Local Network Research Articles

Relational Skills and Corporate Productivity in a Comparative Size Class Perspective

AbstractDrawing on insights from various fields within game theory literature, such as strategic interactions, social dilemmas, gift exchange, and procedural utility, we argue that corporate social responsibility (CSR) and relational skills—whether between firms, employers and workers, among workers themselves, or with stakeholders—are associated with positive effects on productivity. We test this hypothesis using a comparative approach across small, medium, and large Italian firms, leveraging data from the 2019 CSR survey conducted by the Italian Statistical Institute (ISTAT). Our analysis reveals that firm size plays a crucial role in the impact of relational skills on added value per worker, even after controlling for relevant factors. Key components of relational skills identified in our study include corporate policies focusing on workers' well-being, prioritizing teamwork attitudes in recruitment, supporting initiatives within the local productive network, and involving stakeholders in CSR initiatives. Our findings indicate that stakeholder engagement positively impacts all firm sizes, while worker well-being is particularly significant for small and medium firms, local network initiatives for medium and large firms, and teamwork for medium-sized firms. Instrumental variable estimates find evidence of a causal link beyond these correlations. We conclude that firm size exhibits an inverse U-shaped effect on the impact of teamwork skills, reduces the influence of gift exchange mechanisms, and strengthens the effects of investments in the local productive environment on added value per worker.

Mining local and global spatiotemporal features for tactile object recognition

The tactile object recognition (TOR) is highly important for environmental perception of robots. The previous works usually utilize single scale convolution which cannot simultaneously extract local and global spatiotemporal features of tactile data, which leads to low accuracy in TOR task. To address above problem, this article proposes a local and global residual (LGR-18) network which is mainly consisted of multiple local and global convolution (LGC) blocks. An LGC block contains two pairs of local convolution (LC) and global convolution (GC) modules. The LC module mainly utilizes a temporal shift operation and a 2D convolution layer to extract local spatiotemporal features. The GC module extracts global spatiotemporal features by fusing multiple 1D and 2D convolutions which can expand the receptive field in temporal and spatial dimensions. Consequently, our LGR-18 network can extract local-global spatiotemporal features without using 3D convolutions which usually require a large number of parameters. The effectiveness of LC module, GC module and LGC block is verified by ablation studies. Quantitative comparisons with state-of-the-art methods reveal the excellent capability of our method.

Wearing their heart on a wall: the World’s Biggest Comic

Abstract An arts-based project, using a community development approach engaging people in a community of place to address suicide prevention, is potentially powerful. This involves linking community development and mental health promotion practice. The challenge in doing this is that, conceptually, the integration between the two is incomplete and there is a lack of guidance about effective community processes. This paper analyses community development processes to create the World’s Biggest Comic (WBC). The WBC was an innovative, large scale public art project engaging local artists, some with a lived experience of mental illness, to tell a story of Will and Hope. A 15-part comic story, printed on vinyl canvas panels and mounted on buildings, covered 600 m2. https://www.facebook.com/watch/?v=217471332673743 Conducted in a small rural South Australian community by the local volunteer Suicide Prevention Network, the WBC aimed to raise awareness, break down stigma, and prompt life-saving conversations. Results of a mixed method impact evaluation are presented using a community development framework that considers ‘people’, ‘space’, and ‘place’. The use of sociological concepts about community and the community field, the value of relationships, and listening to the voices of those with a lived experience of mental illness, may assist practice. Some potential learnings and pitfalls in using a community development approach in mental health promotion are discussed.

Gaitdlf: global and local fusion for skeleton-based gait recognition in the wild

AbstractA new trend in long-range biometrics, gait recognition, is finding application in a number of different fields including video surveillance. Recently, with the increase in robustness of the pose estimator and the presence of various unpredictable factors in realistic gait recognition, skeleton-based methods with higher robustness have emerged to better meet the challenging gait recognition needs. However, existing approaches primarily focus on extracting global skeletal features, neglecting the intricate motion information of local body parts and overlooking inter-limb relationships. Our solution to these challenges is the dynamic local fusion network (GaitDLF), a novel gait neural network for complex environments that includes a detail-aware stream in addition to the previous direct extraction of global skeleton features, which provides an enhanced representation of gait features. To extract discriminative local motion information, we introduce predefined body part assignments for each joint in the skeletal structure. By segmenting and mapping the overall skeleton based on these limb site divisions, limb-level motion features can be obtained. In addition, we will dynamically fuse the motion features from different limbs and enhance the motion feature representation of each limb by global context information and local context information of the limb-level motion features. The ability to extract gait features between individuals can be improved by aggregating local motion features from different body parts. Based on experiments on CASIA-B, Gait3D, and GREW, we show that our model extracts more comprehensive gait features than the state-of-the-art skeleton-based method, demonstrating that our method is better suited to detecting gait in complex environments in the wild than the appearance-based method.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice.

