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Discovery of a 459 Hz Burst Oscillation in XTE J1810?189 with NICER
We present a detailed temporal study of a type I X-ray burst from the neutron star low-mass X-ray binary (NS-LMXB) XTE J1810?189, observed on 2023 April 27 using the Neutron Star Interior Composition Explorer. The burst exhibited a rapid rise time of 2.55 s, followed by an exponential decay lasting for 7.5 s, with a total duration of ?13 s. Type I X-ray bursts are driven by thermonuclear burning on the surface of a neutron star in an NS-LMXB. As these bursts originate from the stellar surface they can exhibit highly coherent signals known as burst oscillations, which serve as probes of the neutron stars spin frequency. We report the detection of a burst oscillation signal at ?459 Hz at the cooling tail of the burst. The oscillation showed a strong Leahy-normalized power of PL = 35.95 at 458.92 Hz, corresponding to a single-trial significance of 5.53? and a multiple-trial corrected significance of 3.14?. The folded pulse profile in the 0.212 keV band is well described by a constant plus sinusoid with a fractional rms amplitude of 14.63%. These results suggest that the burst oscillation frequency of XTE J1810-189 directly reflects on the neutron stars spin, measured here to be ?2.18 ms, placing it among the rapidly rotating NS-LMXBs. This burst oscillation signal at the cooling tail of the burst can be interpreted through surface mode model or the asymmetric cooling wake model. 2025. The Author(s). Published by the American Astronomical Society. -
Discovery of an M-type companion to the Herbig Ae Star V1787 Ori
The intermediate-mass Herbig Ae star V1787 Ori is a member of the L1641 star-forming region in the Orion A molecular cloud. We report the detection of an M-type companion to V1787 Ori at a projected separation of 6.66 arcsec (corresponding to 2577 au), from the analysis of VLT/NACO adaptive optics Ks-band image. Using astrometric data from Gaia DR2, we show that V1787 Ori A and B share similar distance (d ?387 pc) and proper motion, indicating that they are physically associated. We estimate the spectral type of V1787 Ori B to be M5 2 from colour-spectral type calibration tables and template matching using SpeX spectral library. By fitting PARSEC models in the Pan-STARRS colour-magnitude diagram, we find that V1787 Ori B has an age of 8.1$^{+1.7}_{-1.5}$ Myr and a mass of 0.39$^{+0.02}_{-0.05}$ M. We show that V1787 Ori is a pre-main-sequence wide binary system with a mass ratio of 0.23. Such a low-mass ratio system is rarely identified in Herbig Ae/Be binary systems. We conclude this work with a discussion on possible mechanisms for the formation of V1787 Ori wide binary system. 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Discovery of fullerenes in the shell of candidate luminous blue variable WRAY 16-232
We report the discovery of fullerene in the circumstellar environment of WRAY 16-232, a strong candidate luminous blue variable. Multiple pointings of archival Spitzer Infrared Spectrograph spectra reveal, for the first time, the presence of prominent vibrational bands of C60 at 17.4 and 18.9 ?m in a luminous blue variable (LBV) envelope, along with the strong polycyclic aromatic hydrocarbon features. These observations suggest that, despite the harsh radiative conditions, large carbonaceous molecules can form, process, and survive in the ejecta of massive stars. Complementary optical spectroscopy with South African Large Telescope High-Resolution Spectrograph shows multiple P Cygni profiles in H ?, He i, and Fe ii lines, which are indicative of a dense, expanding wind and substantial mass-loss. Furthermore, analysis of decade long photometric data shows short-term brightness variations of ?0.5 mag. These results not only reinforce the classification of WRAY 16-232 as a strong LBV candidate but also provide new insights into the mechanisms of dust formation and the chemical enrichment of the interstellar medium by massive stars. We discuss various scenarios for fullerene formation in such environments, and find that shock processing due to wind-wind interactions could be playing a vital role. The shell of WRAY 16-232 has an ideal UV field strength and the time-scales appear to match with shock processing time-scales. The results highlight the need for further high spatial/spectral resolution and temporal observations to confirm the formation and survival scenario of C60 in its shell. The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society. -
Discovery of inverse-Compton X-ray emission and estimate of the volume-averaged magnetic field in a galaxy group
