Feature selection is a crucial preprocessing step for classification, inherently forming a bi-objective optimization problem aimed at minimizing feature subset size and classification error. However, most existing multi-objective feature selection methods fail to adequately account for potential feature interactions during the population initialization and underutilize historical information during the evolutionary process. Such issues may affect the breadth and depth of population search within high-dimensional feature spaces. To address these limitations, this paper proposes a multi-objective feature selection approach based on the importance-guided and historical information-assisted strategies (MOFS-IH). The main characteristics are twofold. First, the importance-guided population initialization strategy dynamically stratifies the feature space via information gain, thereby generating a well-distributed initial population that better captures feature interactions and facilitates rapid convergence. Second, the historical information-assisted population update mechanism leverages recorded feature-level information to guide the search toward promising feature subsets, thereby enabling more efficient utilization of historical evolutionary information. Extensive experiments on 15 benchmark datasets demonstrate that MOFS-IH outperforms six state-of-the-art multi-objective feature selection algorithms in terms of the quality of the obtained feature subsets.
Organisational learning is a double-edged sword because it is contextual and subject to interpretation by the top managers. Top managers’ cognitions influence how they interpret and process organisational learning, and how they deploy resources. Drawing on organisational learning and upper-echelon theories, we argue for the joint, complementary effects of a firm’s cross-border acquisition (CBA) experience and the chief executive officer’s (CEO) cognitive orientation, measured by their international experience and tenure at the firm, on the pace of CBAs. The results from analyses of an unbalanced panel of 1583 observations across 369 CBAs from 205 Indian MNEs support our hypothesis. Interestingly, we find negative effects of the firm’s CBA experience and mixed effects of the CEO’s cognitions. However, the joint effect is positive and stronger, as hypothesised. We mainly contribute to the CBA literature by demonstrating the role of ‘experience cognition complementarity’ on the CBA pace.
We report the temperature-dependent photoluminescence properties of 2-thenoyltrifluoroacetone (TTFA)-sensitised NaMgF3:Eu (0.1 mol% to 5 mol%) nanoparticles. Maximum sensitisation was observed for 1%Eu doping, and the sensitised emissions were dominated by highly distorted Eu3+ ions in the nanoparticle shell region. All Eu3+ emissions decreased as the temperature increased from 300 K to 460 K. This thermal quenching is well described by a kinetic model featuring thermally-induced back transfer from the Eu3+ 5D0 state to the TTFA T1 triplet state prior to non-radiative decay. The model places the T1 state 0.44 eV above the 5D0 state and 0.21 eV above the Eu3+ 5D1 state in TTFA-sensitised NaMgF3:Eu. The ratio of the magnetic dipole (5D0 → 7F1) and forced electric dipole (5D0 → 7F2) emission intensities was also temperature dependent. Thus, TTFA-sensitised NaMgF3:Eu nanoparticles have potential for both intensity-based and ratiometric photoluminescent nanothermometry with relative sensitivities up to 2.7%/K.
Covering: January to the end of December 2024This review covers the literature published in 2024 for marine natural products (MNPs), with 617 citations (578 for the period January to December 2024) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, the submerged parts of mangroves and other intertidal plants. The emphasis is on new compounds (1256 in 336 papers for 2024), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. An analysis of the role of artificial intelligence in marine natural products research is discussed.
Disasters’ adverse impacts on human health are not limited to physical health but extend to mental health and well-being. Multiple causal mechanisms can impact mental health, including direct physical injury and disability, psychological trauma, the loss of loved ones, displacement, or socioeconomic stressors associated with the disaster recovery process. Here, we review the literature that focuses on the economic quantifications of the mental health consequences of disasters, including both how economic circumstances can contribute to or ameliorate the mental health impacts, and how these mental health impacts can shape people’s economic trajectories. We review several methods that can be used to quantify these costs. This literature also includes several cost-effectiveness studies of interventions that have been evaluated with quality-adjusted life years (QALY) metrics. We evaluate the strengths and weaknesses of each approach and describe their quantifications. Since these quantifications conclude that these mental health costs can be high, their exclusion from standard disaster risk assessments can lead to the underestimation of disasters’ total social costs, and consequently to an underinvestment in disaster risk-reduction measures. Quantifying these mental health costs may hence yield a more comprehensive understanding of disaster losses and help inform decisions about the appropriate levels of investments in prevention and mitigation.
