Search Results (103)

Attention-deficit/hyperactivity disorder (ADHD), brain–derived neurotrophic factor (BDNF), and enuresis are interconnected in several ways, primarily through their potential links to neurodevelopmental factors and brain function. ADHD is considered a neurobehavioral and neuropsychiatric condition characterized by numerous comorbidities, and it represents one of the most frequently encountered neuropsychiatric disorders in clinical practice. Enuresis constitutes a subgroup of intermittent incontinence occurring during sleep that can be further subdivided into monosymptomatic (MNE) and non-monosymptomatic enuresis (NMNE). BDNF plays a crucial role in neurodevelopment, including neuronal growth, proliferation, survival, differentiation, and synaptic plasticity. This narrative review synthesized available literature identified through a systematic search of PubMed/MEDLINE, Science Direct, and Scopus databases (January 2000–December 2025). However, the evidence base is heterogeneous, and findings regarding BDNF in ADHD are inconsistent. Studies examining BDNF in enuresis often involve urinary BDNF, which reflects local bladder production rather than central BDNF activity. Further research is needed to clarify the specific roles of BDNF in the development and manifestation of these conditions and to fully elucidate the complex relationship between BDNF, ADHD, and enuresis. Full article

Digital trade rules have proliferated rapidly, yet the literature still treats institutional environments and firm behavior in a comparative-static manner, overlooking the feedback loops and stock-like accumulation dynamics through which regulatory openness shapes firm capabilities over time. Drawing on general systems theory and system dynamics, this paper models the digital trade rule regime as an “institutional system” and the overseas subsidiary network of digital MNEs as an “enterprise system,” linked through three capability stocks (market, production, knowledge), cross-subsystem coupling, absorptive capacity modulation, and five internal feedback loops. We derive a reduced-form dynamic panel equation mapping structural parameters onto estimable coefficients, and test its static counterpart using data on 6850 subsidiaries of UNCTAD’s top 100 digital MNEs (2000–2024) matched with the TAPED database. Three findings emerge. First, institutional openness—measured by rule depth and breadth—exerts a positive causal effect on subsidiary ROA, surviving IV estimation and multiple robustness checks. Second, the effect transmits through market expansion, production efficiency, and knowledge accumulation channels, confirmed by Baron–Kenny mediation with Sobel tests. Third, the New Digital Economy (NDE) module displays point estimates 4–8 times larger than other modules, and joint Wald tests reject coefficient equality, providing qualified support for Meadows’ leverage-point hierarchy. Our contribution lies in bridging system dynamics modeling with econometric causal identification, and in unifying transaction cost theory, the OLI paradigm, and the knowledge-based view within a single open-system framework. Full article

Alzheimer’s disease (AD) is the most prevalent cause of dementia and can be conceptualized as a tauopathy initiated by the accumulation of amyloid-β (Aβ) in the brain. The clinical introduction of anti-Aβ antibody therapies has marked the beginning of a new era in disease-modifying treatment for dementia. While the deleterious effects of Aβ on postsynaptic spines and axonal microtubules have been increasingly clarified, recent studies have shifted attention beyond extracellular Aβ deposition as senile plaques to the pathogenic significance of intracellular Aβ. In particular, accumulating evidence highlights lysosomes as critical sites of intracellular Aβ toxicity. Interactions between Aβ and gangliosides, v-ATPase-dependent lysosomal acidification, and lysosomal membrane integrity are the key determinants of disease progression. In parallel, additional molecular players, including components of the complement cascade and asparaginyl endopeptidase, have been implicated in linking Aβ pathology to tau dysregulation and neurodegeneration. As therapeutic strategies targeting Aβ enter clinical practice, these emerging pathways represent promising targets for the next generation of AD treatment. Here, we summarize current insights and ongoing therapeutic developments centered on these mechanisms. Full article

This study empirically examines how artificial intelligence (AI) adoption and innovation shape product market dynamics and financial performance in multinational enterprises (MNEs) using a global firm sample over 1980–2023. We construct an unbalanced panel dataset by integrating textual analysis, manual verification, and data merged from nine major databases, identifying 411 AI-classified MNEs and a matched 411 non-AI MNEs. Using panel regression models with industry and year fixed effects, we test how AI intensity (the proportion of AI-related products/assets) and R&D—individually and jointly—affect product portfolio breadth and change, market share, industry concentration (HHI), and profitability. The results show that greater AI integration is associated with higher product diversification and a stronger competitive positioning, and that the interaction of AI and R&D is particularly important for explaining market share, concentration, and profitability differences across AI and non-AI MNEs. Overall, the findings highlight the strategic value of aligning AI adoption with innovation investments to strengthen product market outcomes and financial performance in global markets. Full article

