Volume 12 Number 4 (April )

Exploring the Potential of Recycled Aggregates in Modern Construction Challenges and Innovations

Nur Hanisa Hapendi, Dayang Siti Hazimmah Ali

The reliance of the construction industry reliance on natural aggregates has led to significant environmental concerns, including resource depletion and habitat destruction. This paper explores the potential of recycled aggregates as sustainable alternatives to natural materials, emphasizing their environmental and economic benefits. It provides a comprehensive review of various types of recycled aggregates, their extraction processes, associated challenges, and recent innovations to improve their applicability. Key findings highlight the effectiveness of advanced treatment techniques and material modifications in enhancing the performance of recycled aggregates. The paper concludes with recommendations for future research and standardization efforts, aiming to integrate recycled aggregates into mainstream construction and contribute to a more sustainable built environment.

Numerical modeling of artificial egg incubator efficiency

Mbaya Ilunga Edouard, Binda Sombani Mafille

In summary, this work presented the essential points enabling the process of operating the artificial incubator, having sought to establish the link between theory and reality, the experimental phase was tackled. This involved manipulating the prototype artificial incubator materialized in the mechanical workshop of the Institut Supérieur de Techniques Appliquées de Kinshasa. The Matlab software was used to run simulations which produced results showing the variation in egg hatching rate as a function of temperature, the variation in internal temperature as a function of the incubator's external temperature, and the variation in humidity as a function of time (days). The results found were discussed with those found by other researchers.

Food and Beverage Establishment in Post-Pandemic: Business Design and Customer Satisfaction

Janry Paul Aganon Santos

This study discovers the business strategies food and beverage businesses use in Nueva Ecija to satisfy customers post-pandemic. With the profound changes in consumer behavior and expectations, while experiencing the pandemic, several establishments would need to adapt their operation and marketing strategies to address shifting customer necessities. The study found that the essential approach was to introduce upgraded health and safety protocols and expand capacity in service delivery. Providing a safe and clean location has become one of the most critical aspects of customer visits. This will instill more trust in the customers' minds, considering responsibly checking everything daily regarding regular sanitization and general cleanliness, as well as compliance with other health protocols and an extremely high standard of hygiene. This, along with good queue and wait time management and contactless methods of payment, will also improve the experience. Business strategies are also changing in response to changes in customer behavior, and digitalization marketing is evolving. Additionally, targeted promotions, discounts, and loyalty programs well-suited for a post-pandemic world enhanced customer retention and satisfaction. In addition, relationships between customers and establishments were improved through personalized marketing campaigns, more transparent communication, and security measures. Improving service delivery has also been critical to customer satisfaction. Those who retrained their staff to operate in the post-pandemic period improved service quality significantly.

High prevalence of cognitive impairment in elderly with diabetes mellitus and associated factors

Cognitive impairment in people with diabetes mellitus (DM) is multifactorial, with evidence that their poor metabolic control is related to impaired cognitive function. Background/Objectives: Identify the prevalence of diabetic older with cognitive impairment and characterize the factors associated with this finding. Methods: Cross-sectional, quantitative, descriptive and analytical research carried out with 246 diabetic older adults followed up in a specialized center in northeastern Brazil. Sociodemographic and clinical variables related to DM2 and evidence of cognitive decline were assessed using the Mini Mental State Examination (MMSE). Results: There was a prevalence of cognitive impairment in 74.4% of the studied sample with an association between cognitive impairment and the variables advanced age (p = 0.003) and female gender (p = 0.025). There was statistical significance between the presence of self-reported cognitive problems and cognitive impairment at evaluation (p < 0.001) and a 1.4 times higher prevalence ratio of cognitive impairment in diabetic underweight than in obese elderly (p = 0.020). Conclusions: The research reveals the high prevalence of cognitive impairment in older adults with diabetes, especially in women and advanced age. It also corroborates the association already described in the international literature between subjective complaints of memory and nutritional status and cognitive decline.

Systematic review on the relationship between HIV, Antiretroviral Therapy (ART), hyperglycemia and hypercholesteremia

HIV (Human Immunodeficiency Virus) infection continues to be a global public health concern, with millions of people living with the virus around the world, largely due to advances in Antiretroviral Therapy (ART) associated with ostensible prevention campaigns. However, if on the one hand a significant increase in the survival of patients with HIV is observed with the use of ART, on the other hand several studies warn of the adverse effects of prolonged use of ART on the metabolic system, particularly in the induction of a persistent hyperglycemic and hypercholesterolemic state, disorders that contribute to the overall reduction in survival due to the emergence of complications related to prolonged hyperglycemia and dyslipidemia, which motivated the present systematic review on the topic.

