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A review of waste paper recycling networks focusing on quantitative methods and sustainability

• Published: 13 October 2020
• Volume 23 , pages 55–76, ( 2021 )

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• Cristiane Maria Defalque   ORCID: orcid.org/0000-0001-6984-5405 1 , 2 ,
• Fernando Augusto Silva Marins 1 ,
• Aneirson Francisco da Silva 1 &
• Elen Yanina Aguirre Rodríguez 1  

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A discussion is currently under way in the literature on the sustainable benefits of recycling material, particularly paper, which has high global consumption and polluting capacity. Optimized planning of waste paper recycling networks stimulates sustainable processing efficiency, motivating the investigation of quantitative methods to guide decision-making. The objective of this article is to review papers that present quantitative models for planning waste paper recycling networks considering optimization of the echelons of this process, to analyze the evolution of research, find research opportunities and contribute to future research. The article presents an analysis of five categories of the selected studies: I—evolution of publications; II—echelons considered in different waste paper recycling systems; III—the sustainability pillars considered in the objectives of the formulated model; IV—formulations and techniques used; and V—uncertainty analysis. The proposal for waste paper recycling networks involves summary of the echelons considered in selected articles, to help future analysis. Research suggestions involving sustainability objectives, especially considering social issues, using different solution techniques and considering uncertainty were identified. This study, by reviewing the articles and identifying possibilities for future research, contributes to the development of research using quantitative methods for the efficient management of waste paper recycling networks or similar arrangements.

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Source: prepared by the authors. The data were obtained from Scopus— www.scopus.com and Web of Science— www.webofknowledge.com . The maps were built using VOSviewer [ 63 ]

Source: prepared by the authors

Source: prepared by the authors. Selected articles (Table 3 ) available in databases and other references described in “ Research method ”

Source: prepared by the author. Selected articles (Table 3 ) available in databases and other references described in “ Research method ”. Number of citations obtained from Scopus— www.scopus.com and Web of Science— www.webofknowledge.com

Source: prepared by the authors, based on echelons considered in the analyzed articles (Table 3 )

Source: prepared by the authors, based on echelons and operations verified in the analyzed articles (Table 3 )

Source: prepared by the authors, based on analyses of the selected articles (Table 3 )

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This study was financed in part by the National Council for Scientific and Technological Development (CNPq—302730/2018; CNPq—303350/2018-0), the São Paulo State Research Foundation (FAPESP—2018/06858-0; FAPESP—2018/14433-0) and the Coordination for the Improvement of Higher Education Personnel—Brazil (CAPES)—Finance Code 001.

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Defalque, C.M., Marins, F.A.S., da Silva, A.F. et al. A review of waste paper recycling networks focusing on quantitative methods and sustainability. J Mater Cycles Waste Manag 23 , 55–76 (2021). https://doi.org/10.1007/s10163-020-01124-0

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Shredder machine for plastic recycling: A review paper

J H Wong 1 , M J H Gan 1,2 , B L Chua 1 , M Gakim 1 and N J Siambun 1,2

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 1217 , Material and Energy Engineering for Sustainable Advancement (MEESA 2021) 29th-30th September 2021, Kota Kinabalu, Sabah, Malaysia Citation J H Wong et al 2022 IOP Conf. Ser.: Mater. Sci. Eng. 1217 012007 DOI 10.1088/1757-899X/1217/1/012007

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1 Mechanical Engineering, Faculty of Engineering, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia

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Plastic pollution has become a global concern due to poor plastic waste disposal management. As an alternative, independent recycling efforts are necessary. A Plastic shredder machine is a preliminary machine used to cut plastic waste into small pieces before turning it into useful products. The concept design of the shredder machine that is currently available is fairly similar. The shaft and blades are the critical components in the shredder machine that determines its performance. The geometry and orientation of the blades that were fitted into the single or double-shafts were found to directly affect the shredding performance. Therefore, this article aims to review the various geometry and orientations of the blades that give direct effect on the shredding performance, as well as identifying the research gaps related to the shredder machine for plastic waste materials

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A comprehensive review on brain–computer interface (bci)-based machine and deep learning algorithms for stroke rehabilitation.

1. Introduction

2. background.

