Olivia Emelie Engström, Hisayo Katsui, Lieketseng Ned
People who sustain traumatic brain injuries (TBIs) often experience unmet rehabilitation needs. The aim of our research was to explore how the invisible aspects of traumatic brain injury affect the experiences of survivors of TBI in accessing the necessary medical, social, and financial assistance. Using Giorgi’s descriptive phenomenological inquiry, we purposefully sampled 11 participants who had experienced TBI when aged 13–27 for interviews. The time since their injuries ranged from 7 to 37 years. Three key themes emerged: (1) lack of knowledge and guidance in medical services, (2) lack of social service assistance, and (3) battles with insurance companies. Our findings show that, due to the hidden nature of TBI-related disabilities and a general lack of societal knowledge about TBI outcomes, survivors face significant difficulties in accessing essential medical, social, and financial services. This study underscores the critical need to address the challenges faced by youth survivors of TBI, as their injuries occur during a pivotal developmental phase when they are developing psychosocial skills, pursuing education, and transitioning into the workforce. Delays or lack of proper medical, social, and financial support hinder rehabilitation and the successful reintegration of these youth into society.
Vocational rehabilitation. Employment of people with disabilities
A modern üzleti környezetben a márkaépítés szerepe túlmutat a termékek és szolgáltatások azonosításán: alapvető tényezővé vált a vállalati siker szempontjából. A vezetői énmárka és a munkáltatói márka fontos stratégiai eszközként jelenik meg a versenyelőny elérésében, a lojalitás és az elkötelezettség növelésében. Az énmárka, mint az egyéni értékek és képességek tudatos kommunikációja, segíti a vezetőket a hitelesség és bizalom erősítésében.
A vezetői énmárka a vezető személyiségének és vezetői stílusának olyan koherens összefoglalása, amely inspiráló példaként szolgál a szervezeten belül. Emellett a munkáltatói márka a vállalati kultúrát és munkahelyi értékeket tükrözi, amely vonzóvá teheti a szervezetet a tehetséges munkavállalók számára. A tanulmány célja, hogy bemutassa, miként járulhat hozzá az énmárka, ezen belül a vezetői énmárka és a munkáltatói márka együttesen a vállalat eredményesebb működéséhez, rámutatva a márkázás különböző szintjeinek szinergiájára és ezek gazdasági, társadalmi hatásaira.
Grik Tadevosyan, Maksim Osipenko, Demetros Aschu
et al.
This paper introduces a safe swarm of drones capable of performing landings in crowded environments robustly by relying on Reinforcement Learning techniques combined with Safe Learning. The developed system allows us to teach the swarm of drones with different dynamics to land on moving landing pads in an environment while avoiding collisions with obstacles and between agents. The safe barrier net algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control. Experimental results show that our system achieves landing accuracy of 2.25 cm with a mean time of 17 s and collision-free landings, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for applications in environments where safety and precision are paramount.
I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.
The landing gear system is a major aircraft subsystem that must withstand extreme forces during ground maneuvers and absorb vibrations. While traditional systems perform well under normal conditions, their efficiency drops under varying landing and runway scenarios. This study addresses this issue by simultaneously optimizing controller coefficients, parameters of a nonlinear hydraulic actuator integrated into the traditional shock absorber, and a vibration absorber using a bee-inspired multi-objective algorithm. To demonstrate adaptability, the paper includes sensitivity analysis for three-point landings affected by added payload and touchdown speed, and robustness analysis for one- and two-point landings under emergency wind conditions. The dynamic flight equations of an Airbus A320-200 during landing are derived and solved numerically. Results show that the active shock absorber system, optimized via two bee-based algorithms, outperforms the passive system in reducing bounce and pitch displacements and momenta, suspension travel, and impact force in both time and frequency domains. This leads to significantly improved passenger comfort and potentially longer structural fatigue life, demonstrating industrial applicability.
Scientific contributions are a direct reflection of a research paper's value, illustrating its impact on existing theories or practices. Existing measurement methods assess contributions based on the authors' perceived or self-identified contributions, while the actual contributions made by the papers are rarely investigated. This study measures the actual contributions of papers published in Nature and Science using 1.53 million citation contexts from citing literature and explores the impact pattern of division of labor (DOL) inputs on the actual contributions of papers from an input-output perspective. Results show that experimental contributions are predominant, contrasting with the theoretical and methodological contributions self-identified by authors. This highlights a notable discrepancy between actual contributions and authors' self-perceptions, indicating an 'ideal bias'. There is no significant correlation between the overall labor input pattern and the actual contribution pattern of papers, but a positive correlation appears between input and output for specific types of scientific contributions, reflecting a 'more effort, more gain' effect. Different types of DOL input in papers exhibit a notable co-occurrence trend. However, once the paper reaches the dissemination stage, the co-occurrence of different types of actual contributions becomes weaker, indicating that a paper's contributions are often focused on a single type.
