Search Results (38,258)

The integration of systems for Adaptive Business Intelligence (ABI) in the healthcare industry has the potential to revolutionize and reform the way organizations approach data analysis and decision-making. By providing real-time actionable insights and enabling organizations to continuously adapt and evolve, ABI has the potential to drive better outcomes, reduce costs, and improve the overall quality of patient care. The ABI Interoperability System was designed to facilitate the usage and integration of ABI systems in healthcare environments through interoperability resources like Health Level 7 (HL7) or Fast Healthcare Interoperability Resources (FHIR). The present article briefly describes both versions of this software, learning about their differences and improvements, and how they affect the solution. The changes introduced in the new version of the system will tackle code quality with automated tests, development workflow, and developer experience, with the introduction of Continuous Integration and Delivery pipelines in the development workflow, new support for the FHIR pattern, and address a few security concerns about the architecture. The second revision of the system features a more refined, modern, and secure architecture and has proven to be more performant and efficient than its predecessor. As it stands, the Interoperability System poses a significant step forward toward interoperability and ease of integration in the healthcare ecosystem. Full article

This long-term field study conducted in Yancheng, China, evaluated the effects of diverse crop rotation sequences on rice growth, yield, and soil properties. Six rotation treatments were implemented from 2016 to 2023 as follows: rice–wheat (control), rice––rape, rice–hairy vetch, rice–barley, rice–faba bean, and rice–winter fallow. Rice growth parameters, yield components, biomass accumulation, and soil properties were measured. Results showed that legume-based rotations, particularly rice–faba bean and rice–hairy vetch, significantly improved rice growth and yield compared to the rice–wheat control. The rice–faba bean rotation increased yield by 19.1% to 8.73 t/ha compared to 7.33 t/ha for the control, while rice–hairy vetch increased yield by 11.9% to 8.20 t/ha. These rotations also demonstrated higher biomass production efficiency, with increases of 33.33% and 25.00%, respectively, in spring crop biomass. Soil nutrients improvements were observed, particularly in available nitrogen, potassium, and electrical conductivity. Legume-based rotations increased the available nitrogen by up to 35.9% compared to the control. The study highlights the potential of diversified crop rotations, especially those incorporating legumes, to enhance rice productivity and soil health in subtropical regions. These findings have important implications for developing sustainable and resilient rice-based cropping systems to address challenges of food security and environmental sustainability in the face of climate change and resource constraints. Full article

As a popular vegetable in Yunnan Province, China, taro flowers are delicious but contain substances that can cause numbing and mucous membrane damage. Prolonged high-temperature cooking is used by locals to mitigate these effects, though its mechanisms were previously unexplored. This study confirms the presence of needle-like calcium oxalate crystals in taro flowers and shows that prolonged steaming reduces their quantity, size, and sharpness, making them safer to eat. Microscopic observations revealed numerous sharp-tipped (~50 μm) calcium oxalate crystals in fresh taro flowers. After 2 h of steam heating, there were significantly fewer (~80% reduction) and smaller crystals (~70% reduction). Ion chromatography showed no significant change (p > 0.05) in calcium oxalate content (remaining ~2.5% of dry weight) after heating. Higher temperatures increase calcium oxalate solubility, causing gradual dissolution and the likely formation of small irregular structures, thus reducing the numbing effect. Prolonged cooking could be applied to other plant-based foods and medicines rich in these crystals. By analyzing statistics related to taro and taro flowers, the estimated potential economic benefits of commercializing taro flowers were USD 2.58–12.92 billion annually, potentially improving food security, creating jobs, and promoting development across regions where taro is largely cultivated in the Global South. Full article

