Considering the status of water resources in the Urmia Lake basin (ULB) and the necessity for real-time monitoring of crop production and water consumption in this area, the objective of current research was to calculate actual evapotranspiration (ETa), actual crop coefficient (Kc act), water consumption, dry biomass, grain yield, and water productivity of irrigated wheat in the ULB using remote sensing techniques. For this purpose, the ULB was divided into six sub-basins. To calculate reference evapotranspiration (ETo), meteorological data and the FAO56 Penman-Monteith (P-M) equation were used, and ETa was calculated utilizing the Surface Energy Balance Algorithm for Land (SEBAL) and 43 Landsat 8 and 9 satellite images. Furthermore, to calculate biomass, grain yield, and crop water productivity (WPc), the Radiation Use Efficiency (RUE) model was employed. In this study, 1115 irrigated wheat farms were monitored. The results indicated that the average ETo in the ULB during the irrigated wheat growing season is 802 mm. Also, Kc act differed from FAO coefficients (Kc) across growth stages, being higher during initial and final stages but lower during development and intermediate stages. According to the results, the average water consumption volume for the entire basin was determined to be 4566 m3/ha. Also, the overall average Harvest Index (HI) for the ULB was found to be 0.42. The results of the biomass and grain yield investigation revealed significant differences in their values among different sub-basins. Additionally, the overall average WPc for irrigated wheat in the ULB was calculated to be 0.94 kg/m3.
Thayná Figueredo Góis, Amanda Maria Paixão Soares, Anna Gabriela Souto Maior Nascimento
Introdução: A efetividade do tratamento farmacológico está intimamente relacionada à disponibilidade do medicamento de
forma acessível ao usuário1. O Componente Especializado da Assistência Farmacêutica (CEAF) busca garantir a integralidade do
tratamento medicamentoso para agravos crônicos e raros, com custos de tratamento mais elevados ou de maior complexidade2. Durante o fluxo padrão, do cadastro à dispensação do medicamento, por vezes se faz necessário o comparecimento do paciente ou familiar à sede do CEAF, fato que, em alguns casos, se torna uma barreira de acesso; principalmente para pacientes idosos, com mobilidade reduzida ou vulneráveis financeiramente, comprometendo assim a disponibilidade do medicamento ao usuário e a adesão ao tratamento farmacológico3. O Programa Remédio em Casa é uma iniciativa pública que, inicialmente, foi instituída visando à entrega domiciliar de medicamentos a pacientes atendidos nas unidades de atenção básica. Mas, recentemente, observa-se a expansão ou implementação do programa no CEAF em alguns estados4,5. Objetivo: Neste contexto, este estudo foi conduzido com o objetivo de avaliar a extensão, em âmbito nacional, do Programa Remédio em Casa no Componente Especializado da Assistência Farmacêutica (CEAF), e o seu impacto no tratamento medicamentoso de pacientes vulneráveis portadores de agravos crônicos e raros. Material e Método: Tratou-se de um estudo quantitativo de caráter descritivo, cuja análise foi realizada a partir de dados coletados em publicações científicas, sites oficiais do governo ou contato direto com as coordenações dos CEAFs estaduais. Resultados: No levantamento realizado, encontraram-se registros do programa de entrega domiciliar de medicamentos do CEAF em 50% dos estados brasileiros e no Distrito Federal, sendo eles Acre, Alagoas, Bahia, Ceará, Espírito Santo, Mato Grosso, Mato Grosso do Sul, Paraná, Pernambuco, Rio Grande do Sul, Rondônia, São Paulo e Sergipe; com variações no nome do programa como “Medicamento em Casa”, “Remédio aqui em Casa” ou apenas “Entrega à Domicílio do CEAF”. Sobre os demais estados, não foram encontrados registros públicos a respeito, tampouco houve sucesso nas tentativas de contato. Como impacto do programa nos estados em que está implantado, têm-se uma maior adesão terapêutica e alto nível de satisfação dos pacientes beneficiados, com geração de bem-estar e qualidade de vida, além do melhoramento do fluxo de atendimento a todos os usuários, com redução de filas e do tempo de espera dos atendimentos presenciais. Conclusões: Assim, conclui-se que o serviço de entrega domiciliar de medicamentos do CEAF, comumente nomeado “Programa Remédio em Casa”, é uma estratégia eficiente de apoio à saúde pública, através da garantia do acesso ao medicamento e melhoria da adesão terapêutica. Além da promoção da inclusão social, ao fortalecer a cidadania de uma parcela vulnerável da população, é evidenciado, ainda, a grande possibilidade de replicação, adaptabilidade e expansão do projeto para os demais estados do país.
