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Keywords = elections forecasting

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29 pages, 1042 KiB  
Article
Macro-Scale Temporal Attenuation for Electoral Forecasting: A Retrospective Study on Recent Elections
by Alexandru Topîrceanu
Mathematics 2025, 13(4), 604; https://doi.org/10.3390/math13040604 - 12 Feb 2025
Viewed by 1058
Abstract
Forecasting election outcomes is a complex scientific challenge with notable societal implications. Existing approaches often combine statistical analysis, machine learning, and economic indicators. However, research in network science has emphasized the importance of temporal factors in the dissemination of opinions. This study presents [...] Read more.
Forecasting election outcomes is a complex scientific challenge with notable societal implications. Existing approaches often combine statistical analysis, machine learning, and economic indicators. However, research in network science has emphasized the importance of temporal factors in the dissemination of opinions. This study presents a macro-scale temporal attenuation (TA) model, which integrates micro-scale opinion dynamics and temporal epidemic theories to enhance forecasting accuracy using pre-election poll data. The findings suggest that the timing of opinion polls significantly influences opinion fluctuations, particularly as election dates approach. Opinion “pulse” is modeled as a temporal function that increases with new poll inputs and declines during stable periods. Two practical variants of the TA model, ETA and PTA, were tested on datasets from ten elections held between 2020 and 2024 around the world. The results indicate that the TA model outperformed several statistical methods, ARIMA models, and best pollster predictions (BPPs) in six out of ten elections. The two TA implementations achieved an average forecasting error of 6.92–6.95 percentage points across all datasets, compared to 7.65 points for BPP and 14.42 points for other statistical methods, demonstrating a performance improvement of 10–83%. Additionally, the TA methods maintained robust performance even with limited poll availability. As global pre-election survey data become more accessible, the TA model is expected to serve as a valuable complement to advanced election-forecasting techniques. Full article
(This article belongs to the Special Issue Advances in Multi-Criteria Decision Making Methods with Applications)
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25 pages, 4129 KiB  
Article
Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals
by Raghu Raman, Sangeetha Gunasekar, Deepa Kaliyaperumal and Prema Nedungadi
Sustainability 2024, 16(21), 9144; https://doi.org/10.3390/su16219144 - 22 Oct 2024
Cited by 13 | Viewed by 5899
Abstract
The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the [...] Read more.
The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development. Full article
(This article belongs to the Special Issue Energy Economics and Energy Policy towards Sustainability)
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13 pages, 251 KiB  
Article
Exploring Inflammatory Parameters in Lung Cancer Patients: A Retrospective Analysis
by Catalin Vladut Ionut Feier, Calin Muntean, Alaviana Monique Faur, Vasile Gaborean, Ioan Adrian Petrache and Gabriel Veniamin Cozma
J. Pers. Med. 2024, 14(6), 552; https://doi.org/10.3390/jpm14060552 - 22 May 2024
Cited by 10 | Viewed by 1955
Abstract
Inflammation-related parameters serve as pivotal indicators in the prognosis and management of lung cancer. This retrospective investigation aimed to explore the relationship between inflammatory markers and diverse clinical variables in non-small-cell lung cancer patients. A cohort of 187 individuals undergoing elective lobectomy for [...] Read more.
Inflammation-related parameters serve as pivotal indicators in the prognosis and management of lung cancer. This retrospective investigation aimed to explore the relationship between inflammatory markers and diverse clinical variables in non-small-cell lung cancer patients. A cohort of 187 individuals undergoing elective lobectomy for lung cancer was retrospectively analyzed, spanning an 11-year data collection period. Six inflammation ratios derived from complete peripheral blood counts were assessed. Significantly elevated levels of neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), platelet-to-lymphocyte ratio (PLR) (p = 0.001), Aggregate Index of Systemic Inflammation (AISI) (p = 0.015), Systemic Inflammation Response Index (SIRI) (p = 0.004), and Systemic Immune Inflammation Index (SII) (p = 0.004) were observed in patients with advanced T stages. Significantly, elevated values (p < 0.05) of these parameters were observed in the study’s smoker patients compared to non-smokers. A statistically significant correlation was identified between the NLR parameter and tumor size (p = 0.07, r = 0.204), alongside a significant elevation in SIRI (p = 0.041) among patients experiencing postoperative complications. Inflammatory biomarkers emerge as invaluable prognostic indicators for patients with non-small-cell lung cancer, offering potential utility in forecasting their prognosis. Full article
(This article belongs to the Special Issue Respiratory Health and Chronic Disease Management)
16 pages, 3793 KiB  
Article
Method to Forecast the Presidential Election Results Based on Simulation and Machine Learning
by Luis Zuloaga-Rotta, Rubén Borja-Rosales, Mirko Jerber Rodríguez Mallma, David Mauricio and Nelson Maculan
Computation 2024, 12(3), 38; https://doi.org/10.3390/computation12030038 - 22 Feb 2024
Cited by 1 | Viewed by 10150
Abstract
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in forecasting tasks is [...] Read more.
