Search Results (1,507)

In mineral processing and metallurgy, total iron grade serves as a critical indicator guiding the entire production chain from crushing to smelting, directly influencing the quality and yield of steel products. To address the limitations of conventional matrix effect correction methods in X-ray fluorescence (XRF) analysis—such as low accuracy, high time consumption, and labor-intensive procedures—this study proposes a novel hybrid model (DSCN-LS) integrating least squares (LS) with dynamically regularized stochastic configuration networks (DSCNs) for total iron ore grade quantification. Through feature analysis, we decompose the grade modeling problem into a linear structural component and nonlinear residual terms. The linear component is resolved by means of LS, while the nonlinear terms are processed by the DSCN with a dynamic regularization strategy. This strategy implements node-specific weighted regularization: weak constraints preserve salient features in high-weight-norm nodes, while strong regularization suppresses redundant information in low-weight-norm nodes, collectively enhancing model generalizability and robustness. Notably, the model was trained and validated using datasets collected directly from industrial sites, ensuring that the results reflect real-world production scenarios. Industrial validation demonstrates that the proposed method achieves an average absolute error of 0.3092, a root mean square error of 0.5561, and a coefficient of determination (R²) of 99.91% in total iron grade estimation. All metrics surpass existing benchmarks, confirming significant improvements in accuracy and operational practicality for XRF detection under complex industrial conditions Full article

Early cost assessment is an essential part of building construction strategy; however, preliminary estimates are occasionally unreliable given incomplete data, which causes budgetary overruns. In general, traditional prediction techniques are imprecise and sluggish, particularly while the project scope is still unclear. By introducing a hybrid framework that utilizes ANNs for renovation cost estimation and features enhancements by the TOPSIS method to guarantee contextual relevance and input accuracy, the present research overcomes these drawbacks. Utilizing data from projects that are structurally and contextually comparable enhances the model’s predicted reliability and robustness. The study builds, trains, and tests two ANN models using IBM SPSS Statistics software, which is based on a thorough literature review and actual renovation data from construction businesses. One model utilized 53 data points from prior building renovation projects, whereas the second model employed 11 data points from post-TOPSIS technique building renovation projects. The Radial Basis Function (RBF) procedure is the basis for models that include 14 input data such as total initial cost, estimated completion time, initial demolition drainage cost, initial cost of plumbing work, initial heating cost, initial cost of electrical work, initial cost of masonry coatings, initial cost of plasterboard construction, initial bathroom cost, initial flooring costs, initial frame cost, initial door cost, initial paint cost, and initial kitchen construction cost, and one output data, the total final cost. The models show excellent performance with near 0.5 relative error and up to 0.3 monetary units sum of squares error before applying the TOPSIS method and nearly 0.6 relative error and up to 0.8 monetary units sum of squares error after the TOPSIS implementation, proving the usefulness and demonstrating the speed of the ANN in estimating overall renovation costs in combination with the TOPSIS approach. By employing this hybridized approach, the entire contingent procedure is expedited and accomplished more rapidly. Full article

Nigeria’s aquaculture sector, which has been dominated by the production of African catfish (Clarias gariepinus), has held considerable potential to improve national food security, support livelihoods, and contribute to economic growth. Although Nigeria has been ranked among the world’s leading producers of farmed catfish, a persistent fish supply deficit that exceeds 2.5 million metric tons annually has been reported. This gap has been sustained by factors such as low productivity, genetic decline, inadequate hatchery systems, and limited export competitiveness. A comprehensive perspective is presented in this review, in which findings from recent researches, field surveys, and stakeholder consultations have been synthesized. The dominance of hybrid species such as Heteroclarias, which has been driven by consumer demand due to fast growth and large body size, is highlighted. Additionally, ecological and genetic concerns resulting from unregulated breeding are emphasized. Major systemic constraints, which include poor broodstock management, weak hatchery infrastructure, low technical capacity, and poor supply chain governance, have also been identified. A strategic approach involves modernizing fish breeding programs by developing a robust and active Fish Breeding Community of Practice (FCoP), enhancing physical infrastructure, improving data collection and management, standardizing germplasm exchange procedures, and increasing the number and capacity of fish breeders and technicians in breeding programs. Identifying traits preferred by farmers and end-users ensures that fish breeding is demand-driven and inclusive. Building capacity in genomic resources to implement an accurate predictive platform for performance assessment will significantly shorten the breeding cycle and increase the rate of genetic progress. This will be complemented by the adoption of modern aquaculture technologies, such as recirculating aquaculture systems, and the development of institutional frameworks for production, certification, and traceability schemes. Capacity development, which should be promoted through collaboration among academic institutions, industry actors, and government agencies, has been recommended. The alignment of aquaculture expansion with environmental sustainability, improved biosecurity, and habitat protection has been considered critical. By outlining strategies for innovation, investment, and policy reform, this review provides a roadmap through which Nigeria’s catfish industry can be transformed into a globally competitive and sustainable sector. Full article

