Search results for: lagrangian trajectory

Authors: Sophy Joseph

Abstract:

The study uses the concept of ‘Settler city’ to understand the nature of peripheralization that a neoliberal city initiates. The settler city designs powerless communities without inherent rights, title and sovereignty. Kathputli Colony, home to generations of artists/craftsmen, who have kept heritage of arts/crafts alive, has undergone eviction of its population from urban space. The proposed study, ‘Neoliberal Settler City: Socio-spatial segregation and livelihood of artists/craftsmen in Delhi’ would problematize the settler city as a colonial technology. The colonial regime has ‘erased’ the ‘unwanted’ as primitive and swept them to peripheries in the city. This study would also highlight how structural change in political economy has undermined their crafts/arts by depriving them from practicing/performing it with dignity in urban space. The interconnections between citizenship and In-Situ Private Public Partnership in Kathputli rehabilitation has become part of academic exercise. However, a comprehensive study connecting inherent characteristics of neoliberal settler city, trajectory of political economy of unorganized workers - artists/craftsmen and legal containment and exclusion leading to dispossession and marginalization of communities from the city site, is relevant to contextualize the trauma of spatial segregation. This study would deal with political, cultural, social and economic dominant behavior of the structure in the state formation, accumulation of property and design of urban space, fueled by segregation of marginalized/unorganized communities and disowning the ‘footloose proletariat’, the migrant workforce. The methodology of study involves qualitative research amongst communities and the field work-oral testimonies and personal accounts- becomes the primary material to theorize the realities. The secondary materials in the forms of archival materials about historical evolution of Delhi as a planned city from various archives, would be used. As the study also adopt ‘narrative approach’ in qualitative study, the life experiences of craftsmen/artists as performers and emotional trauma of losing their livelihood and space forms an important record to understand the instability and insecurity that marginalization and development attributes on urban poor. The study attempts to prove that though there was a change in political tradition from colonialism to constitutional democracy, new state still follows the policy of segregation and dispossession of the communities. It is this dispossession from the space, deprivation of livelihood and non-consultative process in rehabilitation that reflects the neoliberal approach of the state and also critical findings in the study. This study would entail critical spatial lens analyzing ethnographic and sociological data, representational practices and development debates to understand ‘urban otherization’ against craftsmen/artists. This seeks to develop a conceptual framework for understanding the resistance of communities against primitivity attached with them and to decolonize the city. This would help to contextualize the demand for declaring Kathputli Colony as ‘heritage artists village’. The conceptualization and contextualization would help to argue for right to city of the communities, collective rights to property, services and self-determination. The aspirations of the communities also help to draw normative orientation towards decolonization. It is important to study this site as part of the framework, ‘inclusive cities’ because cities are rarely noted as important sites of ‘community struggles’.

Keywords: neoliberal settler city, socio-spatial segregation, the livelihood of artists/craftsmen, dispossession of indigenous communities, urban planning and cultural uprooting

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Authors: Tom S. Owens, Tom A. Calverley, Benjamin S. Stacey, Christopher J. Marley, George Rose, Lewis Fall, Gareth L. Jones, Priscilla Williams, John P. R. Williams, Martin Steggall, Damian M. Bailey