Pharmacy and materia medica, Pharmaceutical industry
Biochar and microorganisms are widely used soil amendments, but the effects of their combined application on crops and soils have not been thoroughly evaluated. We conducted a meta-analysis of 103 studies to examine the effects of biochar-microbe combined (BCM) application on crop physiology ecology and soil function. Together, they significantly improved crop growth and soil properties, with shoot dry weight increasing by 51.79 % and soil organic carbon increasing by 49.38 % compared with the control. BCM application can activate the antioxidant defense system in crops, reduce malondialdehyde levels by 18.24 %, and enhance soil bacterial abundance by 71.9 %. Their effect on soil pH was negatively correlated with the initial soil pH. The effects of BCM application vary with the properties of different biochar sources, pyrolysis temperatures, microbial species, and experiment types. Integrating microbes with low-temperature (≤ 500°C) wood-based biochar showed even better effects, not only enhancing crop growth performance and chlorophyll content, but also producing significant improvements in soil physicochemical properties. The combined application of different functional microorganisms and biochar resulted in varying effects on crop biomass accumulation, growth, and soil quality. In the study, BCM was evaluated for its ecological consequences on crops and soils, and we recommend prioritizing low-temperature biochar in combination with microorganisms to maximize crop and soil improvement.
The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.
O objetivo do artigo é demonstrar as mudanças demográficas que ocorreram no estado do Paraná e nos municípios polo da Mesorregião Oeste Paranaense, Cascavel, Foz do Iguaçu e Toledo, por meio das medidas de fecundidade (Taxa de Fecundidade Total (TFT), Taxa Líquida de Reprodução (TLR)), de mortalidade (tabela de vida e expectativa de vida ao nascer), de envelhecimento populacional e estrutura etária (Índice de Envelhecimento (IE) e pirâmides etárias), nos anos de 2000, 2010 e 2022. Os resultados corroboram as teorias e indicam que existe uma relação entre os fatores socioeconômicos e as medidas demográficas encontradas. Foz do Iguaçu apresenta maior fecundidade, as menores expectativas de vida ao nascer e uma população mais jovem, bem como indicadores de desenvolvimento mais defasados. O contrário ocorre com Toledo, que apresenta menor fecundidade, maior expectativa de vida ao nascer, uma população mais envelhecida e melhores indicadores de desenvolvimento. O município de Cascavel e o estado do Paraná apresentam uma situação intermediária.
Abstract: The objective of the paper is to demonstrate the demographic changes that occurred in the state of Paraná and in the pole municipalities of the West Parana Mesoregion, Cascavel, Foz do Iguaçu and Toledo, through fertility measures (Total Fertility Rate (TFR), Net Reproduction Rate Reproduction (NRR)), mortality (life table and life expectancy at birth), population aging and age structure (Aging Index (IE) and age pyramids), in the years 2000, 2010 and 2022. The results corroborate theories and indicate that there is a relationship between socioeconomic factors and the demographic measures found. Foz do Iguaçu has higher fertility, lower life expectancy at birth and a younger population, as well as lagging development indicators. The opposite occurs in Toledo, which has lower fertility, higher life expectancy at birth, an older population and better development indicators. The municipality of Cascavel and the state of Paraná present an intermediate situation.
Rural households live on income much lower than the national average and experience income inequality much higher than the general population. This excess inequality is primarily due to the internal heterogeneity caused by the different nature of household income sources. The purpose of the study was then to assess the level of rural household income inequality and to decompose the inequality index by the main sources of income. The chosen inequality index was Theil-T. The research drew on unidentifiable microdata from the Household Budget Survey conducted by the CSO in 2019-2021.The study found that rural household inequality was slightly higher than that of all Polish households over the analyzed period. Among the various income-source groups, the highest inequality affected farmer households. This group also contributed most to the overall level of inequality in rural areas (44% in 2019 and over 46% in 2021). The pandemic saw an increase in inequality for all identified groups of rural households (the largest – for farmer households) and a decrease in between-group inequality.
Md. Abdul Awal, Pronab Kumar Paul Partha, Md Rafiul Islam
Rodent is one of the major stored pests in Bangladesh, which causes a huge problem in the food sector. They substantially harm to crops and other valuable items on a large scale. Chemically rodent repellents already exist, but because of their toxicity and high price, they are not recommended for use in food storage. The aim of the study was to develop an electronic rodent-repelling circuit that is capable of generating variable ultrasonic frequencies. These frequencies cause discomfort for pests including rats, and nocturnal insects, which affects their aural senses. A device was successfully designed and constructed by microcontroller, printed circuit board, passive infrared sensor, liquid crystal display, Relay module, and other relative components. The device was vigorously tested in Precision Lab, Bangladesh Agricultural University. Laboratory tests were conducted to evaluate the device's repellent efficacy, considering factors such as range, coverage area, power consumption, and durability. The device functioned as it detected the presence of rodents and then released frequency by the positive signal for 30 s. The device generated ultrasonic signals within the range of 20–125 kHz and detected signals from any live substances up to 4.5 m with an error of a maximum of ±0.428 %. The frequency range of rodent hearing is approximately 20 Hz to 300 kHz, with the greatest sensitivity between 30 and 70 kHz, affecting their aural senses and causing discomfort. Therefore, the developed ultrasonic rodent-repellent circuit can be a practical and sustainable solution for repelling stored rodents, which mitigates storage losses caused by rodent infestations.
