The Sultanate of Oman is focusing on the logistics/supply chain sector as part of its Vision 2040 initiative, which aims to make Oman a logistics hub at the regional and international levels. However, effective workforce localisation, or Omanisation, remains a challenge due to operational, skills, and sustainability gaps. This document examines the issues logistics companies in Oman encounter regarding workforce localisation and the efficiency of their operations. The author employed a qualitative multiple-case study methodology and obtained information through semi-structured interviews with participants from the logistics sector, including managers, human resources personnel, and policymakers. The author found that, despite Omanisation policies, the logistics sector continues to rely on expatriate labour. Omanisation challenges include a lack of local employees’ technical skills, insufficient vocational training, and over-reliance on expatriate labour. Additionally, the author discusses how operational inefficiencies and the reluctance to adopt digital tools hinder the localisation process. The author discusses a strategic alignment framework that integrates workforce localisation, process improvement, and digital enhancement to improve localisation outcomes. The author’s findings contribute to the body of knowledge in logistics and supply chain management by providing insight into the potential operational integration of nationalisation policies aimed at achieving sustainability in Oman.

Building structural integrity evaluation is important in the long-term safety and resilience of buildings. Visual inspection techniques that have been in place do not have the capability of detecting beneath surface cracks, or cracks that may develop at an early stage, that would compromise the structural performance. This paper introduces a smart crack sensor with a combination of Ultrasonic Guided Wave (UGW) and Acoustic Emission (AE) as a guide to the complicated Structural Health Monitoring (SHM) of building structures. The system proposed is based on the UGW-based wave propagation analysis along with the AE signal monitoring, which will detect, localize, and characterize surface and internal cracks with the highest accuracy. Algorithms of machine learning are used to comprehend complicated acoustic signals and distinguish between crack initiation, crack propagation, and the noise in the environment. It is experimentally verified on reinforced concrete specimens that the coupled UGW-AE methodology is more sensitive and accurate than the uniaxial methodologies. These are possible through the combination of real-time data acquisition, fusion of signals, and smart pattern recognition that allows early detection of damage and provides the ability to monitor the damage continuously. The study will help in the emergence of an intelligent, non-destructive, and scalable SHM system capable of improving structural dependability and maintenance effectiveness in contemporary infrastructure systems.

The construction of shallow foundations on soft and over-consolidated organic soil may be challenging. The difficulty arises from the shear strength of the soil, its compressibility, and the ability of the soil to settle. The aspect of soil consolidation is also crucial in the long run. This is because it is associated with soil strengthening and improvement in the bearing capacity of the foundation. This study evaluates the effects of consolidation settlement on the bearing capacity of strip shallow foundations on over-consolidated organic soil through a series of FEM-based calculations in PLAXIS 2D. In this calculation, two factors are considered. The factors include the width of the strip foundation, ranging from 0.5 m to 3.0 m, and the thickness of the over-consolidated organic soil, ranging from 2 m to 12 m. To gain insight into how consolidation can affect the soil behavior during settlement, the calculation was done using a time-dependent loading condition. The results have emphasized that the increase in bearing capacity due to consolidation occurs to a large extent in the first 1000 to 1500 days. For all the test results, wider foundations performed with a higher bearing capacity. The results have emphasized that for high organic soil conditions, higher organic soil thickness resulted in less performance in the initial stages but contributed more to the advancements in strength. These observations emphasize the need for proper consideration of both the time involved for soil consolidation, the dimensions of the foundations, and the soil thickness in designing a shallow foundation.

