Health Damage Research Articles

Genetic Screening-Emerging Issues.

In many countries, some form of genetic screening is offered to all or part of the population, either in the form of well-organized screening programs or in a less formalized way. Screening can be offered at different phases of life, such as preconception, prenatal, neonatal and later in life. Screening should only be offered if the advantages outweigh the disadvantages. Technical innovations in testing and treatment are driving changes in the field of prenatal and neonatal screening, where many jurisdictions have organized population-based screening programs. As a result, a greater number and wider range of conditions are being added to the programs, which can benefit couples' reproductive autonomy (preconception and prenatal screening) and improve early diagnosis to prevent irreversible health damage in children (neonatal screening) and in adults (cancer and cascade screening). While many developments in screening are technology-driven, citizens may also express a demand for innovation in screening, as was the case with non-invasive prenatal testing. Relatively new emerging issues for genetic screening, especially if testing is performed using DNA sequencing, relate to organization, data storage and interpretation, benefit-harm ratio and distributive justice, information provision and follow-up, all connected to acceptability in current healthcare systems.

Mediation of mitochondrial DNA copy number and oxidative stress in fluoride-related bone mineral density alteration in Chinese farmers.

Excessive fluoride can adversely affect bone mineral density (BMD). Oxidative stress and mitochondrial dysfunction are crucial mechanisms of health damage induced by fluoride. Here, a cross-sectional survey involving 907 Chinese farmers (aged 18-60) was carried out in Tongxu County in 2017, aiming to investigate the significance of mitochondrial DNA copy number (mtDNAcn) and oxidative stress in fluoride-related BMD change. Concentrations of urinary fluoride (UF), serum oxidative stress biomarkers, including total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA), as well as relative mtDNAcn in peripheral blood were determined. The multivariable linear model and mediation analysis were performed to assess associations between UF, oxidative stress, and relative mtDNAcn with BMD. Results showed that GSH-Px levels increased by 6.98 U/mL [95% confidence interval (CI) 3.41-10.56)] with each 1.0mg/L increment of UF. After stratification, the T-AOC, relative mtDNAcn, and BMD decreased by 0.04mmol/L (-0.08 ~ -0.01), 0.29-unit (-0.55 ~ -0.04), and 0.18-unit (-0.33 ~ -0.03) with every 1.0mg/L elevation of UF in the excessive fluoride group (EFG, adults with UF > 1.6mg/L), respectively. Furthermore, T-AOC and relative mtDNAcn were favorably related to the BMD in the EFG (β = 0.82, 95%CI 0.16-1.48 for T-AOC; β = 0.11, 95%CI 0.02-0.19 for relative mtDNAcn). Mediation analysis showed that relative mtDNAcn and T-AOC mediated 15.4% and 17.1% of the connection between excessive fluoride and reduced BMD, respectively. Findings suggested that excessive fluoride was related to lower BMD in adults, and the decrement of T-AOC and relative mtDNAcn partially mediate this relationship.

HYBRID MODEL AND FRAMEWORK FOR PREDICTING AIR POLLUTANTS IN SMART CITIES

The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations.

Amplified positive effects on air quality, health, and renewable energy under China’s carbon neutral target

China pledged to achieve carbon neutrality by 2060 to combat global climate change, yet the resulting multi-aspect domestic impacts are not fully analysed due to an incomplete understanding of the underlying anthropogenic–natural interactions. Building an integrated cross-disciplinary modelling framework that can capture the feedbacks of changing aerosols on meteorology, here we highlight the amplified air quality, human health and renewable energy self-reinforcing synergies of China’s carbon neutral target in comparison to the baseline in 2015 and 2060. We find that owing to emissions reduction and more favourable meteorological conditions caused by less aerosol, achieving China’s carbon neutrality target in 2060 reduces national population-weighted PM2.5 concentrations and associated premature deaths by ~39 μg m−3 and 1.13 (95% confidence interval: 0.97–1.29) million while boosting provincial solar (wind) power performance by up to ~10% (~6%) with mostly decreased resource variability in comparison to the 2060 baseline. Enhanced renewable performance along with low-carbon energy transition may provide additional opportunities to address the remaining air pollution and associated human health damages upon achieving carbon neutrality. Our results highlight that global developing and polluting countries’ pledge for carbon neutrality can produce important positive feedbacks between aerosols mitigation, air quality improvement and enhanced renewable energy, which can be amplified via weakened aerosol–meteorology interactions and better atmospheric dispersion.

