Proactive Prevention Research Articles

Proactive Wildfire Detection and Management using AI, ML, and 5G Technology in the United States

Over the past few years, wildfires have become a worldwide environmental emergency, resulting in substantial harm to natural habitats and playing a part in the acceleration of climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite improvements in detection techniques, the rising occurrence of wildfires demands creative solutions for prompt identification and effective control. This research investigates proactive methods for detecting and handling wildfires in the United States, utilizing Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. Utilizing advanced technology could save lives and prevent significant economic losses caused by wildfires. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.

The Threats of Climate Change in Nepal: Natural Catastrophes and Global Conflict Frontiers

Nepal, nestled amidst the majestic Himalayas, is uniquely positioned with its delicate geological makeup. This renders it vulnerable to escalating environmental threats, primarily driven by global climate change-induced adversities. These climatic adversities not only disrupt the ecological balance but also create a space for multiple conflict frontiers. The primary objective of this research is to delve into the multifaceted conflicts that include competition for scarce resources, decision-making process, and vested interest of foreign powers that may arise from the climatic challenges especially flash floods, glacier lake outbursts, droughts, landslides, and other catastrophic disasters. The study aims to provide a comprehensive understanding of climatic-induced disaster events and their significant role in the origin of conflicts. Employing a qualitative research design using the tool of comparative analysis, the study investigates the conflicts triggered by these disasters of the past two decades. The study will draw insightful parallels between Nepal's unique situation and other global regions. I will spotlight regions like South Sudan, Rwanda, Syria, and, South and Southeast Asia where climate-induced factors, such as acute water and food shortages, have ignited community tensions and other resource-based conflicts in the historical review and analysis. Within Nepal's borders, I will undertake a detailed historical analysis of the specific cases due to climate change. The study aims to bridge the knowledge gap between climate change and conflict dynamics, offering actionable insights for holistic resource management, disaster risk mitigation, and proactive conflict prevention.

Enhancing infrastructural dynamic responses to critical residents’ needs for urban resilience through machine learning and hypernetwork analysis

The increasing frequency of extreme weather events poses ever greater challenges to urban resilience and residents’ quality of life. Despite a growing trend advocating for an anthropocentric approach to urban resilience, there remains an inadequate understanding of the evolving hierarchical needs of residents during post-disaster periods, especially considering the interplay with infrastructure service restoration. This study aims to address the gap by elucidating how emergency governance and urban infrastructure repairs can effectively address critical residents’ needs, providing empirical insights for improved resource allocation in infrastructure rush-repair scenarios. First, we categorize residents’ needs into three layers (safety and health, social livelihood and civic engagement) using Latent Dirichlet Allocation topic modeling. Subsequently, we present an urban resilience assessment framework that traces the recovery of residents’ needs alongside dynamic infrastructural functionality restoration, benchmarking against pre-disaster levels. Additionally, hypernetwork analyses are adopted to identify critical and evolving patterns of residents’ post-disaster needs over time. The robustness of our proposed framework is validated through its application to a dataset comprising 220,567 records from residents’ appeals during three recurrent rainfall events in Beijing. Theoretically, this study models the dynamic interactions between residents’ needs and infrastructure response during urban post-disaster recovery. Practically, the pinpointed critical needs guide efficient infrastructure rush-repairs and proactive disaster prevention in infrastructure maintenance.

Can Ensemble Techniques and Large-Scale Fire Datasets Improve Predictions of Forest Fire Probability Due to Climate Change?—A Case Study from the Republic of Korea

The frequency of forest fires worldwide has increased recently due to climate change, leading to severe and widespread damage. In this study, we investigate potential changes in the fire susceptibility of areas in South Korea arising from climate change. We constructed a dataset of large-scale forest fires from the past decade and employed it in machine learning models that integrate climatic, socioeconomic, and environmental variables to assess the risk of forest fires. According to the results of these models, the eastern region is identified as highly vulnerable to forest fires during the baseline period, while the western region is classified as relatively safe. However, in the future, certain areas along the western coast are predicted to become more susceptible to forest fires. Consequently, as climate change continues, the risk of domestic forest fires is expected to increase, leading to the need for proactive prevention measures and careful management. This study contributes to the understanding of forest fire occurrences under diverse climate scenarios.

