Data For Risk Assessment Research Articles

Short-term exposure to okadaic acid induces behavioral and physiological responses in sea urchin (Strongylocentrotus intermedius)

Massive harmful algal blooms (HABs) have increased the risk of marine organisms encountering the dinoflagellate toxin, okadaic acid (OA). Strongylocentrotus intermedius, a globally significant benthic aquaculture species, has a large appetite for benthic algae. During red tide events, there is a high risk of red tide toxin accumulation. This study systematically evaluated the potential impact of short-term OA exposure on the behavior and physiological functions of juvenile S. intermedius. From typical (5 μg/L) to extreme OA concentrations (20 μg/L) during HAB outbreaks, OA exposure gradually inhibited a series of tube foot-related behaviors (sheltering, foraging, righting, and tube-foot tenacity). At OA concentrations during HAB outbreaks (5 μg/L), the tube foot function of S. intermedius was progressively inhibited. Further physiological indicator analyses revealed that the activity of antioxidants increased over a short period to prevent damage from reactive oxygen species induced by OA. However, OA ultimately suppressed the immune response of S. intermedius, leading to apoptosis. Although HAB-associated concentrations of OA (5 μg/L) did not induce a continuous increase in the integrated biological response index of S. intermedius, this study speculated that HABs pose a future risk to echinoderm species. Notably, principal component analysis results showed that OA exposure eventually induced significant changes in the production of O2-, malondialdehyde, and total glutathione, as well as in glutathione S-transferase activity and caspase-7, -8, and -9 levels. This study provides preliminary evidence of OA’s toxic effects on sea urchins and essential data for urgent risk assessments of algal toxin pollution in aquaculture during HABs.

Are Environmental Levels of Nonsteroidal Anti-Inflammatory Drugs a Reason for Concern? Chronic Life-Cycle Effects of Naproxen in Zebrafish.

The nonsteroidal anti-inflammatory drug naproxen (NPX) is among the most consumed pharmaceuticals worldwide, being detected in surface waters within the ng to μg/L range. Considering the limited chronic ecotoxicity data available for NPX in aquatic ecosystems, the present study aimed at evaluating its impact in the model organism Danio rerio, following a full life-cycle exposure to environmentally relevant concentrations (0.1 to 5.0 μg/L). An integration of apical endpoints, i.e., survival, growth, and reproduction, with gonad histopathology and gene transcription (RNA-seq) was performed to provide additional insights into the mode of action (MoA) of NPX. NPX decreased zebrafish growth and reproduction and led to histopathological alterations in gonads at concentrations as low as 0.1 μg/L. At the molecular level, 0.7 μg/L of NPX led to a disruption in gonads transcription of genes involved in several biological processes associated with reproduction, mainly involving steroid hormone biosynthesis and epigenetic/epitranscriptomic machineries. Collectively, these results show that environmentally realistic concentrations of NPX affect zebrafish reproduction and associated signaling pathways, indicating that current hazard and risk assessment data for NPX underestimate the environmental risk of this pharmaceutical.

Can an Expert Opinion Mitigate Racially Biased Diversion Decisions? An Empirical Examination in the Context of Re-Offense Risk Assessment

Using a vignette-based experimental design, this study tested whether the racial or ethnic identity of a justice-impacted person influences jury-eligible individuals’ perceptions of post-conviction risk, placement (incarceration vs. community supervision), and need for mandated treatment. Furthermore, we examined whether the inclusion of data from a reoffense risk tool could mitigate racial bias. Participants recruited through Amazon’s MTurk ( N = 448) were randomly assigned to conditions varying by race/ethnicity of an examinee (Black, Latino, White) and if/how risk assessment data were included (no risk data, risk data only, and risk data reported by a qualified examiner). There was no statistically significant effect of the examinee’s racial/ethnic identity on ratings of reoffense risk or placement decisions; however, the Black examinee was more likely to be mandated with treatment. Regardless of examinee identity, participants exposed to risk information proffered by an expert more often calibrated their risk decisions to the examinee’s actual risk level.

Risk-benefit analysis of a multi-site radiographer comment model for emergency departments.