Self-diagnosis of structural damage in self-powered piezoelectric composites

At present, nondestructive testing and non-self-powered structure health monitoring methods are usually used to judge the damage of fiber reinforced composites. However, these methods have some limitations due to the lack of real-time characteristics, the need for external power supply or the complex structure of sensor. In this work, the piezoelectric nanogenerator mechanism was integrated into the traditional composites to develop the piezoelectric composite with damage self-diagnosis function. The piezoelectric composites can realize structural health monitoring and damage localization without reducing the original mechanical properties. The effects of PVDF and barium titanate on the mechanical properties of the composites were investigated by experiments and simulations. Furthermore, the damage self-diagnosis characteristics of composites under bending load and impact load were studied. The algebraic relationship between the damage area and the open circuit voltage under impact load was established. In addition, the damage localization of composite materials is realized by designing an Application (APP). In general, this work has successfully set a precedent for the application of nanogenerator mechanism to the self-powered and self-diagnosis of composite damage, which has the potential to be used in any field related to composites, such as aerospace, etc.

Enhanced oxidative potential and cytotoxicity of high-temperature treated model soot particles due to carbonaceous core oxidation

When diesel soot particles experience high-temperature treatment during exhaust purification aiming to reduce emission, their physicochemical properties and toxicity would change as well. However, rare studies investigated the relationship between properties and health effects of soot under high temperatures. This study compared the physicochemical properties, oxidative potential (OP, dithiothreitol (DTT) method), and cytotoxicity of model diesel soot before and after 300°C and 500°C treatments in air. It was observed that the oxygen-containing functional groups, environmental persistent free radicals, surface area and pore volume increased a lot after the 500°C process, accompanied with the appearance of hollow core and decreased particle size in the images collected by high-resolution transmission electron microscopy, indicating the deep oxidation into carbonaceous core of soot. In addition, the enhancements of OPDTT and cytotoxicity were in accordance with the changes in physicochemical properties. The toxic increment at 500°C was the most due to core oxidation, whether it was compared with 300°C heating or the surface oxidation of light irradiation and ozone aging, implying that carbonaceous core oxidation caused worse health damage. Hence, it is necessary to consider the impact of widely used diesel exhaust treatment involving high temperatures, considering the potential toxicity of the resulting particles.

Research on health monitoring and damage recognition algorithm of building structures based on image processing

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%.

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity-based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full-scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full-scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full-scale engineering structure.

Regulatory T cells limit age-associated retinal inflammation and neurodegeneration

BackgroundAgeing is the principal risk factor for retinal degenerative diseases, which are the commonest cause of blindness in the developed countries. These conditions include age-related macular degeneration or diabetic retinopathy. Regulatory T cells play a vital role in immunoregulation of the nervous system by limiting inflammation and tissue damage in health and disease. Because the retina was long-considered an immunoprivileged site, the precise contribution of regulatory T cells in retinal homeostasis and in age-related retinal diseases remains unknown.MethodsRegulatory T cells were selectively depleted in both young (2–4 months) and aged (18–23 months) FoxP3-DTR mice. We evaluated neuroretinal degeneration, gliosis, subretinal space phagocyte infiltration, and retinal pigmented epithelium morphology through immunofluorescence analysis. Subsequently, aged Treg depleted animals underwent adoptive transfer of both young and aged regulatory T cells from wild-type mice, and the resulting impact on neurodegeneration was assessed. Statistical analyses employed included the U-Mann Whitney test, and for comparisons involving more than two groups, 1-way ANOVA analysis followed by Bonferroni’s post hoc test.ResultsOur study shows that regulatory T cell elimination leads to retinal pigment epithelium cell dysmorphology and accumulation of phagocytes in the subretinal space of young and aged mice. However, only aged mice experience retinal neurodegeneration and gliosis. Surprisingly, adoptive transfer of young but not aged regulatory T cells reverse these changes.ConclusionOur findings demonstrate an essential role for regulatory T cells in maintaining age retinal homeostasis and preventing age-related neurodegeneration. This previously undescribed role of regulatory T cells in limiting retinal inflammation, RPE/choroid epithelium damage and subsequently photoreceptor loss with age, opens novel avenues to explore regulatory T cell neuroprotective and anti-inflammatory properties as potential therapeutic approaches for age-related retinal diseases.

