Search Results (4,171)

Many real-world expensive industrial and engineering multi-objective optimization problems (MOPs) are driven by historical, experimental, or simulation data. In such scenarios, due to the expensive cost and time required, we are only left with a small amount of labeled data to perform the optimization. These offline data-driven MOPs are usually solved by multi-objective evolutionary algorithms (MOEAs) with the help of surrogate models constructed from offline historical data. The key challenge in developing these data-driven MOEAs is that they have to replace multiple conflicting fitness functions by approximating these objective functions, which may produce cumulative approximation errors and misguide the search. In order to build a reliable surrogate model from a small amount of multi-output offline data and solve the DDMOPs, we have proposed an adaptive model selection method with a reliable individual-based model management-driven MOEA. The proposed algorithm dynamically selects between DNN and XGBoost by comparing their k-fold cross-validation MAE error, which can capture the true generalization ability of the surrogates on unseen data. Then, the selected surrogate is updated with a reliable individual selection strategy, where the individual who is closest, both in the decision and objective space, to the most preferred solution among labeled offline data is chosen. As a result, these two strategies guide the underlying MOEA to the Pareto optimal solutions. The empirical results of the ZDT and DTLZ benchmark test suite validate the use of the three state-of-the-art offline DDMOEAs, showing that our algorithm is able to achieve highly competitive results in terms of convergence and diversity for 2–3 objectives. Finally, our algorithm is applied to an offline data-driven multi-objective problem—transonic airfoil (RAE 2822) shape optimization— to validate its efficiency on real-world DDMOPs. Full article

Circular economy (CE) is a model that is gaining significance in the context of sustainable development and environmental protection, focusing on minimizing waste generation and maximizing the use of available resources through recycling and extending product life cycles. The implementation of CE in various European Union countries demonstrates diverse approaches to resource management, waste production, and energy efficiency improvement. These differences primarily stem from varying strategies, national policies, levels of social awareness, and technological advancements. The article identifies the key challenges and barriers associated with CE implementation in selected countries—Poland, the Netherlands, and Romania—and highlights specific areas requiring improvement and adaptation. It emphasizes the critical role of aligning national policies with the EU guidelines, promoting ecological education, and investing in innovative technologies and solutions that support sustainable development. Additionally, it points to the need for developing appropriate waste management infrastructure and encouraging businesses and consumers to change habits and engage in pro-environmental actions. Full article

Nicotinamide mononucleotide (NMN) has emerged as a promising non-natural cofactor with significant potential to transform biocatalysis, synthetic biology, and therapeutic applications. By modulating NAD⁺ metabolism, NMN offers unique advantages in enzymatic reactions, metabolic engineering, and regenerative medicine. This review provides a comprehensive analysis of NMN’s biochemical properties, mechanisms of action, and diverse applications. Emphasis is placed on its role in addressing challenges in multi-enzyme cascades, biofuel production, and the synthesis of high-value chemicals. The paper also highlights critical research gaps, including the need for scalable NMN synthesis methods, improved integration into enzymatic systems, and comprehensive toxicity studies for therapeutic use. Emerging technologies such as AI-driven enzyme design and CRISPR-based genome engineering are discussed as transformative tools for optimizing NMN-dependent pathways. Furthermore, the synergistic potential of NMN with synthetic biology innovations, such as cell-free systems and dynamic regulatory networks, is explored, paving the way for precise and modular biotechnological solutions. Looking forward, NMN’s versatility as a cofactor positions it as a pivotal tool in advancing sustainable bioprocessing and precision medicine. Addressing current limitations through interdisciplinary approaches will enable NMN to redefine the boundaries of metabolic engineering and therapeutic innovation. This review serves as a roadmap for leveraging NMN’s potential across diverse scientific and industrial domains. Full article

