Resource Consumption Research Articles

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

Investigations on Application of Probabilistic and Mathematical Computing in Design and Statistical Analysis of Lightweight Cryptography

This research provides in-depth investigation into the application and role of probability and mathematics in lightweight cryptography featuring IoT software. The IoT device fleet growth is continuously accompanied by its resources, often poor. It creates a critical need for cryptographic solutions that will be effective and a heavy burden. The research article underlines the use of latest probabilistic models and mathematical concepts to create cryptographic algorithms that are cryptographically powerful and IoT-embedded devices can process them. The study commences with its deep mathematics investigation and focuses on consequently essential permutations, combinations, modular arithmetic and prime number theory, all of which represents the cornerstone of strong cryptographic algorithms development. The article then proceeds to demonstrate the use of probabilistic tools for scrutinizing the security and the effectiveness of these algorithms. A novel approach to grasping the vulnerabilities that may occur in the course of cryptographic implementations is also brought to light. A remarkable part of the investigation is toward designing small-sized cryptographic algorithms by using methods of mathematics computations to decrease the size of key, complexity and implementations of increased safety margin. The research process involves a thorough statistical demonstration of the algorithmic performance in several agendas of IoT exposing the resistance of the algorithms against cryptographic issues and the intensity of their resource consumption on ordinary IoT devices. By conducting these investigations, the paper contributes a lot of useful information on the harmonious relationship between probability methods, mathematical computing, and cryptographic development as well as providing the solid foundation for creating a lightweight cryptography which can be used in the Internet of Things (IoT) and other similar environments that have their unique needs and constraints. However, it not only advances the field of cryptography itself, but also outlines a practical roadmap on how to design secure, efficient cryptosystems, that can be used in its subsequent applications in the small but fast growing IoT technology landscape.

Supramolecularly Connected Armor-like Nanostructure Enables Mechanically Robust Radiative Cooling Materials.

Passive daytime radiative cooling (PDRC) is a promising practice to realize sustainable thermal management with no energy and resources consumption. However, there remains a challenge of simultaneously integrating desired solar reflectivity, environmental durability, and mechanical robustness for polymeric composites with nanophotonic structures. Herein, inspired by a classical armor shell of a pangolin, we adopt a generic design strategy that harnesses supramolecular bonds between the TiO2-decorated mica microplates and cellulose nanofibers to collectively produce strong interfacial interactions for fabricating interlayer nanostructured PDRC materials. Owing to the strong light scattering excited by hierarchical nanophotonic structures, the bioinspired film demonstrates a desired reflectivity (92%) and emissivity (91%) and an excellent temperature drop of 10 °C under direct sunlight. Notably, the film guarantees high strength (41.7 MPa), toughness (10.4 MJ m-3), and excellent environmental durability. This strategy provides possibilities in designing polymeric PDRC materials, further establishing a blueprint for other functional applications like soft robots, wearable devices, etc.

Performance evaluation of a wet air oxidation reactor with external circulation for high-chemical oxygen demand wastewater treatment

The presence of high-concentration organic contaminants in wastewater poses considerable risks to the health of both humans and ecosystems. Wet air oxidation (WAO) is an attractive treatment option, but current dilution and retreatment processes do not align with economic and environmental principles. Here we report an external circulation wet oxidation treatment process that utilizes oxidizing liquids to reduce feed concentration. By coupling energy consumption model with computational fluid dynamics model, we explore the optimal parameters for an industrial external circulation reactor. A 90 kt/a external circulation wastewater treatment system has been established, achieving a 21.6% increase in chemical oxygen demand (COD) removal efficiency and a 34.8% reduction in energy consumption. These findings provide a promising pathway for the efficient, low-energy treatment of high-concentration organic wastewater while minimizing freshwater resource consumption.

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

Reduced-order methods for neutron transport kinetics problem based on proper orthogonal decomposition and dynamic mode decomposition

This work proposes two reduced-order methods to address the high computing resource consumption of neutron transport kinetics problems. The first method is based on proper orthogonal decomposition (POD), which integrates the POD basis with the governing equation. The second method is based on dynamic mode decomposition (DMD), which constructs an approximate linear relationship to predict the time-dependent physical quantities. Numerical results demonstrate that both two reduced-order methods can achieve high accuracy and efficient prediction. For the selected cases, POD can obtain a speed-up ratio of 105 – 429, while DMD can obtain a speed-up ratio of 2300 – 12500. For step transient situations, POD and DMD predict power errors do not exceed 0.42% and 0.05%. For ramp transient situations, POD and DMD predict power errors do not exceed 0.69% and 1.9%. This work can provide some useful suggestions for applications and further development in neutron transport kinetics reduced-order model.

