Search results for: suicide rates

Authors: Pablo Reche

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Since 1980’s aquaculture has become the biggest economic activity in southern Chile, being Salmo salar and Oncorhynchus mykiss the main finfish species. High fish density makes both species prone to contract diseases, what drives the industry to big losses, affecting greatly the local economy. Three are the most concerning infective agents, the infectious salmon anemia virus (ISAv), the bacteria Piscirickettsia salmonis and the copepod Caligus rogercresseyi. To regulate the industry the government arranged the salmon farms within management areas named as barrios, which coordinate the fallowing periods and antibiotics treatments of their salmon farms. In turn, barrios are gathered into larger management areas, named as macrozonas whose purpose is to minimize the risk of disease transmission between them and to enclose the outbreaks within their boundaries. However, disease outbreaks still happen and transmission to neighbor sites enlarges the initial event. Salmon disease agents are mostly transported passively by local currents. Thus, to understand how transmission occurs it must be firstly studied the physical environment. In Chile, salmon farming takes place in the inner seas of the southernmost regions of western Patagonia, between 41.5ºS-55ºS. This coastal marine system is characterised by western winds, latitudinally modulated by the position of the South-Eats Pacific high-pressure centre, high precipitation rates and freshwater inflows from the numerous glaciers (including the largest ice cap out of Antarctic and Greenland). All of these forcings meet in a complex bathymetry and coastline system - deep fjords, shallow sills, narrow straits, channels, archipelagos, inlets, and isolated inner seas- driving an estuarine circulation (fast outflows westwards on surface and slow deeper inflows eastwards). Such a complex system is modelled on the numerical model MIKE3, upon whose 3D current fields particle-track-biological models (one for each infective agent) are decoupled. Each agent biology is parameterized by functions for maturation and mortality (reproduction not included). Such parameterizations are depending upon environmental factors, like temperature and salinity, so their lifespan will depend upon the environmental conditions those virtual agents encounter on their way while passively transported. CLIC (Connectivity-Langrangian–IFOP-Chile) is a service platform that supports the graphical visualization of the connectivity matrices calculated from the particle trajectories files resultant of the particle-track-biological models. On CLIC users can select, from a high-resolution grid (~1km), the areas the connectivity will be calculated between them. These areas can be barrios and macrozonas. Users also can select what nodes of these areas are allowed to release and scatter particles from, depth and frequency of the initial particle release, climatic scenario (winter/summer) and type of particle (ISAv, Piscirickettsia salmonis, Caligus rogercresseyi plus an option for lifeless particles). Results include probabilities downstream (where the particles go) and upstream (where the particles come from), particle age and vertical distribution, all of them aiming to understand how currently connectivity works to eventually propose a minimum risk zonation for aquaculture purpose. Preliminary results in Chiloe inner sea shows that the risk depends not only upon dynamic conditions but upon barrios location with respect to their neighbors.

Keywords: aquaculture zonation, Caligus rogercresseyi, Chilean Patagonia, coastal oceanography, connectivity, infectious salmon anemia virus, Piscirickettsia salmonis

Procedia PDF Downloads 155

Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

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Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