• Unlocking the potential of brain–computer interfaces (BCIs) for brain rehabilitation goes hand in hand with the remarkable power of machine learning. These intricate algorithms act as digital guides, navigating the complex landscape of brain signals to reveal the hidden pathways to recovery. By learning to recognize the unique patterns within each person’s neural code, machine learning techniques like support vector machines, deep neural networks, and random forests become adept at deciphering intentions and translating them into tangible actions. This remarkable synergy opens a world of possibilities for individualized rehabilitation, allowing us to harness the power of our own minds to regain control and rebuild skills [ 21 , 22 , 23 , 24 ].
• BCIs are able to adapt to variations in a user’s brain impulses over time because of machine learning. Increasing accuracy and resilience, adaptive models continuously modify their parameters in response to fresh data [ 20 , 25 , 26 , 27 ].
• Tailored rehabilitation interventions are made possible by machine learning. Rehabilitative techniques can be adjusted as necessary by training models to identify each user’s unique brain patterns [ 7 , 11 , 19 , 28 , 29 , 30 ].
• Instant feedback: Machine learning can process brain signals very quickly, so BCIs can react in real time. This is like having a virtual coach that gives you feedback right away, helping you learn faster [ 14 , 17 ].
• Predicting success: Machine learning can look at brain activity and predict how well someone will do in rehab. This helps doctors make better treatment plans and obtain better results [ 20 ].
• Seeing the whole picture: Machine learning can combine different types of brain data, like EEGs and fMRIs, to obtain a more complete understanding of how the brain is working during rehab [ 17 , 20 ].

3. Diverse Applications of EEG, Machine Learning, and Deep Learning in Rehabilitation

• Motor Rehabilitation: The utilization of machine learning algorithms in decoding EEG signals during motor imagery tasks opens avenues for controlling external devices, including robotic exoskeletons and prosthetics [ 13 , 19 , 31 , 33 ]. Systems providing feedback in real time not only instruct users through precise motor tasks but also contribute significantly to motor skill relearning and the promotion of neuroplasticity [ 14 , 19 , 32 , 33 ].
• Cognitive Rehabilitation: EEG signals play a pivotal role in gauging and enhancing attention stages using Neurofeedback techniques [ 8 , 11 , 13 , 34 ]. The adaptability of machine learning facilitates the tailoring of training procedures tailored to individual mental states, while the nuanced capabilities of deep learning contribute to the design of personalized memory training tasks [ 7 , 8 , 13 , 34 ].
• Neuropsychiatric Rehabilitation: EEG neurotraining emerges as a beneficial instrument for handling stress and anxiety, utilizing artificial intelligence to recognize patterns associated with stress and triggering interventions for relaxation [ 10 , 28 , 35 , 36 ]. Moreover, neurotraining based on EEG assists those with ADHD in refining concentration and controlling attention by reinforcing preferred brain activity patterns [ 13 ].
• Walking Rehabilitation: Integrating ML and EEG with systems that capture movements allows for a comprehensive analysis of gait patterns, providing real-time feedback during walking exercises and potentially revolutionizing rehabilitation approaches [ 13 , 31 , 34 , 37 ].
• Visual and Hearing Restoration: Protocols relying on EEG, in collaboration with advanced machine learning approaches, provide an intricate structure for creating customized training activities for individuals experiencing sensory perception challenges in both vision and hearing [ 32 , 38 ].
• Multimodal Rehabilitation: The integration of EEG with other cutting-edge technologies, such as virtual reality or functional near-infrared spectroscopy, paves the way for innovative multimodal rehabilitation approaches [ 19 , 20 , 33 , 39 , 40 ].

4. EEG Signal Acquisition and Motor Imagery Training

4.1. eeg-based signal acquisition, 4.2. patient training on motor imagery tasks.

• Warm-up (t = 0 s): The user focuses on a fixation point or cue to settle their mind and prepare for the upcoming task. MI Task: A visual cue, such as an arrow, instructs the user to perform a specific MI task (e.g., imagine left-hand movement).
• Data Collection (t = 3 s): During the task period, EEG sensors capture the user’s brain activity, recording the unique electrical signature of the imagined movement.
• Machine Learning (t = 3–7 s): The collected data are then analyzed by a machine learning algorithm. This algorithm identifies the key features that distinguish different MI tasks from the EEG signals.
• No Feedback: In the initial phase, no feedback is provided to the user. This allows the machine learning algorithm to focus solely on understanding the user’s individual EEG patterns.
• MI Task Repetition: Once the system has been calibrated, users repeat the MI tasks. Real-time Feedback: This time, the system provides feedback in real time. For example, a bar might grow longer or change color based on the system’s confidence in recognizing the current MI task.
• Refinement and Repetition: With each trial, the user receives feedback and can adjust their mental strategies to produce clearer EEG patterns. This iterative process strengthens the connection between the imagined movement and the corresponding EEG signature.
• Gradual Improvement: The Graz training paradigm involves multiple training sessions, spread over days or weeks. With each session, the user’s ability to generate distinct EEG patterns for different MI tasks improves, leading to more accurate recognition by the system.
• Customization: The training protocol can be adapted to individual needs and goals. The specific MI tasks, cues, and feedback types can be tailored to suit different applications, such as controlling a prosthetic limb or navigating a virtual environment.

