This paper proposes an adaptive Overcurrent (OC) relay protection scheme that combines load forecasting and clustering-based operating state identification to dynamically adjust relay settings. A Long Short-Term Memory (LSTM) model predicts feeder current, allowing the system to proactively select appropriate relay setting groups for different load conditions. The method is validated on the IEEE 33-bus distribution network. Results show that the forecasting model achieves an Mean Absolute Percentage Error (MAPE) of 2.94 %, while the relay settings derived from predicted loads closely match those obtained from actual data. The difference in operating time is negligible between forecast-based and actual coordination and a safety protection mechanism is employed to deal with the forecast or classification errors by switching to a safe backup protection group. These results demonstrate the effectiveness of the proposed approach in maintaining reliable protection performance under changing operating conditions through SCADA, and are activated using IEC 61,850 protocols and GOOSE-based group switching mechanisms. The time-domain analysis shows a worst-case timing deviation below 41 ms, well within the 300 ms Coordination Time Interval (CTI) requirement.
Metal complexes constitute a key component of modern chemistry due to their structural diversity, adjustable electronic properties, and broad applicability in catalysis, materials science, and biomedicine. In recent years, artificial intelligence (AI) has increasingly contributed to accelerating the discovery and development of metal complexes by supporting several stages of research, including molecular design, synthesis, characterization, and functional optimization. These advances have improved the efficiency of discovery processes while also enhancing sustainability and the reliability of predictive models. This review first considers traditional coordination chemistry approaches alongside recently developed environmentally sustainable methods for the synthesis of metal complexes. Particular attention is given to ongoing challenges associated with reaction optimization, scalability, and reproducibility. In addition to experimental methodologies, machine learning methods are increasingly employed to complement conventional strategies by enabling a rapid estimate of physicochemical properties and catalytic activity. In the biomedical field, particular focus is placed on platinum- and ruthenium-based anticancer complexes. In this area, AI-assisted drug discovery strategies, computational molecular design, and predictive modeling have supported the development of next-generation metal-based therapeutics characterized by improved selectivity and reduced toxicity. The review also examines catalytic applications of metal complexes, including cross-coupling reactions, hydrogenation, transfer hydrogenation, electrocatalysis, photoredox catalysis, carbon dioxide activation, and asymmetric catalysis. Recent developments in hybrid photoelectrodes, supported catalytic systems, redox-active ligands, and rational molecular catalyst design are also considered. Furthermore, emerging AI-driven approaches for catalyst discovery, such as generative models, inverse design strategies, closed-loop automated experimentation, and high-throughput virtual screening, are presented as effective tools for accelerating catalyst development. Finally, the review discusses current limitations associated with AI-based methodologies, particularly those related to data availability, model interpretability, and generalizability. Future directions emphasize the importance of integrating explainable AI with mechanistic understanding in coordination chemistry in order to improve the reliability and interpretability of catalyst and drug discovery processes.
