Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.
Electrical engineering. Electronics. Nuclear engineering, Energy industries. Energy policy. Fuel trade
Sopitta Thurachen, Josef Taher, Matti Lehtomäki
et al.
Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores the use of Point Transformer v2 (PTv2), an advanced deep neural network architecture designed for point cloud data, for land cover mapping through semantic segmentation of multispectral LiDAR data in real-world riverine environments. We utilize the geometric and spectral information from the 3-channel LiDAR point cloud to map land cover classes, including sand, gravel, low vegetation, high vegetation, forest floor, and water. The PTv2 model was trained and evaluated on point cloud data from the Oulanka river in northern Finland using both geometry and spectral features. To improve the model's generalization in new riverine environments, we additionally investigate multi-dataset training that adds sparsely annotated data from an additional river dataset. Results demonstrated that using the full-feature configuration resulted in performance with a mean Intersection over Union (mIoU) of 0.950, significantly outperforming the geometry baseline. Other ablation studies revealed that intensity and reflectance features were the key for accurate land cover mapping. The multi-dataset training experiment showed improved generalization performance, suggesting potential for developing more robust models despite limited high-quality annotated data. Our work demonstrates the potential of applying transformer-based architectures to multispectral point clouds in riverine environments. The approach offers new capabilities for monitoring sediment transport and other river management applications.
Approximately two-thirds of the Earth, the only known inhabited planet, is covered in ocean. Why not 0.01% or 99.99%? It has been previously suggested that this may represent a certain degree of fine-tuning, and thus perhaps observers are a-priori more likely to develop on those rare worlds with nearly equal land-ocean ratios, such as our own. In this work, we take the single datum of the Earth and then use Bayesian inference to compare four models for the probability distribution of a planet becoming inhabited by observers as a function of land-fraction, $f$, which we classify as i) land-centric ii) ocean-centric iii) equi-centric and iv) indifference. We find that no model is strongly favoured over the others, but that 1) the land-centric model is disfavoured over all others, and, 2) the equi-centric model is favoured over all competitors. Further, we show that more extreme models with heavy tail-weighting are strongly disfavoured even when conditioned upon the Earth alone. For example, a land-centric model where the median planet has $f=0.82$ (or greater) is in strong tension with our existence. Finally, we consider the potential addition of more data via Mars or exoplanets. Should paleo-Mars have once harboured life and had $f<0.20$, then this would strongly favour the ocean-centric model for life, over a land-centric hypothesis. We show that strong evidence for/against the equi-centric model versus its competitors would likely require at least a dozen inhabited exoplanets, offering a well-motivated sample size for future experiments.
Ali Nasiri, Esmaeil Salimi, Morteza Delfan Azari
et al.
Flood zoning has extensive applications in flood management and is considered one of the fundamental and critical pieces of information in flood risk management. Flood zoning in urban areas is much more challenging than modeling in floodplain and river areas due to the two-dimensional nature of the flow and, on the other hand, the density of urban features such as buildings, streets, boulevards, and public pathways. In this study, flood zoning for districts 21 and 22 of Tehran was conducted under the current conditions, where the area is almost devoid of surface water collection channels, using a physically-based rainfall-runoff model and two-dimensional hydraulic routing which is the novelty aspect of the article. For this purpose, the HEC-HMS model was used to estimate the runoff from the mountains, and the MIKE model was used to simulate urban rainfall-runoff. According to the modeling results, the areas affected by a 50-year flood event were identified using an integrated modeling approach in districts 21 and 22, covering 8% of these areas. In these areas, the maximum flood depth is 11.8 meters in Vardavard river and the highest speed is 4.5 meters per second at the beginning of Hashemzadeh street (south of Kharrazi highway). The results indicate that in the event of extreme events such as a 50-year rainfall, a significant portion of the highways and main communication arteries of Tehran leading westward would be disrupted, and traffic would be impossible. Moreover, various land uses would fall within the flood zone, and due to the absence of a surface water network, waterlogging conditions throughout districts 21 and 22 of Tehran are predictable. Therefore, the development of a surface water collection network is one of the main priorities for reducing flood risk in these areas.
Risk in industry. Risk management, Industrial safety. Industrial accident prevention
Gianna Williams, Maya De Los Santos, Alexandra To
et al.
