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Shallow entangled circuits for quantum time series prediction on IBM devices

Abstract Forecasting temporal dynamics underpins many areas of science and engineering, from large-scale atmospheric prediction to nanoscale quantum control. Classical approaches, including autoregressive models and deep neural networks, have advanced sequential learning often at the expense of known model order, or large dataset and parameters, resulting in computational cost. Here, we investigate whether quantum entanglement can serve as a resource for temporal pattern learning using shallow and structured quantum circuits. We have proposed a Quantum Time Series (QTS) framework that encodes normalised sequential data into single-qubit rotations and captures temporal correlations through forward and cross-entanglement layers. Among several encoding schemes, phase encoding-based sparse entanglement provides hardware efficiency by scaling to larger qubit systems with linear circuit depth and two-qubit complexity of $$\mathcal {O}(n)$$ for qubit size n . This offers a reduction in parameters and depth compared with deep variational quantum circuits such as Heisenberg-inspired circuits, and random-parametric unitary architectures. Experiments on synthetic and geophysical datasets show that shallow QTS circuits reproduce complex temporal pattern from limited data by leveraging structured quantum entanglement. Executions on IBM’s Heron and Eagle-class processors demonstrate robustness and scalability up to 100 qubits. These results suggest that structured entanglement may offer a short-term memory effect for time-series analysis, providing a scalable route for near-term quantum applications.

Leveraging artificial intelligence for predictive customer churn modeling in telecommunications: a framework for enhanced customer relationship management

Abstract Customer churn remains a critical challenge in the telecommunications industry, impacting profitability and long-term customer value. This study proposes an Artificial Intelligence (AI)-driven framework integrated within Customer Relationship Management (CRM) systems to proactively identify and retain high-risk customers. Using a Random Forest classifier on a publicly available telecom dataset ( N  = 2,668), the model achieved an accuracy of 95.13% and an AUC of 0.89 . Techniques such as SMOTE and class weighting were applied to address class imbalance (14.6% churn). Comparative experiments with XGBoost , SVM , and ANN confirmed the robustness of the proposed model. Feature importance analysis revealed that total day minutes, total day charge, and customer service calls were the most influential predictors. The study contributes by linking explainable AI insights to CRM operationalization, providing actionable strategies for proactive customer engagement and retention.

Increased plasma fibronectin mirrors intimal phenotypic switching of vascular smooth muscle cells in moyamoya arteriopathy

Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves

Location allocation and capacity optimization for a PV and battery integrated hybrid community electric vehicle charging station

Effect of microbubble-assisted gemcitabine delivery with repeated ultrasound exposure in a pancreatic cancer organ-on-a-chip model

Abstract The fibrotic stroma of solid tumours poses a physical barrier to drug delivery and effective treatment. Interaction between cancerous epithelial cells and their surrounding stromal partners results in the development of a rigid, collagenous matrix environment with reduced interstitial flow, crucial for drug delivery to cancer cells, particularly in pancreatic ductal adenocarcinoma (PDAC), an aggressive pancreatic cancer with poor prognosis. Therefore, evaluating novel drug delivery mechanisms using appropriate stroma-mimicking 3D culture models is essential. We previously demonstrated, using a 21-day cultured microfluidic PDAC model that mimics the rigid, collagenous stroma, reduced interstitial flow through the tumour model. In this study, we evaluated the use of microbubbles and ultrasound as an alternative method for disrupting our model’s fibrotic stroma to restore interstitial flow and improve gemcitabine delivery and efficacy. Literature shows microbubbles in 2D and 3D static cultures enhance drug delivery and effects by increasing cell membrane permeability through oscillation and bursting under ultrasound (sonoporation). Here, we observed continuous microbubble oscillation and bursting under repeated ultrasound exposure, leading to continuous matrix-microbubble and PDAC cell-microbubble interactions, which improved the gemcitabine effect. This study emphasises the need for disease-specific in vitro models to assess novel drug delivery mechanisms and improve therapeutic outcomes.