‘Nowhere and no one is safe’: spatial analysis of damage to critical civilian infrastructure in the Gaza Strip during the first phase of the Israeli military campaign, 7 October to 22 November 2023

BackgroundSince the Hamas attacks in Israel on 7 October 2023, the Israeli military has launched an assault in the Gaza Strip, which included over 12,000 targets struck and over 25,000 tons of incendiary munitions used by 2 November 2023. The objectives of this study include: (1) the descriptive and inferential spatial analysis of damage to critical civilian infrastructure (health, education, and water facilities) across the Gaza Strip during the first phase of the military campaign, defined as 7 October to 22 November 2023 and (2) the analysis of damage clustering around critical civilian infrastructure to explore broader questions about Israel’s adherence to International Humanitarian Law (IHL).MethodsWe applied multi-temporal coherent change detection on Copernicus Sentinel 1-A Synthetic Aperture Radar (SAR) imagery to detect signals indicative of damage to the built environment through 22 November 2023. Specific locations of health, education, and water facilities were delineated using open-source building footprint and cross-checked with geocoded data from OCHA, OpenStreetMap, and Humanitarian OpenStreetMap Team. We then assessed the retrieval of damage at and with close proximity to sites of health, education, and water infrastructure in addition to designated evacuation corridors and civilian protection zones. The Global Moran’s I autocorrelation inference statistic was used to determine whether health, education, and water facility infrastructure damage was spatially random or clustered.ResultsDuring the period under investigation, in the entire Gaza Strip, 60.8% (n = 59) of health, 68.2% (n = 324) of education, and 42.1% (n = 64) of water facilities sustained infrastructure damage. Furthermore, 35.1% (n = 34) of health, 40.2% (n = 191) of education, and 36.8% (n = 56) of water facilities were functionally destroyed. Applying the Global Moran’s I spatial inference statistic to facilities demonstrated a high degree of damage clustering for all three types of critical civilian infrastructure, with Z-scores indicating < 1% likelihood of cluster damage occurring by random chance.ConclusionSpatial statistical analysis suggests widespread damage to critical civilian infrastructure that should have been provided protection under IHL. These findings raise serious allegations about the violation of IHL, especially in light of Israeli officials’ statements explicitly inciting violence and displacement and multiple widely reported acts of collective punishment.

Feature extraction based on dynamic response measurements for structural damage identification: a comparative study

ABSTRACT Sudden unexpected loads, such as wind and earthquakes, can cause significant damage and notable changes in the characteristics of structures. Additionally, unfavorable environmental conditions can lead to erosion and deterioration of materials and structural integrity. Therefore, structural health monitoring (SHM) and damage identification have become critical concerns in civil engineering. Feature extraction from dynamic response measurements is a crucial aspect of structural damage identification. Various methods and techniques have been developed to extract features for identifying damage and monitoring the health of structures. This paper presents a comparative study of three different damage-sensitive features: resonant frequency, discrete wavelet transform component energy (DWTCE), and wavelet packet transform component energy (WPTCE), which are used in damage identification. The displacement and acceleration responses were used in the feature extraction process. This study assesses the sensitivity of each feature to structural damage using a derivative-based method. The results showed that WPTCE extracted from the acceleration response data can effectively capture structural damage due to its high sensitivity.

