Optimal Model Research Articles

A new method of rapid-intelligent assessment for the dynamic service performance in high-speed railway subgrade

ABSTRACT There is a lack of a rapid, advanced, and comprehensive method for assessing the dynamic service performance in high-speed railway subgrade, which leads to an inability to pre-assess the health state of the subgrades in advance. In this paper, an innovative method of rapid-intelligent assessment for the dynamic service performance (RADSP) in high-speed railway subgrade was proposed. Firstly, a large-scale exciter was used to simulate the train loading effect, which could rapidly obtain the assessment metrics for subgrade dynamic service performance, including subgrade dynamic response (SDR) and subgrade cumulative deformation (SCD), where the SDR includes dynamic stress and acceleration. Then, to explore the future development trend of SCD during operation, an intelligent and advanced prediction of SCD was conducted using the optimal machine learning (ML) prediction model. Finally, by combining the results of the SDR and SCD assessments to achieve the RADSP in high-speed railway subgrade. Moreover, the method was applied to assess the dynamic service performance of diatomaceous earth subgrade structures with four different waterproofing structure layers (WSL): the middle of subgrade surface layer (WSL-1), the bottom of subgrade surface layer (WSL-2), the middle of subgrade bottom layer (WSL-3), and the bottom of subgrade bottom layer (WSL-4). The results indicate that: 1) During the excitation experiments, the maximum values for SDR (dynamic stress, acceleration) and SCD exceed the respective limits of 50 kPa, 10 m/s2, and 15 mm under WSL-1 and WSL-4. 2) The ensemble model, AdaBoost-GPR, is selected as the optimal ML model to predict the SCD values. Notably, the prediction SCD value exceeds the 15 mm limit under WSL-3, indicating that the SCD during future operation does not meet the requirements for pre-assessment. 3) Under WSL-2, both SDR and SCD assessments satisfy the limit value requirements, indicating that the dynamic service performance is better. Based on the proposed method, it is found that when designing waterproof-drainage subgrade structures in diatomaceous earth areas, it is recommended to position the WSL at the bottom of subgrade surface layer. The research findings serve as a crucial reference and valuable lesson for the health monitoring of high-speed railway subgrades.

Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients

ABSTRACT Objective Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients. Methods A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated. Results Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs. Conclusion The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

Bioinformatics analysis of PANoptosis regulators in the diagnosis and subtyping of steroid-induced osteonecrosis of the femoral head.

In this study, we aimed to investigate the involvement of PANoptosis, a form of regulated cell death, in the development of steroid-induced osteonecrosis of the femoral head (SONFH). The underlying pathogenesis of PANoptosis in SONFH remains unclear. To address this, we employed bioinformatics approaches to analyze the key genes associated with PANoptosis. Our analysis was based on the GSE123568 dataset, allowing us to investigate both the expression profiles of PANoptosis-related genes (PRGs) and the immune profiles in SONFHallowing us to investigate the expression profiles of PRGs as well as the immune profiles in SONFH. We conducted cluster classification based on PRGs and assessed immune cell infiltration. Additionally, we used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific hub genes. Furthermore, we developed an optimal machine learning model to identify the key predictive genes responsible for SONFH progression. We also constructed a nomogram model with high predictive accuracy for assessing risk factors in SONFH patients, and validated the model using external data (area under the curve; AUC = 1.000). Furthermore, we identified potential drug targets for SONFH through the Coremine medical database. Using the optimal machine learning model, we found that 2 PRGs, CASP1 and MLKL, were significantly correlated with the key predictive genes and exhibited higher expression levels in SONFH. Our analysis revealed the existence of 2 distinct PANoptosis molecular subtypes (C1 and C2) within SONFH. Importantly, we observed significant variations in the distribution of immune cells across these subtypes, with C2 displaying higher levels of immune cell infiltration. Gene set variation analysis indicated that C2 was closely associated with multiple immune responses. In conclusion, our study sheds light on the intricate relationship between PANoptosis and SONFH. We successfully developed a risk predictive model for SONFH patients and different SONFH subtypes. These findings enhance our understanding of the pathogenesis of SONFH and offer potential insights into therapeutic strategies.

Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multicenter study comprising 404 individuals. In the application phase, we evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. Through 4D-DIA proteomic analysis, we identified 7 potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of 3 EVs surface proteins, FIBG, PDGF-β and TGF-β, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an AUC of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-β, and TGF-β as novel diagnostic biomarkers for CRC. And the development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach

BackgroundThe role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2–4 gliomas.MethodsThis retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.ResultsWe comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients’ age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].ConclusionsImaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Research on a Calculation Model of Ankle-Joint-Torque-Based sEMG

The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle.