COMPREHENSIVE ANALYSIS OF CATHETER-ASSOCIATED URINARY TRACT INFECTIONS: RISK FACTORS, MICROBIAL DYNAMICS, AND ANTIMICROBIAL CHALLENGES IN A TERTIARY CARE SETTING

Introduction: Indwelling urinary catheters are frequently employed in medical procedures; yet, they pose a considerable risk, with catheter-associated urinary tract infections (CAUTIs) being a global healthcare challenge. CAUTIs account for 30–40% of nosocomial infections, contributing to heightened morbidity and mortality. Objective: Our objectives included investigating CAUTI incidence, exploring factors contributing to UTI risk, examining the correlation between bacteriuria and symptomatic CAUTI, and recording microbiological profiles and antibiotic sensitivities for insights into microbial dynamics and treatment strategies. Method: We aimed to assess patient and catheter-related factors influencing urinary tract infections (UTIs) in 105 catheterized patients. Results: Among the studied cohort, with an average age of 47.73 years, various catheter sizes were employed, and post-operative catheterization was the predominant indication. Microbial presence in urine samples was detected in 31.4% of cases, with a 21% prevalence of symptomatic UTIs. Diabetes mellitus showed a significant association with increased UTI occurrences (p=0.019). Notably, catheterization outside the operating theater (OT) and prolonged catheterization duration were significantly associated with increased infection rates (p=0.000 and p=0.003, respectively). Despite antibiotic treatment, Escherichia coli remained the most prevalent organism causing infection (17.1%), and antibiotic resistance trends were observed, highlighting the challenges of antimicrobial stewardship. Conclusion: The study emphasizes the importance of preventive measures, such as reducing catheterization duration and meticulous diabetes management, to mitigate the burden of CAUTIs. In conclusion, this study provides valuable insights into the prevalence, risk factors, and microbial dynamics of CAUTIs, urging a shift toward proactive prevention strategies and judicious antibiotic use to enhance patient outcomes and alleviate the strain on health-care resources.

The worsening effect of paroxysmal atrial fibrillation on left ventricular function and deformation in type 2 diabetes mellitus patients: a 3.0 T cardiovascular magnetic resonance feature tracking study

BackgroundAtrial fibrillation (AF) has been linked to an increased risk of cardiovascular death, overall mortality and heart failure in patients with type 2 diabetes mellitus (T2DM). The present study investigated the additive effects of paroxysmal AF on left ventricular (LV) function and deformation in T2DM patients with or without AF using the cardiovascular magnetic resonance feature tracking (CMR-FT) technique.MethodsThe present study encompassed 225 T2DM patients differentiated by the presence or absence of paroxysmal AF [T2DM(AF+) and T2DM(AF−), respectively], along with 75 age and sex matched controls, all of whom underwent CMR examination. LV function and global strains, including radial, circumferential and longitudinal peak strain (PS), as well as peak systolic and diastolic strain rates (PSSR and PDSR, respectively), were measured and compared among the groups. Multivariable linear regression analysis was used to examine the factors associated with LV global strains in patients with T2DM.ResultsThe T2DM(AF+) group was the oldest, had the highest LV end‑systolic volume index, lowest LV ejection fraction and estimated glomerular filtration rate compared to the control and T2DM(AF−) groups, and presented a shorter diabetes duration and lower HbA1c than the T2DM(AF−) group. LV PS-radial, PS-longitudinal and PDSR-radial declined successively from controls through the T2DM(AF−) group to the T2DM(AF+) group (all p < 0.001). Compared to the control group, LV PS-circumferential, PSSR-radial and PDSR-circumferential were decreased in the T2DM(AF+) group (all p < 0.001) but preserved in the T2DM(AF−) group. Among all clinical indices, AF was independently associated with worsening LV PS-longitudinal (β = 2.218, p < 0.001), PS-circumferential (β = 3.948, p < 0.001), PS-radial (β = − 8.40, p < 0.001), PSSR-radial and -circumferential (β = − 0.345 and 0.101, p = 0.002 and 0.014, respectively), PDSR-radial and -circumferential (β = 0.359 and − 0.14, p = 0.022 and 0.003, respectively).ConclusionsIn patients with T2DM, the presence of paroxysmal AF further exacerbates LV function and deformation. Proactive prevention, regular detection and early intervention of AF could potentially benefit T2DM patients.

