Hasil untuk "Environmental technology. Sanitary engineering"

Zhen Wang, Qing Xiang, Daixiong Zhang et al.

Biochar-based photocatalytic materials have shown broad application potential in environmental remediation, including the degradation of organic pollutants in aquatic systems, the reduction of heavy metal ions, and the purification of gaseous contaminants. These advantages stem from their high specific surface area, excellent electrical conductivity, and interfacial regulation capabilities. This review systematically summarizes the structural characteristics of biochar and its multiscale regulation strategies, with particular emphasis on its synergistic roles in the construction of Z-scheme and S-scheme heterojunctions, the regulation of photogenerated carrier dynamics, and the generation and transformation of reactive oxygen species (ROS). The practical performance of biochar-based composites in treating diverse pollutants is critically examined, followed by a comprehensive analysis of current challenges, including raw material variability, material stability, limited mechanistic understanding, insufficient engineering adaptability, and the absence of standardized performance evaluation frameworks. Based on these insights, this paper proposes a forward-looking development strategy centered on green synthesis and data-driven design optimization, offering theoretical guidance and technical pathways to accelerate the large-scale and sustainable application of biochar-based photocatalytic materials in complex environmental systems.

Jiaqi Zhang, Jun Ma, Yaqian Xu et al.

Water temperature is an important index that affects physical, chemical and biological reactions in water environments, and accurate water temperature prediction is important. Water temperature prediction in a data-deficient area along the Yangtze River trunk stream was selected as the research object, the factors influencing water temperature changes, such as air temperature, latitude and elevation, were analyzed, and the main factors were determined. A linear regression equation of water temperature and air temperature under different climate types was constructed. The Air2stream model was used for water temperature prediction, and the model prediction accuracies were compared. (1) Water temperature changes are mainly controlled by air temperature, and (2) the averaged root mean square error (RMSE) of water temperatures predicted by the linear regression equation and Air2stream model were 1.79 °C and 1.40 °C, respectively. The averaged determination coefficients (R2) for the Air2stream model under the plateau alpine and subtropical monsoon climate types were 0.97 and 0.95, respectively. (3) The prediction accuracy of the Air2stream model exceeded that of the linear regression equation. Although the phenomenon of water temperature lagging behind air temperature is becoming increasingly obvious in high-flow areas, the water temperature prediction method of the water temperature-air temperature linear regression equation coupled with the Air2stream model can provide more reliable prediction results, thereby providinge a reference for water temperature prediction in data-deficient areas.

Yanqin Chen, Donghui Wang, Xueli Wang et al.

Abstract To enhance the performance of biochar made from almond shells for adsorption of phenol pollutants in water, we prepared an almond shell-based biochar and modified it through combined pyrolysis with KOH and EDTA-4Na at 750 °C, yielding almond shell-based modified activated carbon (A-BC); the mass ratio of biochar, EDTA-4Na, and KOH was 1:1:3. A-BC was characterized by using Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, the Brunauer–Emmett–Teller method, and X-ray Diffraction. The adsorption conditions of A-BC for phenol were optimized through single-factor experiments, and the adsorption mechanism was explored through kinetics and thermodynamics assays. The results show that A-BC exhibits a honeycomb-like structure with a specific surface area of 1050 m2 g−1 and a micropore ratio of 86%. A-BC is rich in functional groups (-OH, -CH2, N–C, C-H, N–H) closely related to phenol adsorption. The adsorption of phenol by A-BC is a spontaneous exothermic process involving both physical adsorption and chemical adsorption (including hydrogen bonding and π-π interactions). The pseudo-second-order kinetic model adequately describes the adsorption process, which consists of liquid film diffusion, surface adsorption, and intraparticle diffusion stages. At 25 °C, with an A-BC dosage of 1.0 g L−1, initial phenol concentration of 400 mg L−1, and contact time of 60 min, A-BC exhibited significant adsorption capacities of 161 and 149 mg g−1 for simulated water and phenol-containing wastewater from coal chemical industries, respectively. A-BC demonstrated good reuse performance and strong adsorption capacity for phenol, indicating its potential application in treating phenol-containing wastewater from coal chemical industries.