With the rapid advancement of the manufacturing industry and the widespread implementation of intelligent manufacturing systems, the energy-aware distributed heterogeneous flexible job shop scheduling problem (DHFJSP) has emerged as a critical challenge in optimizing modern production systems. This study introduces an innovative method to reduce both the makespan and the total energy consumption (TEC) in the context of the DHFJSP. A deep reinforcement advantage Actor-Critic-based co-evolution algorithm (DRAACCE) is proposed to address the issue, which leverages the powerful decision-making and perception abilities of the advantage Actor-Critic (AAC) method. The DRAACCE algorithm consists of three main components: First, to ensure a balance between global and local search capabilities, we propose a new co-evolutionary strategy. This enables the algorithm to explore the solution space efficiently while maintaining robust exploration and exploitation. Next, a novel evolution strategy is introduced to improve the algorithm’s convergence rate and solution diversity, ensuring that the search process is both fast and effective. Finally, we integrate deep reinforcement learning with the advantage Actor-Critic framework to select elite solutions, enhancing the optimization process and leading to superior performance in minimizing both TEC and makespan. Extensive experiments validate the effectiveness of the proposed DRAACCE algorithm. The experimental results show that DRAACCE significantly outperforms existing state-of-the-art methods on all 20 instances and a real-world case, achieving better solutions in terms of both makespan and TEC. Full article

Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and electric vehicle technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations and long charging times. A critical challenge is maximizing passenger travel satisfaction while reducing the AEV idle time. This involves coordinating passenger transport and charging tasks via leveraging the information from charging stations, passenger transport, and AEV data. There are four important contributions in this paper. Firstly, we introduce an integrated scheduling model that considers both passenger transport and charging tasks. Secondly, we propose a multi-level differentiated charging threshold strategy, which dynamically adjusts the charging threshold based on both AEV battery levels and the availability of charging stations, reducing competition among vehicles and minimizing waiting times. Thirdly, we develop a rapid strategy to optimize the selection of charging stations by combining geographic and deviation distance. Fourthly, we design a new evolutionary algorithm to solve the proposed model, in which a buffer space is introduced to promote diversity within the population. Finally, experimental results show that compared to the existing state-of-the-art scheduling algorithms, the proposed algorithm shortens the running time of scheduling algorithms by 6.72% and reduces the idle driving time of AEVs by 6.53%, which proves the effectiveness and efficiency of the proposed model and algorithm. Full article

Heterocyclic pharmaceuticals are emerging contaminants due to their toxic, carcinogenic nature and detrimental impact on the natural ecosystem. These compounds pose a significant environmental concern given their widespread use in medical therapy, constituting over 90% of new medications. Their unique chemical structure contributes to their persistence in various environmental matrices, necessitating urgent measures to mitigate their risks. This review comprehensively examines the sources, environmental fate, toxicity, and long-term risks associated with heterocyclic pharmaceuticals, proposing potential remediation strategies. The article commences with an overview of the diverse types of heterocyclic pharmaceuticals and their applications, focusing on compounds containing heteroatoms such as nitrogen, oxygen, and sulfur. Subsequently, it explores the sources and pathways through which these pollutants enter the environment, including wastewater discharge, agricultural runoff, improper disposal, resistance to biodegradation, and bioaccumulation. The toxic effects and long-term consequences of exposure to heterocyclic pharmaceuticals are then discussed, encompassing neurotoxicity, genotoxicity, mutagenesis, cardiovascular and metabolic toxicity, carcinogenicity, and teratogenesis. Additionally, this review summarizes various remediation strategies and treatment solutions aimed at reducing the environmental impact of these compounds, drawing insights from the literature. The research concludes by identifying critical areas for future research, emphasizing the urgent need for more effective remediation strategies to address the growing concern posed by these emerging contaminants. Full article

