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Abstract With the advent of artificial intelligence (AI), it is now possible to design diverse and novel molecules from previously unexplored chemical space. However, a challenge for chemists is the synthesis of such molecules. Recently, there have been attempts to develop AI models for retrosynthesis prediction, which rely on the availability of a high-quality training dataset. In this work, we explore the suitability of large language models (LLMs) for extraction of high-quality chemical reaction data from patent documents. A comparative study on the same set of patents from an earlier study showed that the proposed automated approach can enhance the current datasets by addition of 26% new reactions. Several challenges were identified during reaction mining, and for some of them alternative solutions were proposed. A detailed analysis was also performed wherein several wrong entries were identified in the previously curated dataset. Reactions extracted using the proposed pipeline over a larger patent dataset can improve the accuracy and efficiency of synthesis prediction models in future. Scientific contribution In this work we evaluated the suitability of large language models for mining a high-quality chemical reaction dataset from patent literature. We showed that the proposed approach can significantly improve the quantity of the reaction database by identifying more chemical reactions and improve the quality of the reaction database by correcting previous errors/false positives.

Background: Vision impairment (VI) in the elderly is a significant health challenge, leading to functional limitations and economic hardship. This study aims to examine education-based inequalities in the prevalence of VI among older adults (aged 45 years and above) in India. Methods: The sample included Data from 59,555 older adults from the Longitudinal Ageing Study in India (LASI), 2017–18. We analyzed the prevalence of VI (distance and near vision impairment) among older adults. Using concentration curves we compared VI inequality along the axes of wealth and education. Erreygers Concentration Index (ECI) was used to measure education-based inequalities in the prevalence of VI, and a decomposition analysis was conducted to identify the socio-economic factors contributing to the inequalities. Results: Overall, 79 % (95 % CI: 78.91, 79.76) of the older adults had VI based on 20/40 visual acuity. Our findings depict that there was significant education-based inequality in the prevalence of VI [ECI: -0.1792, (p

In the original publication [...]

This study addresses the persistent challenge of polysulfide dissolution in lithium–sulfur (Li–S) batteries by introducing magnesium oxide (MgO) nanoparticles as a novel additive. MgO was integrated with sulfur using a scalable process involving solid-state melt diffusion treatment followed by planetary ball milling. XRD measurements confirmed that sulfur (S8) retains its orthorhombic crystalline structure (space group Fddd) following the MgO incorporation, with minimal peak shifts indicating slight lattice distortion, while the increased peak intensity suggests enhanced crystallinity due to MgO acting as a nucleation site. Additionally, Raman spectroscopy demonstrated sulfur’s characteristic vibrational modes consistent with group theory (point group D2h) and highlighted multiwalled carbon nanotube (MWCNT′s) D, G, and 2D bands, with a low ID/IG ratio (0.47), which indicated low defects and high crystallinity in the prepared cathode. The S–MgO composite cathode exhibited superior electrochemical behavior, with an initial discharge capacity (950 mA h g−1 at 0.1 C), significantly improved compared to pristine sulfur’s. The presence of MgO effectively mitigated the polysulfide shuttle effect by trapping polysulfides, leading to enhanced stability over 400 cycles and the consistent coulombic efficiency of over 99.5%. After 400 cycles, EDS and SEM analyses confirmed the structural integrity of the electrode, with only minor fractures and slight sulfur content loss. Electrochemical impedance spectroscopy further confirmed the enhanced performance.

Lithium–sulfur batteries (LiSBs) offer high energy density, cost-effectiveness, and eco-friendliness, making them promising for future energy storage. This study explores using BiFeO3 (BFO) nanoparticles (NPs) to tackle challenges such as lithium polysulfides (LiPs) and shuttle issues in LiSBs. It employs a solid-state melt diffusion technique, encapsulates sulfur in single-walled carbon nanotubes (SCNTs), and utilizes BFO for effective polysulfide control. Herein, composite cathodes of sulfur (S)/SCNTs (abbr. SCNT) were fabricated, and cells were designed using a BFO-coated separator (SCNT-BFS). In addition, a cathode modification was performed with composite S/SCNTs/BFO (SCNT-BF), and a comparative analysis was conducted to assess the effectiveness of the BFO in the separator and the cathode. Cyclic voltammetry measurements revealed that the increased current peak intensity at lower reduction potential in SCNT-BF and SCNT-BFS indicated control of higher-order LiPs (Li2Sx, where 4 ≤ x ≤ 8), resulting in the generation of more stable lower-order products (Li2S2/Li2S). The charge/discharge analysis revealed controlled LiPs, resulting in high-capacity retention in SCNT-BF (∼75%) and SCNT-BFS (∼88%) over 200 cycles, which yielded capacities of 526 and 700 mAh/g at C/8 (1C = 1675 mA/g). These promising results suggest that incorporating BFO into the cathode and separator can advance the commercialization of durable LiSBs.

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.