Abeer Kazmi, Gaojie Li, Jingjing Yang et al.

Cadmium (Cd) is a toxic metal that poses an environmental risk, but its effects on amphibious plants like Hygrophila difformis, which thrive in both terrestrial and submerged conditions, remain unexplored. This study investigates the morphological, anatomical, and physiological responses of H. difformis to Cd exposure. H. difformis was exposed to 0, 1, 2, and 4 mg/L Cd for 30 days under both terrestrial and submerged conditions. Leaves were examined at 10, 20, and 30 days for morphological and physiological changes. At lower Cd concentration (1 mg/L), leaf morphology showed minimal changes compared to the control. Submerged control leaves were highly dissected (26.1 ± 0.45), whereas dissection was substantially reduced at 4 mg/L Cd (16.96 ± 0.67), indicating a profound impact on heterophylly. Cd stress affected leaf size significantly, particularly in submerged plants (17.8 ± 3.22 cm²) compared to controls (39.2 ± 2.84 cm²). Furthermore, compared to terrestrial leaves (4.27 ± 0.31 mg/kg), submerged leaves accumulated significantly higher content of Cd (45.2 ± 6.66 mg/kg), indicating higher absorption under aquatic conditions. Terrestrial leaves appeared more resistant; however, higher concentrations caused tissue damage. Following 30 days of treatment, qualitative TEM-based anatomical analysis revealed noticeable cell shrinkage and fewer visible chloroplasts in submerged leaves compared to controls, while terrestrial leaves exhibited thicker cell walls. Cd exposure also inhibited photosynthesis, reducing pigment levels and enzyme activity. Interestingly, Rubisco activity increased in submerged leaves after 30 days of high Cd exposure, preventing the transition from C3 to C4 photosynthesis. H. difformis exhibits poor growth under Cd stress and can serve as a bioindicator for heavy metal pollution.

Morgan M. Fong, Hilary Egan, Marc Day et al.

Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Fabian C. Peña

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.

Fernando Ayach, Vitor Lameirão, Raul Leão et al.

Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.

Ofek Aloni, Gal Perelman, Barak Fishbain

Synthetic datasets are widely used in many applications, such as missing data imputation, examining non-stationary scenarios, in simulations, training data-driven models, and analyzing system robustness. Typically, synthetic data are based on historical data obtained from the observed system. The data needs to represent a specific behavior of the system, yet be new and diverse enough so that the system is challenged with a broad range of inputs. This paper presents a method, based on discrete Fourier transform, for generating synthetic time series with similar statistical moments for any given signal. The suggested method makes it possible to control the level of similarity between the given signal and the generated synthetic signals. Proof shows analytically that this method preserves the first two statistical moments of the input signal, and its autocorrelation function. The method is compared to known methods, ARMA, GAN, and CoSMoS. A large variety of environmental datasets with different temporal resolutions, and from different domains are used, testing the generality and flexibility of the method. A Python library implementing this method is made available as open-source software.

Jens Strauss, Maija E Marushchak, Lona van Delden et al.

Permafrost regions, characterised by extensive belowground excess ice, are highly vulnerable to rapid thaw, particularly in areas such as the Yedoma domain. This region is known to freeze-lock a globally significant stock of soil nitrogen (N). However, the fate of this N upon permafrost thaw remains largely unknown. In this study, we assess the impact of climate warming on the size and dynamics of the soil N pool in (sub-)Arctic ecosystems, drawing upon recently published data and literature. Our findings suggest that climate warming and increased thaw depths will result in an expansion of the reactive soil N pool due to the larger volume of (seasonally) thawed soil. Dissolved organic N emerges as the predominant N form for rapid cycling within (sub-)Arctic ecosystems. The fate of newly thawed N from permafrost is primarily influenced by plant uptake, microbial immobilisation, changes in decomposition rates due to improved N availability, as well as lateral flow. The Yedoma domain contains substantial N pools, and the partial but increasing thaw of this previously frozen N has the potential to amplify climate feedbacks through additional nitrous oxide (N _2 O) emissions. Our ballpark estimate indicates that the Yedoma domain may contribute approximately 6% of the global annual rate of N _2 O emissions from soils under natural vegetation. However, the released soil N could also mitigate climate feedbacks by promoting enhanced vegetation carbon uptake. The likelihood and rate of N _2 O production are highest in permafrost thaw sites with intermediate moisture content and disturbed vegetation, but accurately predicting future landscape and hydrology changes in the Yedoma domain remains challenging. Nevertheless, it is evident that the permafrost-climate feedback will be significantly influenced by the quantity and mobilisation state of this unconsidered N pool.

