| 1. |
- Carlsson, Ella, et al.
(författare)
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Mass composition of the escaping plasma at Mars
- 2006
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Ingår i: Icarus. - : Elsevier BV. - 0019-1035 .- 1090-2643. ; 182:2, s. 320-328
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Tidskriftsartikel (refereegranskat)abstract
- Data from the Ion Mass Analyzer (IMA) sensor of the ASPERA-3 instrument suite on Mars Express have been analyzed to determine the mass composition of the escaping ion species at Mars. We have examined 77 different ion-beam events and we present the results in terms of flux ratios between the following ion species: CO2+/O+ and O-2(+)/O+. The following ratios averaged over all events and energies were identified: CO2+/O+ = 0.2 and O-2(+)/O+ = 0.9. The values measured are significantly higher, by a factor of 10 for O-2(+)/O+, than a contemporary modeled ratio for the maximum fluxes which the martian ionosphere can supply. The most abundant ion species was found to be O+, followed by O-2(+) and CO2+. We estimate the loss of CO2+ to be 4.0 x 10(24) s(-1) (0.29 kg s(-1)) by using the previous measurements of Phobos-2 in our calculations. The dependence of the ion ratios in relation to their energy ranges we studied, 0.3-3.0 keV, indicated that no clear correlation was found.
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| 2. |
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| 3. |
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| 4. |
- Ingri, Johan, et al.
(författare)
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Hydrogeochemistry of sulfur isotopes in the Kalix River catchment, northern Sweden
- 1997
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Ingår i: Applied Geochemistry. - 0883-2927 .- 1872-9134. ; 12:4, s. 483-496
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Tidskriftsartikel (refereegranskat)abstract
- The 34S-to-32S ratio in dissolved SO4 has been studied in the Kalix River, Northern Sweden, and its catchment. Weekly sampling over 17 months revealed temporal variations from +5.3‰ up to +7.4‰ in the δ34S values in the river. Snow and rain samples showed lower δ34S values (average +5.6‰ and +5.0‰, respectively). The atmosphere is the major source for S in surface waters in the catchment, and the heavier δ34S values in the river are a result of SO4 reduction within the catchment. Most of the temporal variations in the δ34S value in the river are caused by a mixing of water from the mountain areas (relatively light δ34S) and the woodland. The δ34S value is relatively heavy in the woodland tributaries because of bacterial SO4 reduction in peatland areas influenced by groundwater. The highest δ34S values were measured during the spring flood, in June and in November. These heavy δ34S values are related to different types of water with diverse origins. The heavy δ34S values coinciding with the early spring flood originate from peatland areas in the woodland. Relatively heavy δ34S values (up to +14.4‰) were registered in mire water. Smaller variations of the δ34S value during summer and early autumn most likely were caused by the input of ground-mire water during heavy rains. A correlation between increased TOC concentrations and increased δ34S values was observed. The heavy δ34S values in June and November probably originate from SO4 reduction in bottom water and sediments in lakes within the catchment. Bottom water, enriched in 34S---SO4, was transported in the river during the spring and autumn overturn.
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| 5. |
- Reiz, S, et al.
(författare)
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Epidural morphine for postoperative pain relief
- 1981
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Ingår i: Acta Anaesthesiologica Scandinavica. - : Wiley. - 0001-5172 .- 1399-6576. ; 25:2, s. 111-114
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Tidskriftsartikel (refereegranskat)abstract
- Thirty-three patients were randomly assigned to two groups to study the analgesic potency, duration of action and side effects of epidural and intramuscular morphine after hip surgery. Two milligrams of preservative-free morphine chloride in 10 ml of normal saline in the epidural space was compared to 10 mg of intramuscularly administered morphine. There was a more rapid onset of action after intramuscular morphine. However, the quality of pain relief was substantially higher and the duration of action markedly longer after epidural morphine. The total dose required in the epidural group was 3.6 mg and in the intramuscular group 41 mg during the 15-h observation period. The side effects of epidural morphine were few and mild, the most embarrassing being urinary retention (20%). Nausea and/or vomiting was less common after epidural morphine (20% versus 55%). Pruritus or respiratory depression which have been reported previously were not encountered. However, it is recommended that preservative-free solution are used to avoid itching and that the patients are monitored, as respiratory depression may occur long after administration of epidural opiate
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| 6. |
- Reusch, T. B. H., et al.