Victor Radermecker, Andrea Zanon, Nancy Thomas
et al.
Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, pre-processing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end to end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a pre-processing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks.
Jesús Alejandro Loera-Ponce, Diego A. Mercado-Ravell, Israel Becerra-Durán
et al.
In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of-the-art visual transformer network, for the semantic segmentation of complex, unstructured urban environments. This approach yields valuable information that can be utilized in smart autonomous landing missions, particularly in emergency landing scenarios resulting from system failures or human errors. The assessment is done in real-time flight, when images of an RGB camera at the Unmanned Aerial Vehicle (UAV) are segmented with the SegFormer into the most common classes found in urban environments. These classes are then mapped into a level of risk, considering in general, potential material damage, damaging the drone itself and endanger people. The proposed strategy is validated through several case studies, demonstrating the huge potential of semantic segmentation-based strategies to determining the safest landing areas for autonomous emergency landing, which we believe will help unleash the full potential of UAVs on civil applications within urban areas.
Chinese-style modernization involves the modernization of a large population, requiring top-level design in terms of scale and structure. The population perspective in Xi Jinping's Thought on Socialism with Chinese Characteristics for a New Era serves as the fundamental guide for population policies. The three-child policy and delayed retirement will affect the supply of labor in China and challenge the previous assessments of China's Lewis Turning Point. This study examines the rural surplus labor transfer from 2013 to 2022 based on urban and rural data. The results indicate that China's overall wage levels have continuously increased, the urban-rural income gap has narrowed, and the transfer of surplus rural labor has slowed. China has passed the first turning point and entered a transitional phase. Factors such as the level of agricultural mechanization, urbanization rate, and urban-rural income gap are more significant in influencing the transfer of surplus labor than the normal working-age population ratio. The delayed retirement policy has a more immediate impact on the supply and transfer of rural surplus labor than the three-child policy. Additionally, delayed retirement can offset the negative impact of the reduced relative surplus labor supply caused by the three-child policy, although the three-child policy could increase the future absolute surplus labor supply.
In the Papua region, the expansion of the palm oil industry increased by up to 71% from 2011 to 2019, with the largest affected area located in Merauke regency. The objective of this research is to develop policy strategies with impact simulations related to economic, socio-cultural, and environmental issues for the sustainability of the palm oil industry with or without a moratorium on further expansion of land area. This study employs a system dynamics approach. The dynamic system is carried out through conceptual development, model specification, model verification, scenario development, and validity testing by measuring the absolute percentage error (MAPE). Model development and validation were carried out using PowerSim v.7 software. The policy scenario is simulated from the palm oil expansion policy beguan in 2018, until the research year ends in 2030. The validated results, forming the basis for simulations, exhibit high accuracy with a MAPE of less than 5%. The percentage deviations of 4.21% for production and 0.28% for land area is observed based on actual data from 2018 to 2022. The simulation of a 20% expansion scenario shows a significant increase in production to 1 million metric tons per year but also a 1.3% rise in waste generation, with an average waste volume of 350,000 tons per year. The expanded area will experience a 50% increase in the labor force to meet the higher production demand. The proposed comprehensive strategy includes regional contract regulations, diligent monitoring of land clearing, community empowerment, and indigenous peoples' involvement. It also promotes smallholder plantations, the utilization of waste for energy, and alternative markets for crude palm oil (CPO). Conversely, the dynamic model scenario with a moratorium on land expansion resulted in a production output of 600,000 metric tons, slower job market growth, and a 1.3% increase in waste generation, with an average waste volume of 195,000 tons per year. The strategy proposed uses proper waste processing, production adjustments, regulation of community rights and boundaries, resource development, and involvement of local palm farmers. These simulations offer valuable insights for sustainable decision-making, emphasizing the need to balance economic growth, environmental protection, and community well-being in the development of the palm oil industry. Key words: dynamical system, moratorium, palm oil, scenario strategy, sustainability
Samuel David Vargas-Neira, Rodrigo Rodríguez-Cepeda
El presente artículo muestra los resultados de una intervención didáctica piloto, en la cual se diseñaron e implementaron algunos Trabajos Prácticos de Laboratorio (TPL) contextualizados, con el propósito de evaluar su incidencia en el aprendizaje significativo de parámetros fisicoquímicos de calidad en aceites comestibles. La intervención se realizó con un grupo de 14 estudiantes de ingeniería de alimentos, donde se analizaron las respuestas de los estudiantes a cuestionarios inicial y final, además de informes de trabajo práctico, desde un enfoque cualitativo. Se identificó que el diseño e implementación de TPL favorecen el aprendizaje significativo de los conceptos químicos asociados a la calidad de los aceites en los ingenieros en formación. En este sentido, los TPL fomentan criterios para evaluar la calidad de un producto en cuanto a aceptación o rechazo, lo cual permite desarrollar habilidades para la toma de decisiones.
