Protostellar streamers are elongated structures that channel material from larger scale onto disks, influencing their physical and chemical evolution. The M512 protostar in Orion/Lynds 1641 hosts one of the most massive and extended streamer discovered so far, offering a unique opportunity to study these processes. We investigate the morphology, chemistry, and origin of this streamer,and its potential impact on the protostellar disk. Using archival ALMA observations of C18O, DCO+, N2D+, and HCO+, we compare their spatial distributions through moment maps and spatial profiles. The streamer shows clear chemical stratification: C18O lies on the western side of the protostar, N2D+ is farther out to the east, and DCO+ is in the middle. This suggests that the structure has been shaped by environmental effects rather than tracing a single coherent infalling flow, with only the densest gas near the protostar likely to accrete onto the disk. Overall, the bulk of the streamer reflects the physical and chemical imprint of the surrounding cloud, highlighting the importance of environmental shaping in interpreting streamer-disk connections and their role in disk growth.
Designing environments that maximize the rate of emergent behavior development in AI agents remains an open problem. We present the first systematic study of stress-performance relationships in large language model (LLM) multi-agent systems, drawing an explicit parallel to the Yerkes-Dodson law from cognitive psychology. Using a grid-world survival arena, we conduct 22 experiments across four phases, varying environmental pressure through resource scarcity (upkeep cost) and reproductive competition (sexual selection). Our key finding is that cooperative behavior follows an inverted-U curve: trade interactions peak at 29 under medium pressure (upkeep=5), while both low and extreme pressure produce 8--12 trades. Under extreme pressure, behavioral repertoire collapses to movement-only within 5--12 turns. We further show that sexual selection -- a softer pressure mechanism where all agents survive but not all reproduce -- eliminates inter-agent aggression entirely and produces communicative behavior absent under survival pressure. These results suggest that environmental pressure calibration is a viable curriculum design strategy for LLM agent development, analogous to the inverted-U relationship between arousal and performance in biological systems.
Abstract Background Although the effects of artificial light at night (ALAN) on human health are well-documented, its impact on the incidence of anxiety remains unclear. Method To evaluate the potential association between ALAN and the incidence of anxiety, this ecological study analysed the age-standardised anxiety incidence rate (ASAIR) for 174 countries and territories obtained from the Global Burden of Disease Study (2000–2019). The annual average ALAN intensity for each country was estimated using night-time satellite images. Initially, a Geographic Detector (GD) was employed to examine the spatial association between the ALAN intensity and the ASAIR. Thereafter, linear mixed-effects models (LMEMs) were applied to assess the impact of ALAN intensity on the incidence of anxiety. Results From 2000 to 2019, the global ASAIR for anxiety was 603.78 per 100,000 population, and the mean ALAN intensity was 4.95 digital numbers. The GD analysis revealed a statistically significant spatial association between ALAN intensity and the ASAIR over the study period. Moreover, results from LMEMs demonstrated that, after adjusting for potential confounders, ALAN intensity was significantly associated with an increased ASAIR, with an adjusted β coefficient of 2.29 (95% confidence interval: 1.48–2.92). Notably, ALAN-associated adverse effects appeared more pronounced among females and individuals aged 19 years or younger. The findings of the subgroup and sensitivity analyses were largely consistent with the primary results. Conclusion From a global perspective, ALAN may be associated with an increased incidence of anxiety. These findings have implications for urban planning and the development of public health policies targeting light pollution mitigation; nevertheless, further validation is necessary.
We show that the $p$-part of the degree of an irreducible character of a symmetric group is completely determined by the set of vanishing elements of $p$-power order. As a corollary we deduce that the set of zeros of prime power order controls the degree of such a character. The same problem is analysed for alternating groups, where we show that when $p=2$ this data can only be determined up to two possibilities. We prove analogous statements for the defect of the $p$-block containing the character and for the $p$-height of the character.
This study constructs a novel analytical general equilibrium model to compare environmental policies in a setting where oligopolistic energy firms engage in third-degree price discrimination across residential consumers and industrial firms. Closed-form solutions demonstrate the impact on prices and quantities. The resulting welfare change is decomposed across three distortions: output, price discrimination, and externality. This study finds that the output distortion and price discrimination welfare effects generally move in opposite directions under policies such as an emission tax or a two-part instrument. Numerical analysis compares policies and finds scenarios where the output distortion and price discrimination welfare changes fully offset and thus leaves the net welfare gain of the externality correction. In this way, environmental policy can be designed to mitigate output distortion welfare concerns when firms have market power.
