Future Situations Research Articles

The Influence of Incentives on Drivers’ Aggressive Behaviors Toward Automated Vehicles

In the process of merging into the mixed traffic flow composed of human driven vehicles, automated vehicles may be attacked by human drivers. The purpose of this study was to compare the inhibitory effect of four incentive measures, including reward, punishment, education, and warning, on aggressive behaviors of drivers in automated vehicles and to explore their impact mechanism. In this study, 410 participants were recruited and divided into five groups, one of which was the control group, and the other four groups were given an incentive measure. The study found that reward and punishment significantly inhibit aggressive behaviors, although their influence mechanisms are different, while education and warning have no significant inhibition effect. Our research expands the theory of human-vehicle interaction, enriches the specific measures to inhibit the aggressive behavior of humans to autonomous driving, and provides some empirical suggestions for the future hybrid traffic situation of humans and vehicles. In the face of the upcoming mixed traffic of people and vehicles in the future, in order to ensure the good order of the road, it is necessary to take effective measures against human drivers to reduce their attacks on automated vehicles. On the one hand, relevant government departments need to play a guiding role in educating human drivers; on the other hand, manufacturers in related industries need to consider the above factors in the design of automated vehicles functions to make human-vehicle hybrid traffic safe and efficient.

How ChatGPT shapes the future labour market situation of software engineers: A Finnish Delphi study

ChatGPT is changing our working lives, conjuring visions of revolutionary shifts ahead. ChatGPT is a chatbot based on generative artificial intelligence, which can, e.g., write computer code. This article explores how it might shape the future labour market situation of software engineers. A Delphi study with 14 experts was conducted in Finland. The first round identified possible futures, and the second round assessed their probabilities. Five scenarios prevailed: the unlikely scenario that the status quo persists; the ambivalent scenario that ChatGPT can replace software engineers to a large extent; the likely scenario that computer departments in startups embrace ChatGPT; the likely scenario that ChatGPT use proliferates among software engineers to increase productivity; and the highly likely scenario that ChatGPT makes computer programming accessible to the masses. Findings contradict previous discussions that technological advancements might take over especially routine tasks. ChatGPT can also take over non-routine tasks. Moreover, findings underline that digitalisation does not only bring about a choice between upskilling and employability loss, but also a democratisation of knowledge and expertise. Software engineers and companies might use the findings as an impetus for upskilling, while universities might feel nudged to incorporate ChatGPT more strongly into their curricula.

Kidney Transplantation During COVID-19 Outbreak: Data From Public and Non-Public Dialysis Network

COVID-19 pandemic highly influenced on medical services in Poland. All restrictions, and additional procedures as well as numerous infections among medical staff highly affected transplantation in the country.Authors analyzed reports prepared by Polish Transplant Coordination Center Poltransplant and internal Fresenius Nephrocare Poland reports to assess difference in number of patients qualified for kidney transplantation and transplanted in pandemic period in comparison in pre-pandemic year.During SARS-CoV-2 outbreak number of qualified and transplanted patients was significantly lower comparing with pre-pandemic period. The most worsened data concerns the new qualifications which was significantly lower in the first year of pandemic as the effect of all restrictions implemented.The number of Kidney Tx procedures provided during 2-years pandemic period decreased significantly (-20.8%) in 2020 and in the second year the negative trend has been continued (-0.8%).Situation of private dialysis provider concerning number of active patients on waiting list for kidney transplantation was a bit better – decreased from 265 to 239 in 2020 (-9.8%) and increased to 259 in 2021 (+8.4%).The decline in the number of patients treated in Fresenius Nephrocare dialysis centers transplanted was deeper and felt down 27.8% in 2020 in comparison with 2019. In 2021 number of transplanted patients slightly increase about 2.5%.Results – decreased number of qualified and transplanted patients during SARS-CoV-2 outbreak clearly show the need of undertake multidisciplinary discussion among all stakeholders to create new procedures and processes which will help to protect the healthcare system and patients in case of future crisis situation.

How do you happen to feel? The effect of certainty–uncertainty appraisals of incidental emotions on consumer affective forecasting error

AbstractConsumers frequently forecast how their choices will make them feel in future consumption situations. One possible influence during such affective forecasting is their current emotional state. Although the impact of incidental emotions on consumer decision making is well established, limited research has examined the impact of incidental emotions on affective forecasting. The purpose of the present research is to contribute to the understanding of the effects of certainty–uncertainty, as a key appraisal dimension of incidental emotions, on the process and outcome of affective forecasting. The results of four experimental studies demonstrate that experiencing uncertainty associated incidental emotions, such as fear and hope, during affective forecasting, leads to smaller forecasting error compared with experiencing certainty emotions, such as anger and happiness. Furthermore, heuristic processing, as a result of the certainty versus uncertainty appraisals, plays a mediating role in the effect of certainty–uncertainty on forecasting error. The findings of the present research extend the effects of the certainty–uncertainty appraisals in the context of consumer affective forecasting and elucidate the impact of incidental emotions on decision making.

Crop Price Prediction using Machine Learning

Crop planning for the next season in agriculture is a difficult task for farmers because it is difficult to predict the price of the product in a particular season depending on the weather. Accurate and timely estimation of crop prices is important in making planting, harvesting and marketing decisions. Crop prices cannot be predicted accurately using crop price methods. This problem can be solved with machine learning models that will predict crop prices, show crop analysis patterns and present future situations to farmers. Farmers choose the right crop to produce crops; this includes crop selection, setting crop standards and storage. harvested crops provide sufficient information to estimate the appropriate crop price as a business. It will be used for past, present and future rainfall as well as last year's value. Based on this framework, machine learning algorithms are used to predict crop prices and produce accurate crop price prediction results. It helps farmers and others in agriculture make better decisions about crops. With the help of this survey, researchers will help find the best ideas and methods to predict crop prices using machine learning.

Present and future situation of West Nile virus in the Afro‐Palaearctic pathogeographic system

West Nile virus (WNV) is a globally widespread arthropod‐borne virus that poses a significant public health concern. Mosquitoes transmit the virus in an enzootic cycle among birds, which act as reservoirs. Climate plays a crucial role in these outbreaks as mosquitoes are highly influenced by climatic conditions, and bird migrations are also affected by weather patterns. Consequently, changes in climate can potentially impact the occurrence of WNV outbreaks. We used biogeographic modelling based on machine learning algorithms and fuzzy logic to analyse and evaluate separately the risk of WNV outbreaks in two different biogeographic regions, the Afrotropical and the Western Palaearctic region. By employing fuzzy logic tools, we constructed a comprehensive risk model that integrates the Afro‐Palaearctic system as a unified operational unit for WNV spread. This innovative approach recognizes the Afro‐Palaearctic region as a pathogeographic system, characterized by biannual connections facilitated by billions of migratory bird reservoirs carrying the disease. Subsequently, we forecasted the effects of different climate change scenarios on the spread of WNV in the Afro‐Palaearctic system for the years 2040 and 2070. Our findings revealed an increasing epidemic and epizootic risk south of the Sahara. However, the area where an upsurge in risk was forecasted the most lies within Europe, with the anticipation of risk expansion into regions presently situated beyond the virus' distribution range, including central and northern Europe. Gaining insight into the risk within the Afro‐Palaearctic system is crucial for establishing coordinated and international One Health surveillance efforts. This becomes particularly relevant in the face of ongoing climate change, which disrupts the ecological equilibrium among vectors, reservoirs, and human populations. We show that the application of biogeographical tools to assess risk of infectious disease, i.e. pathogeography, is a promising approach for understanding distribution patterns of zoonotic diseases and for anticipating their future spread.

The synergy effect of multi-country policy actions announced in reaction to global risk: A network structure perspective.

The policy actions of countries reflect adaptive responses of local components within the system to the dynamic global risk landscape. These responses can generate interactions and synergy effects on alleviating the evolution of global risks. Adopting a network perspective, the study proposes a theoretical framework that connects three structural characteristics of policy synergy, namely, synergy scale, alignment intensity, and timing synchronization. Focusing on the Covid-19 pandemic as a typical global risk context, the study finds that policy synergy with a larger scale, stronger alignment intensity, and more synchronized timing has a positive impact on mitigating global risks. The effect of alignment intensity is particularly pronounced when polycentric governance involves 20 countries facing severe risks, whereas the effect of timing synchronization is more significant when the multicenter group comprises more countries. Building upon the concept of an efficient scale of polycentric governance in various dimensions, this study develops a policy synergy index model. Through multiple empirical analyses, this study validates the causal relationship between policy synergy and the future evolution of global pandemic risk. Policymakers can leverage the dynamic changes in the policy synergy to predict future risk situations and implement well-rounded and appropriate policy actions, thereby enhancing the efficacy of the synergy effect of multi-country policy actions for risk governance.

Behavioural evidence to inform the COVID-19 pandemic response: Ireland's Social Activity Measure (SAM)

Abstract Humankind's main defence against the virus that cuases COVID-19 (SARS-CoV-2), besides vaccine development, was co-ordinated behaviour change. In many countries, co-ordination was assisted by tracking surveys designed to measure self-reported behaviour and attitudes. This paper describes an alternative, complementary approach, which was undertaken in close collaboration with officials in the Department of the Taoiseach (Irish Prime Minister). We adapted the Day Reconstruction Method (DRM) to develop the ‘Social Activity Measure’ (SAM). The study was conducted fortnightly for 18 months, with findings delivered directly to the Department. This paper describes the method and shows how SAM generated a detailed picture of where and why transmission risk occurred. By using the DRM, we built aggregate measures from narrative accounts of how individuals spent their previous day. SAM recorded the amount, location and type of social activity, including the incidence of close contact and mask-wearing, as well as compliance with public health restrictions by shops and businesses. The method also permitted a detailed analysis of how public perceptions and comprehension are related to behaviour. The results informed government communications and strategies for lifting public health restrictions. The method could be applied to other future situations that might require co-ordinated public behaviour over an extended period.

La Cooperación Internacional para el Desarrollo en América Latina: Una perspectiva histórica

Less than ten years after the 2030 Agenda ends, and more than seventy years after the emergence of the first development cooperation strategies, questions and challenges regarding its scope are emerging. This article proposes to recover the historical process of International Development Cooperation (IDC) from 1945 to the present, from the perspective of Latin America and the Caribbean. For which it aims to recover the Latin American contribution in the construction of this process to rethink a cooperation capable of facing present challenges, such as those posed by the COVID-19 pandemic and anticipate future situations, as an approach to advance the process of transformation and search for alternatives to generate more equitable structural relations from and for the region without depending on traditional North-South aid.

Could insects be an alternative food source? A comprehensive review.

According to the United Nations, more than 800 million people are exposed to starvation. It is predicted that the world population will face much more serious starvation for reasons such as global warming, diseases, economic problems, rapid urbanization, and destruction of agricultural areas and water resources. Thus, there are significant hesitations about the sustainability of food resources, and the search for alternative food sources has increased. One of the leading alternative food sources is insects. Although the use of edible insects has been accepted in some areas of the world, entomophagy is not preferred in some countries due to sociocultural conditions, health concerns, neophobia, and entomophobia. Many people do not accept the direct consumption of raw insects, but insects can be transformed into more preferred forms by using different cooking techniques. Some ground edible insects are satisfactory in terms of nutritional value and have a reasonable level of acceptability when added to products such as bread, tortilla, and pasta in varying percentages. The world market value of edible insects was estimated to be US$3.2 million in 2021 and US$17.6 billion in 2032. In this review, the current and future situation of insects as an alternative food source is comprehensively discussed.

The Impact of COVID-19 on Children, Young People, and Families in Sihanoukville, Cambodia: The Mitigating Work of M’Lop Tapang

ABSTRACT Globally, children have been adversely affected by the wide variety of impacts of SARS-CoV-2 (Coronavirus | COVID-19). Vulnerable children who depend on the support of education and health and social care systems were left unprotected as these systems were weakened by the pandemic. COVID-19 has exposed the already fragile situations in which many children and young people live and that thousands of children and young people would need ongoing support but remained invisible to statutory authorities. COVID-19 has also been a stark reminder of the vulnerability of individuals and societies and it has exposed deep divisions, inequalities, and injustices between different countries and groups of people. M’Lop Tapang is a local nonprofit organization registered with the Royal Government of Cambodia. This report discusses the efforts of M’Lop Tapang following the declaration of the COVID-19 global pandemic in early 2020, to address the local community needs; to ensure the voices of children remained heard; and to promote children’s rights remaining a priority throughout the remainder of the pandemic. What is already known on this topic Vulnerable children are dependent on remaining visible to education and health and social care systems to ensure they are safeguarded and protected. Poor safety, financial instability, unemployment, challenges to children’s rights and displacement all have the potential to increase vulnerabilities and intensify inequalities. The COVID-19 global pandemic has widened the global lens for the nursing profession and to effect change, children’s nurses need to think globally and act locally. What this study adds This study provides insights into the challenges faced by children and families in Sihanoukville, Cambodia, during the early part of the SARS-CoV-2 (COVID19) pandemic in 2020–2021. The economic impact of COVID-19 on children and families living in Sihanoukville, Cambodia, between 2020 and 2021 is reviewed. The learning from the pragmatic and rapid interventions of M’Lop Tapang, Sihanoukville during the early phase of the SARS-CoV-2 (COVID-19) pandemic, are of use in other countries around the world in future epidemic or pandemic situations.

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann–Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Settler Colonialism and Decolonization

Abstract This essay makes two arguments. First, it argues that the analytical frame of settler colonialism is very productive in order to understand, describe, and at times even predict certain future trends of the Israeli state and society, while putting aside questions of legitimation or justification. On the other hand, this analytical frame is underdetermined when it comes to offering possible solutions for the future of Palestine-Israel. From the mere fact that Israel is a settler colonial project no particular future normative solution could be deduced logically. Future situations are political, not logical, and as such they are subject to different kinds of considerations including ethical and practical ones. Furthermore, we bear certain responsibility for political decisions that we do not while making logical judgments, and this different must be kept.

A COMPARISON OF THE THREE TECHNIQUES IN PREDICTING BREAKING STRENGTH OF COTTON AND BLENDED WOVEN FABRICS

With the development of technology, artificial intelligence applications in the textile industry are increasing. The uses of these methods present very good results in cases where statistical methods are lacking in the accurate evaluation and analysis of the past data of the enterprises and the estimation of their future situations. In this study, some models are developed, based on this relationship, to estimate the breaking strength of cotton woven fabrics and polyester/viscose blended woven fabrics separately. Breaking strength is considered one of the most important performance characteristics of woven fabrics. It is mostly determined by the structural elements of the fabric. Multiple linear regression, artificial neural networks and random forest algorithms are employed to perform statistical and stochastic analyses on these elements by using industrial data. A total of 147 fabric data sets in warp and weft directions were used for training and test data in cotton fabrics, and 53 fabric data sets in warp and weft directions in blended fabrics. Appropriate models are generated by using Minitab Statistics and Matlab software. Yarn linear densities, yarn production methods, twist amounts, fabric densities, crimp ratios, unit area weights, various weave factors and fabric structure factors were selected as variables of the models estimating the breaking strength of fabrics in both warp and weft directions. These factors were included in the models separately, and the subset that gave the best results was selected and the models were revised. For the three models created, it was seen that the regression models and models based on artificial neural networks performed well in both cotton fabrics and blended fabrics, while random forest algorithms were not very accurate in estimating the breaking strength.

Knowledge, Attitude, and Practices (KAPs) on COVID-19 of junior and senior high school students

During the COVID-19 pandemic, students faced increased risk of contracting the virus due to the relaxation of health protocols by the Philippine government and the authorization of face-to-face instruction in schools by the Department of Education. There is little data on basic education school students' awareness of COVID-19 in the Philippines, particularly in Cebu City. A cross-sectional descriptive-correlational research design was utilized to determine junior and senior high school students' knowledge, attitudes, and practices regarding COVID-19. In this study, a survey questionnaire was used to compare and attempt to identify relationships between the demographic profile of students and their KAPs regarding COVID-19. This study also investigated the relationships between students' KAPs. JHS and SHS students exhibited outstanding KAPs for COVID-19. Students' demographic characteristics, including grade level and family income, were significantly associated with their KAPs toward the disease. However, their gender was not significantly associated with their KAPs. There was a significant relationship between students' COVID-19 KAPs. It is imperative that the DepEd develop strategies and programs to combat the situation due to pandemic and prepare for similar situations in future. With the aid of curriculum designers, science educators must adapt the science education curriculum to address current and future health concerns.

Vegetable and potato growing in Kazakhstan as the main direction of agricultural production

The goal is to assess the development of potato and vegetable growing and their impact on the state of domestic and regional agricultural markets, to formulate proposals for stabilizing and improving the situation in the near future. Methods – comparative analysis, economicstatistical and structural approaches. Results – the authors note that in order to achieve the required level of food security, it is necessary to increase the volume of cultivation of these products in large agricultural enterprises, which will contribute to the growth of commercial production of vegetables and potatoes of proper quality. The state and potential of vegetable and potato farms, their regional characteristics, formation of vegetable market, and state support for vegetable growing industry at the national level are considered. The issues of regulating cultivation and sale of vegetable products are analyzed, the level of its consumption in the constituent entities of the Republic of Kazakhstan and factors determining it are shown. Monitoring of the areas of potatoes grown in open ground, tools and mechanisms for stimulating their expansion, the share of gross potato harvest for all categories of farms in the republic, the dynamics of prices of agricultural producers and consumer prices was carried out. Taking into account the scientific views and opinions of researchers, analysis of the category “sustainable development” is presented. Conclusions – it is necessary to carry out reclamation measures related to the involvement of agricultural land in circulation for vegetable and potato growing, increase in state support for agricultural producers, as well as funds to reimburse part of costs incurred for production of greenhouse vegetables, and technical re-equipment of production facilities. The implementation of investment projects is important, which will make it possible in the medium term to achieve complete food independence for vegetables and potatoes and ensure an acceptable price environment for their producers and consumers.

Comparing friction behavior and mechanism of three generation bearing steel (GCr15, M50, CSS-42L) paired silicon nitride under ester oil lubrication from room temperature to 350 °C

The different generations of bearing steel are designed to withstand high temperatures, quick speeds, and heavy loads and meet the requirements of aviation, aerospace, and advanced equipment production. In this study, three generation bearing systems (Si3N4/GCr15, Si3N4/M50, and Si3N4/CSS-42L) with pentaerythritol ester lubricants were selected to investigate their friction behavior and evolutionary mechanisms from room temperature to 350 °C. The results indicate that the coefficient of friction (COF) values for all three systems initially decreased and then increased as the temperature increased. The COF of Si3N4/GCr15 was not more than 0.170 when the temperature did not exceed 150 °C. Furthermore, the wear rate of Si3N4/M50 and Si3N4/CSS-42L are 2.67 × 10−4 mm3·N−1·m−1 and 2.04 × 10−4 mm3·N−1·m−1 at 260 °C, which are only 15.4% and 11.8% of Si3N4/GCr15. This discrepancy can be attributed to the formation of metal oxides on the worn surface of Si3N4/M50 and Si3N4/CSS-42L, and was easier to form on the worn surface of CSS-42L. Therefore, the applicable temperature range for GCr15 usually below 150 °C and for Si3N4/M50 and Si3N4/CSS-42L, it was from 150 to 260 °C. These results disclose the friction behavior and mechanism of these three generation bearing steel are different, and can be a guideline to choose proper bearing steel in future situation.

