Processing Techniques Research Articles

Transformations of Critical Lithium Ores to Battery-Grade Materials: From Mine to Precursors

The escalating demand for lithium has intensified the need to process critical lithium ores into battery-grade materials efficiently. This review paper overviews the transformation processes and cost of converting critical lithium ores, primarily spodumene and brine, into high-purity battery-grade precursors. We systematically examine the study findings on various approaches for lithium recovery from spodumene and brine. Dense media separation (DMS) and froth flotation are the most often used processes for spodumene beneficiation. Magnetic separation (MS) and ore gravity concentration techniques in spodumene processing have also been considered. To produce battery-grade lithium salts, the beneficiated-concentrated spodumene must be treated further, with or without heat, in the presence of acidic or alkaline media. As a result, various pyro and hydrometallurgical techniques have been explored. Moreover, the process of extracting lithium from brine through precipitation, liquid–liquid extraction, and polymer inclusion membrane separation employing different organic, inorganic, and composite polymer sorbents has also been reviewed.

Membrane Separations in Biomass Processing.

The development of integrated biorefineries and the greater utilization of biomass resources to reduce dependence on fossil fuel-derived products require research emphasis not just on conversion strategies but also on improving separations associated with biorefining. A significant roadblock towards developing biorefineries is the lack of effective separation techniques evidenced by the lack of literature in this area. Additionally, high conversion yields may only be realized if effective separations generate feedstock of sufficient purity - this makes research into biomass conversion strategies all the more critical. In this review, the challenges associated with biomass separations are discussed, followed by an overview of the most appropriate separation strategies for processing biomass. One of the unit operations most appealing for biorefining, membrane separations (MS), is then considered along with a review of the recent literature utilizing this technique in biomass processing.

Job Title Predictor System

In this paper, we propose a job title recommendation system using a combination of Natural Language Processing (NLP) techniques and machine learning. We implement a TF-IDF Vectorizer and cosine similarity to recommend jobs based on user inputs like skills, experience, industry, and role category. The system was built using Python and integrated into a user-friendly interface using Streamlit, enabling personalized recommendations. We evaluate the accuracy of recommendations and discuss potential improvements

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Monitoring event data extracted from online news for outbreak detection

Abstract Introduction Digital disease surveillance (DDS) detects public health events from internet-based data e.g., online news. Event features depicting epidemiological and social characteristics of health events can be extracted from news using the natural language process techniques. However, few studies have leveraged the event features to support anomaly detection in DDS. We aimed to understand the distribution of the event features and explore anomaly detection using the frequency of these features. Methods We collected event data from COVID-19-related news collected from October 1 to December 31, 2021, sourced from BioCaster, an infectious-disease-focused DDS system. The predefined event features in BioCaster include disease, pathogen, location and 14 binary features, such as if an event was caused by an unclassified virus. We described the distribution of the features and detected changes in the frequency of event features using a Bayesian online change point detection. We compared the change points with the number of new cases and of genomic samples collected. Results We included 170,168 news articles reporting COVID-19 in 155 countries. The event feature indicating that an event was caused by an unclassified virus was identified as positive among 3831 (2.25%) news and 12.91% of news had positive value for the feature indicating cases who had travelled across international borders. The change points detected from these two features were temporally correlated to the emergence of the Omicron variant in corresponding countries, which was more significant in countries with at least 300 news articles. Conversely, event features irrelevant to this case study, e.g., if the cases were military workers, were identified as negative in all news and no change points were detected. Conclusions Our study highlights the potential of monitoring the frequency of event features extracted from online news for anomaly detection in DDS, which relies on sufficient news coverage. Key messages • Monitoring the event features extracted from online news provide is useful approach for automatic anomaly detection in digital disease surveillance. • Increasing media coverage is fundamental for improving the early detection in a digital disease surveillance system.

Integrated Pockels Modulators on Silicon Photonics Platform

AbstractElectro‐optic (EO) modulators are essential components in various fields, including optical communication, free‐space communication, microwave photonics, sensing, and light detection and ranging. The EO modulation enables the fast conversion of electric signals into optical signals, facilitating the precise manipulation of light. With advancements in fabrication processing techniques, next‐generation integrated EO modulators have demonstrated substantial improvements in modulation efficiency, bandwidth, and footprint. Here, the latest research progress in integrated EO modulation, focusing on the principle of the Pockels effect, key modulation metrics, novel EO thin‐film material platforms, and innovative device architectures is overviewed. Finally, it is evaluated different schemes and provide perspectives on future trends in developing integrated EO modulators, highlighting both the advantages and challenges of integrated EO modulation, including waveguide and electrode engineering, integrated methods, and other applications for large‐scale photonic integrated circuits.

