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In this paper, we discuss the state of the art and current trends in designing and optimizing ETL workflows. We explain the existing techniques for: (1) constructing a conceptual and a logical model of an ETL workflow, (2) its corresponding physical implementation, and (3) its optimization, illustrated by examples. The discussed techniques are analyzed w.r.t. their advantages, disadvantages, and challenges in the context of metrics such as autonomous behavior, support for quality metrics, and support for ETL activities as user-defined functions. We draw conclusions on still open research and technological issues in the field of ETL. Finally, we propose a theoretical ETL framework for ETL optimization.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R 2 ) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.

Purpose Collaborative research and development have remained a pertinent mechanism for conducting technological innovations. With the lens of knowledge-based view (KBV), this study aims to examine the role of changes in knowledge couplings and network cohesion to elevate innovation performance. Design/methodology/approach Data analysis has been performed on 53,459 patents through regression analysis with random effects. These independent and joint patents are extracted from Derwent Innovation Database. Findings Findings explicate that change in external existing or existing and new knowledge couplings have inverted U-shaped effects on a firm’s innovation performance. Changes in internal existing or existing and new knowledge couplings have direct positive effects on firm’s innovation performance. The moderation effect of network cohesion flattens the inverted U-shaped effect of external new and existing knowledge coupling, whereas it has no significant effect on external existing knowledge coupling. Network cohesion further elevates the effects of internal knowledge couplings – existing or existing and new. Research limitations/implications This study theoretically contributes to KBV and innovation management literature by highlighting the scope of changes in internal and external knowledge couplings and subsequent output. Network cohesion flattens the curviness of changes in external new and existing knowledge couplings, which is a contribution to strategic management literature. Practical implications Organizations need to carefully manage changes in knowledge couplings and ensure their benefits (obtain new knowledge domain or new combination) outweigh liabilities (damages to organizational routines or increase in collaboration costs). Managers must consider four kinds of knowledge coupling changes along with developing network cohesion as an R&D strategy. Originality/value This study is one of its types to flatten the curve through network cohesion. This study divided the changes in knowledge coupling into four types and two dimensions; external existing and new and existing knowledge couplings and internal existing and new and existing knowledge couplings.

PurposePrevious research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.Design/methodology/approachThe authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.FindingsResults indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.Originality/valueFrom the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

Central nervous system (CNS) tumors represent a significant global health burden, with their incidence affected by genetic and environmental factors alongside substantial underreporting in low and middle-income countries (LMICs).1 Among primary CNS tumors, meningiomas are the most common benign neoplasms (41.7%), and glioblastomas the most frequent malignant type (13.9%).2 Secondary CNS metastases constitute the largest share of cases, with an incidence of 24.2 per 100,000 population.3 Globally, a higher incidence has been observed in males.4 Significant regional discordance exists in brain tumor incidence. In Punjab, Pakistan, a crude estimated annual incidence rate of brain tumors is 0.21 per 100,000 population.5 In contrast, the global age-standardized incidence rate for primary malignant brain and other CNS tumors is reported at 3.5 per 100,000 population, likely due to robust surveillance systems and higher diagnostic sensitivity.4 Surgical intervention remains the cornerstone of treatment for CNS tumors, with various techniques. These include open, minimally invasive, endoscopic, and microscopic approaches, employed based on tumor type, size and location. Regardless of the surgical approach, patients remain at risk of complications. While some perioperative complications such as sensory and motor deficits, hydrocephalus, and seizures are well-documented in the literature, certain other sequelae remain largely unrecognized and underreported. We wish to bring attention to those overlooked perioperative sequelae which are often considered trivial and thus dismissed in clinical discussions. Several aspects of the perioperative experience remain largely unaddressed despite their impact on patient well-being. Preoperative head shaving without prior discussion causes body image problems for patients unprepared for the change.6 Catheterization, though routine, can lead to catheter-related bladder discomfort.7 Intraoperatively, blood transfusions are often addressed only in terms of necessity, with limited attention to potential complications or patient preferences. Postoperative recovery presents further challenges. Heavy dressings add discomfort, and multiple intravenous and arterial lines may cause pain. Food and water restrictions further increase patient distress. The intensive care unit (ICU) setting itself can be overwhelming, with continuous noise, bright lights, and frequent interventions causing disruption in rest. An open and transparent discussion of risks, benefits, and alternative treatments is fundamental to patient-centered care. The focus is currently primarily being placed on life-threatening or functionally debilitating complications. The omission of conversation regarding the aforementioned sequelae is often a result of these issues being perceived as minor. However, patients cannot make fully informed decisions about their care if they are unaware of these other factors. Neglecting to disclose such pertinent details raises ethical concerns, potentially conflicting with the medical ethical principles of autonomy and informed consent. A comprehensive preoperative discussion that includes these aspects can significantly enhance patient satisfaction, trust in the medical team, and overall perception of care. This is particularly crucial in neuro-oncology patients, who often face heightened emotional vulnerability due to the nature of their diagnosis and treatment. Hence, we aim to highlight these perioperative sequelae in neuro-oncology patients. While often considered inconsequential, these factors continue to impact patient well-being. This letter underscores the need for a comprehensive preoperative discussion and transparent communication as key components of informed consent. Implementing this approach can enhance patient satisfaction and trust, ultimately improving the overall surgical experience for neurooncology patients. Healthcare providers should be encouraged and trained to always disclose these seemingly insignificant sequelae that impact patients' physical and psychological equilibrium. Comprehensive discussion of both major and minor complications should be performed. A detailed informed consent paper can be introduced as a part of the pre-operative discussion. The consent paper should hierarchically contain all the minor and major complications to ensure that the patients are fully informed about all aspects of care. Future studies should assess whether discussing these overlooked postoperative sequelae leads to measurable improvements in patient outcomes, satisfaction scores, psychological well-being, and physical comfort. Studying these outcomes could provide insights into the role of informed discussions in enhancing holistic patient care. Such findings might enhance patient care strategies and their overall well-being.

Effective decision-making in complex environments often requires handling fuzzy data with interdependencies. The generalized Heronian mean and geometric Heronian mean operators have proven useful for such analysis. However, existing methods struggle to capture correlations among pq-quasi rung orthopair fuzzy (pqQROF) numbers. This study addresses this gap by extending these operators to the pqQROF setting and developing a novel hybrid decision-making framework. The proposed framework integrates a distance measure, rank sum (RS) method, and aggregation operators to determine both objective and subjective criteria weights. The applicability of the approach is demonstrated through two case studies: project selection and university selection. Finally, a comparative evaluation of the developed operators against existing ones is conducted to assess their effectiveness and highlight the superiority of the proposed decision-making algorithm.

This research focuses on the automation of an existing structural health monitoring system of a bridge using the BIMification approach. This process starts with the Finite Element Analysis (FEA) of an existing bridge for the numerical calculations of static and dynamic parameters. The validation of the FE model and existing SHM system was carried out by the field load testing (Static and dynamic) of the bridge. Further, this study tries to fill the research gap in the area of automatic FE model generation by using a novel methodology that can generate a BIM-based FE model using Visual Programming Language (VPL) scripts. This script can be exported to any FE software to develop the geometry of the FE model. Moreover, the SHM devices are deployed to the Building Information modelling (BIM) model of the bridge to generate the BIM-based sensory model (as per the existing SHM system). In this way, the BIM model is used to manage and monitor the SHM system and control its sensory elements. These sensors are then linked with the self-generated (Internet of Things) IoT platform (coded in Arduino), developing a smart SHM system of the bridge. Resultantly, the system features visualisation and remote accessibility to bridge health monitoring data.

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R 2 ), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R 2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.