In tropical areas, brick and wood are commonly utilised for wall construction. However, building occupants may experience temperature discomfort if inappropriate materials are used. To identify the ideal most comfortable temperature range and to increase energy efficiency, a field study was conducted in Majene City, Indonesia (tropical climate). Wooden-walled and brick-walled classrooms were used to measure environmental parameters, and questionnaires were distributed to assess students' thermal comfort. The neutral temperature of actual voting, obtained using Griffith's method and regression analysis, was compared with the result from the PMV/PPD and adaptive methods. The results showed that whereas classrooms with wooden walls had higher air temperatures, from 27.64 °C to 33.19 °C from morning to midday, classrooms with brick walls had air temperatures approximately between 27.52 °C and 30.67 °C from morning to midday. In addition, student responses indicate that brick-walled classrooms are more comfortable and have better thermal performance than wooden-walled classrooms. Griffith's method and regression analysis showed that the neutral temperatures of brick-walled classrooms (28.50 °C and 29.57 °C, respectively) were lower than those of wooden-walled classrooms (28.93 °C and 29.99 °C, respectively).

The study concludes the potential for conducting a computational analysis on the conventional design of the Savonius rotor made with Poly-Lactic Acid (PLA), which is a 3-D printer material. This material can be molded into any shape with the 3D printer capabilities. The output from the simulation results thus provides an effective design solution for the purpose of constructing the Sovonius Rotor to be produced by a 3D printing technology. A computer design model thus aids in conducting simulation studies to find the torque and Coefficient of Performance (COP) as the primary performance indicators, followed by structural analysis on the rotor blades as the secondary performance indicator. It is a dual simulation study for the evaluation of torque based on the best design of the rotor, for the production of these designs based on the material selection after structural analysis simulation test, both conducted by ANSYS. The material selection was based on easily available metallic and composite material choices, such as aluminum, steel, and PLA. These materials were tested for their structural deformity under wind velocities of 6 m/s, 8 m/s, and 10 m/s. The COP was found to be maximum at 10 m/s for the configuration of the turbine used. The use of PLA is an affordable 3D printing material for building a small Vertical-Axis Wind Turbine (VAWT). PLA is a light material having low density and thermal conductivity as compared to steel and aluminum (88%), tested for producing stress values of 14 mPa, 156 mPa, and 10638 MPa, respectively, at 10 m/s. Deformation over rotor blades was found to be highest for aluminum (88%) at 1 m, and lowest for steel at 0.015 m. Though steel shows more reliable deformation results, PLA is a more preferred material for complex designs of 3-D printing turbine construction due to the lower material cost than steel.

Bomb attacks inside or near buildings can cause Catastrophic damage to structures inside and outside the structure, collapse walls, blow up large sections of windows, and disrupt security systems. The casualties of the victims can be multiplied, including the direct effects of the explosion, the destruction of structures, and the impact of debris. Indirect effects can be combined to prevent a rapid evacuation, resulting in more casualties. In addition, the high loads due to chemical gas explosions can cause dynamic loads in many structures that are greater than the original design loads. In view of the threat of these extreme loading conditions, the behavior of the building structure under explosive charges is studied. A column under axial force and the blast load were simulated and analyzed. The ANSYS finite element platform is used to model columns with various support end conditions.

Recent research has shown that it is vital to comprehend the distribution of trips within urban areas to undertake successful transport planning, especially within the municipal areas that grow at a dominant rate. This paper is a model of the trip distribution of Sangli-Miraj-Kupwad Municipal Corporation based on a household survey and two complementary analytical models. On the one hand, the structured Household Interview Survey provided socio-economic and travel-related data, which, in turn, created an in-depth portrait of household features, travel purposes, and mode preferences in 12 Traffic Analysis Zones. Based on these data, an Origin-Destination matrix was built using the Doubly Constrained Gravity Model with the help of Excel so that both trip productions and attractions were equal to the observed values by balancing αᵢ and βⱼ factors with each other. Second, the deterrence parameters were estimated in RStudio, and inter-zonal flows were sensitive to the cost of traveling, providing another independent statistical assessment of how the model behaves. A combination of these two methods allows obtaining more concise insight into the interactions in spatial travel using strict matrix balancing and the estimation of behavioural parameters. The framework that has been obtained offers an efficient representation of mobility patterns and helps make decisions based on data related to the planning of urban transportation in the study area.

