This study critically examines the contributions of the researchers’ contributions to geopolymer concrete (GPC) within the context of global research. The study covers key aspects including workability, strength properties, microstructural, and long-term durability behavior of GPC produced using fly ash alone and in combination with other agricultural and industrial by-products. The findings indicate that fly ash contain GPC shows comparable or superior strength and durability assessment to standard concrete, along with improved resistance to chemical degradation and a denser microstructure. The review also highlights the mix proportions, curing and concentration on the overall presentation of GPC. A unique contribution of this study lies in consolidating the researchers’ experimental results with existing literature to provide a coherent understanding of material behavior and optimization strategies for sustainable construction. In summary, the paper highlights the potential of fly ash–based GPC as an environmentally sustainable substitute for traditional cement-based systems.
With the rise of big data, numerous large-scale applications have shifted away from relational databases (RDB) to NoSQL stores owing to enhanced performance and flexibility. However, migrating from RDB to NoSQL stores involves tedious schema transformation. This is because existing NoSQL schema design approaches mostly rely on rules of thumb or guidelines for manually choosing a good schema. We have proposed a novel workload-driven and service level agreement (SLA)-aware model for relational to document store schema transformation. The proposed model is simultaneously automatic, workload-driven, and SLA-aware. The proposed model has three phases: model input, midway transformations, and model output. The proposed model begins with an extended entity-relationship schema along with workload information and SLA specifications as inputs. The paper proposes two algorithms for the midway transformations phase: (i) entity and relationship transformations, and (ii) workload-driven and SLA-aware refinements and transformations. The third phase includes generating the document store schema as model output. To validate the effectiveness of our research, we conducted an experimental evaluation using a case study in the e-commerce sector. The performance of the proposed model (P) is compared with the four existing workload-driven models, namely, GAF(G), UAF (U), QPG (Q), and Hypergraph (H), for relational to document store schema transformation. Our results show that the proposed model consistently improves query execution time, reduces read and write latency, and enhances aggregation pipeline performance. Finally, our results illustrate that our proposed model requires less storage space, is highly scalable, and demonstrates improved throughput and latency compared to existing models.
Diabetic foot ulcers (DFU) are a severe complication often leading to non-traumatic amputations if caught too late. While infrared thermography acts as a tool for early intervention by detecting hidden thermal anomalies, traditional AI models frequently act as black boxes that treat patients in isolation. This study introduces a shift toward Relational Manifold Learning through the k-GCNDFU framework. By transforming patient profiles into a connected Patient Similarity Network (PSN), our approach captures deep structural dependencies across a balanced manifold of 753 unique instances. Our six-layer Graph Convolutional Network (GCN) achieved a classification accuracy of 89.7%± 2.7% , a mean AUC of 0.952 ± 0.025 , and a Matthews Correlation Coefficient (MCC) of 0.782 ± 0.059 . Furthermore, GNNExplainer provides transparency by identifying the Medial Calcaneal Artery (MCA) as a key physiological driver of detection. These results demonstrate that the proposed relational approach offers a consistent, reliable and interpretable tool for the early detection of diabetic foot complications.
We present an integrated quantum-to-device-to-data framework for rational bandgap engineering of compositionally graded Ba(Hf Ba(Hf1-Ba(Hf1-x Ba(Hf1-xZr Ba(Hf1-xZrx Ba(Hf1-xZrx)S Ba(Hf1-xZrx)S3 absorbers and its impact on photovoltaic performance. Starting from a quantum-mechanical foundation (Bloch states, virtual crystal approximation, and bowing), four analytic grading laws (linear, parabolic, power-law, and logarithmic) were implemented in SCAPS-1D at 300 K and exhaustively explored via batch simulations by varying absorber thickness (0.1-1.0 mu mu m), left-end composition (x = 0 0-1), and a profile-specific parameter (uniform ND for linear/logarithmic; bowing factor b for parabolic; exponent m for power-law). Multi-dimensional contour maps of power conversion efficiency (n), open-circuit voltage (VOC), short-circuit current density (JSC), and fill factor (FF) were used to identify optimized device architectures; the best-performing designs (linear: n = 24.22%, parabolic: n = 25.08%, power-law: n = 25.45%, logarithmic: n = 18.52%) were analyzed through spatial band diagrams, J-V/EQE responses, and generation-recombination profiles. The optical and transport implications of grading were interpreted using relations such as the absorption edge Ac = 1239.8/Eg, penetration depth d = 1/ 1/, and the graded quasi-electric field 1dEc . Parallelly, machine learning regression models (SVR, RF, and ensemble stacks; hyperparameters d tuned, e.g., SVR: C = 100, e = 0.01, RBF; RF: n nestimators = 200) were trained on the simulated dataset to predict n, VOC, JSC, and FF, achieving high fidelity (R2 >= 0.99, low MAE/RMSE). SHAP interpretability consistently ranked absorber thickness as the principal driver of n and JSC, left-end composition as the dominant influence on V VOC and FF, and the curvature parameters (b, m) as secondary tuning knobs. Comparative analysis with literature demonstrates that the proposed TiO2/Ba(Hf1-xZrx)S3/PTAA architectures are competitive in PCE and FF while exhibiting distinct spectral and voltage trade-offs. The combined simulation-ML pipeline provides both quantitative optimization and physically interpretable design rules, motivating experimental validation, stability testing, and multi-objective ML-driven design for scalable graded-absorber photovoltaics. E Egrad = -
Energy efficiency and minimization of redundant transmissions are critical challenges in Wireless Sensor Networks (WSNs), especially in heterogeneous IoT environments where sensor nodes (SNs) are resource-constrained and deployed in remote or inaccessible areas. This paper aims to address the dual problem of uneven energy distribution and limited network lifespan by proposing a novel Artificial Protozoa Optimizer-based Cluster Head Selection (APO-CHS) algorithm. The proposed APO-CHS is inspired by the adaptive behavior of Euglena, integrating foraging, dormancy, and reproduction mechanisms to optimize cluster head and relay node selection through a multi-objective fitness function. The function incorporates residual energy, node density, neighbor distance, and energy consumption rate to guide the selection process effectively. Additionally, to tackle communication inefficiency, a lightweight data aggregation scheme is employed. This scheme reduces redundant transmissions by introducing a multi-level aggregation model that eliminates full, partial, and duplicate data in both intra-and inter-cluster communication. The simulation results demonstrate that the proposed framework improves network stability by 29.24%, extends network lifetime by 283.96%, and increases throughput by over 60% compared to baseline methods, thus making it a highly efficient and scalable solution for energy-aware IoT-enabled WSN applications.
