Transformation Steps Research Articles

Unveiling electrochemical insights of lithium manganese oxide cathodes from manganese ore for enhanced lithium-ion battery performance

Implementing manganese-based electrode materials in lithium-ion batteries (LIBs) faces several challenges due to the low grade of manganese ore, which necessitates multiple purification and transformation steps before acquiring battery-grade electrode materials, increasing costs. At present, most Lithium Manganese Oxide (LMO) materials are synthesized using electrolytic manganese dioxide, and the development of new processes, such as hydrometallurgical processes is important for achieving a cost-effective synthesis of LMO materials. In this work, we develop a full synthesis process of LMO materials from manganese ore, through acid leaching, forming manganese sulfate monohydrate (MnSO4·H2O), an optimized thermal decomposition (at 900, 950 or 1000 °C) producing different Mn3O4 materials and a solid-state reaction, achieving the synthesis of LMO. The latter was used as a cathode material for LIB exhibiting a specific capacity comparable to the state-of-the-art LMO cathode with a remarkable cycling stability of 800 cycles with <20 % in capacity loss. These performances were attributed to the excellent redox reversibility of the LMO cathode, characterized by voltammetry and in operando and in situ characterization by Raman and XRD.

Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review.

Gastrointestinal (GI) cancers represent some of the most common and lethal malignancies globally, underscoring the urgent need for improved diagnostic strategies. Traditional diagnostic methods, while effective to some degree, are often invasive and unsuit-able for regular screenings. This review article explores integrating machine learning (ML) with liquid biopsy techniques as a revolutionary approach to enhance the detection and monitoring of GI cancers. Liquid biopsies offer a non-invasive alternative for cancer detection through the analysis of circulating tumor DNA (ctDNA) and other biomarkers, which when combined with ML, can significantly improve diagnostic accuracy and patient outcomes. We conducted a comprehensive review of recent advancements in liquid biopsy and ML, focusing on their synergistic potential in the early detection of GI cancers. The review addresses the application of next-generation sequencing and digital droplet PCR in enhancing the sensitivity and specificity of liquid biopsies. Machine learning algorithms have demonstrated remarkable ability in navigating complex datasets and identifying diagnostically significant patterns in ctDNA and other circu-lating biomarkers. Innovations such as machine learning-enhanced "fragmentomics" and tomographic phase imaging flow cytometry illustrate significant strides in non-invasive cancer diagnostics, offering enhanced detection capabilities with high accuracy. The integration of ML in liquid biopsy represents a transformative step in the early detection and personalized treatment of GI cancers. Future research should focus on overcoming current limitations, such as the heterogeneity of tumor-derived genetic materials and the standardization of liquid biopsy protocols, to fully realize the potential of this technol-ogy in clinical settings.

Integrated Geotechnical Modelling and Real-time Analysis for Predicting Earthquake-Induced Landslides and Rockfalls in the East African Fracture Zone

Background: The East African Fracture Zone (EAFZ) stands as a testament to the dynamic forces of Earth, marked by heightened seismic activity that frequently triggers geotechnical disasters such as landslides and rockfalls. Traditionally, the study of earthquake-induced geological risks has been reactive, with a focus on post-incident analysis. While significant advances have been made in spatial analysis and risk mapping, the capabilities for real-time prediction and proactive mitigation are still limited. Objectives: Current study presents an approach to predicting earthquake-induced landslides and rockfalls in the EAFZ. The aim is to change the perception of the problem by viewing disasters as manageable risks and informing decision-making in urban planning and disaster mitigation strategies. Methods: A combination of geotechnical engineering, remote sensing, artificial intelligence and machine learning, and socio-economic analysis were used to develop a holistic software framework that solves the complex problem of earthquake prediction without any problems. Results: A software model has been developed that includes a dynamic learning component that refines its predictions with new data, allowing for a deeper understanding of geological subtleties and socio-economic impacts. Considerable attention is paid to the tangible consequences of landslides and rockfalls, including human, property and economic losses. Despite the inevitable challenges of data accuracy and natural unpredictability, the proposed approach opens up new possibilities for proactive disaster management. The results demonstrate a transformational step in data-driven geotechnics and highlight the global applicability of the methods proposed in this work. Conclusion: In this investigation, was taken a pioneering stride in the realm of geotechnical hazard analysis and prediction, focusing on the complex terrains of the East African Fracture Zone (EAFZ). The results provided critical insights into the dynamics of geotechnical hazards in the EAFZ, laying the foundation for future innovations and enhanced safety measures in vulnerable communities.

