The inability of graphite to store Na+ ions at low potentials necessitates the use of disordered materials as anodes in Na-ion batteries, wherein complex degradative pathways limit cycle life. This study delineates the influence of synthetically induced material disorder in facilitating stable, high-rate sodium ion storage in a series of anatase titania analogs. The degree of disorder in the titanium oxyhydroxide (TiO x (OH) y -T anneal) materials, synthesized via a simple, solution-phase sol-gel protocol employing a Ti(III) precursor, is systematically varied by tuning the postsynthesis annealing temperature (T anneal). The variable disorder in TiO x (OH) y -T anneal stems from the distribution of residual Ti(III), N-dopants, and O-vacancies in these materials as a function of T anneal, as mapped by XPS, bulk-sensitive resonance inelastic X-ray scattering (RIXS), and EPR spectroscopy. In terms of electrochemical performance, we observe a unique, nonmonotonic dependence of Na+ storage capacity on the synthetically induced degree of disorder in TiO x (OH) y -T anneal, instead of a simple inverse size dependence, as normally seen in the case of fully crystalline anatase materials. Further in contrast to crystalline anatase, the disorder in TiO x (OH) y -T anneal opens up a bulk mode of Na+ storage instead of a purely surface confined or capacitive mode, as determined by scan rate-dependent cyclic voltammetry. TiO x (OH) y -400, with an intermediary amount of disorder in the prepared series of materials, shows the highest specific capacity (similar to 188 mAh/g) and cycling stability for Na+ storage at elevated rates. These results are in agreement with our first-principles simulated data exhibiting a higher number of possible sites that Na+ can occupy at an intermediate degree of disorder in a TiO x (OH) y structure.
Modern supply chains are increasingly expected to meet ambitious sustainability targets, yet they often suffer from limited visibility into upstream relationships, environmental risks, and ethical sourcing practices. This paper presents an artificial intelligence (AI)-based approach for supporting sustainability-oriented decision-making in supply chains through knowledge graph completion and link prediction. We construct a multi-relational supply chain knowledge graph that captures heterogeneous entities and relationships, including suppliers, products, certifications, and locations, and apply graph neural networks to infer missing links and sustainability-related attributes. By enabling reasoning over incomplete and sparse data, the proposed approach supports feasibility-oriented decisions, such as identifying alternative supplier relationships and assessing sustainability alignment across multi-tier networks. Building on recent advances in knowledge graph reasoning and heterogeneous graph learning, the framework integrates relational structure with inductive learning to provide interpretable recommendations under uncertainty. The approach is evaluated on two real-world supply chain datasets, demonstrating its applicability in complex, data-sparse settings. The results indicate that graph-based AI can provide a practical foundation for transparent and sustainability-aware supply chain decision support.
PurposeThis study examined the effect of strategic resource allocation on competitiveness of SMEs, accounting for the moderating role of environmental dynamism.Design/methodology/approachUsing a two-stage sampling approach, this study used a dataset of 219 respondents from across the six geopolitical zones country. Primary data was employed using structured questionnaire. Partial least square structural equation model was used to analyse the data with the aid of SmartPLS 3.3.FindingsThe study found that strategic resource allocation has significant effect on agro-allied firms' competitiveness, and environmental dynamism also showed a significant effect on competitiveness. The study also found that environmental dynamism moderates the direct link between strategic resource allocation and agro-allied firms' competitiveness. The study concludes that agro-allied SMEs that desire improved competitiveness should ensure that resources are strategically allocated irrespective of the dynamic nature of their operating environment.Originality/valueThis study adds a new layer to the resource-based view theory as it validates it and also extends that possession of resources should be balanced with effective resource orchestration to achieve competitive advantage. This study addressed the question of how strategic resource allocation can drive competitiveness in firms while also answering the question of whether environmental dynamism is a major boundary condition that can affect this relation. Contrary to the norm in most studies that environmental dynamism could affect performance negatively, the study extends the literature on how a firm can gain competitive performance through environmental dynamism.
Schizophrenia, a severe neurodevelopmental disorder, is influenced by oxidative stress, neuroinflammation, apoptosis, and cholinergic system dysfunction. Given the multifactorial nature of schizophrenia, targeting multiple pathological pathways simultaneously may offer superior therapeutic benefits compared to single-target approaches, particularly for refractory symptoms. Eugenol, a natural phenylpropanoid in clove oil, was investigated for potential therapeutic effects on schizophrenia-like behaviors in mice. Male C57BL/6 mice (n = 10 per group) were administered ketamine (30 mg/kg, i.p.) for 7 consecutive days to induce schizophrenia-like phenotypes. Eugenol (50 and 100 mg/kg, p.o.) was co-administered daily with ketamine. Behavioral assessments, including locomotor activity, pre-pulse inhibition (PPI), novel object recognition (NOR), and social interaction, were performed. Following behavioral tests, brain tissues (prefrontal cortex and hippocampus) were collected for biochemical analyses. Oxidative stress markers (MDA, GSH, SOD, CAT), apoptotic markers (Caspase-3, Bax, Bcl-2), neuroinflammatory cytokines (TNF-α, IL-1β, IL-6), and acetylcholinesterase (AChE) activity were quantified using spectrophotometric and ELISA methods. Ketamine administration significantly induced hyperactivity (p < 0.001), impaired PPI (p < 0.01), reduced NOR (p < 0.001), and decreased social interaction (p < 0.001). Biochemically, ketamine increased MDA, Caspase-3, Bax, TNF-α, IL-1β, IL-6, and AChE activity (p < 0.05 to p < 0.001), while decreasing GSH, SOD, CAT, and Bcl-2 in both prefrontal cortex and hippocampus (p < 0.05–p < 0.001). Eugenol treatment, particularly at 100 mg/kg, significantly ameliorated these behavioral deficits and biochemical alterations (all p < 0.05 vs. ketamine group). Eugenol reversed ketamine-induced oxidative stress by reducing lipid peroxidation and enhancing antioxidant defenses. It attenuated apoptosis by modulating Caspase-3, Bax, and Bcl-2 levels. Furthermore, eugenol suppressed neuroinflammation by reducing pro-inflammatory cytokine levels and restored cholinergic balance by inhibiting AChE activity. These findings suggest that eugenol holds significant promise as a potential adjuvant therapeutic agent for schizophrenia, attributable to its multifaceted neuroprotective effects against oxidative stress, apoptosis, neuroinflammation, and cholinergic dysfunction.
A mechanism-data fusion method is reported for the quantitative prediction of transmitted wavefront error (TWE) in planar optical components subjected to multi-point adhesive bonding. In this framework, TWE sources are explicitly decoupled into two competing physical mechanisms: refractive index variations governed by the photoelastic effect and surface deformations driven by the Poisson effect. To address the measurement inaccessibility of the rear surface under loading, a bounding analysis strategy is implemented to establish physically consistent theoretical prediction intervals. Experimental results show that, for the investigated N-BK7 optical component and loading conditions, the TWE contribution associated with Poisson-induced surface deformation is approximately one order of magnitude larger than that associated with the photoelastic effect. This result quantitatively clarifies the relative importance of these competing mechanisms in the assembly-induced wavefront error. The predictive model is validated through interferometric measurements, where measured wavefront RMS values generally fall within the predicted bounds, with a mean relative prediction error of approximately 10%. These results indicate that the proposed framework can quantitatively capture the dominant physical mechanisms governing assembly-induced wavefront error and offer a mechanism-informed basis for the predictive analysis of bonded opto-mechanical assemblies.