Composite modular panels are increasingly used in modern buildings, yet their layered behavior makes mechanical characterization and modeling difficult. This study presents a novel hybrid framework that integrates analytical, numerical, and AI-driven approaches for the mechanical characterization of composite panels. The system combines a layered concrete configuration with embedded steel reinforcement, and its performance was evaluated through experimental testing, analytical formulation, finite element simulations, and artificial intelligence techniques. Full-scale bending and shear tests were conducted and results in terms of displacements were compared with in silico simulations. The equivalent elastic modulus and thickness were suggested via a closed-form analytical procedure and validated numerically, showing less than 3% deviation from experiments. These equivalent parameters were used to simulate the dynamic response of a two-storey prototype building under harmonic excitation, with simulated modal periods differing by less than 10% from experimental data. To generalize the method, a parametric dataset of 218 panel configurations was generated by varying material and geometric properties. Machine learning models including Artificial Neural Network, Random Forest, Gradient Boosting, and Extra Trees were trained on this dataset, achieving R² > 0.98 for both targets. A graphical user interface was developed to integrate the trained models into an engineering tool for fast prediction of equivalent properties. The proposed methodology provides a unified and computationally efficient approach that combines physical accuracy with practical usability, enabling rapid design and optimization of composite panel structures. Full article

Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at breast height (DBH) and tree height, within a small urban park in Zadar, Croatia. Accuracy assessment of the ULS and TLS-derived DBH was conducted based on traditional ground-based measurement (TGBM) data. For ULS, an automatic Spatix workflow was applied that classified points into a Tree class, segmented trees using trunk-based logic, and estimated DBH by fitting a circle to a 1.3 m slice; tree height was computed from the ground-normalized cloud with the Output Tree Cells tool. A semi-automatic CloudCompare/ArcMap workflow used CSF ground filtering, Connected Components segmentation, extraction of a 10 cm slice, manual trunk vectorization, and DBH calculation via Minimum Bounding Geometry. TLS scans, processed in FARO SCENE, were then analyzed in Spatix using the same automatic trunk-fitting procedure to derive DBH and height. Accuracy for DBH was evaluated against TGBM; comparative performance was summarized with standard error metrics, while ULS and TLS tree heights were compared using Concordance Correlation Coefficient (CCC) and Bland–Altman statistics. Results indicate that the semi-automatic approach outperformed the automatic approach in deriving DBH. TLS-derived DBH values demonstrated higher consistency and agreement with TGBM, as evidenced by their strong linear correlation, minimal bias, and narrow residual spread, while ULS exhibited greater variability and systematic deviation. Tree height comparisons between ULS and TLS revealed that ULS consistently produced slightly higher and more uniform measurements. This study highlights limitations in the evaluated techniques and proposes a hybrid approach combining ULS scanning with personal laser scanning (PLS) systems to enhance data accuracy in urban forest assessments. Full article

