45+ cells, CD8 T cells, and increased expression of TIGIT, IDO and IFN-y. Additionally, higher B7-H3 in non MPR pts may indicate a clinically actionable strategy for further evaluation. Ongoing analyses of longitudinal samples and correlations with clinical outcomes are underway and will be presented. Clinical trial information: NCT02519322 .

Immunotherapy plus chemotherapy versus chemotherapy alone in perioperative treatment for locally advanced gastric cancer: A systematic review and meta-analysis.

e16083 Background: The standard care for locally advanced gastric (LAGC) or gastroesophageal junction (GEJ) cancer is perioperative chemotherapy (CTX) with surgery. The therapeutic landscape has evolved with PD-1 and PD-L1 inhibitors, showing promise in advanced or metastatic disease. Investigating their role in locally advanced disease is an active research area. In response, we conducted a meta-analysis to evaluate the effectiveness of perioperative immunotherapy with CTX in LAGC/GEJ patients. Methods: We systematically searched PubMed, Embase, and Cochrane for randomized controlled trials (RCTs) comparing perioperative PD-1 and PD-L1 inhibitors in combination with CTX versus CTX alone for LAGC/GEJ. Evaluated outcomes included pathological complete response (pCR), R0 resection rate, downstaging rate, and treatment-related adverse events (TRAE) with a grade ≥ 3. Heterogeneity was assessed using I2 statistics, employing a random-effects model for analysis. Statistical analyses were performed using R software version 4.3.2. Results: Among 2,452 patients from six RCTs, 1,228 (50.08%) received immunotherapy. Combining immunotherapy with CTX showed substantial improvements in pCR (RR 2.83; 95% CI 1.78-4.50; p < 0.001). This effect persisted in the FLOT CTX subgroup (RR 2.15; 95% CI 1.37-3.38; p < 0.001). Additionally, downstaging outcomes were superior in the immunotherapy group, with ypT0 (RR 1.96; 95% CI 1.50-2.55; p < 0.001) and ypN0 (RR 1.36; 95% CI 1.19-1.54; p < 0.001). However, no significant differences were observed in the R0 resection rate (RR 1.01; 95% CI 0.98-1.04; p = 0.603) and TRAE grade 3 or higher (RR 1.05; 95% CI 0.98-1.13; p = 0.177). Conclusions: These findings emphasize the effectiveness of combining PD-1 and PD-L1 inhibitors with CTX, indicating an improved pathological response compared to CTX alone. As expected, these positive outcomes persisted in the FLOT regimen. Our results provide evidence supporting the combination of immunotherapy and CTX as a perioperative treatment option for LAGC/GEJ patients.

New Mesoscale Phase Field Model for Analysis of FRP-to-Concrete Bonded Joints

New Mesoscale Phase Field Model for Analysis of FRP-to-Concrete Bonded Joints

Temporal fiber-optic fluorescence study of FAD-NADH interactions in phantoms for potential applications in rapid liquid optical biopsy procedures

Body fluids carry several biomarkers and genetic material that enable the detection of carcinogenesis and tumor metastasis. Owing to this, liquid biopsy is gaining importance and entering clinical practice as an adjunct modality to the current standard of practice in cancer detection. Liquid biopsy involving specimens such as urine is non-invasive and has biomarkers reflecting the tumor microenvironment. We investigate the normal and altered levels of cellular metabolic by-products in liquid phantoms and demonstrate their photon-induced excited-state interaction behaviour for potential diagnostic applications. Short-span temporal analysis of metabolic by-products FAD and NADH in liquid phantoms with fiber optic fluorescence spectroscopy is carried out. The mixed liquid phantom of FAD and NADH, showed excited state interactions that manifested as simultaneous inverse variation in the fluorescence emission from FAD and NADH in varying pH environments. Linear discriminant analysis of the time-varying integrated fluorescence intensity resulted in a 100% accuracy in classifying liquid samples between high and low metabolite concentrations. An extension of application of the experiment model for analysis of body fluids such as urine is also discussed.

Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks

The catastrophic consequences of lithium-ion battery (LIB) accidents have attracted high social attention. Accordingly, risk analysis is indispensable for risk prevention and control of LIBs. However, it is difficult to establish a recognized physics-informed risk analysis model due to the complex material characteristics and aging mechanisms of LIBs. Meanwhile, data-driven approach requires historical information of LIBs and does not rely on knowledge of the internal mechanisms of LIBs. This study proposes a method integrating the physics-informed Bayesian network (BN) (mapping from fault tree) and data-driven BN (learning from data) to conduct risk analysis of LIBs. First, we establish physics-informed and data-driven BNs. Subsequently, we bridge physics-informed and data-driven BNs to establish a Bayesian network for risk analysis of LIB accidents. Second, we set up safety barriers in the system, including detectors, emergency response, and firefighting facilities. Third, we evaluate the effectiveness of safety barriers. We validate the proposed model using data from LIBs in air transportation. Our results indicate that safety barriers can reduce the accidental risk of LIBs. Eventually, we propose suggestions for the risk control of LIBs in air transportation. This study can provide theoretical basis for the risk prevention and control of LIB accidents.