In this study, we introduce a novel, functionally driven method for model pruning guided by sensitivity analysis. Conventional model compression techniques often rely on proxy metrics, such as weight magnitude, which may not accurately reflect a component’s true functional importance. The proposed method directly assesses this by systematically injecting controlled noise into network layers and measuring the resultant perturbation on inference output. Components exhibiting low sensitivity to this noise are identified as functionally redundant and are pruned. We validated the method on EEGNet, a compact convolutional neural network, using the MNE Sample Event-Related Potential (ERP) dataset, a widely used benchmark for electroencephalography classification. After training the baseline model, we generated a sensitivity profile by quantifying how noise injection at different layers impacts predictive accuracy. This profile then guided targeted pruning of less influential convolutional kernels and weights. Experimental results demonstrate the method’s efficacy, achieving a significant reduction in both parameter count and computational complexity. Crucially, the pruned model retains classification accuracy nearly identical to the original, heavyweight EEGNet. This confirms that sensitivity-guided pruning effectively removes redundancy without degrading performance. In conclusion, our noise injection framework provides a more direct and interpretable criterion for neural network simplification. By linking component pruning to functional impact, our method enables a more precise and efficient model reduction than traditional heuristic-based approaches. The method developed presents a practical pathway toward developing lightweight, accurate, and low-latency models essential for real-world neuro-computational applications. Future work will focus on automating the pruning pipeline and extending the framework’s applicability to diverse neural architectures. Full article

Continuous blood pressure (BP) measurement is essential for real-time hypertension management and the prevention of related complications. To address this need, a cuffless BP estimation technique utilizing biosignals from wearable devices has gained significant attention. This study proposes a feasibility approach that integrates microneedle array electrodes (MNE) for ECG acquisition with photoplethysmogram (PPG) sensors for cuffless BP estimation. The algorithm employed is a baseline multivariate regression model using PTT and RR intervals, while the novelty lies in the hardware design aimed at improving signal quality and long-term wearability. The algorithm’s performance was validated using the Medical Information Mart for Intensive Care (MIMIC) database, achieving a mean error range of ±5.28 mmHg for the SBP and ±2.81 mmHg for the DBP. Additionally, the comparison with 253 measurements from three volunteers against an automated sphygmomanometer indicated an accuracy within ±25%. Therefore, these findings demonstrate the feasibility of an MNE-based ECG with PPG for BP integration for cuffless monitoring of SBP and DBP in daily life. The MIMIC-based evaluation was performed to verify the feasibility of the regression model under ideal public-database conditions. The volunteer experiment, performed with the developed MNE-ECG hardware, served as a separate preliminary feasibility test to observe hardware behavior in real-world measurements. Full article

The recent rise in anti-globalization sentiment has renewed interest in how tariffs influence the location decisions of multinational enterprises (MNEs). However, these decisions have also been reshaped by ongoing geopolitical tensions-a factor that remains underexplored in the existing literature. In this study, we construct a panel dataset comprising 283,272 country-country-industry observations spanning the years 2009 to 2021. The data are drawn from the WITS, BvD, World Bank, and GDELT databases. Using fixed-effects regression, fixed-effects logit, and fixed-effects negative binomial models, we examine how MNEs respond to tariffs under varying levels of geopolitical risk. Our analysis yields three key insights. First, in contexts of low or no geopolitical risk, higher tariffs increase the likelihood of international investment by MNEs, consistent with the “tariff jumping” hypothesis. However, under high geopolitical risk, this effect disappears-regardless of tariff levels, MNEs are not more likely to invest abroad. Second, tariff increases can escalate low levels of geopolitical tension between home and host countries, further discouraging international investment. In contrast, high levels of geopolitical risk are not significantly correlated with tariff changes. Third, when low-level geopolitical tensions arise, MNEs may redirect investment to neighboring countries or major trading partners of the host country as a way to access its market indirectly. Full article