Spatial and temporal patterns of fuelwood consumption and its associated CO2 emissions in Muzaffarabad division, a western Himalayan region

BackgroundIn the Himalayan region, fuelwood serves as a critical energy source for rural communities. Being vital for meeting energy needs, fuelwood combustion is a source of carbon dioxide (CO2) emission and, consequently, global warming, as well as deforestation and public health damage. Therefore, quantifying fuelwood consumption patterns and its associated CO2 emissions is essential to understand the environmental impact and promote sustainable resource management.MethodsThis research conducts an evaluation of fuelwood burning patterns and the associated CO2 emissions in Azad Jammu and Kashmir (AJK), situated within the western Himalayan region. The study entails an extensive survey of 24 villages representing 240 households, equally distributed between the subtropical and temperate regions, each comprising 120 households. Data collection was executed through a combination of direct queries and the weight survey method, following standard protocols.ResultsIn the study area, the mean annual fuelwood comsumption per household amounts to 24.28 ± 3.1 Mg (or 3.195 ± 1 Mg capita−1). A variance was observed between subtropical and temperate zones, with the latter exhibiting higher consumption rates. The consequential CO2 emissions were assessed as 41.88 ± 4.5 Mg per household (5.51 ± 0.6 Mg capita−1). On a daily basis, households consumed an average of 66.52 ± 6.4 kg of fuelwood (8.75 ± 1.5 kg capita−1), resulting in a daily CO2 release rate of 114.745 ± 8.6 kg (15.095 ± 2 kg capita−1). The findings unveiled seasonal variations, indicating increased fuelwood consumption and emissions during the winter season. Statistical analysis shed light on the significance of altitude and family size in shaping the patterns of fuelwood use.ConclusionsThe results revealed the importance of prioritizing forest conservation and strategically implementing sustainable practices, including reforestation, afforestation, responsible harvesting, and actively promoting sustainable fuel sources. This research highlights the vital role of well-designed policies focused on preserving ecosystems and improving energy management. Policy intervention can ensure the sustainable stewardship of local and regional forest resources.

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

In this paper, A high-order response surface method is proposed for finite element model updating of continuous beam bridges. Firstly, based on visual inspection and environmental vibration testing, the peak picking (PP) method and random subspace identification (SSI) method are used to identify the dynamic characteristic parameters of the structure. Then, the finite element model of the continuous beam bridge is updated based on the third-order response surface method. It can be concluded that the results of the updated finite element model are in good agreement with the test results, and the maximum error between the calculated and measured frequency is less than 3%, with MAC values greater than 85%. Moreover, the updated finite element model can reflect the current situation of real bridges and serve as the basis for bridge health monitoring, damage detection, and safety assessment.

Risk assessment and source analysis of heavy metals in soil around an asbestos mine in an arid plateau region, China

Asbestos is widely used in construction, manufacturing, and other common industrial fields. Human activities such as mining, processing, and transportation can release heavy metals from asbestos into the surrounding soil environment, posing a health hazard to the mining area's environment and its surrounding residents. The purpose of the present study was to determine the extent of ecological and human health damage caused by asbestos pollution, as well as the primary contributors to the contamination, by examining a large asbestos mine and the surrounding soil in China. The level of heavy metal pollution in soil and sources were analyzed using methods such as the geo-accumulation index (Igeo), potential ecological risk index (RI), and positive matrix factorization (PMF) model. A Monte Carlo simulation-based health risk model was employed to assess the health risks of heavy metals in the study area’s soil to human beings. The results showed that the concentrations of As, Pb, Cr, Cu, and Ni in the soil were 1.74, 0.13, 13.31, 0.33, and 33.37 times higher than the local soil background values, respectively. The Igeo assessment indicated significant accumulation effects for Ni, Cr, and As. The RI evaluation revealed extremely high comprehensive ecological risks (RI ≥ 444) in the vicinity of the waste residue heap and beneficiation area, with Ni exhibiting strong individual potential ecological risk (Eir ≥ 320). The soil health risk assessment demonstrated that As and Cr posed carcinogenic risks to adults, with mean carcinogenic indices (CR) of 1.56E − 05 and 4.14E − 06, respectively. As, Cr, and Cd posed carcinogenic risks to children, with mean CRs of 1.08E − 04, 1.61E − 05, and 2.68E − 06, respectively. Cr also posed certain non-carcinogenic risks to both adults and children. The PMF model identified asbestos contamination as the primary source of heavy metals in the soil surrounding the asbestos mining area, contributing to 79.0%. According to this study, it is recommended that management exercise oversight and regulation over the concentrations of Ni, Cr, Cd, and As in the soil adjacent to asbestos mines, establish a designated control zone to restrict population activities, and locate residential zones at a safe distance from the asbestos mine production zone.

