Feasibility of an environmentally friendly method of contaminant flushing in water distribution systems using containment ponds

Water distribution system flushing is one way to get rid of contamination. In conventional flushing, all the contaminated water gets discharged to the environment, thereby harming it. A new method is proposed here as an alternative solution, in which a containment pond lined with impermeable material will be constructed in a suitable place within the municipality. Network modelling was performed to investigate the feasibility of the new method. It was found that (1) the proposed flushing method can successfully reduce environmental impacts compared to hydrant flushing only, (2) a containment pond cannot clear the system periphery away from the containment pond, (3) the best location of a containment pond is not always at the furthest location from the source reservoir, and (4) for some systems, some pond locations might be better from an economic perspective, while other locations will be better environmentally.

An Enhanced Packet Transmission Framework for Handling Real Time Burst Traffic in Wireless Sensor Networks

Congestion control for real time traffic is an important network measure to be handled in case of repeated event triggers, continuous packet re-transmissions, node interference, node deaths and node failures in Wireless Sensor Networks (WSNs). Network modelling for transmission of packets from source node to sink using probabilistic M/Pareto and Poisson processes have been examined in the past. The existing methodologies are deficit in designing a queuing framework considering other network parameters such as energy consumption and delay for alleviating congestion and thereby efficiently routing packets to sink by reducing packet drops. To overcome this fall back, a Minimum Weight Estimation for Mitigating Congestion during Real Time Burst Traffic (MWCBT) framework is proposed. This gives a precautionary solution against heavy traffic occupancy among the interim and sink-neighbouring nodes in WSNs is proposed. Routing of packets using a congestion-free path is required to increase the node lifespan. An optimal M/Pareto stochastic traffic generator is used in combination with traffic factors such as energy and delay to predict amount of traffic across nodes. A simpler congestion prediction mechanism is performed to control the occurrence of heavy-tailed traffic distributions. A torrent weight value for incoming traffic is generated at each node periodically that directs routing of data packets to sink. The devised MWCBT framework supervises real-time traffic congestion and is found to be more optimal than the existing approaches for network traffic modelling. The proposed approach achieves greater packet delivery ratio and less node congestion compared to the existing network modelling techniques.

Pore Network Modelling of Porous Media for Carbon Dioxide Sequestration: A Case Study of Pakistan

Carbon dioxide (CO2) storage in natural rocks is an important strategy for reducing and capturing greenhouse gas emissions in the atmosphere. The amount of CO2 stored in a natural reservoir such as natural rocks is the major challenge for any economically viable CO2 storage. The intricate nature of the porous media and the estimates of the replacement of residing aqueous media with the invading CO2 is the challenge. The current study uses MATLAB to construct a similar porous network model for simulation of complex porous storage. The model is designed to mimic the overall properties of the natural porous media in terms of permeability, porosity and inter-pore connectivity. Here a dynamic pore network is simulated and validated, firstly in the case of a porous network with one fluid invading empty network. Subsequently, the simulations for an invading fluid (CO2) capturing the porous media with filled aqueous brine solution are also carried out in a dynamic fashion. This resembles the actual storage process of CO2 sequestration in natural rocks. While the sensitivity analysis suggests that the differential pressure and porosity have a direct effect on saturation, increasing differential pressure or porosity increases the saturation of CO2 storage. The results for typically occurring rocks in Pakistan are also studies and related with the findings of the study.

Back to Life, Back to Reality: A Multi-Level Dynamic Network Analysis of Student Mental Health Upon Return to Campus During the COVID-19 Pandemic

Lockdown measures during the COVID-19 pandemic resulted in drastic disruptions of university students’ everyday life and study mode, such as marked reductions in face-to-face teaching activities. Previous research on student mental health during the pandemic found that prolonged campus relocation had negative effects on students’ mental well-being. However, these studies focussed on the initial lockdown period, or periods of active lockdown measures. This longitudinal study collected 456 observations of 23 undergraduate students in the Netherlands using ecological momentary assessment data on mental health related items (anxiety, stress, social context) during the first two weeks of on-campus teaching after prolonged lockdown measures. Using multi-level dynamic network modelling, we analysed the temporal and contemporaneous interplay of students’ mental health factors following the return to campus in September 2021. On average, students reported low to medium scores on stress and anxiety both before and after the assessment period. Results of network analyses showed that students experienced social unease in relation to accumulating difficulties at university and vice versa. Furthermore, there were clusters of different states of social unease next to clusters of stress, anger, loss of control, and feeling upset. Lastly, we found beneficial effects of self-efficacy on experiencing social comfort in university. We discuss implications for potential interventions in universities, such as the promotion of self-efficacy, providing guidance in structuring study load, as well as help with stress management.