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in forecasting tasks is promising because the former presents good results but requires a good balance between data quantity and quality, and the latter supplies said requirement; nonetheless, each technique has its limitations, parameters, processes, and application contexts, which should be treated as a whole to improve the results. This study proposes a systematic method to build a model to forecast the PERs with high precision, based on the factors that influence the voter’s preferences and the use of ML and Simulation techniques. The method consists of four phases, uses contextual and synthetic data, and follows a procedure that guarantees high precision in predicting the PER. The method was applied to real cases in Brazil, Uruguay, and Peru, resulting in a predictive model with 100% agreement with the actual first-round results for all cases. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems)
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11 pages, 747 KiB  
Brief Report
How Do Older Patients with End-Stage Osteoarthritis of the Hip Eat Prior to Hip Replacement? A Preliminary Snapshot That Highlights a Poor Diet
by Matteo Briguglio, Paolo Sirtori, Laura Mangiavini, Sara Buzzi, Claudio Cordani, Maria Francesca Zerni, Thomas W. Wainwright, Nicola Ursino, Giuseppe M. Peretti and Giuseppe Banfi
Nutrients 2023, 15(23), 4868; https://doi.org/10.3390/nu15234868 - 22 Nov 2023
Cited by 3 | Viewed by 2193
Abstract
Diet quantity and quality in older adults is critical for the proper functioning of the musculoskeletal system. In view of hip surgery, old patients should consume 1.2–1.5 g of proteins and 27–30 kcal per kilo of body weight daily, and adhere to healthy [...] Read more.
Diet quantity and quality in older adults is critical for the proper functioning of the musculoskeletal system. In view of hip surgery, old patients should consume 1.2–1.5 g of proteins and 27–30 kcal per kilo of body weight daily, and adhere to healthy eating habits. In this analytical study, we studied diet quantity and quality in relation to the clinical chemistry and functional status of 57 older adults undergoing elective hip replacement. Nine in ten patients did not meet suggested protein and energy intakes and only one in ten patients exhibited high adherence to the Mediterranean diet. Legume consumption adjusted for sex, age, body mass index, and health status successfully forecasted haemoglobin levels (p < 0.05), and patients regularly consuming olive oil reported minor hip disability compared to those using it less frequently (p < 0.05). Patients who reported daily ingestion of <1 serving of meat versus those consuming >1.5 servings had greater cumulative comorbidity (p < 0.05), with meat consumption independently predicting walking ability, mobility, and balance in the fully adjusted model (p < 0.01). In conclusion, our patients seem to eat poorly. There is room for improvement in pre-operative pathways to make older adults eat better, but there is a need to plan an interventional study to fully understand the cause–effect of a dietary pattern or specific food in enhancing recovery after surgery. Full article
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19 pages, 1644 KiB  
Review
Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention
by Khushi Saigal, Anmol Bharat Patel and Brandon Lucke-Wold
Medicina 2023, 59(10), 1714; https://doi.org/10.3390/medicina59101714 - 25 Sep 2023
Cited by 5 | Viewed by 2417
Abstract
Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in [...] Read more.
Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings. Full article
(This article belongs to the Section Neurology)
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29 pages, 2108 KiB  
Article
Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm
by Vikneswari Someetheram, Muhammad Fadhil Marsani, Mohd Shareduwan Mohd Kasihmuddin, Nur Ezlin Zamri, Siti Syatirah Muhammad Sidik, Siti Zulaikha Mohd Jamaludin and Mohd. Asyraf Mansor
Mathematics 2022, 10(24), 4734; https://doi.org/10.3390/math10244734 - 13 Dec 2022
Cited by 13 | Viewed by 2521
Abstract
Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly [...] Read more.
Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search. Full article
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12 pages, 1754 KiB  
Article
Assessing the Accuracy of Google Trends for Predicting Presidential Elections: The Case of Chile, 2006–2021
by Francisco Vergara-Perucich
Data 2022, 7(11), 143; https://doi.org/10.3390/data7110143 - 27 Oct 2022
Cited by 3 | Viewed by 3037
Abstract
This article presents the results of reviewing the predictive capacity of Google Trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn [...] Read more.
This article presents the results of reviewing the predictive capacity of Google Trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analyzed were organized on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the Auto Regressive Integrated Moving Average (ARIMA) technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organizing the data: the linear series, the series transformed by moving average, and the series transformed by Hodrick–Prescott. The results indicate that the method offers the optimal predictive ability. Full article
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16 pages, 2535 KiB  
Article
Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
by Jordi Cusidó, Joan Comalrena, Hamidreza Alavi and Laia Llunas
Appl. Sci. 2022, 12(21), 10764; https://doi.org/10.3390/app122110764 - 24 Oct 2022
Cited by 12 | Viewed by 4215
Abstract
Having an increasing number of patients in the emergency department constitutes an obstacle to the admissions process and hinders the emergency department (ED)’s ability to deal with the continuously arriving demand for new admissions. In addition, forecasting is an important aid in many [...] Read more.
Having an increasing number of patients in the emergency department constitutes an obstacle to the admissions process and hinders the emergency department (ED)’s ability to deal with the continuously arriving demand for new admissions. In addition, forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. Therefore, this paper aims to develop a precise prediction model for admissions in the Integral Healthcare System for Public Use in Catalonia. These models assist in reducing overcrowding in emergency rooms and improve the quality of care offered to patients. Data from 60 EDs were analyzed to determine the likelihood of hospital admission based on information readily available at the time of arrival in the ED. The first part of the study targeted the obtention of models with high accuracy and area under the curve (AUC), while the second part targeted the obtention of models with a sensitivity higher than 0.975 and analyzed the possible benefits that could come from the application of such models. From the 3,189,204 ED visits included in the study, 11.02% ended in admission to the hospital. The gradient boosting machine method was used to predict a binary outcome of either admission or discharge. Full article
(This article belongs to the Section Applied Thermal Engineering)
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17 pages, 1770 KiB  
Article
Is EU Fiscal Governance Effective? A Case Study for the Period 1999–2019
by Panagiotis Liargovas and Vasilis Pilichos
Economies 2022, 10(8), 187; https://doi.org/10.3390/economies10080187 - 30 Jul 2022
Cited by 2 | Viewed by 2382
Abstract
This paper examines the factors that influence the effectiveness of fiscal governance in the EU through a panel of 19 Eurozone countries for the period 1999–2019 using an OLS method. The results show the positive effects of economic growth, inflation and the change [...] Read more.
This paper examines the factors that influence the effectiveness of fiscal governance in the EU through a panel of 19 Eurozone countries for the period 1999–2019 using an OLS method. The results show the positive effects of economic growth, inflation and the change in the general government balance on the fiscal forecast error. Furthermore, the fiscal forecast error is negatively affected by the level of public debt and by elections. Fiscal transparency is integrated into the analysis through independent financial institutions, which positively influence the general government balance forecast error. Finally, Economic Adjustment Programs have a positive effect on the fiscal forecast error, thus improving the efficiency of fiscal governance. This paper suggests that independent budgetary institutions, such as fiscal councils, and the delegation of further responsibilities to them increase countries’ sustainability of public finances. Full article
(This article belongs to the Section Macroeconomics, Monetary Economics, and Financial Markets)
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12 pages, 1468 KiB  
Article
Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis
by Clemens Huettenbrink, Wolfgang Hitzl, Sascha Pahernik, Jens Kubitz, Valentin Popeneciu and Jascha Ell
J. Pers. Med. 2022, 12(5), 784; https://doi.org/10.3390/jpm12050784 - 12 May 2022
Cited by 3 | Viewed by 2025
Abstract
When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction [...] Read more.
When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction models for ureterorenoscopy. Routinely acquired Computer Tomography (CT) imaging data and patient data were used as data sources. Machine learning models were trained and tested to predict the need for laser lithotripsy and to forecast the expected duration of ureterorenoscopy on the bases of 474 patients over a period from May 2016 to December 2019. Negative predictive value for use of laser lithotripsy was 92%, and positive predictive value 91% before application of the reject option, increasing to 97% and 94% after application of the reject option. Similar results were found for duration of surgery at ≤30 min. This combined prediction is possible for 54% of patients. Factors influencing prediction of laser application and duration ≤30 min are age, sex, height, weight, Body Mass Index (BMI), stone size, stone volume, stone density, and presence of a ureteral stent. Neuronal networks for prediction help to identify patients with an operative time ≤30 min who did not require laser lithotripsy. Thus, surgical planning and resource allocation can be optimised to increase efficiency in the Operating Room (OR). Full article
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16 pages, 1742 KiB  
Article
Analysis of Construction Cost and Investment Planning Using Time Series Data
by Fengchang Jiang, John Awaitey and Haiyan Xie
Sustainability 2022, 14(3), 1703; https://doi.org/10.3390/su14031703 - 1 Feb 2022
Cited by 9 | Viewed by 4604
Abstract
Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting [...] Read more.
Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting multivariate cost prediction models in predicting construction cost index (CCI) and other independent variables from September 2021 to December 2022. The United States was selected as the focal country for the study because of its size and influence. Specifically, we used the Statistical Package for Social Sciences (SPSS) software and R-programming applications to forecast the elected variables based on the literature review. These forecasted values were compared to the CCI using Pearson correlations to assess influencing factors. The results indicated that the ARIMA model is the best forecasting model since it has the highest model-fit correlation. Additionally, the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are the influencing factors of investments decisions in short to medium ranges. This result provides insights to managers and cost planners in determining the best model to adopt. The improved accuracies of the influencing factors will help to enhance the control, competitiveness, and capability of futuristic decision-making of the cost of materials and labor in the construction industry. Full article
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16 pages, 1393 KiB  
Article
Analyzing Political Polarization on Social Media by Deleting Bot Spamming
by Riccardo Cantini, Fabrizio Marozzo, Domenico Talia and Paolo Trunfio
Big Data Cogn. Comput. 2022, 6(1), 3; https://doi.org/10.3390/bdcc6010003 - 4 Jan 2022
Cited by 17 | Viewed by 8687
Abstract
Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred [...] Read more.
Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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12 pages, 1156 KiB  
Article
Dehydration before Major Urological Surgery and the Perioperative Pattern of Plasma Creatinine: A Prospective Cohort Series
by Lukas M. Löffel, Dominique A. Engel, Christian M. Beilstein, Robert G. Hahn, Marc A. Furrer and Patrick Y. Wuethrich
J. Clin. Med. 2021, 10(24), 5817; https://doi.org/10.3390/jcm10245817 - 13 Dec 2021
Cited by 5 | Viewed by 2442
Abstract
Preoperative dehydration is usually found in 30–50% of surgical patients, but the incidence is unknown in the urologic population. We determined the prevalence of preoperative dehydration in major elective urological surgery and studied its association with postoperative outcome, with special attention to plasma [...] Read more.
Preoperative dehydration is usually found in 30–50% of surgical patients, but the incidence is unknown in the urologic population. We determined the prevalence of preoperative dehydration in major elective urological surgery and studied its association with postoperative outcome, with special attention to plasma creatinine changes. We recruited 187 patients scheduled for major abdominal urological surgery to participate in a single-center study that used the fluid retention index (FRI), which is a composite index of four urinary biomarkers that correlate with renal water conservation, to assess the presence of dehydration. Secondary outcomes were postoperative nausea and vomiting (PONV), return of gastrointestinal function, in-hospital complications, quality of recovery, and plasma creatinine. The proportion of dehydrated patients at surgery was 20.4%. Dehydration did not correlate with quality of recovery, PONV, or other complications, but dehydrated patients showed later defecation (p = 0.02) and significant elevations of plasma creatinine after surgery. The elevations were also greater when plasma creatinine had increased rather than decreased during the 24 h prior to surgery (p < 0.001). Overall, the increase in plasma creatinine at 6 h after surgery correlated well with elevations on postoperative days one and two. In conclusion, we found preoperative dehydration in one-fifth of the patients. Dehydration was associated with delayed defecation and elevated postoperative plasma creatinine. The preoperative plasma creatinine pattern could independently forecast more pronounced increases during the early postoperative period. Full article
(This article belongs to the Section Anesthesiology)
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30 pages, 2873 KiB  
Article
MetroScan: A Quick Scan Appraisal Capability to Identify Value Adding Sustainable Transport Initiatives
by David A. Hensher, Chinh Quoc Ho, Wen Liu, Edward Wei, Richard Ellison, Kyle Schroeckenthaler, Derek Cutler and Glen Weisbrod
Sustainability 2020, 12(19), 7861; https://doi.org/10.3390/su12197861 - 23 Sep 2020
Cited by 6 | Viewed by 4204
Abstract
One of the most important features of comprehensive land use and transport planning is an ability to identify candidate projects and policies that are adding value to the sustainable performance of transport networks and to the economy as a whole. Standard methods of [...] Read more.
One of the most important features of comprehensive land use and transport planning is an ability to identify candidate projects and policies that are adding value to the sustainable performance of transport networks and to the economy as a whole. Standard methods of identifying a shortlist of projects to assess are often qualitative in nature and/or influenced by prejudices of elected officials or their advisers without a systematic way of narrowing the many potential options to evaluate, in sufficient detail, a truly value-adding set. There is a case to be made for having a capability to undertake, in a timely manner, a scan of a large number of potentially worthy projects and policies that can offer forecasts of passenger and freight demand, benefit–costs ratios and economy-wide outcomes. Such a framework would then be meaningful in the sense of offering outputs that are similar to those that are the focus of assessments that are typically spread over many months, if not years, on very few projects, which may exclude those which have the greatest merit. This paper introduces MetroScan, a strategic-level transport and land use planning application system that allows for mapping of passenger and freight activity, as well as an endogenous treatment of the location of households and firms. We summarise the analytical framework of MetroScan and show its capability (including the many useful outputs) with a case study for a 25 percent reduction in public transport fares across the entire network. Full article
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