Procedia PDF Downloads 182

Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

Procedia PDF Downloads 66

Authors: Anisa Suraya Ab Razak, Izza Hayat

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Introduction: The diagnosis and management of severe hyponatremia in neuropsychiatric patients present a significant challenge to physicians. Several factors contribute, including diagnostic shadowing and attributing abnormal behavior to intellectual disability or psychiatric conditions. Hyponatraemia is the commonest electrolyte abnormality in the inpatient population, ranging from mild/asymptomatic, moderate to severe levels with life-threatening symptoms such as seizures, coma and death. There are several documented fatal case reports in the literature of severe hyponatremia secondary to psychogenic polydipsia, often diagnosed only in autopsy. This paper presents a case study of acute severe hyponatremia in a neuropsychiatric patient with early diagnosis and admission to intensive care. Case study: A 21-year old Caucasian male with known epilepsy and learning disability was admitted from residential living with generalized tonic-clonic self-terminating seizures after refusing medications for several weeks. Evidence of superficial head injury was detected on physical examination. His laboratory data demonstrated mild hyponatremia (125 mmol/L). Computed tomography imaging of his brain demonstrated no acute bleed or space-occupying lesion. He exhibited abnormal behavior - restlessness, drinking water from bathroom taps, inability to engage, paranoia, and hypersexuality. No collateral history was available to establish his baseline behavior. He was loaded with intravenous sodium valproate and leveritircaetam. Three hours later, he developed vomiting and a generalized tonic-clonic seizure lasting forty seconds. He remained drowsy for several hours and regained minimal recovery of consciousness. A repeat set of blood tests demonstrated profound hyponatremia (117 mmol/L). Outcomes: He was referred to intensive care for peripheral intravenous infusion of 2.7% sodium chloride solution with two-hourly laboratory monitoring of sodium concentration. Laboratory monitoring identified dangerously rapid correction of serum sodium concentration, and hypertonic saline was switched to a 5% dextrose solution to reduce the risk of acute large-volume fluid shifts from the cerebral intracellular compartment to the extracellular compartment. He underwent urethral catheterization and produced 8 liters of urine over 24 hours. Serum sodium concentration remained stable after 24 hours of correction fluids. His GCS recovered to baseline after 48 hours with improvement in behavior -he engaged with healthcare professionals, understood the importance of taking medications, admitted to illicit drug use and drinking massive amounts of water. He was transferred from high-dependency care to ward level and was initiated on multiple trials of anti-epileptics before achieving seizure-free days two weeks after resolution of acute hyponatremia. Conclusion: Psychogenic polydipsia is often found in young patients with intellectual disability or psychiatric disorders. Patients drink large volumes of water daily ranging from ten to forty liters, resulting in acute severe hyponatremia with mortality rates as high as 20%. Poor outcomes are due to challenges faced by physicians in making an early diagnosis and treating acute hyponatremia safely. A low index of suspicion of water intoxication is required in this population, including patients with known epilepsy. Monitoring urine output proved to be clinically effective in aiding diagnosis. Early referral and admission to intensive care should be considered for safe correction of sodium concentration while minimizing risk of fatal complications e.g. central pontine myelinolysis.

Keywords: epilepsy, psychogenic polydipsia, seizure, severe hyponatremia

Procedia PDF Downloads 122

Authors: Tsegay Kahsay Gebrekidan, Gebremariam Gebrezgabher Gebremedhin, Abraha Kahsay Weldemariam, Meaza Kidane Teferi

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Municipal solid waste management (MSWM) in Ethiopia is a complex issue with institutional, social, political, environmental, and economic dimensions, impacting sustainable development. Effective MSWM planning necessitates understanding the generation rate and composition of waste. This systematic review synthesizes qualitative and quantitative data from various sources to aggregate current knowledge, identify gaps, and provide a comprehensive understanding of municipal solid waste management in Ethiopia. The findings reveal that the generation rate of municipal solid waste in Ethiopia is 0.38 kg/ca/day, with the waste composition being predominantly food waste, followed by ash, dust, and sand, and yard waste. Over 85% of this MSW is either reusable or recyclable, with a significant portion being organic matter (73.13% biodegradable) and 11.78% recyclable materials. Physicochemical analyses reveal that Ethiopian MSW is suitable for composting and biogas production, offering opportunities to reduce environmental pollution, and GHGs, support urban agriculture, and create job opportunities. However; challenges persist, including a lack of political will, weak municipal planning, limited community awareness, and inadequate waste management infrastructure, and only 31.8% of MSW is collected legally, leading to inefficient and harmful disposal practices. To improve MSWM, Ethiopia should focus on public awareness; increased funding, infrastructure investment, private sector partnerships, and implementing the 4 R principles (reduce, reuse, and recycle). An integrated approach involving government, industry, and civil society is essential. Further research on the physicochemical properties and strategic uses of MSW is needed to enhance management practices. Implications: The comprehensive study of municipal solid waste management (MSWM) in Ethiopia reveals the intricate interplay of institutional, social, political, environmental, and economic factors that influence the nation’s sustainable development. The findings underscore the urgent need for tailored, integrated waste management strategies that are informed by a thorough understanding of MSW generation rates, composition, and current management practices. Ethiopia’s lower per capita MSW generation compared to developed countries and the predominantly organic composition of its waste present significant opportunities for sustainable waste management practices such as composting and recycling. These practices can not only minimize the environmental impact but also support urban greening, agriculture, and renewable energy production. The high organic content, suitable physicochemical properties of MSW for composting, and potential for biogas and briquette production highlight pathways for creating employment, reducing waste, and enhancing soil fertility. Despite these opportunities, Ethiopia faces substantial challenges due to inadequate political will, weak municipal planning, limited community awareness, insufficient waste management infrastructure, and poor policy implementation. The high rate of illegal waste disposal further exacerbates environmental and health issues, emphasizing the need for a more effective and integrated MSWM approach. To address these challenges and harness the potential of MSW, Ethiopia must prioritize increasing public awareness; investing in infrastructure, fostering private sector partnerships, and implementing the principles of reduce, reuse, and recycle (3 R). Developing strategies that involve all stakeholders and turning waste into valuable resources is crucial. Government, industry, and civil society must collaborate to implement integrated MSWM systems that focus on waste reduction at the source, alternative material use, and advanced recycling technologies. Further research at both federal and regional levels is essential to optimize the physicochemical analysis and strategic use of MSW. Prompt action is required to transform waste management into a pillar of sustainable urban development, ultimately improving environmental quality and human health in Ethiopia.