Enhancing educational efficiency: Generative AI chatbots and DevOps in Education 4.0

AbstractThe emergence of new technologies has developed a new industrial need for engineering graduate students. This modern industry needs a modern educational framework to support further development. This research revolves around developing the learning methodological approach for university courses that aligns with Education 4.0 and can fulfill the essential knowledge requirements for future engineers in Industry 4.0. We used a constructive approach as a baseline methodology already proven in the field of engineering education. On the other hand, we modified this approach by implementing Industry 4.0 standards, such as Agile methodology, DevOps tools, and Artificial intelligence chatbots. In this way, we managed to establish one more step in the digital transformation of education as one of the paramount cornerstones of Industry 4.0. As a result, during the course, Agile methodology and DevOps tools created a learning environment very similar to the real software development environment in companies. The efficiency of the delivery of the learning material also increased by implementation and integration of AI chatbots. This was tracked by the number of projects developed during course implementation. The study showed that in the field of education, the implementation of novel approaches developed in the industry to increase efficiency can be implemented in the educational environment. It also showed that the implementation of those methodologies does not hinder but improves the efficiency of the educational cycle.

The Crystal Chemistry of Boussingaultite, (NH4)2Mg(SO4)2·6H2O, and Its Derivatives in a Wide Temperature Range

The crystal structure, thermal behavior, and vibrational spectra of the anthropogenic analogue of boussingaultite, (NH4)2Mg(SO4)2·6H2O, and its dehydrated counterpart efremovite, (NH4)2Mg2(SO4)3, were studied in detail. The sample from the Chelyabinsk burning coal dumps has the composition of (NH4)1.92(Mg1.02Mn0.01Fe0.01)∑1.04(SO4)2·6H2O and crystallizes in the space group P21/a, with a = 9.3183(4), b = 12.6070(4), c = 6.2054(3) Å, β = 107.115(5)°, V = 696.70(5) Å3 (at 20 °C), Z = 2. The thermal evolution steps are as follows: boussingaultite (NH4)2Mg(SO4)2·6H2O (25–90 °C) → X-ray amorphous phase (100–150 °C) → efremovite (NH4)2Mg2(SO4)3 (160–340 °C) → MgSO4 Cmcm + Pbnm (340–580 °C) → MgSO4 Pbnm (580–700 °C). Thermal expansion is anisotropic, with the coefficients (×106 °C−1) α11 = 52(2), α22 = 68(2), α33 = –89(3), and αv = 31(3) at T = –123 °C; and α11 = 53(2), α22 = 67(2), α33 = 15(1), and αv = 136(3) at T = 60 °C. The maximal thermal expansion is along the b-axis and is due to straightening of corrugated pseudolayers (within the ab plane) of Mg(H2O)6 octahedra and SO4 tetrahedra with NH4 groups in the interlayer space. Vibrational spectroscopy outlines the general trend of dehydration and deammonization as the difference in the temperature intervals of these transformation steps allows separation of O–H and N–H vibrations in the process of dehydration by infrared and Raman spectroscopy. The intermediate partially dehydrated modification of boussingaultite was detected by in situ Raman spectroscopy at 110 °C that may correspond to ammonium leonite.

Clinical knowledge modeling: An essential step in the digital transformation of healthcare

Clinical knowledge modeling: An essential step in the digital transformation of healthcare

Study on the Electronic Design Automation of Taximeter

The integration of Field Programmable Gate Arrays (FPGAs) into the design of digital taximeters signifies a transformative step forward in the field of Electronic Design Automation (EDA), catering to the evolving needs of urban transportation systems. This paper details the development and implementation of an FPGA-based digital taximeter that aims to enhance fare calculation accuracy, operational reliability, and adaptability to regulatory changes through the use of sophisticated technology. FPGAs are chosen for their ability to perform high-speed processing and to be reprogrammed in the field, which allows the taximeter system to adapt quickly to new fare structures and transportation regulations.

