Classical Statistical Analysis Research Articles

Design, Development and Testing of Electronic Class Attendance using Fingerprint

Electronic Class Attendance System (e-CAS) is an easy way to keep track of attendance of students and lecturers in School. It will cover the requirements of the personnel department in terms of day-to-day monitoring of students' and lecturers activities. Hence, students’ attendance is an important issue every institution must take into consideration to be productive. Hence, this research aims to design, develop and test using fingerprints. The methodologies used were miniature techniques and image processing techniques for design and development. This target was mainly decomposed into image pre-processing, feature extraction and feature match. For each sub-task, some classical and up-to-date methods in literature were analysed. Based on the analysis, an integrated solution for electronic class attendance was developed for demonstration. However, Poison probability process, Biometrics as well as NP-chart of Quality control were adopted for classical statistical analysis of quality and functionality of the e-Class Attendance System (e-CAS). The project on electronic class attendance system was coded by PHP and the hardware was developed with ATMEGA series of microcontrollers. The result when experimented achieved the objective of improving the performance and consistency of the electronic class attendance system based on template matching techniques as well as class attendance for both students and lecturers.

Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights

BackgroundData sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes.MethodsMulticenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve.ResultsThe Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232–0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821–0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522–0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM).ConclusionsThis study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.

Mapping terrestrial mammals in China based on species richness of functional groups

Biodiversity loss is one of the most critical environmental issues globally. Functional diversity and functional groups, extending and expanding the concept of species richness, provide a more comprehensive understanding of biodiversity. By integrating mathematical indices calculation with classical geospatial statistical analysis and mapping techniques, we explored and visually illustrated the spatial patterns and regularities of functional diversity. This approach shed light on the functional diversity of China’s terrestrial mammals from a macroecological perspective. We found that in terms of overall quantity composition, underground-living mammals (UG groups) had the highest proportion (55.1%) in the vertical niche dimension, while nocturnal mammals (NO groups) dominated (58.6%) the in temporal niche dimension. Employing grid cells as fundamental units, in most grid cells, the proportion of UG and NO groups exceeded 50%, consistent with the overall quantity results. Examining the spatial distribution of functional group species richness from different perspectives reveals varying patterns. For instance, standard deviations of vertical and temporal niches showed different spatial clustering traits on either side of the Hu Huanyong Line. In a specific dimension of functional diversity, the numerical differences among functional groups are larger in grasslands with homogenous vegetation, while in forests with diverse vegetation types, these differences are minimized. Additionally, if there is a large disparity in species richness among functional groups in one dimension, there may be a smaller disparity in another. These findings offer a macroecological perspective for biodiversity research, providing insights for interdisciplinary studies in basic ecology, conservation biology, and geography, and offering scientific guidance for mammal conservation.

A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network

Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimensional (2D) convolutional neural network (CNN) machine learning model to identify risk factors from preoperative magnetic resonance imaging. All adults who underwent endoscopic endonasal transsphenoidal surgery at our institution from January 2007 to March 2023 who had accessible preoperative stereotactic magnetic resonance imaging were included. A retrospective classic statistical analysis was performed to identify demographic, clinical, and anatomic risk factors of intraoperative CSF leak. Stereotactic T2-weighted brain magnetic resonance imaging scans were used to train and test a 2D CNN model. Of 220 included patients, 81 (36.8%) experienced intraoperative CSF leak. Among all preoperative variables, visual disturbance was the only statistically significant identified risk factor (P= 0.008). The trained 2D CNN model predicted CSF leak with 92% accuracy and area under receiver operating characteristic curve of 0.90 (sensitivity of 86% and specificity of 93%). Class activation mapping of this model revealed that anatomic regions of CSF flow were most important in predicting CSF leak. Further review of the class activation mapping gradients revealed regions of the diaphragma sellae, clinoid processes, temporal horns, and optic nerves to have anatomic correlation to intraoperative CSF leak risk. Additionally, visual disturbances from anatomic compression of the optic chiasm were the only identified clinical risk factor. Our 2D CNN model can help a treating team to better anticipate and prepare for intraoperative CSF leak.