Research on Emergency Logistics Model of Agricultural Products Based on Coupling of Petri Net and Blockchain

In this COVID-19 epidemic, due to insufficient awareness of the impact of sudden public health emergencies on agricultural logistics at this stage, agricultural products were left unsold, stocks were backlogged, and losses were severe. In the process of distribution, we should not only ensure a short time cycle and avoid the contamination of agricultural products by foreign bacteria, but also pay attention to the waste of human, material, and financial resources. Therefore, this study mainly adopts the combination of the petrochemical network and block chain to build an agricultural products emergency logistics model. This paper first shows the operation mechanism of the petri dish network and blockchain coupling in the form of a graph and then uses the culture network modelling and simulation tool PIPE to directly verify the construction model. It is proved that the structure and overall business process of the agricultural products logistics system constructed by combining the Petri net and block chain are reasonable, reliable, and feasible in practical application and development. It is hoped that this study can provide a reference direction for agricultural emergency logistics.

Unifying of Steady State and Transient Simulations Methodologies for Increasing Oil Production of Integrated Network of Wells, Pipeline and Topside Processing Equipment

Aging facilities and increasing complexity in operations (e.g., increasing water cut, slugging, sand or wax production) continue to widen the gap between actual production and the full potential of the field. To enable production optimization scenarios within an integrated system comprises of reservoirs, wells and surface facilities, the application of an integrated network modelling has been applied. The highlight of this paper is the synergy of Integrated Production Network Modelling (IPNM) utilizing Steady State Simulator (PROSPER-GAP) and the Transient Simulator (OLGA) tools to identify potential quick gains through gaslift optimization as well as mid and long-term system optimization alternatives. The synergy enables significant reduction in transient simulation time and reduced challenges in OLGA well matching, especially in selecting accurate modelling parameters e.g., well inflow performance (validated well (string) production data, reservoir pressure, temperature and fluid properties and the Absolute Open Flow (AOF) of each well). The paper showcased the successful production gain achieved as well as the workflows and methodologies applied for both Steady State Integrated Production Modelling (IPM Steady State) and Integrated Transient Network Modelling (IPM Transient) as tools for production enhancement. Even though IPM Steady State shows promising results in term of field optimization potential, to increase accuracy and reduce uncertainties, IPM Transient is recommended to be performed to mimic the actual transient phenomena happening in the well to facilities

Graph Neural Network Modelling as a potentially effective Method for predicting and analyzing Procedures based on Patient Diagnoses

BackgroundCurrently, the healthcare sector strives to increase the quality of patient management and improve the economic performance of healthcare providers. The data contained in electronic health records (EHRs) offer the potential to discover relevant patterns that aim to relate diseases and therapies, and thus discover patterns that could help identify empirical medical guidelines that reflect best practices in the healthcare system. Based on this pattern identification, it is then possible to implement recommendation systems based on the idea that a higher volume of procedures is associated with high-quality models.MethodsAlthough there are several applications that use machine learning methods to identify these patterns, this identification is still a challenge, in part because these methods often ignore the basic structure of the population, considering the similarity of diagnoses and patient typology. To this end, we have developed graph methods that aim to cluster similar patients. In such models, patients are linked when the same or similar patterns can be observed for these patients, a concept that enables the construction of a network-like structure. This structure can then be analyzed with Graph Neural Networks (GNN) to identify relevant labels, in this case the appropriate medical procedures.ResultsWe report the construction of a patient Graph structure based on basic patient’s information like age and gender as well as the diagnoses and trained GNNs models to identify the corresponding patient’s therapies using a synthetic patient database. We compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and compared the performance of the different model methods. We have found that GNNs are superior, with an average improvement of the f1 score of 6.48% respect to the baseline models. In addition, the GNNs are useful for performing additional clustering analyses that allow specific identification of specific therapeutic clusters related to a particular combination of diagnoses.ConclusionsWe found that GNNs are a promising way to model the distribution of diagnoses in a patient population and thus better model how similar patients can be identified based on the combination of morbidities and comorbidities. Nevertheless, network building is still challenging and prone to prejudice, as it depends on how ICD distribution affects the patient network embedding space. This network setup requires not only a high quality of the underlying diagnostic ecosystem, but also a good understanding of how to identify related patients by disease. For this reason, additional work is needed to improve and better standardize patient embedding in graph structures for future investigations and applications of services based on this technology, and therefore is not yet an interventional study.