The study of multi-discs hydraulic brake pressure dynamic characteristics

Abstract The pressure dynamic characteristics of hydraulic brakes directly impact the efficiency of the brake control system and aircraft stop distance. A simulation model of hydraulic brake is established with Simulink software. The simulation results are compared with the test results, which proves the reliability of the analysis model. The simulation results visually give the microscopic process of pressure rise and fall in the brake assembly. The study of dynamic characteristics is performed with the correlated simulation model to figure out the reasons for the slow rate of pressure rise and fall, for which suggestions are proposed for future system design improvements.

Efficiency of local public spending in Cameroon: Does population size matter?

AbstractPromoting decentralization is currently high on the global agenda. To date, the existing literature in the African context, and more specifically in Cameroon, has not established a link between the size of local authorities and the efficiency of their infrastructure spending. This study therefore attempts to fill this gap in the literature by empirically examining the effect of population size on the efficiency of local public spending in Cameroon. Using the two‐stage Data Envelopment Analysis model on a sample of 100 communes for the period 2017–2020 to estimate composite efficiency scores and the censored Tobit model to determine the effect of population size on the efficiency of local public spending, the results show that population size and density positively and very significantly affect the efficiency of local public spending. In light of these results, we recommend that the state make population size the main allocation key for transfers and subsidies to local authorities and that communes organize themselves into inter‐municipalities to benefit from economies of scale and curb spillover effects.

Analysis of Influence of Different Running Speeds on Lubrication Performance of Plain Bearings

Abstract Given the influence of the running speed of rotating machinery on the lubrication performance of sliding bearings, based on the hydrodynamic lubrication theory, a numerical analysis model of hydrodynamic lubrication of rolling bearings is established and solved by the finite difference method. The lubrication characteristics of rolling bearings, such as oil film pressure, oil film temperature, oil film thickness, and temperature rise at different running speeds (10 r/min, 30 r/min, 120 r/min, 160 r/min), are obtained. The results show that the sliding bearing shows different trends at different rotational speeds, the maximum oil film temperature is 70.49℃, the film thickness ratio corresponding to the minimum oil film thickness is much more than 3, and the maximum oil film pressure and temperature rise meet the allowable requirements.

Economic Analysis of Solid Oxide Fuel Cell Systems Utilizing Natural Gas as Fuel

Solid oxide fuel cell power generation systems are devices that utilize solid electrolytes to transfer ions for electrochemical energy conversion. A wide range of gases can be used as fuel gas, including hydrogen, natural gas, and carbon monoxide. Considering the high cost of pure hydrogen, hydrogen production from natural gas reforming has become a hot research area. In this study, the 4F-LCA method was employed to construct an evaluation framework, with a particular emphasis on the cost analysis of solid oxide fuel cell power generation systems, and uses a bottom-up approach to build a system economic analysis model to visualize the major costs involved in the system. An economic benefit analysis and sensitivity analysis were carried out for the 2 kW natural gas solid oxide fuel cell as a case by taking the financial net present value (NPV), internal rate of return (IRR) and payback period into account. In this study, the investment cost and payback period of a 2 kW solid oxide fuel cell system are obtained, which can provide a reference for the project construction and operation of solid oxide fuel cell systems.

Health-related quality of life (HRQoL) outcomes from the randomized, double-blind phase 3 KEYNOTE-671 study of perioperative pembrolizumab for early-stage non-small-cell lung cancer (NSCLC).

8012 Background: Neoadjuvant pembrolizumab + cisplatin-based chemotherapy (neoadj pembro + chemo), resection, and adjuvant (adj) pembro (pembro arm; n=397) significantly improved EFS, OS, pCR, and mPR and had an expected safety profile versus neoadj placebo (pbo) + chemo, resection, and adj pbo (pbo arm; n=400) in patients (pts) with resectable stage II, IIIA, or IIIB (N2) NSCLC. We present prespecified pt-reported outcome (PRO) endpoints from KEYNOTE-671. Methods: Pts completed EORTC QLQ-C30 and QLQ-LC13 questionnaires at baseline (BL), the last scheduled presurgery visit, adj cycles 1-4, 7, 10, and 13, and each post treatment visit. A constrained longitudinal data analysis model was used to estimate least squares mean (LSM) score changes from BL to neoadj wk 11 and adj wk 10 (latest time of ≥60% completion and ≥80% compliance) in QLQ-C30 global health status (GHS)/QoL, physical functioning (PF), role functioning (RF), and dyspnea and QLC-LC13 cough and chest pain in all treated pts who completed ≥1 PRO assessment. Data are from interim analysis 2 (10 Jul 2023 cutoff). Results: Across arms, questionnaire completion was ≥87% at neoadj wk 11 and ≥62% at adj wk 10; compliance was ≥87% and ≥92%, respectively. There were no differences in LSM change from BL in the neoadj or adj phase for any PRO score (Table). Conclusions: Adding perioperative pembro maintained HRQoL in both the neoadj and adj settings versus neoadj chemo and surgery alone in pts with resectable early-stage NSCLC. Together with the significant efficacy improvements and absence of new safety signals, HRQoL data support the perioperative pembro regimen as a new standard of care. Clinical trial information: NCT03425643 . [Table: see text]