The global AI surge demands crowdworkers from diverse languages and cultures. They are pivotal in labeling data for enabling global AI systems. Despite global significance, research has primarily focused on understanding the perspectives and experiences of US and India crowdworkers, leaving a notable gap. To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries. We discovered that these workers exhibited pride and respect for their digital labor, with strong support and admiration from their families. Notably, crowd work was also seen as a stepping stone to financial and professional independence. Surprisingly, despite wanting more connection, these workers also felt isolated from peers and doubtful of others' labor quality. They resisted collaboration and gender-based tools, valuing gender-neutrality. Our work advances HCI understanding of Latin American and Caribbean crowdwork, offering insights for digital resistance tools for the region.
Naveen Sudharsan, Manmeet Singh, Sasanka Talukdar
et al.
Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.
African oil palm has the highest productivity amongst cultivated oleaginous crops. Species can constitute a single crop capable to fulfill the growing global demand for vegetable oils, which is estimated to reach 240 million tons by 2050. Two types of vegetable oil are extracted from the palm fruit on commercial scale. The crude palm oil and kernel palm oil have different fatty acid profiles, which increases versatility of the crop in industrial applications. Plantations of the current varieties have economic life-span around 25–30 years and produce fruits around the year. Thus, predictable annual palm oil supply enables marketing plans and adjustments in line with the economic forecasts. Oil palm cultivation is one of the most profitable land uses in the humid tropics. Oil palm fruits are the richest plant source of pro-vitamin A and vitamin E. Hence, crop both alleviates poverty, and could provide a simple practical solution to eliminate global pro-vitamin A deficiency. Oil palm is a perennial, evergreen tree adapted to cultivation in biodiversity rich equatorial land areas. The growing demand for the palm oil threatens the future of the rain forests and has a large negative impact on biodiversity. Plant science faces three major challenges to make oil palm the key element of building the future sustainable world. The global average yield of 3.5 tons of oil per hectare (t) should be raised to the full yield potential estimated at 11–18t. The tree architecture must be changed to lower labor intensity and improve mechanization of the harvest. Oil composition should be tailored to the evolving needs of the food, oleochemical and fuel industries. The release of the oil palm reference genome sequence in 2013 was the key step toward this goal. The molecular bases of agronomically important traits can be and are beginning to be understood at the single base pair resolution, enabling gene-centered breeding and engineering of this remarkable crop.
This article aims to analyze how organizational purpose influences the meaning of work and employee engagement. The article is relevant due to the scarcity of studies on the subject, but also because organizations have faced numerous challenges linked to new ways of working, resignations from professionals who are looking for greater personal and professional fulfillment, among other factors that impact the meaning of work and employee engagement. Given this scenario, this article makes contributions as it identifies: (i) how the phenomenon “organizational purpose” manifests itself in practice; (ii) its relevance; (iii) its positive impact on the meaning of work and engagement and (iv) the importance of the role of leadership in this process. To achieve the objective of this work, ten semi-structured, qualitative research studies were carried out, using the thematic analysis technique to process the data. The study concludes that: (i) the organizational purpose needs to be authentic; (ii) that there is a direct relationship between a company with an organizational purpose and the positive impact it generates on the meanings of work; (iii) the organizational purpose is the “differentiator” in engagement; (iv) and leadership is a driver and guardian of purpose.
Maksym Trotsko, Viktor Hudyma, Andrii Diadechko
et al.
Мета роботи: розробити узагальнену математичну модель функціонування сектору безпеки і оборони України в умовах невизначеності та ризиків, притаманних впливу гібридних засобів противника, а також дослідити синергетичний ефект впливу взаємосумісності на спроможності сектору безпеки і оборони протидіяти стратегії застосування гібридної боротьби.
Метод дослідження: методи комплексного аналізу та синтезу, метод нелінійного математичного моделювання.
Результати дослідження: визначено, що існує взаємозв’язок між невизначеністю та ризиками гібридних загроз, а також доведене існування компенсуючого впливу з боку сектору безпеки та оборони держави на застосування противником заходів гібридного впливу.
Теоретична цінність дослідження: теоретичні положення, висновки та рекомендації, викладені в роботі, можуть стати основою для подальших наукових досліджень й дискусій з питань підвищення можливостей сектору безпеки та оборони України протидіяти гібридним засобам противника.