Scalable electrospinning using a desktop, high throughput, self-contained system

Electrospinning is a specialized processing technique for the formation of submicron diameter fibers of polymeric and ceramic materials using an electrostatic field. The process has multiple advantages over other nano- and micro- fiber synthesis methods; however, generally suffers from very low fabrication speeds, making it undesirable for scalability. This work assesses the performance of a needle-less, self-contained, high throughput electrospinning system. It further compares the fiber fabrication rates obtained versus two single needle setups with different collectors: (i) a conventional single needle and flat plate geometry, and (ii) a single needle with a rotating collector geometry. Polyvinylpyrrolidone (PVP) in ethanol was used as the model material. The fabrication rate of the high throughput system “HTES” was measured at about 2.6 g/h and was about 15 times that collected of the flat plate. Comparing it to other systems reported in the literature also proved it to be a viable option for high throughput, lab scale electrospinning.

Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers

This study examines the formidable and complex challenge of insider threats to organizational security, addressing risks such as ransomware incidents, data breaches, and extortion attempts. The research involves six experiments utilizing email, HTTP, and file content data. To combat insider threats, emerging Natural Language Processing techniques are employed in conjunction with powerful Machine Learning classifiers, specifically XGBoost and AdaBoost. The focus is on recognizing the sentiment and context of malicious actions, which are considered less prone to change compared to commonly tracked metrics like location and time of access. To enhance detection, a term frequency-inverse document frequency-based approach is introduced, providing a more robust, adaptable, and maintainable method. Moreover, the study acknowledges the significant impact of hyperparameter selection on classifier performance and employs various contemporary optimizers, including a modified version of the red fox optimization algorithm. The proposed approach undergoes testing in three simulated scenarios using a public dataset, showcasing commendable outcomes.

Bearing fault diagnosis by sparse frequency spiral spectrum driven NAF-LDM under strong noise and small samples

Abstract The complexity of background noise and the scarcity of real fault samples seriously affect the diagnostic accuracy of the model. To address this, a noise-robust two-dimensional feature map, the sparse frequency spiral spectrum (SFSM), based on sparse representation theory, is proposed. A bridge penalty coefficient is applied to the sparse representation model to accurately select impact components, and the fast iterative shrinkage threshold algorithm is used to solve for sparse representation coefficients. Sparse reconstructed signals are obtained by convolving the impact patterns with these coefficients, leading to a sparse reconstruction algorithm with reduced computational complexity. Furthermore, the novel non-linear activation-free blocks (NAF Blocks) are embedded into the latent diffusion model to augment small samples, significantly improving image generation speed and quality. The integration of the Swin transformer for feature extraction and classification further enhances diagnostic performance. The superiority of this method is validated on the XJTU-SY dataset, a bearing experimental platform dataset, and enterprise engineering dataset. Experimental results demonstrate that the structural and generalization advantages of NAF Blocks are crucial for improving image quality and inference speed. The noise suppression capability of the proposed method, facilitated by the SFSM feature processing technique, is confirmed through ablation and noise robustness tests. Finally, the Swin transformer’s excellent feature extraction and classification capabilities for SFSM are verified. The proposed method achieves diagnostic accuracies of 99.10% and 98.7% on the XJTU-SY and experimental platform datasets, respectively.

Enhancing the Nutritional and Bioactive Properties of Bee Pollen: A Comprehensive Review of Processing Techniques

Bee pollen is recognized as a superfood due to its high content of nutrients and bioactive compounds. However, its bioavailability is restricted by a degradation-resistant outer layer known as exine. Physical and biotechnological techniques have recently been developed to degrade this layer and improve pollen’s nutritional and functional profile. This review examines how processing methods such as fermentation, enzymatic hydrolysis, ultrasound, and drying affect pollen’s chemical profile, nutrient content, and bioactive compounds. The review also considers changes in exine structure and possible synergistic effects between these methods. In addition, the challenges associated with the commercialization of processed bee pollen are examined, including issues such as product standardization, stability during storage, and market acceptance. The objective was to provide a understanding of the efficacy of these techniques, their physicochemical conditions, and their effect on the nutritional value of the pollen. The work also analyzes whether pollen transformation is necessary to maximize its benefits and offers conclusions based on the analysis of available methods, helping to determine whether pollen transformation is a valid strategy for inclusion in functional foods and its impact on consumer health. Although the literature reports that pollen transformation influences its final quality, further studies are needed to demonstrate the need for pollen exine modification, which could lead to greater market availability of pollen-based products with functional properties.