Navigating the theories in the nexus between war and peace

This research examines the relationship between war and peace within the realm of international relations, drawing from a diverse array of theories. It investigates conflict, ranging from power dynamics to ideological disputes, while also evaluating the ethical dimensions of the Just War theory. Through the application of the Just War theory to the dynamic interplay between war and peace as a theoretical framework, this study underscores the vital significance of fostering peaceful dispute resolution, upholding ethical standards in warfare, and undertaking comprehensive post-conflict reconstruction efforts. Employing a historical research design, the study gathers data from secondary sources including journals, books, and ancient encyclopedias, ensuring the credibility of its analysis. Utilizing a descriptive method of data analysis, the research employs deductive logic to maintain a coherent framework and ensure balanced interpretation. Despite disagreements among theories, the study prioritizes accuracy to draw appropriate conclusions. The findings of this study reveal the profound interconnectedness between war and peace, highlighting how conflict arises from a myriad of factors including power struggles, ideological differences, and diplomatic failures. Furthermore, the ethical evaluation of war through the Just War theory provides valuable insights into the moral complexities surrounding armed conflict and its implications for peace-building efforts. Considering these findings, several recommendations are proposed to address the persistent challenge of achieving lasting peace amidst armed conflicts. Emphasizing diplomacy as a primary approach to conflict resolution, prioritizing ethical standards in warfare, and fostering peace education to cultivate values of tolerance and non-violent conflict resolution from an early age are key strategies identified for promoting sustainable peace. By offering recommendations for proactive conflict prevention and strategies in international cooperation, this research aims to contribute to the global pursuit of a more equitable, harmonious, and peaceful world order. Through its rigorous analysis and actionable insights, the study seeks to inform policymaking and advocacy efforts aimed at fostering sustainable peace on a global scale.

Sound flanking through common low-voltage electrical conduit in multi-family residential buildings

Sound flanking between suites in multi-family residential buildings is a prevalent issue in architectural acoustics and building construction that can decrease the sound insulation performance of suite-demising partitions, compromising acoustical privacy and comfort for residents. In recent times, a specific deficiency regarding the sealing of common low-voltage electrical conduit routed between residential suites has been increasing in frequency in construction of residential buildings, resulting in otherwise appropriately designed suite demising configurations performing poorly in situ, and in some cases, even failing Ontario Building Code requirements for sound insulation between dwelling spaces during site testing. This article presents test data sampled from multiple residential buildings in Ontario, highlighting the extent of this issue and its implications, along with discussion regarding the proactive prevention and post-construction rectification of this issue.

Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia

A prioritisation study was conducted to address the lack of adequate information about potential pests likely to be introduced in Zambia and become invasive. The study was conducted by subject matter experts from relevant institutions in and outside Zambia. Although this study focused on major pest categories, this paper only addresses bacteria and Protista. A list of 306 bacterial and 10 Protista species adjudged to affect plants was generated using CABI’s Horizon Scanning Tool. The 316 (total) pest species were refined to focus on pests that affect value chains important to Zambia’s economy. This resulted in a final list of 133 bacteria and eight Protista. Four additional bacteria species considered of phytosanitary interest were added and all 137 bacteria and eight Protista species were subjected to a rapid risk assessment using agreed guidelines. Vectors reported to transmit any of the pathogenic organisms were also subjected to a risk assessment. A proportion of 53% (n = 77 of 145) comprising 73 bacteria and four Protista species were reported as present in Africa. Of these, 42 (57%, n = 73) bacterial species and two (n=4) Protista species were reported in neighbouring countries. Considering a cut-off of 54, the highest scoring pests were 40 bacteria (highest score of 140) and three Protista (highest score of 125). Three actions were suggested for high-scoring pests, a detection surveillance, a pest-initiated pest risk analysis (PRA) or a detection surveillance followed by pest-initiated PRA. A “no action” was suggested where the risk was very low although, for some pathogenic organisms, a “no action” was followed by periodic monitoring. This information will contribute towards proactive prevention and management of biological invasions.