A Two‐Stage Multi‐Objective Optimal Scheduling Model for Community Integrated Energy System

Community integrated energy system coupled with renewable energy generation provides an effective solution to improve economy and reduce carbon emissions. This paper establishes a two‐stage multi‐objective optimal scheduling model for community integrated energy system. During the day‐ahead scheduling stage, a comprehensive customer dissatisfaction model based on Kano model is established, and the model takes total operating cost, comprehensive customer dissatisfaction, and carbon emissions as multi‐objectives. The Non‐dominate Sorting Genetic Algorithmic‐II (NSGA‐II) and the CRITIC‐TOPSIS evaluation model are used to develop a day‐ahead scheduling scheme. On the premise of ensuring customer dissatisfaction, the intra‐day scheduling stage evens out the uncertainty of wind power and photovoltaic power through rolling optimization and improves the reliability of system operation. Simulation results show that the proposed model reduces the total operating cost and carbon emissions by 8.1% and 12.8%, respectively, which validates the effectiveness of the proposed model. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Abstract PO1-15-05: Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer

Abstract Background: Neoadjuvant pertuzumab and trastuzumab in combination with chemotherapy can achieve pathological complete response (pCR, defined as ypT0/is ypN0) in approximately 40–60% of patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Patients who achieve pCR have significantly improved survival. Therefore, the prediction of pCR is crucial for optimizing neoadjuvant therapy. Methods: To identify a gene expression signature that could predict pCR in patients with HER2+ BC, we used the nCounter Breast Cancer 360TM V2 panel to quantify the expression of 758 genes in 104 HER2+ BC samples from patients who received neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab (TCHP) chemotherapy and subsequent curative breast surgery. Data normalization, differentially expressed gene (DEG) analysis, and gene set enrichment analysis (GSEA) were performed using the NanoTube Bioconductor package. Using the elastic net logistic regression model with optimal hyperparameters determined by 5-fold cross-validation (5-CV), we developed a chemosensitivity score based on the expression of significant DEGs (genes with absolute log2-fold-change ≥0.5 and q-value < 0.1). We tested its predictive value for pCR using multivariable logistic regression analysis. Results: The median age at diagnosis was 49.5 years. The clinical stage was II in 53 (51%) patients and III in 51 (49%) patients at diagnosis. Ninety (86.5%) patients had a HER2 immunohistochemistry (IHC) score of 3+, while the remaining 14 (13.5%) patients had HER2 IHC 2+ but HER2 amplification detected by in situ hybridization. Pathological evaluation of surgical specimens revealed that 55 (52.9%) patients achieved pCR. Menopausal status (p = 0.01), PAM50 intrinsic subtype (p = 0.002), hormone receptor (HR) status (p < 0.001), and HER2 IHC score (p = 0.019) were significantly correlated with the pCR rate. DEG analysis using the PAM50 intrinsic subtype, HR status, and HER2 IHC score as covariates revealed 6 downregulated DEGs and 33 upregulated DEGs in patients who achieved pCR compared to patients who did not (Table 1). The 39 DEGs were significantly enriched in nine preselected MSigDB hallmark gene sets representing immunological processes (p < 0.001). The optimal elastic net logistic regression model containing 39 DEGs achieved the mean 5-CV area under the receiver operating characteristic curve of 0.81. The chemosensitivity score calculated using the optimal model parameters was significantly correlated with pCR after adjustment for the PAM50 intrinsic subtype, HR status, and HER2 IHC score (p < 0.001). GSEA revealed eight hallmark gene sets that were significantly up- or downregulated in samples with pCR, including epithelial-mesenchymal transition (normalized enrichment score [NES] = -2.16; q < 0.001), late estrogen response (NES = -2.01; q = 0.004), early estrogen response (NES = -1.972; q = 0.005), and inflammatory response (NES = 1.53; q = 0.06). Conclusion: Our findings demonstrate that gene expression patterns can predict the response to neoadjuvant TCHP chemotherapy in HER2+ BC. The functional characterization of 39 DEGs suggests that the tumor microenvironment, including adjacent immune cells, plays a significant role in modulating the response to neoadjuvant chemotherapy in HER2+ BC. Table 1. Significant DEGs Citation Format: Junghoon Shin, Ji-Yeon Kim, Eun Yoon Cho, Seok Won Kim, Jeong Eon Lee, Hyunwoo Lee, Yeon Hee Park, Jin Seok Ahn, Young-Hyuck Im. Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-05.