The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure prediction are essential elements of modern fleet management. This paper presents an analysis of the reliability of fuel systems based on operational data from ten bulk carriers operated by Polska Żegluga Morska in Szczecin. The analysis combined classical statistical methods with artificial intelligence algorithms to develop a hybrid diagnostic and forecasting framework. The Weibull lifetime distribution was applied to estimate time-to-failure parameters, revealing mixed failure mechanisms—random failures (k < 1) and aging-related processes (k > 1). Using the k-means algorithm, ships were automatically classified into two reliability groups: high-failure-rate units and stable operational vessels. Individual linear regression models were then developed for each ship to forecast the time to the next failure, achieving satisfactory predictive performance (R² > 0.75 for most vessels). Sensitivity analysis quantified model robustness under different disturbance scenarios, yielding mean Relative Prediction Deviation (RPD) values of approximately 65% for Missing Data, 60% for False Failure, and 26% for Data Noise. These results confirm that the proposed hybrid reliability–AI framework is resistant to random noise but sensitive to incomplete or erroneous historical data. The developed approach provides an interpretable and effective tool for predictive maintenance, supporting reliability management and operational decision-making in marine engine systems. The article presents a hybrid model that has been developed to enable the detailed characterization of emergency processes and the identification of the most important factors that influence damage forecasting. For systems with variable failure risk, it was found that both classical probabilistic models and machine learning methods must be considered to interpret damage patterns correctly. Implementing data filtration and validation procedures before using data in artificial intelligence models has been shown to improve forecast stability and increase the usefulness of forecasts for planning repairs. Full article

Background/Objectives: Minimally invasive direct coronary artery bypass (MIDCAB) surgery, performed through a left minithoracotomy, has emerged as an alternative to conventional coronary artery bypass grafting (CABG), which requires a full sternotomy. This procedure is ideal for patients with isolated proximal left anterior descending (LAD) artery disease or high surgical risk. The aim of this study was to assess the long-term clinical outcomes of MIDCAB performed at a single center with stratification by revascularization strategy. Methods: A total of 480 patients who underwent off-pump MIDCAB between 2012 and 2024 at a single center were retrospectively analyzed and categorized into three distinct groups: complete revascularization (MIDCAB-CR), hybrid coronary revascularization (MIDCAB-HCR) and incomplete revascularization (MIDCAB-IR). Short- and long-term outcomes, including mortality, major adverse cardiac and cerebral events (MACCE) and LITA–LAD graft patency were evaluated. Median follow-up was 3.39 years. Results: In-hospital mortality was 1.4%. At a median follow-up duration of 3.39 years, the overall LITA–LAD graft patency was 94.4% with 5- and 10-year survival rates of 78% and 60%, respectively. MIDCAB-CR and MIDCAB-HCR groups showed comparable long-term survival and freedom from MACCE, both significantly better than those observed in the MIDCAB-IR groups. Conclusions: These findings support the safety and durability of MIDCAB as an effective revascularization strategy, especially when performed as complete or hybrid revascularization. Incomplete revascularization may be considered in selected high-risk patients but is associated with worse outcomes. Full article

The integration of generative artificial intelligence, particularly large language models, into education presents opportunities for both personalised learning and pedagogical challenges. This study focuses on electrical engineering laboratory education. We developed a configurable prototype of a generative artificial intelligence powered tutoring tool, implemented it in an undergraduate electrical engineering laboratory course, and analysed 208 student–tutoring tool interactions using a mixed-methods approach that combined research team evaluation with learner feedback. The findings show that student prompts were predominantly procedural or factual, with limited conceptual or metacognitive engagement. Structured prompt styles produced clearer and more coherent responses and were rated the highest by students, while approaches aimed at fostering reasoning and reflection were valued mainly by the research team for their pedagogical depth. This contrast highlights a consistent preference–pedagogy gap, indicating the need to embed stronger instructional guidance into artificial intelligence tutoring. To bridge this gap, a promising direction is the development of pedagogically enriched AI tutors that integrate features such as adaptive prompting, hybrid strategy blending, and retrieval-augmented feedback to balance clarity, engagement, and depth. The results provide practical and conceptual value relevant to educators, developers, and researchers interested in artificial intelligence tutors that are both engaging and pedagogically sound. For educators, the study clarifies how students interact with tutors, helping align artificial intelligence use with instructional goals. For developers, it highlights the importance of designing systems that combine usability with educational value. For researchers, the findings identify directions for further study on how design choices in artificial intelligence tutoring affect learning processes and pedagogical alignment across STEM contexts. On a broader level, the study contributes to a more transparent, equitable, and sustainable integration of generative AI in education. Full article