Observed in a significant fraction of clusters and groups of galaxies, diffuse radio synchrotron emission reveals the presence of relativistic electrons and magnetic fields permeating large scale systems of galaxies. Although, these non-thermal electrons are expected to upscatter cosmic microwave background photons up to hard X-ray energies, such inverse-Compton (IC) X-ray emission has so far not been unambiguously detected on cluster/group scales. Using deep, new proprietary XMM-Newton observations (?200 ks of clean exposure), we report a 4.6 ? detection of extended IC X-ray emission in MRC 0116 +111, an extraordinary group of galaxies at z = 0.131. Assuming a spectral slope derived from low frequency radio data, the detection remains robust to systematic uncertainties. Together with low frequency radio data from the Giant Metrewave Radio Telescope (GMRT), this detection provides an estimate for the volume-averaged magnetic field of (1.9 0.3) ?G within the central part of the group. This value can serve as an anchor for studies of magnetic fields in the largest gravitationally bound systems in the Universe. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Discovery of quasi-periodic oscillations in the persistent X-ray emission of accreting binary X-ray pulsar LMC X-4
We report the discovery of quasi-periodic oscillations (QPOs) in the high-mass X-ray binary (HMXB) pulsar LMC X-4 in its non-flaring (persistent) state using observations with XMM-Newton. In addition to the 74 mHz coherent pulsations, the persistent emission light curve shows a QPO feature in the frequency range of 20-30 mHz. Quasi-periodic flares have been previously observed from LMC X-4 in observations made with Rossi X-ray Timing Explorer (RXTE). However, this is the first time QPOs have been observed in the persistent emission observations of LMC X-4. QPOs in X-ray binaries are generally thought to be related to the rotation of the inhomogeneous matter distribution in the inner accretion disc. In HMXBs such as LMC X-4 where the compact object is a neutron star with a high magnetic field, the radius of the inner accretion disc is determined by the mass accretion rate and the magnetic moment of the neutron star. In such systems, the QPO feature, along with the pulse period and X-ray luminosity measurement, helps us to constrain the magnetic field strength of the neutron star. We use considerations of magnetospheric accretion to have an approximate value of the magnetic field strength of the neutron star in LMC X-4. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Discrete financial in sentimental analysis using exploring patterns and trends
In todays rapidly evolving financial environment, its crucial for investors and decision-makers to effectively analyze stakeholder communications to gain valuable insights. This research conducts a comprehensive evaluation of a range of models that utilize machine learning, such as CNN (Convolutional Neural Network), LR (Logistic Regression), Doc2vec, and LSTM (Long Short-Term Memory), to determine their efficacy in interpreting investors sentiments and predicting business assessments and trading dynamics. The justification for preferring deep neural architectures compared to conventional data analysis lies in the challenge of handling extensive amounts of diverse and unorganized data. Deep learning techniques have shown impressive capacity in automatically detecting complex characteristics and unveiling concealed patterns within written records, rendering them well-suited for sentiment analysis in financial dialogue. This research questions the notion that depending exclusively on data from a solitary origin leads to persistently effective investment moves. In fact, stakeholder communication is impacted by numerous influential elements, leading to diverse sentiments and sentiments. Through our comparative assessment, we aim to illuminate how various deep learning models can adeptly capture the intricate nuances of sentiment within fiscal messaging. 2024, Taru Publications. All rights reserved. -
Discrete Integrity Assuring Slice-Based Secured Data Aggregation Scheme for Wireless Sensor Network (DIA-SSDAS)