Keywords: biodegradable, healthy environment, integrated solid waste management, municipal

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Authors: Jocelyn Doucet, Jean-Philippe Laviolette, Ali Eslami

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The end-of-life management and recycling of polymer wastes remains a key environment issue in on-going efforts to increase resource efficiency and attaining GHG emission reduction targets. Half of all the plastics ever produced were made in the last 13 years, and only about 16% of that plastic waste is collected for recycling, while 25% is incinerated, 40% is landfilled, and 19% is unmanaged and leaks in the environment and waterways. In addition to the plastic collection issue, the UN recently published a report on chemicals in plastics, which adds another layer of challenge when integrating recycled content containing toxic products into new products. To tackle these important issues, innovative solutions are required. Chemical recycling of plastics provides new complementary alternatives to the current recycled plastic market by converting waste material into a high value chemical commodity that can be reintegrated in a variety of applications, making the total market size of the output – virgin-like, high value products - larger than the market size of the input – plastic waste. Access to high-quality feedstock also remains a major obstacle, primarily due to material contamination issues. Pyrowave approaches this challenge with its innovative nano-recycling technology, which purifies polymers at the molecular level, removing undesirable contaminants and restoring the resin to its virgin state without having to depolymerise it. This breakthrough approach expands the range of plastics that can be effectively recycled, including mixed plastics with various contaminants such as lead, inorganic pigments, and flame retardants. The technology allows yields below 100ppm, and purity can be adjusted to an infinitesimal level depending on the customer's specifications. The separation of the polymer and contaminants in Pyrowave's nano-recycling process offers the unique ability to customize the solution on targeted additives and contaminants to be removed based on the difference in molecular size. This precise control enables the attainment of a final polymer purity equivalent to virgin resin. The patented process involves dissolving the contaminated material using a specially formulated solvent, purifying the mixture at the molecular level, and subsequently extracting the solvent to yield a purified polymer resin that can directly be reintegrated in new products without further treatment. Notably, this technology offers simplicity, effectiveness, and flexibility while minimizing environmental impact and preserving valuable resources in the manufacturing circuit. Pyrowave has successfully applied this nano-recycling technology to decontaminate polymers and supply purified, high-quality recycled plastics to critical industries, including food-contact compliance. The technology is low-carbon, electrified, and provides 100% traceable resins with properties identical to those of virgin resins. Additionally, the issue of low recycling rates and the limited market for traditionally hard-to-recycle plastic waste has fueled the need for new complementary alternatives. Chemical recycling, such as Pyrowave's microwave depolymerization, presents a sustainable and efficient solution by converting plastic waste into high-value commodities. By employing microwave catalytic depolymerization, Pyrowave enables a truly circular economy of plastics, particularly in treating polystyrene waste to produce virgin-like styrene monomers. This revolutionary approach boasts low energy consumption, high yields, and a reduced carbon footprint. Pyrowave offers a portfolio of sustainable, low-carbon, electric solutions to give plastic waste a second life and paves the way to the new circular economy of plastics. Here, particularly for polystyrene, we show that styrene monomer yields from Pyrowave’s polystyrene microwave depolymerization reactor is 2,2 to 1,5 times higher than that of the thermal conventional pyrolysis. In addition, we provide a detailed understanding of the microwave assisted depolymerization via analyzing the effects of microwave power, pyrolysis time, microwave receptor and temperature on the styrene product yields. Furthermore, we investigate life cycle environmental impact assessment of microwave assisted pyrolysis of polystyrene in commercial-scale production. Finally, it is worth pointing out that Pyrowave is able to treat several tons of polystyrene to produce virgin styrene monomers and manage waste/contaminated polymeric materials as well in a truly circular economy.

