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The Mordell-Tornheim zeta function: Kronecker limit type formula, series evaluations and applications
No full textIn this paper, we establish Kronecker limit type formulas for the Mordell-Tornheim zeta function ?(r,s,t,x) as a function of the second as well as the third arguments. As an application of these formulas, we obtain results of Herglotz, Ramanujan, Guinand, Zagier and Vlasenko-Zagier as corollaries. We show that the Mordell-Tornheim zeta function lies centrally between many modular relations in the literature, thus providing the means to view them under one umbrella. We also give series evaluations of ?(r,s,t,x) in terms of Herglotz-Zagier function, Vlasenko-Zagier function and their derivatives. Using our new perspective of modular relations, we obtain a new infinite family of results called mixed functional equations
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning
No full textThe success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Learning (SSL) problem aims to enhance performance on downstream tasks by strategically sampling a subset of images (the Core-Set) from a large pool of unlabeled data (the OpenSet). In this paper, we propose a novel method, BloomCoreset, that significantly reduces sampling time from Open-Set while preserving the quality of samples in the coreset. To achieve this, we utilize Bloom filters as an innovative hashing mechanism to store both low- and high-level features of the fine-grained dataset, as captured by Open-CLIP, in a space-efficient manner that enables rapid retrieval of the coreset from the Open-Set. To show the effectiveness of the sampled coreset, we integrate the proposed method into the state-of-the-art fine-grained SSL framework, SimCore [1]. The proposed algorithm drastically outperforms the sampling strategy of the baseline in [1] with a 98.5% reduction in sampling time with a mere 0.83% average trade-off in accuracy calculated across 11 downstream datasets. We have made the code publicly available
Incentive-based demand response program with phase unbalance mitigation: A bilevel approach
No full textThis article proposes an adaptable incentive framework for an incentive-based demand response (IBDR) program. The framework is based on changes in demand from end-consumers using the bilevel approach to optimize the scheduling of flexible loads. The distribution system operator (DSO) acts as a leader with a multi-objective optimization problem. The objective is to maximize profit while minimizing network energy loss and peak load at the point of common coupling. The DSO's strategy involves changing demand-based adaptive incentive offers to enhance end-consumers participation in the DR program. Furthermore, the DSO aimed to mitigate phase unbalancing as an objective to address power quality issues caused by imbalances in phase voltage and power. Aggregators are regarded as followers in the bilevel approach, aiming to maximize incentives for mitigating the discomfort caused by scheduling flexible energy resources in the IBDR program. By utilizing Karush-Kuhn–Tucker conditions, the previously mentioned bilevel problem transformed into a single-level optimization problem. This work examined two case studies to determine the effectiveness of the proposed adaptable IBDR model. The efficacy of the proposed framework was assessed on a modified IEEE 25 bus unbalanced distribution system. The evaluation reveals that adaptive IBDR confers advantages to all participants, including DSO and end-consumers
A Unified Browser-Based Consent Management Framework
No full textToday's users are concerned about the privacy of their personal or sensitive information on the Web because of the different techniques employed to track their online activities and behavior. Privacy laws like the GDPR, CCPA, etc., allow users to decide whether they would like to share their personal data online and what and how much they are willing to share. These laws require the websites to be transparent to the users about what information they are collecting and how that information shall be used, and insist that the websites obtain explicit consent from the users before collecting this information. Recent studies in the area show that websites often utilize dark patterns to affect the consent choices of the users, thereby tricking them into consenting to share more information than what they actually intend. To counter this, researchers have proposed various tools and extensions to automate the task of consent management by selecting default options for the user. However, these tools are not always accurate and are often bypassable by websites.We propose an alternate consent management system that shifts the trust from the web servers to browsers, i.e., instead of relying on servers to obtain and comply with the consent provided by the user, we delegate this task to the web browser. In our approach, the browser obtains and stores the consent of the user for the visited websites. The cookies set by the websites are then subject to this consent provided by the user, as each of the cookies carries an additional attribute that identifies their category. This approach provides an easier way for users to manage consent for different websites without having to search for policies and compliance with the websites while also solving the language barrier. We modified the Nightly Firefox build to add an additional cookie attribute that stores the purpose of every cookie and have included a consent preference option in the browser settings to provide a means for the user to specify their consent. We believe that this approach would provide a cleaner methodology for consent management
Effect of synthesis methods, biocompatibility and photoluminescence of scheelite type sodium lanthanide double tungstates
