Medi-Caps University

Abstract A comprehensive investigation of {\color{red}one-electron} quantum dots {\color{red}(OEQDs)} in free and confined conditions is 
presented, with emphasis placed on degeneracy patterns, information-theoretic measures, and response properties. Highly accurate 
results connecting $1s$, $2p$, $2s$ and $3d$ states are obtained by employing the generalized pseudospectral method. The effects of 
the potential {\color{red} depth ($A$), cavity constant ($B$)}, and the confinement radius $r_{c}$ are systematically analyzed. Localization 
is found to be enhanced with increasing $A$, while delocalization is induced by $B$; in the asymptotic limits $B \to 0$ and $B \to \infty$, 
hydrogenic behavior is recovered. Three generalized scaling relations are derived, through which a unified framework connecting effective 
Hamiltonians is established. Accidental degeneracies among the $2s$–$2p$, $3p$–$3d$, and $3s$–$3d$ states are identified, and the validity of a virial-like theorem in {\color{red}OEQDs} is confirmed. Localization–delocalization trends and similarities between degenerate states are characterized using Shannon entropy and relative Shannon entropy, and a relation connecting Shannon entropy, oscillator strengths, and orbital energies is proposed and validated. Polarizability calculations disclose the presence of tune-out points in both free and confined {\color{red}(OEQDs)}, characterized by vanishing dipole response. It is thereby demonstrated that the electronic structure and spectroscopic features of {\color{red}OEQDs} can be precisely controlled through appropriate variation of $A$, $B$, and $r_{c}$, with implications for confined atomic systems and prospective qubit architectures. \\

The emerging demand of precision-machined components has pushed the medical, aerospace and automotive industries to enhance their focus more on areas of high-quality machining and its predictive modeling, that enhances production efficiency through accurate planning and execution. This study aims to develop a new method based on African vultures optimization algorithm (AVOA) optimized support vector machine (SVM) for optimizing and upgrading the machining performance prediction in turning of aluminum 7068 alloy using a graphene nanoparticle-based coolant. The experiments were conducted using Taguchi L27 orthogonal array for analyzing the impact of key process parameters such as cutting speed, feed rate, and cutting depth, on machining characteristics of surface roughness (Ra) and material removal rate (MRR). The nonlinear correlation between input features and target variables were learned, illustrating the need of developing AVOA-SVM regression models for predicting and analysing machining characteristics. The nonlinear correlation between input features and target variables were learned using Pearson correlation coefficient. The SVM model prediction accuracy was improved through optimizing its hyperparameters employing AVOA algorithm. The efficiency and prediction performance of the proposed methods are verified by experimental trials. The results revealed that the proposed AVOA-SVM regression model can predict surface quality and MRR values accurately and efficiently on partial datasets information. The results were compared with SVM, ANN, DT, AVOA-DT and AVOA-RF models, it was found that AVOA-SVM outperformed in terms of diverse evaluation metrics such as accuracy, RMSE, MAE and MAPE % with greater generalizations. These findings demonstrated improved machining quality and higher MRR, thus providing inspiration for predictive modeling of other machining characteristics along with the intelligent expansion of manufacturing industry.

Efficient sampling of low-energy conformational states remains a central challenge in protein folding simulations. In this study, we investigated the performance and limitations of standard Monte Carlo (MC) and parallel tempering Monte Carlo (PTMC) methods in exploring protein energy landscapes using two representative systems: the small peptide Met-enkephalin and the FBP28 WW-Domain protein. For Met-enkephalin, standard MC simulations achieved equilibrium at higher temperatures but showed insufficient sampling in low-energy regions. In contrast, PTMC significantly improved sampling efficiency across a wide temperature range, particularly in the low-energy regime, enabling robust exploration of the energy landscape. PTMC successfully identified the global minimum structure of Met-enkephalin with an energy of -11.23 kcal/mol and a root-mean-square deviation of 1.18 Å from the reference structure. The applicability of PTMC to larger proteins was further examined through extensive simulations of the WW-Domain, revealing pronounced energy fluctuations and folding–unfolding transitions at higher temperatures, while sampling at lower temperatures remained limited. The number of visits per unit energy range for two consecutive temperatures have also monitored to check the exchange rate and conformational sampling of the entire landscape. The average energy and specific heat with respect to temperature have also calculated to check the folding unfolding transition of WW-Domain. These results demonstrated the superiority of PTMC over standard MC for protein folding studies; however, they also highlighted challenges associated with larger protein systems.

ABSTRACT Static random access memory (SRAM) faces significant reliability challenges in radiation-prone environments, as high-energy particles can induce single-event upsets (SEUs) by disrupting stored data at sensitive nodes. In this work, we aim to design a radiation-hardened SRAM cell that can withstand harsh radiation environments in space. In order to design the DFRH_14T SRAM cells, we utilised two feedback mechanisms. Firstly, we used two crossed coupled latches with an interlocked connection, which creates positive feedback and restores the affected node value. The second method involved using two PMOS transistors controlled by a word-line signal. These transistors connect their drain and source to the internal stored nodes, generating positive feedback. Both feedback machines enable the system to withstand increased radiation levels. Additionally, in hold mode, its beta value is increasing, which helps to improve the hold noise margin. Drives by two transistors make write and read operations faster, which helps to reduce read and write delay. We address the HALF selection issue, by implemented a divided word line, which significantly enhanced the WL driving strength.

Abstract Anaerobic digestion (AD) is vital in achieving sustainable waste management and renewable energy goals. This review critically evaluates the recent advancements and bottlenecks in biogas technology, focusing on improving process efficiency, environmental performance, and integration within circular economy frameworks. The analysis reveals that synergistic pretreatment. Approaches—including combined thermal–alkaline and biological techniques—can enhance methane yield by 30%–60% for lignocellulosic and food waste substrates. The co‐digestion of organic wastes with microalgae improves process stability, nutrient recycling, and biogas productivity by up to 40%, while supporting carbon sequestration. Regarding upgrading, nanocellulose‐ and MOF‐based membranes offer higher methane purity (>98%) and reduced energy input compared to conventional systems. Despite these technological advancements, challenges persist due to inconsistent operational parameters, inadequate feedstock management, weak policy implementation, and limited public participation. The review emphasizes that standardized monitoring frameworks, decentralized biogas models, and integration with SDG‐driven policies are essential to realizing the full potential of AD systems. Overall, the review concludes that targeted innovation and policy synergy can make biogas technology a key enabler of SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action).