Development of a dispersive liquid-liquid microextraction method for the evaluation of maternal-fetal exposure to cocaine employing human umbilical cord tissue.

Illicit drug use is a serious and complex public health problem, not only due to the severity of the health damage but also to the social implications, such as marginalization and drug trafficking. Currently, cocaine (COC) is among the most abused drugs worldwide with about 22 million users. Drug abuse has also been found in women during the pregnancy period, which has shed light on a new group for epidemiology. The diagnosis of COC use in these cases usually depends largely on the mother's reports, which in several cases omit or deny consumption. Therefore, considering physical-chemical methods of sample preparation and exposure biomarkers, the development of analytic toxicological methods can help to confirm drug use during pregnancy. Thus, the objective of the present work was to develop an analytical method based on dispersive liquid-liquid microextraction for the determination of COC analytes, using umbilical cord tissue as an alternative biological matrix, and detection by gas chromatography coupled to mass spectrometry. Therefore, after optimization, the dispersive liquid-liquid microextraction method was fully validated for quantification of COC, benzoylecgonine, cocaethylene, ecgonine, ecgonine methyl ester and norcocaine. The limits of detection were between 15 and 25 ng/g, the limits of quantification were 30 ng/g for ecgonine and 25 ng/g for the other analytes. Linearity ranged from the limits of quantification to 1,000 ng/g. Coefficients of variation for intra-assay precision were <18.5%, inter-assay was <8.75% and bias was <16.4% for all controls. The developed method was applied in 10 suspected positive samples, based on the mother's report and maternal urine screening and confirmation. COC, benzoylecgonine, ecgonine and ecgonine methyl ester were quantified in four umbilical cords with concentrations that ranged from 39.6 to 420.5 ng/g.

Characterization of the Neurochemical and Behavioral Effects of the Phenethylamine 2-Cl-4,5-MDMA in Adolescent and Adult Male Rats.

The proliferation of novel psychoactive substances (NPS) in the drug market raises concerns about uncertainty on their pharmacological profile and the health hazard linked to their use. Within the category of synthetic stimulant NPS, the phenethylamine 2-Cl-4,5-methylenedioxymethamphetamine (2-Cl-4,5-MDMA) has been linked to severe intoxication requiring hospitalization. Thereby, the characterization of its pharmacological profile is urgently warranted. By in vivo brain microdialysis in adolescent and adult male rats we investigated the effects of 2-Cl-4,5-MDMA on dopamine (DA) and serotonin (5-HT) neurotransmission in twobrain areas critical for the motivational and rewarding properties of drugs, the nucleus accumbens (NAc) shell and the medial prefrontal cortex (mPFC). Moreover, we evaluated the locomotor and stereotyped activity induced by 2-Cl-4,5-MDMA and the emission of 50-kHz ultrasonic vocalizations (USVs) to characterize its affective properties. 2-Cl-4,5-MDMA increased dialysate DA and 5-HT in a dose-, brain area-, and age-dependent manner. Notably, 2-Cl-4,5-MDMA more markedly increased dialysate DA in the NAc shell and mPFC of adult than adolescent rats, while the opposite was observed on dialysate 5-HT in the NAc shell, with adolescent rats being more responsive. Furthermore, 2-Cl-4,5-MDMA stimulated locomotion and stereotyped activity in both adolescent and adult rats, although to a greater extent in adolescents. Finally, 2-Cl-4,5-MDMA did not stimulate the emission of50-kHz USVs. This is the first pharmacological characterization of 2-Cl-4,5-MDMA demonstrating that its neurochemical and behavioral effects may differ between adolescence and adulthood. These preclinical data could help understanding the central effects of 2-Cl-4,5-MDMA by increasing awareness on possible health damage in users.

