The alluvial clay deposits at Al-Fao, Southern Iraq, with deep soft clay, offer a great foundation challenge due to low bearing capacity and high risk of settlements. To address these issues, this study evaluated the performance mechanism of floating geogrid-encased stone columns (GESCs) through three-dimensional finite element analysis using PLAXIS 3D with a hardening soil (HS) constitutive model. A parametric study was conducted based on column diameter (0.4–0.8 m), a slenderness ratio (L/D = 3–30), and encasement lengths of (1/3 L, 2/3 L, and Full L). The results demonstrated that increasing the column diameter is the most effective strategy, achieving a maximum bearing capacity ratio (BCR) of 1.75 compared to unimproved soil. Notably, the findings revealed that a 2/3 L partial encasement provides performance nearly identical to full-length encasement (with a difference of less than 0.5%) while significantly reducing material costs by 33%. The geogrid encasement provided an improvement factor (IF) of 1.09 over ordinary stone columns (OSCs). This efficiency is attributed to the encasement’s ability to restrain bulging failure within the upper active zone. The study concluded that 2/3 L partial encasement offers superior technical and economic benefits for floating systems in deep soft clay deposits.

This research investigates the aerodynamic performance and dynamic response of high-speed elevators. The study was conducted using a numerical model based on a two-way air-structure coupling. This is achieved by integrating computational fluid dynamics (CFD) and finite element analysis (FEA) techniques. Three different elevator cabin designs (flat, elliptical, and dome) were analyzed at different operating speeds (6, 8, 10, and 12 m/s) to evaluate the effect of geometry on flow and vibration characteristics. The results showed that the dome cabin shape achieved the best overall performance, contributing to reductions of approximately 41% in acceleration, 35% in deformation, 28% in stress, and the vibration frequency by approximately 50–60% compared to the flat shape. It also exhibited a significant reduction in vibration amplitude. Furthermore, a critical dynamic amplification region was identified at approximately 10 m/s, where the response reaches its peak. This region should be considered when designing damping systems. This improvement is attributed to the streamlined properties of the cabin’s dome shape, which reduce flow decoupling and pressure fluctuations. The results show that improving the streamlined shape may reduce air resistance, thereby positively impacting the required operating power.

Multi-aspect sentiment analysis aims to identify different aspects and associated sentiments within user-generated reviews. In recent years, bidirectional encoder representations from transformer (BERT) have been widely used for sentiment analysis due to its strong ability to capture contextual information. However, BERT has limitations in explicitly identifying aspect boundaries and aligning sentiments, especially when multiple aspects with different sentiments appear in the same review. To address this issue, we propose a combination of rule-based and bidirectional encoder representations from transformer (RB-BERT). The main idea of RB-BERT is to utilize domain-specific linguistic rules to automatically generate weak labels for aspect and sentiment pairs, which are then used to fine-tune the pretrained BERT model. A key contribution of this study is addressing BERT’s limitations in aspect-based sentiment analysis (ABSA) by enhancing aspect identification and sentiment assignment. The dataset consists of 3811 user reviews about Sarangan Lake, a popular tourist attraction in East Java, Indonesia. We collected the dataset from Google Maps. The aspects used in this study are scenery view, price, and local environment. The sentiment polarities are positive and negative. We applied four rule levels to enhance the BERT model. The first rule handles aspect extraction, the second addresses sentiment extraction, and the third determines the dominant sentiment based on the frequency of positive and negative words. The fourth rule combines aspects and sentiment in each review to produce a label. BERT tokenization and BERT embeddings are used for feature extraction, with a fully connected linear layer serving as the classification head. RB-BERT performs best with a precision value of 0.9218, a recall of 0.9748, a Micro-F1 of 0.9476, and a Hamming Loss value of 0.0132. Thus, RB-BERT can be used as an approach to perform automatic labeling in multilabel classification by offering speed, low cost, and good performance.

This article presents a detailed account of the design and development process of an Internet of Things (IoT)-based smart electronic system for the remote, real-time monitoring of important tractor performance parameters using embedded sensors. The proposed system includes three measurement modules, namely draft force, slip ratio, and fuel consumption, which were developed using ESP32 microcontrollers and a Wi-Fi network. The measurement process included a T12C pressure transducer, MPU6050 IMU sensor, and two YF-S401 flow sensors. The proposed system was tested through field experiments, and it was established that the two measurements were in close agreement with the results obtained through conventional measurement methodologies, thereby achieving accuracy levels of 96.81% in draft force, 97.35% in slip ratio, and 98.39% in fuel consumption. Thus, it can be established that the proposed system is effective in improving accuracy levels and facilitating decision-making.