The potential value of oral microbial signatures for prediction of oral squamous cell carcinoma based on machine learning algorithms.

This study aimed to explore the potential predictive value of oral microbial signatures for oral squamous cell carcinoma (OSCC) risk based on machine learning algorithms. The oral microbiome signatures were assessed in the unstimulated saliva samples of 80 OSCC patients and 179 healthy individuals using 16S rRNA gene sequencing. Four different machine learning classifiers were used to develop prediction models. Compared with control participants, OSCC patients had a higher microbial dysbiosis index (MDI, p < 0.001). Among four machine learning classifiers, random forest (RF) provided the best predictive performance, followed by the support vector machines, artificial neural networks and naive Bayes. After controlling the potential confounders using propensity score matching, the optimal RF model was further developed incorporating a minimal set of 20 bacteria genera, exhibiting better predictive performance than the MDI (AUC: 0.992 vs. 0.775, p < 0.001). The novel MDI and RF model developed in this study based on oral microbiome signatures may serve as noninvasive tools for predicting OSCC risk.

Direct Retrieval of Orthographic Representations in Chinese Handwritten Production: Evidence from a Dynamic Causal Modeling Study.

This present study identified an optimal model representing the relationship between orthography and phonology in Chinese handwritten production using dynamic causal modeling, and further explored how this model was modulated by word frequency and syllable frequency. Each model contained five volumes of interest in the left hemisphere (inferior frontal gyrus [IFG], middle frontal gyrus [MFG], angular gyrus [AG], supramarginal gyrus, and superior frontal gyrus), with the IFG as the driven input area. Results showed the superiority of a model in which both the MFG and the AG connected with the IFG, supporting the orthography autonomy hypothesis. Word frequency modulated the AG → superior frontal gyrus connection (information flow from the orthographic lexicon to the orthographic buffer), and syllable frequency affected the IFG → MFG connection (information transmission from the semantic system to the phonological lexicon). This study thus provides new insights into the connectivity architecture of neural substrates involved in writing.

Application of Machine Learning Techniques for the Diagnosis of Obstructive Sleep Apnea/Hypopnea Syndrome

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a condition linked to severe cardiovascular and neuropsychological consequences, characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to compromised ventilation, hypoxemia, and micro-arousals. Polysomnography (PSG) serves as the gold standard for confirming OSAHS, yet its extended duration, high cost, and limited availability pose significant challenges. In this paper, we employ a range of machine learning techniques, including Neural Networks, Decision Trees, Random Forests, and Extra Trees, for OSAHS diagnosis. This approach aims to achieve a diagnostic process that is not only more accessible but also more efficient. The dataset utilized in this study consists of records from 601 adults assessed between 2014 and 2016 at a specialized sleep medical center in Colombia. This research underscores the efficacy of ensemble methods, specifically Random Forests and Extra Trees, achieving an area under the Receiver Operating Characteristic (ROC) curve of 89.2% and 89.6%, respectively. Additionally, a web application has been devised, integrating the optimal model, empowering qualified medical practitioners to make informed decisions through patient registration, an input of 18 variables, and the utilization of the Random Forests model for OSAHS screening.

Combining principal component analysis and logistic regression for multifactorial fall risk prediction among community-dwelling older adults

Falls require comprehensive assessment in older adults due to their diverse risk factors. This study aimed to develop an effective fall risk prediction model for community-dwelling older adults by integrating principal component analysis (PCA) with machine learning. Data were collected for 45 fall-related variables from 1630 older adults in Taiwan, and models were developed using PCA and logistic regression. The optimal model, PCA with stepwise logistic regression, had an area under the receiver operating characteristic curve of 0.78, sensitivity of 74 %, specificity of 70 %, and accuracy of 71 %. While dimensionality reduction via PCA is not essential, it aids practicality. Our framework combines PCA and logistic regression, providing a reliable method for fall risk prediction to support consistent screening and targeted health promotion. The key innovation is using PCA prior to logistic regression, overcoming conventional limitations. This offers an effective community-based fall screening tool for older adults.

Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders

Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer’s disease, and Non-Alzheimer’s dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.

