Accurate first-principle bandgap predictions in strain-engineered ternary III-V semiconductors

Tuning the bandgap in ternary III-V semiconductors via modification of the composition or the strain in the material is a major approach for the design of optoelectronic materials. Experimental approaches screening a large range of possible target structures are hampered by the tremendous effort to optimize the material synthesis for every target structure. We present an approach based on density functional theory efficiently capable of providing the bandgap as a function of composition and strain. Using a specific density functional designed for accurate bandgap computation (TB09) together with a band unfolding procedure and special quasirandom structures, we develop a computational protocol efficiently able to predict bandgaps. The approach's accuracy is validated by comparison to selected experimental data. We thus map the phase space of composition and strain (we call this the ``bandgap phase diagram'') for several important III-V compound semiconductors: GaAsP, GaAsN, GaPSb, GaAsSb, GaPBi, and GaAsBi. We show the application of these diagrams for identifying the most promising materials for device design. Furthermore, our computational protocol can easily be generalized to explore the vast chemical space of III-V materials with all other possible combinations of III- and V-elements.Comment: 13 pages, 7 figures, GitHub (https://bmondal94.github.io/Bandgap-Phase-Diagram/

From First-Principles to Machine Learning: Advancing Bandgap Predictions in Strain-Engineered III-V Semiconductors

Semiconductor compounds composed of elements from groups 13 and 15 (main groups III and V) of the periodic table, commonly referred to as III-V semiconductors, are integral to modern (opto-)electronics. They play a critical role in applications such as solar cells, light-emitting diodes, optical telecommunication, laser technology, photodetectors, and high-speed electronics. The performance and characteristics of these devices heavily rely on the bandgap value and its direct or indirect character. Consequently, tailoring bandgaps to specific applications is a major goal in semiconductor research field. This holds immense importance in advancing the capabilities and efficiency of semiconductor-based technologies. This thesis focuses on two primary approaches for tuning bandgaps in III-V semiconductors: varying composition and applying strain to the materials. To identify tailored materials for specific applications, it is crucial to assess the dependence of bandgaps on composition and strain across a broad range of materials. However, experimental methods face limitations in exploring the vast chemical space of combinations of III- and V-elements with variations in composition and strain due to challenges in synthesizing new materials. In this thesis, a density functional theory (DFT)-based first-principles approach is established to accurately predict bandgaps in strained III-V compound semiconductor materials. A robust scheme is developed within the DFT framework to accurately model the application of various types of strain on a material. The study reveals that not only do the bandgap values change under strain but also the nature of the bandgap can transition from direct to indirect or vice versa. The established DFT protocol enables a comprehensive mapping of bandgap properties with composition and strain in multinary III-V semiconductors, facilitating efficient screening of promising materials for device designs. The investigated materials span binary III-V systems such as GaAs, GaP, GaSb, InP, InAs, InSb, and Si, as well as various ternary materials including GaAsP, GaAsN, GaPSb, GaAsSb, GaPBi, and GaAsBi. Furthermore, as the composition-strain space expands, standalone DFT approaches become computationally demanding for higher-order systems, such as quaternary and pentanary III-V semiconductor materials. The number of DFT calculations required increases significantly in those systems (~millions). To address this, a hybrid approach is developed by integrating a support vector machine-based supervised machine learning (ML) model with DFT. This hybrid DFT-ML approach reduces the number of DFT calculations required by a factor of 1000 while maintaining high prediction accuracy. The effectiveness of this approach is demonstrated through the mapping of bandgaps in the III-V quaternary compound GaAsPSb across its entire composition range and a wide range of strain values, which would otherwise be impractical with standalone DFT method. This hybrid approach enables computationally efficient bandgap predictions across a diverse range of materials and strains, offering a rapid virtual screening capability for the discovery of novel semiconductor materials in (opto-)electronic applications

A study of Ki-67 expression and its clinicopathological determinants in nondysplastic oral leukoplakia