Aiman Eid Al-Rawajfeh, Albara Ibrahim Alrawashdeh, Mohammad Taha Etiwi et al.

In this comprehensive study, a novel adsorbent material comprising silver nanoparticles (Ag-NPs) zeolite composite was synthesized and characterized for its efficacy in removing bromide ions from water. Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray Diffraction (XRD) analyses confirmed the successful integration of Ag-NPs within the zeolite framework, ensuring structural integrity and chemical composition. Under varying operational conditions, the batch experiments consistently exhibited the zeolite with Ag-NPs as superior in bromide removal efficacy compared to zeolite alone, both achieving over 96% removal efficiency. Moreover, the adsorption kinetics aligned well with a second-order reaction model. Furthermore, column studies revealed that zeolite with Ag-NPs performed better than zeolite in maintaining efficient bromide removal over extended durations, highlighting its long-term operational potential. Thermodynamic investigations indicated the exothermic and spontaneous nature of the bromide ion adsorption process, supported by negative ΔG° values. Across temperatures ranging from 22 to 52 °C, the ΔG° values were observed to range from − 1.275 to − 0.049 kJ/mol for bromide removal using zeolite and − 1.90 to − 0.909 kJ/mol for zeolite+Ag-NPs, indicating their favorable adsorption characteristics. Furthermore, the study revealed the remarkable antimicrobial properties of the zeolite with Ag-NPs, as evidenced by a significant reduction in bacterial growth compared to pure zeolite, suggesting its dual application potential for both bromide removal and water disinfection. In conclusion, the integration of Ag-NPs into zeolite represents a promising potential for sustainable and multifunctional water treatment solutions.

T. P. M. Leger, C. D. Clark, C. Huynh et al.

<p>The Greenland Ice Sheet is a large contributor to global sea level rise, and current mass losses are projected to accelerate. However, model projections of future ice sheet evolution are limited by the fact that the ice sheet is not in equilibrium with present-day climate but is still adjusting to past changes that occurred over thousands of years. While the influence of such committed adjustments on future ice sheet evolution remains unquantified, it could be addressed by calibrating numerical ice sheet models over larger timescales and, importantly, against empirical data on ice margin positions. To enable such paleo data–model interactions, we need Greenland-wide empirical reconstructions of past ice sheet extent that combine geomorphological and geochronological evidence. Despite an increasing number of field studies producing new chronologies, such a reconstruction is currently lacking in Greenland. Furthermore, a time slice reconstruction can help to (i) answer open questions regarding the rate and pattern of ice margin evolution in Greenland since the glacial maximum, (ii) develop a standardised record of empirical data, and (iii) identify new sites for future field campaigns. Based on these motivations, we here present PaleoGrIS 1.0, a new Greenland-wide isochrone reconstruction of ice sheet extent evolution through the Late Glacial and early- to mid-Holocene informed by both geomorphological and geochronological markers. Our isochrones have a temporal resolution of 500 years and span <span class="inline-formula">∼</span> 7.5 kyr from approximately 14 to 6.5 kyr BP. We describe the resulting reconstruction of the shrinking ice sheet and conduct a series of ice-sheet-wide and regional analyses to quantify retreat rates, areal extent change, and their variability across space and time. During the Late Glacial and early- to mid-Holocene, we find the Greenland Ice Sheet has lost about one-third of its areal extent (0.89 million km<span class="inline-formula">²</span>). Between <span class="inline-formula">∼</span> 14 and <span class="inline-formula">∼</span> 8.5 kyr BP, it experienced a near-constant rate of areal extent loss of 170 <span class="inline-formula">±</span> 27 km<span class="inline-formula">²</span> yr<span class="inline-formula">⁻¹</span>. We find that the ice-sheet-scale pattern of margin retreat is well correlated to atmospheric and oceanic temperature variations, which implies a high sensitivity of the ice sheet to deglacial warming. However, during the Holocene, we observe inertia in the ice sheet system that likely caused a centennial- to millennial-scale time lag in ice extent response. At the regional scale, we observe highly heterogeneous deglacial responses in ice extent evident in both the magnitude and rate of retreat. We hypothesise that non-climatic factors, such as the asymmetrical nature of continental shelves and onshore bed topographies, play important roles in determining the regional- to valley-scale dynamics. PaleoGrIS 1.0 is an open-access database designed to be used by both the empirical and numerical modelling communities. It should prove a useful basis for improved future versions of the reconstruction when new geomorphological and geochronological data become available.</p>

