Global Connectivity Research Articles

Automated classification of remote sensing satellite images using deep learning based vision transformer

AbstractAutomatic classification of remote sensing images using machine learning techniques is challenging due to the complex features of the images. The images are characterized by features such as multi-resolution, heterogeneous appearance and multi-spectral channels. Deep learning methods have achieved promising results in the analysis of remote sensing satellite images in the recent past. However, deep learning methods based on convolutional neural networks (CNN) experience difficulties in the analysis of intrinsic objects from satellite images. These techniques have not achieved optimum performance in the analysis of remote sensing satellite images due to their complex features, such as coarse resolution, cloud masking, varied sizes of embedded objects and appearance. The receptive fields in convolutional operations are not able to establish long-range dependencies and lack global contextual connectivity for effective feature extraction. To address this problem, we propose an improved deep learning-based vision transformer model for the efficient analysis of remote sensing images. The proposed model incorporates a multi-head local self-attention mechanism with patch shifting procedure to provide both local and global context for effective extraction of multi-scale and multi-resolution spatial features of remote sensing images. The proposed model is also enhanced by fine-tuning the hyper-parameters by introducing dropout modules and a decay linear learning rate scheduler. This approach leverages local self-attention for learning and extraction of the complex features in satellite images. Four distinct remote sensing image datasets, namely RSSCN, EuroSat, UC Merced (UCM) and SIRI-WHU, were subjected to experiments and analysis. The results show some improvement in the proposed vision transformer on the CNN-based methods.

Fault diagnosis of reducers based on digital twins and deep learning

A new method was proposed to address fault diagnosis by applying the digital twin (DT) high-fidelity behavior and the deep learning (DL) data mining capabilities. Subsequently, the proposed fault distribution GAN (FDGAN) was built to map virtual and physical entities for the data from the established test platform. Finally, the MobileViG was employed to validate the model and diagnose faults. The accuracy of the proposed method with training samples of 600 and 800 were 88.4% and 99.5%, respectively. These accuracies surpass those of other methods based on CycleGAN (98.86%), CACGAN (94.92%), ACGAN (86.45%), ML1D-GAN (82.33%), and transfer learning (99.38%). Therefore, with the integration of global connectivity, an innovative network structure, and training methods, FDGAN can effectively address challenges such as network degradation, limited feature extraction in small windows, and insufficient model robustness.

Correlations between alterations in global brain functional connectivity in patients with major depressive disorder and their genetic characteristics

This study aims to elucidate the neuroimaging changes associated with major depressive disorder (MDD) and their relationship with genetic characteristics. We conducted a global-brain functional connectivity (GFC) and genetic-neuroimaging correlation analysis on 42 MDD patients and 42 healthy controls (HCs), exploring the correlation between GFC abnormalities and clinical variables. Results showed that compared to HCs, MDD patients had significantly decreased GFC values in the bilateral posterior cingulate cortex/precuneus and increased GFC values in the left and right cerebellum Crus I/II. Additionally, a negative correlation was observed between the GFC values of the left cerebellum Crus I/II and subjective support scores, as well as social support revalued scale total scores. We identified genes associated with GFC changes in MDD, which are enriched in biological processes such as synaptic transmission and ion transport. Our findings indicate the presence of abnormal GFC values in severe depression, complementing the pathological research on the condition. Furthermore, this study provides preliminary evidence for the correlation between social support levels and brain functional connectivity, offering insights into the potential association between GFC changes and gene expression in MDD patients.

From Hong Kong to the UK: A Community and Public Health Nursing Researcher's Reflections on an International Fellowship.

Our globalized world means that most public health challenges cannot be tackled alone; they require unified and collaborative working across countries. Overseas fellowships offer an immersive experience that empowers nursing researchers to enhance global collaborations and synergize diverse research expertise from various cultural contexts. This enriched collaboration leads to more innovative solutions to public health issues and significantly enhances the overall impact of their research. This paper presents a self-reflection from the perspective of a community and public health nursing researcher, based in Hong Kong. During a 2-month research secondment at a university in the UK, I explored ways to leverage my fellowship to strengthen global connections, expand collaborations, and enhance my research capabilities in addressing health equity issues among LGBTQ+ populations, particularly concerning sexual violence. By sharing my journey, I aim to inspire more nursing researchers to engage with global academic communities and collectively advance global public health initiatives.

Sprache als Brücke: die Kunst der Übersetzung

The present article reveals the intricate art of conveying meaning across languages, surpassing linguistic barriers in order to promote global communication. Translation is not merely an automated task; it is a nuanced skill that involves apprehending the essence of one culture and transferring it into another. Culture plays an essential role in translation, influencing the choice of words and expressions used to ensure that the message is rendered genuinely in the target language. Despite its importance, translation has its own challenges, ranging from linguistic nuances and cultural subtleties to the complexities of maintaining the same meaning. However, as technology advances and global connections expand, the future of translation holds promise for modern solutions, such as machine learning and artificial intelligence, which may transform the way we bridge languages and cultures in the digital era.