Background: Drug-coated balloons (DCBs) have transformed percutaneous coronary intervention (PCI) by delivering antiproliferative drugs without leaving a permanent scaffold. DCB is initially indicated for in-stent restenosis (ISR) and now has expanded indication for treating small vessel disease and bifurcation lesions. However, there is a heterogeneity in the patient and lesion selection, lesion preparation techniques, and the optimal duration of dual antiplatelet therapy after DCB angioplasty. The Cardiovascular Intervention Association of Thailand (CIAT) developed a consensus statement on DCB use in coronary interventions. Methods: The CIAT expert panel systematically reviewed randomized controlled trials, meta-analyses, and real-world studies evaluating DCB therapy. Procedural strategies, imaging guidance, physiologic assessment, and antiplatelet therapy protocols were appraised. The recommendations were developed and put to an online vote. Consensus was defined when the recommendation reached 80% of votes in support of “agree” or “neutral”. Results: Clinical evidence demonstrates that DCBs achieve comparable outcomes to drug-eluting stents (DESs) in selected lesions while enabling shorter durations of dual antiplatelet therapy (DAPT), particularly beneficial for high-bleeding-risk patients. Optimal outcomes require meticulous lesion preparation, appropriate balloon sizing, and controlled vessel dissection. Intravascular imaging and physiologic assessment further refine procedural precision, while hybrid strategies combining DCBs and DESs address complex lesions and multivessel disease. The final document presents 15 consensus statements addressing indications, procedural techniques, imaging and physiologic guidance, and antiplatelet therapy recommendations. Conclusions: DCB angioplasty can be an alternative or complement to therapeutic options to DESs across multiple clinical and anatomical scenarios. The CIAT consensus provided structured recommendations to support DCB therapy in contemporary practice. Full article

Background and Objectives: Pulmonary sequestration (PS) is a rare congenital lung malformation. In adults, intralobar disease with recurrent infection or hemoptysis predominates. Cross-sectional imaging (CTA/MRA) is central to mapping the aberrant systemic supply; catheter angiography is used when noninvasive imaging is inconclusive or when an endovascular procedure is planned. We aimed to synthesize adult PS cases treated with endovascular or hybrid approaches and to summarize case selection, techniques, and outcomes. Methods: We conducted a PRISMA-2020-informed systematic review. We searched PubMed and Scopus from 1 January 2000 to 31 May 2025. Two reviewers extracted data independently; due to heterogeneity, we performed a narrative synthesis and a JBI-adapted qualitative risk-of-bias appraisal. Eligible studies enrolled adults (≥18 years) with imaging-confirmed PS treated with embolization, stent-graft exclusion, or hybrid therapy; prespecified outcomes included technical and clinical success, complications, recurrence, and re-intervention. The review was not registered. Results: Of 93 records screened, 41 publications reporting 48 adults were included. Twenty-five patients were managed endovascularly and 23 with hybrid therapy. Intralobar sequestration predominated (36/48); feeding arteries most often arose from the descending thoracic aorta (28/48). Complications were reported in 10 cases, mostly minor; three embolization cases required re-intervention. Conclusions: Endovascular therapy is useful for selected anatomies and urgent bleeding control, while hybrid strategies may benefit large, complex, or aneurysmal feeding arteries. The evidence base is limited to small case reports/series with heterogeneous outcome definitions and follow-up, precluding quantitative synthesis. Standardized outcome definitions, structured follow-up, and prospective registries are needed. Full article

Thoracic aortic atresia in adults represents a rare and extreme manifestation of aortic coarctation, marked by complete luminal occlusion and frequently compensated by extensive collateral circulation. This narrative review critically evaluates existing literature and institutional experience concerning surgical and endovascular repair strategies for aortic atresia, synthesizing evidence from related aortic arch pathologies due to the absence of direct comparative studies. Both treatment modalities—open surgical repair and catheter-based recanalization with stenting—have evolved significantly, presenting distinct advantages and limitations influenced by patient anatomy, age, and comorbidities. While surgical repair remains the standard for neonates, infants, and complex cases due to superior long-term durability and blood pressure control, endovascular procedures using chronic total occlusion technique and covered stents offer less invasive alternatives with rapid recovery, particularly in adults with suitable anatomic characteristics. The review highlights procedural considerations, including technical approaches, stent selection, and potential complications such as restenosis, hypertension, and vascular injury. Individualized, multidisciplinary decision-making remains essential, with shared consensus guiding therapy in the absence of randomized trials. The article identifies critical gaps in knowledge, emphasizing the need for multicenter, long-term studies and technological advances—including hybrid and personalized strategies for optimal management and for improving outcomes in this challenging congenital condition. Full article