Managing high-salinity industrial wastewater poses environmental and operational challenges, particularly in recovering valuable salts like Na₂SO₄. Traditional methods such as evaporation and distillation are energy-intensive (2200 kJ/kg) and environmentally unsustainable. Addressing these limitations, this study investigates the application and optimisation of pipe freeze-crystallization (PFC), an innovative, energy efficient technology operating at 330 kJ/kg, to achieve zero-waste treatment objectives. This research used OLI ESP software to model the crystallization dynamics, accurately predicting Na₂SO₄ recovery and reductions in sulphate concentrations from 74.3 g/L to 6.9 g/L at temperatures below −2 °C. The recovered Na₂SO₄ was analysed using X-ray diffraction with its purity increasing over the years from 50% to 84.9%. Over a four-year operational period at a demonstration plant in Olifantsfontein, South Africa, modifications including extending pipe length from 90 m to 120 m and increasing pipe diameter from 20 mm to 25 mm improved salt recovery rates from 3.5 t/month to 9.1 t/month. Enhanced chiller performance sustained sub-zero temperatures, achieving a cooling capacity of 7 kW while enabling consistent salt and ice recovery. Results showed that feedwater composition substantially influenced crystallization dynamics, with high NaCl concentrations delaying Na₂SO₄ crystallization. The plant’s adaptability to diverse feedwaters and scalability for broader industrial applications highlights its potential as a cost-effective solution. These findings establish PFC as a transformative technology for sustainable saline wastewater treatment, offering industry compliance with environmental regulations, and economic benefits through resource recovery. Full article

The decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with solutions in the neighborhood, the obtained set of solutions is not sufficiently diverse, leading to poorer convergence properties. In order to adequately acquire a high-quality set of solutions, this algorithm requires a large number of population iterations, which in turn results in relatively low computational efficiency. To address this issue, this paper proposes an algorithm termed MOEA/D-NRD, which is based on neighborhood region domination in the MOEA/D framework. In the improved algorithm, domination relationships are determined by comparing offspring solutions against neighborhood ideal points and neighborhood worst points. By selecting appropriate solution sets within these comparison regions, the solution sets can approach the ideal points more and faster, thereby accelerating population convergence and enhancing the computational efficiency of the algorithm. Full article

Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of “noise-bias cascade” to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment. Recommendations: To effectively mitigate bias, we assert the need to implement additional measures such as rigorous study design; appropriate statistical analysis; transparent reporting; and diverse research representation. Furthermore, we strongly recommend the integration of uncertainty measures during model deployment to ensure the utmost fairness and inclusivity. These comprehensive recommendations aim to minimize both bias and noise, thereby improving the performance of future medical decision support systems. Full article

As highway tunnel operations continue over time, structural defects, particularly cracks, have been observed to increase annually. Coupled with the rapid expansion of tunnel networks, traditional manual inspection methods have proven inadequate to meet current demands. In recent years, machine vision and deep learning technologies have gained significant attention in civil engineering for the detection and analysis of structural defects. However, rapid and accurate defect identification in highway tunnels presents challenges due to complex background conditions, numerous interfering factors, and the relatively low proportion of cracks within the structure. Additionally, the intensive labor requirements and limited efficiency in labeling training datasets for deep learning pose significant constraints on the deployment of intelligent crack segmentation algorithms. To address these limitations, this study proposes an automatic labeling and optimization algorithm for crack sample sets, utilizing crack features and the watershed algorithm to enable efficient automated segmentation with minimal human input. Furthermore, the deep learning-based crack segmentation network was optimized through comparative analysis of various network depths and residual structure configurations to achieve the best possible model performance. Enhanced accuracy was attained by incorporating axis extraction and watershed filling algorithms to refine segmentation outcomes. Under diverse lining surface conditions and multiple interference factors, the proposed approach achieved a crack segmentation accuracy of 98.78%, with an Intersection over Union (IoU) of 72.41%, providing a robust solution for crack segmentation in tunnels with complex backgrounds. Full article

Background: Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions. Full article

