Environmental Research Research Articles

Non-drug therapies for patients with vibration disease associated with exposure to local vibration

Non-drug methods include acupuncture, the most effective way to regulate the body's energy system. Pulsed magnetic stimulation (PMS) is based on the application of the principles of electromagnetic induction, causes hyperpolarization or depolarization of nerve cells. The methods are practically not used in the clinic of occupational diseases, and their effectiveness in the treatment of vibration disease (VD) has not been established. The study aims to evaluate changes in the central nervous system and peripheral nerves, as well as in the psychological status of patients with vibration disease after sessions of acupuncture and pulsed magnetic stimulation. Using the acupuncture method, 24 male patients were treated with a diagnosis of VD associated with exposure to local vibration (average age 49.9±3.8 years, average length of service — 19.4±4.3 years), PMS — 24 people with a diagnosis of VD associated with exposure to local vibration (average age 48.8±3.4 years, average length of service — 18.1±3.4 years). The treatment methods performed made it possible to improve the quality of therapy, which was confirmed by a positive change in the indicators of visual and somatosensory evoked potentials, data from electroneuromyographic studies, vibration sensitivity and algesimetry, the state of the mnestic and attentional spheres of activity improved. The use of the acupuncture method allowed to improve the indicators of bioelectric activity of the brain: the amplitude of the peak N1 of visual evoked potentials increased (from 3.2 to 6.7 MV, p<0.05), the latency of the peak P200 decreased from 178.0 to 142.5 ms, p<0.05. The threshold of pain sensitivity on the zygomatic bone, on the phalanx of the 2nd finger of the hand and on the protruding part of the inner ankle decreased by 63, 125 and 250 Hz, p<0.05. Vibration sensitivity improved (on the ulnar process, phalanx of 2 fingers of the hand, p<0.01, on the tubercle of the tibia, p<0.05). Magnetic stimulation contributed to the improvement of cerebral changes in the treated patients — the latency of auditory evoked potentials decreased (p<0.03 for peak P1 and p<0.05 for peak P2), the amplitude of visual evoked potentials increased (p<0.05), the bioelectric activity of the occipital lobes of the brain increased. The duration of the afferent excitation wave at the level of the cervical region (latency N11 and N13, p<0.05) and thalamic structures (latency P25, p<0.05) increased. Non-drug therapy allowed to restore the time of excitation by the motor component of axons in patients on the upper extremities during acupuncture, on the upper and lower extremities during treatment with magnetic stimulation (p<0.05), improved indicators characterizing the mnestic-attentional and psychoemotional spheres of the treated. Ethics. Conclusion of the MEС of the Federal State Budgetary Scientific Institution "East Siberian Institute of Medical and Environmental Research" No. 32 dated 09/10/2019.

The global relevance of locally grounded ethnobiology.

While ethnobiology is a discipline that focuses on the local, it has an outstanding, but not yet fully realized potential to address global issues. Part of this unrealized potential is that universalistic approaches often do not fully recognize culturally grounded perspectives and there are multiple challenges with scaling up place-based research. However, scalability is paramount to ensure that the intimate and context-specific diversity of human-environmental relationships and understandings are recognized in global-scale planning and policy development. Here, we identify four pathways to enable the scalability of place-based ethnobiological research from the ground up: local-to-global dialogues, aggregation of published data, multi-sited studies, and geospatial analyses. We also discuss some major challenges and consideration to encourage continuous reflexivity in these endeavours and to ensure that scalability does not contribute to unnecessarily decontextualizing, co-opting, or overwriting the epistemologies of Indigenous Peoples and local communities. As ethnobiology navigates multiple scales of time and space and seeks to increase its breadth, this study shows that the use of deliberately global approaches, when carefully nested within rich field-based and ecological and ethnographically grounded data, can contribute to: (1) upscaling case-specific insights to unveil global patterns and dynamics in the biocultural contexts of Indigenous Peoples and local communities; (2) bringing ethnobiological knowledge into resolutions that can influence global environmental research and policy agendas; and (3) enriching ethnobiology's field-based ethos with a deliberate global analytical focus.