No full textThe scheelite type sodium lanthanide double tungstates NaRE(WO4)2 (RE = Sm3+, Ho3+ and Pr3+) were synthesized by three different synthesis methods, namely solid-state reaction, hydrothermal method and solvothermal method using ethylene glycol. The synthesized samples were characterized by X-ray diffraction (XRD), scanning electron microscopy, UV-DRS and photoluminescence spectroscopy. The phase pure sample preparations with the Scheelite-like crystal structure in I41/a space group were established by XRD. Among the three synthesis methods, the broader XRD peaks were noticed for the solvothermal samples and resulting in crystallite sizes of ~ 11–28 nm. The SEM micrographs supported the XRD results and the agglomeration of particles with similar morphology was confirmed for all synthesis methods. The smallest particle size was obtained in the solvothermal method with average particle size distributions of ~ 100–140 nm. Indeed, the synthesis methods and particle size played a crucial role in the photoluminescence (PL) emission spectra. The PL emission intensity was very low for as-synthesized solvothermal samples where the particle size was considerably smaller compared to the other two methods. However, the calcination of solvothermal samples at different temperatures has improved their PL emission such that the emission intensity gradually increased with increasing the calcination temperature. Simultaneously, the SEM confirmed that the particle size remained the same till the calcination temperature of 600 °C. The microbial cell viability and cytotoxicity experiments were performed on the solvothermal samples using E. coli, S. aureus and HeLa cells, respectively. The biological studies demonstrated the biocompatibility of synthesized samples. The biocompatibility and smaller particle size in the solvothermal method could be useful in developing improved phosphors for cell imaging and theranostic applications
AI-driven gait classification using portable wearable sensors: advances and case study
No full textThis chapter explores the latest advancements in AI-driven gait classification. The work mainly focuses on developing and implementing techniques for characterizing and measuring human gait using a wearable portable sensor system. By leveraging deep learning (DL) and machine learning (ML) models, we aim to accurately classify different types of gait activity performed by the user. The chapter also presents a review of recent advancements in gait classification, focusing on machine learning and deep learning techniques. A case study is discussed to illustrate the practical applications and benefits of these technologies. For the case study, we collect the gait data using IMU (Inertial Measurement Unit) sensor attached on right shank of a subject. The data is collected from 20 young subjects, who performed six gait activities: (i) walking uphill, (ii) walking downhill, (iii) walking on ground, (iv) climbing upstairs, (v) climbing downstairs, and (vi) standing, all at a speed comfortable to them. We train and test multiple ML and DL models for gait classification, presenting comprehensive experimental results with performance evaluation. Overall, this chapter explores advancements in gait classification with a focus on wearable sensor systems and machine learning techniques, along with a case study relevant to gait rehabilitation applications
DNA nanocarrier-mediated co-Delivery of cisplatin and silibinin for enhanced treatment of oral squamous cell carcinoma
No full textHilbert–Kunz multiplicity of powers of ideals in dimension two
No full textWe study the behavior of the Hilbert–Kunz multiplicity of powers of an ideal in a local ring. In dimension 2, we provide answers to some problems raised by Smirnov, and give a criterion to answer one of his questions in terms of a “Ratliff–Rush version” of the Hilbert–Kunz multiplicity
Monsoon decline versus summertime intensification of carbon and nitrogen fixation in a shallow tropical lake
No full textIn recent years, changes in lake water levels have emerged as a significant environmental concern, largely attributable to the impacts of climate change and anthropogenic activities. Reductions in lake water levels alter light availability, mixing patterns, nutrient concentrations, biomass, and nutrient assimilation rates. Despite being notable contributors to inland water areas, shallow lakes have received comparatively less attention globally and play a significant role in carbon and nitrogen biogeochemical cycles. This study explores the seasonal influence of water temperature, salinity, and nutrient concentration on phytoplankton primary production, nitrogen assimilation, and dinitrogen fixation rates in a shallow tropical freshwater lake as the water level increases from summer to monsoon. Our results reveal that when water level was reduced during summer, concentrations of particulate and dissolved forms of carbon and nitrogen increased, as did phytoplankton primary production, nitrogen assimilation, and N2 fixation rates. Higher rates during summer were attributed to increased light availability, temperature, biomass, and nitrogenous nutrient concentrations with reduction in water level. Lower rates during monsoon were due to the dilution of lake water caused by precipitation and runoff resulting in limited biomass and supply of nitrogenous nutrients. This seasonal variation can cascade through trophic levels, affecting primary producers and potentially leading to shifts in community structure and biodiversity. Overall, this study provides valuable insights into the interactions among water level changes, environmental parameters, phytoplankton primary production, nitrogen assimilation, and N2 fixation rates in tropical semi-arid freshwater shallow lakes facing the threat of lake desiccation
