Bad Events Research Articles

Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul

Accurate prediction of PM2.5 concentrations is essential for public health management, especially in areas affected by long-range pollutant transport. This study presents a hybrid model combining convolutional long short-term memory (ConvLSTM) and deep neural networks (DNNs) to enhance PM2.5 forecasting in Seoul, South Korea. The hybrid model leverages ConvLSTM’s ability to capture spatiotemporal dependencies and DNN’s strength in feature extraction, enabling it to outperform standalone CMAQ and DNN models. For the T1 forecast (6 h averages), the ConvLSTM-DNN model exhibited superior performance, with an RMSE of 7.2 µg/m3 compared to DNN’s 8.5 µg/m3 and CMAQ’s 10.1 µg/m3. The model also maintained high categorical accuracy (ACC) and probability of detection (POD) for critical PM2.5 levels while reducing false alarms (FARs), particularly in bad and very bad events. Although its performance decreases over extended forecast periods, the ConvLSTM-DNN model demonstrates its utility as a robust forecasting tool. Future work will focus on optimizing the network structure to improve long-term forecast accuracy.

Venturing Travelers vs. Home Stayers Under Extreme Risk

ABSTRACT This study explores risk-taking during a pandemic, a severe risk condition similar to terrorism and natural disasters. It examines differences in sociodemographics and psychographics between individuals who traveled during the COVID-19 Pandemic and those who stayed home. Conducted as an online survey in 2022, the study used a randomized experiment. T-tests revealed significant differences in only two sociodemographics and two risk-taking factors of probability neglect and proximity-to-self, but not in personality traits of risk-taking, thrill-seeking, or self-confidence. Travelers were more likely to have young children and higher income while non-travelers scored higher in neglecting the probability of bad events happening to them, potentially because they avoided travel as they perceived a high likelihood of bad events happening while traveling.

Health survey on anxiety, depression, and stress in Afghanistan: A large-scale cross-sectional study amid ongoing challenges

BackgroundOne of the most significant events in recent Afghan history is the rise of the Taliban and the war that followed, which had profound impacts on the lives of Afghans. The present study examined the mental health of Afghans living under the Taliban government.MethodsBetween June 5, 2023 and February 12, 2024, a cross-sectional study was conducted among the Afghan population in three key regions of Afghanistan. Data were collected using a pre-tested structured questionnaire. The 21-item Depression, Anxiety and Stress scale (DASS-21) was utilized to assess depression, anxiety and stress of the Afghan population. Ethical permission for this study was granted by the Afghanistan Center for Epidemiological Studies (ACES). Logistic regression models were employed to explore the relationship between socio-demographic characteristics and depression, anxiety, and stress among 2,698 participants.ResultsThe prevalence of depression was 72.05%, anxiety was 71.94%, and stress was 66.49%. Multiple regression analysis indicated that gender (being female), economic status (being poor), residency (living in rural areas), education level (being illiterate), being a cigarette smoker, and having experienced a bad event during the past month were significantly associated with depression, anxiety and stress.ConclusionThe findings of the present study show very high levels of anxiety, depression, and stress, most likely reflecting the profound impact of recent political, social, and economic changes. Notably, a significant majority of participants, particularly females and individuals above 35 years of age, reported severe to extremely severe mental health symptoms. The mental health crisis in Afghanistan is a complex and urgent issue that requires a comprehensive and compassionate response.

The Benefits of Bad Events for Narrative Characters: Limits of Symhedonia as a Hidden Gem

The Benefits of Bad Events for Narrative Characters: Limits of Symhedonia as a Hidden Gem

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.

Fake news text detection based on convolutional neural network

The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.

Do investors prefer multiple small bad news events or a single big one? Evidence from the Chinese stock market

This paper focuses on the relation between the hedonic editing hypothesis and the stock market. Based on the data of China's stock market from 2014 to 2020 and the utilization of an exogenous shock arising from the “New Regulation of Insider Selling”, we find that investors will respond more pessimistically to the negative information that is on the same event but released multiple times. This result provides new evidence for the hedonic editing hypothesis to be tenable in the capital market.