In a wireless sensor network, data privacy with a minimum network bandwidth usage is addressed using homomorphic-based data aggregation schemes. Most of the schemes which ensure the end-to-end privacy provide collective integrity verification of aggregated data at the receiver end. The presence of corrupted values affects the integrity of the aggregated data and results in the rejection of the whole data by the base station (BS) thereby leading to the wastage of bandwidth and other resources of energy constraint wireless sensor network. In this paper, we propose a secured data aggregation scheme by slicing the data generated by each sensor node deployed in layered topology and enabling en route aggregation. Novel encoding of data and hash slices based on child order is proposed to enable concatenation-based additive aggregation and smooth extraction of slices from the aggregate by the BS. Elliptic curve-based homomorphic encryption is adopted to ensure end-to-end confidentiality. To the best of our knowledge, the proposed scheme is the first which facilitates the BS to perform node-wise integrity verification, filter out only the corrupted portion, and implement dynamic query over the received data. Communication- and computation-based performance analysis shows the efficiency of the proposed scheme for varied network sizes. The scheme can resist eavesdropping attack, node compromising attack, replay attack, malleability attack, selective dropping attack, and collusion attack. 2021 D. Vinodha and E. A. Mary Anita. -
Discriminated-SDS: A Novel Hybrid Approach for Optimizing EEG Based Brain-Computer Interface Signals Faced by Metaheuristic Algorithms
Brain Computer Interfaces (BCIs) will convert the thoughts of individuals with physical disabilities into commands for devices to enable them autonomous mobility. The Electroencephalogram (EEG) is widely favoured as a control signal due to its ease of acquisition compared to invasive recordings. While the affordability of EEG equipment allows for the use of numerous recording channels, this abundance increases computational complexity, necessitating optimal channel selection strategies to improve efficiency and classification accuracy. Deep Neural Networks (DNNs) often face scalability issues with multidimensional, locally correlated inputs, making them impractical for such applications. Convolutional Neural Networks (CNNs) are efficient for analysing BCI data but require careful hyperparameter tuning to achieve optimal performance. This paper introduces a framework for classifying BCI channel selection using deep learning techniques. The study primarily concentrates on refining the hyper parameters of deep learning algorithms through metaheuristic techniques, specifically employing Discriminated Stochastic Diffusion Search (SDS) to enhance BCI channel selection. The findings indicate that the proposed hyperparameter optimization methods, such as Discriminated-SDS, significantly enhance classification accuracy. The proposed D-SDS balances exploration and exploitation, mitigates the local optima issue, and is especially advantageous for intricate deep learner architectures such as VGGNet, ResNet, and InceptionNet. Hyperparameter optimization in EEG-based BCI systems can substantially improve performance, enhancing their efficiency and reliability. 2026, Iquz Galaxy Publisher. All rights reserved. -
Discrimination between scheduled and non-scheduled groups in access to basic services in urban India
Access to basic services such as water, sanitation, and electricity is a key determinant of an individuals well-being. Nevertheless, access to these services is unequally distributed among different social groups in many countries. India is no exception, with the scheduled castes (SC) and scheduled tribes (ST) being one of the countrys most marginalised and disadvantaged groups. This paper analyses the disparities in access to basic services between scheduled and non-scheduled households, investigates the factors contributing to the unequal access, and suggests policy recommendations. Using data from the National Sample Survey 76th Round, we analyse the access to basic services such as durable housing, improved water and sanitation, and access to electricity. The papers objectives are (a) to investigate the factors impacting the quality of basic service delivery in urban India separately for scheduled and non-scheduled households and (b) to quantify the discrimination between scheduled and non-scheduled households in urban India concerning access to quality of basic services through computing a comprehensive index and by using the Fairlie decomposition approach. The analysis corroborates the finding that systemic discrimination exists between scheduled and non-scheduled households in urban India regarding access to good quality basic services up to an extent of 24%. 2024 The Authors. -
Discrimination Experiences of Old Settlers in Sikkim: A Qualitative Exploration
