Constructor University

Environmental, social, and governance (ESG) reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global sustainability goals, and limited stakeholder usability. Numerous studies prove that ontology-based solutions can address several challenges that occur during ESG reporting activities. Although semantic technologies offer valuable benefits for ESG reporting, their utilization in this field remains constrained. Most ontology-based solutions remain in developmental stages, and they are not broadly utilized since organizations lack an understanding of how these tools would help address their reporting problems. This study performs a systematic literature review (SLR) that investigates 19 peer-reviewed studies obtained from Scopus and Web of Science under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) standards. The SLR identifies critical gaps: (1) existing ontology-driven solutions can address key problems in current ESG reporting; (2) quantitative evaluation methods are rarely integrated with semantic tools, limiting actionable insights; and (3) alignment with evolving standards like the Sustainable Development Goals (SDGs) remains superficial. Based on the SLR insights, this research develops a novel framework through SLR findings by combining ontology-driven methods with quantitative assessment techniques. The framework achieves standardization of various reporting standards through an ESG ontology system that maps essential concepts to build an extensive taxonomy. SDG targets become mutually compatible through established SDG ontologies to allow businesses to measure their activities against international sustainability goals. Fuzzy Multi-Criteria Decision-Making (MCDM) techniques used in combination with an ESG maturity model create quantitative measures to assess ESG performance. The method produces measurable performance indicators that are supported by clear semantic links that allow valid benchmark assessments combined with better data unification and improved decision-making capabilities. The research creates operational frameworks that enable ESG information interoperability, which advance sustainability governance innovation and guide ESG ontology transformations.

Piezocatalytic hydrogen evolution enables the conversion of mechanical energy into chemical fuels, but its efficiency is constrained by a trade-off between piezoelectric polarization and electronic conductivity. Strong piezoelectric polarization is essential for sufficient driving force, yet highly polar materials typically suffer from poor conductivity, which limits bulk-to-surface charge transport. Conversely, enhancing conductivity often compromises piezoelectric performance, resulting in a bottleneck in piezocatalysis. Herein, we decouple piezoelectricity and conductivity using atomically dispersed nickel single atoms on amino-functionalized UiO-66 (Ni SAs@UiO-66-NH2). Introducing polar amino groups and asymmetric Ni─N coordination significantly enhances the piezoelectric response, increasing the piezoelectric coefficient d33 from 48 to 242 pm V-1. Simultaneously, hydrogen adsorption at Ni sites under mechanical stress triggers a pressure-induced semiconductor-to-metal transition, creating transient metallic conduction pathways that facilitate efficient electron extraction without sacrificing bulk polarization. As a result, hydrogen adsorption sites shift from framework carbons to Ni centers, yielding near-optimal H* adsorption energetics (ΔGH * approximately 0.12 eV at 100 MPa), and enabling rapid polarization-driven hydrogen evolution. Consequently, the Ni SAs@UiO-66-NH2 catalyst achieves exceptional hydrogen evolution rate of 1871 µmol g-1 h-1 in deionized water and 17613 µmol g-1 h-1 in methanol-containing media, surpassing reported MOF-based piezocatalysts and competing with leading photo-piezocatalytic and photocatalytic systems.

As markets become increasingly digitalised, understanding the emotional and cognitive drivers of consumer behaviour is essential for developing responsive and targeted marketing strategies. This study contributes to the development of Customer Digital Twins (CDTs) by integrating neuromarketing techniques with social media analytics, positioning the work at the intersection of data science and behavioural psychology. The proposed framework combines electroencephalography (EEG) recordings with sentiment analysis of user-generated content to capture complementary dimensions of consumer emotion and decision making. An experimental design was implemented in which participants’ emotional responses were monitored via EEG during an online shopping task, followed by an ethical priming intervention and sentiment-based analysis of post-task responses. The study focuses on a fast-fashion e-commerce setting in which ethically framed information is introduced to examine how value-laden cues reshape cognitive and emotional states. The results show that EEG-derived indicators, including engagement, workload, and emotional valence, and text-based sentiment measures provide partially complementary views of consumer states, while also highlighting significant challenges in their direct integration. The joint use of these modalities offers richer descriptive insight into customer motivation and behaviour, but simple fusion strategies exhibit limited predictive power under real-world, asynchronous conditions. The findings identify both technical and ethical challenges associated with multimodal consumer modelling and establish a foundation for future work on temporally aligned and dynamically adaptive customer digital twin architectures. A proof-of-concept modelling exercise further explores the feasibility of using combined neuro-sentiment features to predict valence-related indices, serving as an initial diagnostic decision analytics layer rather than a fully realised digital twin. • Integrate brain signal data and social media text to construct ethical customer digital twins. • Model emotional persistence to enhance the analysis of customer decision behaviour. • Predict emotional states using fused brain and sentiment features. • Develop a reproducible analytics pipeline for customer digital twin construction. • Support ethical marketing decisions through emotion-aware customer modelling.

