Real-world Scenarios Research Articles

Computational investigation of stochastic Zika virus optimal control model using Legendre spectral method

This study presents a computational investigation of a stochastic Zika virus along with optimal control model using the Legendre spectral collocation method (LSCM). By accumulation of stochasticity into the model through the proposed stochastic differential equations, we appropriating the random fluctuations essential in the progression and disease transmission. The stability, convergence and accuracy properties of the LSCM are conscientiously analyzed and also demonstrating its strength for solving the complex epidemiological models. Moreover, the study evaluates the various control strategies, such as treatment, prevention and treatment pesticide control, and identifies optimal combinations that the intervention costs and also minimize the proposed infection rates. The basic properties of the given model, such as the reproduction number, were determined with and without the presence of the control strategies. For R0<0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_0<0$$\\end{document}, the model satisfies the disease-free equilibrium, in this case the disease die out after some time, while for R0>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_0>1$$\\end{document}, then endemic equilibrium is satisfied, in this case the disease spread in the population at higher scale. The fundamental findings acknowledge the significant impact of stochastic phonemes on the robustness and effectiveness of control strategies that accelerating the need for cost-effective and multi-faceted approaches. In last the results provide the valuable insights for public health department to enabling more impressive mitigation of Zika virus outbreaks and management in real-world scenarios.

Premarket and postmarket regulatory challenges for mechanical ventilators: scenario in Brazil and comparison with other countries

BackgroundThis study critically assesses the regulatory challenges faced in Brazil for mechanical ventilators amid the COVID-19 pandemic. It evaluates the effectiveness of expedited regulatory actions, compares strategies with other countries, and proposes future optimization in emergency conditions. Methods: Premarket submissions of mechanical ventilators were surveyed, involving accompanying certification documentation, rejections, approvals, and cancellations. Postmarket occurrences were surveyed until June 2023, evaluating their incidence and presence of certifications. Regulatory pathways in other countries were reviewed for comparison. Results: Brazil’s regulatory strategy increased its ventilator supply capacity 17 times in 10 months. However, the absence of essential certifications in 95.52% of the premarket submissions, such as the Certificate of Conformity issued by the Brazilian Conformity Assessment System (CC-SBAC), raised safety and performance concerns. Among temporarily approved ventilators, the main cause of cancellation (76.92%) was the absence of certifications after the temporal approval, notwithstanding the already installed devices maintaining authorization for use in health services. Postmarket alerts lacked CC-SBAC issuance in 75% of the cases when sponsors reported the occurrences. It highlighted the importance of ongoing technical assistance and monitoring, particularly for devices lacking proper certifications. Conclusions: The extraordinary regulatory conditions in Brazil significantly impacted ventilator premarket evaluation. However, lacking essential certifications led to post-approval cancellations. Postmarket occurrences emphasized necessary vigilance, with most alerts involving uncertified devices. Improved premarket evaluation, considering component shortages and real-world usage scenarios, as collaborative efforts from other countries and international bodies, is crucial for ensuring the safety and effectiveness of ventilators in public health emergencies.

A framework for early-stage sustainability assessment of innovation projects enabled by weighted sum multi-criteria decision analysis in the presence of uncertainty

A two-level hierarchical framework for early-stage sustainability assessment (FESSA) amongst a set of alternatives applicable from the earliest stages of process or product development is introduced, and its use in combination with an improved method weighted-sum method multi-criteria decision analysis (WSM-MCDA) in the presence of uncertainty is presented through application to a case study based upon a real-world decision scenario from speciality polymer manufacture. The approach taken addresses the challenge faced by those responsible for innovation management in the manufacturing process industries to make simultaneously timely and rational decisions early in the innovation cycle when knowledge gaps and uncertainty about the options tend to be at their highest. The Computed Uncertainty Range Evaluations (CURE) WSM-MCDA method provides better discrimination than the existing Multiple Attribute Range Evaluations (MARE) method without the computational burden of generating heuristic outcome distributions via Monte-Carlo simulation.

Tri-Phase Implementation of an Innovative Fuzzy Logic Approach for Decision-Making

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach’s durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims.

TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing

Multi-access edge computing (MEC) has attracted the interest of the research and industrial community to support Internet of things (IoT) applications by enabling efficient data processing and minimizing latency. This paper presents significant contributions toward optimizing the resource allocation and enhancing the decision-making process in edge computing environments. Specifically, the TRUST-ME model is introduced, which consists of multiple edge servers and IoT devices, i.e., users, with varied computing tasks offloaded to the MEC servers. A utility function was designed to quantify the benefits in terms of latency and cost for the IoT device while utilizing the MEC servers’ computing capacities. The core innovation of our work is a novel trust model that was designed to evaluate the IoT devices’ confidence in MEC servers. This model integrates both direct and indirect trust and reflects the trustworthiness of the servers based on the direct interactions and social feedback from other devices using the same servers. This dual trust approach helps with accurately gauging the reliability of MEC services and ensuring more informed decision making. A reinforcement learning framework based on the optimistic Q-learning with an upper confidence bounds action selection algorithm enables the IoT devices to autonomously select a MEC server to process their computing tasks. Also, a multilateral bargaining model is proposed for fair resource allocation of the MEC servers’ computing resources to the users while accounting for their computing demands. Numerical simulations demonstrated the operational effectiveness, convergence, and scalability of the TRUST-ME model, which was validated through real-world scenarios and comprehensive comparative evaluations against existing approaches.

