Environmental Effects Research Articles

The Effects of Correctional Environments on Correctional Mental Health Professionals: A Phenomenological Qualitative Study

ABSTRACT Despite their role on the frontlines of mental health services in the U.S., there is a paucity of research on the correctional mental health workforce. This article reports the findings of a phenomenological, qualitative study that explores how correctional mental health professionals perceive the effects of working in correctional environments. Qualitative interviews were conducted with correctional mental health professionals in Colorado (n = 22). Theoretical considerations on the experiences of correctional mental health professionals including burnout, trauma/secondary trauma, dehumanization, moral injury, corrections fatigue, and post-traumatic growth. Findings reveal that correctional mental health professionals’ experiences fall into five thematic categories: 1—relationships between mental health and corrections, 2—absence of clinical supervision, 3—harms of the correctional environment, 4—“a different world:” professional isolation, and 5—the power of purpose. Narrative excerpts from the participants are provided throughout to illustrate study themes. Study strengths and limitations, directions for future research and practice implications are also discussed.

Orchestration Workflows in Distributed Systems: A Systematic Analysis of Efficiency Optimization and Service Coordination

This article presents a comprehensive analysis of orchestration workflows in modern software architectures, examining their role in optimizing service efficiency and coordination across distributed systems. Through a systematic investigation of implementation patterns and operational strategies, we demonstrate how orchestrated workflows enhance service integration, automate complex processes, and improve system reliability. The article employs a mixed-method approach, combining quantitative performance metrics from real-world implementations with qualitative analysis of architectural patterns across diverse enterprise environments. The findings reveal that organizations implementing robust orchestration workflows experienced a 47% reduction in service integration failures, 35% improvement in resource utilization, and 62% faster deployment cycles. The article also identifies critical success factors in orchestration implementation, including dynamic resource management, automated error handling, and comprehensive monitoring frameworks. Furthermore, The article proposes a novel framework for evaluating orchestration effectiveness in cloud-native environments, considering factors such as service mesh integration, container orchestration, and distributed tracing. This article contributes to the growing body of knowledge in service optimization by providing empirical evidence of orchestration benefits and offering practical guidelines for implementing scalable, resilient software systems through advanced workflow orchestration strategies.

The Effectiveness of Green Marketing in Starbucks: An Assessment

Green marketing practices have become increasingly significant as businesses aim to reduce their environmental impact and cater to the growing number of environmentally conscious consumers. The primary goal of green marketing extends beyond appealing to eco-conscious buyers; it also aims to generate positive environmental and societal effects. Green marketing integrates sustainability principles across various aspects of marketing, including product design, packaging, messaging, production processes, and promotion. Its importance lies in enhancing profitability, fostering brand loyalty, and attracting a new market segment that prioritizes sustainability. For instance, businesses may promote reduced emissions in manufacturing or use post-consumer recycled materials in packaging. Some also demonstrate environmental responsibility by donating a portion of sales proceeds to eco-friendly initiatives, such as tree-planting campaigns. This study examines how Starbucks' green marketing practices have contributed to its overall success, profitability, brand reputation, customer loyalty, and employee engagement. Additionally, it investigates challenges and limitations Starbucks may have faced in implementing these practices and explores potential areas for improvement. The study’s findings will provide valuable insights for other businesses aiming to adopt green marketing strategies and contribute to the broader discourse on sustainability's role in business.

Physico-Chemical Analysis of Textile Effluents from Bagru, Jaipur (Rajasthan, India): Implications for Environmental Impact and Treatment Needs

The textile industry is one of the largest water polluters worldwide. The coloured and bad odour effluent discharged from such industries pollutes not only the water bodies but also affects groundwater quality. In the present study, thirty-five water samples were collected from different dyeing units of the Bagru textile area (Rajasthan, India) famous for using natural dyes for textile processing. The physico-chemical analysis of these water samples shows that all parameters exceeded the permitted range recommended by the Central Pollution Control Board (CPCB) and World Health Organization (WHO). The analysis revealed the use of synthetic dyes in Bagru's textile industry for dyeing and printing. Based on the estimated characteristics, these textile effluents must be properly treated before releasing into the environment as the discharge of untreated effluents into local water bodies leads to significant water pollution, adversely affecting aquatic ecosystems and biodiversity. Additionally, contaminants can permeate the soil, compromising agricultural productivity and food safety. The air quality is also compromised due to the release of volatile organic compounds (VOCs) during the dyeing process, posing respiratory risks to nearby communities. This research underscores the urgent need for improved waste management practices and regulatory measures to mitigate the adverse environmental effects associated with synthetic dyes in Bagru's textile industry.