Geopolitical risk (GPR) poses a significant obstacle to the achievement of sustainable development goals, yet its nuanced impact on the environmental, social, and governance (ESG) performance of multinational enterprises (MNEs) remains insufficiently examined. This study explores the influence of GPR on ESG performance by utilizing a comprehensive dataset of 12,699 subsidiaries of Chinese MNEs. The empirical results reveal an inverted U-shaped relationship between GPR and ESG performance: at moderate levels of geopolitical risk, firms tend to proactively improve their ESG practices as a risk management strategy. However, as GPR intensifies beyond a certain threshold, this approach loses its effectiveness, leading to deteriorating ESG outcomes. Further investigation uncovers the moderating roles of firm-specific advantages (FSAs) and country-specific advantages (CSAs). Robust FSAs equip firms with a greater capacity to uphold ESG standards under rising geopolitical uncertainty, while high CSAs strengthen subsidiaries’ incentives to engage in ESG activities to buffer against external political threats. Subgroup analyses demonstrate that service-oriented MNEs, state-owned enterprises, and subsidiaries operating in high-income countries are particularly susceptible to the negative consequences of heightened GPR. By shedding light on the complex interplay between geopolitical risk and corporate sustainability, this study extends the ESG literature and provides practical implications for researchers, corporate strategists, and policymakers aiming to foster resilient and responsible global business operations. Full article

In the context of economic globalization, robust corporate social responsibility (CSR) serves as a critical source of legitimacy and competitive advantage for multinational enterprises (MNEs). However, institutional and competitive disparities between host and home countries frequently lead overseas subsidiaries of MNEs to deviate from parent company standards by substituting symbolic for substantive CSR practices and thereby creating potential threats to MNEs’ group-wide reputation. Although external stakeholder monitoring is widely recognized, most studies adopt static, dyadic perspectives and thus rarely examine the dynamic interplay between external monitoring and MNEs’ CSR governance. To address this gap, this study constructs a tripartite evolutionary game model involving the parent company, overseas subsidiaries, and external stakeholders, systematically analyzes the evolutionary pathways and the stability of their strategic interactions and uses numerical simulations to identify the conditions for system equilibriums and the influence of key parameters. The findings demonstrate that moderate incentives and penalties from the parent company and active monitoring by external stakeholders significantly promote overseas subsidiaries’ adoption of substantive CSR. This equilibrium becomes more stable when the benefits of substantive CSR increase or its costs decrease for overseas subsidiaries. However, excessive incentive expenditures may weaken the parent company’s willingness to implement strict supervision. Furthermore, information synergies and collaborative governance between the parent company and external stakeholders reduce cross-border supervision and coordination costs, thereby increasing the likelihood of an equilibrium with strict supervision and substantive CSR. By moving beyond conventional static and binary analytical frameworks, this study proposes governance pathways, including optimizing incentive mechanisms, strengthening external stakeholder monitoring, and fostering information synergies, thereby offering new theoretical perspectives and managerial implications for understanding the evolution of CSR behavior in MNEs. Full article

Amid intensified global technological competition and increasing restrictions on cross-border knowledge transfer, enhancing the ability to identify, integrate, and recombine diverse technological knowledge has become a critical strategy for strengthening the innovation capabilities of multinational enterprises (MNEs). Based on multidimensional proximity theory and dynamic capability theory, this study takes R&D units within Huawei’s global R&D network as the research object. It constructs a cross-border collaboration framework under the dual boundaries of organization-geography to explore the differences in the role of technological distance on the innovation performance of R&D units in different cooperation scenarios. This study also introduces technological diversification as a mediating variable to reveal the conversion path from heterogeneous knowledge input to innovation output. The findings indicate: (1) A nonlinear relationship exists between technological distance and innovation performance. In local-internal and international-internal collaborations, this relationship follows an inverted U-shaped pattern, whereas in local-external collaborations, it shows a significant positive effect. (2) In international-external collaboration, due to the dual absence of geographical and organizational proximity, the positive effect of technological distance on innovation performance is not significant. (3) The technological diversification capability of R&D units is a crucial mediating factor in the process by which technological distance affects innovation performance, thereby fostering the efficiency of heterogeneous knowledge absorption and recombination. The study examines the micro-mechanisms underlying cross-border collaborations and capability building in MNEs’ R&D units from dual perspectives of contextual fit and capability development, providing theoretical support and practical guidance for MNEs to optimize international technological collaboration mechanisms and improve innovation performance. Full article

The digital transformation of multinational enterprises (MNEs) in emerging economies has gained increasing interest in academic circles and business communities. However, how and when digital transformation affects green innovation in MNEs remains unknown. Using the theory of organizational learning, this study explores the relationship between digital transformation and the green innovation performance of MNEs, reveals the underlying theoretical mechanisms (i.e., absorptive capacity), and identifies the moderating role of the degree of internationalization and state ownership. Data from Chinese listed MNEs from 2010 to 2019 were used to examine mediating and moderating effects, and robustness tests were performed. The results show that (1) digital transformation can enhance green innovation in MNEs by improving their absorptive capacity; (2) a high degree of internationalization can strengthen the positive relationship between digital transformation and absorptive capacity; and (3) the positive effect of digital transformation on absorptive capacity is stronger in state-owned MNEs than in private MNEs. This study provides a comprehensive theoretical framework for understanding how and when digital transformation affects green innovation in MNEs. The results provide insights into digital transformation and can inform policies regarding green innovation performance and sustainable development in firms. Full article