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that AI has the potential to improve climate modeling’s regional accuracy and computing efficiency. Machine learning downscaling better captures local precipitation extremes than GCMs, while hybrid AI–physics models cut ensemble costs by reducing computational demand without sacrificing accuracy. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, highlighting that AI complements GCMs in regional downscaling and extremes, while GCMs provide stronger global consistency, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what AI has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate’s future, we must keep pushing the limits of the existing modeling tools. Full article

An assembly line is a manufacturing process commonly used in the production of commodity goods. The assembly process is divided into elementary tasks that are sequentially performed at serially arranged workstations. Among the various challenges that must be addressed during the design and operation of an assembly line, the assembly line balancing problem involves the assignment of tasks to different workstations. In its simplest form, this problem aims to distribute assembly operations among the workstations efficiently. An efficient line is one that optimizes a specific objective function, usually associated with maximizing throughput or minimizing resource requirements. In this study, we adopt a bi-objective approach to find a Pareto set of efficient solutions balancing throughput and resource requirements. To address this problem, we propose a multi-objective evolutionary method, complemented by single- and multi-objective local search procedures that leverage a polynomially solvable case of the problem. We then compare the results of these methods, including their hybridizations, through a computational experiment demonstrating the ability to achieve high-quality solutions. Full article

Hybrid woven composite materials and structures have important application value in modern engineering because of their high specific stiffness, specific strength and excellent impact resistance. The mechanical properties of carbon/aramid fiber hybrid woven composite laminates under repeated low-velocity impacts were studied in this paper. This study aims to understand the behavior of these materials under repeated impact conditions and to evaluate their damage resistance and failure mechanisms. The materials and methods used are introduced in detail, including the preparation of samples, the experimental apparatus for impact testing, and the methods of damage assessment and data analysis. The experimental setup simulated real impact scenarios and followed procedures to collect and analyze data. The low-velocity impact tests were carried out in accordance with ASTM D7136 test standard. The experimental results show that with the increase in impact energy, the damage of laminates includes delamination, matrix cracking and fiber fracture. The damage threshold and damage propagation rate are affected by the type of fiber used and its lay-up direction in the composite. Compared with (0,90)₁₂ laminates, [(0,90)/(±45)]_3s laminates show more obvious damage expansion, which highlights the importance of fiber orientation in the impact durability design of laminates. The results can be used to design and optimize the structure of hybrid woven composite laminates. Full article

Background: Leishmania amazonensis, one of the causative agents of cutaneous leishmaniasis, is responsible for a neglected tropical disease affecting nearly one million individuals worldwide. Although clinical treatments are available, their effectiveness is often compromised by high toxicity and limited selectivity. Methods: From a database, 64 chalcone derivatives were studied using Comparative Molecular Similarity Indices Analysis (CoMSIA) and Hansch analysis (2D-QSAR) to construct predictive computational models. Internal and external validation criteria were applied to identify structural determinants associated with antileishmanial activity. Based on these insights, twelve novel quinoline–chalcone hybrids were designed, synthesized, and biologically evaluated against L. amazonensis. Results: The most robust CoMSIA model combined steric and hydrogen-bond acceptor fields (CoMSIA-SA), while Hansch analysis highlighted electronic descriptors—specifically LUMO energy and the global electrophilicity index—as critical for parasite growth inhibition. Guided by these computational findings, a new series of 12 hybrid quinoline–chalcone derivatives (E001–E012) was synthesized through a two-step procedure, achieving overall yields of 43–71%. Biological assays demonstrated that four compounds displayed inhibitory activity comparable to amphotericin B. Furthermore, physicochemical profiling and in silico pharmacokinetic predictions for the most active compounds (E003, E005, E006, and E011) indicated favorable biocompatibility and drug-like properties. Conclusions: These results underscore the value of an integrative computational–experimental approach in the rational design of next-generation L. amazonensis inhibitors, reinforcing chalcone-based scaffolds as promising pharmacophoric frameworks for antileishmanial drug discovery. Full article