Exploring false negatives in self-reported trauma exposure status.

Trauma exposure is often assessed using checklists such as the Life Events Checklist for DSM-5 (LEC-5; Weathers et al., 2013b). When participants endorse multiple events, respondents are asked to identify a single, worst event (i.e., index event). Recent work indicates that the "worst event" method leads to a concerning number of false negatives. The purpose of the current study was to replicate previous findings of false negatives and extend them by examining characteristics associated with false negatives, such as trauma type, means of exposure, recency of trauma, and posttraumatic stress disorder (PTSD) symptom severity. Adults (n = 476) provided data on trauma history assessed using a revised version of the LEC-5 that asked participants to provide follow-up information for each traumatic event endorsed. Participants also provided demographic data and completed the PTSD Checklist for DSM-5. Results: Two hundred thirty-four participants (49.16%) reported a worst event that met the DSM-5 definition of Criterion A trauma ("primary Criterion A" group). However, of the 242 participants who did not, 138 participants (57.02%, or 28.99% of the total sample) reported a secondary event that did meet Criterion A ("secondary Criterion A" group). The secondary Criterion A group most commonly reported serious life-threatening illnesses/injuries and "other" stressful life experiences as their index trauma that did not fulfill Criterion A. Participants in the primary and secondary Criterion A groups reported similar levels of PTSD symptoms. No differences were observed in means of exposure and recency of index trauma between the Criterion A groups. Findings raise questions regarding the efficiency and accuracy of the worst event method to determine trauma exposure status via self-report. Researchers should consider alternative methods for assessing trauma exposure rather than relying on the worst event scoring method. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Emotional Impact of Patient Loss on Brazilian Veterinarians.

Veterinarians, unlike human physicians, could potentially care for the patient for several years, from conception to end-of-life care. Because of their close relationship with the animal, healthcare providers (for example, veterinarians and staff) are more likely to be affected by bad events and end-of-life care. The purpose of this study was to assess the emotional impact of patients' deaths on Brazilian veterinarians; 549 Brazilian veterinarians (78.3% females) completed a 20-item online questionnaire. Females were more emotionally affected than males by having to talk to the owner about their animal's death and more emotionally affected by the animal's death itself. Furthermore, the emotional impact of an animal's death was heavily influenced by the number of animals euthanized and varied greatly across veterinarians based on their age, with vets over 50 years old being less affected than vets between the ages of 31 and 40. The majority of responders (91.0%) were not trained to deal with grief during their degree. Those who had some training reported being less affected by bereavement. These findings indicate that patient death is a significant emotional concern for veterinarians. Specific education during the degree course, aimed at preparing future veterinarians to deal with death and death communication, is lacking but necessary.

RESILIENSI REMAJA YANG PERNAH MENGALAMI KEHAMILAN TIDAK DI INGINKAN AKIBAT HUBUNGAN SEKS SEBELUM MENIKAH: STUDI KUALITATIF

Teenagers who are unmarried and experience unwanted pregnancies get negative effects, namely experiencing psychological stress. The main stress experienced is fear of stigmatization from the environment, negative reactions from partners, parents feel disappointed, and expulsion from school. Teenagers get a lot of negative impacts and experience difficult situations from unwanted pregnancies due to premarital sexual relations and new roles as young mothers so they need to have the ability to adapt and survive when experiencing bad or broken events in their lives which are known as resilience. The method used in this study is qualitative while the focus of the research is to describe the resilience of adolescents who have experienced unwanted pregnancies as a result of premarital sex. The results of this study are that all informants are currently in the process of becoming resilient. The resilience within the three informants comes from three sources, namely I have, I am, and I can resilience.
 