Real-world efficacy and safety of pembrolizumab plus lenvatinib in patients with metastatic renal cell carcinoma: a multi-institutional retrospective study

Influence of pulverization on the micropore structure of coal and its fractal characteristics

Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms

Abstract Immunization is a cost-effective public health intervention globally, including in Ethiopia. However, the study focused on children aged 0–59 months and analyzed factors influencing incomplete immunization using ensemble machine learning techniques. A total of 16,394 EDHS datasets were used, with 80% for training and 20% for testing sets. Accordingly, the training set consisted of 13,115 samples, while the testing set contained 3,279 samples. Ensemble learning algorithms were employed, including Bagging methods (Bagging meta-estimator, Random Forest), Boosting methods (Gradient Boosting, XGBoost, LightGBM, AdaBoost, and CatBoost), and Voting ensembles combining both bagging and boosting models. Additionally, Stacking was performed using XGBoost and CatBoost as base models, with other machine learning algorithms such as Random Forest, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Logistic Regression as meta-models. All models were implemented using the Python programming language. On the tested data, bagging meta-estimator + XGBoost voting model executed the highest performance result of accuracy (95.94%), f1-score (95.89%), recall (94.81%), and precision (97.07%), for visualizing using a confusion matrix and AUC-ROC value of 96%, and the cross-validation score of 95.75% for its reliability. Also, the most influential factors for incomplete immunization include marital status, residence, and others. This study aims to identify key factors influencing immunization coverage among Ethiopian children under the age of five and improve with ensemble machine learning algorithms. The findings provide valuable insights for targeted interventions, supporting improved immunization practices and contributing to better child health outcomes.

A novel ZnO-MgO-Gd₂O₃ nanocomposite synthesized with Ocimum basilicum seed extract for enhanced photocatalysis

Impact of pancreatic proenzymes on pancreatic ductal adenocarcinoma associated fibroblasts

A mathematical formulation and computational exploration of Yayoi Kusama’s tentacle artworks

Synergistic antibiofilm activity of methylene blue and silver nanoparticle-mediated photothermal therapy against Enterococcus faecalis biofilm

Abstract Biofilm formation by Enterococcus faecalis ( E. faecalis) in root canals is a significant challenge in endodontic therapy, often leading to persistent infections and treatment failures. This research paper investigates the antibiofilm efficacy of methylene blue mediated photothermal treatment (MB-PTT), as compared to the sole effect of diode laser, PTT, and sodium hypochlorite (NaOCl) on E. faecalis biofilms. 45 maxillary central incisors were decoronated, prepared and infected by E faecalis for seven days. Forty samples were randomly allocated as follows; GI; irrigated with 2.6% NaOCl, GII; irradiated with 660 nm diode laser (250 mW) for 180 s. GIII; Silver nanoparticles (AgNPs) with diode laser application at same parameters (AgNPs-PTT), GIV: accompanied MB and AgNPs-PTT, while 5 samples were kept as control for biofilm formation. The antibiofilm effect was demonstrated both by bacterial colonies counting (CFU/ml) and scanning electron microscope images. The results highlight the potential of all experimental treatment modalities ( P  < 0.01), However complete absence of detactable bacterial colonies was only evident when MB was coupled with AgNPs-PTT. Accompanied MB with PTT is a promising approach with effective antibiofilm activity against E. faecalis biofilms.

Elucidating the molecular compatibility mechanism to guide the optimization of Straw-Derived asphalt

Time-series analysis of vitiligo-related online search behavior in response to ambient air pollutants

The emerging contribution of Tigris Euphrates basin dust emissions to extreme dust activity over the Arabian Peninsula

An intelligent framework for visually impaired people through indoor object Detection-Based assistive system using YOLO with recurrent neural networks

Microfluidic droplet cultivation preserves microalgae diversity in screening systems

Hyper-spectral imaging with up-converted mid-infrared single-photons

Hyperchaos and the fusion of Moore’s automaton with gold sequences for augmented medical image encryption

Abstract This study presents a sophisticated encryption methodology specifically designed for the secure transfer of medical images across cloud services. The initial phase of the algorithm involves the consolidation of multiple images to form a single augmented image, which is then subjected to the first layer of encryption. This layer employs an encryption key and an S-box generated through a Memristive Coupled Neural Network Model (MCNNM), establishing a strong foundation for security. Following this, the novel integration of Moore’s Automaton with Gold sequences is applied as a confusion mechanism, intrinsically scrambling the image structure to effectively disrupt pixel correlations. The encryption process iterates over N cycles, significantly deepening the level of encryption with each iteration. Performance evaluations reflect a considerable key space of $$2^{2020}$$ and a high encryption rate of 15.5 Mbps, while rigorous statistical tests validate the algorithm’s resilience. The encryption system proposed in this manuscript not only ensures a formidable level of security but is also pragmatically designed for application in the protection of sensitive healthcare data.