Chiral Peropyrene by Selective Dimerization of Phenalenyl.

Two aryl substituted phenalenyl derivatives were synthesized, providing an opportunity to study the steric effects on selectivity of phenalenyl dimerization. Owing to σ-dimerization serving as the decisive step in phenalenyl-peropyrene transformation, a chiral peropyrene compound was generated by dimerization of triarylphenalenyl, while tetraarylphenalenyl did not afford any dimerized product. The structure and properties of chiral peropyrene were elaborated. Our study showcases that phenalenyl dimerization could function as a useful tool to synthesize fascinating π-conjugated hydrocarbons.

Steps of Transformational Leadership by School Principals to Enhance Educator Quality in Early Childhood Education Institutions

PAUD educators hold professional positions, which means they must maintain a high level of professionalism due to their constant interaction with students. The challenges faced by PAUD (Early Childhood Education) educators can hinder the achievement of quality education. The leadership of the PAUD principal plays a significant role in enhancing the quality of educators. This study aims to identify the steps taken by the principal’s transformational leadership to improve educator quality. The research was conducted at Al Irsyad Islamic Kindergarten in Madiun. A qualitative approach was employed, using a case study method. Data collection involved observations, interviews, and documentation. The survey results indicate that enhancing the quality of educators through transformational leadership requires specific steps in leadership transformation. Key actions include determining task priorities, assessing educator readiness, reviewing performance, and formulating a vision. Quality PAUD educators are essential for producing quality students, ultimately contributing to the development of a glorious generation in the future.

Distinct copy number signatures between residual benign and transformed areas of carcinoma ex pleomorphic adenoma

The mechanisms involved with the pathogenesis of carcinoma ex pleomorphic adenoma (CXPA) seem to be associated with the accumulation of molecular alterations in the pleomorphic adenoma (PA). In this sense, using array-based comparative genomic hybridization (aCGH) a rare series of 27 cases of CXPA and 14 residual PA (rPA) adjacent to the transformation area, we investigated the profile of the copy number alterations (CNAs) comparing benign residual and transformed areas. The main findings were correlated with the histopathological classification by histologic subtype and degree of invasion. The distribution of losses (p = 0.187) and amplifications (p = 0.172) was not statistically different between rPA and CXPA. The number of gains was increased in the transformed areas compared to the benign residual areas (p = 0.005). PLAG1 gain was maintained along the malignant transformation, as it was observed in both residual PA and CXPA samples, likely being an earlier event during transformation. The amplification of GRB7 and ERBB2 may also be an initial step in the malignant transformation of PA to CXPA (salivary duct carcinoma subtype). Furthermore, the amplification of HMGA2 and RPSAP52 were the most prevalent alterations among the studied samples. It was noteworthy that amplified genes in the transformed areas of the tumors were enriched for biological processes related to immune signaling. In conclusion, our results underscored for the first-time crucial CNAs in CXPA, some of them shared with the residual benign area adjacent to the transformation site. These CNAs included PLAG1 gain, as well as amplification of GRB7, ERBB2, HMGA2, and RPSAP52.

Responding to COVID-19: an exploration of EU country responses and directions for further research.