Using Data Visualization Techniques to Assess Perception of Professionalism, Professional Identity and Development: A Case Study on Closed-Type Questionnaires

Introduction. Professional identity, professionalism, and professional development are concepts that attract research interest over the years. The concepts are complex, multi-faceted, evolving over time, and varying depending on the pre-primary teachers’ (i.e., preschool educators’) and primary school teachers’ context. The conceptualization by teachers themselves is of particular interest. Goal. Explore the use of artificial intelligence techniques and specifically dimensionality reduction methods, in analyzing in-service teachers’ perceptions of the three concepts as captured in closed-type questions with Likert-scale responses. Methods of the Research. Model Likert-scale responses of closed-type questionnaire as multi-dimensional question space. Apply dimensionality reduction techniques to visualize responses in two dimensions. Translate back question distances into theoretical concepts and identify unexplored topics. Results. The dimensionality reduction disclosed novel correlations between the concepts and their components, not previously reported in published literature, where classical statistical analysis was employed. Conclusions. We confirm the applicability and usefulness of the dimensionality reduction method for the analysis of human responses in closed-type questionnaires. Further, we identify through dimensionality reduction two novel research questions that should be further studied in the future.

Long cell-free DNA fragments predict early-progression in patients with advanced or metastatic cancer treated with immune-checkpoint inhibition.

e14550 Background: Immune-checkpoint inhibition (ICI) has significantly advanced cancer treatment, but many patients experience early progression (EP). Establishing reliable and early predictive biomarkers for guiding clinical practice is essential. Analysis of genome-wide cfDNA fragmentation profiles (fragmentome) is a promising non-invasive method for assessing treatment response independently of a specific molecular target, cancer type, and treatment. SChISM (Size CfDNA Immunotherapies Signature Monitoring) is a clinical study that monitors plasmatic cfDNA concentration and quantitative characteristics of the fragmentome in advanced pan-cancer patients, treated with ICI (n = 139). The aim is to predict EP, defined as progression at the first imaging evaluation, using pre-treatment data. Methods: Using BIABooster analysis technology from ADELIS, we assessed the predictive performances of plasmatic fragmentome-derived metrics: concentration, size distribution of first, second peaks and specific size ranges (total p=11 variables). Classical statistical analyses were conducted including hypothesis testing and modeling for association with EP (logistic regression) or progression-free-survival (PFS, Cox proportional hazard regression) in both the univariate and multivariate settings. Predictive analysis of EP was also performed. The dataset was split between a training (n=99) and test (n=40) set, using stratified sampling. Optimal thresholds were determined on the training dataset through receiver-operator characteristics (ROC) curve analysis, and confidence intervals determined using bootstrap resampling. Classification metrics were assessed in both the training and testing set. The entire process was bootstrapped 100 times to assess the robustness of the results. Results: Four out of the eleven features were associated with EP or PFS. Quantity of long fragments over 1650 bp showed the best discriminatory power (median AUC = 0.7, 95% CI: 0.59-0.80) of EP. Long fragments were significantly different between the EP groups (t-test p-val < 0.001) and PFS groups (log-rank p-val < 0.001). Patients with higher quantity of fragments over 1650 bp were likely not to progress before the first evaluation in both the univariate (odd ratio OR = 0.37 (0.22-0.64), p-val < 0.001) and multivariate (OR = 0.45 (0.25-0.81), p-val = 0.008) settings. Accuracy was 0.71 (0.67-0.73), positive predictive value was 0.59 (0.53-0.63) and negative predictive value was 0.78 (0.75-0.79). Conclusions: These findings highlight the association of high-molecular-weight fragments with the early progressors ICI-treated. Thus, the improvement of the knowledge in the long fragments’ biology could potentially help in preventing or discontinuing unnecessary treatments for patients who are unlikely to respond to ICI.

A refined approach for evaluating small datasets via binary classification using machine learning.

Classical statistical analysis of data can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are frequently limited to small datasets, which raises several questions regarding biases and coincidentally positive results. In this study, we present a refined approach for evaluating the performance of a binary classification based on machine learning for small datasets. The approach includes a non-parametric permutation test as a method to quantify the probability of the results generalising to new data. Furthermore, we found that a repeated nested cross-validation is almost free of biases and yields reliable results that are only slightly dependent on chance. Considering the advantages of several evaluation metrics, we suggest a combination of more than one metric to train and evaluate machine learning classifiers. In the specific case that both classes are equally important, the Matthews correlation coefficient exhibits the lowest bias and chance for coincidentally good results. The results indicate that it is essential to avoid several biases when analysing small datasets using machine learning.

