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In recent years, the use of gold nanorods (AuNRs) has garnered considerable attention in biomedical applications due to their unique optical and physicochemical properties. They have been considered as potential tools for the advanced treatment of diseases by various stimuli such as magnetic fields, pH, temperature and light in the fields of targeted therapy, imaging and drug delivery. Their biocompatibility and tunable plasmonic properties make them a versatile platform for a range of biomedical applications. While endogenous stimuli have limited cargo delivery control at specific sites, exogenous stimuli are a more favored approach despite their circumscribed penetration depth for releasing the cargo at the specific target. Dual/multi-stimuli responsive AuNTs can be triggered by multiple stimuli for enhanced control and specificity in biomedical applications. This review provides to provide a summary of the biomedical applications of stimuli-responsive AuNRs, including their endogenous and exogenous properties, as well as their dual/multi-functionality and potential for clinical delivery. This review provides a comprehensive review on the improvement of therapeutic efficacy and the effective control of drug release with AuNRs, highlights AuNRs design strategies in recent years, discusses the advantages or challenges so far in the field of AuNRs. Finally, we have addressed the clinical translation bio-integrated nanoassemblies (CTBNs) in this field., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R 2 , R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R 2 and R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R 2 and R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R 2 and R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model's capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model's superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application., (© 2024. The Author(s).)

Household energy consumption (HEC) is one of the major contributors to global emissions, making it a critical area for addressing sustainability challenges. The impact of personality traits on human behaviour is significant in shaping HEC patterns, and therefore, have important implications for sustainability policies. This study aims to investigate role of biologically predicted big-two personality traits (i.e., stability and plasticity), a higher order solution to five-factor traits and orthogonal traits, on HEC. To that end, a structural equation model is developed using a national household survey in Australia. The performance of the model is benchmarked against a five-factor (i.e., agreeableness, consciousness, emotional stability, extraversion and openness) personality trait model. The performance of the models is measured using six goodness-of-fit indices, all of which show a superior performance in the big-two traits model. The results indicate that a higher score in stability poses energy-intensive behaviour, while a higher plasticity score poses energy-saving behaviours. The plasticity trait is linked to environmentally friendly behaviours, while the stability trait is associated with environmentally unfavourable behavioural practices. The effects of socioeconomic status on HEC are mediated by stability and plasticity to identify those who are more likely to change their energy consumption habits as the target group for policy intervention. This study can assist policy makers to determine energy-intensive and energy-saving behaviours from the big-two traits, and to develop more effective and targeted sustainability policies that can help in reducing HEC and promote sustainable living in societies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Dial a ride problem (DARP) is a complex version of the pick-up and delivery problem with many practical applications in the field of transportation. This study proposes an enhanced deterministic annealing algorithm for the solution of large-scale multi-vehicle DARPs. The proposed method always explores the feasible search space; therefore, a feasible solution is guaranteed at any point of termination. This method utilises advanced local search operators to accelerate the search for optimal solutions and it relies on a linearly decreasing deterministic annealing schedule to limit poor jumps during the course of search. This study puts forward a systematic series of experiments to compare the performance of solution methods from various angles. The proposed method is compared with the most efficient methods reported in the literature i.e., the Adaptive Large Neighbourhood Search (ALNS), Evolutionary Local Search (ELS), and Deterministic Annealing (DA) using standard benchmarks. The results suggest that the proposed algorithm is on average faster than the state-of-the-art algorithms in reaching competitive objective values across the range of benchmarks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mortazavi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)