Madhava Krishna, Bhagesh Gaur, Arsh Verma et al.

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.

Alex Saturday, Susan Kangume, Wilson Bamwerinde

Abstract The purpose of this study was to investigate the content and dynamics of nutrients in the shallow (max. 6 m) Lake Mulehe. We collected 54 water samples from nine sampling stations between the wet season (March–May 2020 and dry season (June–August 2020). Nutrients; ammonia–nitrogen (NH4–N), nitrate–nitrogen (NO3–N), nitrite–nitrogen (NO2–N), total nitrogen (TN), total phosphorus (TP) and soluble reactive phosphorus (SRP) were investigated in accordance with APHA 2017 standard procedures. Besides, physical parameters: Temperature, pH, turbidity, electrical conductivity and dissolved oxygen were measured in situ. The water quality index (WQI) was used to determine the water quality of Lake Muhele  using drinking water quality standards developed by the Uganda National Bureau of Standards and the World Health Organization. Results indicated that nutrients (TN, NO3–N, TP, NH4-N, NO2–N and SRP) did not differ substantially between study stations (p > 0.05) but did reveal significant differences (p < 0.05) across study months. Besides, nutrient levels differed significantly between seasons (p < 0.05) except for SRP and NH4–N. The WQI values varied from 36.0 to 74.5, with a mean of 58.69. The recorded overall WQI value places Lake Mulehe’s water quality into the ‘poor’ category in terms of worthiness for human consumption. The study, therefore, recommends continuous pollution monitoring and enforcement of local regulations to reduce pollution in the lake as a result of anthropogenic activities.

Manqing Luo, Xiaowei Xie, Jieyu Wu et al.

Background: Long-term ambient particulate matter (PM) exposure exerts detrimental effects on cardiovascular health. Evidence on the relation of chronically exposed ambient PM10 and PM2.5 with coronary stenosis remains lacking. Our aim was to investigate the association of PM10 and PM2.5 with coronary stenosis in patients undergoing coronary angiography. Methods: We performed a retrospective cohort study consisting of 7513 individuals who underwent coronary angiography in Fujian Province, China, from January 2019 to December 2021. We calculated a modified Gensini score (GS) to represent the degree of stenosis in coronary arteries by selective coronary angiography. We fitted linear regressions and logistic models to assess the association of PM10 and PM2.5 with coronary stenosis. We employed restricted cubic splines to describe the exposure-response curves. We performed mediation analyses to assess the potential mediators. Results: Long-term ambient PM10 and PM2.5 (prior three years average) exposure was significantly associated with the GS, with a breakpoint concentration of 47.5 μg/m3 and 25.8 μg/m3 for PM10 and PM2.5, respectively, above which we found a linear positive exposure-response relationship of ambient PM with GS. Each 10 µg /m3 increase in PM10 exposure (β: 4.81, 95 % CI: 0.44–9.19) and PM2.5 exposure [β: 10.50, 95 % CI: 3.14–17.86] were positively related to the GS. The adjusted odds ratio (OR) for each 10 µg/m3 increment in PM10 exposure on severe coronary stenosis was 1.33 (95 % CI: 1.04–1.76). Correspondingly, the adjusted OR for PM2.5 was 1.87 (95 % CI: 1.24–2.99). The mediation analysis indicated that the effect of PM10 on coronary stenosis may be partially mediated through total cholesterol, low-density lipoprotein cholesterol, apolipoprotein B, serum creatinine and blood urea nitrogen, and the effect of PM2.5 may be mediated in part by hemoglobin A1c. Conclusion: Our study provides the first evidence that chronic ambient PM10 and PM2.5 exposure was associated with coronary stenosis assessed by GS in patients with suspected coronary artery disease and reveals its potential mediators.