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Introduction and aims: Sports-related concussion (SRC) represents a significant and growing public health concern in rugby union, yet remains one of the least understood injuries facing the health community today. Alongside increasing SRC incidence rates, there is concern that prior recurrent concussion may contribute to long-term neurologic sequelae in later-life. This may be due to an accelerated decline in cerebral perfusion, a major risk factor for neurocognitive decline and neurodegeneration, though the underlying mechanisms remain to be established. The present study hypothesised that recurrent concussion in current professional rugby union players would result in elevated systemic oxidative-nitrosative stress, reflected by a free radical-mediated reduction in nitric oxide (NO) bioavailability and impaired cerebrovascular and cognitive function. Methodology: A longitudinal study design was adopted across the 2017-2018 rugby union season. Ethical approval was obtained from the University of South Wales Ethics Committee. Data collection is ongoing, and therefore the current report documents result from the pre-season and first half of the in-season data collection. Participants were initially divided into two subgroups; 23 professional rugby union players (aged 26 ± 5 years) and 22 non-concussed controls (27 ± 8 years). Pre-season measurements were performed for cerebrovascular function (Doppler ultrasound of middle cerebral artery velocity (MCAv) in response to hypocapnia/normocapnia/hypercapnia), cephalic venous concentrations of the ascorbate radical (A•-, electron paramagnetic resonance spectroscopy), NO (ozone-based chemiluminescence) and cognition (neuropsychometric tests). Notational analysis was performed to assess contact in the rugby group throughout each competitive game. Results: 1001 tackles and 62 injuries, including three concussions were observed across the first half of the season. However, no associations were apparent between number of tackles and any injury type (P > 0.05). The rugby group expressed greater oxidative stress as indicated by increased A•- (P < 0.05 vs. control) and a subsequent decrease in NO bioavailability (P < 0.05 vs. control). The rugby group performed worse in the Ray Auditory Verbal Learning Test B (RAVLT-B, learning, and memory) and the Grooved Pegboard test using both the dominant and non-dominant hands (visuomotor coordination, P < 0.05 vs. control). There were no between-group differences in cerebral perfusion at baseline (MCAv: 54 ± 13 vs. 59 ± 12, P > 0.05). Likewise, no between-group differences in CVRCO2Hypo (2.58 ± 1.01 vs. 2.58 ± 0.75, P > 0.05) or CVRCO2Hyper (2.69 ± 1.07 vs. 3.35 ± 1.28, P > 0.05) were observed. Conclusion: The present study identified that the rugby union players are characterized by impaired cognitive function subsequent to elevated systemic-oxidative-nitrosative stress. However, this appears to be independent of any functional impairment in cerebrovascular function. Given the potential long-term trajectory towards accelerated cognitive decline in populations exposed to SRC, prophylaxis to increase NO bioavailability warrants consideration.

Keywords: cognition, concussion, mild traumatic brain injury, rugby

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Authors: Dean Sinković, Tea Golja, Morena Paulišić

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Upon the end of the vicious war in former Yugoslavia in 1995, international development community widely promoted microfinance as the key development framework to eradicate poverty, create jobs, increase income. Widespread claims were made that microfinance institutions would play vital role in creating a bedrock for sustainable ‘bottom-up’ economic development trajectory, thus, helping newly formed states to find proper way from economic post-war depression. This uplifting neoliberal narrative has no empirical support in the Republic of Croatia. Firstly, the type of enterprises created via microfinance sector are small, unskilled, labor intensive, no technology and with huge debt burden. This results in extremely high failure rates of microenterprises and poor individuals plunging into even deeper poverty, acute indebtedness and social marginalization. Secondly, evidence shows that microcredit is exact reflection of dangerous and destructive sub-prime lending model with ‘boom-to-bust’ scenarios in which benefits are solely extracted by the tiny financial and political elite working around the microfinance sector. We argue that microcredit providers are not proper financial structures through which developing countries should look way out of underdevelopment and poverty. In order to achieve sustainable long-term growth goals, public policy needs to focus on creating, supporting and facilitating the small and mid-size enterprises development. These enterprises should be technically sophisticated, capable of creating new capabilities and innovations, with managerial expertise (skills formation) and inter-connected with other organizations (i.e. clusters, networks, supply chains, etc.). Evidence from South-East Europe suggest that such structures are not created via microfinance model but can be fostered through various forms of social enterprises. Various legal entities may operate as social enterprises: limited liability private company, limited liability public company, cooperative, associations, foundations, institutions, Mutual Insurances and Credit union. Our main hypothesis is that cooperatives are potential agents of social and economic transformation and community development in the region. Financial cooperatives are structures that can foster more efficient allocation of financial resources involving deeper democratic arrangements and more socially just outcomes. In Croatia, pioneers of the first social enterprises were civil society organizations whilst forming a separated legal entity. (i.e. cooperatives, associations, commercial companies working on the principles of returning the investment to the founder). Ever since 1995 cooperatives in Croatia have not grown by pursuing their own internal growth but mostly by relying on external financial support. The greater part of today’s registered cooperatives tend to be agricultural (39%), followed by war veterans cooperatives (38%) and others. There are no financial cooperatives in Croatia. Due to the above mentioned we look at the historical developments and the prevailing social enterprises forms and discuss their advantages and disadvantages as potential agents for social and economic transformation and community development in the region. There is an evident lack of understanding of this business model and of its potential for social and economic development followed by an unfavorable institutional environment. Thus, we discuss the role of governance and management in the formation of social enterprises in Croatia, stressing the challenges for the governance of the country’s social enterprise movement.