(författare)
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The Baltic Sea as a time machine for the future coastal ocean
- 2018
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Ingår i: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 4:5
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Tidskriftsartikel (refereegranskat)abstract
- Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use are difficult, because of multiple interacting pressures, uncertain projections, and a lack of test cases for management. We argue that the Baltic Sea can serve as a time machine to study consequences and mitigation of future coastal perturbations, due to its unique combination of an early history of multistressor disturbance and ecosystem deterioration and early implementation of cross-border environmental management to address these problems. The Baltic Sea also stands out in providing a strong scientific foundation and accessibility to long-term data series that provide a unique opportunity to assess the efficacy of management actions to address the breakdown of ecosystem functions. Trend reversals such as the return of top predators, recovering fish stocks, and reduced input of nutrient and harmful substances could be achieved only by implementing an international, cooperative governance structure transcending its complex multistate policy setting, with integrated management of watershed and sea. The Baltic Sea also demonstrates how rapidly progressing global pressures, particularly warming of Baltic waters and the surrounding catchment area, can offset the efficacy of current management approaches. This situation calls for management that is (i) conservative to provide a buffer against regionally unmanageable global perturbations, (ii) adaptive to react to new management challenges, and, ultimately, (iii) multisectorial and integrative to address conflicts associated with economic trade-offs.
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| 7. |
- Shafkat Raihan, S.M., et al.
(författare)
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A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data
- 2022
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Ingår i: Proceedings of the International Conference on Big Data, IoT, and Machine Learning. - Singapore : Springer. ; , s. 483-496
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Konferensbidrag (refereegranskat)abstract
- In the prevailing COVID-19 pandemic, accurate diagnosis plays a vital role in preventing the mass transmission of the SARS-CoV-2 virus. Especially patients with pneumonia need correct diagnosis for proper treatment of their respiratory distress. However, the current standard diagnosis method, RT-PCR testing has a significant false negative and false positive rate. As alternatives, diagnosis methods based on artificial intelligence can be applied for faster and more accurate diagnosis. Currently, various machine learning and deep learning techniques are being researched on to develop better COVID-19 diagnosis system. However, these approaches do not consider the uncertainty in data. Deep learning approaches use backpropagation. It is an unexplainable black box approach and is prone to problems like catastrophic forgetting. This article applies a belief rule-based expert system (BRBES) for diagnosis of COVID-19 on hematological data and CT scan data of lung tissue infection of adult pneumonia patients. The system is optimized with nature-inspired optimization algorithm—BRBES-based adaptive differential evolution (BRBaDE). This model has been evaluated on a real-world dataset of COVID-19 patients published in a previous work. Also, performance of the BRBaDE has been compared with BRBES optimized with genetic algorithm and MATLAB’s fmincon function where BRBaDE outperformed genetic algorithm and fmincon and showed best accuracy of 73.91%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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| 8. |
- Abedin, Md. Zainal, et al.
(författare)
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Performance Analysis of Anomaly Based Network Intrusion Detection Systems
- 2018
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Ingår i: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops). - Piscataway, NJ : IEEE Computer Society. ; , s. 1-7
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Konferensbidrag (refereegranskat)abstract
- Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool.
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| 9. |
- Ahmed, Shamim, et al.
(författare)
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A Comparative Analysis of LIME and SHAP Interpreters with Explainable ML-Based Diabetes Predictions
- 2024
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Ingår i: IEEE Access. - : IEEE. - 2169-3536.
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Tidskriftsartikel (refereegranskat)abstract
- Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we investigate how to generate local and global explanations for a machine-learning model built on a logistic regression architecture. We trained on 253,680 survey responses from diabetes patients using the explainable AI techniques LIME and SHAP. LIME and SHAP were then used to explain the predictions produced by the logistic regression and Random forest-based model on the validation and test sets.With a discussion of future work, the comparative analysis and discussion of various experimental findings between LIME and SHAP are provided, along with their strengths and weaknesses in terms of interpretation. With a high accuracy of 86% on the test set, we used LR architecture with a spatial attention mechanism, demonstrating the possibility of merging machine learning and explainable AI to improve diabetes prediction, diagnosis, and treatment.We also focus on various applications, difficulties, and probable future directions of machine learning models for LIME and SHAP interpreters.
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