Smart Watches Lack Skin Smarts: Current and Future Dermatologic Applications in Device Metrics.

Smartwatches have proven life-saving in medical specialties such as cardiology. Smartwatches actively warn us of arrhythmia risk and loud noise exposure. However, dermatologic health metrics are rarely monitored, and users are never alerted of potential skin health issues. Furthermore, the role of these devices within dermatology has not been evaluated in the literature. This study aims to analyze the current data points monitored by smartwatches and discuss potential adaptations to support dermatologic patient education and improve clinical management. Methods: The top three smartwatches per global market share were identified and analyzed to determine the health data points they monitor and the alerts they provide. These data points were grouped and compared based on their corresponding body systems. Results: Cardiovascular health comprises the highest percentage of data points collected with an average of 41% while dermatologic health averaged only 11%. Conclusion: Dermatology is grossly underrepresented in current smartwatch devices. There is an important need to expand the dermatologic health metrics tracked by adapting existing smartwatch technology. From proactive cancer prevention to disease-specific reactive interventions, smartwatches can play a significant role in improving dermatological health and reducing healthcare costs.

Development and validation of fall risk perception scale for patients with Parkinson's disease.

Perception assessment plays an important role in fall risk awareness and fall prevention. Parkinson's disease patients with motor dysfunction are at high risk of falling. Currently, no instrument has been explicitly crafted to assess the risk perception of fall in PD patients. The purpose of this study was to develop and validate the fall risk perception scale for PD patients (FRPS-PD), providing healthcare professionals with a effective assessment tool to enhance proactive fall prevention initiatives. Based on the Proactive Health theory and Risk Perception Attitude (RPA) Framework, the questionnaire was developed through literature review, semi-structure interview, expert consultation and pilot testing. A total of 428 patients with PD from Grade A tertiary hospitals in Shanghai, Hangzhou and Anhui from January 2023 to July 2023 were recruited. The items and dimensions in the scale were explored and confirmed using item-analysis, content validity, exploratory factor analytical (EFA), confirmatory factor analytical (CFA), internal consistency and test-retest reliability analysis. A total of 16-items, 2-dimensions structure were identified, including 12 items of risk perception and 4 items of self-efficacy dimension. The cumulative variance of EFA model was 73.669%, further CFA showed that acceptable model fit (χ2/df = 2.226, RMSEA = 0.074, NF = 0.928, TLI = 0.951, CFI = 0.959, GFI = 0.887 and AGFI = 0.848). The content validity index was 0.956. The reliability of the scale was 0.952 using Cronbach's α coefficient method. The test-retest reliability was 0.944. The FRPS-PD is a valid and reliable measurement for evaluating fall risk perception level for individuals with PD in mainland China.

Kano Model for Apartment-Unit Specialized Planning Guidelines to Prevent Infectious Diseases

Owing to the continued occurrence of infectious diseases, proactive prevention and management plans are required. This study aimed to develop design guidelines to effectively respond to infectious diseases based on the needs of apartment residents, which focused on a South Korean setting. The research method included a literature review to identify apartment planning concepts for preventing and managing infectious diseases, a survey of 300 participants using the Kano model, and an analysis of the quality attributes (QAs) of the survey results to prioritize design guidelines. After reviewing 20 studies, 65 items related to apartment-unit planning for infectious disease prevention, including 108 keywords, were identified. Using thematic analysis, the keywords converged into three planning concepts: hygiene, convenience, and comfort. Based on the literature review, 27 survey questions were derived, and a Kano model QA analysis was performed. As a result, 17 attractive QAs, two one-dimensional QAs, seven indifferent QAs, and one reverse QA were identified. Among these, 13 items that had a significant impact on residents’ satisfaction were classified as essential requirements, and the remaining 14 items were classified as recommended design guidelines. The results of this study provide insights into an evidence-based framework for complex building design guidelines to prevent the spread of infectious diseases.

Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.

Association between cognitive dysfunction and diabetes in patients over 65 years old: a cross-sectional study using propensity score matching.

To investigate the association between diabetes and cognitive dysfunction in the elderly population, and examine the impact of cognitive dysfunction on level of activities of daily living (ADL) in patients with diabetes. Data analysis was conducted on 2,951 individuals aged over 65 years from the Chinese Longitudinal Healthy Longevity Survey cohort. Propensity score matching was utilized to mitigate selection bias. Multivariate binary logistic regression was performed to analyse the association between diabetes and cognitive dysfunction in the study subjects. In addition, the relationship between ADL and cognitive function in patients with diabetes was analysed using the Wilcoxon rank-sum test. A significant association (p = 0.017) was found between diabetes and the occurrence of cognitive dysfunction in older adults. Subgroup analyses revealed that diabetes patients with cognitive dysfunction exhibited a worse ADL dependence compared with those without cognitive dysfunction (p < 0.001). These findings indicate that diabetes is associated with cognitive dysfunction in older adults. Meanwhile, there is an association between cognitive impairment and ADL level in subjects with diabetes. As such, healthcare professionals should pay close attention to the occurrence of cognitive dysfunction and ADL decline during diagnosis and treatment, and proactive prevention and intervention strategies should be implemented.

Personality and burnout complaints: The mediating role of proactive burnout prevention behaviors at work.

The aim of this study is to gain insight into how and why certain personality traits are related to experiencing burnout complaints. Drawing on insights from a stimulus-organism-response (SOR) model of personality and affect and conservation of resources (COR) theory, we specifically focus on the role of five proactive behaviors to prevent burnout (PBPBs) at work. Two research questions are addressed: (1) How are the HEXACO personality traits related to burnout complaints, and (2) to what extent do the PBPBs aimed at increasing resources act as mediators between the engagement dimensions of personality and burnout complaints. We set up a two-wave survey examining HEXACO personality, PBPBs at work, and burnout complaints in a sample of 172 employees. For our analyses we relied on multiple regression analyses and structural equation modeling. Our analyses revealed that employees high on Extraversion and Conscientiousness and low on Emotionality are less inclined to experience burnout complaints. For the trait of Conscientiousness, this could be partly explained because conscientious employees demonstrate more proactive behavior aimed at maintaining or increasing job control. Our research contributes to the burnout and personality literature as we offer insight into why specific personality dimensions are related to burnout complaints. Specific proactive behaviors aimed at increasing job resources appeared to play a small, yet relevant role in this respect, specifically for Conscientiousness. By studying the relationship between personality and behavior in association with burnout complaints, this study adds to our understanding of personalized preventive actions in the work context that can reduce burnout complaints.

An Overview on Revolutionizing Multi Disease Prediction System: Opportunity through Artificial Intelligence

Abstract: A revolutionary change towards proactive and individualised healthcare management has been sparked by the introduction of machine learning technology in the modern healthcare environment. In order to predict numerous diseases at once, this study presents the numerous Disease Prediction System (MDPS), a novel method that uses cutting-edge machine learning techniques to concurrently predict the likelihood of diabetes (both Type 1 and Type 2), Parkinson's disease, and heart diseases and create a thorough health profile for each person, the MDPS incorporates data from a wide variety of sources, such as genetic information, health records, lifestyle factors, and environmental information. diseases. To this end, the system makes use of advanced machine learning models, including ensemble approaches, deep learning, and Bayesian networks. The interface is easy to use, making it easier for both individuals and healthcare professionals to interpret the results. This promotes proactive disease prevention, efficient resource allocation, customised healthcare programmes, and research possibilities to better understand complicated health dynamics.