Evaluating Competitiveness of Container Shipping Operators in the Sustainability and Digitalization Era

This study aims to comprehensively analyze the operational status and international competitiveness of global container shipping enterprises in the era of sustainability and digitalization within the field of international container transportation. Utilizing the entropy method, this study quantitatively evaluates the direct operational strength of 14 leading container shipping enterprises while considering multiple factors including environmental protection, service quality, company scale, customer satisfaction, research and development level, and brand influence. The contributions of this study to the existing knowledge are primarily manifested in several aspects: firstly, by constructing a comprehensive evaluation framework, it offers a new perspective for assessing the international competitiveness of container shipping enterprises, facilitating a more comprehensive understanding of their strengths and weaknesses. Secondly, this study emphasizes the crucial roles of environmental protection and customer service in the competitiveness of shipping enterprises, providing new strategic directions for the industry’s sustainable development and digital transformation. Lastly, through detailed analysis of the operational performance of different companies, this study provides specific improvement suggestions for shipping enterprises, aiding them in achieving more precise management and more efficient development. The research findings demonstrate that companies exhibit varied performance in different aspects, showcasing their respective strengths and challenges. Particularly, this study identifies leading enterprises that have made significant progress in environmental technology innovation and customer service, while also highlighting deficiencies in some companies regarding scale expansion and brand building. These findings not only offer valuable insights for the development of the shipping industry but also serve as a window for policymakers, investors, and consumers to gain a deeper understanding of the shipping market. Through the thorough analysis conducted in this study, we aim to contribute to the sustainable development and digital transformation of the global container shipping industry.

A multi-task learning network based on transformer network for airborne electromagnetic detection imaging and denoising

Abstract As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration.

지방대학의 중도 탈락에 영향을 미치는 대학 요인 분석

Objectives This study aims to analyze the determinants of dropout rates at a rural public university systematically. The study aims to contribute to the formulation of effective policies to reduce dropout rates. Methods Lasso regression was chosen as the research method for this study because it is suitable for selecting important variables in complex data sets. Results The analysis of the results indicates that the most decisive factor significantly influencing the dropout rate is being ‘not enrolled in a university.’ Additionally, research funding per full-time faculty member, which reflects the research environment of the university, was also found to be an important factor. Conclusions To address the issue of student dropout, the study recommends that universities create policies to encourage students to persist in their studies and improve the quality of the research environment. The study also provides practical directions for policy makers and educational planners to reduce dropout rates by enhancing stu-dent support and improving the quality of education.

School students’ perceptions of trees and wooden objects in their immediate home environment: Possible implications for school environments

AbstractWe present a county-wide statistically representative survey with 430 Hungarian school students demonstrating that students who have trees in the parental home environment show more positive attitude towards trees and wood than their peers who do not have any. We aimed at finding out about the current attitudes of the upcoming generation with regard to the use of wood and wood materials, and also about the question as to whether they would consciously use wood materials in the future. Results show that school students who have trees in their parental home environment show significantly higher scores on the cognitive, affective, and conative domains of attitude towards trees and wood compared to those who do not have trees in their parental home environment. The present study contributes to outdoor environmental education research in that we provide quantitative evidence for the positive effect of the immediate environment of children on the attitude towards wood and trees. The findings suggest the imperative need for further novel educational interventions in school gardens related to trees and wooden objects.

A Structured Literature Review on the Research and Design of Rehabilitation Environments.