During COVID-19, scientists advising policymakers were forced to deal with high uncertainty and risks in an environment of unknowns. Evidence on which policies and measures were effective in responding to the pandemic remains underdeveloped to answer the key question 'what worked and why?'. This study aims to provide a basis for studies to go further to answer this critical question, by starting to look efficacy or how countries ensured that health services remained available and what measures were enacted to protect and treat their populations and workers. We applied a three-phase sequential mixed methods design. In phase one, we started with a qualitative content analysis of the EU Country Profile reports to retrieve and analyse data on COVID-19 responses taken by 29 countries in the European region. Phase two is the step of data transformation, converting qualitative data into numerical codes that can be statistically analysed, which are then used in a quantitative cross-national comparative analysis that comprises phase three. The quantifying process resulted in a numerical indicator to measure the 'response efficacy' of the 29 countries, which is used in phase three's association of the response measure with country performance indicators that were derived from European Centre for Disease Control (ECDC) COVID-19 case and death rate data. Through comparing the frequency of COVID-19 measures taken, we found that many countries in the European region undertook similar actions but with differing effects. The cross-national analysis revealed an expected relationship: a lower COVID-19 response efficacy appeared to be related to a higher case and death rates. Still, marked variation for countries with similar response efficacy indicators was found, signalling that the combination and sequence of implementation of COVID-19 responses is possibly just as important as their efficacy in terms of which response measures were implemented. Many European countries employed similar COVID-19 measures but still had a wide variation in their case and death rates. To unravel the question 'what worked and why?', we suggest directions from which more refined research can be designed that will eventually contribute to mitigate the impact of future pandemics and to be better prepared for their economic and human burden.

InChI 1.07 available on GitHub

Abstract The latest InChI version 1.07 has been approved by IUPAC and the InChI Trust—a transformational step in our development to support the standard. Now on Github, with a standard MIT license—fully tested for back-compatibility, many bugfixes, and a foundation for future extension and maintenance.

Identification of a staphylococcal dipeptidase involved in the production of human body odor

The production of human body odour is the result of the action of commensal skin bacteria, including Staphylococcus hominis, acting to biotransform odourless apocrine gland secretions into volatile chemicals like thioalcohols such as 3-methyl-3-sulphanylhexan-1-ol (3M3SH). As the secreted odour precursor Cys-Gly-3M3SH contains a dipeptide, yet the final enzyme in the biotransformation pathway only functions on Cys-3M3SH, we sought to identify the remaining step in this human-adapted biochemical pathway using a novel coupled enzyme assay. Purification of this activity from S. hominis extracts led to the identification of the M20A-family PepV peptidase (ShPepV) as the primary Cys-Gly-3M3SH dipeptidase. To establish whether this was a primary substrate for PepV, the recombinant protein was purified and demonstrated broad activity against diverse dipeptides. The binding site for Cys-Gly-3M3SH was predicted using modelling, which suggested mutations that might accommodate this ligand more favourably. Indeed, a D437A resulted in an almost 6-fold increase in the kcat/KM, while other introduced mutations reduced or abolished function. Together these data identify an enzyme capable of catalysing the missing step in an ancient human-specific biochemical transformation and suggest that the production of 3M3SH neither uses a dedicated transporter nor peptidase for its breakdown, with only the final cleavage step, catalysed by PatB C-S β-lyase, being a unique enzyme.

Enhancing Submarine Propulsion: Hydrogen SI Engine With EGR for NOx Reduction Monitored by AI

AbstractThis research paper underscores the critical importance of advancing submarine technology by proposing a fully hydrogen‐powered submarine integrated with an air‐independent propulsion (AIP) system and artificial intelligence (AI)‐driven safety measures. Such an innovative approach not only addresses the limitations of traditional diesel‐electric submarines but also promises to revolutionize naval operations. The adoption of hydrogen as the primary power source significantly extends the submarine's operational range, reducing its dependence on fossil fuels and minimizing environmental impact. The incorporation of AIP technology enables sustained underwater endurance, reducing vulnerability and enhancing mission effectiveness. Concurrently, AI‐driven safety measures provide robust leak detection and safe hydrogen handling, ensuring the well‐being of the crew while optimizing operational efficiency. This integrated solution represents a transformative step toward achieving a more sustainable, capable, and stealthy submarine fleet for modern warfare scenarios.

Elucidating the Fundamental Process of Methyl-(5hydroxymethyl) Furan-2-Carboxylate Toxin Biosynthesis in Curvularia lunata Causing Maize Leaf Spot.