Critical/urgent X-ray findings are not always communicated in an appropriate time frame to ED physicians. The practice of radiographers alerting referrers to clinically significant image findings (verbally, via image flags or written comment) is noted internationally but risk assessment data is unavailable in the literature. A hybrid radiographer comment and alert model was piloted in New South Wales and a risk-benefit assessment conducted for timely and safe communication of abnormal X-ray appearances to ED physicians. Radiographer comments (n = 1102) were provided to five New South Wales EDs by 69 radiographers for a period of 3-12 months. Site auditors classified comments as true positive (TP), false positive (FP) or indeterminate (ID) with respect to the radiology report. FP comments were investigated with ED referrers and a low-medium-high-risk assessment was provided by two independent reviewers. A total of 42 FP (3.9%; 95% confidence interval [CI] 2.9-5.3) comments were analysed for any adverse outcomes. Risk assessments demonstrated 37 low, 5 low-moderate and no high-risk cases. A total of 282 direct or potential patient benefits were identified (26.4%; 95% CI 23.8-29.1). A total of 42 radiology report discrepancies were incidentally found: (3.9%; 95% CI 2.9-5.3). Audit results demonstrated areas where the radiographer comment could mitigate risk. The provision of radiographer alerts with a written comment for ED was found to be low risk to patients in the pilot study. Radiographers communicating directly with the emergency team when abnormal image appearances are detected can reduce diagnostic error and improve patient safety and health outcomes.

Impacts of aquaculture practices on the sustainability of social-ecological systems in coastal zones of the Mekong Delta

Coastal and deltaic regions face increasing natural and human-induced hazards, such as cyclones, storm surges, erosion, and saline intrusion, exacerbated by climate change. In tropical zones, mangrove ecosystems provide vital protection against these threats, yet the conversion of mangroves to shrimp aquaculture undermines these benefits, even in protected areas.The Mekong Delta in Vietnam, historically rich in biodiversity and diverse habitats like mangroves and mudflats, has seen local communities thrive. However, rising global market demands have led to more intensive aquaculture models, posing new risks.This research examines the impact of shrimp farming models, from extensive to hyper-intensive, under multiple natural hazards and transformative processes like land conversion and mangrove restoration. Using risk assessment tools and data from local community interviews and focus groups, we present a model showcasing the influence of aquaculture on socio-ecological systems.Key findings reveal the interplay between livelihoods and landscape conversion, highlighting the risks and benefits of various shrimp farming models. The study stresses the need for stakeholder collaboration and improved wastewater treatment to enhance sustainability and environmental health in the Mekong Delta.

Exploring The Impact of Internet of Things (Iot) Technologies on Tax Compliance: Opportunities, Challenges and Policy Implications

The proliferation of Internet of Things (IoT) technologies has revolutionized various aspects of modern life, including business operations and government services. This research paper delves into the intersection of IoT technologies and tax compliance, aiming to elucidate the opportunities, challenges, and policy implications arising from their integration. Through a comprehensive review and analysis of existing literature, this paper seeks to provide insights into the transformative potential of IoT in tax compliance, while also highlighting potential hurdles and offering policy recommendations. IoT technologies offer a myriad of opportunities to enhance tax compliance processes for both taxpayers and tax authorities. The ability of IoT devices to collect real-time data and automate various tasks can streamline recordkeeping, improve accuracy in reporting, and enable proactive compliance measures. For taxpayers, IoT-enabled systems can simplify tax documentation, minimize errors, and provide timely reminders for compliance deadlines. Likewise, tax authorities can leverage IoT data for better monitoring, enforcement, and risk assessment, thus enhancing overall tax administration efficiency. However, the integration of IoT in tax compliance also presents notable challenges. Privacy and data security concerns emerge as paramount considerations, given the sensitive nature of financial information involved in taxation. The vast amount of data generated by IoT devices also raises questions regarding data governance, ownership, and liability. Moreover, the digital divide may exacerbate existing disparities in access to IoT technologies, potentially marginalizing certain segments of the population and hindering equitable tax compliance. In light of these opportunities and challenges, policymakers must carefully design regulations and guidelines to harness the benefits of IoT while mitigating associated risks. Clear standards for data protection, encryption, and user consent are essential to safeguard taxpayer privacy and instill trust in IoT-enabled tax compliance systems. Additionally, implementation of measures to bridge the digital divide and ensure equitable access to IoT technologies, thereby promoting inclusivity in tax compliance efforts. Furthermore, tax authorities need to develop robust frameworks for data governance, encompassing data collection, storage, sharing, and usage. Collaboration between public and private sectors is crucial to foster innovation in IoT-based tax compliance solutions while adhering to regulatory requirements and ethical principles. In conclusion, the integration of IoT technologies in tax compliance holds immense potential to revolutionize the way in which taxes administered and enforced. However, realizing this potential necessitates a holistic approach that addresses technical, legal, and socio-economic considerations. By embracing innovation while safeguarding privacy and inclusivity, governments can harness the transformative power of IoT to foster more efficient, transparent, and equitable tax systems.