This literature review is conducted to identify knowledge gaps and shape a framework for the development of guidelines and future research on programming and design of rehabilitation environments. Patients suffering from trauma, stroke, neurological or cardiopulmonary conditions, or recovering from surgery or cancer treatment require rehabilitation services. A comprehensive rehabilitation program can support continuum of care for inpatient and outpatient groups. However, within most facilities, rehabilitation environments are found to be outdated and undersized compared to other programs or lack the correct adjacencies within the facility. Unfortunately, this deficiency is echoed by limited guidelines on programming, planning, and design of these environments. General guidelines derived from healthcare environments research is not adaptable to rehabilitation environments, because the paradigm used in most healthcare environment research does not address specific needs of rehabilitation patients in regaining confidence or relearning daily life skills. We conducted a structured literature review, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses as a basis for reporting the available body of work on evidence-based research in rehabilitation environments. Through analysis of the limited literature, specific mediators such as patient confidence and motivation were identified. An environment that creates a balance between privacy and social interaction can promote these mediators. Creating enriched environments through elements that engage the senses and encourage more social and physical interaction is essential for recovery. Finally, accessibility and wayfinding are of great importance in these environments due to potential limited mobility or cognitive impairments of patients.

Matrix-free laser desorption/ionization mass spectrometry imaging for rapid evaluation of wood biomass conversion.

This study overcomes traditional biomass analysis limitations by introducing a pioneering matrix-free laser desorption/ionization (LDI) approach in mass spectrometry imaging (MSI) for efficient lignin evaluation in wood. The innovative acetic acid-peracetic acid (APA) treatment significantly enhances lignin detection, enabling high-throughput, on-site analysis. Wood slices, softwood from a conifer tree (Japanese cypress) and hardwood from a broadleaf tree (Japanese beech), were analyzed using MSI with a Fourier transform ion cyclotron resonance mass spectrometer. The developed APA treatment demonstrated effectiveness for MSI analysis of biomass. Our imaging technique successfully distinguishes between earlywood and latewood and enables the distinct visualization of lignin in these and other wood tissues, such as the radial parenchyma. This approach reveals significant contrasts in MSI. It has identified intense ions from β-O-4-type lignin, specifically in the radial parenchyma of hardwood, highlighting the method's precision and utility in wood tissue analysis. The benefits of matrix-free LDI include reduced peak overlap, consistent sample quality, preservation of natural sample properties, enhanced analytical accuracy, and reduced operational costs. This innovative approach is poised to become a standard method for rapid and precise biomass evaluation and has important applications in environmental research and sustainable resource management and is crucial for the effective management of diverse biomass, paving the way towards a sustainable, circular society.

Non-Enzymatic Glucose Detection by Fe2O3 Nanorods-Reduced Graphene under Physiological pH

Background: Non-enzymatic detection has become a research hotspot because of its alternativity in solving problems compared to enzymatic biosensors, but most of those sensors require a strong basic pH environment (higher than 10) to active their surface, restricting their use in clinical detection because the pH of body fluid is around 7.4. Furthermore, metal oxide sensors with specific morphologies are reported to have a fast electrocatalytic response. Therefore, Fe2O3 nanocomposites with porous structure are selected for glucose detection research in a physiological pH environment. Objective: The study aimed to assess the potential use of porous reduced graphene oxide-Fe2O3 nanorods in glucose detection in a physiological pH environment. Method: Hydrothermal method was used to prepare porous Fe2O3-rGO NRs (Nanorods) and hollow Fe2O3/C nanoparticles. Cyclic voltammetry and electrochemical impedance spectroscopy were used to evaluate the performance of our materials. Results: Porous-reduced graphene oxide-Fe2O3 nanorods have exhibited better performance than hollow carbon-Fe2O3 core-shell nanoparticles for glucose detection in a physiological pH environment. Conclusion: Non-enzymatic glucose sensing based upon cavity Fe2O3-rGO NRs under a physiological pH environment has been successfully realized, attributing to their high electron mobility and large specific surface area. Furthermore, the results of this work indicate that the glucose sensor prepared here has shown good repeatability and stability, which suggests its potential use in clinical detection.