Race-based stigma and discrimination have been extensively studied from the perspective of the northeastern community due to their minority status in most states of India. Discrimination experiences of the mainland Indians in the northeastern states, where they are a minority, are little discussed. The Rajya Sabha (upper house of the parliament) Committee of Petitions in 2014 acknowledged that the old settlers were treated as second-class citizens in Sikkim. In the present study, we explored the existence and manifestation of discrimination experiences of old settlers who settled in Sikkim before 1975 and perceive themselves to be stigmatized. This study focused on Sikkim because the state merged with India in 1975 and has had less time integrating with migrants or mainlanders than other northeastern states. We conducted nine semi-structured interviews with seven male and two female participants from the Marwari, Bihari, and Punjabi mainland communities. Using thematic analysis, we developed 1 global theme, 2 organizing themes, and 24 basic themes. The analysis showed the existence of discrimination and racism against old settlers and their manifestations at institutional and interpersonal levels. The findings are important from a policymaking perspective as they provide evidence to the conclusion reached by the Rajya Sabha Committee on Petitions and provide valued suggestions for reports on race-based discrimination in India. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2023. -
Discriminative Gait Features Based on Signal Properties of Silhouette Centroids
Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier. 2019, Springer Nature Singapore Pte Ltd. -
Discussion on ostracised transgender individuals and entrepreneurship through review of literature
Transgender individuals are the most deteriorated individuals in society. They face a wide range of trodden lives and setbacks in their everyday life. They encounter challenges and difficulties from the time they violate the social norms, they are also humiliated from their biological families and are sent to live a life of their own. In India, the Mughal period was termed to be the golden years for transgender individuals. It was after colonisation and implementation of the Criminal Tribes Act 1871 transgender individuals were treated brutally and eventually begging and sex work became their only source of income. Alongside, entrepreneurship proved to be a success factor as it brought the shunned women into the mainstream society. Thereby, entrepreneurship increases social capital and thus encourages transgenders in job creation activities. Despite a dire situation, there are transgender individuals who have faced all odds and have proved to set benchmarks in the society in varied fields. There are sporadic transgender individual entrepreneurs in the country who have paved their way into the entrepreneurial world, which is an important area to be explored. The study focuses on literature relating to transgender individuals, challenges faced by transgender individuals, entrepreneurial motivations and also transgender entrepreneurs. 2020 SERSC. -
Disease Identification for Tea Leaves Using Explainable Artificial Intelligence
Infection can consequently reduce both quality and yield, and causes major threats to tea production round the world. It is therefore sometime difficult to achieve fast, reliable, and precise identification of disease in tea plants and hence the need to embrace new methods of disease identification. To enable realisation of accurately understandable models for classification of the diseases in tea leaves, Explainable Artificial Intelligence (XAI) approaches are applied in this work. In order to train and test machine learning models, we collected a set of repos of high-resolution images of tea leaves affected by various diseases along with meta information. CNN models were trained with the help of our approach and adopting XAI tools as tools for explanation of predictions. From this study, the field of agricultural AI is benefitted from the illustration of how XAI might enhance disease management strategies in tea agriculture. The results demonstrate an accuracy of 87.85%, with precision, recall and F1-scores ranging between 0.78 and 0.95 across different classes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Disease Identification from Illegible Medical Prescriptions Using OCR and NLP Techniques
Medical prescriptions that are challenging to interpret present significant issues for the healthcare industry because they increase the possibility of errors in patient care and medication administration. This study presents an efficient workflow that uses Optical Character Recognition (OCR) technology, specifically, Tesseract OCR, along with a preprocessing step to extract text from handwritten prescriptions. The preprocessing stage uses grayscale conversion, noise reduction, and contrast enhancement to increase the accuracy of OCR. Significant results from experiments on a publicly accessible dataset show that preprocessing greatly improves performance, lowering the error rate from 34.7 to 18.3% and raising average accuracy from 65.3 to 81.7%. The enhanced accuracy outweighs the modest increase in processing time (from 0.8 to 1.2s), emphasizing the potential of using these techniques in practical healthcare applications. The studys findings also demonstrated the successful analysis of the text using Natural Language Processing (NLP) and Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) techniques by identifying four distinct diseases, Common Cold, Diabetes Mellitus, Bronchitis, and disease caused by Anemia, as validated by a medical professional. This demonstrates the systems potential to improve health care processes by automatically digitizing handwritten prescriptions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Diseased Leaf Identification Using Bag-of-Features and Sigmoidal Spider Monkey Optimization
