To develop energy-efficient devices for a sustainable future, this study investigated thulium-doped lanthanum zirconate (La2Zr2O7) as a phosphor for solid-state lighting. The material was synthesized via the sol–gel technique. X-ray diffraction (XRD) analysis was used to confirm the phosphor phase, with no impurity peaks observed. The phosphor is single-phase, has a pyrochlore cubic formation, and the crystallite size was found to be around 36 nm. Fourier transform infrared (FTIR) spectroscopy was used to identify the various bonds in the host lattice. The energy bandgap of the optimized phosphor was calculated to be 4.87 eV using the diffuse reflectance data by applying the Kubelka–Munk function. Photoluminescence (PL) emission and excitation studies were also conducted on these phosphors. A sharp blue luminescence, centered at 460 nm under 360 nm excitation, was observed, originating from the 1D2 → 3F4 transition. The intensity of this luminescence increased up to 0.05 mol of Tm3+ ions in the host lattice. Dipole–dipole interaction was confirmed as the interaction between the activator ions, leading to the quenching of the concentration. The Commission Internationale de l’Eclairage (CIE) coordinates of the prepared phosphors lay in the blue region, with the optimized sample exhibiting the highest color purity of around 76
The rapid adoption of artificial intelligence (AI) in the financial sector has intensified concerns regarding responsible use, governance, and long-term capability development. While prior studies examine AI adoption, regulation, ethical principles, or performance outcomes, limited empirical research explains how banking and regulated financial service organizations systematically build responsible AI capabilities as part of their strategic management processes. In particular, existing studies do not structurally model the interdependencies among AI management factors nor link them to dynamic organizational capabilities. Addressing this gap, this study presents an original empirical investigation of responsible AI management in commercial banks and regulated financial institutions engaged in risk management, compliance, credit assessment, and customer-facing financial services. The study makes an original theoretical contribution by integrating Fuzzy Interpretive Structural Modelling (FISM) with Dynamic Capabilities Theory (DCT) to develop a new, theory-informed capability-building framework. A three-phase mixed-methods design was employed. In the first phase, open-ended questionnaires and in-depth interviews with 26 domain experts, along with Nominal Group Technique (NGT) sessions involving 11 experts, identified 14 critical factors influencing responsible AI management. In the second phase, FISM was applied to model the hierarchical and contextual interrelationships among these factors. In the final phase, follow-up interviews mapped these factors to the sensing, seizing, and transforming dimensions of DCT. The findings generate new empirical insights demonstrating that responsible AI in banking functions as a second-order dynamic capability extending beyond technological readiness. While financial institutions possess foundational AI resources, they lack specialized skills and governance structures required for responsible AI integration. Ethical governance mechanisms, workforce development, legacy system alignment, and technology partnerships emerge as key driving enablers. The study offers an original, empirically grounded framework that advances responsible AI scholarship by linking structural factor modelling with dynamic capability development, providing a structured roadmap for financial managers and policymakers seeking to align AI innovation with risk management and ethical accountability.
Beetroot (Beta vulgaris) is a highly nutritious and antioxidant-rich root crop that is used for preparation of several food items i.e. candy, paste, halwa etc., as well as natural colorant. The main objective of this research work was to standardize the process for preparation of beetroot candy (BRC) using jaggery as a sweetening agent. The samples were analyzed for sensory characteristics [body and texture (8.62±0.02), flavor (8.56±0.03), color and appearance (8.67±0.03), sweetness (8.46±0.03) and overall acceptability (8.66±0.03)], chemical composition [fat (0.20±0.00
Magic state distillation is a leading but costly approach to fault-tolerant quantum computation, and it is important to explore all possible ways of minimizing its overhead cost. The number of ancillas required to produce a magic state within a target error rate epsilon is O[ln(gamma) (epsilon(-1))] where gamma is known as the yield parameter. Hastings and Haah derived a family of distillation protocols with sublogarithmic overhead (i.e., gamma < 1) based on punctured Reed-Muller codes. Building on work by Campbell et al. and Krishna-Tillich, we generalize the Hastings-Haah construction to qudits of arbitrary prime dimension p. Although existing qubit-based constructions achieve sublogarithmic overhead, these advantages only emerge at block sizes that are prohibitively large for practical implementation. In contrast, using an analytically tractable puncturing scheme, we show that the block size required to achieve sublogarithmic overhead decreases drastically as p increases for our qudit construction, with yield parameter approaching 1/ln p as p ->infinity. We also perform a small computational search for optimal puncture locations, which results in several interesting triorthogonal codes, including a [[519, 106, 5]](5) code with gamma = 0.99. These results demonstrate that qudit-based distillation routines allow sublogarithmic overheads to be realized at much smaller and more physically viable block lengths.
A number of technological advancements have made bioremediation an emerging and innovative technology, including its economic viability, increased competence, and natural environment friendliness. The efficiency, scalability, and specificity of conventional physical, chemical, and biological remediation techniques are still limited, despite their partial success. Recent developments in CRISPR-based genome engineering have made it possible to precisely manipulate metal transporters, detoxification enzymes, and stress-response pathways in microorganisms and plants, opening up new possibilities to improve bioremediation. This review offers a thorough and integrated examination of enzyme engineering, biosensing systems, microbial bioremediation, and CRISPR-enabled phytoremediation. This work is novel because it presents a unified roadmap for next-generation bioremediation technologies by integrating CRISPR editing with multi-omics, synthetic biology, and emerging CRISPR-based biosensors. We also go over ecological risks, current difficulties, legal issues, and potential field deployment scenarios in the future. These revelations collectively demonstrate the revolutionary potential of CRISPR in creating highly effective, sustainable, and scalable remedies for heavy metal pollution.
