Preemptive Measures Research Articles

Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation

Accurate monitoring of capacity degradation of a lithium-ion battery is important as it enables the user to manage the battery usage for optimal performance/lifetime and to take preemptive measures against any potential explosion or fire. Battery capacity fades gradually through repetitive charging and discharging until it reaches the so called ‘knee-point’, after which it goes through rapid and irreversible deterioration to reach its end-of-life. It is crucial to forecast the knee-point early and accurately for safety and economic use of the battery. Machine learning based methods have been used to predict the knee-point with early cycles cell data. Despite some notable progress made, the existing methods make the unrealistic assumption of constant cycle-to-cycle charge/discharge operation. In this study, a novel two-stage deep learning method is proposed for online knee-point prediction under variable battery usage. A CNN-based model extracts temporal features across past and current cycles to sort out those that should be monitored closely for near-term failures, and then predict the number of cycles left to reach the knee-point for them. The proposed method extracts features from time-series data and thus reflects dynamic changes in battery properties, resulting in improved prediction performance under realistic scenarios.

The COVID-19 Pandemic Response System at University Level: The Case of Safe Campus Model at Ewha Womans University

In response to the changes in the Coronavirus disease 2019 (COVID-19) epidemic situation, Ewha Womans University established Ewha Safe Campus (ESC), an on-campus infection outbreak management system, to allow students and faculty members to safely resume face-to face classes in 2022. The COVID-19 testing station, Ewha Safe Station, is the core element of ESC. Symptomatic students and faculty members perform a combo swab self-PCR test or receive a nasopharyngeal swab PCR test from experts to prevent the spread of COVID-19 through early detection and management. ESC is significant in that it detects infection risks and proactively implements preemptive measures in a university. The COVID-19 health response system model at the university level was applied for the first time in South Korea, reaching a milestone in the history of university health in South Korea. In particular, it is highly valuable that the test was free of charge, as it enabled all of the examinees to have easy access to the test through joint cooperation with the Seegene Medical Foundation. This is a successful example of cooperation between schools and private institutions for public health improvement. In the future, the direct and indirect effects of the establishment and implementation of ESC need to be evaluated and confirmed, and areas requiring improvements need to be identified in preparation for another infectious disease outbreak in the future.

Chronic postsurgical pain in children and young adults with cancer and choice of regional anesthesia for amputation and limb-sparing surgery.

Patients undergoing limb amputation (LA) or limb-sparing (LS) for lower extremity oncologic diagnoses are at similar risk for chronic postsurgical pain of neuropathic nature (CPSP/NP). Regional anesthesia (RA) techniques are pre-emptive measures to prevent the occurrence of the CPS/NP. However, recommendations for epidural (EP) versus peripheral nerve blocks (PNBs) lack in pediatric literature. This study investigates the incidence and duration of CPSP/NP and describes NP-directed regimens. Data on demographics, use of EP or PNB, duration of CPSP/NP, and NP-directed medication were retrospectively collected for LA and LS between 2009 and 2019. Mixed effects logistic regression was used to compare the odds of CPSP/NP between EP and PNB. Cox PH model with adjustment for clustering due to multiple surgeries on patients was used to quantify rate of pain relief between surgery groups (LA vs. LS) and RA groups (EP vs. PNB). The incidence of CPSP/NP was 36 (23.8%) after 165 surgeries (150 patients). The odds of CPSP/NP after PNB were 2.5 times those of CPSP/NP after EP (p=.11). The rate of pain relief at any instant after the EP was 1.2 times that after PNB (p=.3). The rate of pain relief for LS with EP was 1.9 times that of pain relief for LA with EP, a statistically significant difference (p=.03). Gabapentin was used (94.5%), with addition of amitriptyline (24.2%) and both amitriptyline and methadone (12.7%). The LS with the EP group had a significantly higher rate of relief of CPSP/NP than LA with EP. Odds of CPSP/NP after PNB were 2.5 times those of CPSP after EP.