 Key word: Premarital sex, Resilience, Unwanted Pregnancy

LINGUISTIC APPROACH TO THE INTERPRETATION OF THE CONCEPT OF ‘A COMMUNITY WITH A SHARED FUTURE FOR MANKIND’

This article is devoted to the interpretation of the key Chinese diplomatic concept of ‘Renlei Mingyun gongtongti’ (‘a community with a shared future for mankind’) based on the theory of Natural Semantic Metalanguage (NSM), which tries to reduce the semantics of all lexicons to a limited set of semantic primitives. The goals of the study are, firstly, to reveal the essence of this concept in Chinese, secondly, to analyze the options for its translation into English and Russian, and thirdly, to interpret it with the help of Minimal Language, which is the latest development of the theory of NSM. ‘Renlei Mingyun gongtongti’ consists of three parts: ‘renlei’ means the whole of humanity, ‘mingyun’ means all the good and bad events (opportunities and challenges) that humanity will face in the future, and ‘gongtongti’ means the mutual exchange of opportunities and responsibility to challenges. Due to the complexity of these concepts, questions of their perception also arise. These questions force us to find another way of interpretation, namely with the help of words that are semantically simpler, objective and almost universal in all languages of the world, which help to maximize the simplicity and universality of the interpretation of more complex diplomatic concepts. The scientific novelty of the study is that this is the first attempt by Chinese russianists to interpret this diplomatic concept using Minimal Language.

Experiences of Indonesian Adolescents Losing Parents Due to COVID-19: a Phenomenological Study

Background: Adolescents losing their parents is the worst event they experience during the COVID-19 pandemic. Adolescents raise a traumatic response after a loss occurs.Objective: This study aims to explore the description of the condition of adolescents in dealing with the loss of parents due to COVID-19.Methods: Qualitative research design with a phenomenological approach. The number of participants is 12 people with the criteria of youth aged 10-19 years consisting of men and women who have lost their parents due to COVID-19. Interview data were processed using the Colaizzi technique.Results: Four themes through the theme analysis, namely adolescents are not ready to face sudden loss, adolescents experience suffering due to the loss of parents, adolescents find it difficult to go through the loss process, and changes occur in adolescents due to the loss of parents.Conclusion: The findings of this study show that the sudden loss of parents due to COVID-19 harms adolescent mental health. As a result, mental health nurses in the community need to detect adolescents early for signs of stress and anxiety due to the loss of parents and take steps to strengthen adolescents' self-defense after loss.

A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia.

In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.

PERAN SASTRA DALAM PENDIDIKAN ISLAM

This paper describes the urgency of literary works as an important learning material in Islamic boarding schools. This condition can be seen in the story learning method which can shape character through appreciation of every side of life so that people can learn to imitate these characters when they are good, and also not to repeat bad events in the story. In this case, literary works can become learning media and methods, even learning strategies and insights in looking at things with a new paradigm. This paper concludes that reading Islamic literature more often can form a broad perspective for its readers, so that they can better appreciate the greatness of God.

Why the 2022 Po River drought is the worst in the past two centuries.

The causes of recent hydrological droughts and their future evolution under a changing climate are still poorly understood. Banking on a 216-year river flow time series at the Po River outlet, we show that the 2022 hydrological drought is the worst event (30% lower than the second worst, with a six-century return period), part of an increasing trend in severe drought occurrence. The decline in summer river flows (-4.14 cubic meters per second per year), which is more relevant than the precipitation decline, is attributed to a combination of changes in the precipitation regime, resulting in a decline of snow fraction (-0.6% per year) and snowmelt (-0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year) and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological impact of climate change is exacerbated by local changes in hydrologic seasonality and water use.

Post-FOMC Drift

We study the patterns of stock returns around the Federal Reserve monetary policy announcements. Much of the existing literature interprets changes in short rates around the announcement windows as policy surprises. In contrast, we follow the “Fed information effect” literature, which posits that financial markets react to central bank announcements not just for unexpected changes in monetary policy stances (monetary policy news), but also for central bank’s assessment of economic conditions (non-monetary policy news). We identify the good/bad news using a combination of sign restrictions with high-frequency financial data. “Bad news” events are times when the market interpreted the Fed decisions/announcements as revealing negative Fed information about the economy, and vice versa for “good news” events. A novel finding is that following bad news events, we observe significantly positive stock returns in a 20-day period. This observation is largely consistent with a story of asymmetric effects of good and bad news on the level of uncertainty. Further analysis shows that the post-FOMC drift to economic news in Fed announcements is a market-wide phenomenon. A trading strategy that buys following “bad news” earns an excess return of 2.5% per year with a Sharpe ratio of 0.43.