Using the Super-learner to Predict the Chemical Acute Toxicity on Rats

With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD50) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relationship models relying on single algorithm always lack interpretability and precision, given the complexity of the mechanisms underlying acute toxicity. To address these issues, this study has developed a predictive framework using an ensemble learning model based on Super-learner. Particularly, we first obtained LD50 data for 9,843 compounds and constructed 16 meta models using 4 molecular descriptors and machine learning algorithms. The Super-learner model performed well, achieving R² values of 0.61 and 0.64 in five-fold cross-validation and test sets, respectively, with corresponding root mean square errors of 0.55 and 0.64, significantly outperforming the results of individual model. Additionally, we incorporated data filtering and applicability domain methods, which demonstrated that the Super-learner can mitigate the impact of dataset noise to some extent. The model achieved an R² of 0.76 within an applicability domain, ensuring prediction accuracy within the chemical space. Compared to previous studies, the model developed here using Super-learner generally achieved better performance across a larger applicability domain. Finally, we has launched an online tool (http://sltox.hhra.net), allowing users to quickly predict LD50 of compounds, greatly simplifying the chemical safety assessment process. This study not only provides an effective and cost-efficient method for predicting chemical toxicity but also offers technical support and data for risk assessments of chemicals.

FLOOD RISK MAPPING IN THE IRTYSH RIVER BASIN USING SATELLITE DATA

Floods are among the most frequent and devastating natural disasters, causing significant economic damage and loss of life worldwide. Effective flood risk management relies on accurate modeling techniques that can predict vulnerable areas and assess potential impacts. In this study, flood dynamics are simulated in the Irtysh River Basin near Ust-Kamenogorsk, a city in East Kazakhstan prone to seasonal flooding using high-resolution satellite imagery and digital elevation data. The primary objective is to visually model flood risks based on terrain characteristics. The study utilizes imagery sourced from the Mapbox platform, which combines data from MODIS, Landsat 7, Maxar, and the Google Earth Engine, providing access to Sentinel-2 surface reflectance imagery at 10-meter resolution. Elevation data from the Copernicus global digital elevation model, with a 30-meter resolution, is used to simulate flood progression. The flood simulation involves calculating flood depth relative to the terrain’s elevation, allowing for a pixel-by-pixel determination of submerged areas. Each simulation incrementally increases water levels to generate a sequence of images, showcasing the progression of flooding over time. The study describes hydraulic soil characteristics usage, and focuses on visualizing flood risk based on terrain data and water level changes. The simulation results indicate that flooding initially impacts riverbanks as water flow starts from the northwest of the city with critical infrastructure becoming vulnerable once water levels exceed 2 meters from the lowest elevation point. These findings highlight the potential of high-resolution satellite imagery and terrain data for flood risk assessment and improving urban flood preparedness. The results provide valuable insights into flood progression enabling more informed decision-making for disaster mitigation.