The Nexus between Macroeconomic Variables and Demand for Life Insurance in Malaysia

The insurance industry’s growth has an all-encompassing impact on the economic state of a nation. In the recent era of COVID-19 pandemic, insurance companies experienced a slowdown in the premiums particularly in the life sector. Premium volumes worldwide declined as consumers chose to reduce discretionary expenditure on life insurance policies. Life insurance is one of the ways to provide income protection for the dependents or beneficiaries upon the passing of an insured person, total permanent disability, or maturity of the policy contract. This study therefore takes stock of the recent events in examining the nexus between macroeconomic variables and life insurance demand in Malaysia. The ordinary least square (OLS) methodology is employed using 34 years data spanning from 1988 until 2021. The findings from this study revealed that apart from household savings and the stock market, inflation, income, and unemployment are significant factors in determining the life insurance demand. These empirical findings are expected to contribute to enriching the existing literature and to create awareness of the benefits that life insurance may offer in potential risks transfer to the insurer. From the macroeconomic perspective, the findings may assist policymakers in developing pre-emptive measures to protect life insurance businesses from the negative repercussions of lower market confidence following an economic downturn. An insight into the long-run relationship between the macroeconomic variables and life insurance demand using cointegration techniques is suggested for future researchers.

Is there a Kuznets curve for forest product footprint? – empirical evidence from India

The study examines the validity of the Environmental Kuznets Curve for the forest product footprint in India. Based on the Autoregressive Distributive Lag Model and its non-linear version, we show that India has an N-shaped forest product footprint-induced environmental Kuznets curve. By employing the necessary and sufficient conditions, we show the existence of two real and distinct per capita Gross Domestic Product turning points. The study predicts the out-of-sample turning points using the average annual per capita growth rate and warns about a rise in forest product footprint by 2023. The government of India must, therefore, take pre-emptive measures for the protection of forests.

Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

The bulk of the fault tolerance techniques that are in use today laid their primary emphasis, in the event that a virtual machine fails, on the production of clones to replace it, rather than on the early prediction of the failure itself in advance. Several of the currently used techniques give migration priority over recovery in the event that a virtual machine (VM) fails. This is due to resource constraints and concerns with server availability. Examples of algorithms with a single objective include fault tolerance, migration prediction, and simply expecting failure. Another example is fault tolerance. In this research, we are aiming to determine the most effective strategy to transition from a system that is not operating well to one that does. It is essential to be able to predict the failure of a virtual machine in a timely manner due of issues such as squandered resources, energy, and cost. Since the beginning of cloud computing, there has been an issue with the dependability of virtual computers, often known as VMs. As an integral component of a fault tolerance system, preemptive measures are an absolute need in order to guarantee the continuation of service. As a consequence of this, it is vital to work toward enhancing and emphasizing the proactive failure prediction of virtual machines. The key motivations for this are decreased periods of downtime and enhanced scalability. A technique was utilized to transfer the resources that were predicted to fail from one virtual machine (VM) to another VM in a safe manner. Using the compression strategy reduced the amount of time needed to complete the migration, and resource utilization increased. This article provides artificial intelligence that enables effective fault prediction techniques in cloud computing to improve resource optimization.

중산층의 주거지위는 지난 10년간 어떻게 변했는가?

This article explores the middle class segment of the population in terms of housing status. The middle class is defined as those living in households with incomes ranging between 75% and 200% of the national median presented by the OECD (2019). The housing status is operationally defined by compounding three factors: housing tenure types, housing-related debt, and real estate assets. Microdata from the Household Welfare Financial Survey (2012~2021) of the National Statistical Office were used in the analysis. The main results of identifying the role of housing in maintaining and reproducing the middle class over the past decade are as follows. First, although the proportion of middle-class population has somewhat increased recently, this does not mean that the economic contributions of the middle class has increased. Consequently, it is necessary to develop new indicators that can represent a society with a sizable middle class. Second, housing polarization, which is being differentiated within the middle class, was clearly revealed. Addressing the missing middle issues or marginalized middle class, it is necessary to identify the policy needs and prepare preemptive measures such as expanding affordable housing and alleviating the housing cost burden. Third, in the recent global discourse on housing, South Korea was no exception. The middle class with housing loans become richer compared to middle class without housing loans. Those in the middle class with real estate assets become richer than those without. On the other hand, for the middle class without home ownership but who took out housing loans for rent, the house only represents a debt to be paid back and an object to be struggled with.