Karthik Muthineni, Alexander Artemenko, Josep Vidal
et al.
The fifth generation (5G) mobile communication technology integrates communication, positioning, and mapping functionalities as an in-built feature. This has drawn significant attention from industries owing to the capability of replacing the traditional wireless technologies used in industries with 5G infrastructure that can be used for both connectivity and positioning. To this end, we identify the Automated Guided Vehicle (AGV) as a primary use case to benefit from the 5G functionalities. Given that there have been various works focusing on 5G positioning, it is necessary to analyze the existing works about their applicability with AGVs in industrial environments and provide insights to future research. In this paper, we present state of the art in 5G-based positioning, with a focus on key features, such as Millimeter Wave (mmWave) system, Massive Multiple Input Multiple Output (MIMO), Ultra-Dense Network (UDN), Device-to-Device (D2D) communication, and Reconfigurable Intelligent Surface (RIS). Moreover, we present the shortcomings in the current state of the art. Additionally, we propose enhanced techniques that can complement the accuracy of 5G-based positioning in controlled industrial environments.
We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation), to capture the behavioral signature of wash trading. Using US stocks as a benchmark, we demonstrate that joint elevation in both liquidity metrics indicates wash trading in crypto assets. A simulated regulatory treatment that removes likely wash trades confirms this dynamic: it reduces liquidity diffusion significantly while leaving liquidity jump largely unaffected. These findings align with a theoretical model in which manipulative traders amplify both the level and variance of price pressure, whereas passive investors affect only the level. Our model offers practical tools for investors to assess market quality and for regulators to monitor manipulation risk on crypto exchanges without oversight.
Ahmad Al-Mallahi, Manoj Natarajan, Alimohammad Shirzadifar
A communication algorithm to send data via the standard communication protocol for agricultural implements, ISO 11783, was developed and tested in this research work. The goal was to enable a universal method to integrate machine vision systems to sprayers so that sensor-based real-time spot spraying becomes possible in the future. The algorithm receives pest detection results from a machine vision and constructs messages compatible with ISO 11783 (also known as ISOBUS), which gets sent to the sprayer. This allows for simultaneous opening of different nozzles on the sprayer even when pests are detected by different cameras at different locations across the boom. The results on our spraying apparatus which consisted of 6 nozzles showed that correct spraying commands were sent to the nozzles 100% of the times for all possible 64 simultaneous nozzle opening/closing combinations. The algorithm was designed to construct CAN frames based on arithmetic rather than logic operations. Also, the increase of data load on the ISOBUS of the sprayer was limited to 5.2%. Moreover, no data loss between the machine vision system and the sprayer was observed even when the data frames were sent every 10 milliseconds. The electronic control unit (ECU) on which the algorithm was written has two interfaces, one communicates with the machine vision system using serial communication, and the other communicates with the sprayer using the ISOBUS. The universal serial communication protocol used in this development allows for immediate integration to the ECU which creates the messages according to the specifications of the nozzle control unit of the sprayer.
Puti Retno Ali , Suprihatin , Nastiti Siswi Indrasti
Current business practices are no longer relevant in the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) environment. VUCA reflects the speed of change, uncertainty, complexity, and ambiguity in the business world. Companies need to be agile to adapt to the prevailing VUCA situations. This study aims to provide an understanding of business agility from the perspective of stakeholder needs through a model. The method used is a focus group discussion involving the relevant stakeholders, with a case study conducted in an integrated chicken meat company. The results of the study reveal that an agile ecosystem stakeholder network model consists of seven stakeholder elements: academia, business, community, government, media, financial institutions, and customers as the core element of the model. The "customer" stakeholder element is the most crucial, while the other stakeholder elements influence each other in their actions, with their ultimate goal of meeting customer needs. By placing the customer element at the core of the model, the business environment can be aligned and better outcomes can be achieved. The recommendation derived from this study is the explicit formulation of company strategies in achieving business agility.
