Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

<span>An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.</span>

Contribution of Civil Protection to the Urban Economy: Evidence from a Small-Sized Greek City

Civil protection has attracted considerable attention due to its role in disaster management and preparedness, being essential in alerting the public about potential disasters and crisis recovery measures. However, there is limited research on civil protection and its vital role in urban economy recovery. Therefore, we sought to comprehensively investigate the impact of civil protection on economic growth and the development of the urban economy, focusing on a small-sized Greek city, Kozani, as a case study. We utilized data from 160 residents of Kozani. The study findings confirmed that the key focus areas of civil protection, namely, the national early warning system, crisis preparedness measures and economy rescue operations, significantly affect economic growth and development. Furthermore, the key strategies essential for improved civil protection, such as government support, positively affect economic growth.

Small-holder farmers knowledge and information on the impact of climate variability & extremes on livestock production in Limpopo & Mpumalanga Provinces

A study was carried out to investigate the level of awareness, knowledge and information of small-holder farmers (SHLF) on the impact of climatic change (CC) and extremes on livestock production in Limpopo and Mpumalanga Provinces. At least 366 smallholder farmers were interviewed using a semi-structured questionnaire to elicit responses on vulnerability. Almost all the farmers (96 %) have heard about CC only a few farmers (4 %) did not know CC. The medium for the conveyance of CC information was the main radio (94.32%). Newspapers and television were also efficient mediums in the conveyance of this information, each with the outreach of 16.76 and 32.67%, respectively. Central to the impact of CC was the fact that (90%) of the farmers confirmed that there was a change in grass availability; which contributed to major livestock fatalities of which over half of the farmers (55.19%) attested as the cause. The study found that 86.67% of SHLF who attended awareness meetings indicated that the discussions prioritized adapting to CC. However, SHLF (80.77%) did not have an early warning system. This was coupled with a lack of contingency plans by (84.36%) the farmers to deal with the impact of the said drought on their farms. SHLF (19%) who had facilitated contingency plans indicated that improved aspects of the plan should incorporate the support of their provision feeds, drilling of boreholes, and erection of dams. Based on SHLF perceptions there is a need for strategic shifts from grazing to small scale feed-lots.

A Novel Financial Risk Early Warning Strategy Based on Decision Tree Algorithm

This paper requires a lot of assumptions for financial risk, which cannot use all of the data and is often limited to financial data; and in the past, most early warning models for financial crises did not, so they could not track the fluctuation and change trend of financial indicators. A decision tree algorithm model is used to propose a financial risk early warning method. Enterprises have suffered as a result of the financial crisis, and some have even gone bankrupt. Any financial crisis, on the other hand, has a gradual and deteriorating course. As a result, it is critical to track and monitor the company's financial operations so that early warning signs of a financial crisis can be identified and effective measures taken to mitigate the company’s business risk. This paper establishes a financial early warning system to predict financial operations using the decision tree algorithm in big data. Operators can take measures to improve their enterprise’s operation and prevent the failure of the embryonic stage of the financial crisis, to avoid greater losses after discovering the bud of the enterprise’s financial crisis, and to avoid greater losses after discovering the bud of the enterprise’s financial crisis. This prediction can be used by banks and other financial institutions to help them make loan decisions and keep track of their loans. Relevant businesses can use this signal to make credit decisions and effectively manage accounts receivable; CPAs can use this early warning information to determine their audit procedures, assess the enterprise's prospects, and reduce audit risk. As a result, the principle of steady operation should guide modern enterprise management. Prepare emergency plans in advance of a business risk or financial crisis to resolve the financial crisis and reduce the financial risk.

Multi-Task Learning with Recurrent Neural Networks for ARDS Prediction using only EHR Data: Model Development and Validation Study (Preprint)

BACKGROUND Acute Respiratory Distress Syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes. OBJECTIVE To perform an exploration of how multi-label classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS. METHODS The electronic health record dataset included 40,073 patient encounters from 7 hospitals from 4/20/2018 to 3/17/2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia and Covid-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic (AUROC). Heatmaps to visualize attention scores were generated to provide interpretability to the NNs. Finally, cluster analysis was performed to identify potential phenotypic subgroups of ARDS patients. RESULTS The single RNN model trained to classify 13 outputs outperformed the XGBoost model for ARDS prediction, achieving an AUROC of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in increasing performance. Earlier diagnosis of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations. CONCLUSIONS The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with means to take early action.

Review of Tsunami early warning system and coastal resilience with a focus on Indian Ocean

Purpose This paper aims to provide a general overview of the international Tsunami warning system mandated by the United Nations, particularly on cataloging past studies and a strategic focus in the Indian Ocean, particularly on the Bay of Bengal region. Design/methodology/approach Present research assimilates the secondary non-classified data on the Tsunami warning system installed in the Indian Ocean. Qualitative review and exploratory research methodology have been followed to provide a holistic profile of the Tsunami rarly warning system (TEWS) and its role in coastal resilience. Findings The study finds the need for strategic focus to expand and interlink regional early warning cooperation mechanisms and partnerships to enhance capacities through cooperation and international assistance and mobilize resources necessary to maintain the TEWS in the Indian Ocean region. The enhanced capacity of the TEWS certainly improves the resilience of Indian Ocean coastal communities and infrastructures. Originality/value The study is original research and useful for policy planning and regional cooperation on data interlinkages for effective TEWS in the Indian Ocean region.

Detecting Extreme Rainfall Events Using the WRF-ERDS Workflow: The 15 July 2020 Palermo Case Study

In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to the heavy rainfall event that affected the city of Palermo on 15 July 2020, causing urban flooding due to an exceptional rainfall amount of more than 130 mm recorded in about 2.5 h. This rainfall event was not properly forecasted by meteorological models operational at the time of the event, thus not allowing to issue an adequate alert over that area. The results highlight that the improvement in the quantitative precipitation scenario forecast skills, supported by the adoption of the H2020 LEXIS computing facilities and by the assimilation of in situ observations, allowed the ERDS system to improve the prediction of the peak rainfall depths, thus paving the way to the potential issuing of an alert over the Palermo area.

Detection Of Water Levels In Sewering Channels As Flood Disaster Early Warning System

In 2020 a number of areas in DKI Jakarta were hit by floods, one of which was the North Tanjung Duren Village, Grogol Petamburan District, West Jakarta. The extreme rainfall of 377 mm/day has flooded almost all areas of DKI Jakarta and its surroundings. In addition, poor drainage causes water to stagnate, causing flooding. However, the absence of an early flood information system makes residents restless when the rainy season arrives. The Community Service (Community service activity) activity carried out aims to provide training to the community in the Tanjung Duren Utara Village area RW 04, RT 0010 regarding water level detection equipment placed in culverts. This tool serves to provide early information if the volume of water in the culvert is full of information in the form of a siren sound. The method used in this Community service activity activity is to provide online counseling and training to the community in RT 0010 / RW 04, North Tanjung Duren Village, Grogol Petamburan District, West Jakarta. The results obtained are an increase in public understanding regarding the dangers of flooding to household electricity, as indicated by the results of the pre-test and post-test which are obtained an average of 43 and 90, respectively. Public understanding of the dangers of flooding to electricity has increased up to 109.3%. In addition, the average partner satisfaction with the material presented is 92%, this shows that the material presented by the presenters team is very useful for the extension participants.