Green synthesis of Fe[sbnd]Ni[sbnd]Sn trimetallic oxides from Moringa oleifera for photocatalytic degradation of Reactive Red 6B dye

The nanocomposite FeNiSn trimetallic oxides were synthesized using an aqueous Moringa Oleifera leaf extract. The prepared nanocomposite was analyzed using SEM, FTIR, XRD, N2 adsorption-desorption, Zeta potential, and UV–vis analysis. A common Azo dye named Reactive Red 6B (RR-6B), widely applied in the dyeing industry and causing environmental pollution, was selected to examine the photodegradation of FeNiSn trimetallic oxides nanocomposite under sunlight irradiation, and it was shown that this dye could be successfully degraded by prepared nanocomposite. Effects of parameters such as inorganic salt concentration, H2O2 concentration, contact time, pH, and impact of different concentrations of catalyst and dye were examined to find out degradation conditions. This prepared nanocomposite was applied for the photodegradation of RR-6B dye, and 95 % degradation efficiency was obtained at pH 4, 10 mg of catalyst concentration, 10 ppm of dye concentration, and contact time of 2 h. Degradation kinetics were carried out using Langmuir-Hinshelwood and first-order reaction models. These results showed a facile technique for forming nanocomposite to efficiently degrade organic contaminants from wastewater, which directly impacts industrial wastewater treatment. The antibacterial activity was performed using the Time assay kill method for Gram-positive S. aureus and Gram-negative E. coli bacteria. The results of antibacterial activity showed an inhibition effect on bacterial growth by trimetallic oxides NPs, suggesting their potential use in antimicrobial coatings and materials. These findings underscore the practical applications of our research, demonstrating its relevance and potential impact in real-world scenarios.

Verifying the Generalization of Deep Learning to Out-of-Distribution Domains

Deep neural networks (DNNs) play a crucial role in the field of machine learning, demonstrating state-of-the-art performance across various application domains. However, despite their success, DNN-based models may occasionally exhibit challenges with generalization, i.e., may fail to handle inputs that were not encountered during training. This limitation is a significant challenge when it comes to deploying deep learning for safety-critical tasks, as well as in real-world settings characterized by substantial variability. We introduce a novel approach for harnessing DNN verification technology to identify DNN-driven decision rules that exhibit robust generalization to previously unencountered input domains. Our method assesses generalization within an input domain by measuring the level of agreement between independently trained deep neural networks for inputs in this domain. We also efficiently realize our approach by using off-the-shelf DNN verification engines, and extensively evaluate it on both supervised and unsupervised DNN benchmarks, including a deep reinforcement learning (DRL) system for Internet congestion control—demonstrating the applicability of our approach for real-world settings. Moreover, our research introduces a fresh objective for formal verification, offering the prospect of mitigating the challenges linked to deploying DNN-driven systems in real-world scenarios.

Port performance indicators construction based on the AIS-generated trajectory segmentation and classification

AbstractWith coverage of worldwide shipping routes and straightforward accessibility, vessels’ navigational data from AIS have emerged as a potential leading source of knowledge for maritime stakeholders. However, transforming the raw AIS messages into meaningful indicators requires comprehensive work that involves enrichment with multiple relevant data sources. In this study, a fain-grained technique to process AIS tracks for the purpose of constructing port performance indicators is proposed. The technique involves trajectory segmentation to separate the stopping and underway segments of AIS-generated trajectories and trajectory classification to identify moored and at-anchor vessels from the stopping segments. With the fusion of relevant information, the method estimates the port boundary and identifies the associated berthing and anchorage locations to estimate turnaround time and construct port connectivity indicators. AIS datasets recorded over eight months covering the waters of Indonesia, Malaysia, and Singapore, known as one of the world’s busiest shipping routes, are selected to evaluate the proposed method. The trajectory segmentation demonstrated 98–99% accuracy, while the classification achieved 95–97%. The estimation of the vessel turnaround time closely matched the UNCTAD data with an error rate of 2.9%. These results prove the proposed approach’s practicality in contributing to the real-world scenario.

TLDM: An Enhanced Traffic Light Detection Model Based on YOLOv5

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.