Comparative assessment of acute neurotoxicity of real-world ultra-fine black carbon emitted from residential solid fuel combustion

Incomplete combustion of residential solid fuel is one of the main anthropogenic sources for black carbon (BC). Fresh BC, mainly enriched in ultra-fine fraction of particles, can directly cross blood-brain barrier and are reported to be associated with neurodegenerative diseases. Because of the difficulties in collection and purification of BC from ambient particles, there are still significant knowledge gaps in understanding neurotoxicity caused by real-world BC. The purpose of this study is to compare the neurotoxic effects caused by BCs emitted from combustion of six residential solid fuels, and try to reveal associated biological mechanisms in SH-SY5Y cells. Two straw BC (Wheat-BC and Corn-BC) showed highest neurotoxic effects followed by wood BC (Pine-BC and Aspen-BC) and coal BC (Xvzhou and Longkou Coal), as indicated by viability, lactic dehydrogenase, malondialdehyde, adenosine triphosphate and acetylcholine levels. Coal BC caused nearly no toxicity in human neuroblastoma (SH-SY5Y) cells within highest dose of 200 μg/mL. RNA sequence and bioinformatics analysis were applied to effectively identify differential genes and signaling pathways. Based on Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Protein Protein Interaction network (PPI network) construction, we found biomass BC affected mitochondrial function, interfered with cellular metabolic processes, disturbed redox homeostasis, and finally resulted in cellular damages. Coal-BC mainly caused cytokine/chemokine related inflammatory responses. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blotting methods were further applied to find out related signaling pathways. Biomass BC activated IL6R/JAK3/STAT3 and JAK3/STAT6 pathways leading to oxidative stress and inflammatory responses. Coal BC activated JAK3/STAT3 pathway leading to chemokine related responses. This study revealed the heterogeneity in neurotoxicity of BCs from different combustion sources and provided important data for health risk assessment. BC-related neurotoxicity should be considered when making air pollution emission control strategies, with residential biomass receiving more policy attention.

Distribution and Ecological Risk of Ludwigia peploides in South Korea.

The number of alien species introduced into South Korea continues to increase over the years. In particular, several plants have been introduced as ornamentals. Ludwigia peploides, which is native to the Americas and Australia, is believed to have been planted as an ornamental aquatic plant called "water primrose" and "primrose". It spread to natural ecosystems through rivers, and its distribution is gradually expanding in Suwon, Hwaseong, Busan, and Jeju. However, there has been no specific study on the ecological risk of L. peploides introduced into South Korea. This study, therefore, investigates the distribution status and ecological risks of L. peploides in South Korea through field surveys and allelopathic material analysis, as well as assessing abiotic risk factors. The distribution was confirmed at a total of 19 sites, with high-density mats of a single species forming along the water's edge and on the water surface. The maximum distribution area was 13,922 m2 in Chilgok Reservoir in Anseong. Stems and plant fragments transported along waterways were continuously forming colonies through vegetative propagation. When evaluating the overall risk, it is determined that L. peploides has a high potential to cause significant damage to the ecosystem if not managed promptly. Therefore, continuous monitoring is necessary to effectively manage and prevent the habitat expansion of L. peploides. The results of this study are expected to aid in the identification of the current distribution and potential ecological risks of L. peploides in South Korea, providing essential data for ecological risk assessment and the development of effective management strategies.

Nationwide survey in Korea for risk assessment of pesticide residues in fishery and seaweed products

Pesticide residues in fishery products are a concern due to environmental contamination. However, it is not sufficiently managed because of a lack of established standards. This study was conducted to monitor pesticide residues in fishery and seaweed product samples and assess the risk to humans. A total of 454 fishery products and 38 seaweed products were examined, and seven pesticides were detected. Among these, ethoxyquin was the most frequently detected. The risk of ethoxyquin via chronic exposure was 0.16 % and 83 % for the average and 97.5th intake, respectively. A risk exceeding 80 % is concerning; however, this concentration is considered safe in real-world circumstances, such as washing and cooking processes. Therefore, the detected pesticide residues in this study were assessed to be safe level for Korean intake level of fishery products. However, DDT, the prohibited pesticide decades ago, was still found and it is necessary to keep monitor considering persistent of pesticides. Considering newly regulated pesticides, sufficient monitoring and risk assessment data such as this study should be provided for policy standards setting and management.