Практична цінність дослідження: реалізація рекомендацій і пропозицій, обґрунтованих у роботі, які спрямовані, на основі процесів військової стандартизації, на забезпечення взаємосумісності складових сектору безпеки і оборони України, а також міжнародних партнерів, дозволить протидіяти стратегії противника щодо застосування заходів гібридної боротьби.
Цінність дослідження: в даному дослідженні моделювання процесів функціонування сектору безпеки та оборони України в умовах невизначеності та ризиків, притаманних впливу гібридних засобів противника ще не були предметом комплексного наукового дослідження.
Cleonice Lisbete Silva Gama, Marília Miranda Forte Gomes, Ari Melo Mariano
Introdução
A aquisição e distribuição de medicamentos no Sistema Único de Saúde é um dos grandes desafios para os gestores, dispendendo um alto volume de recursos públicos, que competem com as necessidades de financiamento dos demais serviços de saúde. Nesse cenário, os medicamentos de alto custo representam um desafio para as políticas de financiamento e acesso equitativo. O objetivo deste trabalho foi realizar uma revisão bibliométrica para identificar as principais tendências de pesquisa em relação aos medicamentos de alto custo, as áreas de maior interesse e as regiões geográficas que lideram a pesquisa neste tema.
Métodos
Foi utilizado o método da Teoria do Enfoque Meta-Analítico Consolidado – TEMAC, que utiliza três etapas para identificar literaturas relevantes sobre o tema abordado, sendo que na primeira etapa é feita uma preparação para a pesquisa nas bases de dados, na segunda é feita as inter-relações entre os dados dos registros encontrados e, na última etapa, realiza-se a análise dos gráficos de coupling, co-citation e co-occurrence gerados no VOSviewer 1.6.84. Foram utilizados os descritores “high--cost medications” ou “high-cost drugs”.
Resultados
Nas bases escolhidas, obteve-se 519 registros, sendo 215 na Web of Science (WoS) e 304 na Scopus, no período entre 1980 e 2022, sendo em sua maioria artigos (cerca de 72%), os quais se relacionam principalmente com os desafios econômicos e políticos associados a medicamentos de alto custo. Os países que mais publicaram sobre o tema foram os Estados Unidos, Brasil, Reino Unido e Austrália. Nos últimos anos, o interesse na área tem crescido, com um aumento significativo na quantidade de publicações. Os artigos mais citados sobre o tema geralmente se concentram em doenças crônicas ou raras e em novas terapias que possam ajudar a melhorar a qualidade de vida dos pacientes, principalmente para o câncer, e estão voltados para questões de custo e eficácia dos tratamentos, bem como preocupações éticas e legais. Por meio das análises de co-occurrence, co-citation e coupling, foi possível identificar que a temática do estudo está centrada em torno de termos como: medicamentos de alto custo, câncer, saúde, usuário, custos econômicos.
Discussão e conclusões
As conclusões mais importantes da pesquisa indicam que o custo dos medicamentos é um desafio significativo para os sistemas de saúde em todo o mundo, concentrando-se em encontrar soluções para tornar os tratamentos mais acessíveis e eficazes, incluindo novas abordagens de financiamento, como parcerias público-privadas e modelos de pagamento baseados em resultados. Também se destaca a importância de políticas de saúde pública eficazes para garantir que os pacientes possam acessar tratamentos de alta qualidade sem enfrentar dificuldades financeiras significativas. Esses resultados sugerem que os medicamentos de alto custo são utilizados principalmente para o tratamento de doenças graves e raras, e que o custo desses medicamentos é uma preocupação importante para os sistemas de saúde. Essas informações são relevantes para identificar as principais tendências de pesquisa em relação aos medicamentos de alto custo, as áreas de maior interesse e as regiões geográficas que lideram a pesquisa nessa área. Isso pode ajudar os pesquisadores a identificar lacunas na pesquisa e a direcionar seus estudos para áreas de maior interesse e relevância.
Pharmacy and materia medica, Pharmaceutical industry
The accuracy assessment of remote-sensing derived built-up land data represents a specific case of binary map comparison, where class imbalance varies considerably across rural-urban trajectories. Thus, local accuracy characterization of such datasets requires specific strategies that are robust to low sample sizes and different levels of class imbalance. Herein, we examine the suitability of commonly used spatial agreement measures for their localized accuracy characterization of built-up land layers across the rural-urban continuum, using the Global Human Settlement Layer and a reference database of built-up land derived from cadastral and building footprint data.