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within 'remapped' forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within ‘remapped’ forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

‘Botha Cake’ and ‘Belgian Onion Soup:’ Gendered Patriotism Through Three South African, First World War, Community Cookbooks

ABSTRACT This article considers three South African community cookbooks – including The Overseas Contingent Fund Recipe Book (Turffontein, 1915) and the Paarl Cookery Book in Aid of War Funds (Paarl, 1918) alongside Wartime Cookery (Cape Town, 1915) – as expressions of gendered patriotism. These community-created cookbooks enabled contributors to participate meaningfully in the First World War by drawing together two established gendered practices: philanthropic fundraising and food and household management. This ‘domesticity in action’ enabled participants to contribute to, and feel connected to, both local and trans-empire causes. These values of philanthropy, economy and patriotism are revealed in the constitutive components of the texts – the titles, aims, prefaces and even the recipes themselves. Apart from supporting specific funds, the call for economy in the cookbooks reflected both experiences and perceptions of wartime material hardship in South Africa. A further consideration of the contributors involved with the books’ compilations highlights the intersection of local and trans-empire networks, and wartime (white) identity politics within urban South Africa. The networks suggest locally-grounded loyalisms, many marked by personal connections to men serving, as well as friend and family networks with overseas reach. Lastly, the article is a reminder of the importance of cookbooks as neglected sources in the writing of history.

Comparison of WebSocket and Hypertext Transfer Protocol for Transfer of Electronic Health Records.

Electronic health records (EHR) emerged as a digital record of the data that is generated in the healthcare. In this paper the transfer times of EHRs using the Hypertext Transfer Protocol and WebSocket in both local network and wide area network (WAN) are compared. A python web application to serve Fast Health Interoperability Resources (FHIR) records is created and the transfer times of the EHRs over both HTTP and WebSocket connection are measured. 45000 test Patient resources in 20, 50, 100 and 200 resources per Bundle transfers are used. WebSocket showed much better transfer times of large amount of data. These were 18 s shorter in the local network and 342 s shorter in WAN for the 20 resource per Bundle transfer. RESTful APIs are a convenient way to implement EHR servers; on the other hand, HTTP becomes a bottleneck when transferring large amount of data. WebSocket shows better transfer times and thus its superiority in such situations. The problem can be addressed by developing a new communication protocol or by using network tunneling to handle large data transfer of EHRs.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Understanding Chinese international students’ language ideologies and multilingual practices in English-medium instruction programs in Germany

Analysed through the lens of language ideology, this qualitative study delved into the multilingual experiences of Chinese international students in English-medium instruction (EMI) studies in Germany. Drawing on semi-structured interview data from 16 Chinese postgraduate students in an international university, this study probed into these students’ language ideologies related to English, German, and Mandarin Chinese. Data analysis showed that their language ideologies were complex and intertwined with academic success, social integration, future employment, solidarity within ethnic groups, and widening local networks. It was also found that the participants’ multiple while often conflicting language ideologies were pivotal in shaping their investment in and usage of particular languages. The findings of this study contribute to a deeper understanding of the diversity inherent in international students’ language ideologies in EMI and have significant implications for the internationalisation of multilingual environments in higher education (HE).

A universal medically assisted model for anatomical landmark detection in radioactive images

AbstractAnatomical landmark detection is a critical task in medical image analysis with significant research and practical value. The analysis of landmarks in radiological images is beneficial for its diagnosis and treatment. Currently, most methods are applied to datasets from specific anatomical regions, with only a few deep neural network models designed for mixed datasets. Meanwhile, the precise and fast localization of landmarks remains a significant challenge due to interdependencies among landmarks and the demanding accuracy requirements in medical clinical applications. In this work, we leverage the advantages of attention mechanisms for focusing on specific information, and propose a universal landmark detection model trained on mixed‐domain radioactive image datasets. The model consists of three main components: the local network, the global network, and the attention feature extraction module. The local network employs a U‐Net architecture with depthwise separable convolutions to extract local image features. Notably, we introduce partial depthwise separable convolutions as an option for further lightweight models. The global network comprises a sequence of dilated convolutions with varying dilation rates, expanding the receptive field to extract global features. The attention feature extraction module efficiently utilizes intermediate outputs to refine the precise location information of landmarks. Additionally, we introduce the regional attention loss function to provide the model with landmark offset information. We conduct experiments on three publicly available x‐ray image datasets covering head, hand, and chest regions. The experimental results demonstrate that the proposed universal model achieves fast convergence and excellent performance across multiple datasets.