Genetic and Neuronal Basis for Facial Emotion Perception in Humans and Macaques

Abstract The ability to rapidly recognize basic facial emotions (e.g., fear) is crucial for social interactions and adaptive functioning. To date, the origin of facial emotion recognition ability remains equivocal. Using a classical twin design in humans, we found a clear dissection of low and high spatial frequencies (LSF and HSF) in facial emotion perception: whereas genetic factors contributed to individual variation in LSF processing, HSF processing is largely shaped by environmental effects. Furthermore, the ability to recognize facial emotion of LSF content genetically correlated with the function of the amygdala. Crucially, single-unit recording of the amygdala in macaques further revealed the dissociation between LSF and HSF processing in facial emotion perception, indicating the existence of an evolutionarily conserved mechanism. This cross-species study enhances insights into the neurobiological dual-route model (subcortical vs. cortical) of emotion perception and illuminates the origin and the functional development of the emotional brain in primates.

Toxicity of polystyrene nanoplastic and copper oxide nanoparticle in Artemiasalina: Single and combined effects on stress responses

Nanoparticles, such as copper oxide nanoparticle (CuO NP) and polystyrene nanoplastic (PSNP), are increasingly released into aquatic environments, and pose potential risks to aquatic animals such as brine shrimps. Understanding the toxicity of these nanoparticles, especially when combined, is very important to assess their environmental effects. Therefore, this work describes the toxicity of polystyrene nanoplastic (PSNP) and CuO nanoparticles (CuO NPs) for brine shrimp (Artemiasalina). The body length and stress biomarkers, including the activity of SOD, CAT, GST, Acid phosphatase, AChE, level of MDA and GSH, and expression of the hsp70 gene were quantified. The 48h-EC50 values for PSNP, CuO NPs, and their combination were determined as 1.024 and 5.089, and 0.512 mg L−1, respectively. The combined exposure groups showed the highest growth inhibition. This was associated with increased activity of SOD and GST, decreased activity of CAT, a significant decrease in the level of GSH, a significant increase in the MDA level, and expression of the hsp70 gene (P < 0.05). Moreover, an increased ACP and reduced AChE activity were observed in exposure groups. This study indicated that PSNP and CuO NPs have synergistic toxicity for A.salina, underscoring the importance of further investigation into their combined effect on aquatic animals.

Inhibition of the miR-1914-5p increases the oxidative metabolism in cellular model of steatosis by modulating the Sirt1-PGC-1α pathway and systemic cellular activity.

Metabolic associated fatty liver disease (MAFLD) is considered an indicator of metabolic syndrome, which affects millions of people around the world and no effective treatment is currently available. MAFLD involves a wide spectrum of liver damage, that initiates from steatosis (fatty live) and may progress to more complex pathophysiology. Then, details in lipid metabolism controlling should be explored aiming to control the fatty liver. In this context, the miR-1914-5p can be considered a potential biotechnology tool to control lipid metabolism in hepatic cells. This miRNA finds potential mRNA binding sequences in more than 100 molecules correlated with energy production and lipid metabolism pointed in bioinformatic platforms. The present study addressed the miR-1914-5p effects in hepatic HepG2/LX-2 co-cultured cells in a in vitro steatotic environment stablished by the addition of 400 μM of a mixture of oleic and palmitic acids. The analyses demonstrated that the inhibition of the miRNA reduced energetic metabolites such as total lipids, triglycerides, cholesterol and even glucose. In addition, the miR-inhibitor-transfected cells did not present any deleterious effect in cellular environment by controlling reactive oxygen species production (ROS), mitochondrial membrane potential (ΔΨm) and even the pro-inflammatory environment. Moreover, the functional effect of the investigated miR, suggested its close connection to the modulation of Sirt-1-PGC1-α pathway, a master switch metabolic route that controlls cellular energetic metabolism. Our assays also suggested a synergistic effect of this miR-1914-5p in cell metabolism, which should be considered as a strong candidate to control steatotic environment in future clinical trials.