We propose a method for autonomous precision drone landing with fiducial markers and a gimbal-mounted, multi-payload camera with wide-angle, zoom, and IR sensors. The method has minimal data requirements; it depends primarily on the direction from the drone to the landing pad, enabling it to switch dynamically between the camera's different sensors and zoom factors, and minimizing auxiliary sensor requirements. It eliminates the need for data such as altitude above ground level, straight-line distance to the landing pad, fiducial marker size, and 6 DoF marker pose (of which the orientation is problematic). We leverage the zoom and wide-angle cameras, as well as visual April Tag fiducial markers to conduct successful precision landings from much longer distances than in previous work (168m horizontal distance, 102m altitude). We use two types of April Tags in the IR spectrum - active and passive - for precision landing both at daytime and nighttime, instead of simple IR beacons used in most previous work. The active IR landing pad is heated; the novel, passive one is unpowered, at ambient temperature, and depends on its high reflectivity and an IR differential between the ground and the sky. Finally, we propose a high-level control policy to manage initial search for the landing pad and subsequent searches if it is lost - not addressed in previous work. The method demonstrates successful landings with the landing skids at least touching the landing pad, achieving an average error of 0.19m. It also demonstrates successful recovery and landing when the landing pad is temporarily obscured.
Hakan T. Otal, Elyse Zavar, Sherri B. Binder
et al.
Environmental disasters such as floods, hurricanes, and wildfires have increasingly threatened communities worldwide, prompting various mitigation strategies. Among these, property buyouts have emerged as a prominent approach to reducing vulnerability to future disasters. This strategy involves governments purchasing at-risk properties from willing sellers and converting the land into open space, ostensibly reducing future disaster risk and impact. However, the aftermath of these buyouts, particularly concerning land-use patterns and community impacts, remains under-explored. This research aims to fill this gap by employing innovative techniques like satellite imagery analysis and deep learning to study these patterns. To achieve this goal, we employed FEMA's Hazard Mitigation Grant Program (HMGP) buyout dataset, encompassing over 41,004 addresses of these buyout properties from 1989 to 2017. Leveraging Google's Maps Static API, we gathered 40,053 satellite images corresponding to these buyout lands. Subsequently, we implemented five cutting-edge machine learning models to evaluate their performance in classifying land cover types. Notably, this task involved multi-class classification, and our model achieved an outstanding ROC-AUC score of 98.86%
Risma Neswati, Nurfadila Jamaluddin Suppe, Sumbangan Baja
et al.
The success of agricultural operations is highly dependent on the site selected, which affects sustainability, and it is important to solve problems associated with activities and efficient land use. However, many researchers have selected sites based solely on climate and soil characteristics and have ignored farmer preferences, which has resulted in the failure to meet sustainable agriculture goals, and a proper strategy is therefore required to anticipate related problems. This study was conducted to: (1) analyze plantation development priorities based on the hierarchy of farmers’ preferences, (2) identify the relationship between successful plantations, climate, and soil fertility. The attributes employed to assess farmers’ preferences included price, production, and price stability over the past five years, while annual rainfall, annual temperature, and soil fertility were used to assess land suitability. Farmers’ preferences were analyzed using the discrete choice experiment (DCE) method, and land suitability was analyzed using the fuzzy method. The farmer preference analysis showed that coffee was the priority crop of farmers in most of the research areas, and cocoa was the lowest cultivation priority. Coffee had a higher land suitability index than other plants, ranging from 0.62 to 0.92, and it was dominant within the optimal suitability class. Clove, pepper, and cocoa plants belonged to the moderate land suitability class with indexes of 0.6–0.91, 0.56–0.88, and 0.4–0.86 for pepper, clove, and cocoa, respectively. A regression analysis was conducted to determine the relationship between the priority of cultivated plants based on farmers’ preference and land suitability, and a positive relationship (moderate strength) was determined. These research results show that when selecting priority crops, 21% of farmers’ decisions are influenced by land suitability.