The anchor cables of slopes are affected by long-term environmental corrosion, geotechnical creep, and adverse weather, resulting in gradual loss of tensile force, which can lead to structural failure and subsequent safety accidents. The authors of this paper conducted research based on the magnetic induction density distribution characteristics of permanent magnets, including model derivation, theoretical simulation, and indoor experiments, aiming to propose a new anchor cable force monitoring technology with high sensitivity, strong applicability, and good stability. Based on the molecular circulation model and the Biot–Savart law, the analytical expression of the spatial magnetic field distribution of a rectangular permanent magnet was derived and, combined with the stress–strain relationship characteristics of anchor cables, a theoretical model for the relationship between anchor cable tensile force and magnetic induction density was established. MATLAB (R2018b) was used to simulate and analyze the spatial magnetic field distribution and the force–magnetism relationship. The analysis showed that the magnetic induction density along the central axis of the permanent magnet approximately exhibited a symmetrical quadratic curve distribution, and its value was significantly negatively correlated with the anchor cable force. Based on this, a new anchor cable force monitoring technology was proposed, and an indoor experimental platform was established. The indoor experimental studies further confirmed the negative correlation between force and magnetism (i.e., as the tensile force increases, the magnetic induction strength decreases, and as the tensile force decreases, the magnetic induction strength increases). The fitting results of the force–magnetism curve show that a quadratic function can better describe the correspondence between magnetic induction density and anchor cable force. Reproducibility analysis of the experimental data showed low dispersion in magnetic induction values under various design loads, along with good stability, validating the effectiveness and applicability of the proposed anchor cable force monitoring technology.
Oktay Zorlu, Bedirhan Güneşdoğmuş, Sefa Sözer
et al.
Abstract Energy efficiency in food preparation is a critical yet often overlooked aspect of sustainability. Despite tea being one of the most consumed beverages worldwide, research on the energy efficiency of its brewing process—particularly at the household level—remains limited. This study addresses this gap by investigating the energy cost, efficiency, and environmental footprint of Turkish-style tea brewing, a method characterized by its unique double teapot system and prolonged steeping process. Experimental tests were conducted on a standard kitchen stove using three burner sizes and varying flame modes. Energy efficiency, analyzed using the First Law of Thermodynamics, ranged from 31% to 70%, with specific energy consumption between 0.53 and 0.93 kJ/kg. Results reveal a trade-off between energy efficiency and brewing time, highlighting the need for optimized techniques to reduce energy waste. Given the massive global tea consumption, this study provides valuable insights for future research on sustainable and energy-efficient food preparation practices.
Nutrition. Foods and food supply, Food processing and manufacture
Abstract Cadmium (Cd2+) contamination threatens plant viability and human health by disrupting cellular homeostasis and metabolic processes. Investigating the molecular mechanism underlying Cd2+ tolerance in plants is necessary to remediate Cd2+-contaminated soil. This study presents an integrated physiological, metabolomic, and transcriptomic analysis of the roots, stems, and leaves in response to Cd2+ stress. The study found that Cd2+ accumulation was significantly lower in Cd2+-tolerant cotton. Under 4 mM Cd2+ stress, the Cd2+ content in cotton increased significantly, accompanied by elevated levels of malondialdehyde (MDA), proline (Pro), and hydrogen peroxide (H2O2), as well as noticeable damage to the cellular ultrastructure. Metabolomic profiling analysis revealed that Cd2+ stress significantly affected the distribution of lipids, amino acids, and organic acids in different tissues. The metabolic pathways of alanine, aspartate, and glutamate are closely associated with Cd2+ stress, and the induced elevation of GABA levels plays a crucial role in cotton’s adaptation to Cd2+ stress. Exogenous GABA application significantly enhances Cd2+ tolerance in cotton by reducing Cd2+ accumulation and decreasing the content of Pro, MDA, and H2O2. Silencing of the γ-aminobutyric acid (GABA) biosynthetic gene glutamate decarboxylase (GhGAD6) resulted in increased Cd2+ sensitivity, demonstrating that GABA alleviates Cd2+ toxicity in cotton through reducing Cd2+ accumulation and scavenging ROS. These findings elucidate the molecular basis of Cd2+ stress tolerance in plants and provide a key for the effective strategy of enhancing Cd2+ tolerance in cotton.
Barbara Klik, Zbigniew Mazur, Agata Krasnodębska
et al.