Keywords: nanorecycling, nanomaterials, plastic recycling, depolymerization

Procedia PDF Downloads 66

Authors: Mina Naeini, Thomas A. Adams II

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Solid Oxide Fuel Cells (SOFCs) feature high electrical efficiency and generate substantial amounts of waste heat that make them suitable for integrated community energy systems (ICEs). By harvesting and distributing the waste heat through hot water pipelines, SOFCs can meet thermal demand of the communities. Therefore, they can replace traditional gas boilers and reduce greenhouse gas (GHG) emissions. Despite these advantages of SOFCs over competing power generation units, this technology has not been successfully commercialized in large-scale to replace traditional generators in ICEs. One reason is that SOFC performance deteriorates over long-term operation, which makes it difficult to find the proper sizing of the cells for a particular ICE system. In order to find the optimal sizing and operating conditions of SOFCs in a community, a proper knowledge of degradation mechanisms and effects of operating conditions on SOFCs long-time performance is required. The simplified SOFC models that exist in the current literature usually do not provide realistic results since they usually underestimate rate of performance drop by making too many assumptions or generalizations. In addition, some of these models have been obtained from experimental data by curve-fitting methods. Although these models are valid for the range of operating conditions in which experiments were conducted, they cannot be generalized to other conditions and so have limited use for most ICEs. In the present study, a general, detailed degradation-based model is proposed that predicts the performance of conventional SOFCs over a long period of time at different operating conditions. Conventional SOFCs are composed of Yttria Stabilized Zirconia (YSZ) as electrolyte, Ni-cermet anodes, and LaSr₁₋ₓMnₓO₃ (LSM) cathodes. The following degradation processes are considered in this model: oxidation and coarsening of nickel particles in the Ni-cermet anodes, changes in the pore radius in anode, electrolyte, and anode electrical conductivity degradation, and sulfur poisoning of the anode compartment. This model helps decision makers discover the optimal sizing and operation of the cells for a stable, efficient performance with the fewest assumptions. It is suitable for a wide variety of applications. Sulfur contamination of the anode compartment is an important cause of performance drop in cells supplied with hydrocarbon-based fuel sources. H₂S, which is often added to hydrocarbon fuels as an odorant, can diminish catalytic behavior of Ni-based anodes by lowering their electrochemical activity and hydrocarbon conversion properties. Therefore, the existing models in the literature for H₂-supplied SOFCs cannot be applied to hydrocarbon-fueled SOFCs as they only account for the electrochemical activity reduction. A regression model is developed in the current work for sulfur contamination of the SOFCs fed with hydrocarbon fuel sources. The model is developed as a function of current density and H₂S concentration in the fuel. To the best of authors' knowledge, it is the first model that accounts for impact of current density on sulfur poisoning of cells supplied with hydrocarbon-based fuels. Proposed model has wide validity over a range of parameters and is consistent across multiple studies by different independent groups. Simulations using the degradation-based model illustrated that SOFCs voltage drops significantly in the first 1500 hours of operation. After that, cells exhibit a slower degradation rate. The present analysis allowed us to discover the reason for various degradation rate values reported in literature for conventional SOFCs. In fact, the reason why literature reports very different degradation rates, is that literature is inconsistent in definition of how degradation rate is calculated. In the literature, the degradation rate has been calculated as the slope of voltage versus time plot with the unit of voltage drop percentage per 1000 hours operation. Due to the nonlinear profile of voltage over time, degradation rate magnitude depends on the magnitude of time steps selected to calculate the curve's slope. To avoid this issue, instantaneous rate of performance drop is used in the present work. According to a sensitivity analysis, the current density has the highest impact on degradation rate compared to other operating factors, while temperature and hydrogen partial pressure affect SOFCs performance less. The findings demonstrated that a cell running at lower current density performs better in long-term in terms of total average energy delivered per year, even though initially it generates less power than if it had a higher current density. This is because of the dominant and devastating impact of large current densities on the long-term performance of SOFCs, as explained by the model.

Keywords: degradation rate, long-term performance, optimal operation, solid oxide fuel cells, SOFCs

Procedia PDF Downloads 132