Effects of various cold plasma atmospheres and external influencing factors on the adsorption of formaldehyde gas by bamboo-based carbon microfibers

To effectively enhance the adsorption performance of gaseous formaldehyde (HCHO) by bamboo-based carbon microfibers (BCMFs), the active oxygen (O) and nitrogen (N)-containing functional groups were rapidly introduced by cold plasma. The effects of plasma time, plasma atmospheres including oxygen (O2), nitrogen (N2), air, and argon (Ar) as well as ambient temperature, flow rate, and initial concentration of gaseous HCHO on the adsorption performance of BCMFs were comprehensively investigated. The typical kinetic, isothermal, and thermodynamic models were also evaluated. The O2-plasma activated BCMFs showed the most serious etching effects among four atmospheres. The adsorption capacities were ranked as BCMF-800-N-10 (203.87 mg g−1) > BCMF-800-Ar-10 (166.26 mg g−1) > BCMF-800-Air-10 (151.28 mg g−1) > BCMF-800-O-10 (144.21 mg g−1), which were 112.99%, 73.69%, 58.04%, and 50.66% higher than the control, respectively. The O–CO and pyrrolic N groups were more easily formed onto BCMF-800-N-10. The Langmuir and the Freundlich models displayed the excellent fitting properties. The optimal kinetic model tended to transfer to the pseudo-second-order from the pseudo-first-order after cold plasma modification. The BCMFs activated by plasma showed a synergistic single/multi-layer adsorption mechanism with a complex physicochemical adsorption. The HCHO adsorption performance was positively correlated with its concentrations but negatively correlated with environmental temperature.

Rapid determination of residual pefloxacin in mutton based on hyperspectral imaging and data fusion

Non-destructive detection of veterinary drug residues in meat products is critical for ensuring food safety and human health. The feasibility of using visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging (HSI) system combined with data fusion for the prediction of pefloxacin residues in mutton was investigated in this study. A partial least squares regression optimization (PLSR) model was developed by multivariate data processing methods. The results showed that the low-level fusion (LLF) produced better results with R2P = 0.907 and root mean square error of prediction (RMSEP) = 0.462 as compared to individual data blocks. Furthermore, intermediate-level fusion (ILF) based on iterative retention of informative variables (IRIV) algorithm showed the best results with R2P = 0.940 and RMSEP = 0.375. Finally, the visualized distributions of Vis-NIR and NIR based on the optimal model combination were mapped. The results have demonstrated that a combination of HSI with data fusion may be applied to rapidly and non-destructively detect pefloxacin residues in mutton.

Machine learning in epidemiology: Neural networks forecasting of monkeypox cases.

This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting the spread and severity of cases using data from June 3 to December 31, 2022, and evaluates them against test data from January 1 to February 7, 2023. The study highlights the potential of neural networks in epidemiology, especially concerning recent monkeypox outbreaks. It provides a comparative analysis of the models, emphasizing their capabilities in public health strategies. The research identifies optimal model configurations and underscores the efficiency of the Levenberg-Marquardt algorithm in training. The findings suggest that ANN models, particularly those with optimized Root Mean Squared Error, Mean Absolute Percentage Error, and the Coefficient of Determination values, are effective in infectious disease forecasting and can significantly enhance public health responses.

Enhancing the exploitation of natural resources for green energy: An application of LSTM-Based Meta-model for Aluminum Prices Forecasting

Efficient resource allocation for electric car production can be achieved by anticipating aluminum future pricing. For the electric car sector to maintain a steady supply chain, efficient production, and long-term growth, accurate forecasts are essential for policymakers to plan resource policies. The accuracy of ensemble learning models in predicting Aluminum prices was analyzed using Gene Expression programming and long short-term memory (LSTM) method. The analysis was based on monthly frequency spot settlement price data of Aluminum, copper, silver, and crude oil prices, as well as currency inflation rates of USA, China, and Peru. The study period spanned from January 1, 1994 to December 31, 2022, and also included the gross domestic product (GDP) of USA, China, and other metal minerals. Initially, we assess the level of multicollinearity between each chosen input parameter and Aluminum price volatility using Variance inflation factor-based multicollinearity. The primary innovation of this study lies in the creation of a highly precise model that utilizes the LSTM algorithm to estimate monthly Aluminum prices. This model serves as a revolutionary forecasting method. An extensive evaluation of various ensemble learning models reveals that XGBoost is the optimal model for accurately forecasting monthly Aluminum prices. The LSTM-based recurrent neural network model exhibited the lowest error, with a Mean Relative Error (MRE) ranging from 0.001 to 0.008, RMSE ranging from 54.527 to 136.044, and R2 ranging from 0.95 to 0.983. The findings demonstrated that the LSTM-based super learner models had superior performance compared to the CatBoost, LightBoost, Random Forest, XGBoost, and AdaBoost models, as evidenced by higher values of the determination coefficient (R2), value account for (VAF), and other 10 error analysis evaluators. Therefore, the utilization of an LSTM-based super learner model can serve as a dependable approach for forecasting future monthly Aluminum prices. In addition, this study introduces a logical mathematical model utilizing gene expression programming (GEP) to anticipate future Aluminum prices, which can be utilized by other researchers. Thus, we conclude that both the GEP and ensemble learning models, particularly the LSTM-based Super learner model, are appropriate for precise metal price predictions, which can benefit policymakers and assist in resource policy planning.