Diane A. Mielewczyk, Chris N. Glover, Chantelle E. Klaczek et al.

Fish mortality is associated with harmful algal blooms, although whether toxicity is related directly to the presence of cyanotoxins or the prevailing water chemistry remains unclear. Similarly, while planktivorous fish may be exposed to toxin through the diet, the hazard posed by waterborne extracellular toxin to carnivorous fish is less well understood. In this study rainbow trout (Oncorhynchus mykiss) were exposed for up to 28 d to waterborne microcystin-LR at nominal concentrations of 1.5 and 50 µg L−1 (measured values 2 and 49 µg L−1, respectively). The former represents the Canadian drinking water guideline, and the latter an elevated environmental level. This study hypothesised that waterborne toxin exposure would specifically impact gill function, and given the importance of this tissue in freshwater fish ion regulation, effects on plasma ions and branchial ion transporter activity would be observed. Microcystin-LR exposure resulted in a significant and persistent hypocalcaemia at the higher exposure concentration, but plasma sodium and branchial activities of the sodium/potassium ATPase, proton ATPase and calcium ATPase enzymes remained unaffected. An in vitro assessment failed to show any effect of microcystin-LR on branchial calcium ATPase activity even at exposure concentrations as high as 1000 µg L−1. A transient increase in hepatic alkaline phosphatase activity was also observed at 49 µg L−1, but there were no effects of toxin exposure on branchial or hepatic lactate dehydrogenase activity. These results suggest that microcystin-LR exposure does not have a general effect on ion regulation, but instead produces a novel and specific impact on calcium metabolism in rainbow trout, although the mechanism underlying this effect remains unknown.

Carlos Andres Gallegos-Riofrío, Hassan Arab, Amaya Carrasco-Torrontegui et al.

We explore two as-yet-unconnected trends: evidence of nature's effects on mental health/wellbeing, and acknowledgment that behavioral research is overwhelmingly informed by globally non-representative societies. We assess geographies, ethnicities, and conceptualizations in 174 peer-reviewed studies of nature's mental-health/wellbeing connection. Findings reveal a Western-World bias: over-representation of White participants; ethnicity overlooked (62% of studies do not report participants' ethnicity); narrow views of mental health/wellbeing; and nature operationalized largely as greenspace and forests. Because planetary health is largely contingent on the Ethnosphere (the planet's rich cultural web), we encourage future studies to test nature's mental health/wellbeing effects pluralistically and beyond unrepresentative subsets of humankind.

Aletta Lucia Meinsma, Sanne Willemijn Kristensen, W. Gudrun Reijnierse et al.

Researchers point to four potential issues related to the popularisation of quantum science and technology. These include a lack of explaining underlying quantum concepts of quantum 2.0 technology, framing quantum science and technology as spooky and enigmatic, framing quantum technology narrowly in terms of public good and having a strong focus on quantum computing. To date, no research has yet assessed whether these potential issues are actually present in popular communication about quantum science. In this content analysis, we have examined the presence of these potential issues in 501 TEDx talks with quantum science and technology content. Results show that while most experts (70%) explained at least one underlying quantum concept (superposition, entanglement or contextuality) of quantum 2.0 technology, only 28% of the non-experts did so. Secondly, the spooky/enigmatic frame was present in about a quarter of the talks. Thirdly, a narrow public good frame was found, predominantly by highlighting the benefits of quantum science and technology (found in over 6 times more talks than risks). Finally, the main focus was on quantum computing at the expense of other quantum technologies. In conclusion, the proposed frames are indeed found in TEDx talks, there is indeed a focus on quantum computing, but at least experts explain underlying quantum concepts often.