Language Preservation through Popularization of Regional Indian Music: A Data-Driven Approach

The increased global connectivity due to internet penetration has resulted in the dominance of international languages and the decline of regional languages. To help preserve these languages, this study identifies the non-native regions in India that offer the most promising opportunities for expanding the popularity of music for each of the 14 regional languages. It analyses the audio features of 41,000 songs from 14 Indian languages on Spotify, employing two approaches – clustering algorithms and Random Forest Regressors. An overlap between the regions identified by both methods for any given language is considered a robust result, providing greater confidence in the results. The results show that while geographical proximity is a significant factor in determining non-native market fits for the music of a given language, alignment in preferences of audio features driven by cultural similarities plays an integral role as well. The findings have important implications for music industry stakeholders, including artists, management, digital platforms, and governments, who can leverage this data to devise strategies to expand the reach of regional music. This includes designing targeted marketing strategies, fostering cross-regional artist collaborations, and optimizing content recommendation algorithms, among others.

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites.

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

Innovations and Challenges in Primary School English Education in the Industrial Era 4.0

The existence, dynamics, and applicability of English language instruction at the basic school level to the disruptive technology era are all covered in detail in this study. This paper aims to characterize research problems using descriptive-analytical approaches and research methodologies of the literature study type, utilizing literature data from pertinent books, journals, papers, and scientific publications. This study outlines a number of prerequisites for English language instruction in the modern day, including infrastructure and technology, curriculum and instructional strategies, certified teachers, instructional materials and resources, and parental support. The results also show that there are opportunities that can be targeted, such as more extensive access to language learning materials, dynamic and interesting learning opportunities, global connectivity and language exchange, digital applications for language practice, and the provision of a useful system for evaluation and feedback. In the meanwhile, issues include unequal access to technology, unequal user technical proficiency, linguistic difficulties, students' short attention spans and lack of focus, and threats to digital security.

The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis

BackgroundIndividuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.MethodsWe recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.ResultsThe MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.ConclusionMUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

The impact of an innovative education and outreach project

In 1995, the ATLAS Experiment at the Large Hadron Collider embarked on an ambitious journey, driven by a passion to ignite curiosity and inspire future scientists. Our quest wasn’t just about uncovering the universe’s mysteries; it was a cosmic odyssey to share the thrill of scientific exploration with all. Our exploratory collection included engaging classroom activities, a dynamic website, captivating videos, a vibrant YouTube channel, global social media connections, and a captivating planetarium show, all intertwined with the exciting search for the elusive Higgs Boson. This scientific program celebrates the enduring curiosity of humanity and the passion that fuels discovery.

The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis.

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, SD = 0.41) were divided into good (n = 154), competent (n = 147), and struggling readers (n = 152) based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.

Large Language Models Meet Next-Generation Networking Technologies: A Review

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer’s disease

Abstract Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer’s disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepened the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

THE WAR IN UKRAINE AND ITS EXPANSION IN THE BALKANS: GLOBAL IMPACT ASSESSMENT AND OPPORTUNITIES FOR DIPLOMATIC SOLUTIONS

This paper aims to document the actuality of the war between Ukraine and Russia, as well as its global impact. The current event of war/conflict with immediate effect in our region and more widely imposes the evidence of the economic circumstances as well as efforts for solutions! The war in Ukraine is causing a major humanitarian crisis. Ukraine's economy is collapsing and undoubtedly the range of economic consequences will affect the globe given the economic complexity. The trauma suffered by the population will have long-term consequences. The above is the overture and this paper, based on the bibliography and references provided, aims to provide an overview of the chronology of the events surrounding the conflict, the history of the conflict, the consequences and impact of the crisis/war, of an economic, developmental nature, the response of international factors, as well as a perspective on the possibilities for a diplomatic solution. The researched topic explains and provides data about the global implications and impacts, reflects on the economic effects, the effects to the food crisis and argues that carefully calibrated policies and diplomatic channels will be decisive in the unforeseen solution. Ominously, due to the pressure of higher prices for food and other essentials, governments may be tempted to implement price control and subsidy policies, but these may prove counterproductive in the global economy. However, the last chapter underlines that what should not be neglected is the possibility of a diplomatic solution and that it should be based on the mutual acceptance of the parties to give hope for long-term peace. We are limited about predictions, where it will end and what the situation will look like in the medium term, though, this should not stop us from the possibility of recording and evidence that will serve as a comparison for the future, and about the situation and global connections, in the world we live today. The world has changed, and it is a fact that crises wherever they are, bring obstacles in communication and connections of a socio-economic nature, in other parts of the globe.

