Dead time is a major source of instability and performance loss in process control systems, requiring effective compensation strategies. This study investigates temperature regulation in a two-tank thermal system with transport delay using two control configurations: PID with Smith Predictor (PID-SP) and PID with Internal Model Control (PID-IMC). A 10 L laboratory-scale stirred-tank heater was modeled as a first-order-plus-deadtime (FOPDT) process, and controller parameters were tuned using the Process Reaction Curve (PRC) method. Closed-loop simulations in Scilab/XCOS were performed for both regulatory and servo control cases under various delay conditions. The results show that PID-IMC achieves faster settling, smaller integral absolute error (IAE), and smoother manipulated-variable responses, while PID-SP offers better robustness to delay variations. The key finding highlights that PID-IMC provides higher precision for well-modeled processes, whereas PID-SP ensures stable performance under model uncertainty. These insights are valuable for improving process control design and implementation in industrial thermal systems with significant dead time.

This study investigates the causes of recurring road failures at Ekiti State University, Ado-Ekiti, Nigeria, using geophysical methods. The research aims to identify subsurface conditions contributing to these failures, assess the geological and geotechnical properties of the soil and rock, and recommend sustainable construction practices. Electrical resistivity using dipole-dipole configuration and two-dimensional (2D) electrical imaging where used. Data were collected through Ohmega Resistivity Meter, electrodes, and connecting cables, with measurements processed using Dipro-Win software. The results revealed four geological formations: topsoil with resistivity values ranging from 22 – 128 Ωm, a weathered layer (128 – 737 Ωm), a partly weathered basement (737 – 4232 Ωm), and fresh basement rock (>4232 Ωm). Weak zones with low resistivity, suspected as clayey material, were found between stations 3 and 7, with a thickness of less than 2.5 meters. These zones are associated with road cracks and failures. Moderate resistivity zones (likely laterite) between 7 and 10 meters demonstrated field competence. Below these layers, partly weathered and fractured zones were identified above fresh basement rock, with signs of weathering toward the east of the study area. According to the findings, the electrical resistivity approach was a useful tool for identifying the reasons behind road failures along Ekiti State University’s Management Science Road to college of medicine. For road rehabilitation projects, the discovered subsurface anomalies such as inadequately compacted soil and underground water channels offer important insights. The study shows how geophysical techniques can be used to solve problems with road infrastructure and shows how they can be used more widely in road engineering and maintenance projects.

Historic cities have heritage cores which are critical parts of the city that reduce cultural, architectural and historical awareness. The ever-growing rate of urbanization and the increasing need to establish modern transport infrastructure in these precincts pose a strong challenge in conservation. Infrastructure projects like extensions of the metro, expansion of roads and development of transit interchanges are often in opposition to the aims of heritage preservation and impose physical degradation, socio-economic disruption and visual intrusion, endangering the coherence and authenticity of heritage cores. The paper is a systematic review of the spatial, functional, and governance-related issues, occurring between the development of the transport infrastructure and the conservation of heritage cores. It includes a systematic review of the literature, as well as compares world-case studies to identify the causal preconditions and common trends. Results highlight disjointed governing frameworks, legal barriers, and inadequate heritage impact assessment processes that contribute to the heightening of the clashes that occur between the compulsions of mobility and the requirements of conservation. The paper examines most effective practices and effective planning strategies that have developed out of global models, such as the Historic Urban Landscape Methodology by UNESCO and principles of adaptive reuse. Improved technology e.g. GIS based risk mapping and vibration mitigation measures are also evaluated on ability to reduce undesirable effects. The paper supports the idea of participatory governance, combining land-use planning and transport planning, and multi-stakeholder cooperation as the key measures to promote sustainable urban development in respect of heritage values. The knowledge they create is added to the development of a comprehensive planning structure that balances the development of transport infrastructure with the preservation of heritage cores. The framework was developed to help urban planners, policy makers and conservation practitioners to develop resilient, inclusive and culturally aware urban environments. It also reaffirms the need to connect conservation priorities to the changing needs of urban mobility and therefore secure sustainable destinies of historic cities in the world.

This study explores the potential use of Tunisian raw materials in ceramic applications. Clay mineral collected from Kef Abed (KA) in northern Tunisia (Numidian zone) was blended with limestone (Abiod formation) and dolomitic sedimentary rock as auxiliary fluxes in ceramic bodies. The Kef Abed clay is predominantly kaolinitic, with other associated minerals such as illite, smectite, quartz, feldspar, and hematite. Two different granulometry distributions of KA clay were blended with varying percentages of limestone (5%, 15%, and 25%), while dolomite was used at a fixed percentage (5%). Ceramic bodies were formed by pressing at 7 tons and subsequently fired at 500°C, 750°C, and 1000°C. The chemical and mineralogical compositions were analyzed using ICP and X-ray diffraction, respectively. FTIR and TG-DTA were employed to characterize the raw and purified KA clay samples, while TEM images were used to investigate the fired materials. The thermal behavior study of KA clay (both raw and purified) using X-ray data and TEM images revealed the presence of a mullite phase, with its quantity increasing as the granulometry decreased. Mineralogical analysis indicated that ceramics tempered with 5% limestone and 5% dolomite are particularly suitable for ceramic applications