The framework of Hammer (CAFÉ) credit rating for capital markets in China with international credit rating standards

AbstractThe goal of this paper is to discuss how we establish the so‐called “Hammer (CAFÉ) Credit System” by applying the Gibbs sampling algorithm under the framework of a big data approach supported by both traditional structured and unstructured data as a breakthrough, in particular, to extract those highly related risk features in depicting default (bad) events and related fraudulent behaviors (action) by following the “five step principle” incorporated with the international credit rating standards in the practice. The analysis shows that our Hammer (CAFÉ) Credit System is able to handle current three issues raised by the credit rating business for capital markets in China, which are as follows: (1) The rating is falsely high, (2) the differentiation of credit rating grades is insufficient, and 3) the performance in predicting early warning and related issues is poor. In addition, the Hammer (CAFÉ) credit discussed in this paper is supported by clearly defining the “BBB” grade rate as the basic investment level associated with the annualized rate of default probability, and the credit transition matrix for “AAA‐A” to “CCC‐C” credit grades in accordance with international standards used in the practice of risk management and decision services.

Depression, anxiety and quality of life of Afghan women living in urban areas under the Taliban government: a cross-sectional study

ObjectivesAccording to the World Health Organization, depression is a common mental health illness that is characterised by a persistent feeling of sadness and loss of interest. The present study examined...

The impact of robotics on development, of hot and cold executive functions, and their role in improving them in special education

Executive function (EF) skills are neurocognitive skills that support the reflective, top-down coordination and control of other brain functions, and there is neural and behavioral evidence for a continuum from more "cool" EF skills activated in emotionally neutral contexts to more "hot" EF skills required for motivationally significant tendency reversal. EF problems are transdiagnostic markers of abnormal development. A neurodevelopmental model follows the path from bad childhood events and stress to disturbance of the development of brain systems supporting reflection and EF skills and an increased risk for general psychopathology traits. Educational robotics is generally concerned with researching the effects of building and programming robots on children's learning and academic accomplishment. We recently discovered that engaging in progressively more difficult robot planning and monitoring (ER-Lab) promotes visual-spatial working memory and response inhibition in early childhood during typical development, and that an ER-Lab can be a viable rehabilitative tool for children with Special Needs. Children with Special Needs (SN) had considerably enhanced inhibition skills, and children with attentional impairment had greater gains in inhibition of motor response tasks than children with a language deficiency. The study's findings and future prospects for how ER-Lab programs could become a strong tool in classrooms with special needs children are highlighted. The key also conclusion was that there was a considerable improvement in visuo-spatial attention as well as a significant effect on robot programming skills. According to research, EF abilities can be developed through scaffolded training and are a promising therapeutic and preventive intervention target. Intervention efficacy can be increased by reducing disruptive bottom-up effects like stress, teaching both hot and cool EF skills, and incorporating a reflective, metacognitive component to facilitate far transfer of trained skills.

Behavioral aspects of household portfolio choice: Effects of loss aversion on life insurance uptake and savings

This paper provides empirical evidence that loss aversion decreases insurance demand and increases savings demand using individual-level data on the US elderly. Specifically, loss-averse individuals have a significantly lower ownership rate of term life insurance which is pure insurance, and they have a higher ownership rate of whole life insurance, which has a savings element. This paper also shows that loss-averse individuals own a smaller amount of stock (equity) than others and a greater amount of non-risky financial assets, such as deposits and bonds. This aligns with the prospect theory's prediction that boundedly rational consumers may view pure protection insurance (term life insurance) as a “risky investment” like stocks because the insured may just lose the premiums if a bad event does not occur during the policy term. They instead opt for “safer options” such as whole life insurance, which guarantees payment of a death benefit, or deposits and bonds. Furthermore, this paper provides suggestive evidence that loss aversion has a negative impact on insurance demand beyond the US, by showing that there is a negative association between loss aversion and insurance penetration rates in 51 countries.