Keywords: financial cooperatives, governance and management models, microfinance institutions, social enterprises

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Authors: Nazym Zhumabayeva, Aleksandr Mezin, Alexandra Knysheva

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Women's underrepresentation in STEM is critical, worsened by ineffective engagement in educational practices. UNICEF Kazakhstan and GameLab KBTU's collaborative initiatives aim to enhance female STEM participation by fostering an inclusive environment. Learning from LEVEL UP's 2023 program, which featured a hackathon, the 2024 strategy pivots towards non-competitive gamification. Although the data from last year's project showed higher than average student engagement, observations and in-depth interviews with participants showed that the format was stressful for the girls, making them focus on points rather than on other values. This study presents a gamified educational system, DPG Lab, aimed at incentivizing young women's participation in STEM through the development of digital public goods (DPGs). By prioritizing collaborative gamification elements, the project seeks to create an inclusive learning environment that increases engagement and interest in STEM among young women. The DPG Lab aims to find a solution to minimize competition and support collaboration. The project is designed to motivate female participants towards the development of digital solutions through an introduction to the concept of DPGs. It consists of a short online course, a simulation videogame, and a real-time online quest with an offline finale at the KBTU campus. The online course offers short video lectures on open-source development and DPG standards. The game facilitates the practical application of theoretical knowledge, enriching the learning experience. Learners can also participate in a quest that encourages participants to develop DPG ideas in teams by choosing missions throughout the quest path. At the offline quest finale, the participants will meet in person to exchange experiences and accomplishments without engaging in comparative assessments: the quest ensures that each team’s trajectory is distinct by design. This marks a shift from competitive hackathons to a collaborative format, recognizing the unique contributions and achievements of each participant. The pilot batch of students is scheduled to commence in April 2024, with the finale anticipated in June. It is projected that this group will comprise 50 female high-school students from various regions across Kazakhstan. Expected outcomes include increased engagement and interest in STEM fields among young female participants, positive emotional and psychological impact through an emphasis on collaborative learning environments, and improved understanding and skills in DPG development. GameLab KBTU intends to undertake a hypothesis evaluation, employing a methodology similar to that utilized in the preceding LEVEL UP project. This approach will encompass the compilation of quantitative metrics (conversion funnels, test results, and surveys) and qualitative data from in-depth interviews and observational studies. For comparative analysis, a select group of participants from the previous year's project will be recruited to engage in the DPG Lab. By developing and implementing a gamified framework that emphasizes inclusion, engagement, and collaboration, the study seeks to provide practical knowledge about effective gamification strategies for promoting gender diversity in STEM. The expected outcomes of this initiative can contribute to the broader discussion on gamification in education and gender equality in STEM by offering a replicable and scalable model for similar interventions around the world.

Keywords: collaborative learning, competitive learning, digital public goods, educational gamification, emerging regions, STEM, underprivileged groups

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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