Machine Learning in Allergic Contact Dermatitis: Identifying (Dis)similarities between Polysensitized and Monosensitized Patients

Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of this study is to apply machine learning in order to analyze the association between ACD, polysensitization, individual susceptibility, and patients’ characteristics. Methods: Patch test results and demographics from 400 ACD patients (Study protocol Nr. 3765/2022), categorized as polysensitized or monosensitized, were analyzed. Classic statistical analysis and multiple correspondence analysis (MCA) were utilized to explore relationships among variables. Results: The findings revealed significant associations between patient characteristics and ACD patterns, with hand dermatitis showing the strongest correlation. MCA provided insights into the complex interplay of demographic and clinical factors influencing ACD prevalence. Conclusion: Overall, this study highlights the potential of machine learning in unveiling hidden patterns within dermatological data, paving the way for future advancements in the field.

The screening of cannabis addiction using machine learning, MoCA, and anxiety/depression tests

A new cannabis addiction screening approach is developed based on the use of Machine Learning (ML) alongside with psychological and cognitive assessment tests. The Hospital Anxiety and Depression (HAD) test is used to determine the Absence of Anxiety and Depression “No Disorder” or the presence of “Anxiety Only”, “Depression Only”, and both “Anxiety and Depression”. These features are considered to evaluate the psychological effects of cannabis use. In addition to that, the Montreal Cognitive Assessment (MoCA) test is chosen as a feature to assess the cognitive state of the participants. Also, the Age of First Cannabis Use (AFCU) is included as a feature to incorporate participants with no prior cannabis use alongside with those who have used cannabis before. The portion of participants with prior experience consuming cannabis comprises a subset containing passive users and a subset of users that have developed cannabis addiction. This 6-month study was conducted in Marrakech, Morocco in a center affiliated to the National Association of Drug-Risk Reduction of Morocco known as RdR-Maroc with the participation of 146 subjects. The participants in this study are grouped into two groups of 73 participants each. Both case and control groups were clinically assessed by a professional. In this paper, the classical statistical data analysis for the AFCU (M=15.6; SD=2.4), MoCA (M=25.0; SD=2.4), and HAD (58 cases with no disorder, 88 cases with disorder) failed to separate between the two classes. Also, the feature selection for modeling is performed using Spearman correlation, Kendall correlation, and the Odds Ratio (OR). These statistics showed that the selected features were associated to addiction except for “Anxiety Only”. Moreover, ML models such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), kth-Nearest Neighbor (KNN), and Decision Tree (DT) were used to classify participants into two categories (Addict vs. Non-Addict) based on the selected features mentioned earlier. The Receiver Operating Characteristic (ROC) curve is used to overview the quality of the models and showed that they all captured the nonlinearities underlined in the data. On the other hand, the Area Under the Curve (AUC) allowed the ranking of these models and indicated that the SVM model outperformed all other models with an AUC of 0.97. After that, the SVM model was compared to the Cannabis Use Disorder (CUD) based screening method issued by the fifth edition of the Diagnostic and Statistical Manual of mental disorders (DSM-5). The SVM model's accuracy of 96.6 % outperformed the CUD questionnaires accuracy of 87.7 %. The validity of the proposed approach was investigated where the SVM model presented high validity characteristics (sensitivity=1, specificity=0.93) compared to the CUD questionnaire (sensitivity=0.75, specificity=1). Hence, the proposed approach provided satisfactory results compared to current adopted CUD based screening test. Our findings expand current research on the development of simple and powerful tools for cannabis addiction screening with the purpose of early detection in order to establish preventive and therapeutic strategies that could potentially mitigate the prevalence of cannabis addiction.