Multi-view brain functional connectivity and hierarchical fusion for EEG-based emotion recognition

Emotional states evolve gradually from their onset to full manifestation, reflected in changes in brain functional connectivity. Existing research often focuses only on the final emotional state, missing the rich, dynamic information of emotional evolution. This paper proposes a novel network framework leveraging global and local brain functional connectivity through a multi-view approach. We introduce a global and local connectivity modeling method using electroencephalogram signals to simulate brain connectivity changes across time and specific moments. A residual-based local detail feature extractor combined with a coordinate attention module captures long-range dependencies and maintains positional accuracy. A global feature extractor is also designed for the effective extraction of emotional information. Finally, a hierarchical multi-scale cross-attention feature fusion module integrates features across different levels to enhance emotion recognition performance and robustness. Extensive experiments on three publicly available datasets demonstrate the framework’s superiority and generalization capability.

Alternations voxel-wise interhemispheric and intrahemipheric functional connectivity dynamics in internet gaming disorder

BackgroundCurrently, numerous studies have indicated that individuals with internet gaming disorder (IGDs) have aberrant functional connection patterns between multiple brain regions and networks. However, temporal variability in the intra- and interhemispheric dynamic functional connectivity in IGDs remains unknown. MethodsThis study investigated resting-state functional magnetic resonance data from 55 IGDs and 50 demographically matched healthy controls (HCs). Functional connectivity density (FCD) combined with sliding window analysis is employed to calculate the temporal variability of global functional connectivity. The temporal variability of dynamic functional connectivity further quantified utilizing the standard deviations of global, intra-, and interhemispheric FCD. Finally, correlation analyses were performed between dynamic FCD varience (dFCD) in differential brain regions and clinical behaviors. ResultIGDs showed decreased intra- and interhemispheric dFCD variance in the visual attention network (precuneus and calcarine) and also demonstrated hemispheric-level dFCD variance abnormalities in the posterior cingulate cortex (PCC) compared to HCs. Moreover, abnormal global dFCD variability of the calcarine and ipsilateral dFCD variability of the PCC were negatively correlated with the severity of IGDs in the IGD group. ConclusionOur results demonstrate abberant intra- and interhemispheric dynamic functional connectivity in the visual attention network, which emphasizes the neurobiological basis for impaired concentration in IGDs.

Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning

RS image captioning (RSIC) utilizes natural language to provide a description of image content, assisting in the comprehension of object properties and relationships. Nonetheless, RS images are characterized by variations in object scales, distributions, and quantities, which make it challenging to obtain global semantic information and object connections. To enhance the accuracy of captions produced from RS images, this paper proposes a novel method referred to as Discrete Diffusion Models with Refined Language-Image Pre-trained representations (DDM-RLIP), leveraging an advanced discrete diffusion model (DDM) for nosing and denoising text tokens. DDM-RLIP is based on an advanced DDM-based method designed for natural pictures. The primary approach for refining image representations involves fine-tuning a CLIP image encoder on RS images, followed by adapting the transformer with an additional attention module to focus on crucial image regions and relevant words. Furthermore, experiments were conducted on three datasets, Sydney-Captions, UCM-Captions, and NWPU-Captions, and the results demonstrated the superior performance of the proposed method compared to conventional autoregressive models. On the NWPU-Captions dataset, the CIDEr score improved from 116.4 to 197.7, further validating the efficacy and potential of DDM-RLIP. The implementation codes for our approach DDM-RLIP are available at https://github.com/Leng-bingo/DDM-RLIP.

Conductive Framework Materials for Chemiresistive Detection and Differentiation of Toxic Gases.