Reliable predictions of high temperature events are of great significance to enhance urban resilience in arid regions, especially for cities such as Baghdad which lie at the southern end of the jet stream with summer temperatures frequently exceeding 50 °C. However, linear models such as the autoregressive integrated moving average (ARIMA) are limited; they have difficulties in modeling nonlinear patterns. Deep learning techniques (e.g., long short-term memory (LSTM) networks) pose yet another difficulty as they are sensitive to overfitting and they demand large amounts of data to be trained on. In this paper, introduce a hybrid ARIMA-LSTM based on residual decomposition is proposed. This method takes the best of statistical and deep learning methods. The time series of temperature is decomposed into two parts: the linear part which is modeled by ARIMA and the residual nonlinear part which is modeled by LSTM. Based on the daily temperature information during 2000–2023, this hybrid model outperformed the ARIMA and LSTM models individually. For example, it obtained a mean absolute error (MAE) of 1.56 °C, root mean square error (RMSE) of 2.11 °C and $R^2$ of 0.92. Note that the model remained highly accurate during extreme heat events over 45 °C (producing an MAE of 2.01 °C). These findings point to the model’s potential for early warning and climate adaptation, particularly in dry urban districts confronted with escalating heat stress.

This study examines the effects of viscous dissipation, Joule heating, and coupled heat transfer on dissipative ternary nanofluid flow over a permeable surface. The ternary nanofluid is composed of Al$_2$O$_3$, SiO$_2$, and TiO$_2$ nanoparticles dispersed in water as the base fluid. By introducing suitable similarity transformations, the governing partial differential equations are reduced to a coupled system of ordinary differential equations. The thermal field is analyzed for both prescribed surface temperature (PST) and prescribed heat flux (PHF) conditions, while a temperature-dependent heat source/sink term is incorporated to maintain energy balance within the fluid domain. The resulting energy equation is treated analytically with the aid of Kummer’s function and Laguerre polynomial techniques. The effects of the main controlling parameters, including the inverse Darcy number, magnetic parameter, viscosity-ratio parameter, and radiation parameter, are discussed with the support of graphical results. It is found that an increase in the magnetic parameter reduces the velocity by about 12% and raises the temperature by nearly 18%. These findings provide useful guidance for the design and thermal optimization of engineering systems involving complex nanofluids in porous media, including polymer extrusion and automotive cooling applications.

Energy-efficient path planning for multi-Unmanned Aerial Vehicle (UAV) data-collection missions requires balancing trajectory efficiency, energy consumption, and workload distribution among UAVs. This study presents a controlled computational evaluation of three routing paradigms: random assignment, Greedy nearest-neighbor routing, and Greedy + K-means clustering. The evaluation is conducted using a mission-level energy model that incorporates propulsion energy and mission-phase components, including take-off, hovering, sensing, communication, and landing. Simulation experiments were performed using fleets of 1–10 UAVs serving 100 Points-of-Interest (PoIs) under two spatial deployment scenarios: a structured grid layout and a spatially heterogeneous random layout. Each configuration was executed over 20 independent episodes to ensure statistical robustness. The results demonstrate that routing structure significantly influences geometric mission efficiency. In the propulsion-dominated regime (U $\geq$ 5 under random PoI layouts), Greedy + K-means clustering reduces mission travel distance by approximately 11.6–24.5% compared with Greedy routing, corresponding to an energy reduction of approximately 4.6–10.5%. In contrast, under the phase-dominated regime, where fixed mission-phase energy dominates the total energy budget, performance differences between routing strategies remain below 5%. Statistical analysis further confirms large practical differences in geometric performance across algorithms ($\eta^2$ $>$ 0.86). These findings indicate that routing strategy selection should depend on mission scale and spatial characteristics rather than assuming universal optimality. Greedy routing performs effectively in small or spatially structured deployments, whereas Greedy + K-means clustering provides greater robustness and scalability in larger or spatially heterogeneous missions.

Functional plate is one of the most typical materials used for strengthening of reinforced concrete (RC) structures. This article focuses on using functional plates internally to improve the flexural response of RC beams. For this purpose, experimental and numerical investigations on the flexural behavior and ductility of steel-plated RC beams were conducted. Nine RC beams were cast and cured for 28 days. The steel plates were located at the tension side of the RC beams to investigate their effect on the flexural performance of the tested beams. To achieve the research objective, three configurations of the shape of steel plates were proposed, flat, curved, and rounded. The results demonstrate that using embedded steel plates is effective and significantly enhanced the flexural performance of concrete beams. The strengthening delayed the first cracking appearance and increasing of ultimate load up to 45% compared to the reference beam. Further, there was an improvement in ductility and stiffness behaviours by 202% and 46%, respectively, particularly for beams with constrained flat steel plates, which exhibited the highest performance gain. The experimental and finite element (FE) results showed a good agreement in terms of cracking behavior and with approximately 6% maximum ultimate load difference.