Beach protection is vital to reduce the damage to shorelines and coastal areas; one of the artificial protections that can be utilized is the tetrapod. However, much damage occurred when using a traditional tetrapod due to the lack of stability coefficient (K_D). Therefore, this research aims to increase the stability coefficient by providing minor modifications to the cape of the tetrapod, such as round-caped or cube-caped. The modification seeks to hold the drag force from the wave and offer a good interlocking in between the tetrapod. This research applied physical model test research using a breakwater model made from the proposed innovative tetrapod with numerous variations in dimensions and layers simulated with several scenarios. The analysis was carried out by graphing the relationship between the parameters of the measurement results and the relationship between dimensionless parameters, such as wave steepness H/gT2, and other essential parameters, such as the K_D stability number and the level of damage in %. The result shows that the modified and innovative tetrapod has a more excellent K_D value than the conventional tetrapod. In addition, the innovative tetrapod with the cube-shaped has a recommended K_D value greater than the round shape. This means that for the modified tetrapod structure and the same level of security, the required weight of the tetrapod with the cube cap will be lighter than the tetrapod with the round cap. These findings have significant practical implications for coastal protection and engineering, potentially leading to more efficient and cost-effective solutions.

Scientists have harnessed the diverse capabilities of nanofluids to solve a variety of engineering and scientific problems due to high-temperature predictions. The contribution of nanoparticles is often discussed in thermal devices, chemical reactions, automobile engines, fusion processes, energy results, and many industrial systems based on unique heat transfer results. Examining bioconvection in non-Newtonian nanofluids reveals diverse applications in advanced fields such as biotechnology, biomechanics, microbiology, computational biology, and medicine. This study investigates the enhancement of heat transfer with the impact of magnetic forces on a linearly stretched surface, examining the two-dimensional Darcy-Forchheimer flow of nanofluids based on blood. The research explores the influence of velocity, temperature, concentration, and microorganism profile on fluid flow assumptions. This investigation utilizes blood as the primary fluid for nanofluids, introducing nanoparticles like zinc oxide  and titanium dioxide (. The study aims to explore their interactions and potential applications in the field of biomedicine. In order to streamline the complex scheme of partial differential equations (PDEs), boundary layer assumptions are employed. Through appropriate transformations, the governing partial differential equations (PDEs) and their associated boundary conditions are transformed into a dimensionless representation. By employing a local non-similarity technique with a second-degree truncation and utilizing MATLAB’s built-in finite difference code (bvp4c), the modified model’s outcomes are obtained. Once the calculated results and published results are satisfactorily aligned, graphical representations are used to illustrate and analyze how changing variables affect the fluid flow characteristics problems under consideration. In order to visualize the numerical variations of the drag coefficient and the Nusselt number, tables have been specially designed. Velocity profile of -blood and -blood decreases for increasing values of  and , while temperature profile increases for increasing values of  and . Concentration profile decreases for increasing values of , and microorganism profile increases for increasing values of . For rising values of  and  the drag coefficient increases and the Nusselt number decreases for rising values of  and  The model introduces a novel approach by conducting a non-similar analysis of the Darchy-Forchheimer bioconvection flow of a two-dimensional blood-based nanofluid in the presence of a magnetic field.

The current era of Industry 4.0, driven by advanced technologies, holds immense potential for revolutionising various industries and fostering substantial economic growth. However, comprehending intricate processes of policy change poses difficulties, impeding necessary adaptations. Public apprehensions are growing about the inertia and efficacy of policy changes, given the influential role of policy environments in shaping development amidst resource constraints. To address these concerns, the study introduces the Kaleidoscope Model of policy change, serving as a roadmap for policymakers to enact effective changes. The study investigates the mediating impact of cultural change within the framework of the Kaleidoscope Model. The study delves into cultural influences by incorporating the Behavior Change Wheel (BCW) Theory. The methodology involves questionnaires survey, analysing using Structural Equation Modelling (SEM). The findings reveal that only the Policy Adoption and Policy Implementation components significantly affect the assessment of the effectiveness of the Construction 4.0 policy. Intriguingly, the final model demonstrates no discernible connection between the Kaleidoscope Model and the cultural influences. This study makes a noteworthy contribution to the realm of political science by furnishing a comprehensive framework and directives for the successful implementation of the Construction 4.0 policy.

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

By reviewing US state-level panel data on infrastructure spending and on per capita income inequality from 1950 to 2010, this paper sets out to test whether an empirical link exists between infrastructure and inequality. Panel regressions with fixed effects show that an increase in the growth rate of spending on highways and higher education in a given decade correlates negatively with Gini indices at the end of the decade, thus suggesting a causal effect from growth in infrastructure spending to a reduction in inequality through better access to education and opportunities for employment. More significantly, this relationship is more pronounced with inequality at the bottom 40 percent of the income distribution. In addition, infrastructure expenditures on highways are shown to be more effective at reducing inequality. By carrying out a counterfactual experiment, the results show that those US states with a significantly higher bottom Gini coefficient in 2010 had underinvested in infrastructure during the previous decade. From a policy-making perspective, new innovations in finance for infrastructure investments are developed, for the US, other industrially advanced countries and also for developing economies.