Search Results (30)

The preferential concentration of metals into finer size fractions (<19 mm) during breakage can be exploited for early rejection of low-grade material, reducing non-ore processing and improving energy and water efficiency. The Cooperative Research Centre for Optimising Resource Extraction (CRC ORE) established a testing regime and developed the Response Ranking (RR) factor to compare fractionation behavior across deposits. RR values range from 200 to negative, with higher values indicating breakage patterns favorable for ore liberation. This study evaluates geological parameters controlling rock breakage in the Gramalote and Telfer West Dome deposits, both intrusion-related gold systems. For this purpose, macroscopic description of drill core was carried out using the Anaconda methodology, along with uncrushed run-of-mine (ROM) samples. In addition, petrophysical datasets including hardness, magnetic susceptibility, hyperspectral data, geochemistry, and calculated mineralogy were used. These datasets were systematically compared with RR values to investigate the relationship between geological attributes and grade-by-size fractionation behavior. Geological description provides a practical basis to identify early separation opportunities and model optimization potential through grade-by-size fractionation. Full article

This study presents the design and evaluation of a multi-model artificial intelligence (AI) framework for proactive quality risk management in projects. A dataset comprising 2000 risk records was developed, containing four columns: Risk Description (input), Risk Category, Trigger, and Impact (outputs). Each output variable was modeled using three independent classifiers, forming a multi-step decision-making pipeline where one input is processed by multiple specialized models. Two feature extraction techniques, Term Frequency–Inverse Document Frequency (TF-IDF) and GloVe100 Word Embeddings, were compared in combination with several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVMs), Random Forest, Multinomial Naive Bayes, and XGBoost. Results showed that model performance varied with task complexity and the number of output classes. Trigger prediction (28 classes), Logistic Regression, and SVM achieved the best performance, with a macro-average F1-score of 0.75, while XGBoost with TF-IDF features produced the highest accuracy for Risk Category classification (five classes). In Impact prediction (15 classes), SVM with Word Embeddings demonstrated superior results. The implementation, conducted in Python (v3.9.12, Anaconda), utilized Scikit-learn, XGBoost, SHAP, and Gensim libraries. SHAP visualizations and confusion matrices enhanced model interpretability. The proposed framework contributes to scalable, text-based, predictive, quality risk management, supporting real-time project decision-making. Full article

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation. Full article

Background/Objectives: Mismatch repair (MMR) deficiency assessment has proven to be a valuable tool for prognostic evaluation and therapeutic management guidance in patients with colorectal cancer (CRC). Our study aimed to investigate the associations between MMR deficiency and a range of clinicopathological parameters. Methods: We conducted a retrospective observational study including 264 patients diagnosed with CRC, for whom immunohistochemical (IHC) data were available. Statistical analysis was performed using the Python 3.12.7 programming language within the Jupyter Notebook environment (Anaconda distribution). Results: MMR deficiency was identified in 18.18% of patients. It was significantly associated with younger age (<50 years), female sex, right-sided tumor location, poor tumor differentiation (G3), smoking, and loss of CDX2 expression (p < 0.001). MLH1 and PMS2 were the most frequently affected proteins, with concurrent loss in 77.08% of MMR-deficient cases. Loss of MLH1 expression correlated with female sex (p = 0.004), right-sided location (p < 0.001), poor differentiation (p < 0.001), and loss of CDX2 expression (p < 0.001). Additionally, the loss of PMS2 expression was associated with female sex (p = 0.015), right-sided tumor location (p = 0.003), and poor differentiation (p < 0.001). No significant associations were identified between MMR status and tumor stage, histological subtype, PLR, or NLR values. Conclusions: Gaining deeper insights into the clinical relevance of MMR status in CRC could contribute to improved testing rates and support the design of tailored management strategies that address the specific biological features of these tumors. Full article