Optimizing oil-source correlation analysis using support vector machines and sensory attention networks

Oil source correlation can be used to identify the origin of crude oil by linking crude oil to source rocks; however, the manual methods, which are limited by the sample or parameter quantity or imbalanced datasets, are facing uncertainties. Although the existing multivariate statistical techniques can alleviate this problem, they are facing difficulties in processing imbalanced datasets and quantifying source beds. Therefore, a novel oil-source correlation analysis model called SVM-SelectKBest combining a support vector machine (SVM) with a feature selection algorithm to mitigate the common issue of dataset imbalance in oil-source correlations is proposed in this paper. The SVM-SelectKBest offers advantages over normal SVM by dynamically selecting the most relevant features and fine-tuning model parameters to achieve higher accuracy and better generalizability in complex datasets. SVM compensates for class imbalances by heavily penalizing the misclassification of the minority class, and SelectKBest streamlines the feature set to enhance SVM's effectiveness on critical variables. Furthermore, a shallow neural network (SensoryAttentionNet) is introduced into the proposed model to address the issue of quantifying the source bed proportions in crude oil. The result show that SVM-SelectKBest has better performance in identifying key geochemical parameters and discriminating oil source correlation, its accuracy in unbalanced datasets is improved by near 40% compared to SVM. The model obtains 25 key geochemical parameters such as C17 n-heptadecane, Pr pristane, and C18 n-octadecane, it achieves F1 scores of 1.0 on the training, validation, and test sets. SensoryAttentionNet also performs robustly, with a low variance of 0.05 between its predicted and actual values. All the results indicate the effectiveness of the proposed method in dealing with the imbalance problem in oil-source source correlation datasets and in determining the proportional contribution of source beds in crude oil.

Sentiment analysis of student feedback using attention-based RNN and transformer embedding

Sentiment analysis systems aim to assess people’s opinions across various domains by collecting and categorizing feedback and reviews. In our study, researchers put forward a sentiment analysis system that leverages three distinct embedding techniques: automatic, global vectors (GloVe) for word representation, and bidirectional encoder representations from transformers (BERT). This system features an attention layer, with the best model chosen through rigorous comparisons. In developing the sentiment analysis model, we employed a hybrid dataset comprising students’ feedback and comments. This dataset comprises 3,820 comments, including 2,773 from formal evaluations and 1,047 generated by ChatGPT and prompting engineering. Our main motivation for integrating generative AI was to balance both positive and negative comments. We also explored recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM), with and without pre-trained GloVe embedding. These techniques produced F-scores ranging from 67% to 69%. On the other hand, the sentiment model based on BERT, particularly its KERAS implementation, achieved higher F-scores ranging from 83% to 87%. The Bi-LSTM architecture outperformed other models and the inclusion of an attention layer further enhanced the performance, resulting in F-scores of 89% and 88% from the Bi-LSTM-BERT sentiment models, respectively.

The statistical analysis based on GTD terrorist incident record data

Terrorism refers to the claims and acts that create social panic, endanger public security, infringe upon persons and property, or threaten state organs and organizations to achieve political and ideological goals through violence, sabotage, intimidation and other means. According to the survey, 92 % of the world's countries have been subjected to terrorist attacks to varying degrees, involving South America, the Middle East and North Africa. Since 1970, there have been about 200,000 terrorist incidents worldwide. Global terrorist attacks have a wide range of targets, involving nearly 20 groups, resulting in up to 280,000 deaths and 360,000 injuries. Based on the GTD database, this paper selects the relevant data from 1998 to 2017, and uses the factor analysis model to establish the evaluation index system of terrorist attack harm from the perspective of terrorist attack harm. At the same time, GIS is used for quantitative analysis of the data, and time series model is used for correlation prediction. The results show that the comprehensive evaluation model of terrorist attacks has objectively and accurately evaluated the terrorist attacks in the past 20 years, among which 9/11 incident has the highest degree of harm, followed by the Salam Embassy bombing. The ranking of the harmfulness of the incident is basically consistent with the reports of mainstream media at home and abroad. From the perspective of the global spatial scope of terrorist attacks, terrorist attacks are shifting from North Africa and the Arabian Peninsula to the eastern front, and the Middle East has continued to gather, and the situation of terrorist attacks in the Middle East is not optimistic. At present and for some time to come, international terrorism will remain at a high level, and terrorist attacks will remain concentrated in the Middle East and the Arabian Peninsula.

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN

Real-time hybrid facial expression analysis model using quantum distance-based classifier and classical artificial neural networks: QUADCANN