5. EEG Signal Processing and Classification Techniques in Rehabilitation Research

• Filtering: The application of low pass, high pass, and notch filters emerges as a crucial step in refining EEG signal quality, as it effectively eliminates undesirable frequency components [ 38 , 47 ].
• Artifact Elimination: Approaches like Independent Component Analysis (ICA) and Principal Component Analysis (PCA) play a crucial role in differentiating and removing disturbances, encompassing eye blinks, muscle movements, and interference from electrocardiograms [ 17 , 18 , 48 ].
• Time Domain Features: The extraction of characteristics like average amplitude, root mean square value, and signal variance represents a nuanced approach to capturing the temporal characteristics inherent in EEG signals [ 19 , 20 , 33 ].
• Frequency Domain Features: Insights into the frequency distribution of brain activity are unveiled through the meticulous examination of power spectral density, spectral entropy, and band power ratios [ 11 , 31 , 47 ].
• Time-Frequency Features: Methods such as wavelet transformation and short-time Fourier transformation enhance complexity by unveiling the dynamic variations in EEG signal characteristics across both time and frequency domains [ 32 , 49 , 50 ].
• Functional Interconnection: Measures encompassing coherence, phase synchronization, and mutual information serve as invaluable tools in assessing the intricate functional relationships between different brain regions [ 33 , 53 , 54 ].
• Graph Theory Analysis: The innovative representation of EEG data as networks, coupled with graph theory metrics, offers a unique lens through which organizational and communication patterns within the brain can be deciphered [ 51 , 52 ].
• Pattern Recognition and Motor Imagery: A specialized focus on processing EEG signals derived from motor imagery tasks facilitates the recognition of specific patterns associated with imagined movements, thereby paving the way for tailored interventions [ 26 , 55 , 56 ].

5.1. Feature and Channel Selection

• Filter Approach: Initiating with the full set of features, filter methods meticulously identify the optimal subset through dedicated selection criteria. These criteria often revolve around key characteristics like information gain, dependency, consistency, correlation, and distance measures [ 57 ]. A significant advantage of filter methods lies in their minimal computational requirements. Additionally, the feature selection process operates independently of the chosen classifier, providing greater flexibility. Widely utilized filter methods include correlation criteria and mutual information techniques, both meticulously honing in on the most informative features within the data landscape.
• Wrapper Approach: Distinct from filter methods, wrapper approaches forge a direct partnership with the classifier to select features meticulously. They iteratively present candidate feature subsets to the classifier, diligently evaluating its performance. This feedback loop guides the selection process, prompting either acceptance of a subset based on established criteria or the proposal of new combinations for further evaluation. Algorithms within this realm encompass searching algorithms and evolutionary algorithms. The former embarks on their quest with an empty set, strategically adding or removing features until the classifier’s performance peaks. Their journey typically concludes when a designated maximum feature subset size is attained. Meanwhile, evolutionary algorithms, such as particle swarm optimization (PSO) [ 58 ], and artificial bee colony (ABC) [ 59 , 60 ], harness nature-inspired optimization techniques to uncover optimal subsets. While wrapper methods excel at identifying feature combinations that yield superior classifier performance compared to filter methods, their computational demands are considerable, rendering them less suitable for handling vast datasets.

5.2. EEG-Based Machine Learning and Deep Learning Algorithms

• Sample Size: Effective with small-to-medium-sized datasets.
• Accuracy: High accuracy in binary and multi-class classification problems as Figure 4 .
• Training Cost: Moderate, with a need for tuning hyperparameters.
• Sample Size: Suitable for small datasets.
• Accuracy: Good for pattern recognition tasks.
• Training Cost: Low, as k-NN is a lazy learner.
• Sample Size: Requires a large dataset.
• Accuracy: High for spatial feature extraction and classification.
• Training Cost: High due to the need for extensive computational resources.
• Sample Size: Requires a large dataset.
• Accuracy: High for sequential data analysis.
• Training Cost: High due to recurrent nature and complex computations.
• Accuracy: High for temporal pattern recognition.
• Training Cost: High due to complex architecture.
• Sample Size: Effective with medium to large datasets.
• Accuracy: Moderate to high, depending on the complexity of the task.
• Training Cost: Moderate.

