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The most commonly occurring cancer among women, breast cancer, causes lakhs of deaths yearly, which can be prevented by early detection and treatment. Detection can be done by using machine learning models on histopathological images that are affordable, reliable and accurate. Previous works in this regard have focused on transfer learning methods combining feature selection using Convolutional Neural Networks (CNNs) and ensemble of gradient-boosting algorithms. However, none of the state-of-the-art techniques capture the multi-objective nature of breast cancer detection and tend to improve a single performance measure such as Accuracy and F1 score, which fail to capture certain essential aspects of the problem as the cost of misclassification varies greatly depending on its type. In this study, a Multi-Objective Hyperparameter Optimization technique for Breast Cancer Prediction is proposed by comparing Random Search, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Bayesian Optimization. This approach is applied to ensemble of three popular Gradient Boosting techniques: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) on features obtained from Inception-ResNet-v2 CNN model applied on the benchmark BreakHis dataset to optimize Precision, Recall, Accuracy and AUC simultaneously. The novel NSGA2-IRv2-CXL model proposed in this study achieves maximum Accuracy of 94.40%, AUC of 98.16, Precision of 95.77%, and Recall of 99.29% for 100x magnification. The study also establishes tradeoffs between performance metrics which opens avenues for further research in multi-objective approaches on breast cancer detection which can provide a much larger view of the strengths and weaknesses of the classification model.

Monkeypox is a zoonotic disease caused by the monkeypox virus, which is a member of the Poxviridae family of viruses. It is transmitted through direct or indirect contact with fluid secretions. Initial symptoms include fever, chills, headache, and malaise, followed by a maculopapular rash that starts on the face and progresses centrifugally. Polymerase chain reaction is the preferred laboratory test for the diagnosis, and management is mostly supportive. The clinical presentation of monkeypox is quite similar to that of another member of the Poxviridae family: smallpox, which wreaked havoc in the 20th century, before being eradicated with the help of the vaccinia virus vaccine in 1977. This vaccine protects not only against smallpox but also monkeypox; therefore, when use of this vaccine was discontinued, monkeypox had a new susceptible population to infect and way to proliferate and evolve. Initially the disease spread in Africa, but now the more evolved monkeypox is quickly spreading to other countries. On July 23, 2022, the World Health Organization declared this multicountry outbreak a public health emergency of international concern. Given its mutating ability and high transmissibility, we need to quickly devise measures to control this virus before it turns into a pandemic.

To design noise-shaping successive approximation register (NS-SAR) analog-to-digital converters (ADCs) with high resolution and good power efficiency, two key bottlenecks need to be addressed. One is to realize a high-order loop filter with low circuit overhead, and the other is to mitigate the thermal noise. This article presents an NS-SAR ADC that synergistically addresses both challenges. To achieve high-order efficiency, it proposes an innovative error feedback-cascaded integrator feedforward (EF-CIFF) structure that realizes third-order noise shaping using only a single amplifier. It combines the merits of both structures, showing improved robustness, and is free of dc offset concern. On reducing the kT/C noise, this work features a sampling kT/C noise cancellation (SNC) technique that reuses the native hardware of the EF-CIFF structure. An open-loop self-quenching floating-inverter dynamic amplifier (FIDA) is used to support all amplification with low noise and power. Prototyped in 65-nm CMOS, this work achieves 84.8-dB signal-to-noise-distortion ratio (SNDR) with 625-kHz bandwidth (BW) (OSR = 8) and 119 $\mu \text{W}$ , leading to 182-dB Schreier Figure of Merit (FoM). It uses only 0.8-pF input capacitance, which is $5\times $ smaller than prior NS-SAR ADCs with similar oversampling ratio (OSR) and SNDR.

Obesity and atrial fibrillation in the elderly population both present as significant health issues worldwide indirectly. Cases of atrial fibrillation are also rising worldwide, making it the most common type of cardiac arrhythmia. There are a variety of risk factors for atrial fibrillation. Modifiable factors include smoking, hypertension, diabetes mellitus, sedentary lifestyle, obesity, and non-modifiable include genetic predisposition. Obesity is a well-known risk factor for multiple systemic conditions like metabolic syndrome, diabetes mellitus type 2, sleep-related disorders, etc. In addition, it contributes directly to the development of various cardiovascular disorders like hypertension, myocardial infarction, and, more notably, atrial fibrillation. There are multiple mechanisms by which obesity in adults increases the risk of atrial fibrillation. Some of them are systemic inflammation caused by the increased secretion of cytokines by adipocytes, neuro-hormonal disturbances and heart structure remodeling, and weight-loss strategies have shown improvements in patients suffering from atrial fibrillation.

The coronavirus disease 2019 (COVID-19) pandemic has led to significant psychological and economical distress. Within a year after COVID-19 was declared a pandemic, several vaccines against COVID-19 were approved for emergency use. The journey from vaccine discovery to global herd immunity against COVID-19 continues to present significant challenges revolving around its development, affordability, accessibility, and acceptability at both a country level and an individual level. The main challenge faced by developed countries is the acceptability of the COVID-19 vaccine and the main challenge faced by developing countries is the affordability and accessibility of the COVID-19 vaccine.

Deviations and deformities of the bony nasal skeleton are amenable to osteotomies in the longitudinal, transverse and the oblique planes. These interventions undertaken either intranasal or percutaneous, translocate the osteo-cartilaginous nasal segments as a single entity. The intermediate osteotomy is usually resorted to, in three situations, namely; a very broad nose with a good nasal height, a deviated nose with a lateral wall longer than the opposite and where required to straighten an excessively convex nasal bone. A lady with quite a broad nose was subjected to bilateral osteotomies of the ‘intermediate’ type, to achieve aesthetic social acceptability.