Simultaneous determination of Copper, Zinc and Nikel in Electroplating Waste water by UV-VIS Spectroscopy Combined with advanced Machine Learning and Deep Learning Models

Monitoring to evaluate wastewater quality during the production process requires simple measurements and Realtime analysis as well. Among common methods for heavy metal analysis, the UV-VIS absorption spectroscopy is considered a potential analytical method due to its low cost and simple operation, direct online integration with treatment tanks. However, it faces limitations in simultaneously analyzing multiple metals due to overlapping absorption spectra. This study applied machine learning (ML) algorithms (Decision Tree (DT), Random Forest(RF) and deep learning (DL) models (Multilayer Perceptron - MLP, and 1D Convolutional Neural Network - 1D-CNN) to improve the accuracy of simultaneous quantitative analysis of three metals—Cu, Zn, and Ni in electroplating wastewater—based on VIS absorption spectra data of their colored complexes in aqueous solution with the PAN reagent in the presence of a surfactant. Large datasets were collected from UV-VIS spectra of 500 wastewater spiked samples in the range of 620-500 nm with a 1 nm interval, resulting in a dataset of size 500x121, followed by the application of ML and DL models using the Python programming language. Model performance was evaluated based on the correlation coefficient (R²) and root mean square error (RMSE). Preprocessing methods such as first-order derivatives and Principal Component Analysis (PCA) were applied to reduce noise in the dataset before training with machine learning algorithms. Results showed that the 1D-CNN model outperformed the others, achieving R² > 0.88 and RMSE < 0.036 for all three analytes. It is supposed by its ability to directly extract nonlinear features from raw data without the need for dimensionality reduction. In contrast, the DT, RF, and even MLP models, which utilized principal component analysis (PCA) for dimensionality reduction, demonstrated significantly lower accuracy due to information loss during the reduction process. The proposed model was successfully applied for rapid and simple metal concentration determination in practical samples using a test kit with reagent, a compact spectrophotometer, and an automated PC-based data reading application. These findings demonstrate that combining UV-VIS spectroscopy with machine learning and deep learning algorithms is an effective and feasible approach for the simultaneous detection of multiple heavy metals in specific matrix wastewater samples.

Hyperautomation with Power Platform: Merging AI, RPA, and Low-Code for Business Efficiency – Exploring how AI Builder, Power Automate, and Dataverse can drive end-to-end enterprise automation

Sarat Piridi, Satyanarayana Asundi, Dr. James C Hyatt

Hyperautomation is studied as to how it can improve business efficiency using the Microsoft Power Platform tools. It further breaks down potential savings in costs caused by AI driven automation, unification of data and workflow orchestration and shows how productivity can be increased along with a raised accuracy. Using literature review and then quantitative evidence, the research gives insights for enterprise digital transformation that are actionable.

Cross-Environment Deployment Strategies for Power Platform Solutions – Investigating best practices for managing multi-environment deployments, from development to production, using managed environments and DevOps

Sarat Piridi, Satyanarayana Asundi, Dr. James C Hyatt

The cross-environment deployment strategies for Power Platform solutions which include moving Power Platform solutions from development to production using managed environments and DevOps practices. The paper takes advantage of ten key academic and industry sources to evaluate frameworks, automation tools and governing models to streamline deployment and enhance system reliability. The measurable benefits of such case studies are reduced deployment time and improved accuracy. With this DevOps deployed throughout cloud and hybrid platform and agile methodology, it facilitates scalable and secure deployments. The insights provided from the findings contribute to organizations wanting to improve performance, maintain consistency, and direct development to meet operational goals in dynamic contexts of enterprise.

Coastline Response to Groins Analysis

Syawaluddin Hutahaean

This research investigates the morphological response of coastlines to the construction of groins, focusing on both single groins and paired groins. The evolution of the coastline is analyzed with the aim of identifying a new stable coastline geometry. The approach involves modeling the stable coastline using polynomial functions, where the polynomial coefficients are determined through the application of point-specific and line-specific characteristics, as well as the principle of mass conservation. The resulting stable coastline configuration typically forms a crenulate-shaped bay an equilibrium shoreline geometry occurring between two headlands. The derived geometry, whether partial or complete, exhibits features consistent with the known characteristics and dimensions of a crenulate-shaped bay.The modeled stable coastline can be calibrated against empirical crenulate bay profiles, confirming its validity as a representation of an actual stable coastal form.

Energy recovery and Analysis of performance criteria of the energy potential of biomass for the production of electricity

P. Kyomba Mulenda, T. Lwamba Muba

This study focuses on the field of energy. Electricity production is an industrial sector that supplies consumers with electrical energy adapted to their needs. With current ecological and environmental requirements in the world, which wants to be more and more sustainable and non-polluting. The electricity production sector is seeking to exploit new sources of clean energy to align with today's sustainable context. The SDG . 7 for example is a leitmotif of the neo-producer of electricity in the 21st century who wants to guarantee access to reliable, sustainable and modern energy services for all at an affordable cost. So we made the choice of biomass in this work, as a source of primary, unlimited, non-polluting energy to produce, following its biochemical decomposition and fermentation, biogas which will be the raw material which will help us in the production of a electricity at a good price and accessible to large masses of the world population, two methods of analyzing energy performance criteria are developed in this work for a good selection of biomass, namely Gaussian Mixture Model (GMM) and DEEPLEARNING.