This study examines the environmental implications of repurposing former railway infrastructure into bicycle paths in northeastern Poland. While lauding the commendable initiative for offering eco-friendly transportation alternatives, the investigation emphasises the potential environmental and consequences linked to contaminated soils resulting from the former railway line, posing risks for both users and the environment. Topsoil samples (0–25 cm) were collected from eight measurement points along the Szczytno - Biskupiec Reszelski railway line (Poland), at varying distances from the railway tracks (5–30 m). Concentrations of specific Potentially Toxic Elements (PTEs), notably Ni, Zn, Pb, and Cd, were analysed in 48 topsoil samples. Furthermore, the soil contamination was assessed by contamination indices, single (Igeo, PI) and integrated (RI, (IPln, MERMQ). The study revealed variations in PTE concentrations, with the highest levels recorded at the fourth and seventh sampling sites (4:34.2–57.5 mg/kg; 7:18.2–42.3 mg/kg). These findings were consistently supported by the MERMQ and RI indices, emphasising the significant risks for the environment and increased soil toxicity. In light of the EU's proposed Soil Monitoring Law, the research emphasises the need for robust data collection, analysis, and contamination monitoring practices to facilitate informed decision-making and sustainable environmental management in repurposed transportation infrastructure.
Solid oxide fuel cell (SOFC) stacks face reliability challenges because multiple degradation mechanisms interact with operational and environmental variability. We develop a hierarchical Bayesian framework that couples a monotone area-specific resistance (ASR) growth law with a Weibull time-to-failure model and employs a Student-t observation layer to down-weight outliers. Using multi-cell data, the approach narrows to 95% predictive-interval widths for ASR and lifetime by up to 33 % relative to a non-hierarchical baseline, and global sensitivity analysis identifies the ASR growth rate as the dominant driver (S<inline-formula> <tex-math notation="LaTeX">$1~\approx ~0.84$ </tex-math></inline-formula>). Scenario projections quantify operational effects: hot–humid climates raise failure probability to <inline-formula> <tex-math notation="LaTeX">$\approx 56$ </tex-math></inline-formula> % versus <inline-formula> <tex-math notation="LaTeX">$\approx 46$ </tex-math></inline-formula> % under cold–dry conditions, whereas moderate load variations are negligible within normal ranges. External validation on a <inline-formula> <tex-math notation="LaTeX">$\sim 93~000$ </tex-math></inline-formula> h record shows low root-mean-square and means absolute errors with near-nominal predictive-interval coverage. Collectively, these results establish a diagnostic-to-decision workflow for reliability modeling that improves confidence in lifetime predictions and supports data-informed operation and maintenance.
Addressing the challenges posed by climate change, biodiversity loss, and environmental pollution requires comprehensive monitoring and effective data management strategies that are applicable across various scales in environmental system science. This paper introduces a versatile and transferable digital ecosystem for managing time series data, designed to adhere to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The system is highly adaptable, cloud-ready, and suitable for deployment in a wide range of settings, from small-scale projects to large-scale monitoring initiatives. The ecosystem comprises three core components: the Sensor Management System (SMS) for detailed metadata registration and management; time$.$IO, a platform for efficient time series data storage, transfer, and real-time visualization; and the System for Automated Quality Control (SaQC), which ensures data integrity through real-time analysis and quality assurance. The modular architecture, combined with standardized protocols and interfaces, ensures that the ecosystem can be easily transferred and deployed across different environments and institutions. This approach enhances data accessibility for a broad spectrum of stakeholders, including researchers, policymakers, and the public, while fostering collaboration and advancing scientific research in environmental monitoring.
Franca Corradini, Carlo Grigioni, Alessandro Antonucci
et al.
Safe road crossing by autonomous wheelchairs can be affected by several environmental factors such as adverse weather conditions influencing the accuracy of artificial vision. Previous studies have addressed experimental evaluation of multi-sensor information fusion to support road-crossing decisions in autonomous wheelchairs. In this study, we focus on the fine-tuning of tracking performance and on its experimental evaluation against outdoor environmental factors such as fog, rain, darkness, etc. It is rather intuitive that those factors can negatively affect the tracking performance; therefore our aim is to provide an approach to quantify their effects in the reference scenario, in order to detect conditions of unacceptable accuracy. In those cases, warnings can be issued and system can be possibly reconfigured to reduce the reputation of less accurate sensors, and thus improve overall safety. Critical situations can be detected by the main sensors or by additional sensors, e.g., light sensors, rain sensors, etc. Results have been achieved by using an available laboratory dataset and by applying appropriate software filters; they show that the approach can be adopted to evaluate video tracking and event detection robustness against outdoor environmental factors in relevant operational scenarios.
Girmaw Abebe Tadesse, Caleb Robinson, Gilles Quentin Hacheme
et al.
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia using sub-meter gray-scale aerial imagery from 1943 and 1972. In this work, we propose a workflow for analyzing historical aerial imagery using a deep semantic segmentation model on sparse hand-labels. To this end, we employ a number of strategies including class-weighting, pseudo-labeling and empirical p-value-based filtering to balance skewed and sparse representations of objects in the ground truth data. Results demonstrate the benefits of these different training strategies resulting in an average $F_1=0.661$ and $F_1=0.755$ over the three objects of interest for the 1943 and 1972 imagery, respectively. We also identified that the average size of Waterhole and Big trees increased while the average size of Omuti homesteads decreased between 1943 and 1972 reflecting some of the local effects of the massive post-Second World War economic, agricultural, demographic, and environmental changes. This work also highlights the untapped potential of historical aerial photographs in understanding long-term environmental changes beyond Namibia (and Africa). With the lack of adequate satellite technology in the past, archival aerial photography offers a great alternative to uncover decades-long environmental changes.
Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The model integrates advanced deep learning techniques, including LSTM and Transformer, and PSO algorithm for parameter optimization, significantly enhancing predictive performance and practical applicability. Results show that our model achieves substantial improvements across various metrics, including a 30% reduction in MAE, a 20% decrease in MAPE, a 25% drop in RMSE, and a 35% decline in MSE. These results validate the model's effectiveness and reliability in renewable energy demand forecasting. This research provides valuable insights for applying deep learning in environmental decision support systems.
We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively.
Riccardo Gianluigi Serio, Maria Michela Dickson, Thomas de Graaff
et al.
Tourism consumption has grown into a major economic factor for modern societies. However, the environmental impact of tourism has become a significant concern, leading to an increased focus on sustainable tourism policies. While governments and institutions have introduced frameworks to promote ecological transition in the tourism sector, the effectiveness of such policies remains unclear. This study provides a seminal attempt to examine the complex relationship between tourism demand and sustainable tourism policies. To do so, a gravity model framework has been adopted to examine incoming international tourism flows in Italian provinces in 2019. The findings reveal a positive association between tourism demand and sustainable labels. This study also suggests that eco-labels are appreciated by tourists and have a role in the destination decision-making process. It highlighted the need for continued research to identify effective sustainable tourism policies that can balance the economic benefits of tourism with environmental considerations
This paper examines the legal and practical aspects of Article 266 of the Serbian Criminal Code, which criminalizes the import, as well as illegal processing, disposal, and storage of hazardous materials in Serbia. The study explores the conceptual underpinnings of the law, emphasizing its role in safeguarding public health and environmental safety. It scrutinizes the application of Article 266, highlighting notable cases and enforcement strategies employed by Serbian judicial authorities, as well as a study cases related to problems of hazardous waste. The paper also delves into the inherent challenges in the legislation`s implementation, including the identification and classification of dangerous substances, the evolving nature of hazardous materials, and jurisdictional issues. Moreover, the study critically analyzes the impact of international conventions and European Union standards on Serbian law, exploring how global trends influence domestic legal frameworks. The paper concludes by offering recommendations for enhancing the efficacy of Article 266, suggesting legislative reforms, raising public awareness, and improving inter-agency collaboration to effectively combat the illegal handling of hazardous materials in Serbia. U radu se analiziraju pravni i praktični aspekti člana 266.
Robert Nicolas Warong, Altje Agustin Musa, Djefry Welly Lumintang
Environmental sector licensing aims to optimize efforts to maintain the carrying capacity and capacity of the LH sustainably. The arrangement of environmental licensing for business actors is implied in Law Number 32 of 2009 concerning Environmental Protection and Management and Government Regulation Number 27 of 2012 concerning Environmental Permits. This normative legal research is, namely the analysis of existing problems and is discussed based on related legal theories and applicable laws and regulations, as well as conceptual approaches and case approaches. In addition, empirical methods are used based on findings in the field. The legal arrangement of the responsibility of the government and regional governments in the PPLH sector has 3 (three) aspects, philosophical, sociological and legal. Although not yet optimal in its implementation, regulations on environmental licensing have brought about improvements in the relationship and authority between the central government and regional governments. However, its implementation must be planned, rational, optimal, responsible and in line with the carrying capacity of natural resources for the welfare of all citizens without overlapping.
The diversity and quality of natural systems have been a puzzle and inspiration for communities studying artificial life. It is now widely admitted that the adaptation mechanisms enabling these properties are largely influenced by the environments they inhabit. Organisms facing environmental variability have two alternative adaptation mechanisms operating at different timescales: \textit{plasticity}, the ability of a phenotype to survive in diverse environments and \textit{evolvability}, the ability to adapt through mutations. Although vital under environmental variability, both mechanisms are associated with fitness costs hypothesized to render them unnecessary in stable environments. In this work, we study the interplay between environmental dynamics and adaptation in a minimal model of the evolution of plasticity and evolvability. We experiment with different types of environments characterized by the presence of niches and a climate function that determines the fitness landscape. We empirically show that environmental dynamics affect plasticity and evolvability differently and that the presence of diverse ecological niches favors adaptability even in stable environments. We perform ablation studies of the selection mechanisms to separate the role of fitness-based selection and niche-limited competition. Results obtained from our minimal model allow us to propose promising research directions in the study of open-endedness in biological and artificial systems.