Development of a comprehensive open access "molecules with androgenic activity resource (MAAR)" to facilitate risk assessment of chemicals.

The increasing prevalence of endocrine-disrupting chemicals (EDCs) and their potential adverse effects on human health underscore the necessity for robust tools to assess and manage associated risks. The androgen receptor (AR) is a critical component of the endocrine system, playing a pivotal role in mediating the biological effects of androgens, which are male sex hormones. Exposure to androgen-disrupting chemicals during critical periods of development, such as fetal development or puberty, may result in adverse effects on reproductive health, including altered sexual differentiation, impaired fertility, and an increased risk of reproductive disorders. Therefore, androgenic activity data is critical for chemical risk assessment. A large amount of androgenic data has been generated using various experimental protocols. Moreover, the data are reported in different formats and in diverse sources. To facilitate utilization of androgenic activity data in chemical risk assessment, the Molecules with Androgenic Activity Resource (MAAR) was developed. MAAR is the first open-access platform designed to streamline and enhance the risk assessment of chemicals with androgenic activity. MAAR's development involved the integration of diverse data sources, including data from public databases and mining literature, to establish a reliable and versatile repository. The platform employs a user-friendly interface, enabling efficient navigation and extraction of pertinent information. MAAR is poised to advance chemical risk assessment by offering unprecedented access to information crucial for evaluating the androgenic potential of a wide array of chemicals. The open-access nature of MAAR promotes transparency and collaboration, fostering a collective effort to address the challenges posed by androgenic EDCs.

Real-world phenotyping and risk assessment of childhood asthma burden using national registries

BackgroundPhenotype classification contributes to risk assessment of asthma. Previous studies have applied this concept primarily to adult populations and in the setting of research protocol assessments which may not be applicable to clinical settings. ObjectiveExploring the value of routinely collected clinical data for phenotype classification and risk assessment of childhood asthma. MethodsUsing hospital and laboratory data, 29,851 children in a Danish nationwide database aged 2–17 years with ICS-treated asthma in 2015 followed for two years (730 days) were classified to have T2 (elevated blood eosinophils (>300 cells/μL) and/or elevated total- or specific-IgE), and/or non-T2 risk factors (in utero tobacco exposure and/or severe viral infections). Logistic regression was applied to quantify associations of risk factors with asthma severity, control, and exacerbation risk. ResultsIn a complete case analysis, 85.8 % children had at least one T2 risk factor and 29.3 % had mixed T2/non-T2 risk factors. Elevated blood eosinophils and total/specific IgE were associated with exacerbations (ORs 1.55 (1.38–1.73) and 1.41 (1.20–1.66) and higher asthma severity (1.42 (1.24–1.63) and 1.31 (1.08–1.60)), respectively.Dose-dependency was observed between blood eosinophil counts, total IgE levels, and risk of adverse outcomes. Furthermore, accumulation of risk factors demonstrated an increasing risk, with children with all four risk factors having a high risk of any adverse asthma-related outcome (OR 3.13 (2.03–4.82) ConclusionAsthma phenotypic markers defined in research protocols can be reliably applied in real-world settings by utilizing data collected during routine clinical care and enable better classification of risk of adverse asthma outcomes.

Global population datasets overestimate flood exposure in Sweden

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Research on the Application of Alternative Data in Credit Risk Management

With the development of financial technology, traditional credit assessment models have gradually shown their limitations. Especially in assessing borrowers with no credit history or weak credit records. The rise of alternative data provides a new dimension for credit risk prediction, including but not limited to social media behavior, online transaction records, geographic location data, etc. This paper explores the current application status, challenges, and future development trends of alternative data in personal credit risk assessment, and explores the application and effects of various forms of alternative data through different classifications. This paper refers to the relevant literature on alternative data and credit risk management and finds that the application of alternative data can not only supplement part of the information reference to enhance the risk management model but also further provide certain credit credentials for groups that cannot obtain credit services with traditional credit data. It has potential contributions to improving credit risk management and promoting the development of inclusive finance.