The Effects of Local Food on Carbon Emissions: The Case of the Republic of Korea

This study addresses the urgent environmental issue of climate change, focusing specifically on the role of local food, which is produced and consumed locally, in helping to reduce carbon emissions by eliminating the need for long-distance transportation. Through quantitative analysis, this study elucidates the potential benefits of environmental impact achieved through the consumption of local food, i.e., the significant reduction in carbon emissions. Specifically, the consumption of local food has been found to yield an annual decrease of 2,421,296 tCO2 emissions, representing 2.5% of the Republic of Korea’s total greenhouse gas emissions from the transportation sector. This reduction further translates into an estimated economic value of USD 54.23 million (KRW 70.5 billion). These findings underscore the potential of local food consumption as a tangible strategy to overcome environmental problems. Moreover, the academic contribution of this study lies in its comprehensive analysis of the economic and empirical impacts of local food consumption on the environment. Moving forward, we propose policies such as supporting local food distribution networks, providing public education on local food, fostering local food industries, and implementing incentives to revitalize local food consumption.

Perturbing cortical networks: in vivo electrophysiological consequences of pan-neuronal chemogenetic manipulations using deschloroclozapine

IntroductionChemogenetic techniques, specifically the use of Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), have become invaluable tools in neuroscience research. Yet, the understanding of how Gq- and Gicoupled DREADDs alter local field potential (LFP) oscillations in vivo remains incomplete.MethodsThis study investigates the in vivo electrophysiological effects of DREADD actuation by deschloroclozapine, on spontaneous firing rate and LFP oscillations recorded from the anterior cingulate cortex in lightly anesthetized male rats.ResultsUnexpectedly, in response to the administration of deschloroclozapine, we observed inhibitory effects with pan-neuronal hM3D(Gq) stimulation, and excitatory effects with pan-neuronal hM4D(Gi) stimulation in a significant portion of neurons. These results emphasize the need to account for indirect perturbation effects at the local neuronal network level in vivo, particularly when not all neurons express the chemogenetic receptors uniformly. In the current study, for instance, the majority of cells that were transduced with both hM3D(Gq) and hM4D(Gi) were GABAergic. Moreover, we found that panneuronal cortical chemogenetic modulation can profoundly alter oscillatory neuronal activity, presenting a potential research tool or therapeutic strategy in several neuropsychiatric models and diseases.DiscussionThese findings help to optimize the use of chemogenetic techniques in neuroscience research and open new possibilities for novel therapeutic strategies.

Framing Europe on the local level – policy networks in German cities and their activities for political cohesion in Europe

ABSTRACT Political practice and academic discourse similarly assume that the local level has the potential to strengthen political cohesion in the European Union: it is the tier closest to citizens and implements a large share of European legislation. But what political cohesion actually is and how it can be achieved is less clear. The paper addresses this gap by analysing the local framing of political cohesion in Europe. Drawing on case studies in eight German cities, local Europe-related networks are investigated, focusing on how their ideas about their role in crafting political cohesion links to their Europe-related activities.

DEVELOPMENT OF DATABASE AND CLIENT-SERVER APPLICATION FOR HIGHER EDUCATIONAL INSTITUTIONS

The electronic diary described in the work is intended for students and teachers in higher education institutions and is based on a client-server architecture, using advanced technologies to provide convenient access to educational information via a local network. The main goal of this project is to improve the interaction between students and teachers, contributing to the increase in productivity and quality of education. The diary provides convenient access to educational information, contributing to effective data management and improving communication between participants of the educational process. It helps students keep track of their grades, class schedules, and teachers to grade and interact with their students, making learning more efficient and convenient.