This research aims to investigate financing decisions of capital-constrained small and medium-sized enterprise (SME) manufacturers and distributors under a Green Supply Chain (GSC) framework. By evaluating the impact of Supply Chain Finance (SCF) instruments, this study utilizes Stackelberg game model to explore a decentralized decision-making system. To our knowledge, this investigation represents the first exploration of game models that uniquely compares financing through trade credit, where the manufacturer offers zero-interest credit without discounts with reverse factoring, while also considering distributor’s efforts on sustainable marketing under the impact of supportive government policies. Our study suggests that manufacturers should adopt reverse factoring for optimal profits and actively participate in distributors’ financing decisions to address inefficiencies in decentralized systems. Furthermore, the distributor’s demand quantity, profits and sustainable marketing efforts show significant increase under reverse factoring, aided by favorable policies. Finally, the results are validated through Python 3.8.8 simulations in the Anaconda distribution, offering meaningful insights for policymakers and supply chain managers. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

Background/Objectives: Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s Disease (PD) and other movement disorders. Despite its benefits, DBS explantation occurs in 5.6% of cases, with costs exceeding USD 22,000 per implant. Traditional statistical methods have struggled to identify reliable risk factors for explantation. We hypothesized that supervised machine learning would more effectively capture complex interactions among perioperative factors, enabling the identification of novel risk factors. Methods: The Medical Informatics Operating Room Vitals and Events Repository was queried for patients with DBS, adequate clinical data, and at least two years of follow-up (n = 38). Fisher’s exact test assessed demographic and medical history variables. Data were analyzed using Anaconda Version 2.3.1. with pandas, numpy, sklearn, sklearn-extra, matplotlin. pyplot, and seaborn. Recursive feature elimination with cross-validation (RFECV) optimized factor selection was used. A multivariate logistic regression model was trained and evaluated using precision, recall, F1-score, and area under the curve (AUC). Results: Fisher’s exact test identified chronic pain (p = 0.0108) and tobacco use (p = 0.0026) as risk factors. RFECV selected 24 optimal features. The logistic regression model demonstrated strong performance (precision: 0.89, recall: 0.86, F1-score: 0.86, AUC: 1.0). Significant risk factors included tobacco use (OR: 3.64; CI: 3.60–3.68), primary PD (OR: 2.01; CI: 1.99–2.02), ASA score (OR: 1.91; CI: 1.90–1.92), chronic pain (OR: 1.82; CI: 1.80–1.85), and diabetes (OR: 1.63; CI: 1.62–1.65). Conclusions: Our study suggests that supervised machine learning can identify risk factors for early DBS explantation. Larger studies are needed to validate our findings. Full article

In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles. Full article

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics. Full article

While elucidating the evolutionary trajectory of green anacondas, we previously documented the existence of two distinct species, Eunectes akayima sp. nov. and Eunectes murinus (Linnaeus, 1758), that separated approximately 10 million years ago. Our research integrates a novel molecular clock approach, focuses on tectonic plate movements with fossil records as minimal chronological markers, and offers a refined understanding of speciation events in relation to major biogeographical occurrences in South America. Mitochondrial DNA analysis demonstrates a significant genetic divergence between the species, which is supported by a notable difference in sexual size dimorphism (SSD) intensity between the two species, along with other morphological differences. This paper also rectifies earlier oversights in the description of the new species and clarifies taxonomic ambiguities in compliance with the International Code of Zoological Nomenclature (henceforth ICZN). In addition, we designate a neotype for E. murinus to stabilize the group. In an effort to honor Indigenous nations, E. akayima sp. nov. derives its name from the Carib language, advocating for the inclusion of traditional names in scientific discourse. Our paper not only contributes to the taxonomic stability of anacondas but also advocates for the usage of Indigenous names in zoological nomenclature by adopting a more inclusive and flexible approach to the ICZN and eliminating unintended exclusionary practices that we have inherited in science as in other disciplines. Full article

