Ecological Task Research Articles

Exploring the Link Between Motor Functions and the Relative Use of the More Affected Arm in Adults with Cerebral Palsy

Individuals with hemiparetic cerebral palsy (CP) exhibit reduced use of their more affected (MA) arm, yet the factors that influence its use during activities of daily living remain elusive. The objectives of this study were to describe the relative use of the MA arm during an ecological task, examine its relationship with the level of impairment, and investigate its association with performance in various unilateral and bilateral tasks. Methods: Participants took part in two sessions comprising robotic assessments and clinical assessments of motor functions, as well as accelerometry measurement during kitchen tasks. Four variables were derived from accelerometry data. Stepwise regression analyses were used to identify the best contributors to the accelerometry variables among robotic and clinical assessments. Results: Nineteen adults with CP (34.3 years old ± 11.5; MACS I = 7, II = 6, III = 6) were included. The Use Ratio measured during the kitchen tasks ranged between 0.10 and 0.63. The best predictors of all accelerometry metrics were two bilateral assessments (r2 = 0.23–0.64). Conclusions: The importance of assessing bilateral tasks was reaffirmed by the key role played by two bilateral tasks in determining the relative use of the MA arm. The results support the use of intensity-based accelerometry metrics to measure MA arm use.

Self-beneficial transactional social dynamics for cooperation in Shwachman-Diamond syndrome: a mixed-subject analysis using computational pragmatics

BackgroundShwachman-Diamond Syndrome (SDS) is a rare genetic disorder with documented cognitive and behavioral challenges. However, its socio-pragmatic dynamics remain underexplored, particularly in cooperative interactions where social norms and economic considerations intersect.ObjectiveThis study investigates the socio-behavioral dynamics of SDS, focusing on how children with the condition navigate cooperative interactions. Using computational pragmatics, we aimed to identify the underlying principles guiding their social behavior.MethodsA cohort of 10 children (5 SDS, 5 matched controls) participated in ecological and cognitive tasks, including the WISC-V “Comprehension” subtest, NEPSY-II social perception tasks, and the Trognon Ecological Side Task for the Assessment of Speech-Act Processing (TEST-ASAP). Dialogues were analyzed using the Topological and Kinetic (2TK) model and a Recurrent Neural Network (RNN), enabling fine-grained computational insights into their interaction patterns.ResultsChildren with SDS exhibited cooperative behaviors shaped by perceived economic benefits, often at the expense of established social norms. Unlike behaviors classically observed in other pathologies such as autism spectrum disorders, where responses are influenced by the directness of communication, SDS behaviors were driven by personal gain, regardless of the indirectness of requests. Computational analyses revealed strong divergences in dialogical alignment when tasks lacked direct benefits, even with corrective prompts.ConclusionSDS children demonstrate a transactional approach to social interactions, prioritizing personal benefits over cooperative norms. Using our unique dialogic and computational frameworks, we show that perceived personal gain strongly shapes their cooperation patterns. These findings underscore the need for targeted interventions to enhance pragmatic skills and adaptive functioning in SDS, given their unique interaction profiles.

O007 Menstrual cycle phase classification methods and the relationship with sleep, mood, and performance in elite female athletes