Light Emitting Diodes (LEDs) have become pervasive in modern lighting and automotive applications. LED drivers regulate LED current which sets their luminous output, where dimming is an important attribute. Dimming techniques fall in one of two categories: "analog" or "duty-cycled" (pulse-width-modulated), and duty-cycled (PWM) dimming decomposes into three further classes: shutdown, shunt- and series-switched. However, a comprehensive analysis of dimming techniques, corresponding power losses, and their dimming capabilities is lacking in the literature. This paper explains and quantifies those in the context of a switched inductor (SL) DC–DC converter. Presented analysis incorporates SL conversion efficiency and models luminous flux, dimming range, and luminous efficiency. This paper reveals and verifies that analog dimming is up to 57% more efficient with the widest dimming range.

Metal–oxide memristive integrated technologies for analogue neuromorphic computing have undergone notable developments in the past decade, but are still not mature enough for very large-scale integration with complementary metal–oxide–semiconductor (CMOS) processes. Although non-volatile floating-gate synapse transistors are a more advanced technology embedded within CMOS processes, their performance as analogue resistive memories remains limited. Here, we report a low-power, two-terminal floating-gate transistor fabricated using standard single-poly technology in a commercial 180 nm CMOS process. Our device, which is integrated with a readout transistor, can operate in an energy-efficient subthreshold memristive mode. At the same time, it is linearized for small-signal changes with a two-orders-of-magnitude resistance dynamic range. Our device can be precisely tuned using optimized switching voltages and times, and can achieve 65 distinct resistive levels and ten-year analogue data retention. We experimentally demonstrate the feasibility of a selector-free integrated memristive array in basic neuromorphic applications, including spike-time-dependent plasticity, vector-matrix multiplication, associative memory and classification training. A floating-gate memristive device fabricated in a commercial 180 nm CMOS process can be integrated into a selector-free memristive array and used to demonstrate basic neuromorphic applications.

The mixing of multiple languages in speech or text is termed as the phenomenon of code-mixing. The easier access of internet to a larger population, fluent in various regional languages and the more convenient usage of ubiquitous languages like English for technical terms, sports related words, scientific concepts, etc. has led to an increasing presence of code-mixed content on the world wide web, especially socializing and microblogging platforms like of Facebook, twitter, Instagram, etc. The code-mixing of Hindi, the predominant language of South Asia, with English is colloquially referred to as “Hinglish”, a portmanteau resulting from the names of these two languages. Hinglish is considerably different from its parent languages in syntax, phonetics, grammar and even usage of punctuations. The accent and sentiments are drawn from Hindi, the vocabulary is comprised of varying English (Roman) transliteration of Hindi words, certain English terms. The proposed research work aims to build a self-sufficient model, independent of models meant for English language, which can classify code-mixed posts or tweets, or any other text material for that matter, into the categories: Non-Offensive, Abusive and Hate-Inducing. Our work proposes two ensemble models. The first consisting of some basic machine learning models, first experimented individually experimented with, then as an ensemble. The other ensemble is made by stacking some deep learning models. The aim was to combine the learning of models as weak learners into one for getting better results.

Hinglish is a portmanteau word for ’Hindi’ and ’English’, and refers to the informal "language" predominantly used in the South-Asian (Indian) Sub-Continent, a blend of the two languages it derives its name from. It considerably differs from the English language in grammar, syntax, punctuations, phonetics and accent, as well as in sentiments.As it is more convenient to use English for certain technical words, sports events, scientific phenomena, and other things, mixed usage of English and regional languages has gained considerable prominence in day-to-day conversations and Social Media. This research aims to create an independent and self-sufficing model that classifies Hingish texts as Hate Speech, Abusive or Non-Offensive.The prevalent use of code-mixed language in the subcontinent, the sensitive nature of hate speeches, and the need of a self-sufficient model for Hinglish, together serve as the motivation for this research.We have used character level embeddings for Hinglish Language which has the potential to most efficiently extract the context from Hinglish sentences given the level of variation in syntax and semantics of the code-mixed (a language that is a combination of two or more languages) language. Later we trained various deep learning classifier models. Hybridisation of GRU with Attention Model performed best among more than 12 models experimented with. The use of Character Level Embeddings, GRU, and attention layer are novel to Hate Speech Detection in Hinglish Code-Mixed Language.

Background: Fungal rhino sinusitis is rampant in the North Indian state of Punjab. The demographic profile, presentation and comorbidities have been analysed.Methods: 110 samples from the nasal cavity were collected in Oto-rhino-Laryngology services and processed in the Microbiology services of Dayanand Medical College and Hospital, Ludhiana. Nasal secretions and surgically excised tissue were processed and subjected to direct microscopy by potassium hydroxide (KOH) preparation as well as inoculated on Sabrouraud’s dextrose agar. Identification of fungal isolates was done as per standard procedures.Results: Fungal sinusitis was most common followed by acute invasive sinusitis, chronic invasive, granuloma (fungal ball) and granulomatous invasive fungal sinusitis. Males were affected more than females and the majority of the patients were from urban background. Allergic form of disease was commonly seen in younger age group as compared to the invasive form of the disease which was seen more in older patients. Nasal discharge was common presentation followed by nasal obstruction. Bronchial asthma and diabetes mellitus were co-morbidities. KOH positivity was 44.5% and fungal culture positivity was 33.6%. The common fungal isolate in this group was Aspergillus flavus (22.7%). The most common fungal isolate in the invasive group was Rhizopus spp. (4.5%) and Mucor spp. (3.6%).Conclusions: Elderly diabetic and asthmatic males are more susceptible to invasive fungal rhino sinusitis while the young males are vulnerable to the allergic fungal rhino sinusitis. Thereby control of diabetic status and appropriate allergy testing and desensitization is emphasised.