This paper investigated the structural, optical, electronic and thermal characteristics of SrSnO3 perovskites that were calculated using the density functional theory. Software called WEIN2K is used to perform the calculation. According to our calculations, the band gap energy of the SrSnO3 is roughly 4.00 eV and it adopts a distorted cubic shape in the space group Pm3-m. The band structure and partial density of state reflects the major contribution of O 2p in the valence band while 5s orbital from Sn in the conduction band. The electron density plot significantly shows the contribution different clusters SrO12 and SnO6 that plays crucial role in electronic and optical properties. The creation of covalent bonds between the atoms of Sn and O as well as the ionic interaction between the atoms of Sr and O are both demonstrated by the electron density graphs and SCF calculation. The refractive index and extinction coefficient directly correlated with the real and imaginary part of complex dielectric function. Real part of dielectric function shows higher values at two major point of energy 3.54 eV and 9.78 eV associated with the absorption and optical activity of SrSnO3. Negative part of imaginary dielectric function part suggests metallic behavior also supported by -grep lapw method. Thermoelectric and thermal conductivity properties suggest the power factor need to be improved for the device application.

In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.

Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic emanation) from a device that performs some cryptographic operation and poses a serious threat in the present IoT era. In the last couple of decades, there have been a large body of research works dedicated to streamlining/improving the attacks or suggesting novel countermeasures to thwart those attacks. However, a closer inspection reveals that a vast majority of published works in the context of symmetric key cryptography is dedicated to block ciphers (or similar designs). This leaves the problem for the stream ciphers wide open. There are few works here and there, but a generic and systematic framework appears to be missing from the literature. Motivating by this observation, we explore the problem of SCA on stream ciphers with extensive details. Loosely speaking, our work picks up from the recent TCHES’21 paper by Sim, Bhasin and Jap. We present a framework by extending the efficiency of their analysis, bringing it into more practical terms. In a nutshell, we develop an automated framework that works as a generic tool to perform SCA on any stream cipher or a similar structure. It combines multiple automated tools (such as, machine learning, mixed integer linear programming, satisfiability modulo theory) under one umbrella, and acts as an end-to-end solution (taking side channel traces and returning the secret key). Our framework efficiently handles noisy data and works even after the cipher reaches its pseudo-random state. We demonstrate its efficacy by taking electromagnetic traces from a 32-bit software platform and performing SCA on a high-profile stream cipher, TRIVIUM, which is also an ISO standard. We show pragmatic key recovery on TRIVIUM during its initialization and also after the cipher reaches its pseudo-random state (i.e., producing key-stream).

One of the major concerns for industries in the modern world is to focus efforts on producing high quality products with minimal costs. Various quality improvement philosophies have emerged in recent times, Six Sigma being one of the most practical and efficient techniques for quality improvement of processes. In this work, Six Sigma based DMAIC (Define, Measure, Analyze, Improve, Control) approach is used to enhance productivity and quality performance, and to make the hot forging process robust to quality variations. Finite element method has been employed for the simulation of hot forging of the connecting rod. The influence of design and process parameters is investigated for the response ‘forging die load’. Analysis of various critical parameters and the interaction among them has been carried out with the help of Taguchi’s method of experimental design. To further optimize the response and make the analysis more precise and robust, response surface methodology has been incorporated. Parameters have been optimized, leading to the accomplishment of a minimized forging die load which is verified using a confirmation experiment. Confirmatory results reveal the potential of the DMAIC approach of Six Sigma in optimizing the process parameters successfully and thereby present significant applicability in the industry.

Liver cancer, also identified as hepatic cell carcinoma, is the fifth most prevalent kind of malignancy globally and the fourth foremost cause of cancer-associated mortality. The development and progression of liver cancer are complex processes that involve multiple genetic and environmental factors. As the diagnosis of liver cancer is still worse, with late-stage patients facing a less than 20% 5-year survival rate, there is a critical need for the development of new and effective therapeutic approaches for liver cancer. Mitochondrial alterations and mitochondrial DNA (mtDNA) mutations have long been associated with cancer pathogenesis, including liver cancer. These alterations not only disrupt cellular bioenergetics but also deteriorate the situation by modifying tumor suppressors and oncogenic proteins. Excessive reactive oxygen species generation and flaws in mitochondrial enzymes are among the factors responsible for mitochondrial dysfunction. Additionally, perturbed microRNA levels have also been linked to mtDNA dysfunction and reactive oxygen species generation. Various pharmacological approaches to target mitochondrial dysfunction and mtDNA mutations in cancer have been proposed as potential therapeutic strategies. These approaches include targeting the electron transport chain, which is responsible for the production of adenosine triphosphate in the mitochondria, or transcriptional inhibition of various proteins involved in the mitochondrial biogenesis pathway. Overall, mtDNA is a crucial component of the cell, and alterations in mtDNA make it an attractive target for therapeutic interventions. Hence, we advocate that understanding the role of mtDNA in cancer pathogenesis is important for the development of targeted therapies for these disorders. [ABSTRACT FROM AUTHOR]