Agricultural products decide the economy of a country like India. The agricultural business has the involvement of a large population. The quality and quantity of agricultural products highly depend on environmental conditions and facilities provided to farmers. Timely and efficient detection of diseases in plants and crops is one of the most critical issues that affect crop production. Therefore, it is highly desirable to develop some cheap and easy-to-handle automated plant disease detection systems for the timely treatment of plants. Leaves are considered a primary source of information about the health of plants. In the case of plants, the disease may be easily visualized and identified by observing its effect on leaves. Therefore, this paper introduces a bag-of-features in sigmoidal spider monkey optimization to identify a diseased leaf, separating the diseased leaf from a healthy leaf. The investigational outcomes show the superiority of the anticipated technique in contrast to other meta-heuristic-based systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Disentangling homeowner motives for solar PV: Psychometric development, validation and invariance test of the Motivation for Rooftop Solar Adoption Scale
The rooftop photovoltaic (PV) adoption of households is shaped by heterogeneous motives of the household that extend beyond economic calculus, yet prior research often measures these motives using ad hoc or single-item indicators, limiting comparability across studies. This study develops and validates the Motivation for Rooftop Solar Adoption Scale (MRSAS) to disentangle the key motivational dimensions that drives the household PV adoption. Following established scale-development guidance, we generated an initial item pool from theory and recent PV-adoption evidence, assessed content adequacy using a structured Q-sort, and then conducted exploratory factor analysis (N = 295), followed by confirmatory factor analysis (N = 312). Results support a parsimonious 22 item six-factor model with Social Influence, Financial Motivation, Environmental Protectionism, Energy Self-Reliance, Facilitating Conditions, and Technophile Attitude as dimensions. The model exhibits excellent confirmatory model fit along with strong reliability and discriminant validity. Multi-group CFA establishes scalar invariance between adopters and non-adopters, aligning with theory, adopters score higher across all six motivational dimensions. The MRSAS provides a psychometrically robust and transferable tool for profiling why households adopt solar, supporting cumulative theory-building and enabling practitioners to tailor incentives, communication and programme design to the motivations that matter. 2026 The Authors. -
Disentangling the association of PAH molecules with star formation
Context. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous complex molecules in the interstellar medium and are used as an indirect indicator of star formation. On the other hand, the ultraviolet (UV) emission from young massive stars directly traces the star formation activity in a galaxy. The James Webb Space Telescope (JWST), along with the UltraViolet Imaging Telescope (UVIT), opened up a new window of opportunity to better understand the properties of PAH molecules that are associated with star-forming regions. Aims. We investigate how the resolved scale properties of PAH molecules in nearby galaxies are affected by star formation. Methods. We analyzed the PAH features observed at 3.3, 7.7, and 11.3 m using F335M, F770W, and F1130W images obtained from the JWST. These images helped us identify and quantify the PAH molecules. Additionally, we used UVIT images to assess the star formation associated with these PAH-emitting regions. Our study focused on three galaxies, namely NGC 628, NGC 1365, and NGC 7496, which were selected based on the availability of both JWST and UVIT images. Bright PAH emission regions were identified in the JWST images, and their corresponding UV emission was estimated using the UVIT images. We quantified the