Ageing in Place: Ensuring Home Safety and Adaptations for the Well-Being of Seniors

This paper investigated the living considerations in ageing, emphasizing the importance of ensuring home safety and adaptations for senior well-being. When ageing in place, seniors' desire to remain in their familiar homes is explored to preserve independence and autonomy. This study delved into the existing literature on home safety for seniors, employing methodologies such as home safety assessments and fall incident surveys. The literature review emphasized the significance of home safety for seniors, particularly in preventing falls, a leading cause of injuries among older adults. Strategies such as home modifications, including the installation of grab bars and non-slip flooring, are examined, along with the integration of assistive technology like wearable devices and smart home automation. The study's methodology involves a comprehensive evaluation of home safety through standardized assessment tools, surveying seniors and analyzing the impact of home modifications and assistive technology on safety and well-being. The study revealed variations in home safety assessment scores among seniors, with higher scores corresponding to safer home environments. The number of fall incidents reported by participants in the past year underscores the need for proactive fall prevention strategies. Home modifications and usage of assistive technology, including wearable devices and smart home automation, positively influenced home safety scores. Recommendations include regular home safety assessments, prioritizing fall prevention strategies, education and awareness, individualized solutions, and continuous monitoring. The study concluded that by implementing these recommendations, seniors' safety and quality of life could be enhanced, enabling them to age comfortably and independently while reducing the risks associated with ageing.

Strategies to prevent cardiovascular disease in Singapore: A call to action from Singapore Heart Foundation, Singapore Cardiac Society and Chapter of Cardiologists of the Academy of Medicine, Singapore.

In 2022, the Minister for Health of Singapore launched Healthier SG, a national strategy in championing the shift towards a population health approach. The Singapore Heart Foundation conducted a series of roundtable discussions, also attended by representatives of the Singapore Cardiac Society and the Chapter of Cardiologists of the Academy of Medicine Singapore. During the meetings, the authors formulated interventions supportive of Healthier SG that specifically aimed to uplift the state of cardiovascular (CV) preventive care in Singapore. In line with Healthier SG, the authors propose a 3-pronged approach ("Healthier Heart SG") to augment the success of Healthier SG in achieving good CV outcomes. This proposal includes the following components: (1) a call to update the standards of care in addressing the 5 main modifiable risk factors of cardiovascular disease (CVD); (2) patient education through cooperation between healthcare professionals and community partners for a whole-of-system approach; and (3) support for integrated care, including access to cardiac rehabilitation in the community, improved referral processes and access to nutrition/dietetics counselling and tobacco cessation, optimal use of information technology, and continued CV research. Healthier Heart SG would bring the standards of care and CV care delivery in Singapore closer to achieving the vision of proactive prevention of CVD and CV morbidity and mortality. This can only be achieved through the concerted efforts of healthcare professionals, policymakers and community partners, coupled with the cooperation of community members.

Risk of introduction and establishment of alien vertebrate species in transboundary neighboring areas

Cross-border neighboring areas could be particularly vulnerable to biological invasions due to short geographic distances and frequent interactions, although the invasion risk remains unevaluated worldwide. Here, based on global datasets of distributions of established alien vertebrates as well as vectors of introduction and establishment, we show that more than one-third of the world’s transboundary neighboring areas are facing high invasion risk of alien vertebrates, especially in Europe, North America, South Asia, and Southeast Asia. The most important predictors of high introduction and establishment risk are bilateral trade, habitat disturbance and the richness of established alien vertebrates. Interestingly, we found that border fences may have limited effects in reducing the risk, as only 7.9% of border fences spatially overlap with hotspots of biological invasion even in the Eurasia areas (13.7% overlap) where physical border barriers are mainly located. We therefore recommend the implementation of immediate and proactive prevention and control measures to cope with cross-border invasions in response to continued globalization.

Sustainable microalgae extraction for proactive water bloom prevention

Sustainable microalgae extraction for proactive water bloom prevention