Context: Oral cancer is the third most prevalent malignancy in India. Leukoplakia is its most common precursor lesion. Aims: This study aimed at evaluation of the Ki-67 expression and thereby detection of the dysplastic potential in histopathologically nondysplastic oral leukoplakia (OL). Secondarily, another purpose was to correlate various clinicopathological factors with the labeling indices (LIs) of Ki-67 in those cases as well. Settings and Design: In total, 97 OL cases were examined. Relevant clinical and demographic information was retrieved from the pro forma, prefilled by the patients themselves during their first visit. Subjects and Methods: Ki-67 immunohistochemical staining was performed on paraffin-embedded tissue samples. Its LIs were calculated and correlated with different clinicopathological parameters using statistical software SPSS version 16.0. Results: 58.8% (57 cases) lesions exhibited a Ki-67 positivity of ≤5%, and 25.8% (25 cases) lesions exhibited it in the range of 6%–25%. Only 15 (15.4%) patches were stained positively between 26% and 60%. Patients' age beyond 50 years, nonhomogeneous leukoplakia, and tobacco addiction were the significant risk factors for high Ki-67 scores (P < 0.05). Conclusions: Ki-67 is an essential immunohistochemical marker for epithelial dysplasia in OL, especially when the conventional histopathology fails to appreciate the same. In this purpose, Ki-67 labeling on a routine basis delivers the most convenient results for patients aged above 50 years, and/or addicted to tobacco products, and/or suffering from nonhomogeneous patches

Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors

Quaternary III-V semiconductors are one of the major promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and characteristics of optoelectronic devices. Experimental approaches screening a large range of possible combinations of III- and V-elements with variations in composition and strain are impractical for every target application. We present a combination of accurate first-principles calculations and machine learning based approaches to predict the properties of the bandgap for quaternary III-V semiconductors. By learning bandgap magnitudes and their nature at density functional theory accuracy based solely on the composition and strain features of the materials as an input, we develop a computationally efficient yet highly accurate machine learning approach that can be applied to a large number of compositions and strain values. This allows for a computationally efficient prediction of a vast range of materials under different strains, offering the possibility for virtual screening of multinary III-V materials for optoelectronic applications

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Not AvailablePea (Pisum sativum) is the most common green pod-shaped vegetable widely grown as a cool-season crop. Green peas are used either- fresh or frozen and canned. India is one of the largest producers of peas in the world and ranks 5th on the of major pea producers. However, fluctuation in the prices of pea are common and lead to often reduced profit to farmers. A prior information about this price could help them in decision making regarding bringing the same for market or opting for processing. For this purpose ARIMA and SARIMA models were used to forecast the prices of pea for Varanasi in Uttar Pradesh using daily time series data of five years from 2017 to 2021. The best model was selected on the basis of R-squared, AIC, BIC, RMSE and MAE. The study revealed that out of ARIMA (3,1,5), SARIMA (1,0,1) (1,0,1) and SARIMA (0,0,1)(0,0,1), the 2nd were best fitted model for forecasting of pea prices for Varanasi. The forecasted values showed that the prices of pea were high in the month of November and February and low in December and January for the forecast year 2022.Not Availabl

A randomized trial of intravenous labetalol & oral nifedipine in severe pregnancy induced hypertension

Background: Hypertension is the most frequently encountered medical disorder in obstetrics practice &amp; remain a major cause of maternal, fetal &amp; neonatal morbidity &amp; mortality. The present study was undertaken to compare the time taken to reach the therapeutic goal blood pressure after using intravenous labetalol &amp; oral nifedipine in severe pregnancy induced hypertension.Methods: Randomly allocated patients received labetalol 20 mg initially, followed by escalating doses of 40, 80, 80 &amp; 80 mg &amp; a placebo tablet every 20 minutes or initially nifedipine tablet 10 mg orally with repeated doses of 20 mg every 20 minutes up to 5 doses &amp; intravenous placebo 0.9% isotonic saline until the therapeutic goal blood pressure, Systolic ≤ 150 mmHg &amp; diastolic ≤ 100 mmHg was achieved. Primary and secondary outcomes like the time interval required to achieve a blood pressure of ≤150/100 mmHg and urinary output, agent failure &amp; adverse effects respectively were reported.Results: Patients received oral nifedipine achieved the goal therapeutic blood pressure more rapidly in 28.2±11.7 minutes (mean±SD) as compared with 48.4±23.5 minutes in those received intravenous labetalol (p=0.001). The nifedipine group also required significantly fewer doses (3.5±0.5 vs 4.5±1.5; p=0.001) to reach the goal blood pressure. Urine output was significantly increased (p&lt;0.001) at one hour after nifedipine therapy (95.6±1.2) compared with labetalol (41.9±1.6 ml) &amp; remained significantly increased at 4,8,16&amp;24 hours after initial therapy. Few adverse effects were reported but not significant. No patients required cross over therapy.Conclusions: Oral nifedipine &amp; intravenous labetalol regimens are effective in the management of severe hypertension in pregnancy; however nifedipine controls hypertension more rapidly &amp; is associated with a significant increase in urinary output