The aim of the article is to assess the proposed EU regulations on the use of ESG (environment, society and corporate governance) in the food sector in Poland. The source of materials for the analysis were reports on sustainable development of the UN, European Environment Agency, European Commission and information from companies that are already implementing ESG principles. The essence and importance of ESG and the strategies used so far for sustainable production practices are discussed. An attempt was made to determine how companies in the food industry using ESG can improve their financial results. Key legal regulations were presented and the importance of ESG factors in building the value of enterprises in the agri-food sector was indicated. It was pointed out that companies from the food industry play an important role in meeting the needs of the population, both in terms of food and in solving the problem of the climate crisis. The growing importance of ESG issues creates both challenges and opportunities for the Polish food sector. Due to the growing interest of consumers and investors in the issues of evaluating companies according to ESG principles, the pressure to report these activities in company reports is growing. It has been shown that the implementation of environmental, social and corporate governance in enterprises is a necessity in the context of generating value and competitive advantage of enterprises.
Risk assessment across industries is paramount for ensuring a robust and sustainable economy. While previous studies have relied heavily on official statistics for their accuracy, they often lag behind real-time developments. Addressing this gap, our research endeavors to integrate market microstructure theory with AI technologies to refine industry risk predictions. This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs). By enhancing the timeliness of risk assessments and delving into the influence of non-traditional factors such as market sentiment and investor behavior, we strive to develop a more holistic and dynamic risk assessment model. One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses. Moreover, textual data about industry analysis necessitates a deeper understanding facilitated by pre-trained language models. To tackle these issues, we propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis. For explicit analysis, we employ a hierarchical data analysis methodology that spans the industry and individual listed company levels. This strategic breakdown helps mitigate the impact of data noise, ensuring a more accurate portrayal of industry dynamics. In parallel, we introduce implicit analysis, where we pre-train an SML to interpret industry trends within the context of current news events. This approach leverages the extensive knowledge embedded in the pre-training corpus, enabling a nuanced understanding of industry trends and their underlying drivers. Experimental results based on our proposed methodology demonstrate its effectiveness in delivering robust industry trend analyses, underscoring its potential to revolutionize risk assessment practices across industries.
Energy security and energy trade are the cornerstones of global economic and social development. The structural robustness of the international oil trade network (iOTN) plays an important role in the global economy. We integrate the machine learning optimization algorithm, game theory, and utility theory for learning an oil trade decision-making model which contains the benefit endowment and cost endowment of economies in international oil trades. We have reconstructed the network degree, clustering coefficient, and closeness of the iOTN well to verify the effectiveness of the model. In the end, policy simulations based on game theory and agent-based model are carried out in a more realistic environment. We find that the export-oriented economies are more vulnerable to be affected than import-oriented economies after receiving external shocks. Moreover, the impact of the increase and decrease of trade friction costs on the international oil trade is asymmetrical and there are significant differences between international organizations.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model's proficiency in extracting robust market features and its adaptability to diverse market conditions.
The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.
Kevin Palmer-Wilson, Tyler Bryant, Peter Wild
et al.
Northern countries face a unique challenge in decarbonizing heating demands. This study compares two pathways to reduce carbon emissions from building heating by (1) replacing natural gas heaters with electric heat pumps or (2) replacing natural gas with renewable gas. Optimal annual system cost and capacity requirements for Metro Vancouver, Canada are assessed for each pathway, under nine scenarios. Results show that either pathway can be lower cost but the range of costs is more narrow for the renewable gas pathway. System cost is sensitive to heat demand, with colder temperatures favouring the renewable gas pathway and milder temperatures favouring the electrification pathway. These results highlight the need for a better understanding of heating profiles and associated energy system requirements.
Julia Knaggs, J. Ross Pruitt, Lindsay Anderson
et al.
Abstract Consumers are known to signal social status through their purchasing behaviors. As the food industry continually expands its use of strategic marketing to reach customers, understanding food’s connection to this kind of status signaling may open the door to explore new markets for farmers. This study explored the influence of social status, physical activity, and socio-demographics on an individual’s willingness to pay for a basket of high-quality organic foods. Over 3 days, participants had their physical activity measured by a pedometer, and they were randomly assigned to a social status condition and subsequently placed bids for the organic food basket using a second-price auction to measure their willingness to pay. High-status individuals were publicly recognized in order to test our hypothesis that individuals will not be motivated to pay more for an organic food basket than low-status counterparts when they have already received recognition for their high status. The results showed that on average non-students were willing to pay significantly more for an organic food basket than student counterparts. Hispanic and Asian shoppers were willing to pay more for an organic food basket than White counterparts. However, physical activity had no significant impact on willingness to pay. Ultimately, our hypothesis was confirmed that recognizing high-status individuals eliminated or reduced the need to showcase social status through higher bids for the organic food baskets.
Nutrition. Foods and food supply, Agricultural industries