The U.S. Hegemonic Model during the Cold War

This paper examines the question about the nature of the US hegemony in the international system during the Cold War. In this paper I will analyze the US hegemonic model during the Cold War, by arguing that the United States promoted and exerted an inclusive hegemony. As theoretical basis for hegemony I will use a mix of theories on hegemony (Robert Gilpin and Antonio Gramsci) and new institutional economics (Daron Acemoglu and James A. Robinson)

Us future strategy in North-East Asia: Balancing or buck-passing?

This paper examines two strategies that United States can use in order to block the rise of China as a regional hegemon in North-East Asia. The two strategies are balancing and buck-passing and I argue that the first is better for United States than the second. After these two strategies will be presented followsthe demonstration that China is a potential hegemon in North-East Asia and that the other powers in the region ar too weak to oppose, and finally, an explanation of the fact will be provided that why balancing is better for USA in this case than the buck-passing.Adrian Eugen Preda is a first year student at Security and Diplomacy Master, National School of Political and Administrative Studies from Bucharest. His research interests are International Relations Theory, Theory of Alliances, Security Studies and Strategic Studies

LIBERAL HEGEMONY AND INEQUALITY IN THE 21ST CENTURY

The subject of inequality represents a present phenomenon in the contemporary society, even though the issue was analyzed by various authors, since the beginning of the Industrial Age. Ideologues, philosophers, economists or social scientists devoted researches on the subject of inequality, offering various interpretations or solutions, more or less viable, about this issue. The present paper will try to argue, from the viewpoint of International Relations Theory, that social inequality represents the expression of a liberal hegemonic model, still influent at the international system. This hegemonic model is based on the liberal ideology, thus assuming the phenomenon and outcome of inequality as natural. From a theoretical point of view, this paper is based on the hegemonic stability theory, an approach from the International Relations Theory. The main actor which sustains this hegemonic model is represented by the United States, as the hegemonic power in the international system, the world leader that sustains the liberal political and economic institutions at the systemic level. From a methodological point of view, the research adopted a qualitative perspective, with the case study on the influence of the liberal hegemonic model on the subject of inequality

Chronic wound management; surgical therapy and complementary nursing with Manuka honey

Objectives. This study aims to analyze the evolution of chronic wounds treated both surgically and by complementary nursing using Manuka honey. The parameters monitored were: presence/persistence of bacterial infection, the duration of healing, the recovery period and the patients\u27 quality of life. Materials and Methods. The study group and the control group each consisted of 10 patients, aged between 50-60 years, with chronic wounds of various etiologies but without other significant systemic pathologies. Data collection was carried out through anamnesis, physical examination and analysis of medical documents. Results. In the study group, the depth of the wound was reduced rapidly and significantly, with complete epithelialization after about four weeks. In the control group, the wound was completely healed by classical treatment, but in eleven weeks and in the form of an unaesthetic keloid scar. The recovery period of a chronic wound appears to be significantly influenced by the use of Manuka honey. Conclusions. Future studies on large groups of patients need to verify the potential therapeutic properties of this compound (anti-inflammatory, antibacterial, antifungal, antioxidant, autolytic debridement, etc.), as well as its adjunctive contribution to wound dressing (maintaining a moist environment and reducing trauma and unpleasant odors)

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients.

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given networks connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to HC. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. K-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions

Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state-of-the-art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural networks decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies

Bioorganically doped sol-gel materials containing amyloglucosidase activity

Amyloglucosidase (AMG) from Aspergillus niger was encapsulated in various matrices derived from tetraethoxysilane, methyltriethoxysilane, phenyltriethoxysilane and vinyltriacetoxysilane by different methods of immobilization. The immobilized enzyme was prepared by entrapment in two steps, in one-step and entrapment/deposition, respectively. The activities of the immobilized AMG were assayed and compared with that of the native enzyme. The effects of the organosilaneprecursors and their molar ratios, the immobilization method, the inorganic support (white ceramic, red ceramic, purolite, alumina, TiO2, celite, zeolite) and enzyme loading upon the immobilized enzyme activity were tested. The efficiency of the sol-gel biocomposites can be improved through combination of the fundamental immobilization techniques and selection of the precursors

Multi-modal deep learning from imaging genomic data for schizophrenia classification

BackgroundSchizophrenia (SZ) is a psychiatric condition that adversely affects an individual’s cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises.MethodsOur proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC).ResultsExperimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%.ConclusionWe proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings