Social Economic Loss Research Articles

Understanding Intimate Partner Femicide and Attempted Intimate Partner Femicide During and After COVID-19: A Comprehensive Strain-Based Approach

Intimate partner femicide (IPF) and attempted intimate partner femicide (AIPF) are severe forms of violence and global phenomena occurring in all societies worldwide. However, explaining IPF and AIPF is a complex and challenging endeavor. This article delves into the evolution of IPF and AIPF in Portugal during and after COVID-19, seeking to comprehend the underlying explanatory factors through a comprehensive strain-based approach. IPF rates during the COVID-19 pandemic have remained relatively stable, comparable to those observed before the pandemic, following a pattern akin to that documented in other countries. However, a significant increase in AIPF occurred during the COVID-19 pandemic, and a slight uptick in IPF indicators was noted toward the end of the pandemic period (2022). Perpetrators faced various strains, such as separation and financial difficulties, along with experiencing emotions like anger, jealousy, anxiety, and depression, leading to instances of IPF and AIPF. The COVID-19 crisis and associated restrictions introduced additional strains, including social isolation, economic losses, anxiety, and widespread fear, which may contribute to psychological distress, stress, and depression, thereby heightening the likelihood of committing IPF and AIPF. The pandemic restrictions also resulted in limited access to support and protection services, hindering victims from seeking help. Establishing a contingency plan for use in future crises (including pandemics) is imperative to promote support for victims and prevent situations of IPF and AIPF.

Vehicular social aspects in data dissemination of urban critical events with QoS and QoE for supporting emergency response

AbstractCritical urban events impact the city agenda, like sustainable urban mobility, safe transportation, and efficient governance, thus leading many times to social disturbance, environmental degradation, and economic and human losses. Such events demand efficient dissemination of their data to the authorities to take rapid and effective actions to minimize their effects. The Social Internet of Vehicles (SIoV) paradigm can aid in monitoring such events in time and space. Vehicles are suitable for monitoring random events in space and time due to their ubiquity. Simultaneously, social relationships between vehicle users establish local communities where social intelligence can help disseminate data collected by VANET to support public management in event recognition. Thus, this article proposes a distributed and robust QoS/QoE aware, social‐based data dissemination solution for critical urban events and investigates how social parameters influence the quality of data dissemination. The impact of social influence, social activity, and friendship strategies was analyzed by simulation. The results show the gains in QoS when using social activity and in QoE subjective metrics when employing social influence.

Investigating the spatial–temporal changes of flood events across the Yangtze River Basin, China: Identification, spatial heterogeneity, and dominant impact factors

Flood is one of the most devastating natural hazards and is responsible for sizeable social-economic losses and substantial fatalities. Flood events with diverse behavior characteristics vary greatly in spatial patterns and bring great challenges for flood management. In this study, the Peak Over Threshold (POT) approach coupled with an event-start-and-end detecting approach was proposed to separate flood events during 2008 ∼ 2018 across the Yangtze River Basin. Ten flood behavior metrics, comprehensively characterizing magnitude, duration, timing, rate of change, and flood forms of flood events, were used to fully describe flood events and then to identify flood event classes. Subsequently, the spatial heterogeneity of flood events was revealed based on behavior similarity classification of numerous events. Furthermore, a Contribution-Based Impact Factor Analysis (CBIFA) method was constructed to investigate the contributions of natural and anthropogenic factors to flood event variations. Results show that: (1) six flood event classes were identified including long-duration and small-rate-of-change flood events (Class 1, 31.00%), sharp-thin and short-tail flood events (Class 2, 20.58%), sharp-thin and early-peak flood events (Class 3, 5.12%), dwarf-fat flood events (Class 4, 18.37%), small and sharp-thin flood events (Class 5, 4.24%), and conventional events (Class 6, 20.67%). (2) Class 1 and Class 4, characterized by small rate of change, long duration and large magnitude, occurred across the whole Yangtze River Basin and mainly in the Middle-Lower Yangtze plains. Class 2 with small magnitude and short duration was mainly distributed in the Qinghai Tibet Plateau. Class 3, Class 5, and Class 6 mainly occurred in mountainous areas and hills in the middle and upper reaches, wherein Class 3 and Class 5 were characterized by large rate of change and short duration. (3) The meteorological factors were the most important impact factors and explained 30.23%∼68.10% of the total flood event variations, followed by geographical (9.19%∼32.10%), human activities (6.56%∼23.17%), soil (9.19%∼19.34%) and vegetation (2.56%∼5.31%) factors. Identifying the spatial pattern of flood events and their driver factors is a crucial step toward accurate flood prediction and will finally provide essential information for reliable flood management.

Bulk assemblies of organic antimony chloride with multiple reversible photoluminescence switching for anti-counterfeiting and information encryption

Forgery and leakage of confidential information will bring great social harm and economic loss, so it is necessary to take simple and effective strategies to ensure information security. Here, two different zero-dimensional (0D) organic antimony chlorides of [Ph3S]2SbCl5·2C2H3N (1) and [Ph3S]2SbCl5 (2) were obtained by inserting the organic ligand of Ph3SCl (triphenylsulfonium chloride) into SbCl3 lattice via different chemical synthesis methods. Under photoexcitation, compound 1 shines bright yellow emission with a photoluminescence quantum efficiency (PLQE) of 98.2%, while compound 2 shows efficient red emission with a PLQE of 99.6%. Both of them exhibit dual-emission bands, which stem from the singlet self-trapped excitons (STEs) and triplet STEs, respectively. Particularly, the dynamic transformation processes between compounds 1 and 2 are witnessed via the acetonitrile (C2H3N) stripping/insertion under different external stimuli, which was demonstrated by the results of powder X-ray diffraction and photoluminescence (PL) spectra. In addition, the continuous heating of compound 2 can make the emission color change from red to yellow again, accompanied by the lattice distortion of [SbCl5]2- pyramid. Finally, the multiple anti-counterfeiting and information encryption models were fabricated based on the reversible PL switching of the title compounds under different external stimuli.

Landslide susceptibility mapping using automatically constructed CNN architectures with pre-slide topographic DEM of deep-seated catastrophic landslides caused by Typhoon Talas

There has been an increasing demand for detailed and accurate landslide maps and inventories in disaster-prone areas of subtropical and temperate zones, particularly in Asia as they can mitigate the impacts of landslides on social infrastructure and economic losses. Hence, in this study, models using automatically constructed high-performing convolutional neural network (CNN) architectures for landslide detection were applied and their outcomes were compared for landslide susceptibility mapping at the Kii peninsula, Japan. First, a total of 38 landslide and 63 non-landslide points were identified and divided into 70% and 30% of training and validation datasets, respectively. Eight landslide influence factors were used: slope angle, eigenvalue ratio, curvature, underground openness, overground openness, topographic witness index, wavelet, and elevation. These factors were selected using a 1-m DEM, which is easy to acquire and process data. Experimental results of model evaluation using receiver operating characteristics (ROC), area under the curve (AUC), and accuracy showed that the optimal models (ROC = 96.0%, accuracy = 88.7%) were more accurate than initial models (ROC = 91.1%, accuracy = 80.7%) in predicting landslides spatially. Furthermore, the landslide susceptibility mapping is consistent with the trends in the distribution of gentle slopes and knick lines unique to the study area and can be used as a powerful method for predicting landslides in future.

The social-economic efficiency of measures on the development of safe labor conditions in oil field enterprises

The paper studies the social-economic efficiency of the measures towards the development of safe conditions of labor in the industrial enterprises. The necessity of the advancement of corresponding legal framework is shown, and various aspects defining industrial safety are characterized as well. The basic procedures of ensuring safe working environment, and the evaluation of the social-economic efficiency of the measures towards the labor protection in the industrial enterprises are reviewed, and the perspectives of its enhancement shown. Special attention is paid to the issues of the monetary evaluation of the measures carried out towards improving the safety of labor conditions; the reasons for the variety of approaches to the estimation of the social injury are studied as well. With the purpose of development of safe working environment in an industrial enterprise, the views on the perspectives of the advancement of the organization of control in the working places are offered. The possibilities of the minimization of the social-economic losses in the system of labor protection are specified.

Credit Card Fraud Detection Using Feature Fusion-based Machine Learning Model

Credit card fraud is a very common means of financial fraud at present. In order to reduce social and personal economic property losses, this study aims to propose a model to predict credit card fraud, so as to detect possible fraudulent transactions in the transaction process. In this paper, the data set to simulate fraudulent transactions be used, and construct the model after processing the characteristic data and selecting the features. In the model construction part, four algorithms are used as the trained classifiers, which are K Nearest Neighbor algorithm, Bgging algorithm, Logistic Regression algorithm and Gaussian Bayesian algorithm. First, the four algorithms are used to train the data set respectively, and then the parameters of each classifier are adjusted respectively, so that each individual classifier achieves the optimal performance. On this basis, a new model is constructed by using the method of model fusion. The classifier constructed by K Nearest Neighbor algorithm, Bagging algorithm and Gaussian Bayesian algorithm is used as the primary learner, and the classifier constructed by logistic regression algorithm is used as the secondary classifier to build a fused model as a whole. The final experimental results will give the classification performance of the five models, including individual models. The results show that the performance of the model after integrating the four individual models is better than that of the single model, which can provide accurate feedback for credit card holders on whether there is fraud.

Stigma, psychosocial and economic effects of yaws in the Philippines: an exploratory, qualitative study

BackgroundYaws is a chronic, non-venereal, highly contagious skin and bone infection affecting children living in impoverished, remote communities and caused by Treponema pallidum subspecie pertenue. Social stigma and economic losses due to yaws have been reported anecdotally in the Southern Philippines but have not been well-documented.ObjectiveTo describe and compare the psychological, social, and economic effects of yaws from the perspective of patients, contacts, and key informants in two areas of the Philippines.Materials and methodsYaws and contacts were identified through clinicoseroprevalence surveys conducted in the Liguasan Marsh area, Mindanao, Southern Philippines in 2017 and among the Aetas, an indigenous people community in Quezon province, Luzon region in 2020. Skin examinations and serologic tests confirmed the diagnosis of active, latent, or past yaws among the children and adults. Trained health personnel conducted in-depth interviews of those affected by yaws and their guardians, household contacts, and key informants, such as health workers regarding their perceptions, feelings, health-seeking behaviors, and effects of yaws on their lives.ResultsA total of 26 participants were interviewed: 17 from Mindanao and 9 from Luzon. Aside from the physical discomforts and embarrassment, yaws was considered stigmatizing in Mindanao, because positive non-treponemal tests or treponemal antibody tests were associated with syphilis and promiscuity. These have led to loss of employment and income opportunities for adults with latent or past yaws. In contrast, the Aetas of Luzon did not perceive yaws as stigmatizing, because it was a common skin problem. Plantar yaws interfered with the Aeta’s gold panning livelihood due to the pain of wounds.ConclusionsYaws is not merely a chronic skin and bone disease. It can lead to significant psychosocial and economic problems as well. Yaws is a generally forgotten disease in the Philippines. There is no yaws surveillance and control program. Treatments are not readily available for the populations affected, thus perpetuating the infection and negative effects.Significance of studyThis is the first study to document the psychosocial and economic effects of yaws among Filipinos. Information campaigns about yaws and a yaws control program are needed to reduce stigma and discrimination.

METHODOLOGY OF REDUCING THE LEVEL OF OCCUPATIONAL INJURIES IN THE FIELD OF ELECTRIC POWER ENGINEERING OF UKRAINE

Statistics from the International Labor Organization show that at this time the level of both occupational and domestic injuries is equivalent to an epidemic. At the same time, studies by the World Health Organization show that today the death rate from accidents ranks third after deaths from cardiovascular disease and cancer. It is important to note that this situation is typical of both developing and economically developed countries. It is obvious that such a situation leads to both significant social losses and significant economic losses. In our country the level of industrial injuries and electric injuries at electric power enterprises is especially high. At the same time, in recent years the level of occupational injuries in the electricity sector of Ukraine has increased and is characterized by an increase in the number of fatal injuries. Based on this, the development of a methodology to reduce the level of this indicator of production activity of enterprises is an urgent task. The main causes of mortality of men of working age in Ukraine are: - industrial accidents; - industrial poisoning; - industrial injuries. Such cases account for about 30… 35% of all deaths under the age of 40. The study revealed the features of the work performed at the enterprises of the industry, which differ in the organization of the workplace and a significant number of high-risk work. Further analysis of reports on labor protection, acts of investigation of accidents at enterprises revealed a number of frequent specific cases of violation of important provisions of legislation, regulations on labor protection, labor protection and electrical safety. The results of researches of the reasons of injuries in industrial conditions in the field of electric power are systematized. This made it possible to identify the main directions for improving the state of labor protection. Structural and systematic implementation of the identified areas is the basis of the developed methodology for improving the state of occupational injuries at electric power companies of Ukraine.

A computational approach for real-time stochastic recovery of electric power networks during a disaster

Disasters are occurring with increasing frequency worldwide, causing significant social hardship and economic losses. Critical infrastructures such as electric power networks are prone to failure under such events, and this significantly impacts the daily lives of people in affected areas. It is hence critical that the restoration planning of these power networks be done proactively. Disaster response in power networks is a well-studied problem, especially for pre-and post-event restoration Similar to pre-event, we consider uncertainty associated with the failure paths, and we look into real-time response while failures are happening. In this regard, at each time step, we move repair teams towards distribution loads based on their current state, their likelihood to fail, and the impact of the damage in case of node failure. We consider large-scale networks (>50 nodes and >20 repair teams) and propose an efficient algorithm to support real-time recovery.In particular, to address the curse of dimensionality, we design a novel approximate dynamic program that (i) evaluates the future impact of current actions using rollout, (ii) reduces the action space relying on aggregate dynamic programming. The proposed approach is applied to the power distribution network in Aguada municipality, Puerto Rico. Our results show that the proposed rollout approach significantly improves the network service level compared to the base heuristic through prepositioning of the repair crew. Moreover, we find that the performance gap grows larger with the concave restoration function (i.e., a decreasing Rate of Increase in the Load Service Level as the recovery progresses) compared to the linear restoration (a constant recovery rate throughout the recovery operation). Finally, the performance gap also grows larger under stronger failure scenarios.

Crime Type Prediction in Saudi Arabia Based on Intelligence Gathering

Abstract One of the biggest social problems currently facing major cities around the globe is the high rate of crime. The largest part of the social-economic loss globally is ascribed to criminal activities. Crime also has direct impacts on the nation’s economy, social constructs and country’s global repute. Inadequate policing capital is one of the biggest challenges facing many global economies. As a result, these resources have to be rationed. This implies that some areas will not be covered extensively thus providing favorable environs for perpetrators. To combat crime, more innovative security measures are needed. In this sense, traditional methods are being replaced with modern approaches of machine learning systems that can predict the occurrence of crime. These crime forecasts can be used by legislatures and law enforcers to make effective and informed approaches that can efficiently eradicate criminals and facilitate nation building. This paper seeks to review the literature on the application of machine learning models in crime prediction and to find the influences that have an impact on crimes in Saudi Arabia. The results show that after the four models were trained and tested, the random forest classifier had the highest accuracy of 97.84%.

A Proposed Framework for Evaluating the Academic-failure Prediction in Distance Learning

Academic failure is a crucial problem that affects not only students but also institutions and countries. Lack of success in the educational process can lead to health and social disorders and economic losses. Consequently, predicting in advance the occurrence of this event is a good prevention and mitigation strategy. This work proposes a framework to evaluate machine learning-based predictive models of academic failure, to facilitate early pedagogical interventions. We took a Brazilian undergraduate course in the distance learning modality as a case study. We run seven classification models on normalized datasets, which comprised grades for three weeks of classes for a total of six weeks. Since it is an imbalanced-data context, adopting a single metric to identify the best predictive model of student failure would not be efficient. Therefore, the proposed framework considers 11 metrics generated by the classifiers run and the application of exclusion and ordering criteria to produce a list of best predictors. Finally, we discussed and presented some possible applications for minimizing the students’ failure.

Strategies for Gully Stabilization and Highland Protection in Chinese Loess Plateau

Loess tablelands are large-scale platforms with flat top surfaces in the center of Chinese Loess Plateau, such as Dongzhiyuan, which is a ∼900 km2 loess stratum, with the thickness larger than 200 m. The break of loess tablelands is widely reported due to the retreat of gully systems, resulting in serious social problems and economic losses. The retreating rate of large gullies has been 0.5–6.6 m/year over the past 2,000 years, estimated by the calculation of erosion modulus, literature archaeology, and field monitoring. The retreating rate of a small gully can reach 7.5–27.0 m/year after a rainstorm. To protect the farmland, buildings, roads, and pipelines near to the gully head areas, a project named Gully Stabilization and Highland Protection (GSHP) was launched by the local government, including the small watershed management (SWM) around loess slopes and gully channels and sponge city construction (SCC) achieved by improving the drainage system of tableland surfaces. These efforts improve the soil and water conservations, although they contribute less to controlling gully expansion. From the perspective of mutual promotion of gravity erosion and hydraulic erosion, this paper proposes a technical framework for GSHP, focusing on the avoidance of geological disaster. This case study reports on an illustrative GSHP project, the restoration of Huoxianggou gully, including the backfilled loess embankment across the gully for convenient urban transportation, and the stabilization of unstable loess slopes. After the implementation, the retreat of the gully head has been stopped, and the soil erosion rate has been reduced by 90%.

African Swine Fever-How to Unravel Fake News in Veterinary Medicine.

Simple SummaryIn recent years, fake scientific news has spread much faster through the Internet and social media within the so-called “infodemic”. African swine fever (ASF) is a perfect case study to prove how fake news can undermine the public health response, even in the veterinary field. ASF is a contagious infective disease exclusively affecting domestic and wild pigs such as wild boars. ASF can cause social damages and economic losses both directly (due to the high mortality rate) and indirectly (due to international sanctions). Although ASF is not a threat to human health, since 2018, newspapers have often reported false or misleading news, ranging from misinterpreted findings/data to fake or alarmistic news. In some cases, fake news was spread, such as the use of snipers at the border of nations to kill wild boars or the possible risks to human health. In order to provide real and fact-based news on epidemics, some organizations have created easy-to-read infographic and iconographic materials, available on their websites, to help the readers identifying the fake news.In recent years, fake scientific news has spread much faster through the Internet and social media within the so-called “infodemic”. African Swine Fever (ASF) is a perfect case study to prove how fake news can undermine the public health response, even in the veterinary field. ASF is a highly contagious infective disease affecting exclusively domestic and wild pigs such as wild boars. ASF can cause social damage and economic losses both directly (due to the high mortality rate) and indirectly (due to international sanctions). Although ASF is not a threat to human health, since 2018 newspapers have often reported false or misleading news, ranging from misinterpreted findings/data to fake or alarmistic news. In some cases, fake news was spread, such as the use of snipers at the border of nations to kill wild boars, or those reports concerning possible risks to human health. In order to provide real and fact-based news on epidemics, some organizations have created easy-to-read infographic and iconographic materials, available on their websites, to help the readers identifying the fake news. Indeed, it is crucial that governments and scientific organizations work against fear and anxiety, using simple and clear communication.

Acupuncturing of myofascial pain trigger points for the treatment of knee osteoarthritis: A systematic review and meta-analysis.

Background:Osteoarthritis of the knee is one of the major disorders leading to social dysfunction, economic loss and social development. This study was conducted to systematically evaluate the efficacy and safety effectiveness of acupuncture inactivation of myofascial pain trigger points in the treatment of osteoarthritis of the knee.Methods:Randomized Controlled Trials (RCTs) on the treatment of knee pain were searched by computer from PubMed, The Cochrane Library, China journal full-text database (CNKI), Chinese biomedical literature database (CBM), China academic journal database (Wanfang Data) databases from the date of creation to December 2021, and the data were analyzed by Reman 5.3 software for data analysis.Results:A total of 724 patients from 9 RCTs were finally included, and the results of meta-analysis showed that the acupuncture myofascial pain trigger point group was better than the control group in terms of total effective rate, cure rate, VAS score, Lysholm score, and WOMAC score.Conclusion:The efficacy and safety of acupuncturing myofascial pain trigger points in the treatment of knee osteoarthritis is positive, but due to the limited number of literature included in this study and the low quality of the included literature, there is still a need for high-quality and large sample size RCTs for the analysis of this treatment option.

Human Rights in Times of the Pandemic: A Dialogue on Migration and Indigenous Rights in Brazil before the Regional Inter-American System

This paper using the deductive method of approach based on regional scope for the protection of human rights, aims to demonstrate that the Brazilian state actions took during the pandemic, in terms of strategies, policies and measures to deal with the COVID-19 pandemic had severe consequences specially for indigenous and migrants population in Brazil, increasing the level of vulnerability of that population in its territory. It will also consider that in spite of adhering the unconditional observance of inter-American and international standards on human rights, the Brazilian government ignored such agreements and therefore causing social economic losses and about hundreds of deaths.

Pigment Characterization of the Giant-Colony-Forming Haptophyte Phaeocystis globosa in the Beibu Gulf Reveals Blooms of Different Origins.

The giant-colony-forming haptophyte Phaeocystis globosa has caused several large-scale blooms in the Beibu Gulf since 2011, but the distribution and dynamics of the blooms remained largely unknown. In this study, colonies of P. globosa, as well as membrane-concentrated phytoplankton samples, were collected during eight cruises in the Beibu Gulf from September 2016 to August 2017. Pigments were analyzed by high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD). The pigment 19'-hexanoyloxyfucoxanthin (hex-fuco), generally considered a diagnostic pigment for Phaeocystis, was not detected in P. globosa colonies in the Beibu Gulf, whereas 19'-butanoyloxyfucoxanthin (but-fuco) was found in all colony samples. Moreover, but-fuco in membrane-concentrated phytoplankton samples exhibited a similar distribution pattern to that of P. globosa colonies, suggesting that but-fuco provided the diagnostic pigment for bloom-forming P. globosa in the Beibu Gulf. Based on the distribution of but-fuco in different water masses in the region prior to the formation of intensive blooms, it is suggested that P. globosa blooms in the Beibu Gulf could originate from two different sources. IMPORTANCE Phaeocystis globosa has formed intensive blooms in the South China Sea and even around the world, causing huge social economic losses and environmental damage. However, little is known about the formation mechanism and dynamics of P. globosa blooms. 19'-Hexanoyloxyfucoxanthin (hex-fuco) is often used as the pigment proxy to estimate Phaeocystis biomass, while this is challenged by the giant-colony-forming P. globosa in the Beibu Gulf, which contains only 19'-butanoyloxyfucoxanthin (but-fuco) but not hex-fuco. Using but-fuco as a diagnostic pigment, we traced two different origins of P. globosa blooms in the Beibu Gulf. This study clarifies the development process of P. globosa blooms in the Beibu Gulf, which provides a basis for the early monitoring and prevention of the blooms.

Risk-Informed Decision-Making for Predisaster Risk Mitigation Planning of Interdependent Infrastructure Systems: Case Study of Jamaica

Hazards cause huge social disruptions and economic losses each year. Community disaster resilience relies on the capacity of critical infrastructure systems to maintain acceptable levels of functionality during a disruptive event and to recover promptly after the event. A proper predisaster risk mitigation planning framework can aid communities by working in advance to understand the preexisting local needs, identify the leaders, form partnerships, establish resources, and seize opportunities to increase community disaster resilience. Although numerous theoretical studies exist on developing models to support infrastructure predisaster risk mitigation, these models have not been properly incorporated into the decision-making process in existing studies. This lack of incorporation impedes the transfer of theoretical knowledge to practical policymaking. High-level predisaster infrastructure risk mitigation decisions for multiple infrastructure systems are especially in need of such an illustration due to the complexity introduced by the scale. This paper defines the interdependent infrastructure risk mitigation (IIRM) problem in a risk-informed decision-making framework and illustrates the entire decision-making process using a case study on predisaster risk mitigation planning of the interdependent critical infrastructure systems in Jamaica. This region is vulnerable to natural hazards but has received little attention in the literature.

Assessment and Prediction of Climate Risks in Three Major Urban Agglomerations of Eastern China

In the context of global climate change and urban expansion, extreme urban weather events occur frequently and cause significant social problems and economic losses. To study the climate risks associated with rapid urbanization in the global context of climate change, the vulnerability degree of urban agglomeration is constructed by the Grey Model (GM (1, 1)). Based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) data sets SSP1-2.6, SSP2-4.5, and SSP5-8.5, drought, heat wave, and flood hazards under different emission scenarios are calculated. The vulnerability degree of the urban agglomeration and the climate change hazard were input into the climate change risk assessment model to evaluate future climate change risk. The analysis results show regional differences, with the Beijing–Tianjin–Hebei urban agglomeration having good urban resilience, the Yangtze River Delta urban agglomeration having slightly higher overall risk, and the Pearl River Delta urban agglomeration having the highest relative risk overall. On the whole, the higher the emission intensity is, the greater the risk of climate change to each urban agglomeration under different emission scenarios.

The epidemic and prevention of plagues in modern Shanghai

It was found that a variety of plagues were prevalent in Shanghai at the end of the Qing Dynasty and the beginning of the Republic of China, by analysing and synthesising the epidemic situation, the causes of the epidemic, the social impact, and prevention and control measures. These plagues led to serious social harm and economic losses. Their causes were believed closely related to some factors, such as urban environment, population flow, and climate changes. Their prevention appeared to be applicable and effective. The Shanghai concession and Chinese authorities organised society to fight against plagues and stabilise the social order. Charity organisations of the local communities were also involved in social support. It was found that more can be done in studies on interdisciplinary synthesis, comparative study of epidemic prevention measures of managers, and of traditional Chinese medicine prevention and treatment.