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Disease and detection is crucial for the protection of forest growth, reproduction, and biodiversity. Traditional detection methods face challenges such as limited coverage, excessive time and resource consumption, and poor accuracy, diminishing the effectiveness of forest disease prevention and control. By addressing these challenges, this study leverages drone remote sensing data combined with deep object detection models, specifically employing the YOLO-v3 algorithm based on loss function optimization, for the efficient and accurate detection of tree diseases and pests. Utilizing drone-mounted cameras, the study captures insect pest image information in pine forest areas, followed by segmentation, merging, and feature extraction processing. The computing system of airborne embedded devices is designed to ensure detection efficiency and accuracy. The improved YOLO-v3 algorithm combined with the CIoU loss function was used to detect forest pests and diseases. Compared to the traditional IoU loss function, CIoU takes into account the overlap area, the distance between the center of the predicted frame and the actual frame, and the consistency of the aspect ratio. The experimental results demonstrate the proposed model’s capability to process pest and disease images at a slightly faster speed, with an average processing time of less than 0.5 s per image, while achieving an accuracy surpassing 95%. The model’s effectiveness in identifying tree pests and diseases with high accuracy and comprehensiveness offers significant potential for developing forest inspection protection and prevention plans. However, limitations exist in the model’s performance in complex forest environments, necessitating further research to improve model universality and adaptability across diverse forest regions. Future directions include exploring advanced deep object detection models to minimize computing resource demands and enhance practical application support for forest protection and pest control.

Analysis of Environmental Impact and Mechanical Properties of Inconel 625 Produced Using Wire Arc Additive Manufacturing

Inconel 625 is a nickel-based superalloy widely used in industries such as energy, space, and defence, due to its strength and corrosion resistance. It is traditionally time- and resource-intensive to machine, leading to increased environmental impact and material waste. Using additive manufacturing (AM) technology enables a reduction in resource consumption during the manufacture of high value components, as material is only deposited where it is required. This study compares the environmental impact of manufacturing an Inconel 625 impeller through machining and wire arc additive manufacturing (WAAM) by employing established life cycle assessment methods. WAAM shows significant advantages, cutting energy consumption threefold and reducing material waste from 85% to 35%. The current work also evaluates the mechanical properties of WAAM-produced components through tensile and axial fatigue testing, in addition to the use of optical and electron microscopy for metallurgical analysis and fractography. This demonstrates yield and ultimate tensile strengths exceeding industrial standards, with comparable or superior fatigue life to other AM methods. The improved fatigue performance extends the service life of components, bolstering sustainability by reducing the need for frequent replacements, thereby lessening associated environmental impacts. These findings underscore the promise of WAAM in enhancing both environmental sustainability and mechanical performance in manufacturing Inconel 625 components.

Possibilities of reinforcement of subtle translucent concrete panel

Textile-reinforced concrete not only has many advantages that lead to less consumption of primary resources, but also enables the creation of thin and subtle elements. The high performance of such elements allows us to create interesting architectural features, such as translucent concrete. This article focusses on the possibility of reinforcing translucent concrete panels.

FEASIBILITY AND MICRO–COSTING ANALYSIS OF A SAME–DAY DISCHARGE PROTOCOL FOR CATHETER ABLATION OF ATRIAL FIBRILLATION: A PILOT STUDY USING CRYOBALLOON ABLATION