The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation. IoT sensors deployed across aquaculture systems continuously track critical parameters such as temperature, pH, dissolved oxygen, salinity, and fish behavior. AI algorithms process these data streams to provide predictive insights into water quality management, disease detection, species identification, biomass estimation, and optimized feeding strategies, among others. Much as AIoT adoption in aquaculture is advantageous on various fronts, there are still numerous challenges, including high implementation costs, data privacy concerns, and the need for scalable and adaptable AI models across diverse aquaculture environments. This review also highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management. Full article

This paper addresses the critical issue of road safety in the indispensable role of transportation for societal well-being and economic growth. Despite global initiatives like Vision Zero, traffic accidents persist, largely influenced by driver behavior. Advanced driver monitoring systems (ADMSs) utilizing computer vision have emerged to mitigate this issue, but existing systems are often costly and inaccessible, particularly for bus companies. This study introduces a lightweight, deep-learning-based ADMS tailored for real-time driver behavior monitoring, addressing practical barriers to enhance safety measures. A meticulously curated dataset, encompassing diverse demographics and lighting conditions, captures 4966 images depicting five key driver behaviors: eye closure, yawning, smoking, mobile phone usage, and seatbelt compliance. Three object detection models—Faster R-CNN, RetinaNet, and YOLOv5—were evaluated using critical performance metrics. YOLOv5 demonstrated exceptional efficiency, achieving an FPS of 125, a compact model size of 42 MB, and an mAP@IoU 50% of 93.6%. Its performance highlights a favorable trade-off between speed, model size, and prediction accuracy, making it ideal for real-time applications. Faster R-CNN achieved an FPS of 8.56, a model size of 835 MB, and an mAP@IoU 50% of 89.93%, while RetinaNet recorded an FPS of 16.24, a model size of 442 MB, and an mAP@IoU 50% of 87.63%. The practical deployment of the ADMS on a mini CPU demonstrated cost-effectiveness and high performance, enhancing accessibility in real-world settings. By elucidating the strengths and limitations of different object detection models, this research contributes to advancing road safety through affordable, efficient, and reliable technology solutions. Full article

The rapid advancement of high-throughput technologies has led to the generation of vast amounts of omics data, including genomics, epigenomics, and metabolomics. Integrating these diverse datasets has become essential for gaining comprehensive insights into complex biological systems and enhancing personalized healthcare solutions. This critical review examines the current state of multi-omics data integration platforms, highlighting both the strengths and limitations of existing tools. By evaluating the latest digital platforms, such as GraphOmics, OmicsAnalyst, and others, the paper explores how they support seamless integration and analysis of omics data in healthcare applications. Special attention is given to their role in clinical decision-making, disease prediction, and personalized medicine, with a focus on their interoperability, scalability, and usability. The review also discusses the challenges these platforms face, such as data complexity, standardization issues, and the need for improved machine learning and AI-based analytics. Finally, the paper proposes directions for future research and development, emphasizing the importance of more advanced, user-friendly, and secure platforms that can better serve comprehensive healthcare needs. Full article

Human interaction with birds has never been more positive and supported by so many private citizens and professional groups. However, direct mortality of birds from anthropogenic causes has increased and has led to significant annual losses of birds. We know of the crucial impact of habitat loss on the survival of birds and its effects on biodiversity. Direct mortality via anthropogenic causes is an additive but biologically important cause of avian decline. This is the focus of this paper. This paper synthesises and interprets the data on direct anthropogenic causes of mortality in birds, and it also discusses emerging and relatively hidden problems, including new challenges that birds may not be able to manage. This paper points out that such deaths occur indiscriminately and have negative behavioural and reproductive consequences even for survivors. All of these factors are important to address, because any functional habitat depends on birds. This paper suggests that some of this death toll can be reduced substantially and immediately, even some of the seemingly intractable problems. This paper also proposes cross-disciplinary solutions, bearing in mind that “ecosystem services” provided by birds benefit us all, and that the continued existence of avian diversity is one cornerstone for human survival. Full article