Abstract Introduction Female athletes are often excluded from sports-science research due to hormonal variations across the menstrual cycle. Effects of menstrual cycle phase on outcomes are inconclusive likely due to between-study variation in phase classification. We aimed to test if the relationship between menstrual cycle phase and sleep, mood, and performance differed depending on whether a continuous, 2-phase, 3-phase, or 4-phase approach was used to classify cycle phase. Methods Elite Australian Rules footballers reporting a regular menstrual cycle and not on contraception (N=18, M-age=24.9±5.1) were recruited. Over two weeks, they completed continuous sleep/wake monitoring via actigraphy and sleep diaries, daily self-reported sleep quality, mood, and performance ratings, and five sessions of cognitive (vigilant attention) and physical (ecological kicking task) performance testing. Using baseline self-report data, menstrual cycle phase for each day of the study was inferred using a continuous (percentage through the cycle), 2-phase (follicular, luteal), 3-phase (menses, mid-late follicular, luteal), and 4-phase (menses, mid-late follicular, early-mid luteal, pre-menses) model. Results No outcome was consistently predicted by all phase-models. Objective sleep efficiency was lower in menses according to the 3- and 4-phase models. Subjective mood and performance were higher in the mid-late follicular according to the 3-phase model. Discussion The relationships between menstrual cycle phase and sleep, mood, and performance depend on how menstrual cycle phases are classified. Discordant findings highlight the importance of methodological differences in athlete menstrual cycle research. The effects of the menstrual cycle phase need to be further studied using consistent and standardised classifications across studies.

Ironies of Generative AI: Understanding and Mitigating Productivity Loss in Human-AI Interaction

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these ‘ironies of automation’ have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users’ roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

Virtual Reality–Based Cognitive Intervention in Serious Mental Illness: Effectiveness for Promotion of Participation

Abstract Date Presented 03/23/24 Cognitive remediation using a virtual reality (VR) platform with ecological environments and functional tasks is feasible in intensive care for people with serious mental illness (SMI) and improves functional capacity, participation, cognition, and symptom severity. Primary Author and Speaker: LENA Lipskaya-Velikovsky Contributing Authors: Reut Komemi, Hana Taubenblat, Dovrat Cohen

Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems.

Cognitive and affective theory of mind in young and elderly patients with multiple sclerosis.

Theory of mind (ToM) deficits have been reported in persons with multiple sclerosis (pwMS). However, most studies have used pictures or written scenarios as stimuli without distinguishing between cognitive and affective ToM, and no studies have investigated older pwMS. We recruited 13 young healthy controls (HC), 14 young pwMS, 14 elderly HC and 15 elderly pwMS. ToM was measured using an adaptation of the Conversations and Insinuations task (Ouellet etal., J. Int. Neuropsychol. Soc., 16, 2010, 287). In this ecological video-based task, participants watch four 2-minute videos of social interactions, which are interrupted by multiple choice questions about either the emotional state (affective ToM) or the intention (cognitive ToM) of the characters. They also underwent a short neuropsychological battery including cognitive, executive and social cognition tasks and questionnaires. We observed a significant interaction between the ToM conditions and the groups regarding ToM performance. Elderly pwMS scored significantly lower than elderly HC and young pwMS in cognitive ToM, but not in affective ToM. They also showed the largest discrepancy between their cognitive and affective ToM. Young pwMS showed relatively preserved ToM in both conditions. Both cognitive and affective ToM correlated with global cognition and executive abilities, but not with social cognitive measures (emotion recognition, real-life empathy). This study suggests that decline in cognitive ToM might be accentuated by advancing age in pwMS. These impairments are most likely underlied by cognitive and executive difficulties, but not by core social cognitive impairments. Future studies should investigate the real-life impacts of ToM impairments in pwMS.

Content of heavy metals in soils of Bidoup Nui Ba National Park (Southern Vietnam)

The study of technogenic pollution of soils with heavy metals (HM) is an essential task for ecology. The analysis of the content of HMs in the park's soils shows the degree of pollution and the sources of its occurrence. The study of the elemental composition of soils is an objective method for assessing the state of the ecosystem. To determine the current state of heavy metal contamination in forest soils, the concentrations of their total forms were analyzed. Heavy metals, including Zn, Pb, Cr, Cu, Hg, Cd, and As, were found in the study area. In addition, the threat of contamination with Cd and As has been identified. The calculation of the total pollution coefficient allows us to assess the level of pollution for the dry season (Zc = 18.45-28.24, average 22.45) as average (moderately hazardous) and for the wet season (Zc = 0.01-5.11, average 1.96) as permissible. This indicates an unfavorable environmental situation. The content of heavy metals in soils depends on the season. Observations show that at the end of the wet season, the concentration of heavy metals decreases, while it increases in the period after the dry season.

Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of images for ecologists to conduct ecological investigations, such as species identification and population estimation. However, the sheer volume of images poses a challenge, and the integration of deep learning into automated ecological investigation tasks remains complex, particularly when dealing with low-quality images in long-term monitoring programs. Existing approaches often struggle to strike a balance between image enhancement and deep learning for ecological tasks, thereby overlooking crucial information contained within low-quality images. This research introduces a pioneering adaptive image processing module (AIP) that seamlessly incorporates image processing into camera trap ecological tasks, elevating the performance of wildlife monitoring activities. Specifically, a differentiable image processing (DIP) module is presented to enhance low-quality images, with its parameters predicted by a Non-local based parameter predictor (NLPP). Additionally, an end-to-end approach based on hybrid data containing both original and synthetic data is proposed, encompassing adaptive image processing methods and downstream tasks for camera traps, adaptable to various scenarios. This approach effectively reduces the manual labor and time required for professional image processing. When applied to real-world camera trap images and synthetic image datasets, our method achieves an accuracy of 92.26% and 86.65% in classifying wildlife, respectively, demonstrating its robustness. By outperforming alternative methods under harsh conditions, the application of the adaptive image processing module instills greater confidence in deep learning applications within complex environments.

Being confident in confidence scores: calibration in deep learning models for camera trap image sequences

AbstractIn ecological studies, machine learning models are increasingly being used for the automatic processing of camera trap images. Although this automation facilitates and accelerates the identification step, the results of these models may lack interpretability and their immediate applicability to ecological downstream tasks (e.g. occupancy estimation) remains questionable. In particular, little is known about their calibration, a property that allows confidence scores to be interpreted as probabilities that model's predictions are true. Using a large and diverse European camera trap dataset, we investigate whether deep learning models for species classification in camera trap images are well calibrated. Additionally, as camera traps are often configured to take multiple photos of the same event, we also explore the calibration of predictions aggregated across sequences of images. Finally, we study the effect and the practicality of a post‐hoc calibration method, i.e. temperature scaling, for predictions made at image and sequence levels. Based on five established models and three independent test sets, we show that averaging the logits over the sequence, selecting an appropriate architecture, and optionally using temperature scaling can produce well‐calibrated models. Our findings have clear implication for, for instance, the calculation of error rates or the selection of confidence score thresholds in ecological studies making use of artificial intelligence models.

Large language models help facilitate the automated synthesis of information on potential pest controllers

Abstract The body of ecological literature, which informs much of our knowledge of the global loss of biodiversity, has been experiencing rapid growth in recent decades. The increasing difficulty of synthesising this literature manually has simultaneously resulted in a growing demand for automated text mining methods. Within the domain of deep learning, large language models (LLMs) have been the subject of considerable attention in recent years due to great leaps in progress and a wide range of potential applications; however, quantitative investigation into their potential in ecology has so far been lacking. In this work, we analyse the ability of GPT‐4 to extract information about invertebrate pests and pest controllers from abstracts of articles on biological pest control, using a bespoke, zero‐shot prompt. Our results show that the performance of GPT‐4 is highly competitive with other state‐of‐the‐art tools used for taxonomic named entity recognition and geographic location extraction tasks. On a held‐out test set, we show that species and geographic locations are extracted with F1‐scores of 99.8% and 95.3%, respectively, and highlight that the model can effectively distinguish between ecological roles of interest such as predators, parasitoids and pests. Moreover, we demonstrate the model's ability to effectively extract and predict taxonomic information across various taxonomic ranks. However, we do report a small number of cases of fabricated information (confabulations). Due to a lack of specialised, pre‐trained ecological language models, general‐purpose LLMs may provide a promising way forward in ecology. Combined with tailored prompt engineering, such models can be employed for a wide range of text mining tasks in ecology, with the potential to greatly reduce time spent on manual screening and labelling of the literature.

