Sigma Research (United States)

Purpose This study aims to examine how urban Indian Generation Z (Gen Z) construct expectations of corporate environmental responsibility (CER) in the context of lived environmental degradation. It explores how everyday experiences of pollution shape corporate expectations, what motivates or constrains Gen Z’s engagement with sustainable behaviours and how social, digital and educational contexts influence their interpretation and evaluation of CER. Design/methodology/approach The study adopts a qualitative, exploratory design based on semi-structured interviews with 48 urban Gen Z participants (aged 21–26), including postgraduate students and early-career professionals. Data were analysed using reflexive thematic analysis, supported by NVivo 14. Stakeholder theory, the theory of planned behaviour (TPB) and the value-belief-norm (VBN) framework were used as sensitising frameworks to inform interpretation, while themes were inductively derived from the data. Findings The findings reveal strong expectations for authenticity, transparency and verifiable corporate environmental action, with participants expressing scepticism towards symbolic or compliance-driven ESG disclosures. Moral responsibility and peer influence emerged as key motivators of sustainable engagement, while affordability, accessibility and convenience constrained behavioural enactment. Social and digital environments heightened scrutiny of corporate claims, positioning Gen Z as active evaluators of CER within digitally mediated stakeholder networks. Research limitations/implications The study focuses on urban, educated and digitally connected Gen Z participants, which may limit transferability to less-connected populations. Future research could adopt longitudinal and comparative designs across regions and economies to examine how expectations evolve over time. Practical implications For organisations, the findings underscore the need to move beyond symbolic ESG communication towards transparent, measurable and locally responsive environmental actions. Digital credibility and consistency are critical for engaging younger stakeholder groups. Social implications Gen Z’s expectations reflect a shift towards collective accountability and heightened scrutiny of corporate environmental conduct. Their evaluative role within digital ecosystems has implications for broader societal debates on environmental governance and CER. Originality/value By shifting focus from consumption intentions to expectations of CER, this study extends stakeholder and behavioural research through a context-rich qualitative account from an emerging economy. It demonstrates how lived environmental exposure and digital information environments reshape stakeholder legitimacy judgements in ways underexplored in existing models.

This paper presents a low-power and high-stability 12T SRAM cell designed using 18nm FinFET technologies and compared with conventional 6T, 8T, and 10T architectures. The proposed design employs read-path isolation, stacked transistors, and a leakage-controlled sleep mechanism to minimize dynamic and static power while improving read/write stability. Simulations were carried out in Cadence Virtuoso using PTM BSIM-CMG models under varied PVT conditions (0.8–1.0 V, –25 °C to 50 °C). The results show significant enhancements in performance metrics, achieving read SNM of 205 mV, hold SNM of 245 mV, and read/write delays of 52ps and 61ps, respectively. The proposed design demonstrates up to 60% reduction in read power and 28% overall power savings compared with traditional SRAM cells. The layout area of 0.61 µm² corresponds to an array density of 1.64 Mbits/mm², only a 1.25× overhead relative to a 6T cell. Monte Carlo simulations with 200 samples confirmed less than ±10% variation in power, ensuring robust and energy-efficient operation. The proposed 12T FinFET SRAM is thus a strong candidate for next-generation low-power, high-density memory applications in advanced VLSI systems.

Alzheimer's Disease (AD) affect millions worldwide and is becoming more prevalent as the population ages. Current understanding of AD pathology centers around monitoring neurodegenerative biomarkers in blood. In recent years there specifically has been a growing interest in measuring the ratio of Aβ42/ Aβ40 but these low abundant biomarkers require higher sensitivity immunoassays for detection in blood samples. We developed and verified a novel SMC® Aβ42/Aβ40 high sensitivity immunoassay kit that can accurately quantitate these biomarkers simultaneously in human plasma samples using FemtoQuest™, the next generation SMC® instrument. Here we report the results from testing plasma samples from healthy and AD patients using the new immunoassay and instrument. First, SMC® Aβ42 and Aβ40 high sensitivity singleplex immunoassays were used to confirm detectable levels of Aβ42 and Aβ40 in plasma. Each singleplex assay identified a clear differentiation between healthy and AD samples with p-value of <0.05. The same set of healthy and AD samples was subsequently tested with the multiplex SMC® Aβ42/Aβ40 high sensitivity immunoassay to simultaneously measure both biomarkers. The Aβ42/Aβ40 ratio resulted in a stronger differentiation of healthy vs AD than individual biomarkers. This study demonstrates the value of using multiplex high sensitivity technology to evaluate multiple biomarkers in the same sample. The SMC® Aβ42/Aβ40 high sensitivity immunoassay kit serves as a powerful non-invasive biomarker tool for monitoring the progression of neurodegenerative diseases such as Alzheimer's disease while saving time and sample. Further studies into other combinations of AD biomarkers will determine if their ratios would also serve as valuable measures of AD related neurodegeneration.