Evaluation of Artificial Intelligence Anxiety Status of Generation Z Candidate Nurses using Machine Learning in Perspective of Leadership

This study aims to determine the artificial intelligence (AI) anxiety levels of Z-generation candidate nurses and the variables affecting the anxiety levels of artificial intelligence by the machine learning (ML) method. Data were collected from 431 candidate nurses by questionnaire using the convenience sampling method. R open access programming language was used for the statistical analysis of the study and the evaluation of significant variables according to their importance levels. The Boruta algorithm, a machine learning method, was used in the determination of the variables affecting the level of artificial intelligence anxiety according to the degree of importance. The findings showed that the most important variable for students' artificial intelligence anxiety level is age. Moreover, there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation. Furthermore, nearly half of the participants (48.5%) had very low anxiety, 12.8% had low anxiety, 30.2% had medium anxiety, 6.5% had high-level anxiety and 2.1% of them had very high levels of anxiety. With this research, the artificial intelligence anxiety of generation Z was determined by determining the demographic characteristics that are effective in artificial intelligence. We concluded that more sensitive analysis and different results can be obtained when using a machine learning algorithm compared to classical statistical analysis in determining the complex relationships in the data.

Female researchers are under-represented in the Colombian science infrastructure.

Worldwide women have increased their participation in STEM, but we are still far from reaching gender parity. Although progress can be seen at the bachelor's and master's level, career advancement of women in research still faces substantial challenges leading to a 'leaky pipeline' phenomenon (i.e., the continuous decrease of women's participation at advanced career stages). Latin America exhibits encouraging rates of women participation in research, but the panorama varies across countries and stages in the academic ladder. This study focuses on women's participation in research in natural sciences in Colombia and investigates career progression, leadership roles, and funding rates by analyzing data on scholarships, grants, rankings, and academic positions. Overall, we found persistent gender imbalances throughout the research ecosystem that were significant using classical statistical analyses. First, although women constitute >50% graduates from bachelors in natural sciences, <40% of researchers in this field are female. Second, women win <30% of research grants, and in turn, their scientific productivity is 2X lower than that of men. Third, because of the less research funding and output women have, their promotion to senior positions in academic and research rankings is slower. In consequence, only ~25% of senior researchers and full professors are women. Fourth, the proportion of women leading research groups and mentoring young scientist in Colombia is <30%. Our study deepens our understanding of gender gaps in STEM research in Colombia, and provides information to design initiatives that effectively target gender disparities by focusing on key areas of intervention, and then gradually building up, rather than tackling structural inequities all at once.

Classical and Spatial Cluster Analysis of Smuggling in Iranian Provinces

Smuggling harms the economy and development of a country. Various statistical methods can be used to determine the geographical distribution of smuggling within a country. In this regard, clustering techniques (both classical and spatial analysis) have been used to present an overview of smuggling in Iranian provinces from 2009 to 2017. This dataset can be transformed into a country map using the ArcGIS and R software. In this study, classical and spatial statistical analysis and geographically weighted regression are used to detect some important geographical dimensions and critical geographical aspects of smuggling in Iran.

Pengaruh Good Corporate Governance, Konservatisme Akuntansi, dan Ukuran Perusahaan Terhadap Manajemen Laba (Studi Empiris pada Perusahaan Manufaktur yang terdaftar di Bursa Efek Indonesia Tahun 2018-2020)

The purpose of this study is to see an estimate of earnings management practices in financial reporting, by analyzing the influence of good corporate governance, accounting conservatism, and company size on earnings management. The population is manufacturing companies listed on the IDX in 2018 – 2020. The sample was taken using a purposive sampling method and a total of 22 companies were obtained, resulting in a total of 66 units of analysis. The instruments used in testing the SPSS-assisted hypothesis, the hypothesis test using the coefficient of determination, t test and F test, and the method of analysis is multiple linear regression analysis, classical assumption test and descriptive statistical analysis. The results prove that GCG and accounting conservatism partially have a significant effect on earnings management, while company size individually does not have a significant effect on earnings management. The results of the F test show that simultaneously GCG, accounting conservatism and company size have a significant influence on earnings management.