Post-synthetic modification of nano-chitosan using gibberellic acid: Foliar application on sorghum under salt stress conditions and estimation of biochemical parameters

The challenge of desert farming with a high salt level has become an ecological task due to salt stress negatively affecting plant growth and reproduction. The current study deals with the cultivation of sorghum under salt stress conditions to counteract the effect of chitosan and gibberellic acid (GA3). Here, the effects of chitosan, GA3 and nano-composite (GA3@chitosan) on biochemical contents, growth and seed yield of sorghum under salinity stress conditions were studied. The results showed that spraying with GA3@chitosan increased sorghum grain yield by 2.07, 1.81 and 1.64 fold higher than salinity stressed plants, chitosan treatment and GA3 treatment, respectively. Additionally, compared to the control of the same variety, the GA3@chitosan spraying treatment improved the concentration of microelements in the grains of the Shandweel-1 and Dorado by 24.51% and 18.39%, respectively for each variety. Furthermore, spraying GA3@chitosan on sorghum varieties increased the accumulation of the macroelements N, P, and K by 34.03%, 47.61%, and 8.67% higher than salt-stressed plants, respectively. On the other hand, the proline and glycinebetaine content in sorghum leaves sprayed with nano-composite were drop by 51.04% and 11.98% less than stressed plants, respectively. The results showed that, in Ras Sudr, the Shandweel-1 variety produced more grain per feddan than the Dorado variety. These findings suggest that GA3@chitosan improves the chemical and biochemical components leading to a decrease in the negative effect of salt stress on the plant which reflects in the high-yield production of cultivated sorghum plants in salt conditions.

Historical and dispersal processes drive community assembly of multiple aquatic taxa in glacierized catchments in the Qinghai-Tibet plateau

Understanding the relative role of dispersal dynamics and niche constraints is not only a core task in community ecology, but also becomes an important prerequisite for bioassessment. Despite the recent progress in our knowledge of community assembly in space and time, patterns and processes underlying biotic communities in alpine glacierized catchments remain mostly ignored. To fill this knowledge gap, we combined the recently proposed dispersal-niche continuum index (DNCI) with traditional constrained ordinations and idealized patterns of species distributions to unravel community assembly mechanisms of different key groups of primary producers and consumers (i.e., phytoplankton, epiphytic algae, zooplankton, macroinvertebrates, and fishes) in rivers in the Qinghai-Tibet Plateau, the World's Third Pole. We tested whether organismal groups with contrasting body sizes differed in their assembly processes, and discussed their applicability in bioassessment in alpine zones. We found that community structure of alpine river biotas was always predominantly explained in terms of dispersal dynamics and historical biogeography. These patterns are most likely the result of differences in species-specific functional attributes, the stochastic colonization-extinction dynamics driven by multi-year glacier disturbances and the repeated hydrodynamic separation among alpine catchments after the rising of the Qilian mountains. Additionally, we found that the strength of dispersal dynamics and niche constraints was partially mediated by organismal body sizes, with dispersal processes being more influential for microscopic primary producers. Finding that zooplankton and macroinvertebrate communities followed clumped species replacement structures (i.e., Clementsian gradients) supports the notion that environmental filtering also contributes to the structure of high-altitude animal communities in glacierized catchments. In terms of the applied fields, we argue that freshwater bioassessment in glacierized catchments can benefit from incorporating the metacommunity perspective and applying novel approaches to (i) detect the optimal spatial scale for species sorting and (ii) identify and eliminate the species that are sensitive to dispersal-related processes.

Lost in space(s): Multimodal neuroimaging of disorientation along the Alzheimer's disease continuum.