The demand for intelligent, safe, and regulated infrastructures has increased due to the growing use of digital healthcare commerce platforms. This research proposes a novel Artificial Intelligence (AI) supported cloud assisted healthcare E-commerce framework with privacy and proactive protection in multiple healthcare transactions. The work incorporated Federated Adaptive Deep Learning with Differential Privacy (FADL-DP) for making smart decision, Blockchain ledger for data integrity and Convolutional Neural Network Gated Recurrent Unit (CNN-GRU) for intrusion detection. The combination of Federated Learning (FL), differential privacy and adaptive optimization improve accuracy and supports the model to train across dispersed hospitals and pharmacies without disclosure of private medical information. The adoption of smart contract with Blockchain ledger enduringly stores the records with tamperproof integrity. The CNN-GRU based Intrusion Detection System (IDS), continuously examines the patterns for spotting of irregularities and also identify cyberattacks instantly. This makes the proposed work a flexible, robust and intelligent healthcare commerce atmosphere that secures clinical data and makes digital health transactions efficient and reliable.

UpadhayaySimultaneous measurement of GLIM and LINA in a synthetic mixture had been accomplished by validation and development of a UV spectrophotometric technique. Technique utilized overlain zero-order spectra with absorbance measurements at 242nm for Glimepiride and 225nm for Linagliptin in methanol. Linearity had been observed in range of 1 to 8µg/mL for Glimepiride and 5 to 40µg/mL for Linagliptin. Method showed excellent precision with %RSD values below 1% for both repeatability and interday precision. Recovery studies confirmed 100% recovery at selected concentrations, indicating accuracy. LOQ and LOD values had been discovered to be 0.28 to 0.30µg/ml and 0.38 to 0.42µg/ml, correspondingly. Assay outcomes of 97.69% for Glimepiride and 101.84% for Linagliptin confirmed the method’s applicability for analysis in synthetic mixtures.

The increasing complexity of contemporary organizational environments has demanded more sophisticated approaches to Business Process Management (BPM). This study presents a critical and in-depth analysis of the evolution of process modeling, investigating emerging synergies between Business Process Model and Notation (BPMN), Process Mining techniques, and advances in Artificial Intelligence (AI). Through a mixed methodology that combines systematic literature review, bibliometric analysis, and multiple case studies, this research examines 000 scientific articles published between 0000 and 0000 in high-impact journals (JCR Q0 and Q0). The results reveal that the integration of these technologies not only optimizes process representation and automation but fundamentally transforms the nature of organizational governance, enabling predictive, adaptive, and evidence-based management. The analysis identifies five critical governance dimensions: (1) strategic-operational alignment, (2) capacity building and change management, (3) integrated technological architecture, (4) intelligent performance metrics, and (5) sustainability and regulatory compliance. The study contributes theoretically by proposing the Integrated Framework for Intelligent Process Governance (IFIPG), which articulates the technological, organizational, and strategic dimensions necessary for successful implementation. Practically, it offers specific guidelines for organizations seeking to overcome challenges of resistance to change, integration complexity, and value measurement. It is concluded that the future of BPM lies in the intelligent convergence of these technologies, mediated by robust governance that balances technological innovation with organizational sustainability.