Emerging trends of environmental accounting research: a science mapping approach

Purpose This paper aims to provide a comprehensive knowledge structure for environmental accounting (EA) research by identifying research hotspots and frontiers and suggesting future trends for scholarly investigation. Design/methodology/approach Using the bibliometric method, the paper analyzed 321 academic articles and reviews in international journals from the Scopus database. Science mapping, including strategic diagrams, network analysis and thematic maps, was used to analyze the evolution of topics and to recommend future research trends. Findings EA research is an emerging trend. This study presents the landscape of EA research by constructing the “synthesis house of knowledge” in EA. Significant EA research areas were identified and future research trends were suggested based on the results. Practical implications This paper provides insights into the current state of EA research and identifies potential future research trends that can help scholars and experts develop and stimulate further advancement of the research in this field. Social implications Results may motivate policymakers and government agencies to formulate regulations to enforce appropriate corporate environmental strategies to better manage environmental costs and reduce community environmental impact. Originality/value The study provides an intellectually structured literature review of the EA research field; identifies the main themes through the innovative use of network analysis, strategic diagrams and thematic maps; highlights research gaps; and offers potential research questions and suggestions for future research. The novelty of this paper lies in the “synthesis house of knowledge” in EA and the research gaps and potential and specific research questions constructed from the findings.

A Critique of Supervisory Practices of Doctoral Student Research in Emerging Higher Education Context, Botswana

The science community in Botswana holds a broad consensus that not enough quality PhDs are being produced to meet the needs of societal developments in the country. There appears to be a connection between the level of knowledge advancement of a nation and its economic wealth. The role of supervision in the quest for a leap towards quality and quantity knowledge output therefore, cannot be overemphasised. This paper provides a critique of supervisory practices of doctoral student research in emerging higher education context of Botswana. The study adopted a desk top research methodology which focused on a scoping view of studies related to the phenomenon of study. The areas interrogated included the initial stage of student recruitment and selection, allocation of supervisors, proposal development, and management environment for research, implementation of the proposal, and document compilation for examination.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Strategies for selecting and managing equipment in a light microscopy facility.

Light microscopy facilities vary in the number of imaging systems and the scope of technologies they support. Each facility must craftan identity through the selection of equipment and development of staff in order to serve the needs of its local research environment. The process of crafting a light microscopy facility can be compared to curation of an art exhibition: great care should be given to the selection and placement of each object in order to make a coherent statement. Lay Description: Light microscopy facilities vary in the number of imaging systems and the scope of technologies they support. Each facility must develop an identity through the selection of equipment and development of staff in order to serve the needs of its local research environment. The process of crafting a light microscopy facility can be compared to curation of an art exhibition: great care should be given to the selection and placement of each object in order to make a coherent statement.

Factors Influencing Perceptions of Trust in Data Infrastructures

Trust is an essential pre-condition for the acceptance of digital infrastructures and services. Transparency has been identified as one mechanism for increasing trustworthiness. Yet, it is difficult to assess to which extent and how exactly different aspects of transparency contribute to trust, or potentially impede it in cases of overwhelming complexity of the information provided. To address these issues, we performed two initial studies to help determining the factors that influence or have impact on trust, focusing on transparency across a range of elements associated with data, data infrastructures and virtual research environments. On one hand, we performed a survey among IT experts in the field of data science focusing on quality aspects in the context of re-using and sharing open source software, assessing issues such as the need for documentation, test cases, and accountability. On the other hand, we complemented this with a set of semi-structured interviews with senior researchers to address specific issues of the degree of transparency achievable with different approaches. They include, for example, the amount of transparency we can achieve with approaches from explainable AI, or the usefulness and limitations of data provenance in determining the suitability of data for reuse and others. Specifically, we consider mechanisms on three levels, i.e. technical, process-oriented as well as social mechanisms. Starting from attributes of trust in the “analogue world”, we aim to understand which of these can be applied in the digital world, how they differ, and what additional mechanisms need to be established, in order to support trust in complex socio-technological processes and their emergent results when the traditional approaches cannot be applied anymore.