Acute limb ischemia due to limb-graft occlusion (LGO) after fenestrated endovascular aneurysm repair (FEVAR) and acute bypass graft occlusion with an ePTFE graft pose critical challenges, necessitating prompt intervention to prevent limb loss. This paper discusses two cases of acute limb ischemia treated with a hybrid approach using the AngioJet™ Ultra Thrombectomy System as an adjunct to Fogarty thrombectomy. Case I involved a 69-year-old male post-FEVAR with contralateral iliac limb graft occlusion of the fenestrated Anaconda™, while Case II featured a 70-year-old male (ASA IV) post-bypass surgery (iliopopliteal arterial bypass with ePTFE Graft) with acute bypass graft occlusion. Both cases underwent successful recanalization using the AngioJet™ Ultra Thrombectomy System (ZelanteDVT™ 8F catheter, Solent™ Proxi 6F catheter) (Boston Scientific, Marlborough, MA, USA), combined with adjunctive techniques including Fogarty thrombectomy, balloon angioplasty, stenting, and local lysis. Immediate postoperative and follow-up assessments after 6 months revealed restored limb perfusion and improved clinical outcomes, with palpable pulses and improved ulcer healing. The aim of this treatment strategy is not only to alleviate limb ischemia but also to preserve future options in the event of graft failure. The use of the AngioJet™ Thrombectomy System in cases of LGO aims not only to clear the thrombus load but also to avoid the need for graft relining. In the case of acute arterial bypass graft occlusion in a patient with ASA IV, the goal of using the thrombectomy device is to preserve the native vessels for future procedures, such as long infragenual bypass, in addition to limb salvage. These cases demonstrate the efficacy of a hybrid surgical approach in managing acute limb ischemia following graft occlusion following FEVAR and bypass surgery. Long-term follow-up will further elucidate the durability of these interventions and their impact on limb salvage and overall patient outcomes. By combining mechanical thrombectomy with adjunctive techniques, such as balloon angioplasty and stenting, this hybrid approach offers a comprehensive solution to acute limb ischemia, addressing both the underlying occlusive pathology and ensuring optimal limb perfusion. Furthermore, the utilization of the AngioJet™ Ultra Thrombectomy System provides a minimally invasive yet effective method for thrombus removal, reducing procedural time and potential complications associated with open surgical techniques. As such, this approach represents a valuable addition to the armamentarium of treatments for acute limb ischemia, particularly in cases of graft occlusion following complex endovascular and bypass procedures. Full article

Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research. Full article

Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009–2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections. Full article

This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML models (e.g., KNN, SVM, Naive Bayes, Gradient Boosting, and Neural Networks), we predict connection strengths on Facebook, focusing on model performance metrics such as accuracy, precision, recall, and F1-score. Our methodology, executed in Python using the Anaconda distribution, unveils insights into trust formation and sustainability on social media, highlighting the potent application of ML in understanding these dynamics. Challenges, including the complexity of modeling social behaviors and ethical data use concerns, are discussed, emphasizing the need for continued innovation. Our findings contribute to the discourse on trust in social networks and suggest future research directions, including the application of our methodologies to other platforms and the study of online trust over time. This work not only advances the academic understanding of digital social interactions but also offers practical implications for developers, policymakers, and online communities. Full article

Anacondas, genus Eunectes, are a group of aquatic snakes with a wide distribution in South America. The taxonomic status of several species has been uncertain and/or controversial. Using genetic data from four recognized anaconda species across nine countries, this study investigates the phylogenetic relationships within the genus Eunectes. A key finding was the identification of two distinct clades within Eunectes murinus, revealing two species as cryptic yet genetically deeply divergent. This has led to the recognition of the Northern Green Anaconda as a separate species (Eunectes akayima sp. nov), distinct from its southern counterpart (E. murinus), the Southern Green Anaconda. Additionally, our data challenge the current understanding of Yellow Anaconda species by proposing the unification of Eunectes deschauenseei and Eunectes beniensis into a single species with Eunectes notaeus. This reclassification is based on comprehensive genetic and phylogeographic analyses, suggesting closer relationships than previously recognized and the realization that our understanding of their geographic ranges is insufficient to justify its use as a separation criterion. We also present a phylogeographic hypothesis that traces the Miocene diversification of anacondas in western South America. Beyond its academic significance, this study has vital implications for the conservation of these iconic reptile species, highlighting our lack of knowledge about the diversity of the South American fauna and the need for revised strategies to conserve the newly identified and reclassified species. Full article