Temporal Forecast of Maize Production in Tanzania: An Autoregressive Integrated Moving Average Approach

This paper aims to model and forecast maize production in Tanzania, emphasizing its crucial contribution to the agricultural sector. Utilizing secondary data on annual maize production in Tanzania from 1961 to 2021, measured in tonnes and sourced from the FAO database, the study employs statistical techniques such as unit root tests, the ARIMA model, and the Ljung-Box test for a comprehensive analysis. The optimal model for forecasting maize production in Tanzania is identified as ARIMA (5,1,5), and the adequacy assessment confirms its effectiveness in predicting maize production values. The ten-year forecast reveals an intermittent pattern, offering valuable insights into anticipated trends. While not explicitly developing an existing theory, this research enhances the understanding of maize production dynamics in Tanzania through the application of advanced statistical methods. Policymakers, agricultural stakeholders, and researchers can leverage these findings to enhance agricultural productivity and food security in Tanzania.

Pumpkin-Based Cookies Formulated from Optimized Pumpkin Flour Blends: Nutritional and Antidiabetic Potentials

The study looked at the optimization of blends of pumpkin pulp flour, pumpkin seed, and pumpkin starch residue for the development and nutritional evaluation of cookies. 3 optimum blends, runs 12, 15, and 16 were selected out of 16 runs using Optimal Model Design of Response Surface Methodology with responses based on proximate analysis. The amino acid profile, anti-diabetic potential, physical properties, and sensory characteristics of the developed cookies were evaluated. The proximate parameters of the cookies were found to significantly (P≤ 0.05) increased with the levels of pumpkin seed. The carbohydrate content of the pumpkin-based Cookies (47.20-49.12 %) was significantly lower than that of the control, 100 % Wheat Flour (60.29 %). The ash content ranged from 6.20-8.19 %, and dietary fibre, 24.39-26.9 %. The amino acid profile compared well with FAO/WHO recommendations for healthy adults. The most abundant amino acids in the samples were glutamic acid, alanine, leucine, aspartate, and valine. The In-vitro Protein Digestibilities of the pumpkin-based cookies were significantly higher than that of the control sample (WFC). The pumpkin-based cookies effectively inhibited α-amylase and α-glucosidase at 59 % and 69 % inhibition rates respectively. The Glycemic Indices of the pumpkin-based cookies were in the range of 50-52 % compared to the control (WFC) which was 61.43 %, thus registering the pumpkin-based cookies as low-glycemic foods. Sensory evaluation revealed that cookies made from 60 % pumpkin pulp, 24.5 % pumpkin seed, and 14.4 % pumpkin starch residues were generally acceptable as best among the pumpkin-based cookies.

Segmentation of hyphae and yeast in fungi-infected tissue slice images and its application in analyzing anti-fungal blue light therapy.

Candida albicans (C. albicans) is a pathogenic fungus that undergoes morphological transitions between hyphal and yeast, adapting to diverse environmental stimuli and exhibiting distinct virulence. Existing researches on anti-fungal blue light (ABL) therapy have either focused solely on hyphae or neglected to differentiate between morphologies, obscuring potential differential effects. To address this gap, we established a novel dataset of 150 C. albicans-infected mouse skin tissue slice images with meticulously annotated hyphae and yeast. Eleven representative convolutional neural networks were trained and evaluated on this dataset using seven metrics to identify the optimal model for segmenting hyphae and yeast in original high pixel size images. Leveraging the segmentation results, we analyzed the differential impact of blue light on the invasion depth and density of both morphologies within the skin tissue. Comparison results demonstrated that U-Net-BN emerged as superior segmentation accuracy compared to other models, achieving the best overall performance. While both hyphae and yeast exhibited significant reductions in invasion depth and density at the highest ABL dose (180J/cm2), only yeast was significantly inhibited at the lower dose (135J/cm2). This novel finding emphasizes the importance of developing more effective treatment strategies for both morphologies.