Towards an understanding of landslide risk assessment and its economic losses: a scientometric analysis

AbstractThis scientometric analysis significantly advances the understanding of landslide risk assessment and economic losses, focusing on scientometric insights. This study aims at analyzing the global trends and structures of landslide risk and economic loss research from 2002 to 2023 using scientometric techniques such as co-authorship, co-word, co-citation, cluster analysis, and trend topics, among others. Thus, analysis of 92 studies gathered from Scopus and Web of Science databases reveals a continuous growth in environmental, social, and quantitative research topics. Predominant contributions hail mainly from China and Italy. The research identifies critical themes, including risk analysis, vulnerability, fragility, and economic losses. The current identified research combines advanced statistical methods, including logistic regression, with climate change scenarios and susceptibility assessments to reveal intricate connections between climatic shifts, hydrogeological hazards, and their economic and environmental impacts. This study provides researchers and practitioners with a comprehensive understanding of the status quo and research trends of ontology research landslide risk and its economic losses. It also promotes further studies in this domain.

Advances and perspectives in environmental health research in China

Advances and perspectives in environmental health research in China

Land Cover Classification Based on Multimodal Remote Sensing Fusion

Abstract. Global high-precision and high timeliness land cover data is a fundamental and strategic resource for global strategic interest maintenance, global environmental change research, and sustainable development planning. However, due to difficulties in obtaining control and reference information from overseas, a single data source cannot effectively cover, and surface coverage classification faces significant challenges in information extraction. Based on this, this article proposes an intelligent interpretation method for typical elements based on multimodal fusion, starting from the characteristics of domestic remote sensing images. It also develops an optical SAR data conversion and complementarity strategy based on convolutional translation networks, as well as a typical element extraction algorithm. This solves the problems of sparse remote sensing images, limited effective observations, and difficult information recognition, thereby achieving automation of typical element information under dense observation time series High precision extraction and analysis.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

The effect of scenario-based training versus video training on nurse anesthesia students’ basic life support knowledge and skill of cardiopulmonary resuscitation: a quasi-experimental comparative study

BackgroundPerforming CPR (Cardiopulmonary Resuscitation) is an extremely intricate skill whose success depends largely on the level of knowledge and skill of Anesthesiology students. Therefore, this research was conducted to compare the effect of the scenario-based training method as opposed to video training method on nurse anesthesia students’ BLS (Basic Life Support) knowledge and skills.MethodsThis randomized quasi-experimental study involved 45 nurse anesthesia students of Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran in 2022–2023. The practical room of the university formed the research environment. The participants were randomly divided into three groups of scenario-based training, video training, and control. Data were collected by a knowledge questionnaire and a BLS skill assessment checklist before and after the intervention.ResultsThere was a significant difference between the students’ scores of BLS knowledge and skill before and after the educational intervention in both SG (scenario group) (p < 0.001) and VG (video group) (p = 0.008) (p < 0.001). However, no significant difference was observed in this regard in the CG (control group) (p = 0.37) (p = 0.16). Also, the mean scores of BLS knowledge and skills in the SG were higher than those in the VG (p < 0.001).ConclusionGiven the beneficial impact of scenario-based education on fostering active participation, critical thinking, utilization of intellectual abilities, and learner creativity, it appears that this approach holds an advantage over video training, particularly when it comes to teaching crucial subjects like Basic Life Support.

Shaping the Future of Environmental Economics: A Bibliometric Review of Current Trends and Future Directions

Environmental economics is increasingly pivotal in addressing global environmental challenges. This study aims to elucidate the research landscape in this field, focusing on prevalent themes such as climate change, carbon emissions, sustainable development, and environmental policy. Employing a bibliometric analysis, we analyzed a substantial corpus of literature from Scopus. We applied Lotka's Law to assess author productivity and distribution and conducted institutional and country-level analyses to map the geographical spread and institutional affiliations in environmental economics research. Our analysis highlights a significant growth in the environmental economics literature, with a notable focus on climate change and sustainability. A concentration of publications among a few authors was observed, indicating influential researchers and potential for increased collaboration. The rise in publications, particularly in China and leading journals like 'The Journal of Cleaner Production,' reflects a global upsurge in environmental economics research. Distinct clusters were identified, covering diverse topics from climate change economics to environmental policy. The study acknowledges limitations due to exclusive reliance on Scopus data, suggesting future research to include other databases like the Web of Science for a more comprehensive analysis. Considering the evolving nature of environmental challenges, we advocate for interdisciplinary approaches and continual updates in research to address emergent trends in this critical field.