The authors of the study believe that urban planning tools and coherent planning of urban green spaces are the primary available methods for mitigating air pollution in Astana. In the survey, urban recreational areas of Astana (urban parks, squares, and gardens) are considered the primary sites of urban green space concentration, serving as the primary source of air filtration and as natural barriers that intercept polluted air flows. However, in addition to the number of recreational areas, they must be distributed evenly throughout the city. In this regard, the study is based on the concept of environmental justice. A statistical juxtaposition of environmental hotspots with social susceptibility features is conducted to determine whether a double burden or double blessing is occurring in the Astana districts. The authors calculated the spatial distribution of air pollution from the city's primary source, Thermal Power Station-2. The study's main finding is that the distribution of urban recreational areas in Astana is unequal. In the city districts most exposed to air pollution and with the lowest income levels, there is a need for additional urban recreational areas to protect residents from air pollution. In contrast, the district with the lowest air pollution and the highest income rate in Astana has the most significant number of urban recreational areas. The study aims to contribute to the emergence of the concept of environmental justice in Kazakhstan by examining the unequal distribution of urban recreational areas in Astana.

Mega highway projects are very sensitive to a number of geotechnical risks in the form of soil instability, seismic events, and environmental change, leading to costly failures. The current risk assessment methods are not capable of integrating the range of datasets—spatial data, temporal data, and real-time streams of data—and thus lack good levels of predictability, combined with a lack of response time. Thus, with a view to addressing these barriers, we introduce the new concept of Big Data Geotechnical Risk Assessment Model (BD-GRAM), which applies big data analytics and advanced machine learning algorithms in order to better predict and mitigate geotechnical risks for different scenarios. BD-GRAM combines various methods adapted to geotechnical data samples. The Graph Convolutional Networks (GCNs) are utilized for spatial and temporal data fusion, where complex spatial dependencies and temporal variations in soil properties, as well as samples of seismic data, are considered. GCNs, with the enhancement of attention mechanisms, have the ability to increase accuracy by as much as 20% compared with the conventional methods. A hybrid model by combining LSTMs and FEA, misleading synergistic use of physical laws, as well as temporal patterns of data pertaining to predictive accuracy, with an improvement of 25%. Near real-time processing on Apache Kafka & Apache Spark enables near real-time continuous monitoring of risk with alerting on. SHAP (Shapley Additive Explanations) ensures interpretability of the model outputs, as well as transparency of the factors driving the risk predictions. Lastly, the system is scalable using GPU-accelerated TensorFlow to Run Masses of datasets & samples. This fully integrated approach is optimized in this way to further enhance predictive accuracy and reduce false-positives and false-negatives, enhancing the speed and response of geotechnical risk assessment responses in real time. BD-GRAM represents an effective way to meet the early identification and mitigation of geotechnical challenges in a scalable data-driven manner for improved resilience and safety of large-scale infrastructures in any given highway scenario.

The urban image is characterized by being the distinctive symbol of a city that seeks to generate an identity and autonomy in its reading process to denote traditions and customs from the perspective of the architectural compositional elements that make up traditional buildings and their evolution towards a contemporary stage that enhances the typological conditions of both homes and businesses in the Urdesa neighborhood of Guayaquil, Ecuador. This study seeks to describe the urban image of this traditional neighborhood of Guayaquil based on a historical analysis using architectural housing evaluation techniques to show the changes in the urban image of housing defined in the formal buildable aspect, relationship with public space and design influence with natural spaces to determine how these indicators have complemented the structure of the urban image, emulating a timeline analysis of the original development to the transformationism of 21st century Urdesa, to be exposed in a photographic memory of architecture denoting the traditional aspects still preserved. This booming contemporary architecture still preserves the urban image of Urdesa. The results were strategies that contribute to revitalizing Urdesa's indigenous image as a traditional neighborhood in Guayaquil, which has a deep-rooted natural, constructivist, and functional connection with the coastal area.