Orientation is a fundamental cognitive faculty and the bedrock of the neurologic examination. Orientation is defined as the alignment between an individual's internal representation and the external world in the spatial, temporal, and social domains. While spatial disorientation is a recognized hallmark of Alzheimer's disease (AD), little is known about disorientation beyond space in AD. This study aimed to explore disorientation in spatial, temporal, and social domains along the AD continuum. Fifty-one participants along the AD continuum performed an ecological orientation task in the spatial, temporal, and social domains while undergoing functional MRI. Disorientation in AD followed a three-way association between orientation domain, brain region, and disease stage. Specifically, patients with early amnestic mild cognitive impairment exhibited spatio-temporal disorientation and reduced brain activity in temporoparietal regions, while patients with AD dementia showed additional social disorientation and reduced brain activity in frontoparietal regions. Furthermore, patterns of hypoactivation overlapped different subnetworks of the default mode network, patterns of fluorodeoxyglucose hypometabolism, and cortical atrophy characteristic of AD. Our results suggest that AD may encompass a disorder of orientation, characterized by a biphasic process manifesting as early spatio-temporal and late social disorientation. As such, disorientation may offer a unique window into the clinicopathological progression of AD. SIGNIFICANCE STATEMENT: Despite extensive research into Alzheimer's disease (AD), its core cognitive deficit remains a matter of debate. In this study, we investigated whether orientation, defined as the ability to align internal representations with the external world in spatial, temporal, and social domains, constitutes a core cognitive deficit in AD. To do so, we used PET-fMRI imaging to collect behavioral, functional, and metabolic data from 51 participants along the AD continuum. Our findings suggest that AD may constitute a disorder of orientation, characterized by an early spatio-temporal disorientation and followed by late social disorientation, manifesting in task-evoked and neurodegenerative changes. We propose that a profile of disorientation across multiple domains offers a unique window into the progression of AD and as such could greatly benefit disease diagnosis, monitoring, and evaluation of treatment response.

Avaliação das funções executivas em adultos pós traumatismo cranioencefálico

Abstract Objective Executive dysfunction after traumatic brain injury may lead to reduced daily functionality due to direct damage to the frontal lobe or to disruption of its connections to other brain regions. This study aimed to compare the performance of adults with and without traumatic brain injury on executive functions tasks. Method The present study had a descriptive transversal design and included 43 participants divided into a clinical group (N = 23, age M = 34.3 [10.86]) and a control group (N = 20, age M = 38.3 [10.7]). The battery consisted of traditional instruments and the Ecological Hotel Task. Results A significant difference was found in the performance of the groups in the planning measures (number of tasks [t = -3.06; p < 0.01] and planning score [t = -3.06; p < 0.01]), highlighting the advantages of using both the traditional performance paradigms and the ecological tasks in neuropsychological assessment of executive functions. Conclusion Results suggested that the ecological task might discriminate better executive dysfunction, emphasizing the importance of using activities that simulate real-life situations of the participants.

Comprehensive integration of the interaction of ecology, science and education in the context of the scientific and technological revolution and the formation of a sustainable society

Taking into account the modern ecological problems accumulated by mankind since the times of antiquity, the article draws attention to the peculiarities of the worldview, and its change, and defines the peculiarities of the philosophy of ecology in the society of sustainable development. In the process of research, the essence and significance of the philosophy of ecology as a direction of philosophical research that forms the basis of worldview orientations was clarified, and by resorting to praxeology, it was possible to reveal the potential opportunities of education in the ecological thinking of the future self. The features of the socio-anthropological awareness of the need for the concept of sustainable development are considered and the philosophy of ecology is in the process of environmentalization of public consciousness. For a better understanding of the need for environmental awareness in society, an important role was played by international concepts and regulatory legal acts, which are the basis of the research and writing of the article itself. They became one of the reasons for the rethinking of the self-based on the ecological tasks in the society of sustainable development. In this context, special attention is paid to education, a social institution, one of the main tasks of which is the formation of the future self by instilling value orientations and the awareness that a person is a part of nature, which at the same time is his home. However, any changes cannot occur without a change in the consciousness of the Self, and in this case, the leading role is given to the philosophy of ecology as a part of philosophical knowledge whose task is to form an ecological worldview.

