Humour is a crucial aspect of human speech, and it is, therefore, imperative to create a system that can offer such detection. While data regarding humour in English speech is plentiful, the same cannot be said for a low-resource language like Hindi. Through this article, we introduce two multimodal datasets for humour detection in the Hindi web series. The dataset was collected from over 500 minutes of conversations amongst the characters of the Hindi web series Kota-Factory and Panchayat . Each dialogue is manually annotated as Humour or Non-Humour. Along with presenting a new Hindi language-based Humour detection dataset, we propose an improved framework for detecting humour in Hindi conversations. We start by preprocessing both datasets to obtain uniformity across the dialogues and datasets. The processed dialogues are then passed through the Skip-gram model for generating Hindi word embedding. The generated Hindi word embedding is then passed onto three convolutional neural network (CNN) architectures simultaneously, each having a different filter size for feature extraction. The extracted features are then passed through stacked Long Short-Term Memory (LSTM) layers for further processing and finally classifying the dialogues as Humour or Non-Humour. We conduct intensive experiments on both proposed Hindi datasets and evaluate several standard performance metrics. The performance of our proposed framework was also compared with several baselines and contemporary algorithms for Humour detection. The results demonstrate the effectiveness of our dataset to be used as a standard dataset for Humour detection in the Hindi web series. The proposed model yields an accuracy of 91.79 and 87.32 while an F1 score of 91.64 and 87.04 in percentage for the Kota-Factory and Panchayat datasets, respectively.

ABSTRACT The article examines the global activities of high-value Indian state-owned enterprises (SOEs) classified by the government as “Maharatnas” and “Navratnas.” Using the FESPET framework, the paper assesses their performance and international presence, revealing their commitment to a public mission beyond financial gains. These SOEs prioritise social spending and often surpass regulatory environmental targets, serving as valuable assets in India and globally. Profitable SOEs demonstrate positive impacts abroad, while financially unsustainable ones fall short in contributing to the non-financial “ESPET” dimension.

In the ever-evolving landscape of online discourse and political dialogue, the rise of hate speech poses a significant challenge to maintaining a respectful and inclusive digital environment. The context becomes particularly complex when considering the Hindi language—a low-resource language with limited available data. To address this pressing concern, we introduce the CHUNAV dataset—a collection of 11,457 Hindi tweets gathered during assembly elections in various states. CHUNAV is purpose-built for hate speech categorization and the identification of target groups. The dataset is a valuable resource for exploring hate speech within the distinctive socio-political context of Indian elections. The tweets within CHUNAV have been meticulously categorized into “Hate” and “Non-Hate” labels, and further subdivided to pinpoint the specific targets of hate speech, including “Individual”, “Organization”, and “Community” labels (as shown in Figure 1). Furthermore, this paper presents multiple benchmark models for hate speech detection, along with an innovative ensemble and oversampling-based method. The paper also delves into the results of topic modeling, all aimed at effectively addressing hate speech and target identification in the Hindi language. This contribution seeks to advance the field of hate speech analysis and foster a safer and more inclusive online space within the distinctive realm of Indian Assembly Elections.

Abstract The efficiency of Latch design and optimization plays a crucial role in shaping the overall performance of digital circuits, impacting power consumption and timing within emerging system-on-chip (SOC) archi-tectures. This paper introduces a methodology for modeling, designing, and optimizing 65nm D-Latch positive-edge-triggered Latch. Leveraging an Arti-ficial neural network (ANN)-based optimization ap-proach, an accurate model is first generated for vari-ous performance metrics using training data acquired from transistor-level models, which surpass tradi-tional methods by over 100 times in speed. Sub-sequently, these precise ANN-based models are em-ployed to optimize design objectives such as setup time, and propagation delay (Data to Output). The utilization of these swift and precise models markedly expedites the design process and yields significantly optimized outcomes. Furthermore, given the ANN’s capacity as a universal approximator capable of cap-turing any nonlinear input-output relationship, the proposed method is adaptable to optimizing circuits for diverse performance metrics, even in the absence of analytical formulas. Additionally, the automation of circuit design through the proposed method sim-plifies the tasks of circuit designers.