Using Prediction Markets as a Tool for Classroom and Civic Engagement

Keeping students actively engaged in their courses and seeking ways to help them understand how to apply course content to real-world scenarios remains a central focus of political science education. In an effort to address this enduring challenge, we implement a prediction market focused on the 2022 congressional midterm elections in our introductory and upper-level political science courses. Using original survey data, obtained through the administration of pre and post surveys, we find statistically significant increases in feelings of pride, hope, trust, and efficacy among market participants. More broadly, we discuss the ways in which this semester-long project, using both the prediction market and classroom assignments, fosters increases in the desire for civic participation and the possibility of sustained civic engagement that extends beyond the classroom. Throughout the paper we seek to provide a useful guide for anyone seeking alternative methods of classroom and civic engagement and different ways by which to achieve it in a variety of courses.

FCTree: Visualization of function calls in execution

Function calls in execution contain rich bivariate, hierarchical, and chronological information. Many visualizations have been adopted to analyze function calls in execution for program testing, vulnerability locating, and malware detection. However, we conducted a pilot study and revealed that existing single-viewed function call visualizations fail to present the bivariate, hierarchical, and chronological information comprehensively. A new function call visualization named FCTree is proposed in this work to deal with this situation. Learned from advantages of existing visualizations and iterative discussions with actual users, FCTree uses a compact and aligned hierarchical layout design to present the bivariate and hierarchical information and adopts a glyph design to present the chronological information. Subjective and objective experiments in the laboratory and a field study in a real-world scenario were conducted to evaluate the effectiveness of FCTree.

DACA: A domain adaptive fault diagnosis approach with class-aware based on cross-domain extreme imbalance data

Existing research on unsupervised domain adaptation (UDA) has primarily centered on mitigating differences in feature distribution across various domains. However, in real-world scenarios, natural data often exhibits not only a disparity in instance counts across different classes within the same domain but also across different domains, leading to imbalances at both the class and domain levels. To address this issue, this study introduces a novel scenario, termed extreme imbalance domain adaptation (EIDA), which encompasses both covariate shifts and labeling shifts. We propose a Domain Adaptation with Class-aware (DACA) framework for bearing fault diagnosis that aims to tackle inter-domain feature shifts as well as intra- and inter-domain category imbalances. Specifically, DACA enhances the recognition accuracy of minority categories without degrading the performance concerning the majority category, reduces the adverse effects of data imbalance during domain transitions, and strengthens the robustness of decision boundaries for improved spatial matching. This is achieved through four sub-modules: adversarial clustering learning, cross-domain category alignment, category-level reweighting, and category-level re-margining. The efficacy of DACA is demonstrated through simulations of three imbalance protocols in the EIDA scenario across two bearing datasets and one gearbox dataset, showcasing significant advancements in cross-domain diagnostic tasks.

Intravitreal Antiangiogenic Treatment for Diabetic Retinopathy: A Mexican Real-Life Scenario Experience.

The objective of this study was to analyze the effectiveness of two intravitreal antiangiogenic drugs, ranibizumab and aflibercept, in a Mexican population over a period of 5 years, evaluating the improvement in visual acuity (VA) and central retinal thickness (CRT) in a real-world scenario. This is a retrospective study with subjects diagnosed with diabetic retinopathy (DR), proliferative diabetic retinopathy (PDR), and diabetic macular edema (DME) receiving intravitreal injections of ranibizumab and/or aflibercept. In this study, we analyzed 588 eyes of 294 patients who received intravitreal antiangiogenic injections. The results showed an improvement regardless of antiangiogenic treatment or diagnosis in both VA and CRT. We found that both aflibercept and ranibizumab improved VA, while subjects with DME responded less to antiangiogenic treatment (p < 0.05), and that this difference did not correspond to the CRT measured by OCT. These results support evidence that intravitreal antiangiogenic medications are effective for ophthalmic complications of diabetes in our population; however, damage to visual structures is not reversed in most patients. And that the perception by the patient (VA) and that of the ophthalmologist (CRT) do not completely correlate in our study.

Real-world effectiveness of the CoronaVac vaccine in a retrospective population-based cohort in four Colombian cities (2021-2022)