star formation properties of these PAH emitting regions using the UVIT images. Furthermore, we investigated the relation between the star formation surface density (?SFR) and the PAH ratios to better understand the impact of star formation on the properties of PAH molecules. Results. Based on the resolved scale study of the PAH-bright regions using JWST images, we found that the fraction of ionized PAH molecules is high in the star-forming regions with high ?SFR. We observed that emission from smaller PAH molecules is higher in star-forming regions with higher ?SFR. Conclusions. Our study suggests that the PAH molecules excited by the photons from star-forming regions with higher ?SFR are dominantly smaller and ionized molecules. UV photons from the star-forming regions could be the reason for the higher fraction of the ionized PAHs. We suggest that the effect of the high temperature in the star-forming regions and the formation of smaller PAH molecules in the star-forming regions might also result in the higher emission in the F335MPAH band. The Authors 2024. -
Disentangling the two sub-populations of early Herbig Be stars using VLT/X-shooter spectra
Context. Early Herbig Be (HBe) stars are massive, young stars accreting through the boundary layer mechanism. However, given the rapid (<2 Myr) evolution of early Herbig stars to the main-sequence phase, studying the evolution of the circumstellar medium around these stars can be a cumbersome exercise. Aims. In this work, we study the sample of early (B0-B5) HBe stars using the correlation between H? emission strength and near-infrared excess, complemented by the analysis of various emission features in the X-shooter spectra. Methods. We segregate the sample of 37 early HBe stars based on the median values of H? equivalent width (EW) and near-infrared index (n(J-H)) distributions. The stars with |H? EW| > 50and n(J-H) > -2 are classified as intense HBe stars and stars with |H? EW| < 50and n(J-H) < -2 as weak HBe stars. Using the VLT/X-shooter spectra of five intense and eight weak HBe stars, we visually checked for the differences in intensity and profiles of various HI and metallic emission lines commonly observed in Herbig stars. Results. We propose that the intense HBe stars possess an inner disk close to the star (as apparent from the high near-infrared excess) and an active circumstellar environment (as seen from the high H? EW value and presence of emission lines belonging to FeII, CaII, OI, and [OI]). However, for weak HBe stars, the inner disk has cleared, and the circumstellar environment appears more evolved than for intense HBe stars. Furthermore, we compiled a sample of ~58 000 emission-line stars published in Gaia DR3 to identify more intense HBe candidates. Further spectroscopic studies of these candidates will help us to understand the evolution of the inner (approximately a few au) disk in early HBe stars. The Authors 2023. -
Dislocation and microindentation analysis of vapour grown Bi 2Te3-xSex whiskers
The structural defects and microhardness of Bi2Te 3-xSex whiskers (x = 0, 0.2 and 0.4 at % Se) grown by physical vapour deposition (PVD) method have been investigated. Concentric pairs of dislocation loops were observed on the as-grown surfaces of short hexagonal prisms. A systematic study of dislocations in these crystals was carried out by chemical etching technique. The effects of Se doping, annealing and quenching on the mechanical properties have also been studied on the prism faces of Bi 2Te3-xSex whiskers. 2008 WILEY-VCH Verlag GmbH & Co. KGaA. -
Disorders in graphene: types, effects and control techniquesa review
The development of graphene and graphene-like materials has been a breakthrough in the field of nanotechnology for its exceptional properties. It exhibits extraordinary properties owing to its unique, regular arrangement of carbon atoms in it. However, graphene is usually modified for specific applications, by introducing disorder in the system. Since disorders are ubiquitous during the synthesis of graphene and graphene-like materials, it is best to exploit the defects for tuning its exceptional properties for suitable device applications. Like in any material, the disorder can drastically affect the properties, and hence they are deliberately incorporated into the material. In this review, we discuss topics related to the creation and configuration of disorders in graphene such as corrugations, topological defects, vacancies, adatoms and sp3-defects. The effects of these disorders on the electrical, thermal, chemical and mechanical properties of graphene are analysed subsequently. Finally, we review earlier works on the modulation of structural defects in graphene for specific applications. 2021, Korean Carbon Society.