Methylmercury in commercial fish species from the Erhai Lake Basin, Southwest China: concentrations, health risk assessment, and implications for future monitoring.

Erhai Lake, a highland lake situated in Southwest China, provides critical aquatic protein sources for the local community, and its preservation is vital due to the sensitivity of alpine freshwater ecosystems to disturbance. However, there is a lack of research on the contamination status of methylmercury (MeHg) in aquatic organisms of the Erhai Lake Basin. MeHg concentrations in important commercial fish species from the Erhai Lake were examined, and the potential health risks associated with human consumption were assessed. Our results showed significant inter-species differences in fish muscle MeHg: the carnivorous S. asotus exhibited the highest level (303ng/g; ww), while that of the detritivorous R. ocellatus was the lowest (3.86ng/g). Moreover, MeHg concentrations in P. fulvidraco and C. auratus collected from the Luoshi River (a major tributary of Erhai Lake) were significantly higher compared to those from the Erhai Lake, indicating possible river-based input of MeHg into the Erhai Lake. Additionally, our study revealed a significant positive correlation between the MeHg levels and the length as well as weight of the examined fish species. All the fish species analyzed in our study had MeHg concentrations within the limits of China's food safety standard. Nevertheless, a relatively low consumption quantity of 16g per day of certain species (i.e., S. asotus) may still pose potential health risks especially for children. The present study provides baseline data for MeHg monitoring and risk assessment in the Erhai Lake Basin, and warrants continued monitoring and source investigation.

Classification of Heart Disease Diagnoses Using Gaussian Naïve Bayes

Machine learning, which is part of artificial intelligence, has been widely applied in various fields, especially the medical field. Machine learning helps doctors make more accurate diagnoses. Heart disease is one of the highest causes of death in the world, so the need for accurate diagnosis is absolute for this disease. There are many algorithms that have been applied in machine learning to classify and detect heart disease, such as Linear Discriminant Analysis [1], KNN, Decision Tree, Random Forest [2], and Logistic Regression [3]. One classification algorithm that has not been implemented is Gaussian Naive Bayes. So, in this research the Gaussian Naive Bayes algorithm will be tested on the cardio health risk assessment dataset. From the research results of applying the Gaussian Naive Bayes algorithm to cardio health risk assessment data, accuracy was 0.87%, precision was 0.88%, recall was 0.90%, and f1-score was 0.89%.

Long-term exposure to climbazole may affect the health of stress-tolerant coral Galaxea fascicularis

The persistence of coral reefs globally is threatened by various forms of chemical pollution. Climbazole, an azole antibacterial agent extensively utilized in pharmaceuticals and personal care products (PPCPs) in everyday life, has been detected in various environment media and proved to have significant adverse effects on aquatic organism. However, the effects of climbazole on coral remain largely unknown. Therefore, in this study, we conducted a 42-day investigation to examine the effects of varying concentrations of climbazole on Galaxea fascicularis (G. fascicularis), a stress-tolerant coral species. Our investigations included coral color observations, physiological experiments, and assessments of microbial diversity. The results showed that, after 42 days of exposure, the coral color in the treatment group exposed to 100 μg/L climbazole significantly decreased by one color category on the reference chart (D6 shifted to D5), while there was no change in the control group. This was accompanied by an increase in oxidative stress and a decrease in photosynthetic capacity in coral specimens. Additionally, there was a notable alteration in microbial diversity, resulting in reduced community stability. Elevated levels of climbazole (100 μg/L) stress led to an increased abundance of potentially pathogenic bacteria such as unclassified Erysipelotrichaceae. However, at an environmentally relevant concentration of 1 μg/L, climbazole decreased the photosynthetic efficiency and induced oxidative stress in the stress-tolerant coral G. fascicularis, while not significantly impacting the microbial community diversity of the coral. The findings of our study have important implications for the protection and management of nearshore coral reefs and offer essential data for ecological risk assessment of climbazole.

Regional ecological risk assessment through the application of production-living-ecology land use function indicators: a case study of the eco-economic zone of Yellow River Delta.

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures.

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.