ConspectusSensing complex gaseous mixtures and identifying their composition and concentration have the potential to achieve unprecedented improvements in environmental monitoring, medical diagnostics, industrial safety, and the food/agriculture industry. Electronically transduced chemical sensors capable of recognizing and differentiating specific target gases and transducing these chemical stimuli in a portable electronic device offer an opportunity for impact by bridging the utility of chemical information with global wireless connectivity. Among electronically transduced chemical sensors, chemiresistors stand out as particularly promising due to combined features of low-power requirements, room temperature operation, non-line-of-sight detection, high portability, and exceptional modularity. Relying on changes in resistance of a functional material triggered by variations in the surrounding chemical environment, these devices have achieved part-per-billion sensitivities of analytes by employing conductive polymers, graphene, carbon nanotubes (CNTs), metal oxides, metal nanoparticles, metal dichalcogenides, or MXenes as sensing materials. Despite these tremendous developments, the need for stable, selective, and sensitive chemiresistors demands continued innovation in material design in order to operate in complex mixtures with interferents as well as variations in humidity and temperature.To fill existing gaps in sensing capabilities, conductive metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have recently emerged as a promising class of materials for chemiresistive sensing. In contrast to previously reported chemiresistors, these materials offer at least three unique features for gas sensing applications: (i) bottom-up synthesis from molecularly precise precursors that allows for strategic control of material-analyte interactions, (ii) intrinsic conductivity that simultaneously facilitates charge transport and signal transduction under low power requirements, and (iii) high surface area that enables the accessibility of abundant active sites and decontamination of gas streams by coordinating to and, sometimes, detoxifying harmful analytes. Through an emphasis on molecular engineering of structure-property relationships in conductive MOFs and COFs, combined with strategic innovations in device integration strategies and device form factor (i.e., the physical dimensions and design of device components), our group has paved the way to demonstrating the multifunctional utility of these materials in the chemiresistive detection of gases and vapors. Backed by spectroscopic assessment of material-analyte interactions, we illustrated how molecular-level features lead to device performance in detection, filtration, and detoxification of gaseous analytes. By merging the bottom-up synthesis of these materials with device integration, we show the versatility and scalability of using these materials in low-power electronic sensing devices. Taken together, our achievements, combined with the progress spearheaded on this class of materials by other researchers, establish conductive MOFs and COFs as promising multifunctional materials for applications in electronically transduced, portable, low-power sensing devices.

Neural correlates of systemic lidocaine administration in healthy adults measured by functional MRI: a single arm open label study.

Intravenous lidocaine is increasingly used as a nonopioid analgesic, but how it acts in the brain is incompletely understood. We conducted a functional MRI study of pain response, resting connectivity, and cognitive task performance in volunteers to elucidate the effects of lidocaine at the brain-systems level. We enrolled 27 adults (age 22-55 yr) in this single-arm, open-label study. Pain response task and resting-state functional MRI scans at 3 T were obtained at baseline and then with a constant effect-site concentration of lidocaine. Electric nerve stimulation, titrated in advance to 7/10 intensity, was used for the pain task (five times every 10 s). Group-level differences in pain task-evoked responses (primary outcome, focused on the insula) and in resting connectivity were compared between baseline and lidocaine conditions, using adjusted P<0.05 to account for multiple comparisons. Pain ratings and performance on a brief battery of computer-based tasks were also recorded. Lidocaine infusion was associated with decreased pain-evoked responses in the insula (left: Z=3.6, P<0.001, right: Z=3.6, P=0.004) and other brain areas including the cingulate gyrus, thalamus, and primary sensory cortex. Resting-state connectivity showed significant diffuse reductions in both region-to-region and global connectivity measures with lidocaine. Small decreases in pain intensity and unpleasantness and worse memory performance were also seen with lidocaine. Lidocaine was associated with broad reductions in functional MRI response to acute pain and modulated whole-brain functional connectivity, predominantly decreasing long-range connectivity. This was accompanied by small but significant decreases in pain perception and memory performance. NCT05501600.

AFGHANISTAN'S POLITICAL CHANGE AND THE BELT AND ROAD INITIATIVE: AN OVERVIEW OF THE SITUATION

This article explores the intricate relationship between Afghanistan's evolving political landscape and the ambitious Belt and Road Initiative (BRI). Situated at the crossroads of geopolitical complexities, Afghanistan has undergone enduring political transformations, prompting a critical review of its international role. As the nation grapples with transformative political journeys, this study scrutinizes the interplay between internal changes and the broader goals of the BRI. Initiated by China, the BRI represents an unprecedented effort in global connectivity and economic cooperation. The article outlines the dynamics of Afghanistan's political scenario and its potential impact on the BRI, revealing the complex relationship between political changes and the future trajectory of this ambitious initiative within Afghanistan's borders. Central to this exploration is the Belt and Road Initiative, a historic initiative spanning continents with the aim to reshape global trade patterns and strengthen economic cooperation. Originating from China, the BRI vision goes beyond infrastructure development, encompassing diplomatic, economic, and cultural dimensions. Understanding the goals and mechanisms of the BRI is fundamental to assessing how Afghanistan's political transformation might align with this transformative initiative. This article aims to dissect the point where Afghanistan's political trajectory converges with the aspirations of the BRI. By detailing the complex interplay between these two dynamic forces, the study explores synergies, conflicts, and potential implications for Afghanistan's geopolitical position and the broader goals of the BRI. As a scholarly endeavor, it contributes nuanced insights to the ongoing discourse on global politics, international cooperation, and the transformative potential of large-scale infrastructure projects in regions undergoing significant political change. Keywords: Afghanistan, Political Change, Belt and Road Initiative, Geopolitics, Infrastructure Development, Economic Dynamics, Regional Cooperation.