Deep insight : Mathematical modeling and statistical analysis for mango leaf disease classification using advanced deep learning models

This paper presents a comprehensive investigation into the mathematical modeling and statistical analysis of classification of mango leaf diseases employing state-of-the-art Deep Learning models. The literature review underscores the significance of automated disease detection in agriculture, with Convolutional Neural Networks (CNNs) emerging as pivotal tools for image-based tasks. The proposed methodology encompasses meticulous preprocessing, optimal hyperparameter tuning, and fine-tuning of pretrained models (Inception V3, MobileNet V3 Small, MobileNet V3 Large, and ResNet50). Results indicate rapid convergence and outstanding accuracy during initial training, with all models achieving 100% accuracy on both validation and test datasets. K-Fold cross-validation affirms the models’ consistency, with Inception V3 demonstrating leading performance. Detailed analyses, including training and loss graphs and confusion matrices, offer nuanced insights and highlight areas for refinement, particularly in distinguishing Healthy leaf, Gall Midge, and Anthracnose. This research positions the proposed methodology as a promising approach with potential applications in real-world agricultural scenarios, where precise disease detection is critical for effective crop management and optimal yields.

Relationship between soil and species diversity in typical forest stands in Xianrendong, Liaoning Province based on plant-soil feedback.

Exploring the relationship between soil properties and species diversity in typical forest stands in Liaoning Xianrendong National Nature Reserve will help maintain the stability of forest communities in the transition zone between flora in Changbai and North China. Based on the plant-soil feedback theory, community sample data from nine typical forest stands in the study area and experimental test data from 54 soil samples, we selected indexes of soil physical and chemical properties based on the minimum data set (temperature, compactness, capillary pore space, bulk weight, capillary water holding capacity, drainage capacity, soil water storage, conductivity, pH, organic matter, Ca, Fe, K, N and P). We adopt the research method of classical statistical analysis. The soil properties of nine typical stands in Xianrendong National Nature Reserve of Liaoning Province were systematically analyzed. The relationship between soil properties and forest stands' species diversity was quantified using correlation and redundancy analyses. The Pearson correlation analysis results showed significant positive correlations between the Gleason abundance index (arbors) with conductivity, pH, organic matter, Ca, N and P; Pielou's evenness index (arbors) with bulk weight and Fe. Significant negative correlations between the Gleason abundance index (arbors) with capillary pore space, bulk weight, drainage capacity, soil water storage and capillary water holding capacity; Simpson dominance index and Shannon-Wiener diversity index with capillary water holding capacity, drainage capacity and soil water storage; Pielou's evenness index (arbors) with Ca and N. The natural moisture content and clay particles are neutral feedback. The results showed that the feedback mechanism of soil physicochemical properties on stand species diversity was complex, which was conducive to species coexistence and community stability.

Spatio-Temporal Dynamics of Forest Fires in Poland and Consequences for Fire Protection Systems: Seeking a Balance between Efficiency and Costs

An important issue from the forest fire protection system perspective is forecasting fires and maintaining a high readiness of firefighting units at a low cost. In Poland, the level of fire protection is defined by the degree of forest fire risk, based on forecasts made for 1 March to 30 September, i.e., the risk period (the so-called fire season). In this paper, based on classical statistical and spatial analysis, we positively verify the research hypothesis that the fire season in Poland changes in terms of start and end dates depending on the region. We therefore propose a modification to the forecasting period, adapted to local conditions and calculated annually on the basis of the previous period. Then, using network analysis, we negatively verify the hypothesis that the size of the burned area is related to the distance from fire brigades, which proves that the reasons for such a differentiation in the number of fires should be sought elsewhere than in the system used for allocating fire units. On the basis of cluster analysis, attention is drawn to areas where large fires break out more often.

Advancing forensic research: An examination of compositional data analysis with an application on petrol fraud detection

In recent years, numerous studies have examined the chemical compounds of petrol and petrol data for forensic research. Standard quantitative methods often assume that the variables or compounds do not have compositional constraints or are not part of a constrained whole, operating within an Euclidean vector space. However, chemical compounds are typically part of a whole, and the appropriate vector space for their analysis is the simplex. Biased and arbitrary results result when statistical analysis are applied on such data without proper pre-processing of such data. Compositional analysis of data has not yet been considered in forensic science. Therefore, we compare classical statistical analysis as applied in forensic research and the new proposed paradigm of compositional data analysis (CoDa). It is demonstrated how such analysis improves the analysis in petrol and forensic science. Our study shows how principal component analysis (PCA) and classification results are affected by the preprocessing steps performed on the raw data.Our results indicate that results from a log ratio analysis provides a better separation between subgroups of the data and leads to an easier interpretation of the results. In addition, with a compositional analysis a higher classification accuracy is obtained. Even a non-linear classification method - in our case a random forest - was shown to perform poorly when applied without using compositional methods. Moreover, normalization of samples due to laboratory/unit-of-measurement effects is no longer necessary, since the composition of an observation is in compositional thinking equivalent to a multiple of it, because the used (log) ratios on raw and log ratio transformed data are equal.Petrol data from different petrol stations in Brazil are used for the demonstration. This data is highly susceptible to counterfeit petrol. Forensic analysis of its chemical elements requires non-biased statistical analysis designed for compositional data to detect fraud.Based on these results, we recommend the use of compositional data methods for gasoline and petrol chemical element analysis and gasoline product characterization, authentication and fraud detection in forensic sciences.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

BackgroundRaman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing. ResultCombining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate，which indicates that it is an extremely promising auxiliary technology for cancer diagnosis. SignificanceThe proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.

(Invited) Modeling the Charge Transfer Reactions at Li/SEI/Electrolyte Interfaces in Lithium-Ion Batteries

Two kinds of charge transfer reactions are critical for the performance and life of lithium battery: the desired ion transfer reaction occurring during each charge/discharge cycle, , and the undesired electron transfer reactions leading to the parasitic chemical decomposition of the electrolyte and solid electrolyte interphase (SEI) formation/growth. The heterogeneous multi-component nature of SEI dominates its ionic and electronic transport properties and controls these two charge transfer reactions. Density Functional Theory (DFT)-informed multiscale modeling has been providing valuable insights under the scarcity of quantitative experiments. For example, the LiF/Li2CO3 interface was demonstrated to increase the ionic conductivity of mixed SEI nanocomposite by forming an ionic space charge region near the interface and promoting more Li-ion interstitials in Li2CO3, although LiF itself has low Li-ion conducting carriers and conductivity. To form a LiF-rich SEI layer, the electrolyte compositions need to be designed. Since the SEI formation occurs on the charged surface, the electric double layer (EDL) structure near the charged surfaces needs to be incorporated into the modeling. Here interactive classical molecular dynamics (MD), DFT, and data statistical analysis were combined to illustrate the effect of EDL on SEI formation in two essential electrolytes, the carbonate-based electrolyte for Li-ion batteries and the ether-based electrolyte for batteries with Li-metal anodes. It was found the effectiveness of adding fluoroethylene carbonate (FEC) to form the beneficial F-containing SEI component (e.g., LiF) varies with the electrolyte and temperature, because of the interplay of ion-solvent interactions with the surface charge. These integrated modeling provided quantitative guidance for electrolyte/SEI/Li-metal interface design.

Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control

Abstract Soil quality is the key to the success or failure of the gully land reclamation project. Studying the variation characteristics of soil nutrients and evaluating the nutrient classes in the gully after the implementation of gully land reclamation project in the loess hilly and gully region can provide a basis for adjusting the spatial layout of gully crops, rational planning and utilization of land resources, and soil nutrient management. In this study, a typical gully in Yan’an City, Shaanxi Province, China was taken as the research object. Based on the data of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) before 0 year (0a) and after 2, 4, and 8 years (2a, 4a, and 8a) gully remediation. From the spatial distribution characteristics of soil nutrients and its levels, the classical statistical analysis, geostatistical analysis, and matter-element model were used to reveal the variation characteristics of soil nutrients in the gully before and after the gully land reclamation project. The results showed that (1) before remediation, except for AK which reached the level of 1–3, the rest of the nutrients content was generally low and the degree of variation was large, after remediation the degree of variation decreased and the coefficient of variation was between 0.23–0.54, the distribution tended to be uniform. (2) SOM content showed a “decrease–increase” change process, reaching the highest after remediation (8a); TN and AP increased first and then decreased; AK showed a “downward–upward–downward” change process. (3) The soil nutrient grade belongs to the appropriate level. After the implementation of the project, only the gully head is reduced to the poor level. After 4a of remediation, the soil nutrient grade is restored and has the potential to continue to improve. Compared with the slope land remediation, the soil nutrient recovery time of gully land creation is shorter, the spatial layout of crops can be adjusted according to the soil nutrient characteristics of different locations in gully, and different levels and methods of management measures can be implemented to increase food production.