The combined pressures of climate change and human activities have exacerbated ecological risks in fragile and sensitive areas. Assessing the ecological restoration status of key nature reserves and developing a new conservation and development framework are fundamental for achieving ecological civilization and enhancing sustainability. As an ecological security barrier in the northwestern alpine region, Qilian Mountain National Park (QMNP), is of great significance for maintaining the sustainable ecological environment of western China. By measuring changes in ecological land use and monitoring key vegetation indicator trends in QMNP, we constructed the Regional Ecological Resilience Indicator (RERI) and proposed a new restoration and restoration framework. The results show that: (1) the ecological land restoration in QMNP was remarkable, with a total of 721.76 km² of non-ecological land converted to ecological land, representing a 1.44% increase. Forest restoration covered 110 km², primarily made up of previously unused land from 2000 to 2020. (2) The average NDVI value increased by 0.025. Regions showing productivity growth (NPP) accounted for 51.82% of the total area from 2000 to 2020. The four typical eco-migration zones reduced the building profile area by 47.72% between 2015 and 2019. The distribution of high Composite Vegetation Index (CFI) values overlapped with concentrated forest restoration areas, revealing two main restoration models: forest conservation and population relocation. (3) RERI calculations divided the park into three ecological zones, Priority Conservation Area (PCA), Optimization and Enhancement Area (OEA), and Concerted Development Area (CDA), leading to the proposal of an ecological restoration and development framework for QMNP, characterized by “three zones, two horizontal axes, and one vertical axis”. Our findings contribute to strengthening the ecological security barrier in northwestern China; they offer new insights for the long-term, stable improvement of the ecological environment in QMNP and in other critical protected area systems globally. Full article

With the construction of smart cities advancing, research on big data and smart cities has become crucial for sustainable development. This study seeks to fill gaps in the literature and elucidate the significance of big data and smart city research, offering a comprehensive analysis that aims to foster academic understanding, promote urban development, and drive technological innovation. Using bibliometric methods and Citespace software (6.2.R3), this study comprehensively examines the research landscape from 2015 to 2023, aiming to understand its dynamics. Under the guidance of the United Nations, global research on big data and smart cities is progressing. Using the Web of Science (WOS) Core Collection as the data source, an exhaustive visual analysis was conducted, revealing various aspects, including the literature output, journal distribution, geographic study trends, research themes, and collaborative networks of scholars and institutions. This study reveals a downward trend despite research growth from 2015 to 2020, focusing on digital technology, smart city innovations, energy management and environmental applications, data security, and sustainable development. However, biases persist towards technology, information silos, homogenised research, and short-sighted strategies. Research should prioritise effectiveness, applications, diverse fields, and interdisciplinary collaboration to advance smart cities comprehensively. In the post-COVID-19 era, using big data to optimise city management is key to fostering intelligent, green, and humane cities and to exploring efficient mechanisms to address urban development challenges in the new era. Full article

The adoption of Data Networks and Application Programming Interfaces (APIs) has become crucial for small and medium enterprises (SMEs) to streamline operations, improve efficiency, and reduce costs. However, SMEs often face challenges such as resource limitations and security vulnerabilities, which hinder their ability to fully leverage these technologies. This systematic review examines the role of Data Networks and APIs in enhancing operational efficiency within SMEs, focusing on key metrics such as speed, cost reduction, scalability, and security challenges. Following PRISMA 2020 guidelines, we conducted a systematic search across multiple databases including Web of Science, Scopus, IEEE Xplore, and Google Scholar. Studies published between 2014 and 2024, focused on SMEs, and addressing the role of Data Networks and APIs in operational efficiency were included. A total of 49 studies met the inclusion criteria and were analyzed for key outcomes related to operational efficiency, cost-effectiveness, and security risks. The review found that Data Networks and APIs significantly improve operational efficiency by increasing process speed (12% increase), reducing operational costs (8% reduction), and enhancing overall productivity. However, security challenges, particularly related to API vulnerabilities, were a major concern, with cyberattacks on APIs increasing by 400% in Q1 2023 alone. Despite these risks, the benefits of implementing Data Networks and APIs in SMEs, particularly in terms of scalability and real-time data processing, were evident across industries. Data Networks and APIs offer substantial improvements in operational efficiency for SMEs, although security remains a significant challenge. Future efforts should focus on developing security frameworks tailored to SMEs while maintaining the operational benefits of these technologies. Further research is needed to explore scalable and secure API models for SMEs. Full article

Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. This study examines the energy, economic, and environmental effects of CPEC’s energy investments in Pakistan, covering a range of power sources, including coal, hydro, solar, wind, and nuclear energy. Utilizing data from 31 CPEC energy projects and employing the GTAP-E-Power model, this research assesses these impacts through seven scenarios, comprehensively analyzing the heterogeneity of different power sources. Our findings reveal that while all types of CPEC energy infrastructure investments contribute to increasing the share of zero-emissions electricity to 49.1% and reducing CO₂ emissions by 18.61 million tons, the economic impacts vary significantly by energy source. The study suggests that it is crucial to prioritize renewable energy investments while addressing immediate power shortages to balance economic growth with environmental sustainability. Policymakers should also consider the potential inter-sectoral substitution effects when applying significant shocks to specific sectors. This analysis informs future energy investment decisions under CPEC and offers insights for other Belt and Road Initiative (BRI) countries aiming to optimize their energy strategies for sustainable development. Full article

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction. Full article

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. Full article

Combining wind and hydropower facilities makes it possible to solve the problems caused by power supply shortages in areas that are remote from the central energy system. Hydropower plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power outputs from wind power plants, and the limitations associated with them are significantly reduced when they are integrated into the regional energy system. Such an integration contributes to increasing the efficiency of renewable energy sources, which in turn reduces our dependence on fossil resources and decreases their harmful impact on the environment, increasing the stability of the power supply to consumers. The results of optimisation calculations show that a consumer load security of 95% allows the set capacity of RESs to be used in the energy complex up to 700 MW. It is shown here that the joint operation of HPPs and WPPs as part of a power complex and hydraulic energy storage allows for the creation of a stable power supply system that can operate even in conditions of variable wind force or uneven water flow. The conclusions obtained allow us to say that the combination of hydro- and wind power facilities makes it possible to solve the problem of power supply deficits in the regions of Kazakhstan that are remote from the central power station. At the same time, hydroelectric power plants and highly manoeuvrable hydroelectric units successfully compensate for the uneven power output from wind power plants and significantly reduce the limitations associated with them during their integration into the regional energy system. Full article

Background: The COVID-19 pandemic has had an impact on the eating habits of the general population, among other reasons, because it has affected access to commercial establishments since some of them closed. This study aimed to describe potential changes that occurred between 2019 and 2022 in the availability and affordability of food and beverages in the food environment of a Brazilian public university. Methods: Cross-sectional and descriptive study conducted at a public university located in Rio de Janeiro, Brazil. Audits were carried out in all establishments selling food and beverages at the university’s main campus in 2019 and 2022. Descriptive analysis with frequencies and means was carried out and the 95% confidence intervals were compared. Results: Over the period, there was a decrease in the on-campus number of establishments, dropping from 20 to 14, and ultra-processed foods became more prevalent. In general, the decrease in the number of establishments led to a reduction in the supply of fresh or minimally processed foods and beverages, and higher average prices were noted. Conclusions: The pandemic seems to have deteriorated the availability and the prices of healthy food in the university food environment, making healthy choices harder for students and the university community. Full article