ObjectivesThe National Vaccination Plan against SARS-CoV-2/COVID-19 was launched by the Ministry of Health and Social Protection on 14 February 2021. The main objective of this study was to evaluate the effectiveness of the CoronaVac in preventing the three clinical outcomes of infection, hospitalisation, or death, in a real-world scenario. DesignThis was a population-based retrospective dynamic cohort study using a multivariate Cox model to calculate hazard ratios to estimate vaccine effectiveness from 17 February 2021 to 30 June 2022. The data were collected from surveillance systems for 12 months for each individual. Four cities were selected on the basis of the reliability of their data bases. ResultsThe rates of CoronaVac effectiveness were 32% (95% confidence interval [CI] 31-33) for preventing infection, 55% (95% CI 54-56) for hospitalisation, and 90% (95% CI 89-90) for death, at the end of follow-up. These findings were more consistent during the first 4 months. Compared with the unvaccinated group, homologous booster doses appeared to increase effectiveness in preventing hospitalisation, whereas heterologous booster doses increased protection for both hospitalisation and death. Booster doses did not improve effectiveness among those already vaccinated with CoronaVac, even when they received heterologous boosters. ConclusionsCoronaVac demonstrated effectiveness in preventing death and hospitalisation during the first year of follow-up, but its effectiveness in preventing infection was lower, decreasing rapidly after the first 4 months of follow-up. The effectiveness was higher among children aged between 3 and 12 years, and among adults aged ≥60 years. Booster doses did not improve effectiveness among those already vaccinated with CoronaVac.

TSMD-Net: A two-stage mixed dehazing network with feature fusion and multi-window self attention on Jetson board

Single image dehazing is a pivotal endeavor focused on enhancing the quality of images by removing haze-induced noise. Achieving this goal requires a delicate equilibrium between retaining spatial intricacies and contextual understanding, particularly crucial in real-world scenarios where complexity is amplified. In response to this challenge, we present the Two-Stage Mixed Dehazing Network (TSMD-Net), designed to reconcile these conflicting objectives by leveraging the foundational U-Net architecture. Our approach entails a dual-phase strategy: initially assimilating contextual comprehension through encoder-decoder frameworks, followed by amalgamating it with high-resolution branches to uphold spatial integrity. To streamline the analysis and comparison of this architecture, we opt to substitute or eliminate nonlinear activation functions with multiplication operations, effectively mitigating system complexity. Furthermore, to foster seamless information exchange across hierarchical layers of the encoder-decoder at varying scales, we introduce the Feature Fusion (FF) mechanism, facilitating the integration of insights from higher layers progressively down to the base layer. This amalgamated data significantly enhances the fidelity of the original image transformation by harmonizing the inclusion of contextual feature maps with the preservation of spatial details, resulting in noise-free, high-quality images. Additionally, we propose the “Multi-Window Self Attention” (MWSA) mechanism, characterized by linear time complexity, as the central block of the encoder-decoder. This module mitigates the confined receptive fields inherent in convolutional neural networks (CNNs), enabling the aggregation of more comprehensive feature-map data. Subsequently, our TSMD-Net undergoes rigorous training and testing on both synthetic and real-world datasets, showcasing superior performance compared to current state-of-the-art (SOTA) networks while demanding fewer processing resources. To validate the robustness and practical utility of our methodology, we implement TSMD-Net on a low-end Jetson Nano board, confirming its effectiveness and efficiency in real-world settings.

Advancing Sustainable Direct Contact Membrane Distillation: Performance and Stability of Novel Polymer Inclusion Membranes

Novel hydrophobic membranes for direct contact membrane distillation (DCMD) were developed using poly(1,1-difluoroethylene) (PVDF) loaded with the ionic liquid methyltrioctylammonium bis(2-ethylhexyl) phosphate (MTOA-DEHP). These polymer inclusion membranes (PIMs) were fabricated via non-solvent induced phase separation (NIPS). Detailed characterization revealed that MTOA-DEHP significantly enhances the morphological and physicochemical properties of the PIMs. Through a series of DCMD experiments, the membrane’s flux, salt rejection, and stability under varying conditions, including different feed solutions (i.e., synthetic NaCl solution and real seawater from the Atlantic Ocean), were assessed. Our results demonstrate that the PVDF/20 %MTOA-DEHP membrane exhibits superior performance, maintaining consistent transmembrane flux values (8.98 ± 0.61 kg·m−2·h−1) over multiple cycles and achieving high salt rejection rates (>99.9 %). Furthermore, the membrane demonstrates excellent stability, with minimal degradation observed even after prolonged operation. The results affirm the viability of PIMs as a promising avenue for achieving sustainable and efficient desalination via DCMD, particularly in real-world operational scenarios.

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage RUL prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Despite operating without prior knowledge of end-of-life (EOL) events, our method consistently achieves lower error rates, boasting mean absolute error (MAE) and mean square error (MSE) of 0.0275 and 0.0014, respectively, compared to existing convolutional neural networks (CNN) and long short-term memory (LSTM)-based methods. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries.

Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles. Despite significant advancements achieved by data-driven methods, diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data. This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations. We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles. Our analysis comprehensively addresses cell inconsistencies, physical interpretations, and charging uncertainties in real-world applications. This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves. By leveraging inexpensive unlabeled data in a self-supervised approach, our method demonstrates improvements in average root mean square errors of 74.54% and 60.50% in the best and worst cases, respectively, compared to the supervised benchmark. This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in real-world scenarios.

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater

To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios.