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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.

Patients with ovarian cancer (OC) often experience anxiety, depression and fear of progression (FOP); however, it is unclear whether surgical complexity has a role to play. We investigated the prevalence of anxiety, depression and FOP at 12 months post-cytoreductive surgery and investigated associations with surgical complexity, patient (age, ethnicity, performance status, BMI) and tumour (stage, disease load) factors. One hundred and forty-one patients with FIGO Stage III–IV OC, who did not have disease progression at 12 months post-surgery, completed the Hospital Anxiety and Depression Scale and FOP short-form questionnaire. Patients underwent surgery with low (40.4%), intermediate (31.2%) and high (28.4%) surgical complexity scores. At 12 months post-surgery, 99 of 141 (70%) patients with advanced OC undergoing surgery experienced clinically significant anxiety, 21 of 141 (14.9%) patients experienced moderate to severe depression and 37 of 140 (26.4%) experienced dysfunctional FOP. No associations were identified between the three different surgical complexity groups with regards to anxiety, depression or FOP scores. Unsurprisingly, given the natural history of the disease, most patients with OC suffer from anxiety, depression and fear of progression after completion of first-line cancer treatment. Surgical complexity at the time of surgery is not associated with a deleterious impact on anxiety, depression or FOP for patients with OC. Patients with OC experience a profound mental health impact and should be offered mental health support throughout their cancer journey.

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).

We investigated URS and impact on survival in whole patient cohorts with AOC treated within gynaecological cancer centres that participated in the previously presented SOCQER 2 study. National cancer registry datasets were used to identify FIGO Stage 3,4 and unknown stage patients from 11 cancer centres that had previously participated in the SOCQER2 study. Patient outcomes’ association with surgical ethos were evaluated using logistic regression and Cox proportional hazards. Centres were classified into three groups based on their surgical complexity scores (SCS); those practicing mainly low complexity, (5/11 centres with >70% low SCS procedures, 759 patients), mainly intermediate (3/11, 35–50% low SCS, 356 patients), or mainly high complexity surgery (3/11, >35% high SCS, 356 patients). Surgery rates were 43.2% vs. 58.4% vs. 60.9%. across mainly low, intermediate and high SCS centres, respectively, p < 0.001. Combined surgery and chemotherapy rates were 39.2% vs. 51.8% vs. 38.3% p < 0.000 across mainly low, intermediate and high complexity groups, respectively. Median survival was 23.1 (95% CI 19.0 to 27.2) vs. 22.0 (95% CI 17.6 to 26.3) vs. 17.9 months (95% CI 15.7 to 20.1), p = 0.043 in mainly high SCS, intermediate, and low SCS centres, respectively. In an age and deprivation adjusted model, compared to patients in the high SCS centres, patients in the low SCS group had an HR of 1.21 (95% CI 1.03 to 1.40) for death. Mainly high/intermediate SCS centres have significantly higher surgery rates and better survival at a population level. Centres that practice mainly low complexity surgery should change practice. This study provides support for the utilization of URS for patients with advanced OC.

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.

Dynamic characteristics and reliability of elastomeric pads are dominant components in the design methodology to satisfy the performance parameters under dynamic operations. The fatigue life of rubber pads fitted with elastomeric pads mainly depends upon elastomers; natural rubber, chloroprene, nitrile butadiene, silicon compounds, etc., additives, accelerators, activators, fillers, anti-degradants, and manufacturing processes. The performance of the elastomeric pad is evaluated based on service operation, simulation, and laboratory testing. New and failed elastomeric (EM) pads have been examined in the Indian Rubber Manufacturer's Research Association IRMRA laboratory for detailed failure analysis of existing EM pads. Further, chemical and physical properties are also evaluated in M&C (Metallurgical and Chemical) and Testing Directorate of Research Designs and Standards Organization (RDSO), labs respectively. The fatigue life of the EM pad is estimated based on the results extracted from the Finite Element (FE) analysis of the EM pad. An attempt is made to modify the design of the EM pad by considering the results of detailed field trials, lab testing, and FE analysis. The modified design satisfies the dynamic characteristic during FE (Structural and Thermal) analysis and lab testing. [ABSTRACT FROM AUTHOR]