IntroductionNeuropsychiatric systemic lupus erythematosus (NPSLE) is a major clinical challenge, characterized by heterogeneous manifestations and the absence of reliable biomarkers. The mechanisms linking systemic autoimmunity to neuronal injury and neuropsychiatric symptoms remain poorly understood.MethodsUsing the lupus-prone MRL/Lpr mouse model, we integrated systemic cytokine profiling, plasma neurofilament light chain (NfL), region-specific CNS cytokine mRNA mapping, cortical metabolomics, and behavioral analyses to dissect immune–metabolic–neuronal interactions.ResultsInflammation was dominated by a Th1 cytokine program, with interferon-gamma emerging as a prominent component of the inflammatory profile. Composite cytokine scores correlated strongly with plasma NfL, establishing an immune–neuronal injury axis. Region-resolved analyses revealed distinct CNS cytokine signatures, including selective hippocampal loss of interleukin-10 and IFNγ-dominated responses in the frontal cortex. Cortical metabolomics demonstrated diversion of tryptophan metabolism away from serotonin toward the kynurenine pathway, with increased quinolinic acid/kynurenic acid (QA/KA) ratio and upregulation of indoleamine 2,3-dioxygenase-1 (Ido1) and kynurenine 3-monooxygenase (Kmo). NfL levels were negatively associated with serotonin and positively with 3-hydroxykynurenine and QA/KA, linking axonal damage to an excitotoxic metabolic environment. Importantly, cortical serotonin levels correlated with exploratory behavior, linking serotonergic depletion to anxiety-like phenotypes.DiscussionTogether, these results support an associative framework in which systemic IFNγ levels are linked to cortical metabolic reprogramming and neuronal vulnerability, bridging peripheral immune activation with serotonergic depletion, melatonin loss, axonal injury, and behavioral dysfunction. Translationally, combined blood-based monitoring of IFNγ, NfL, and kynurenine metabolites may represent a candidate biomarker framework for NPSLE. However, validation in independent patient cohorts will be essential, and therapeutic modulation of IDO1/KMO or serotonergic pathways remains an avenue for future investigation.

Age-related declines in dynamic balance and cognitive control increase fall risk in older adults (OA). Non-invasive brain stimulation, such as anodal transcranial direct current stimulation (a-tDCS), may enhance training outcomes. However, it remains unclear whether stimulation over motor or prefrontal regions is more effective for improving dynamic balance training (DBT) in OA. To compare the effects of a-tDCS over the primary motor cortex (M1) vs. dorsolateral prefrontal cortex (DLPFC) on DBT performance and transfer to static balance in OA, and to explore whether baseline resting-state EEG predicts training outcomes. In a randomized crossover design, 18 healthy OA completed DBT training during three stimulation conditions (M1, DLPFC, and sham). Static balance performance was assessed before and after training. Resting-state EEG was recorded to assess predictors of training success. DBT performance improved significantly more with DLPFC stimulation than with M1 or sham. Negative transfer effects were observed on untrained static balance tasks. Lower baseline alpha power predicted stronger training gains with DLPFC stimulation but weaker gains with M1 stimulation. DLPFC-targeted a-tDCS enhances DBT in OA more effectively than M1 stimulation. Baseline oscillatory brain activity may inform individualized stimulation protocols to optimize balance training outcomes in OA.

Affine frequency division multiplexing (AFDM) is an emerging waveform candidate for future sixth-generation (6G) systems offering a range of promising features, such as enhanced robustness in heterogeneous and high-mobility environments, as well as inherent suitability for integrated sensing and communications (ISAC) applications. In addition, unlike other candidates such as orthogonal time-frequency space (OTFS) modulation, AFDM provides several unique advantages that strengthen its relevance to practical deployment and standardization in 6G. Notably, as a natural generalization of orthogonal frequency division multiplexing (OFDM), strong backward compatibility with existing conventional systems is guaranteed, while also offering novel possibilities in waveform design, for example to enable physical-layer security through its inherent chirp parametrization. In all, this article provides an overview of AFDM, emphasizing its suitability as a candidate waveform for 6G standardization. First, we provide a concise introduction to the fundamental properties and unique characteristics of AFDM, followed by highlights of its advantageous features, and finally a discussion of its potential and challenges in 6G standardization efforts and representative requirements.