Advanced waveform analysis of the electrocardiogram signals using complementary signal processing techniques to investigate the response to a QTc prolonging drug vs control

Abstract Background Thorough QTc (TQT) studies using Electrocardiogram (ECG) data, are a well-established method for testing the pro-arrhythmic propensity of drugs performed during safety studies as part of drug development. ECG analysis is currently reduced to simplified biomarkers, such as rate, intervals and amplitudes. However, ECG waveforms are complex and generally sampled with high fidelity (125-1000 Hz). Our novel mathematical method, Symmetric Projection Attractor Reconstruction (SPAR) analyses the morphology and variability of such waveforms, using every data point to generate a new, faithful 2D waveform representation termed an ‘attractor’. Moxifloxacin is a reversible blocker of the rapid component of the delayed rectifier, potassium current of the cardiac IKr potassium channel and has been shown to cause a mean increase of the QTc interval of 10–14 ms after an oral single dose of 400 mg. Separately, studies have shown that meals can alter QT interval as well as altering the pharmacokinetic profile of moxifloxacin itself with gender and ethnicity impacting individual responses. In-depth knowledge of these physiological factors could support safer medicines development and clinical use. Purpose This project investigated the feasibility of SPAR analysis of ECG signals to detect drug induced changes which a higher degree of sensitivity, to support safety evaluation during medicines development. Methods High resolution triplicate 12 lead ECG time-series data from a Phase one study of healthy human volunteers cross over of placebo, moxifloxacin or moxifloxacin + food was retrospectively analysed using SPAR. Using a bespoke software tool was used to perform SPAR analyses to transform ECG time-series into corresponding attractors. Corresponding single point measurements were previously obtained as part of the original Phase one study. Results Initial evaluation of attractors revealed high inter-individual variation, consistent with previous SPAR ECG analysis. Nevertheless, we identified changes in subjects receiving Moxifloxacin (six hour time point) when compared to those receiving placebo control. These changes were not as evident in previously obtained QT intervals. Conclusion This pilot study illustrates that SPAR is highlighting changes on the ECG that are less obvious from QT data alone. Further evaluation of the whole dataset to identify generalisable SPAR features, and their comparison with existing markers is now necessary. The individualised nature of ECGs and corresponding attractors indicates the method may have highest utility in personalised medicine, evaluating each individual’s change and hence individual risk to drug induced arrhythmia.Visualisations of SPAR analysis of ECGs

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

Separation and Purification of CHO Secretome and Extracellular Vesicles for Proteome Analysis.

For decades, host cell proteins (HCPs) have been investigated as putative contaminants in downstream processing of biopharmaceutical products of Chinese hamster ovary (CHO) cells. However, little is still known about the composition of the entire protein and vesicle environment in CHO cultivations. Ever evolving mass spectrometry techniques allow more and more insights into cell-cell communication processes and the composition of extracellular matrix, proteases, and further actively segregated compounds such as extracellular vesicles (EVs). EVs themselves are a heterologous group consisting of exosomes, ectosomes, and apoptotic vesicles. To specifically analyze these subsets of the secretome and determine beneficial and detrimental factors for a production process, targeted separation and purification techniques are necessary.In this chapter, we present our optimized workflows for a clear differentiation between directly secreted proteins and the vesicular protein content of different fractions (especially exosomal small EVs) from CHO cell supernatant for proteomic analysis by NanoLC ESI-MS.

Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment”

This study employs computational hermeneutics to explore emotional representations in Fyodor Dostoevsky’s Crime and Punishment. Using advanced Natural Language Processing (NLP) techniques, including sentiment analysis powered by the BERT model and other tools, we examine the emotional and existential aspects embedded in the narrative. Our approach combines computational tools with traditional hermeneutic methods to deconstruct the emotional journey of the protagonist, Rodion Raskolnikov. We quantitatively analyze his emotional evolution before and after the pivotal crime, mapping his psychological transition from detachment to engagement. The study focuses on four key emotional dimensions: Attitude, Introspection, Sensitivity, and Temper. By transforming qualitative textual data into a quantitative framework, we provide a novel illustration of Raskolnikov’s psychological progression from a state of depersonalization to full emotional contact. This computational analysis is contextualized within a broader philosophical interpretation, drawing on concepts such as Stoic apatheia and Heideggerian Sorge. Our research serves a dual purpose: it enhances our understanding of Dostoevsky’s work while demonstrating the potential of computational methods in literary analysis. By bridging digital humanities and traditional literary criticism, this study contributes to the growing field of digital hermeneutics and opens new avenues for interdisciplinary research in literature, philosophy, and computational linguistics.