Optimized zone-based vehicle speed estimation and classification

Abstract. This article pioneers the fusion of advanced computer vision, and environmental science in order to be a starting point in ecological tasks and environmental benefits. Utilizing state-of-the-art tools like YOLOv7 and innovative algorithms, the study achieves unmatched accuracy in vehicle identification, classification, tracking, and speed analysis. By optimizing YOLOv7-e6e-1280 architecture using TensorRT and reduced precision, real-time analysis becomes possible without compromising accuracy. The integration of the Vanishing Point Principle for road zoning and zone-based speed calculation provides nuanced insights into driving behaviors. Detailed vehicle classification and robust tracking offer valuable data for urban planning and ecological studies. This approach increase our potential in vehicular analysis, setting new standards for research in urban development, transportation, and environmental science.

Technogenic salinization of podzolic and alluvial soils of the middle taiga subzone of West Siberia

The aim of the study was to identify the main trends and patterns of migration and accumulation of oil pollutants (petroleum products, easily soluble salts) in soils of the middle taiga subzone of West Siberia. Location and time of the study. Soil geography and genesis were studied in 2019 and 2023 in the middle taiga subzone of West Siberia in the middle part of the Ob River in various elementary landscapes. Methods. Oil compounds were determined in hexane extracts from soil using Fluorat-2 apparatus. The qualitative composition of salts was assessed in water extracts. Results. Highly mineralized waters were found to have a more significant impact on the properties of podzolic and alluvial soils in West Siberia than the crude oil. Combined oil and salt pollution was accompanied by a high content of toxic salts in the root-inhabited horizons. The discharge of mineralized water during emergency spills under the conditions of waterlogged taiga landscapes in West Siberia led to the formation of technogenically salinized soils in areas where the natural development of salinization is impossible. Therefore, soil salinization in a humid climate can be considered a superimposed soil-forming process. Conclusions. The results allow assessing the ecological state of soils (their chemistry, degree of salinity, reserve of toxic salts) and develop proposals for the remediation of oil and salt contaminated soils. The combined effect of halogenesis and alkalization processes is not typical for humid soil formation zones, where natural processes of salinization and halogenesis are not developed due to excess moisture and flushing water regime. Technogenic salinization associated with oil production is often found in such regions and does not depend on climatic conditions. Due to the lack of industrial reclamation of soils subjected to technogenic salinization in the middle taiga subzone of West Siberia, the studies of soil ecology in the contaminated areas are one of the urgent tasks of soil science and ecology. At the same time, taking into account the nature of pollution, the chemistry of salinization and soils catenary positions in the landscape will allow adapting the reclamation methods of technogenically saline soils for a specific contaminated area, which will subsequently allow completing soil restoration during reclamation activities.

Efficiency of Heating and Evaporation of Graphene Nanofluid under Solar Radiation

The conversion of solar radiation into steam is a crucial focus in today’s green energy, ecology, and clean water production. This study presents the first investigation of the heating and evaporation process of a rotating graphene nanofluid under the influence of solar simulator radiation. The study examined the influence of various factors on the heating and evaporation process of the graphene nanofluid, including the direction of irradiation, graphene concentration and rotation speed. It was demonstrated that the evaporation rate strongly depends on the graphene concentration and the irradiation method of the samples. The heating characteristics of graphene also depend on the irradiation method. It was shown that graphene heated to a higher temperature when in direct contact with radiation, while graphene within the bulk volume was heated less effectively than the base liquid. Moreover, the application of rotating graphene nanofluid in this research was found to enhance thermal efficiency by 2.5% compared to distilled water, with a graphene volume concentration of 0.1%. Consequently, various effects of the rotating graphene nanofluid volume on hydrodynamic, heat, and mass transfer parameters were identified, which hold significance for both fundamental and applied tasks in energy, chemical technology, and ecology.