Adaptive MSSRCPF filtering based on constrained optimization and fading memory for SINS/GNSS integrated navigation

To address particle degeneracy and the challenge of selecting importance density functions in traditional particle filters for complex nonlinear systems, we propose an adaptive mixed-order spherical simplex-radial cubature particle filter algorithm based on constrained optimization and fading memory. This algorithm integrates constrained optimization, an adaptive fading memory strategy, adaptive adjustment of particle numbers and weights, and the advantages of mixed-order spherical simplex-radial cubature Kalman filtering. By designing the importance density function using a mixed-order integration method, the algorithm significantly improves filtering accuracy over traditional cubature Kalman filters while maintaining lower computational complexity than high-order cubature Kalman filter methods. The adaptive fading memory strategy dynamically adjusts the covariance matrix, enhancing sensitivity to current measurement information and reducing the influence of historical data. By dynamically adjusting the noise covariance matrix and constraining the ratio between the error covariance and measurement noise covariance, the algorithm improves the convergence speed and accuracy of state estimation. An adaptive particle number and weight adjustment strategy based on effective sample size and entropy regularization dynamically adjusts the number of particles to reduce computational complexity while ensuring filtering accuracy, and employs entropy regularization to suppress weight over-concentration, thereby reducing the impact of outliers on the filtering results. Simulation results demonstrate that in complex SINS/GNSS integrated navigation systems, especially under high-noise and nonlinear conditions, the proposed algorithm significantly improves positioning accuracy compared to the CPF method, with the maximum latitude error reduced by approximately 54.8%, the maximum longitude error reduced by approximately 40.5%, and the maximum altitude error reduced by approximately 68.3%. Compared to the FMCPF method, the proposed algorithm also shows clear advantages in positioning accuracy, convergence speed, and computational efficiency, validating its effectiveness and practicality in complex environments.

Ocean-scale patterns of environment and climate changes driving global marine phytoplankton biomass dynamics.

Effects of marine environment and climate changes on phytoplankton dynamics in global oceans have received increasing attention but remain a mystery. This study used a comprehensive approach combining correlation and information flow to explore relationships among phytoplankton biomass, marine environment, and climate forcing based on global observations over the past multi-decadal period. Correlation and causality between phytoplankton biomass and environmental factors exhibit spatial asymmetry-regions where environmental factors directly drive biomass variations were concentrated in oceanic currents and subtropical circulations. Temperature, light, and mixed layer depth show pronounced influences on global phytoplankton interdecadal variations. Climate forcing over interdecadal timescales directly affects phytoplankton biomass in the equatorial Pacific, South Pacific, and Indian Oceans, with more uncertain biomass variability in the equatorial Pacific due to multiple climate events. Our findings revealed that environment and climate changes directly affect phytoplankton interdecadal variability only in specific regions at the oceanic scale.

Assessment of Gold and Mercury Losses in an Artisanal Gold Mining Site in Nigeria and Its Implication on the Local Economy and the Environment

The objective of this work was to establish the gold and mercury losses in an artisanal mining deposit (Uke) in Nigeria to convince miners about their inefficiency and suggest changes in their gold extraction practices. Samples of feeds and tailings from five sluice box concentration processes previously ground in hammer mills below 1 mm (P80 = 0.5 mm) were systematically sampled every 15 min. for 4 h and sent for gold analyses by a fire assay and intensive cyanidation. Dry grain size analyses of primary and amalgamation tailings allowed us to find out in which size fraction gold and mercury are lost. Total mercury losses in sixteen operations were obtained by weighing mercury at the beginning and in all steps of the concentrates’ amalgamation. After analyses, the average gold grade in the feed resulted in 3.80 ± 1.52 ppm (two standard deviations). The gold recovery was 29.24 ± 13.24%, which is low due to a lack of liberation of the fine gold particles from the gangue (silicates). Finer grinding would be necessary. The mercury balance revealed that 42% of the mercury added is lost, in which 26% involves tailings and 16% evaporated. The HgLost-to-AuProduced ratio was found to be 3.35 ± 9.46, which is exceedingly high for this type of amalgamation process that should have this ratio around 1. One reason is the excessive amount of mercury in the amalgams, 76.5 ± 38.12%, when the normal is around 40%–50%. Mercury lost by evaporation in open bonfires is clearly contaminating amalgamation operators (usually children), neighbours, and the environment. The Hg-contaminated tailings and primary tailings are sold to local cyanidation plants, and this can form toxic soluble Hg(CN)2 in the process. The results of this research were brought to the attention of the miners and other stakeholders, including the regulatory agencies of the government. The % gold recovery by amalgamation was established in this study, but if this process recovers 50 to 60% of the liberated gold particles in a concentrate and 30% of gold was recovered in the sluice boxes, then the total gold recovery should be between 15 and 20; i.e., 80 to 85% of gold mined is lost. On average, an operation produces 8.26 g of gold/month, which is split to six miners, representing USD 69/month/miner or USD 2.3/day. It was discussed with miners, authorities, and community members (in particular female miners) how to avoid exposure to mercury, how to improve gold recovery without mercury, and the health and environmental effects of this pollutant.

Enhancing Open-Space Gas Detection Limit: A Novel Environmentally Adaptive Infrared Temperature Prediction Method for Uncooled Spectroscopy

Gas cloud imaging with uncooled infrared spectroscopy is influenced by ambient temperature, complicating the quantitative detection of gas concentrations in open environments. To solve the aforementioned challenges, the paper analyzes the main factors influencing detection errors in uncooled infrared spectroscopy gas cloud imaging and proposes a temperature correction method to address them. Firstly, to mitigate the environmental effects on the radiative temperature output of uncooled infrared detectors, a snapshot-based, multi-band infrared temperature compensation algorithm incorporating environmental awareness was developed. This algorithm enables precise infrared radiation prediction across a wide operating temperature range. Validation tests conducted over the full temperature range of 0 °C to 80 °C demonstrated that the prediction error was maintained within ±0.96 °C. Subsequently, temperature compensation techniques were integrated, resulting in the development of a comprehensive uncooled infrared spectroscopy gas cloud imaging detection method. Ultimately, the detection limits for SF6, ethylene, cyclohexane, and ammonia were enhanced by 50%, 33%, 25%, and 67%, respectively.

Multi-View Fusion-Based Automated Full-Posture Cattle Body Size Measurement

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

Effect of the social environment on olfaction and social skills in wild-type and a mouse model of autism

Autism spectrum disorders (ASD) are complex, polygenic and heterogenous neurodevelopmental conditions. The severity of autism-associated variants is influenced by environmental factors, particularly social experiences during the critical neurodevelopmental period. While early behavioral interventions have shown efficacy in some children with autism, pharmacological support for core features — impairments in social interaction and communication, and stereotyped or restricted behaviors — is currently lacking. In this study, we examined how the social environment influences both wild-type (WT) and Shank3 knockout (KO) mice, a model reflecting core autism-like traits. Our findings revealed that chronic social isolation enhanced social interaction and olfactory neuron responses in WT animals. Furthermore, it restored impairments in social novelty preference and olfactory function, as well as self-grooming in Shank3 KO mice. Conversely, an enriched social environment heightened social interest toward novel conspecifics in WT mice, but elicited the opposite effect in Shank3 KO mice. Notably, Shank3 KO mice displayed distinct social responses when exposed to WT or Shank3 KO mice. These results offer novel insights that could favor the implementation of behavioral interventions and inclusive classroom programs for children with ASD.

Association of neighborhood-level disadvantage beyond individual sociodemographic factors in patients with or at risk of knee osteoarthritis

ObjectiveLower socioeconomic status (SES) is a risk factor for poorer pain-related outcomes. Further, the neighborhood environments of disadvantaged communities can create a milieu of increased stress and deprivation that adversely affects pain-related and other health outcomes. Socioenvironmental variables such as the Area Deprivation Index, which ranks neighborhoods based on socioeconomic factors could be used to capture environmental aspects associated with poor pain outcomes. However, it is unclear whether the ADI could be used as a risk assessment tool in addition to individual-level SES.MethodsThe current study investigated whether neighborhood-level disadvantage impacts knee pain-related outcomes above sociodemographic measures. Participants were 188 community-dwelling adults who self-identified as non-Hispanic Black or non-Hispanic White and reported knee pain. Area Deprivation Index (ADI; measure of neighborhood-level disadvantage) state deciles were derived for each participant. Participants reported educational attainment and annual household income as measures of SES, and completed several measures of pain and function: Short-form McGill Pain Questionnaire, Western Ontario and McMaster Universities Osteoarthritis Index, and Graded Chronic Pain Scale were completed, and movement-evoked pain was assessed following the Short Physical Performance Battery. Hierarchical linear regression analyses were used to assess whether environmental and sociodemographic measures (i.e., ADI 80/20 [80% least disadvantaged and 20% most disadvantaged]; education/income, race) were associated with pain-related clinical outcomes.ResultsLiving in the most deprived neighborhood was associated with poorer clinical knee pain-related outcomes compared to living in less deprived neighborhoods (ps < 0.05). Study site, age, BMI, education, and income explained 11.3–28.5% of the variance across all of the individual pain-related outcomes. However, the ADI accounted for 2.5–4.2% additional variance across multiple pain-related outcomes.ConclusionThe ADI accounted for a significant amount of variance in pain-related outcomes beyond the control variables including education and income. Further, the effect of ADI was similar to or higher than the effect of age and BMI. While the effect of neighborhood environment was modest, a neighborhood-level socioenvironmental variable like ADI might be used by clinicians and researchers to improve the characterization of patients’ risk profile for chronic pain outcomes.

The influence of visual and auditory environments in parks on visitors’ landscape preference, emotional state, and perceived restorativeness

Various auditory and visual factors influence people’s perception and restoration experiences in the spatial environments. However, few studies have treated these factors as a comprehensive system phenomenon to reveal their integrated effects. Therefore, a specific explanation of the primary pathways and factors that contribute to the main effects of park soundscapes and visual features and the resulting perceived restorativeness remains unclear. The study surveyed 861 visitors in five urban parks in China and collected audiovisual information. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the research explored the direct, indirect, and moderating effects of common audiovisual environments in parks on perceived restorativeness, emotional states, and preferences. The results indicate that most natural-related visual and auditory factors provide direct benefits for people’s perceived restorativeness, emotional state, and preference. Additionally, certain landscape features and types of sound sources, such as trees and water sounds, have indirect effects on activating restoration through preference and emotion mediation. The study also reveals the moderating effects of landscape features and sound source types on emotional and perceived restorativeness. Another noteworthy finding is the significant relationship between bird songs and the positive impact on perceived restorativeness, emotional state, and preference, which is closely related to acoustic quality indicators. These findings offer new insights for creating urban park spaces through visual and auditory interactions.

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

Adipocytes reprogram the proteome of breast cancer cells in organotypic three-dimensional cell cultures.

While epidemiological evidence has long linked obesity with an increased risk of breast cancer, the intricate interactions between adipocytes and cancer cells within the tumor microenvironment remain largely uncharted territory. The use of organotypic three-dimensional (3D) cell cultures that more accurately mimic the spatial architecture of tumors represents an innovative approach to this complex issue. In the present study, we investigated the effects of adipocytes on the proteome of Hs578t breast cancer cells cultured in a 3D microenvironment. Using different treatments, we rigorously optimized the experimental conditions to induce the optimal differentiation of 3T3-L1 fibroblasts into mature adipocytes. Then, we grow the Hs578t cells in a simulated microenvironment using an on-top model for organotypic 3D cultures. Our data showed that cancer cells formed 3D stellate-like architectures when grown over an extracellular matrix proteins-enriched scaffold for 48h. Proteomic profiling using LC-MS/MS mass spectrometry of Hs578t cells grown in 3D conditions with or without the adipocyte-enriched culture discovered 916 unique proteins. Of these, 605 showed no significant changes in abundance, whereas 87 proteins were significantly upregulated and 224 downregulated after interaction with fat cells (p < 0.05, FC > 2.0). Bioinformatic analysis of upregulated proteins indicated that the most enriched GO terms and molecular functions were related to lipids transport, cell differentiation, hypoxia response, and cell junctions. In addition, several modulated proteins have been previously associated with breast cancer progression. Interestingly, lipid transport proteins, including PITPNM2, ATP2C1, ABCA12, HDLBP, and APOB, showed perturbations in their expression, which were also associated with low overall survival in breast cancer patients. Functional studies showed that the knockdown of apolipoprotein B (APOB) expression in Hs578t cells reduced the size of 3D cellular structures. Moreover, APOB-knocked cells cocultured with adipocytes for 48h exhibited a significant decrease of intracellular lipids, whereas an increase in the adipocytes was found. Our results indicate that the 3D microenvironment and the adipocytes crosstalk reprogram the proteome of breast cancer cells. These data help us understand the environmental effects in gene expression and contribute to discovering novel tumor proteins with potential intervention in breast cancer therapy.

Novel category of environmental effects on gravitational waves from binaries perturbed by periodic forces

Novel category of environmental effects on gravitational waves from binaries perturbed by periodic forces

Virtual reality in medical education: Effectiveness of Immersive Virtual Anatomy Laboratory (IVAL) compared to traditional learning approaches

Immersive Virtual Anatomy Laboratory (IVAL) is an innovative learning tool that combines virtual reality and serious games elements to enhance anatomy education. This experimental study compares IVAL with traditional learning methods in terms of educational effectiveness and user acceptance. An experimental design was implemented with 120 undergraduate health-science students, randomly assigned to two groups: an experimental group using IVAL, and a control group following traditional learning methods. Data collection focused on quantitative measures such as pretest and posttest vocabulary assessment scores and task completion times, alongside qualitative measures obtained through a user experience questionnaire. This study utilizes the Technology Acceptance Model (TAM), incorporating variables such as Perceived Usefulness and Perceived Ease of Use. Results revealed significant improvements in the experimental group, with a 55.95% increase in vocabulary scores and an 18.75% reduction in task completion times compared to the control group. Qualitative data indicated that IVAL users reported greater Perceived Usefulness of the technology, improved Perceived Ease of Use, a more positive Attitude Towards Using IVAL, and stronger Behavioral Intention to continue using IVAL for anatomy learning. This study demonstrates that the integration of immersive virtual reality in the IVAL approach offers a promising method to enhance anatomy education. The findings provide insights into the effectiveness of immersive learning environments in improving learning outcomes and user acceptance. While further research is needed to explore long-term effects, this innovative approach not only enhances the effectiveness and enjoyment of anatomy learning but also provides valuable data on optimizing educational technology for improved learning outcomes.

Granulometric and Geochemical Distribution of Arsenic in a Mining Environmental Liability in a Semi-arid Area.

This study focuses on the "El Lavadero" tailings deposit, a mining environmental liability (MEL) located near the town of San Felipe de Jesús, Sonora, in northwest Mexico. The objective was to determine the total arsenic (As) content, its granulometric and geochemical distribution, as well as its mobilization capacity and bioavailability. The results from oxidized and unoxidized tailings showed low potential of hydrogen (pH) values (2.4-5.7) and high concentrations of total arsenic (8235-36,004mgkg-1), predominantly in the finer granulometric fractions (< 0.05mm). Arsenic also prevails in the finest fraction of agricultural soil (> 2mm). These fine particles could present adverse environmental effects due to their potential to be transported by leaching and water suspension. In contrast, arsenic in the effluent sediments is primarily found in the coarser fraction (> 2mm). A significant proportion of arsenic in the tailings (5-40%) was found in the non-residual geochemical fractions (I + II + III) (1106-7675mgkg-1), indicating potential for mobilization and bioavailability. Depending on environmental conditions (redox potential and pH), arsenic can redissolve and exhibit high mobility in abiotic media, which may ultimately impact the environment and human health. Therefore, it is crucial to rehabilitate the "El Lavadero" MEL to prevent further environmental damage. This study provides useful information to understand some phenomena in other global mining environmental liabilities, such as mobilization and bioavailability of arsenic and its possible impact on the surrounding environment and biota, contributing to the worldwide research of ecosystems polluted by mining activity, especially in arid and semi-arid climates.