Abstract Background Catheter ablation (CA) is a well–established treatment for atrial fibrillation (AF). Due to the ageing population, increasing prevalence of AF risk factors, improving detection methods for AF, and broadening indications for ablation, CA for AF is the most common resource–consuming ablation procedure, strongly impacting waiting list times and healthcare costs. The combination of these factors and limited bed availability led to development of a same–day discharge (SDD) protocol with acceptable efficacy and safety outcomes. Due to the COVID–19 outbreak, we started the SDD pilot project to reduce patient hospital stays and time spent on the waiting list. Objective We aimed to assess our day–case service‘s overall effectiveness and safety. Furthermore, we aimed to evaluate the health–economic impact of a routine adoption of day–case AF ablation. Methods Patients with symptomatic and drug–refractory PAF or early Pe–AF scheduled for de–novo ablation were included in the study protocol if they had the following clinical and non–clinical criteria for an SDD protocol: 1–Clinical factors: a) stable anticoagulation, b) No history of bleeding, c) No systolic heart failure, d) No history of pulmonary disease, e) No interventional procedure within 60 days, f) BMI &amp;lt;35, g) CHA DS –VASc ≤3, h) non–severely dilated left atrium, i) age &amp;lt;65 years old; 2–Non–clinical factors: a) home residence within 50 km, or if more, with an emergency department (ER) reachable within 30 minutes, b) home assistance to the patients the same day of the procedure. Results Fifty–two patients were included in the SDD protocol and underwent PVI with the Arctic Front Advance cryoballoon ablation catheter. Patients were admitted to a cardiac short–stay unit on the day of ablation at 7 am. SDD procedures were carried out in conscious sedation without the presence of an anesthesiologist. After ablation, the patient was admitted to the recovery area for at least 6 hours. Postprocedural echocardiography was performed to rule out pericardial effusion. The bed occupancy cost reduction was 493€/procedure with the SDD protocol (2415€ vs. 1921€). No minor or major complications were observed in the SDD protocol. Conclusion The adoption of an optimized path would lead to a reduction in the length of hospital stays, optimization of beds, a reduction in waiting lists, an accelerating patient access to care and a reduction in patient management costs by optimizing the consumption of resources.

Federated learning in smart home: A dynamic contract-based incentive approach with task preferences

Federated Learning (FL) is a new type of distributed machine learning paradigm aimed at addressing data privacy and security issues in the Internet of Things (IoT) context. In the training process of federated learning model of smart home, data owners need to constantly update cached data, which lead to resources consumption and service latency. Meanwhile, the update cost of the data owners will change with the passage of time when the model owner requests data update. Therefore, this paper first puts forward an incentive scheme based on two-period dynamic contract theory under the information asymmetry. The scheme can balance the weighted preference of the model owner for age of information (AoI) and service latency, as well as encourage more data owners to participate in model training to increase the utility of model owner. Then we formally prove the feasibility of the proposed dynamic contract, which satisfies the constraints of individual rationality and intertemporal incentive compatibility. The experimental results on MNIST dataset show that the accuracy of the proposed dynamic contract is improved by at least 4% compared with the existing contracts. Additionally, compared with traditional contract and consistent pricing strategy, the model owner obtains more profit from the proposed contract.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants(three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Production of β-cyclodextrin by immobilized CGTase in packed bed bioreactor through the medium component by employing Taguchi methodology

This study investigates the optimization of submerged culture conditions for the production of extracellular cyclodextrin glucanotransferase (CGTase) enzyme by Bacillus macerans ATCC 8244, employing Taguchi orthogonal array (OA) experimental design methodology. Five critical factors - starch, peptone, temperature, pH, and agitation speed - were examined at two levels using an OA layout of L8 for the experimental design. Analysis revealed a significant enhancement in CGTase yield, with the modified culture conditions resulting in a 21.2% increase from 513.416 U/ml to 622.5 U/ml. The successful application of Taguchi's design methodology underscores its efficacy in optimizing enzyme production processes. This study highlights Taguchi's design potential as a valuable tool for improving CGTase production efficiency while minimizing resource consumption. Additionally, the utilization of a packed bed bioreactor (PBR) facilitated continuous production of β-cyclodextrin (β-CD), demonstrating stability over 10 days with only a slight decrease in productivity. The Km,app value was determined to be 23.4 mg starch/ml, indicating a slight increase.

Toward intelligent food drying: Integrating artificial intelligence into drying systems

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies.

A novel low-resource consumption and high-speed hardware implementation of HOG feature extraction on FPGA for human detection

In today’s increasingly complex traffic environment, pedestrian detection has become increasingly important. The Histogram of Oriented Gradients (HOG) algorithm has been proven to be highly efficient in pedestrian detection. This paper proposes a low-resource consumption, high-speed hardware implementation for HOG algorithm. In the case of a slight sacrifice in accuracy, it increases computational speed and reduces resource consumption. Experimental results demonstrate that the implementation achieves a speed of 0.933 pixels per clock cycle and consumes 4117 look-up tables and 4.5 Kbits of block RAMs while its accuracy decreases by 1.2% on the INRIA dataset and by 0.11% on the MIT dataset.

Revolutionizing Combat Casualty Care: The Power of Digital Twins in Optimizing Casualty Care Through Passive Data Collection.

