Climate change is one of the trending terms in the world nowadays due to its profound impact on human health and activity. Extreme drought events and desertification are some of the results of climate change. This study utilized the power of AI tools by using the long short-term memory (LSTM) model to predict the drought index for Anbar Province, Iraq. The data from the standardized precipitation evapotranspiration index (SPEI) for 118 years have been used for the current study. The proposed model employed seven different optimizers to enhance the prediction performance. Based on different performance indicators, the results show that the RMSprop and Adamax optimizers achieved the highest accuracy (90.93% and 90.61%, respectively). Additionally, the models forecasted the next 40 years of the SPEI for the study area, where all the models showed an upward trend in the SPEI. In contrast, the best models expected no increase in the severity of drought. This research highlights the vital role of machine learning models and remote sensing in drought forecasting and the significance of these applications by providing accurate climate data for better water resources management, especially in arid regions like that of Anbar province.

In a dynamic market marked by disruptions like pandemics and recessions, organizations face significant challenges in efficiently managing logistics processes and activities. The primary objective of this article is to propose an integrated four-phase model for assessing the efficiency of retail distribution warehouses based on principal component analysis-data envelopment analysis-improved fuzzy step-wise weight assessment ratio analysis-combined compromise solution (PCA-DEA-IMF SWARA-CoCoSo). The model provides a synergistic effect of all positive sides of the considered methods. PCA-DEA methods are used to reduce the number of variables and to identify efficient warehouses. IMF SWARA is applied to determine criteria weights, while the CoCoSo method is employed in the last phase for ranking efficient warehouses. The model incorporates 18 inputs and 3 outputs, derived from both literature and real-world systems. The proposed model identifies the most efficient warehouses, which can serve as benchmarks for improving the performance of less efficient ones. After implementing PCA-DEA, only seven warehouses were identified as efficient. Subsequently, fixed and variable costs are identified as the two most important criteria. Results of the considered case study indicate that warehouse A4 emerges as the best one, whereas A6 is the least preferred warehouse. This research offers valuable insights and practical implications for organizations operating in dynamic markets, assisting them in achieving operational excellence and improving their supply chain performance.

Motorcycling postures are generically speculated to be physical and physiologically demanding – which in-turn may lead to motorcycling fatigue, and then becoming a possible factor to road accident. The objective of this study was to measure the muscular activities of various motorcycling postures. High muscular activity reading will signifies that motorcycling is indeed physically and physiologically demanding to the motorcyclist. For this particular study, the following postures were tested: i) forward lean, ii) upright sitting, and iii) neutral sitting (as control). Surface electromyography (sEMG) measurement was conducted on the following muscles: i) extensor carpi radialis, ii) upper trapezius iii) latissimus dorsi, and iv) erector spinae. The results showed that for all test subjects, the muscular activities readings for the forward lean posture was actually close to neutral sitting’s. Whilst, the upright sitting had showed much higher muscular activities measurement instead. Conclusively, this study had proven that any types of discomforts associated with the forward lean posture is not originated from muscular activities. Whereas, confirming that any discomforts in regards to the upright sitting is indeed related to muscular activities. Further studies are warranted to discover the actual risk factors that causes physical and physiological discomforts for the forward lean motorcycling posture.

This study aimed to determine effects of diffuser length of open and ducted propellers in UAV applications. This study opted for Computational Fluid Dynamics (CFD) as the primary research method. Results have shown that the diffuser length impacts are largely independent results-wise and show no correlation, however, more data would be needed to form a conclusive statement. From this study, miniature models could be fabricated for the purpose of further testing. For instance, future studies could conduct an experimental approach to attain data that is as close to real-world applications as possible. This will be vital for the industrial application.

Computer numerical control (CNC) machining process simulation is increasingly central to intelligent manufacturing, yet its deployment in brownfield environments remains constrained by legacy controllers, heterogeneous data semantics, limited computational resources, and rising cybersecurity requirements. While digital twins (DTs), artificial intelligence (AI), and multi-physics simulation have matured conceptually, practical adoption, particularly among small and medium-sized enterprises (SMEs), continues to lag behind theoretical capability. This paper presents a PRISMA-guided systematic review of peer-reviewed literature, standards, and industrial reports published between 2019 and 2025, focusing on CNC machining simulation, digital twin architectures, interoperability standards, and intelligent optimisation under brownfield constraints. Rather than proposing new simulation algorithms, the review synthesises fragmented evidence into a deployable, standards-aligned integration perspective. The review consolidates prior work into a seven-layer architecture grounded in ISO 23247, explicitly separating sensing, communication, digital twin entities, analytics, and human–machine interaction. It derives practical decision rules for middleware selection, edge-cloud compute placement under latency constraints, and modelling strategy selection, ranging from mechanistic and finite-element methods to hybrid reduced-order and machine-learning surrogates. An SME-oriented implementation and validation roadmap links staged retrofitting to measurable operational indicators, including overall equipment effectiveness, first-pass yield, downtime, cycle time, and energy intensity.