The present study investigates the relationship between export performance and the level of financial development for 30 Indian states and union territories (UTs) from 2011–12 to 2020–21. The study first ranks the states and UTs on the parameters of financial development and physical infrastructure using the robust principal component analysis method. By applying the instrumental variable fixed effects panel data technique, the study observes that the level of financial development has a significant positive impact on the export performance of the states. The study also finds a positive effect of the availability of physical infrastructure, quality of governance and fiscal position on states’ export performance but does not observe a significant role of political stability in this context. The results suggest that export growth in the economically laggard and Himalayan states can come through policy interventions to improve financial development. In contrast, landlocked states need better physical infrastructure to facilitate easier production and transportation of export goods. The study lends new perspectives to the nascent literature on inter-regional disparities in India’s export performance and is expected to provide insights into improving the sub-national export performance of other developing countries as well. JEL Codes: F14, F36, F4

AbstractAlkali‐metal‐based yttrium fluoride host (AY1‒xF4:xEu3+; A: Li/Na/K) nanophosphors were developed using co‐precipitation method. Phase analysis and emission spectral behavior were established for the said nanophosphors. The highest emission observed for NaYF4 doped with Eu3+ was then optimized to achieve maximum photoluminescence (PL) emission for varied dopant amount. The downconversion and upconversion PL studies revealed pure red emission under n‐ultraviolet and infrared excitations. The CIE coordinates are observed to be lie in the red hue of the CIE diagram. The PL decay curves reported to study lifetime of the emitting state and to identify quantum efficiency using Auzel's model for the said nanophosphor. Thermal stability was also evaluated from temperature‐sensitive luminescent spectra. The studies conducted on AYF4:.05Eu3+ (A: Li/Na/K) nanophosphors contemplates that NaYF4 nanophosphors doped with Eu3+ ions are best suited for biophotonic applications as the later one show relatively high emission.

Thermal additive centrifugal abrasive flow machining hybrid of abrasive flow machining caters to low material removal issues which enhances the acceptability of the process. The present investigation investigates the different centrifugal force-generating electrode geometry performances in the thermal additive centrifugal abrasive flow machining process. The L27 orthogonal array is used for optimizing the process parameters (electrode type, current supply, and the duty cycle) and their effect on material removal and percentage improvement in surface finish (%Δ Ra) are analyzed. Based on the percentage contribution obtained from Taguchi analysis, electrode type is the prominent parameter for material removal (88.27%) and %Δ Ra (78.54%) followed by current (7.43% for material removal and 13.92% for %Δ Ra) and duty cycle (3.95% for material removal and 6.42% for %Δ Ra). The spline electrode with a curved blade is found optimum with 10 A current and 0.76 duty cycle. The average experimental value based on the design of the experiment and the predicted value for material removal is 10.373 and 10.881 mg, respectively. The average experimental value and the predicted value for percentage improvement in the surface are 42.24% and 43.71%, respectively. The percentage error between the predicted and experimental values for material removal and %Δ Ra is 4.66% and 3.15%, respectively.

Abstract In the present study, the photocatalytic degradation of Rhodamine-B dye was carried out. bismuth oxide (α − Bi2O3) and copper oxide (CuO) photo catalyst was prepared for the degradation analysis. During the photocatalysis of Rhodamine-B degradation, the order of removal with different semiconductors was followed in the following order: α − Bi2O3 > CuO. The effect of operating parameters, including solution pH (3–8), catalysts dose (0.2–1.5 g/L), temperature change (5–20 oC), and initial Rhodamine B dye concentration (10–25 mg/L), were systematically examined using α − Bi2O3 photocatalyst under UV-light irradiation. The Rhodamine-B dyes showed the best removal efficiency of 97% at operating conditions of natural pH = 7.0, catalyst dose = 1.5 g/L, temperature = 20 ◦C, and Rh-B concentration = 10 mg/L under control conditions. As − prepared semiconductor materials such as α − Bi2O3 and CuO were characterized by using many techniques like scanning electron microscope, energy dispersive X-ray, Fourier Transmission Infrared spectroscopy, and X − ray diffraction technique. A degradation pathway was also suggested by the identification of reaction intermediates. The reusability test analysis of bismuth oxide confirmed that photocatalysts can be separated after degradation and reused many times, and there were no other changes in structure and morphologies. This study confirmed the simple synthesis approach of semiconductor materials and their uses for the treatment of Rhodamine-B dye.