The potential impact of large-scale combat operations and multidomain operations against peer adversaries poses significant challenges to the Military Health System including large volumes of critically ill and injured casualties, prolonged care times in austere care contexts, limited movement, contested logistics, and denied communications. These challenges contribute to the probability of higher casualty mortality and risk that casualty care hinders commanders' forward momentum or opportunities for overmatch on the battlefield. Novel technical solutions and associated concepts of operation that fundamentally change the delivery of casualty care are necessary to achieve desired medical outcomes that include maximizing Warfighter battle-readiness, minimizing return-to-duty time, optimizing medical evacuation that clears casualties from the battlefield while minimizing casualty morbidity and mortality, and minimizing resource consumption across the care continuum. These novel solutions promise to "automate" certain aspects of casualty care at the level of the individual caregiver and the system level, to unburden our limited number of providers to do more and make better (data-driven) decisions. In this commentary, we describe concepts of casualty digital twins-virtual representations of a casualty's physical journey through the roles of care-and how they, combined with passive data collection about casualty status, caregiver actions, and real-time resource use, can lead to human-machine teaming and increasing automation of casualty care across the care continuum while maintaining or improving outcomes. Our path to combat casualty care automation starts with mapping and modeling the context of casualty care in realistic environments through passive data collection of large amounts of unstructured data to inform machine learning models. These context-aware models will be matched with patient physiology models to create casualty digital twins that better predict casualty needs and resources required and ultimately inform and accelerate decision-making across the continuum of care. We will draw from the experience of the automotive industry as an exemplar for achieving automation in health care and inculcate automation as a mechanism for optimizing the casualty care survival chain.

Research on the Accounting and Spatial Effects of Emergy Ecological Footprint and Industrial Green GDP--The Case of Yangtze River Economic Belt

The booming development of industrial enterprises at the cost of ecological resource consumption has inevitably harmed the ecological environment, especially since the phenomenon of water resource pollution is more serious. Exploring the relationship between the ecological environment and the industrial economy is crucial to the green and sustainable development of the region. This study uses the Spatial Durbin Model (SDM) to investigate the impact of emergy ecological footprint on the industrial green of gross domestic product (GDP) under the perspective of water resources, based on the panel data of 11 provinces in Chinas Yangtze River Economic Belt (YREB) from 2012 to 2021. The results show that first, the overall industrial green GDP of the YREB has increased year by year, and the effect of water resource protection is outstanding. Second, the emergy ecological footprint has a significant positive effect on industrial green GDP, and the spillover effect is also significant. Third, heterogeneity analysis shows that the spatial relationship between emergy ecological footprint and industrial green GDP is different in different regions, and the local effect and neighborhood effect of the high industrial green economic development areas are consistent with those of the YREB, while the relationship between the low industrial green economic development areas is not obvious. This paper is of great significance for the green development of industrial enterprises in the YREB and guides the formulation of sustainable development policies.

Sustainable nanomaterials' role in green supply chains and environmental sustainability

The review explores the pivotal role of sustainable nanomaterials in promoting green supply chains and advancing environmental sustainability. Nanotechnology has emerged as a promising field for developing innovative materials with enhanced properties and reduced environmental impacts. Sustainable nanomaterials, characterized by their eco-friendly synthesis methods, biodegradability, and low toxicity, offer transformative opportunities for enhancing the sustainability of supply chains across diverse industries. This review examines the potential applications of sustainable nanomaterials in green supply chains, encompassing areas such as renewable energy, water purification, waste management, and sustainable packaging. By leveraging the unique properties of nanomaterials, such as high surface area-to-volume ratio, catalytic activity, and tunable properties, businesses can develop sustainable solutions to address pressing environmental challenges. Case studies and examples highlight successful integration of sustainable nanomaterials into supply chain practices, showcasing their ability to reduce resource consumption, minimize waste generation, and mitigate environmental impacts. The review also discusses challenges and considerations associated with the adoption of sustainable nanomaterials, including regulatory compliance, risk assessment, and ethical considerations. Strategies for promoting responsible nanotechnology practices and fostering collaboration among stakeholders are proposed, emphasizing the importance of interdisciplinary approaches and stakeholder engagement in achieving sustainable supply chain goals. In conclusion, the integration of sustainable nanomaterials into green supply chains holds immense potential for driving environmental sustainability, innovation, and long-term prosperity. Keywords: Sustainable Nanomaterials, Green Supply Chains, Environmental Sustainability, Circular Economy, Nanotechnology, Supply Chain Optimization