Electric vehicles (EVs) and their ecosystem have unquestionably made significant technological strides. Indeed, EVs have evolved into sophisticated computer systems with extensive internal and external communication capabilities. This interconnection raises concerns about security, privacy, and the expanding risk of cyber-attacks within the electric vehicle landscape. In particular, the charging infrastructure plays a crucial role in the electric mobility ecosystem. With the proliferation of charging points, new attack vectors are opened up for cybercriminals. The threat landscape targeting charging systems encompasses various types of attacks ranging from physical attacks to data breaches including customer information. In this paper, we aim to leverage the power of model-driven engineering to model and analyze EV charging systems at early stages. We employ domain-specific modeling language (DSML) techniques for the early security modeling and analysis of EV charging infrastructure. We accomplish this by integrating the established EMSA model for electric mobility, which encapsulates all key stakeholders in the ecosystem. To our knowledge, this represents the first instance in the literature of applying DSML within the electric mobility ecosystem, highlighting its innovative nature. Moreover, as our formalization based on DSML is an iterative, continuous, and evolving process, this approach guarantees that our proposed framework adeptly tackles the evolving cyber threats confronting the EV industry. Specifically, we use the Object Constraint Language (OCL) for precise specification and verification of security threats as properties of a modeled system. To validate our framework, we explore a set of representative threats targeting EV charging systems from real-world scenarios. To the best of our knowledge, this is the first attempt to provide a comprehensive security modeling framework for the electric mobility ecosystem. Full article

Clean transition increases the demand for reliable electricity distribution, but while the capacity can be improved through investments, responding to the demand increases costs for the customers. This study presents a methodological improvement to the assessment of the reasonability of pricing, by comprehensively analyzing pricing regulation data to define the total cost of electricity distribution by clustering. A novel systematic view on the volume and distribution of economic steering shows that according to the regulation data in Finland, the total annual cost of distribution for the present level of reliability varies from EUR 490/a in an urban environment to EUR 1220/a per customer in sparsely populated areas. The majority of the total costs of distribution stem from actual utility expenses. The approach and results may be used for implementing TOTEX models for future pricing regulation. Full article

The frequent occurrence of excessive heat events driven by global warming poses a great threat to plant growth and food security. To survive in heat stress (HS) environments, plants have evolved sophisticated response mechanisms, and the transcriptional network that controls the expression levels of HS-inducible genes serves as an essential component of this process. NAC (NAM, ATAF1/2, and CUC2) transcription factors (TFs) play key regulatory roles in the abiotic stress responses of plants. However, the functional roles of NAC TFs in the heat stress response of Chinese cabbage remain elusive. In the present study, we identified the Brassica rapa NAC family transcription factor BrNAC19 as a close homologue of Arabidopsis NAC019 and found that it serves as a positive regulator of the HS response. BrNAC19 displayed inducible gene expression in response to HS, and its subcellular localization showed that it was localized in the nucleus. Heterologous expression of BrNAC19 significantly enhanced the heat tolerance of plants and reduced the accumulation of reactive oxygen species (ROS) under HS conditions. Furthermore, our results demonstrated that BrNAC19 directly targeted and promoted the expression of superoxide dismutase 1 (CSD1) and catalase 2 (CAT2), two antioxidant-enzyme coding genes in Chinese cabbage. Altogether, our results suggest that BrNAC19 enhances heat stress tolerance by positively regulating the expression of genes involved in the HS response and ROS scavenging and exhibits potential as a target gene in Chinese cabbage breeding to increase heat stress tolerance. Full article

After the emergence of the Internet of Things (IoT), the way devices interact with each other changed, as it allowed automation and seamless communication in various fields. However, various challenges related to security and trust have emerged, hindering the widespread adoption of the IoT. Blockchain technology is considered the ideal solution to face these challenges because of its immutable and decentralized nature. This paper explores the potential of blockchain technology to address critical security and trust challenges within the rapidly growing IoT ecosystem. Through a systematic literature review, this study examines how blockchain’s decentralized, immutable, and transparent features contribute to enhancing security and trust in IoT networks. Key findings indicate that blockchain integration can prevent data manipulation, ensure robust identity management, and facilitate transparent, verifiable transactions, supporting both security and trust in IoT systems. These attributes not only improve IoT security but also promote sustainable practices by optimizing resource efficiency, reducing environmental impact, and enhancing resilience in systems like supply chain management and smart grids. Additionally, this study identifies open research challenges and suggests future directions for optimizing blockchain in IoT environments, focusing on scalability, energy-efficient consensus mechanisms, and efficient data processing. Full article