5.3. Performance Indicators and Metrics in Evaluating the Effectiveness of Methods

• Classification Accuracy: The efficacy of EEG-based models is scrutinized through the lens of classification accuracy, providing insights into their ability to distinguish between different classes or states in activities like differentiating motor imagery or identifying cognitive states [ 11 , 13 , 14 , 19 , 31 , 38 , 38 ].
• ROC Curve and AUC: The evaluation of the balance between specificity and sensitivity in classification assignments is facilitated through the utilization of ROC curves and AUC values, adding a layer of sophistication to the assessment process [ 17 , 38 ].
• MSE or RMSE: In the realm of regression functions, the metrics of Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) emerge as crucial, providing a nuanced measure of the accuracy of predictions by quantifying the difference between predicted and actual values [ 13 , 31 ].
• R-squared (R2): The extent to which the regression model fits the data is gauged through the lens of R-squared (R2), offering valuable insights into the predictive power of the model [ 49 , 50 ].
• Real-time Performance Measurements: In situations necessitating instantaneous responsiveness, parameters such as response latency, response time, and overall system latency offer a comprehensive evaluation of the system’s real-world applicability [ 26 , 55 ].

6. Present Constraints in the Ongoing Research on Rehabilitation Utilizing EEG with ML and DL Methods

• Noise and Artifacts: Concerns surrounding data quality, preprocessing methodologies, and the standardization of data gathering protocols cast a spotlight on the imperative need to address these aspects to ensure the reliability and consistency of results.
• Small Sample Sizes: The challenges when acquiring high-quality EEG data from groups of patients contribute to the prevalence of small sample sizes, potentially leading to model overfitting and hindering the generalizability of findings.
• Longitudinal EEG Datasets: The scarcity of longitudinal EEG datasets poses a significant hurdle in monitoring progress during neural rehabilitation. A dedicated focus on long-term research is indispensable for comprehensively understanding the effectiveness of diverse approaches and customizing interventions and treatments accordingly.
• Interpretability of Deep Learning: The opaque nature of deep learning models poses challenges in interpreting results, necessitating research that seamlessly combines deep learning methodologies with insights from neuroscience. This integration is crucial for gaining a deeper understanding of the fundamental neurophysiological mechanisms associated with brain rehabilitation.
• Ethical Concerns in Real-Time Applications: While offline analysis dominates several EEG-based brain rehabilitation techniques, the shift towards real-time applications introduces ethical considerations related to patient consent, data ownership, and privacy. Meticulous attention is required to ensure that these issues are addressed with the utmost care, respecting patients’ rights.
• Bridging the Gap Between Research and Clinical Implementation: Despite the strides made in research in neurological rehabilitation utilizing EEG signals, there remains a discernible discrepancy between academic research and the practical implementation of clinical solutions. Efforts to bridge this gap are essential for the seamless translation of research findings into real-world clinical practices.

7. Comparison of EEG Headsets for Rehabilitation Purposes with Various Datasets

8. most significant eeg- and motor imagery-based studies in the last 7 years, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Elashmawi, W.H.; Ayman, A.; Antoun, M.; Mohamed, H.; Mohamed, S.E.; Amr, H.; Talaat, Y.; Ali, A. A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation. Appl. Sci. 2024 , 14 , 6347. https://doi.org/10.3390/app14146347

Elashmawi WH, Ayman A, Antoun M, Mohamed H, Mohamed SE, Amr H, Talaat Y, Ali A. A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation. Applied Sciences . 2024; 14(14):6347. https://doi.org/10.3390/app14146347

Elashmawi, Walaa H., Abdelrahman Ayman, Mina Antoun, Habiba Mohamed, Shehab Eldeen Mohamed, Habiba Amr, Youssef Talaat, and Ahmed Ali. 2024. "A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation" Applied Sciences 14, no. 14: 6347. https://doi.org/10.3390/app14146347

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Computer Science > Machine Learning

Title: detecting throat cancer from speech signals using machine learning: a scoping literature review.

Abstract: Introduction: Cases of throat cancer are rising worldwide. With survival decreasing significantly at later stages, early detection is vital. Artificial intelligence (AI) and machine learning (ML) have the potential to detect throat cancer from patient speech, facilitating earlier diagnosis and reducing the burden on overstretched healthcare systems. However, no comprehensive review has explored the use of AI and ML for detecting throat cancer from speech. This review aims to fill this gap by evaluating how these technologies perform and identifying issues that need to be addressed in future research. Materials and Methods: We conducted a scoping literature review across three databases: Scopus,Web of Science, and PubMed. We included articles that classified speech using machine learning and specified the inclusion of throat cancer patients in their data. Articles were categorized based on whether they performed binary or multi-class classification. Results: We found 27 articles fitting our inclusion criteria, 12 performing binary classification, 13 performing multi-class classification, and two that do both binary and multiclass classification. The most common classification method used was neural networks, and the most frequently extracted feature was mel-spectrograms. We also documented pre-processing methods and classifier performance. We compared each article against the TRIPOD-AI checklist, which showed a significant lack of open science, with only one article sharing code and only three using open-access data. Conclusion: Open-source code is essential for external validation and further development in this field. Our review indicates that no single method or specific feature consistently outperforms others in detecting throat cancer from speech. Future research should focus on standardizing methodologies and improving the reproducibility of results.

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