Policy styles and pandemic management: The case of Turkey.

Using the concept of a policy style developed by Howlett and Tosun, this article explores the linkages between Turkey's policy style and its pandemic management. Turkey took both preemptive and restrictive measures with respect to the COVID‐19 pandemic. Before the virus hit the country, flights to and from various countries including China were suspended, the Scientific Advisory Board (SAB) was established, and a COVID‐19 disease guide was prepared. After the virus entered the country, immediate restrictive measures, such as national lockdowns and international and intercity travel restrictions were put in place. Turkey adopted a centralized response to the pandemic with the President, the Ministry of Interior, the Ministry of Health, and the SAB acting as central players. From the perspective of policy style, this article shows how Turkey's general characteristics of policy‐making processes have been reflected in its pandemic management policies.

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021.

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5–19.7%, weekday; 19.4–31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8–34.0%, weekend), and from July to September 2021 (3.2–13.1%, weekday; 38.3–44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system called the Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to 3 days in advance at 1 km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is used to test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system. The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1- and 2-days lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

CFD를 이용한 보행자 풍환경 평가 기법의 적합성 분석

Recently, due to increased urbanization with high-rise buildings, there are growing concerns about the wind environment that threatens the safety of pedestrians as well as facility damage caused by the building wind. In order to reduce building wind damage, it is important to improve the wind environment of the urban environment by applying preemptive measures through the evaluation of the wind environment around buildings. Besides conventional wind tunnel tests, CFD has recently been accepted an alternative or a supplementary tool for the assessment of wind environment on the pedestrian level. As one of the practice in the field, MOLIT recently proposed Wind Environment Evaluation Guidelines Using CFD Analysis Techniques. This paper presents a computational study to confirm the feasibility of the pedestrian wind environment evaluation method using CFD based on the guideline. To verify the results of CFD, models and wind tunnel experiment data by provided by Architectual Institute of Japan were employed. Various grid configurations and numerical methods as well as varied wind directions were applied. The comparison between experimental data and the present results show that the grid refinement improves the accuracy and the pedestrian wind can be assessed by using CFD in spite of some deficit and room for improvements. More importantly, the gusts caused by the upwind and downwind in upwind region of the building, and vortex around the building were well simulated.

Peranan Kepolisian Dalam Menanggulangi Tindak Pidana Pencucian Uang (Studi Di Kepolisian Daerah Sumatera Utara)

This study aims to determine and understand the roleof the police in dealing with money laundering. -views inbuilding legal arguments for data collection Researchersalso conduct field studies (Field Research) by conductingDirect Interviews conducted at the North Sumatra Regional Police (Poldasu) The role of the police incarrying out their duties as law enforcement there areobstacles, namely good from internal and external factors Therole of the police in tackling the crime of moneylaundering includes pre-emptive measures. preventive and repressivemeasures This regulation related to money laundering isbased on Law Number 8 of 2010 concerning the preventionand eradication of money laundering in Indonesia, PenalEfforts (repressive) and non-penal (preventive) measuresthat the Police can undertake to tackling the crime of moneylaundering in the context of carrying out its duties as aState apparatus that maintains public security andorder, enforces the law, and protects. Protecting andserving the community is based on Law Number 2 of 2002concerning the Indonesian National Police

SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

Natural disasters have a significant impact on human welfare. In recent years, disasters are more violent and frequent due to climate change, so their impact may be higher if no preemptive measures are taken. In this context, real-time data processing and analysis have shown great potential to support decision-making, rescue, and recovery after a disaster. However, disaster scenarios are challenging due to their highly dynamic nature. In particular, we focus on data traffic and available processing resources. In this work, we propose SLedge—an edge-based processing model that enables mobile devices to support stream processing systems’ tasks under post-disaster scenarios. SLedge relies on a two-level control loop that automatically schedules SPS’s tasks over mobile devices to increase the system’s resilience, reduce latency, and provide accurate outputs. Our results show that SLedge can outperform a cloud-based infrastructure in terms of latency while keeping a low overhead. SLedge processes data up to five times faster than a cloud-based architecture while improving load balancing among processing resources, dealing better with traffic spikes, and reducing data loss and battery drain.

Steroid therapy still plays a crucial role and could serve as a bridge to the next promising treatments in patients with IgG4-related sclerosing cholangitis: Results of a Japanese Nationwide Study.

The acceptable duration of steroid therapy for patients with IgG4-sclerosing cholangitis (SC) has been under debate. Our aim is to clarify the feasible duration of steroid treatment. We retrospectively reviewed the data of patients with IgG4-SC and analyzed the following: biliary status during the steroid therapy, incidence of remission, relapse, relapse-free survival rate, and steroid-related complications (SRCs). Remission was achieved 99.5% (763/767) of patients who received steroid therapy, while remission rate dropped to 63.6% (78/129) of patients who didn't receive it. Relapse was noted in 19.7% (151/763) of the patients who received steroid. Besides, relapse rate went up 38.4% (30/78) of the counterpart. Normalization of the serum total bilirubin and serum alkaline phosphatase (ALP) levels were achieved at two weeks regardless of biliary drainage. Multivariate analysis identified younger onset, MST less than three years, immunosuppressant, and steroid cessation as independent risk factors for relapse. Steroid-free was achieved in the patients underwent MST only 3.4% over 54 months. SRCs were recorded in a total of 99 patients (12.9%) despite sufficient preemptive medications. Multivariate analysis identified history of malignancy and immunosuppressant as independent risk factors for SRCs. Steroid therapy should be continued for no less than three years to reduce the risk of relapse, with use of preemptive measures taken around five years. The biliary drainage might not be mandatory. Steroid as 1st line therapy could serve as a bridge to further promising treatments.

Soil sampling plan design of key facilities for denuclearization based on data quality objective process

The possibility of denuclearization of the Korean Peninsula has been continuously debated, and the initiative participation of the Republic of Korea has necessitated preemptive measures against neighboring countries. In this study, we present a proposal for formulating a site survey plan when the amount of site information provided is insufficient and the accuracy of the information is not guaranteed. Considering a case wherein “a soil sample analysis is used to determine the presence or absence of nuclear activity” in a radiochemical laboratory, which is a typical key facility for denuclearization, the optimal soil sample collection plan is designed based on international guidelines and public information. In the event of denuclearization, a scenario that is not based on the expertise of the sample collector is set, and the data quality objective (DQO) process is applied to ensure reality. Consequently, the primary sample collection points can be derived in consideration of accessibility, and the sample collection scale can be adjusted according to the cost. By applying the DQO process to ensure sample representativeness and reality, reliable and resource-efficient soil sample collection can be achieved in radiochemical laboratories and other denuclearization facilities.

Haemodynamic changes during radical nephrectomy with inferior vena cava thrombectomy: A pilot study.

Haemodynamic changes during radical nephrectomy with inferior vena cava thrombectomy: A pilot study.

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning

BackgroundInflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.AimTo accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice.MethodsWe built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data.ResultsWe included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70–0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61–0.90).ConclusionsThis study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.