A Comprehensive Analysis of Covid-19 Research in Turkish Dentistry

To analyze, classify and summarize current data in Turkish dentistry literature related to COVID-19 in 2-year duration from beginning of the pandemic to the end of 2021. The terms ‘‘COVID-19”, “koronavirüs”, “diş hekimliği”, “pandemic”, “coronavirus”, “dentistry”, “pandemics”, “SARS-Cov-2”’’ were searched separately and in combinations in PubMed and Google Academic databases. Regardless of whether the studies were in Turkish and/or English, it was taken into account that they belong to Turkish dentists, interventional and/or observational, and have been published in the duration starts from the beginning of the pandemic to the end of 2021 and contain information on the COVID-19 pandemic in dentistry. 46 studies in PubMed database and 74 studies in Google Scholar were identified. Studies were classified under 8 main topics (knowledge level and behavioral assessment, psychological effects, research specific to dentistry departments, practices, education, importance of the pandemic, infection control and precautions, social media and social network analysis). Through the findings of the analysis, it will be ensured that dentists and healthcare professionals serving in the field of dentistry will be updated on their knowledge, attitudes and behaviors, be prepared for possible future pandemic situations and shape their perspectives on infectious diseases under the guidance of the literature.

Ultra-short-term operation situation prediction method of active distribution network based on convolutional neural network long short term memory

The large-scale and high-permeability access of distributed generations (DGs) is making the distribution networks develop into active distribution networks (ADNs). The increasing complexity of the structure of ADNs also increases the vulnerabilities for cyberattacks. To accurately predict the operation situation of ADNs no matter whether cyberattacks occurred, this paper proposes an ultra-short-term operation situation prediction method of ADNs based on CNN-LSTM. Firstly, an ADNs situation prediction index system is established, which can comprehensively evaluate the future operation situation of ADNs from different perspectives. Secondly, a multi-feature data reconstruction method based on the time sliding window algorithm is proposed, which is utilized for constructing 2-dimensional matrix and as input datasets for multiple independent CNN-LSTM models to predict each situation prediction index. Then, there are 8 independent CNN-LSTM models shared same input matrix to predict 8 ADNs situation prediction index value without interfering with each other. Among them, CNN is used to extract the spatial correlation feature vectors of ADNs historical operation data and meteorological factor data in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. LSTM is used to extract long-term historical temporal relationship to obtain ADNs operation situation prediction index value. Finally, we utilize the fuzzy analytic hierarchy process to calculate the future operation situation of ultra-short-term ADNs. Taking a real ADNs with 33-node as an example, we compared our proposed methodology to other existing excellent machine learning-based models, the result demonstrates that our proposed prediction method exhibits outstanding performance in time consumption and prediction accuracy.

A novel hybrid STL-transformer-ARIMA architecture for aviation failure events prediction

Accurate prediction of aviation failure events helps to anticipate future safety situations and protect against further uncontrollable accidents. However, the large sample size, complex temporal characteristics, and significant long-term correlation of aviation failure events increase the operational cost of accurate prediction. To address these challenges, this paper proposes a novel approach involving seasonal-trend decomposition using Loess (STL) and a hybrid prediction model consisting of a transformer and autoregressive integrated moving average (ARIMA). First, STL decomposition is utilized to isolate trend, seasonal, and remainder components, contributing to a comprehensive understanding of the events sample characteristics. The trend component is then trained and predicted using transformer, solving the vanishing gradient problem and improving computational efficiency. ARIMA is employed to train and predict the seasonal and remainder components, maintaining accuracy while reducing complexity. Finally, a comparative evaluation between the proposed and multiple existing approaches is conducted using Aviation Safety Reporting System (ASRS) data. The results demonstrate that the STL-transformer-ARIMA provides more accurate predictions of failure events than single model. It also exhibits significant advantages in robustness and generalization capacity compared to single transformer-based predictors. This revealed that the proposed approach performed better in predicting aviation failure events.