Purpose – This paper aims to conceptualize and empirically illustrate the challenges that financial market regulation presents to politicians and the organization tasked with specifying regulations and supervising their implementation in the interest of users and consumers of financial instruments. It analyses the problem from the viewpoint of the governor's dilemma and the control/competence conflict, the linked problem of the rent-seeking of agents/intermediators and consumers of financial instruments. Political accountability problems are enhanced by the materiality of the technologies used, i.e. algo trading. Design/methodology/approach – The paper theoretically conceptualizes and empirically illustrates the argument. Findings – The paper finds that regulators of digitalized financial markets are faced with considerable problems and depend on private agents when regulating financial transactions. However, the new technological instruments also offer new possibilities for securing compliance. Research limitations/implications – Further research should focus more in-depth on the cooperation between public and private actors in the specification and implementation of regulatory details. It should further investigate the conditions which allow regulators to use RegTech in the surveillance of financial firms. Practical implications – Since financial market transactions are opaque for most users, the creation of more transparency is crucial to hold regulators accountable in their activity of surveillance of financial firms. New algorithm-based technologies may lend important support in doing so. Originality/value – By linking the different analytical perspectives, i.e. the governor's dilemma vis-à-vis the intermediator or agent and the possible rent-seeking of intermediators, under the condition of a highly developed technology of financial transactions as well as the market structure, the paper offers new insights into the limits as well as new opportunities of regulating financial markets allowing for political accountability of regulators and financial firms.
Regulation of industry, trade, and commerce. Occupational law, Economic growth, development, planning
In a series of recent papers we developed a formulation of general relativity in which spacetime and the dynamics of matter evolve with a Poincaré invariant parameter $τ$. In this paper, we apply the formalism to derive the metric induced by a `static' event evolving uniformly along its $t$-axis at the spatial origin ${\mathbf x} = 0$. The metric is shown to vary with $t$ and $τ$, as well as spatial distance $r$, taking its maximum value for a test particle at the retarded time $τ= t - r/c$. In the resulting picture, an event localized in space and time produces a metric field similarly localized, where both evolve in $τ$. We first derive this metric as a solution to the wave equation in linearized field theory, and discuss its limitations by studying the geodesic motion it produces for an evolving event. By then examining this solution in the 4+1 formalism, which poses an initial value problem for the metric under $τ$-evolution, we clarify these limitations and indicate how they may be overcome in a solution to the full nonlinear field equations.
ABSTRACT This paper explores reasons why some commercial producers in South Africa are expecting to quit and sell their farms, and others are not. Of 450 respondents to a voluntary survey, distinctly different groups of producers emerged concerning their longer-term strategic planning and how they experience and absorb current threats and challenges. Unsupervised learning on the dataset is imposed using a cluster analysis to explore the commonalities and the underlying factors why producers would expect to exit or not. Factors that we hypothesised might play a role included a producer's age and financial position, rural safety concerns, labor problems, industry-related problems, and opportunities for off-farm earnings. The factors the potentially exiting producers had in common were financial difficulty, which was uncorrelated to turnover, problems with access to dependable labor, uncertainty regarding land reform policy, and rural safety concerns. Intention to retire also played a role, although to a lesser extent. It is more often a combination of factors, rather than a single factor, that makes a producer more likely to decide to quit and sell in the future. With the exclusion of farm safety concerns and labor problems, the identified factors in this study are in step with those found internationally.
G. Komlatsky, M. Slozhenkina, R. V. E`lizbarov
et al.
Organic agriculture is an important area of high-quality food production and one of the stages of transition of agricultural production to green economy rails. At the moment, assortment on the Russian market of organic products is limited and there are no meat products in general. The aim of this study is to analyze the state of domestic pig breeding and the possibilities of conversion a certain segment of the industry to organic production. The methodological basis was the results of researches by domestic, foreign scientists, as well as our own studies. In the course of the work, general methods of scientific knowledge were used, which ensured reliability of the obtained results. Taking into account global trend towards healthy nutrition, Russian pig breeding has all prerequisites for conversion to organic production. Due to the high marginality of organic pig breeding, the presence in Russia of large labor and land resources suitable for organic production, there are all the prerequisites for organic pork to enter the world agrifood market. The implementation of this direction will require small business development, creation of a certification system for meat and meat products, training of qualified organic zootechnicians, as well as development and improvement of technologies.