AbstractA series of new isoxazole‐substituted aryl iodides 1 a–1 d have been synthesized by DIB‐mediated [3+2] cycloaddition reaction of 2‐iodo‐1,3‐bis(prop‐2‐yn‐1‐yloxy) benzene (4) with corresponding benzaldehyde oximes 5 a–5 d. Structure of the synthesized aryl iodides 1 were characterized by IR, 1H NMR, 13C NMR and HRMS. The structure of 1 a was also confirmed by single‐crystal X‐ray crystallography. Further, catalytic activity of iodoarenes 1 a–1 d was screened for the oxidation of hydroquinones and sulfides. On oxidation using aryl iodides 1 with m‐CPBA as terminal oxidant, hydroquinones afforded benzoquinones while sulfides gave corresponding sulfoxides in good to excellent yields. Iodoarene 1 b showed the best catalytic activity for the oxidation of sulfides and hydroquinones. Moreover, iodoarene 1 b, was also utilized for α‐oxytosylation of acetophenones.

Reverse Logistics is support in moving goods from consumer to a facility where re-capturing of value of the End of Life goods is possible. This can be achieved by product recovery management. The recovered material can be reused as raw material in manufacturing giving an edge of cost competitiveness. Besides this, Reverse logistics also mitigates problem of pollution, cheaper raw material, value for waste and making return policy easy in on-line shopping. This paper aims to investigate the responsiveness of reverse logistics using AHP in maintaining cost, product recovery management, environment, customer satisfaction citing examples of major manufacturing sector. The paper highlights the importance of responsiveness of reverse logistics to make it more responsible towards environment and making reverse logistics as a profitable venture. The study also provides roadmap to top management in furnishing their social responsibility to take care of End of Life goods produced from their firms as the human being no more be as wasteful and insensitive towards his surroundings. The best implication of RL may ease the problem of electronics and plastics waste disposal, pollution of land, air and aquatics and vanishing of precious rare metals from the earth.

An analysis on the nonlinear interaction of electromagnetic waves with electron acoustic waves is performed in plasma with two different temperature electron fluids in the presence of a neutralizing static ion background. A newly structured Zakharov’s equations are derived employing two fluid two-time scale theory. These coupled Zakharov’s equations describe the weakly nonlinear interaction of em wave perturbation with electron acoustic waves while propagating through plasma. In the low frequency or adiabatic limit, these Zakharov’s equations may be unified to produce a modified NLSE. A solution of the equation, novel in the literature, is derived following the method shown by Kudryashov. In a resonant regime, the modified NLSE reduces to NLSE. Though a stable solution exists for both cases, instability analysis shows caviton instability may arise. The threshold value of the electric field, at which instability sets in, is virtually zero for the resonant region whereas, apart from that region there is a threshold value of the electric field, determined by the frequency difference of em wave and electron plasma wave. Experimental observations support these results. This study is relevant for laser-plasma interaction and astrophysical and space plasma.

ASA/ANSI S12.42-2010 contains the first standardized test method for measuring the performance of hearing protection devices (HPDs) with high-level impulsive noise. This standard defines the impulsive peak insertion loss (IPIL) as a time-domain metric that quantifies the reduction in peak sound pressure level provided by an HPD for impulsive noises. However, IPIL as defined in S12.42-2010 is dependent on the spectrum of the impulsive noise source used for measurements of HPDs. Recent studies of HPDs with impulsive noise have led to the investigation of frequency-domain metrics and calculation methods. Using frequency-domain calculations allows the impulsive peak level attenuation to be computed for a wide range of impulsive noises, not only those used for the measurement. Consequently, significant revisions to S12.42 are under consideration and a new standard, ISO 4869-7, is being developed in parallel with the ANSI revision. This presentation highlights key proposals and substantive changes including: measurement and calculation of the frequency-dependent impulsive insertion loss (IIL), incorporation of frequency-domain aspects such as real-ear attenuation at threshold (REAT) limits to attenuation, and a new impulsive peak level attenuation metric (IPLA) to be calculated from the REAT-bounded IIL.

Abstract System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analytical solution. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. To date, in order to calculate higher order terms in the Wiener series, vast quantities of data are needed. In this paper, a novel formulation of the Wiener series is developed in the frequency domain which applies to general stochastic inputs with an arbitrary spectrum. It is enhanced by placing Gaussian process priors over the Wiener kernels to enforce prior knowledge of their structure. This significantly reduces the quantity of data required for inference and has the benefit of enabling the calculation of the third order kernel for systems with long memory. The benefits were demonstrated in initial investigations using an idealised nonlinear oscillatory system. Decomposition of the system response into Wiener functionals also sheds light on the learnability of nonlinear dynamical systems, which could be used to assess the value of collecting additional data.

Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In contrast, some recent works explore SE via generative speech synthesis, where the system’s output is synthesized by a neural vocoder after an inherently lossy feature-denoising step. In this paper, we propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We leverage rich representations from self-supervised learning (SSL) speech models to discover relevant features. We conduct a candidate search across 15 potential SSL front-ends and subsequently train our vocoder adversarially with the best SSL configuration. Additionally, we demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation. Finally, we conduct both objective evaluations and subjective listening studies to show our system improves objective metrics and outperforms an existing state-of-the-art SE model subjectively.

Modern noise-cancelling headphones have significantly improved users’ auditory experiences by removing unwanted background noise, but they can also block out sounds that matter to users. Machine learning (ML) models for sound event detection (SED) and speaker identification (SID) can enable headphones to selectively pass through important sounds; however, implementing these models for a user-centric experience presents several unique challenges. First, most people spend limited time customizing their headphones, so the sound detection should work reasonably well out of the box. Second, the models should be able to learn over time the specific sounds that are important to users based on their implicit and explicit interactions. Finally, such models should have a small memory footprint to run on low-power headphones with limited on-chip memory. In this paper, we propose addressing these challenges using HiSSNet (Hierarchical SED and SID Network). HiSSNet is an SEID (SED and SID) model that uses a hierarchical prototypical network to detect both general and specific sounds of interest and characterize both alarm-like and speech sounds. We show that HiSSNet outperforms an SEID model trained using non-hierarchical prototypical networks by 6.9 – 8.6%. When compared to state-of-the-art (SOTA) models trained specifically for SED or SID alone, HiSSNet achieves similar or better performance while reducing the memory footprint required to support multiple capabilities on-device.

Abstract Rapidly returning back the synchronism of an inter-connected multi-area power system under continuous varying load demand conditions is documented as a distinguished issue to be tackled by automatic generation control (AGC). AGC supports to alleviate both the frequency and tie-line power deviations on occurrence of load perturbations. Therefore, a robust and intelligent controller is indispensable for an effective AGC in power system. In the current work, a hybrid fuzzy proportional-integral-derivative with filter (FPIDN) controller is employed for AGC of 2-area non-reheat/reheat thermal power systems. A new adaptive differential evolution (ADE) is exploited to adjust parameters of the controller. The dominance of the endorsed technique is revealed by evaluating it with lately published approaches. Then, the recommended scheme is extended to a realistic 4-area interconnected reheat thermal system, which includes all possible uncertainties such as generation rate constraint, boiler dynamics and governor dead band. It is perceived that the performance of the suggested ADE aided hybrid FPIDN controller provides a better response than other control techniques. Lastly, the robustness of the advocated method is tested for an extensive variety of system parameters. The stability analysis is validated by analyzing frequency domain specifications such as gain margin and phase margin. Finally, real-time hardware-in-the-loop simulations are performed to confirm the applicability of the advocated controller for AGC.

Abstract Slot-filling is considered one of the most important components of task-based interactive systems. The main challenge in slot filling is to assign a semantic label to each text in the utterance which is further useful for generating the response by the interactive system. Another challenge is the non-availability of extensive labeled data. Thus in this research, we primarily focus on developing two approaches namely (i) automatic slot identification through IOB labeling for slot labels creation and preparing a labeled dataset (ii) a self-learning based model where the slot labels are created in step (i) are predicted for an untrained query from the same or different domains. We use BI-LSTM with Elmo Embedding for our proposed frameworks with POS Tags to generate slot-independent tags and label descriptions for slot labeling. We applied and compared our model on the SNIPS and Mobile service provider dataset through the F1 score and analyzed that the efficiency of self-learning-based models highly depends upon the quality and correlation among different domains of the same dataset.

The era of information and communication technologies has produced numerous opportunities in every field. Finance and business sector is most benefitted field from these recent developments. The spotlight quickly shifted from "Fintech," which emerged in the last ten years, to "crypto currency," then to "CBDC," demonstrating that how quickly the world of digital finance is changing. CBDC is similar to blockchain technology but is controlled and regulated by Reserve Bank of India (RBI). The pilot study of retail segment of CBDC has commenced from the beginning of December 01, 2022. This paper made an attempt to delve into the understanding of Central Bank Digital Currency and to probe the motives behind the issuance of CBDC. RBI is currently working toward a phased deployment plan, progressing gradually through several rounds of pilots before the final launch and concurrently looking at use cases that might be adopted with little to no impact.