P083 Introduction of a gout pathway in a London NHS trust

Abstract Background/Aims Gout is a major cause of morbidity with a significant increase in UK hospitalisation identified over the last decade due to acute flare. Patient and physician education alongside a treat-to-target approach with urate-lowering therapy (ULT) are fundamental in its management. Despite being commonly encountered by clinicians, a survey conducted at Imperial College Healthcare NHS trust (ICHNT) demonstrated 49% of responders did not feel confident managing the condition. This project aimed to create a guideline for the management of acute gout at ICHNT to aid prompt diagnosis and effective management. Increasing the proportion of patients commencing ULT through engagement with local primary care networks was prioritised to improve patient outcomes and reduce subsequent hospital attendances. Methods A trust wide guideline for the management of acute gout was created with input from stakeholders (including emergency and acute medicine physicians) and informed by rheumatology clinicians and current guidelines. Latest recommendations from NICE, BSR and EULAR were consulted alongside local guidelines in place across London. An emphasis was placed upon education and initiation of ULT to improve management of acute and chronic gout to prevent future hospital attendances. Staff within the rheumatology department were consulted to agree on timing of ULT initiation and an effective pathway for implementation. Results Through review of local, national and international guidelines, discrepancies in gout management were identified, most notably variation in the timing of ULT initiation (e.g. during the acute presentation, after flare resolution or after subsequent flare). This was reflected in the practice of clinicians within the rheumatology department at ICHNT. Agreed consensus brought the trust guideline predominantly in line with current NICE guidance for management of acute gout with ULT commenced in patients 2-4 weeks after resolution of flare. Initially, this led to delegation of ULT initiation to general practitioners, however, this resulted in concerns over treatment delays and added pressure on an already stretched service. Follow-up within secondary care was also considered, but capacity issues exist. As a next step, a pilot for a pharmacy-led follow-up service within primary care is planned using standardised protocols supported by secondary care. Conclusion The implementation of a local guideline on the management of acute gout is helpful for clinicians from a non-rheumatology background to facilitate prompt diagnosis and effective management of this common, but often poorly treated, condition. A successful pathway to prevent future flares is more challenging, not only due to discrepancies in current guidelines on timing of ULT initiation but also in creating capacity for follow-up and treatment implementation. This continuing project aims to explore alternative pathways such as a pharmacy-led service that improves patient outcomes and reduces future burden upon healthcare services. Disclosure E. Mabey: None. H. Wilson-Morkeh: None. B. Ellis: None.

Reduced Expression of CLEC4G in Neurons Is Associated with Alzheimer’s Disease

CLEC4G, a glycan-binding receptor, has previously been demonstrated to inhibit Aβ generation, yet its brain localization and functions in Alzheimer’s disease (AD) are not clear. We explored the localization, function, and regulatory network of CLEC4G via experiments and analysis of RNA-seq databases. CLEC4G transcripts and proteins were identified in brain tissues, with the highest expression observed in neurons. Notably, AD was associated with reduced levels of CLEC4G transcripts. Bioinformatic analyses revealed interactions between CLEC4G and relevant genes such as BACE1, NPC1, PILRA, TYROBP, MGAT1, and MGAT3, all displaying a negative correlation trend. We further identified the upstream transcriptional regulators NR2F6 and XRCC4 for CLEC4G and confirmed a decrease in CLEC4G expression in APP/PS1 transgenic mice. This study highlights the role of CLEC4G in protecting against AD progression and the significance of CLEC4G for AD research and management.

Urban Road Network Connectivity Analysis Based on GIS for Aurangabad City, Maharashtra

Urban road network plays an important role in urban local network. The urban road network plays a key role in the urban spatial structure. It is the main city social-economy activities and transportation carrier. An important event of the cultural landscape is that transportation serves as a powerful factor in regional as well as national development. In connectivity network analysis, the term connectivity is known as the quantity of connections between nodes in a given network, so it derives the structure of the road network. Road network, major junctions, retail junctions, major corridors, road density and connectivity can be analyzed using the network connectivity indicators on the GIS platform. Road network dataset is created in ArcGIS which provides the number of transport lines and number of Junctions in study region. One of the most basic features of a transportation network is the network index, such as the alpha, beta, gamma index, etc. There are various indices, and each index has its individual equation for different purposes. It requires road network (line), junction (nodes) to draw connectivity index. Roads density is highest in most of the zones in Aurangabad city and lowest road density recorded in zone A in the north and E is south-west direction. he averages score of the gamma index has been increased from 0.50 in 2001 to 0.53 in 2011 where the average value also increased from 0.44 to 0.48 during this period.

Predict-IT - Forecasting District Heating Loads

In the realm of many thermal energy systems, and particularly within district heating networks, heat load forecasts play a pivotal role in optimizing system operation and efficient infrastructure usage. While district heating operators routinely log measurement data, its potential remains underutilized. One essential application of such data is forecasting a network’s heat load based on historical data records. Such forecasts can improve the efficient usage of plant infrastructure and facilitate predictive operational strategies. This paper introduces ”Predict-IT”, a web-based platform designed to standardize the entire forecasting pipeline, making the generation of predictions largely independent of expert knowledge. The Predict-IT platform is powered by a state-of-the-art long short-term memory (LSTM) based neural network algorithm which only requires very little inputs (measured heat load and ambient temperature) to deliver satisfying forecasting accuracy, even a couple of days ahead. The prediction algorithm is validated on two data sets from local Austrian district heating networks, showing the general applicability of the LSTM-based neural network, given an appropriate set of hyperparameters. The Predict-IT platform simplifies the process of forecasting heat loads into a few discrete steps: data upload, algorithm training, heat load forecast generation, and visualization of forecasts. The source code will be open-source, and deployment and installation will be facilitated by an easily installable Docker solution.