Background: Atrial fibrillation (AF) is a prevalent condition impacting millions globally. It poses significant thrombotic risks, ranging from stroke to sudden cardiac death, thus understanding current anticoagulant treatment patterns is critical to optimizing outcomes. Purpose: Study objectives were to gain insight into current anticoagulant treatment landscape and patient characteristics across several AF cohorts, classified by treatment options: no treatment, warfarin, and direct oral anticoagulants (DOACs) as per US prescribing information (standard dose (SD) per label, SD not per label, low dose (LD) per label and LD not per label). Clinical outcomes were compared between patients using DOACs as SD per label and LD not per label overall and by gender. Methods: This was a retrospective, observational study utilizing Optum’s de-identified Market Clarity Data (Optum ® Market Clarity, from January 2017 to September 2024. It only included patients eligible for OAC according to 2019 AHA/ACC/HRS guidelines. A 180-day window was used to assess treatment patterns. Propensity Score Matching including Mahalanobis distance method were used to balance patient characteristics. Cox regression analysis was conducted to calculate hazard ratios for outcomes including stroke, major bleeding (MB), and major adverse cardiac events (MACE, composite of stroke, myocardial infarction, and cardiovascular-related death). Results: After applying inclusion criteria, the study included 701,536 patients, of whom 37% received DOACs, 11% warfarin, and 52% no OAC therapy. Further exclusions (prior OAC use and invalid bodyweight/serum creatinine) in the DOAC group resulted in a final sample of 48,901 patients. Among these, 78% received SD DOACs per label, 4% SD not per label, 9% LD per label, and 9% LD not per label. These trends were consistent across genders. Overall, patients with LD DOAC not per label were associated with higher rates of MACE, similar rates of stroke/SE and similar rates of MB vs. SD patients. Higher rates of MACE with LD not per label were evident in both males and females. Conclusion: More than half of AF patients in the US, regardless of gender, who were clinically eligible did not receive anticoagulants. Although some patients were underdosed with DOACs, they did not have a lower risk of bleeding and their MACE event tended to be worse. These findings suggest the need for therapies that reduce thrombosis while minimizing bleeding risk to optimize patient outcomes.

In the rapidly evolving digital landscape, ensuring trust, transparency, and security in online collaborations remains a significant challenge, particularly for innovators and experts engaged in knowledge exchange. The proposed SPARK-IT platform leverages blockchain, AI-driven matchmaking, decentralized identity management, and tokenomics to foster a secure innovation ecosystem. By utilizing a permissioned blockchain, smart contracts, and decentralized storage, SPARK-IT ensures intellectual property protection, traceability of contributions, and non-repudiation in mentor-innovator engagements.This paper presents the technical architecture of the platform, demonstrating how distributed ledger technology and AI-driven methodologies can establish a human-centered, sustainable and trustworthy online innovation ecosystem. By bridging academia, startups, and industry, SPARK-IT redefines digital trust and collaboration in the innovation economy.

Model-Based Development (MBD) has become an expectation in several industries to support testing & verification during system design. Specifically, MBD reduces the need for physical resources and promotes re-usable digital development frameworks. At the same time, there are still several challenges that exist with using (primarily graphical) MBD tools in industrial settings which are hindering a mature integration for collaboration in Cyber-Physical Systems (CPSs) design and development. In this paper we provide an experience report about using Simulink for code generation & automated testing in industrial settings and discuss the implications for wider model-based adoption. The paper also supports these experiences through results from an internal survey at a company with 23 participants in relation to user experiences of using Simulink. The survey findings highlight concerns in collaboration, particularly from a software engineering perspective.

This study aimed to develop gemcitabine-loaded nanocapsules for pancreatic cancer treatment, optimizing their formulation before evaluating them through in vivo studies. The researchers selected gemcitabine as the model drug because it has established clinical relevance and known pharmacokinetic shortfalls (short half-life and poor bioavailability) with toxic dose limits which suit the evaluation of nanoparticle drug delivery systems. The researchers conducted a 32 factorial design to optimize the formulation through adjustments of PLGA concentration and Tween 80 concentration. The optimal characteristics of F5 among nine formulations included particles of 160 ± 3nm with 87 ± 2% encapsulation efficiency and - 27.8 ± 1.2mV zeta potential. The in vitro drug release testing alongside pharmacokinetic results established that nanoparticle gemcitabine caused extended drug delivery and dramatically better bioavailability in addition to achieving longer systemic circulation than free gemcitabine alone. Evaluation of biodistribution revealed that the product displayed tumor-targeting property and had improved antitumor effect and less side effect compared with the other groups. The findings demonstrate that gemcitabine works as an ideal model chemotherapy drug to measure nanocarrier delivery platforms for treating pancreatic cancer solid tumors.