In central Punjab, rainfall patterns have been significantly impacted by climate change, leading to anomalies that affect agriculture, water resources, and disaster management. Long-term yearly rainfall data from Multan, Bahawalnagar, and Sargodha are analysed in this work utilising time-series and statistical techniques, such as ARIMA, SARIMA, and L-moment analysis. The findings demonstrate that Sargodha exhibits extremely varied and unpredictable rainfall patterns, Bahawalnagar has the most steady and highest rainfall, and Multan has the least but comparatively consistent rainfall. Forecasts for the years 2017 to 2027 indicate that while Sargodha will continue to have unpredictable rainfall patterns, Bahawalnagar and Multan will continue to have consistent tendencies. The likelihood of severe rainfall occurrences is shown by positive skewness in all three cities. These results highlight how crucial local-scale rainfall studies are for wellinformed planning in agriculture, water management, and flood control in the face of climate change. In central Punjab, this study offers unique city-scale evidence of rainfall predictability limits under changing climate conditions.

This research presents an analysis of the estimated solar radiation using Maximum and minimum daily temperatures (Tdmax and Tdmin), and by applying six models. The estimated solar radiation was calibrated by the recently developed CSR model, and extensive digital data sets derived from satellite observations edited in the Atlas Solar Radiation for the Saudi Arabia edited by KACST with the collaboration of Center for renewable energy resources of Colorado. The model performance was analyzed using four statistical models (RMSE, ME, R2, MAE).

The problem of access to clean water by urban poor households in low-income countries persists because the households are not connected to water distribution network (WDN). Hence, they lack public water supply in a poor sanitary environment, and are liable to suffer water-related diseases. This is especially true of the people of Nyanya Mararaba Town in Nigeria’s Federal Capital Territory. This study explores the combination of geospatial technology and four machine learning techniques to develop functional models for predicting volume of water consumed by urban poor households. Random Forest performed better than other machine learning techniques during training with RMSE of 5.99 liter (L) in dry season, and RMSE of 6.59 liter (L) in wet season. Average water consumption in dry and wet seasons are 20 liters per capita per day (LCPD) and 23 LCPD respectively. The functional models were validated with RMSE of 5.67L and 4.92L in dry and wet seasons respectively, so providing a tool for planning water supply to urban poor households where there is no WDN.

The Churiyamai Tunnel that was constructed in 1917 near Hetauda is the first highway tunnel in Nepal. Therefore, this tunnel holds significant historical and infrastructural importance. Over the time, the tunnel suffered structural degradation issues, including partial collapses, developed cracks in linings, material deterioration, and water leakage in various sections. This manuscript describes the ratification work carried at this tunnel with an aim to emphasize the historical and cultural significance. In addition, the manuscript evaluates construction techniques used during its original development, identifies the causes behind the structural anomalies in the tunnel and presents the countermeasures applied to restore the functionality. The maintenance work was conducted between 2021 and 2022 where combination of modern and traditional technologies was utilized to strengthen the structural deficiencies while preserving the tunnel as a heritage structure. The study involved investigating the anomalies inside the tunnel and documenting the restoration techniques applied. It was envisaged through investigations that the tunnel suffered with development of cracks, material degradation, and water leakage. The applied countermeasures successfully restored the functionality of Churiyamai Tunnel and maintained status as a heritage site.

Predicting the porosity and permeability of hydrocarbon reservoirs is a key part of figuring out how much fluid is retained and how much moves through them. However, the absence of conventional porosity logs often complicates these predictions due to factors like borehole instability and logging challenges. This research looks at how well three machine learning algorithms—Linear Regression (LR), Random Forest (RF), and Gradient Boosting (XGBoost)—can predict porosity and permeability from well-log data in the Niger Delta basin. The well-log data includes gamma ray, caliper, density, and compressional sonic logs. The goal of the study was to create and improve machine learning models that could guess the properties of a reservoir without using traditional porosity logs. To get better results, hyperparameters for RF and XGBoost were tweaked, and the models’ work was checked using the coefficient of determination (R²) on training, validation, and blind testing datasets. Results indicated that XGBoost and RF outperformed LR in both porosity and permeability predictions, with R² values reaching 0.94–0.95 for porosity and 0.98–0.99 for permeability in test data. Blind testing further confirmed the robustness of the models, achieving R² values of 0.99 for porosity and 0.999 for permeability. This study adds to what is known in the oil industry by showing how machine learning techniques can be used to accurately predict key reservoir properties. These techniques can be used as a reliable alternative to traditional log data when they are not available, like in the Niger Delta basin. Furthermore, accurate prediction of reservoir properties can optimize operations and reduce uncertainties.

Water is among the most vital natural resources that a nation must consistently protect, conserve, and manage sustainably. It is essential to all forms of life, and any significant shortage can result in a decline in the quality of life and substantial economic losses. Effective water resource management helps overcome this challenge. This study made use of System Dynamic Modeling (SDM) to examine the factor such as water demand, water supply, distribution leaks and water price, to provide a comprehensive understanding of the existing water supply system. A number of scenarios were evaluated, including water price increase, conservation campaigns and reduction of non-revenue water (NRW). Results show that over time, the groundwater supply will no longer be sufficient to meet the demand and that exploration of alternative water resource is recommended. Water conservation campaigns, price increase and NRW reduction will be able to help delay the depletion of the groundwater supply.