Maize leaf spot, which is caused by Curvularia lunata (Wakkre) Boedijn, was epidemic in the maize-growing regions of northeastern and northern China in the mid-1990s, where it led to large yield losses. Since then, the epidemic has evolved into a kind of common disease. In recent years, however, a tendency of becoming an epidemic disease again has been observed in some areas in China due to significant changes in climate, farming, systems and crop varieties. The significance of methyl-(5hydroxymethyl) furan-2-carboxylate (M5HF2C) as a nonspecific host toxin in causing maize leaf spot disease has been demonstrated in previous research. However, the key enzymes involved in M5HF2C toxin synthesis remain unclear. In our study, we demonstrate that the synthesis of M5HF2C toxin starts from a precursor substrate in the pathogen, furfural, which is then catalytically dehydrogenated into furoic acid via an alcohol dehydrogenase (CLADH6). The furoic acid was further confirmed as one of the raw materials for the biosynthesis of M5HF2C toxin based on deletion mutants of the alcohol dehydrogenase gene (Cladh6) in C. lunata, which had reduced M5HF2C toxin-producing ability; however, this ability could be restored in all deletion mutants through complementation with furoic acid, thereby confirming that furoic acid is an intermediate in the biosynthesis of M5HF2C toxin. In summary, the biosynthesis process of M5HF2C toxin in C. lunata involves three transformation steps: (1) from xylose to furfural; (2) then from furfural to furoic acid; and (3) eventually from furoic acid to M5HF2C toxin. Our research findings provide new clues in elucidating the major steps in the process of M5HF2C toxin biosynthesis in C. lunata.

Shortcomings of the Bharatiya Nyaya Sanhita, 2023: An Analytical Perspective

The Bharatiya Nyaya Sanhita, 2023 was enacted on December 25, 2023, to supplant the Indian Penal Code of 1860, marking a significant reform in India's criminal justice system. This legislation, along with the Bharatiya Nagarik Suraksha Sanhita as well as Bharatiya Sakshya Adhiniyam, took impact on 1st July 2024. The changes signify a departure from colonial legal frameworks, aiming for a system aligned with contemporary rights and inclusivity. While the Chief Justice of India and various legal experts have praised the BNS as a transformative step, it still exhibits notable shortcomings. For instance, it fails to address necrophilia, leaving serious offences unregulated. Moreover, Section 69's treatment of "deceitful means" in sexual relations raises issues regarding consent, particularly in contexts involving job offers or promotions. Section 152, which deals with threats to national sovereignty, could lead to overly broad interpretations that impede free expression. Additionally, Sections 84 and 88 reveal gender biases and outdated perspectives on marital rights and women's reproductive decisions. These challenges highlight the urgent need for further review and reform to ensure BNS fulfils its goal of creating a just and equitable legal system.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ~5 and ~25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to cheminformatics by wrapping standard RDKit functionality, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

How Shape Perception Works, in Two Dimensions and Three Dimensions

The ventral visual pathway transforms retinal images into neural representations that support object understanding, including exquisite appreciation of precise 2D pattern shape and 3D volumetric shape. We articulate a framework for understanding the goals of this transformation and how they are achieved by neural coding at successive ventral pathway stages. The critical goals are (a) radical compression to make shape information communicable across axonal bundles and storable in memory, (b) explicit coding to make shape information easily readable by the rest of the brain and thus accessible for cognition and behavioral control, and (c) representational stability to maintain consistent perception across highly variable viewing conditions. We describe how each transformational step in ventral pathway vision serves one or more of these goals. This three-goal framework unifies discoveries about ventral shape processing into a neural explanation for our remarkable experience of shape as a vivid, richly detailed aspect of the natural world.

Analysis, identification, and application of distribution of relaxation times in CMP slurry

This study delves into the mechanistic roles of oxidants, complexing agents, and inhibitors in slurries through of Distribution of Relaxation Times (DRT) analysis. The influence of varying concentrations of these chemicals on electrochemical processes is discussed. This article divides the spectrum obtained through DRT analysis into four different frequency regions and differentiate naming through electrode reaction process: the double-layer charging step, the charge transfer step, the mass transfer step, and the surface transformation step by adsorption or chemical alterations at the electrode surface. The results reveal that the peak observed in the low-frequency range at 10−2–10° Hz is associated with mass transfer step, while the relaxation peak at 10°–102 Hz corresponds to the charge transfer process, while the relaxation peak occurring at 102–104 Hz is linked to the surface transformation step, and the first peak emerging in the 104–106 Hz range is attributed to double-layer charging step.