Remote Sensing-Based Structural Health Monitoring and Damage Assessment

With the rapid development of society and the economy, significant infrastructure, such as roads, buildings, high-speed railways, and bridges, have been built all over the world [...]

Patients' knowledge about the uses, risks, and beliefs surrounding the regulation and safety of Cannabis sativa L. in Peru

In recent decades, there has been a significant rise in the utilization of medical cannabis. Understanding the various facets surrounding its usage and regulation is paramount for patients, health professionals, and policymakers. This cross-sectional study conducted at the CANNAVITAL clinic in Lima, Peru aimed to assess the basic knowledge, attitudes towards use, and beliefs regarding regulation and safety of Cannabis sativa L. Among 86 patients (mean age: 41 ± 7.8 years; 53.4% women) actively receiving medical cannabis treatment for at least one year, we utilized the 22-item KUC-22 questionnaire to evaluate their understanding of cannabis, risk factors, legislation, medical and recreational use. The average duration of medical cannabis use was 3 ± 1.2 years. Results showed that 60.2% of patients were familiar with different forms of cannabis consumption, while 77.3% recognized the importance of product quality. Chronic pain, nausea and vomiting (each 23.9%) were the primary conditions treated with cannabis, followed by cancer and epilepsy (both 21.6%). A significant correlation was found between knowledge and cannabis use (p < 0.001). Furthermore, 92% of patients believed that a medical prescription was necessary, while 84.1% engaged in self-medication practices. Concerning perceptions, 69.3% of participants perceived psychological dependence from long-term cannabis use, and 65.9% believed it could cause health damage. Significant correlations were observed between the perception of risk factors, knowledge of legislation, and cannabis use (p < 0.001). In conclusion, Peruvian patients exhibited a high level of knowledge regarding the risks, uses, and regulatory framework surrounding Cannabis sativa L. These findings provide valuable insights into patients' perspectives on medical cannabis, offering important information for medical providers and researchers aiming to enhance cannabinoid-based therapies.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

Economic impact of All-Terrain Vehicles (ATVs) on local economies: a literature review

&lt;p class="MsoNormal" style="text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;The use of All-Terrain Vehicles (ATVs) has gained considerable attention within the recreation industry. The incorporation of ATVs into tourism and recreational activities has demonstrated substantial economic potential for local economies. However, there is a lack of comprehensive knowledge regarding the economic impact of ATV recreation. This paper conducts an extensive review of existing literature from 2004 to 2022, evaluating the economic impact of ATVs on local economies in the United States. The review aims to identify commonalities and disparities among existing economic impact studies and assess whether these studies have included costs associated with ATV-induced environmental, societal, and public health damages in their economic assessments. To this end, there is a scarcity of scientific peer reviewed journal articles that exclusively focus on the economic impact of ATVs. As such, we reviewed a total of seven publications comprising reports. The results of our analysis shed light on similarities and differences in aspects of resident and non-resident ATV spending, type of ATV expenditures, and the approaches used to evaluate economic impact assessment. The findings indicate that existing studies have assessed the economic impact of ATVs solely based on gross expenditures, lacking comprehensive inclusion of the costs associated with ATV damages to the environment, society, and human health. This study advocates for the need for a more comprehensive consideration of both benefits and costs in the economic impact of ATVs to assist ATV business managers, policymakers, and researchers in making informed decisions and planning future programs.&lt;/span&gt;&lt;/p&gt;