Phthalic acid esters (PAEs) are persistent, toxic endocrine disruptors commonly found in plastics and bottled water, necessitating sensitive analytical monitoring. This study developed a magnetic solid-phase extraction (MSPE) method combined with HPLC-DAD, using treated cypress wood (CW) as a sorbent to extract four PAEs: dimethyl phthalate (DMP), diethyl phthalate (DEP), benzyl butyl phthalate (BBP), and diisobutyl phthalate (DIBP). The wood underwent organic solvent washing, pyrolysis at various temperatures, magnetite loading, and different treatment sequences. The optimal adsorbent, MCW-400 °C-Pet.ether(1:7), was prepared by pyrolysis at 400 °C, washing with petroleum ether, and magnetization at a 1:7 magnetite-to-wood mass ratio, which enhanced porosity, exposed active sites, and improved magnetic separability. The MSPE-HPLC-DAD method was optimized for pH, adsorbent mass, and contact time, achieving low detection limits (0.027–0.098 mg L⁻¹), and high correlation coefficients (R² > 0.986). When applied to bottled water samples, the method produced recovery rates of 67.2–99.0
Purpose This study explains how universities' marketing capabilities translate into measurable strategic performance and tests whether intellectual capital disclosure, across human, structural, and relational dimensions, conditions amplify that relationship in an emerging economy higher education context. Design/methodology/approach We compile a multi-source panel for public and private Jordanian universities, aligning validated measures of marketing capabilities with independently assessed levels of intellectual capital disclosure. Strategic performance is captured through recognised indicators of university value creation. Hypotheses are examined via moderated models with interaction terms, university and year effects, clustered standard errors, and robustness checks. Findings Marketing capabilities are positively and significantly associated with strategic performance. Intellectual capital disclosure strengthens this association, with structural disclosure exerting the largest conditioning effect, followed by human and then relational disclosure. Universities that combine dynamic marketing capabilities with mature, verifiable disclosure architectures convert the marketing narrative more reliably into observable value, admissions, research funding, and partnerships. Research limitations/implications Findings are bounded by a single national setting and a 2014–2024 panel. Publicly reported proxies and content-based coding for disclosure may entail measurement error, and residual endogeneity cannot be ruled out despite controls. Future research should exploit longer multi-country panels and quasi experimental shocks, deploy lagged IV specifications and a multi-level models programme, college, university, and use audited, quality weighted disclosure indices to sharpen identification and generalisability. Originality/value The paper repositions intellectual capital disclosure as a contextual, knowledge infrastructure moderator, not a mere communication output, that raises the marginal returns to dynamic marketing capabilities. It demonstrates the empirical dominance of structural disclosure and disentangles the distinct conditioning effects of human and relational disclosure, integrating RBV KBV with dynamic capabilities, signalling, legitimacy, and complementarity supermodularity in an under researched higher education setting.
Glioblastoma (GBM) remains a lethal malignancy characterized by therapeutic resistance and recurrence. Emerging evidence suggests that senescent niches may shape tumor progression, support tumor stemness, and modulate immune engagement. We integrated transcriptomic data from the Glioma Longitudinal Analysis Consortium (GLASS; 118 primary and 113 recurrent IDH-wildtype GBM samples) with protein-level analysis from an independent cohort of 37 GBM patients (25 primary, 12 recurrent), including 6 matched primary-recurrent pairs. Senescence-, stemness-, and immune-related pathways were assessed using single-sample gene set enrichment analysis (ssGSEA), while immunohistochemistry quantified the expression of Lamin B1, Ki67, p53, SOX2, HLA-DRA, B2M, and CD56. Transcript-level validation was performed using matched-pair Wilcoxon testing in 101 GLASS pairs. Recurrent tumors demonstrated increased enrichment of senescence-associated transcriptional programs, including upregulated KAMMINGA_SENESCENCE and reduced TANG_SENESCENCE_TP53_TARGETS_DN scores. Lamin B1 and Ki67 protein levels were significantly lower in recurrent tumors (p = 0.004 and p = 0.016), while p53 expression increased overall (p = 0.001), suggestive of a senescence enrichment upon recurrence. In the matched analysis (6 pairs; 12 samples total), Lamin B1 and Ki67 generally trended lower at recurrence, although paired differences were not statistically significant. SOX2 expression remained broadly stable at the protein level but showed a modest decrease in RNA expression. Immune markers (HLA-DRA, B2M, CD56) exhibited minimal differences, although HLA-DRA increased significantly overall at recurrence (p = 0.025). Matched transcriptomic analysis in GLASS pairs supported recurrent-specific reductions in LMNB1, MKI67, and SOX2, with no consistent changes in TP53, HLA-DRA, B2M, or NCAM1. Recurrent IDH-wildtype GBM exhibits a transcriptional and protein expression shift towards a senescence-associated state with no concomitant changes in SOX2 and select immune markers.