Hale Pamukçu, Pelin Soyertaş Yapıcıoğlu, Mehmet İrfan Yeşilnacar
This study majorly aimed to determine the effect of optimization on transport routes on the reduction of greenhouse gas (GHG) emissions from municipal solid waste management (MSM) within the scope of European Union (EU) Green Deal. Optimization of collection and transportation routes has been regarded as an effective methodology in order to mitigate the GHG emissions of municipal waste management, recently. Optimization of routes has been obtained using ant colony algorithm (ACA) and Monte Carlo simulation, in this study. In this context, this study investigated to reduce GHG emissions from municipal waste management using optimization of transportation routes in Diyarbakir city in Turkey. Firstly, GHG emissions which are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from waste collection and transport have been calculated using a new developed model based on Tier-I method. Then, Monte Carlo simulation has been used to figure out the GHG emissions. Finally, life cycle assessment (LCA) approach has been applied to determine the GHG emissions. According to the route optimization resulting ACA methodology, nearly 47.43% of reduction on each GHG emissions. Approximately, 58%, 38% and 51% of reduction on CO2, CH4 and N2O emissions respectively has been achieved, in the result of the route optimization using Monte Carlo simulation. The results of LCA methodology revealed that the reduction reached up 45.71% on GHG emissions in terms of Global Warming Potential (GWP). The reduction amounts have been overlapped with each other.
Malvika Kanojia, Prerna Kamani, Gautam Siddharth Kashyap
et al.
Humanity's progress in combating hunger, poverty, and child mortality is marred by escalating environmental degradation due to rising greenhouse gas emissions and climate change impacts. Despite positive developments, ecosystems are suffering globally. Regional strategies for mitigating and adapting to climate change must be viewed from a global perspective. The 2015 UN Sustainable Development Goals reflect the challenge of balancing social and environmental aspects for sustainable development. Agriculture, vital for food production, also threatens Earth systems. A study examines the interplay of land-use impacts, modeling crop and livestock trade, and their effects on climate, biodiversity, water, and land using a Partial-Equilibrium Agricultural Sector Model. Different scenarios involving taxing externalities related to Earth processes were tested. Results show synergies in reducing emissions, biodiversity loss, water use, and phosphorus pollution, driven by shifts in crop management. Nitrogen application and deforestation scenarios exhibit weaker synergies and more conflicts. The study offers insights into SDG interactions and the potential for sustainable farming.
Niken Ayu Permatasari, Indah Hardiyanti, Ani Suryani
et al.
The storage process of shallot powder can cause a quality decrease. This study aims to estimate shelf-life of shallot powder at various storage temperatures and determine quality changes during storage. This study used a Completely Randomized Design with two factors to determine quality changes of shallot powder, i.e. the coating and storage temperature. The statistical analysis results showed that the coating, storage temperature, and interaction between the two factors affected Volatile Reducing Substances (VRS) levels. The two factors and their interaction do not influence the water content. Shelf-life estimation was performed using the Arrhenius method. Coated shallot powder has a longer shelf-life than uncoated shallot powder. Shelf-life determination was performed based on water content parameters. The shelf-life of uncoated shallot powder was 337 days at 30 °C, 322 days at 40 °C, and 309 days at 50 °C. The shelf-life of coated shallot powder was 353 days at 30 °C, 340 days at 40 °C, and 328 days at 50 °C. The coating process can extend the shelf-life of shallot powder.
In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning. While thousands of new satellite images become freely available on a daily basis, getting ground truth data is still very challenging, time consuming and costly. In this paper we present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery to improve performance on downstream dense land cover classification tasks. Performing an extensive experimental evaluation spanning four countries and two continents we use models pre-trained with our proposed method as initialization points for supervised land cover semantic segmentation and observe significant improvements up to 25% absolute mIoU. In every case tested we outperform random initialization, especially so when ground truth data are scarse. Through a series of ablation studies we explore the qualities of the proposed approach and find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme as a replacement to random initialization for Earth observation tasks. Code will be uploaded soon at https://github.com/michaeltrs/DeepSatModels.
We study how Chapter 11 bankruptcies affect local legal labor markets. We document that bankruptcy shocks increase county legal employment and corroborate this finding by exploiting a stipulation of the law known as Forum Shopping during the Court Competition Era (1991-1996). We quantify losses to local communities from firms forum shopping away from their local area as follows. First, we calculate the unrealized potential employment gains implied by our reduced-form results. Second, we structurally estimate a model of legal labor markets and quantify welfare losses. We uncover meaningful costs to local communities from lax bankruptcy venue laws.
Joseph Early, Ying-Jung Deweese, Christine Evers
et al.
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.