Конструкція контролера нечіткої логіки для управління напругою, частотою, струмом та потужністю ізольованої мікромережі на основі трифазної розподіленої генерації

Сьогоднішні екологічно чисті технології, пов'язані з мікромережами, наближаються до розумної системи наномереж. Вона задовольняє попит на електроенергію у всьому світі шляхом належного використання відновлюваних джерел енергії та систем накопичення енергії. Проте, управління електростанцією з використанням мікромереж перейшло на рівень, який вимагає складного і плавного контролю у взаємодії з мережею, включаючи розподілену операцію ізоляції. Динаміка навантаження і похибки є загальними проблемами, що впливають на профіль частоти, напруги та потужності мікромережі, яка є відповідальною за пошкодження навантаження та системи енергопостачання. У статті запропоновано конструкцію надійного контролера нечіткої логіки (FLC) для регулювання характеристик трифазної ізольованої мікромережі. Характеристики запропонованого FLC досліджували за різних умов навантаження, надійність яких оцінювали у стані несправності. Досліджувані характеристики мікромережі забезпечують високе відстеження та надійну роботу запропонованого FLC.Today’s clean technologies related to microgrids are approaching towards the smart nanogrid system. It fulfils the demand of the electricity throughout the world by proper using of renewable energy sources and energy storage systems. Still, the microgrid (MG) power plant control has enriched to a level that it will require complicated and smooth control in the grid interaction including distributed islanding operation. The load dynamics and uncertainties are the common issues which hampers the frequency, voltage and power profile of the MG that is responsible to damage the load and power system. The design of robust fuzzy logic controller (FLC) has been proposed in this research article to regulate the performances of three-phase islanded MG. The performance of the proposed FLC has been examined under different loading condition whose robustness has been evaluated under faulty condition. The investigated performances of the MG ensure high tracking and robust performance of the proposed FLC

Новий блок системи безпеки зі зменшенням відхилення частоти для об'єднаної енергосистеми з урахуванням кібератак

Об'єднана енергосистема є перспективним джерелом електричної енергії, яке задовольняє надлишкові потреби в електроенергії у всьому світі, безпечна та надійна робота якого необхідна для зменшення навантаження та підвищення стійкості. Розвиток інформаційно-комунікаційних технологій (ICT) не лише стимулює, а й гальмує технології, сприяючи кіберзлочинності. Кібератака (CA) на енергосистему в теперішній час стає поширеною проблемою, яка призводить до несанкціонованого доступу до блоку управління енергосистемою і частково або повністю перешкоджає роботі всієї системи, змінюючи конфіденційні дані енергосистеми та блоку управління. Продуктивність енергосистеми регулюється використанням FPID (fractional-order-proportional-integral-derivative) контролера і порівнюється з продуктивністю звичайного PID контролера. Надійна робота енергосистеми повністю залежить від ефективної конструкції контролера, але на параметри контролера значною мірою впливає CA, ушкоджуючи всю систему. Будь-яка зміна блоку управління або параметрів системи може знизити стійкість та стабільність енергосистеми. У статті запропоновано автоматичний метод захисту від CA (ACAMU), щоб повністю уникнути CA та її впливу на систему та контролер, для підвищення безпеки і стійкості енергосистеми, підтримуючи фіксовані дані як для системи, так і для контролера.Interconnected power system is a promising source of electric power that fulfils the excess demand of electricity throughout the world whose safe and reliable operation is necessary for decreasing loadshedding and increasing resiliency. The development of information and communication technology (ICT) not only blessing for us but also hampers our technology by promoting cyber-crime. Cyber-attack (CA) on power system is now becoming a common problem that produces unauthorized access to the control unit of power system and hampers the whole system partially or completely by changing the sensitive data of power system and control unit. The performance of the power system is regulated by employing a fractional-order-proportional-integral-derivative (FPID) controller and is compared with conventional PID controller in this paper. The reliable performance of the power system completely depends on the efficient design of controller, but the parameters of the controller are largely affected by the CA and damage the whole system. Any change of the control unit or the system parameters may decrease the resiliency and the stability of the power system. An automatic cyber-attack mitigation technique (ACAMU) has been proposed in this article to completely mitigate the CA and its impact on the system and controller to enhance the security and resiliency of power system by maintaining a fixed data for both system and controller