Sustainable Solution Processing Toward High‐Efficiency Organic Solar Cells: A Comprehensive Review of Materials, Strategies, and Applications

AbstractOrganic solar cells (OSCs) have emerged as promising candidates for renewable energy harvesting due to their lightweight, flexible, and low‐cost fabrication potential. The efficiency of OSCs is largely determined by the choice of solvents, which significantly affect the film morphology of the active layers, the intermixed donor‐acceptor domains, and overall device performance. Beginning with an introduction to the importance of solvent selection, the screening and classification of solvents, emphasizing their characteristics and classification based on sustainability, solubility, and other additional considerations are explored. Various non‐halogenated solvents, highlighting commonly used aromatic solvents, biomass‐derived solvents, and water/alcohol‐based solvents are explored. The state‐of‐the‐art donor and acceptor materials, focusing on efficient donor materials such as PM6 and D18, and high‐performing Y‐series acceptors are also presented. Strategies for developing high‐performance OSCs processed using non‐halogenated solvents are examined, including solvent engineering with additive and additive‐free approaches, ternary strategies, and layer‐by‐layer techniques. The fabrication of large‐area devices using non‐halogenated solvents is addressed, focusing on blade‐coating, slot‐coating, and other processing techniques. Finally, this review outlines future research directions in the non‐halogenated solvent processing of OSCs, emphasizing the need for continuous innovation to overcome existing limitations and propel OSC technology toward commercial viability.

Finding the human in an era of machine intelligence: A flat ontological analysis of generative AI and language learning

Amid the optimism of generative Artificial Intelligence (gen-AI) in language education, there remains a weak connection to learning practices. The emergence of gen-AI has preceded considerations of how it should be applied in teaching and learning. However, while gen-AI has been justified in terms of the possibilities to enhance learner agency by expanding opportunities to engage with language, such as through the generation of content or the translation of texts, it can also take power away from learners. How can learners be self-determining in light of how choices become increasingly guided by Artificial Intelligence? In this paper, I conceive the arrangements of humans and software as an assemblage of complex and dynamic social, and technical processes. Drawing on a flat ontology, where all agents (human and non-human, material and subjective) have equal ontological status, I argue that learner agency has its origins in the messy and lively interactions between heterogenous actors. In particular, I consider active and passive affects as being part of the same process: active when we bring something into effect ourselves, passive when our self-determination is changed not by our own power, but through external forces acting on it (such as gen-AI). From this, I explore the constraining and enabling potential of artificial intelligence. Finally, I extend this discussion to the emergence of learner agency.

A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real–time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near–infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

The effects of global product design on supply chain efficiency and natural resources management

The significance of global product design as a primary factor in determining supply chain efficiency and natural resource management. This study examines the impact of global product design changes on process and supply chain practices. Specifically, it seeks to understand in promoting sustainable practices across global supply chains. This study, utilizes the fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate the criteria, sub-criteria, and various significant opportunities influencing sustainable product design and supply chain management. The fuzzy AHP method is used to assess the key criteria and their respective sub-criteria. The fuzzy TOPSIS method is used to rank the opportunities in driving sustainable practices across global supply chains. The results of fuzzy AHP show that innovation and product differentiation (C4), economic viability (C1), and supply chain efficiency (C5) are the crucial criteria. While the results of fuzzy TOPSIS present that supply chain transparency (O2) is the most suitable opportunity, followed by circular economy practices (O1) and policy advocacy and compliance (O6) in adopting global product design on supply chain efficiency and natural resources management. Thus, this study is highly relevant to the governments, industry specialists, and policymakers who aim at enhancing the dynamic business environment and sustainable development.

Training a BERT-base NLP Model to Identify Cognitive Distortions and Understand their Intersection with Suicide-Risk

Cognitive distortions are irrational thoughts influencing mental health and are closely linked to self-harm. This paper explores the use of natural language processing (NLP) techniques to identify cognitive distortions and suicide risks in written text. Various models were tested, with BERT-base showing the highest accuracy of 53%. Cognitive distortions were inferred on a dataset of Reddit posts labelled as suicidal and non-suicidal, showing that distortions like overgeneralization and mental filtering are prevalent in suicidal texts. This research highlights the relationship between cognitive distortions and suicide risk, suggesting that certain distortions could be early indicators of suicidal ideation. Findings support using NLP models for mental health interventions, but future improvements, such as more training epochs, multi-task models, and larger datasets, are needed to enhance accuracy and intervention efficacy.

Human–robot interaction: predicting research agenda by long short-term memory

The article addresses the identification and prediction of research topics in human–robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020–2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.