To uncover neurobiological indicators related to autism spectrum disorders and developmental dyslexia, this article gives a full overview of the most recent advances in machine learning and deep learning methods based on electroencephalography. We look into methodological pipelines that include signal gathering, preprocessing, feature engineering, model selection, and interpretability procedures. We based these pipelines on 15 peer-reviewed research papers published between 2013 and 2025. Most of the research employed the 10–20 system for resting-state EEG and followed MATLAB, MNE-Python, or EEGLAB guidelines for preprocessing. The feature sets included spectral power, functional connectivity, task-evoked potentials, and entropy measures. People used many standard ML methods, such as support vector machines and random forests, as well as more advanced models, like deep neural networks and transformer-based architectures. Several studies found that both dyslexic and ASD groups did well at classifying, with accuracy scores between 82% and 99.2%. The new models could be used in therapeutic settings, but there are still problems with how easy they are to understand and how well they apply to a wide range of situations. This is especially true for ASD because its spectrum is so varied. Full article

Background/Objectives: A subset of children with monosymptomatic nocturnal enuresis (MNE) remains unresponsive to standard treatments such as desmopressin and alarm therapy. This study aimed to identify clinical predictors of response to biofeedback therapy in treatment-resistant MNE and to evaluate the role of bladder capacity as a stratification parameter. Methods: In this prospective study, 89 children with treatment-resistant MNE underwent six weekly sessions of biofeedback therapy involving visual pelvic floor feedback. Based on treatment outcomes, patients were classified as complete responders or partial/non-responders. Clinical characteristics including age-adjusted maximal voided volume (MVV), nocturnal polyuria, and wetting frequency were compared. Results: Patients with a complete response had significantly lower baseline MVV and age-adjusted MVV (p < 0.001). Nocturnal overactivity was more common among responders (60.6% vs. 33.9%, p = 0.017), whereas nocturnal polyuria was more frequent in non-responders (p = 0.027). Age-adjusted MVV emerged as the only independent predictor of treatment success in multivariate analysis (p = 0.045), with ROC analysis confirming its predictive value (AUC = 0.767, 95% CI: 0.667–0.866). Conclusions: These findings suggest that reduced bladder capacity and frequent night-time wetting may help identify patients who are more likely to benefit from biofeedback therapy. Bladder capacity assessment may thus serve as a useful tool in tailoring management strategies for refractory MNE. Full article

In the era of digital business model innovation, multinational corporations face a dual challenge: leveraging digital technologies to overcome institutional barriers while reconfiguring value creation in cross-border operations. Grounded in institutional theory and the digital transformation literature, this study investigates how knowledge distance and political distance shape subsidiary exits through a U-shaped relationship, and how digital transformation breadth and depth differentially reconfigure these effects. We conduct empirical research on 1203 Chinese multinational enterprises from 2015 to 2019. The results indicate that both knowledge distance and political distance exhibit a U-shaped relationship with the subsidiary exit. The breadth of digital transformation strengthens the U-shaped relationship between knowledge distance and subsidiary exit but weakens the relationship between political distance and subsidiary exit. The depth of digital transformation mitigates the effects of both knowledge distance and political distance on subsidiary exit. These findings provide a novel explanatory perspective on the ‘Distance Paradox’ in internationalization theory, address a critical gap in the multinational enterprise (MNE) exit literature, and propose a modular governance blueprint for MNEs. Full article

This study explores how headquarters (HQ) digital transformation affects foreign subsidiaries’ performance in emerging market multinational enterprises (EMNEs). Based on the Technology–Organization–Environment (TOE) framework, parenting advantage theory, and loose coupling theory, we propose a multi-level contingency model. Using unbalanced panel data from 5543 foreign subsidiaries of Chinese A-share listed firms (2011–2021), we find that HQ digital transformation significantly improves subsidiary performance. However, this effect is shaped by key organizational and environmental factors. At the organizational level, excessive HQ control weakens the positive impact, while business group affiliation strengthens it. At the environmental level, strong intellectual property rights (IPR) protection enhances the benefits of digital transformation, whereas advanced host-country digital infrastructure substitutes internal support, reducing the effect. Robustness checks with alternative measures and instrumental variable estimation confirm our results. Theoretically, this study opens the “black box” of intra-MNE digital value transmission and identifies boundary conditions under which digital parenting is effective. Practically, it offers insights for EMNEs on optimizing digital strategies amid governance complexity and institutional diversity. Full article