Introducing macrophages to artificial immune systems for earthquake prediction

Earthquake prediction (EQP) is crucial for taking preemptive measures and accurately predicting damage. Several historical seismic-event-based EQP approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, reducing the prediction accuracy. Macrophages play an important role in the immune system by recognizing viruses, apoptotic cells, and normal cells, as well as performing immune responses and suppression to ensure homeostasis; that is, macrophages exhibit strong classification capabilities and self-adaptability. Therefore, in this study, a novel artificial macrophage algorithm (AMA) for EQP is proposed. More specifically, we first introduce the biological mechanism of macrophages to establish recognition and learning mechanisms to identify noise and anomalies. Second, we adopt a distance metric to denote the weights of the AMA, instead of using experience-based parameters. Finally, a stochastic gradient descent is introduced to ensure the adaptability of the AMA. The performance of the AMA was assessed through an analysis of historical seismic events in Sichuan and its surroundings. Our experimental results demonstrate that AMA outperforms state-of-the-art EQP algorithms. The parameters and statistical tests of AMA were further analyzed in this study.

Trust in COVID-19 policy among public health professionals in Israel during the first wave of the pandemic: a cross-sectional study

BackgroundThe COVID-19 pandemic has highlighted the important role of professionals in designing and communicating effective policies. The purpose of this study was to evaluate the level of trust in the COVID-19 national public health policy among public health professionals in Israel and its correlates during the first wave of the pandemic.MethodsA purposive sampling of public health professionals in Israel, through professional and academic public health networks (N = 112). The survey was distributed online during May 2020. Level of trust was measured by the mean of 18 related statements using a 5-point Likert scale, where 1 means not at all and 5 means to a very high extent, and grouped as low and high trust by median (2.75).ResultsA moderate level of trust in policy was found among professionals (Mean: 2.84, 95% Cl: [2.70, 2.98]). The level of trust among public health physicians was somewhat lower than among researchers and other health professionals (Mean: 2.66 vs. 2.81 and 2.96, respectively, p = 0.286), with a higher proportion expressing low trust (70% vs. 51% and 38%, respectively, p < 0.05). Participants with a low compared to high level of trust in policy were less supportive of the use of Israel Security Agency tools for contact tracing (Mean = 2.21 vs. 3.17, p < 0.01), and reported lower levels of trust in the Ministry of Health (Mean = 2.52 vs. 3.91, p < 0.01). A strong positive correlation was found between the level of trust in policy and the level of trust in the Ministry of Health (rs = 0.782, p < 0.01). Most professionals (77%) rated their involvement in decision making as low or not at all, and they reported a lower level of trust in policy than those with high involvement (Mean = 2.76 vs. 3.12, p < 0.05). Regarding trust in the ability of agencies to deal with the COVID-19 crisis, respondents reported high levels of trust in the Association of Public Health Physicians (80%) and in hospitals (79%), but very low levels of trust in the Minister of Health (5%).ConclusionsThis study shows that Israeli public health professionals exhibited moderate levels of trust in COVID-19 national public health policy and varied levels of trust in government agencies during the first wave of COVID-19. The level of trust in policy was lower among most of the participants who were not involved in decision making. The level of trust found is worrisome and should be monitored, because it may harm cooperation, professional response, and public trust. Professionals’ trust in policy-making during early stages of emergencies is important, and preemptive measures should be considered, such as involving professionals in the decision-making process, maintaining transparency of the process, and basing policy on scientific and epidemiological evidence.

Development of pups born to rats established as a model of underweight Japanese women and the onset of impaired glucose tolerance

An increasing number of Japanese women of childbearing age are underweight (BMI <18.5), but the association between this and the increased number of low-birth-weight babies born remains unclear. Here, a rat model was established to mimic the undernutrition (85% of the energy required for those with normal activity levels) experienced by such women and to evaluate the associated impaired glucose tolerance. The undernourished Wistar rat group showed increased serum corticosterone level reflecting stress, and greater adrenal weight and size. It also showed greater insulin resistance, higher expression of FOXO-1, a transcription factor related to muscle atrophy, and lower expression of p-Akt, an insulin-dependent signaling factor. Overall, this work shows the key role of undernutrition during pregnancy as a cause of impaired glucose tolerance and increased diabetes risk in offspring. The findings of this study may inform preemptive measures to prevent the development of metabolic syndrome in offspring of undernourished mothers.