Browse Articles

Correlation of specific addictions with cancer sites identified among patients in Northeast India

Material requirements planning with a novel lot sizing method and a new algorithm for production scheduling

Abstract Material Requirements Planning is an intelligent information system and a decision maker that determines the type of raw materials, the quantity of them, and their supplying time in order to realize production schedule of products and work-in-process items in a manufacturing system. In each manufacturing system, if we split production costs into material, labor, and overhead costs, we will observe that over 60% of total production costs are related to material. Therefore, it is necessary to consider an appropriate inventory control system to manage the costs of inventory system to provide right inventory, with right quantity, at the right time, at the right price, and with appropriate quality. Lot sizing methods in previous researches do not provide feasible and operational solutions, or have parameters such as holding costs and set up costs that are not identified in financial accounts. To solve this research gap, in this paper, we presented a new lot sizing method considering two important parameters including batch size of the items and the minimum order quantity that a supplier provides for the items. Simultaneously, we designed a new production scheduling algorithm in Master Production Schedule component for a manufacturing system with k production lines and m products with n Bill of Materials for each product. At the end, we programmed our new mentioned contributions and developed Material Requirements Planning module of a Supply Chain Management system.

Soybean selection in Kenya enhanced by multi-trait and genotype-by-environment interaction modeling

LC-MS/MS quantification of omadacycline in human plasma for therapeutic drug monitoring: method development and clinical application

Optimization of steel anchor pile configurations for enhanced pullout resistance in expansive soil foundations

Healthy lifestyle and life expectancy with and without major chronic disease – a cohort study

Cell cycle dependence of ERK activation dynamics is regulated by PI3K and PAK1 signaling

Effects of erector spinae plane block on intraoperative blood pressure variability, blood loss, and postoperative pain in transforaminal lumbar interbody fusion

Exploration of soliton solutions of the nonlinear Kraenkel-Manna-Merle system using innovative methods in ferromagnetic materials

Topographic-mediated climate-NPP relationships in subtropical mountain heterogeneity units

Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models

Abstract Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalability, frequently necessitating specialized equipment and expertise. This study presents a deep learning-based automated diagnostic system for rice leaf diseases, leveraging a large-scale dataset comprising annotated images spanning six common rice diseases: bacterial stripe, false smut, leaf blast, neck blast, sheath blight, and brown spot. We evaluated seven advanced deep learning architectures—MobileNetV2, GoogLeNet, EfficientNet, ResNet-34, DenseNet-121, VGG16, and ShuffleNetV2—across a range of performance metrics including precision, recall, and overall diagnostic accuracy. Among these, GoogLeNet, DenseNet-121, ResNet-34, and VGG16 demonstrated superior performance, particularly in minimizing class confusion and enhancing diagnostic accuracy. These models were selected based on diverse architectural principles to ensure complementary feature extraction capabilities. An ensemble model, integrating these four high-performing networks via a simple average fusion strategy, was subsequently developed, significantly reducing misclassification rates and providing robust, scalable diagnostic capabilities suitable for deployment in real-world agricultural settings. The model’s performance was further validated on independent test data collected under varying environmental conditions.

The association between muscle strength and z scores of pulmonary function

Solitary silence and social sounds: music can influence mental imagery, inducing thoughts of social interactions

Spatial assessment of heavy metal contamination in groundwater in the Kadaladi region, Tamil Nadu, India

AI models are neglecting African languages — scientists want to change that

Preoperative albumin-to-fibrinogen ratio as a predictor of postoperative hospital stay in locally advanced esophageal squamous cell carcinoma after neoadjuvant therapy

Abstract Esophageal squamous cell carcinoma (ESCC) is a major global health issue, with postoperative hospital length of stay (LOS) being a critical factor influencing patient outcomes and healthcare costs. This study evaluates the impact of preoperative albumin-to-fibrinogen ratio (AFR) and albumin-to-D-II aggregates ratio (ADR) on LOS in patients with locally advanced ESCC undergoing neoadjuvant therapy. A retrospective study of 135 patients with locally advanced ESCC who underwent esophagectomy after neoadjuvant therapy (July 2013–November 2020). Demographic, clinical, and preoperative blood data were analyzed. LOS was defined from surgery to discharge. AFR and ADR values were calculated, and ROC curves identified optimal cutoffs. Multivariate Cox proportional hazards models and Kaplan–Meier analysis were used to assess relationships between AFR and LOS. The optimal AFR cutoff was 10.34, demonstrating better predictive accuracy for LOS than ADR. High AFR was associated with significantly shorter LOS. Multivariate analysis revealed high AFR, and cholesterol were linked to shorter stays, while older age and high globulin levels were associated with longer stays. Kaplan–Meier analysis confirmed the relationship. Preoperative AFR is a reliable predictor of LOS in advanced ESCC patients after neoadjuvant therapy, offering potential for improved clinical management and resource allocation.

Safe model based optimization balancing exploration and reliability for protein sequence design

Abstract Discovering proteins with desired functionalities using protein engineering is time-consuming. Offline Model-Based Optimization (MBO) accelerates protein sequence design by exploring the vast protein sequence space using a trained proxy model. However, the proxy model often yields excessively good values that are far from the training dataset and causes pathological behavior in the MBO. To address this problem, we propose a mean deviation tree-structured Parzen estimator (MD-TPE) that penalizes unreliable samples located in the out-of-distribution region using the deviation of the predictive distribution of the Gaussian process (GP) model in the objective function to find the solution in the vicinity of the training data, where the proxy model can reliably predict. Upon examining the GFP dataset, compared to TPE, MD-TPE yielded fewer pathological samples. Additionally, it successfully identified mutants with higher binding affinity in the antibody affinity maturation task. Thus, our developed safe optimization approach is useful for protein engineering.

Seismic retrofit of high-rise buildings using buckling-restrained braces: design methodology and performance evaluation

A dual branch feature extraction network for heart sound signal analysis

Geographic-style maps with a local novelty distance help navigate in the materials space

Abstract With the advent of self-driving labs promising to synthesize large numbers of new materials, new automated tools are required for checking potential duplicates in existing structural databases before a material can be claimed as novel. To avoid duplication, we rigorously define the novelty metric of any periodic material as the smallest distance to its nearest neighbor among already known materials. Using ultra-fast structural invariants, all such nearest neighbors can be found within seconds on a typical computer even if a given crystal is disguised by changing a unit cell, perturbing atoms, or replacing chemical elements. This real-time novelty check is demonstrated by finding near-duplicates of the 43 materials produced by Berkeley’s A-lab in the world’s largest collections of inorganic structures, the Inorganic Crystal Structure Database and the Materials Project. To help future self-driving labs successfully identify novel materials, we propose navigation maps of the materials space where any new structure can be quickly located by its invariant descriptors similar to a geographic location on Earth.