Agriculture fundamentally depends on comprehending the complex interplay between crops and their environmental conditions, especially soil ecology. As in India, nearly 60% of people are dependent on agriculture in rural areas, and it also supports related industries like textiles, food processing, and ag machinery by providing raw materials. As in the past, machine learning methodologies are used for various soil ecologies to deliver accurate crop suggestions. Our methodology determines the optimal crops for certain places by evaluating essential soil properties, including pH, nitrogen (N), phosphorus (P), potassium (K) levels, and organic carbon content, in conjunction with weather conditions such as temperature, rainfall, and humidity. The suggested method assesses various machine learning algorithms, including decision trees, random forests, support vector machines, and ensembles, to choose the most effective models for precise predictions. We achieved a top accuracy of 98.4% by random forest and also 99.4% accuracy by decision tree, but with a risk of overfitting. Showing that integrating soil ecology leads to more precise and sustainable crop recommendations.
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.
Grasping the concerns of customers is paramount, serving as a foundation for both attracting and retaining a loyal customer base. While customer satisfaction has been extensively explored across diverse industries, there remains a dearth of insights into how distinct rural bed and breakfasts (RB&Bs) can effectively cater to the specific needs of their target audience. This research utilized latent semantic analysis and text regression techniques on online reviews, uncovering previously unrecognized factors contributing to RB&B customer satisfaction. Furthermore, the study demonstrates that certain factors wield distinct impacts on guest satisfaction within varying RB&B market segments. The implications of these findings extend to empowering RB&B owners with actionable insights to enhance the overall customer experience.
Md. Anisur Rahman Mazumder, Metinee Sangsomboon, Sunantha Ketnawa
et al.
The aim of this research was to develop mushroom–based Northern Thai style sausage (NS). Four mushrooms; split gill (SG), shiitake, oyster (OM) and log white (LW) were screened for NS preparation. Composition analysis suggested that LW had significantly higher amount of protein, fat and ash. LW showed significantly higher amounts of essential amino acids, total polyphenol, and DPPH activity. However, FRAP value was higher in the SG. LW was selected for further processing of NS based on nutritional analysis. LW-NS had higher nutritional value than commercial NS, but lower than northern Thai style beef sausages (NBS). Textural properties of LW-NS are comparable with NBS, but better than commercial NS. The LW-NS showed better (p < 0.05) consumer acceptability than commercial NS. The in vitro digestibility shows that both LW- and SG-NS remained reasonably steady in the gastric stage, but dramatically increased throughout the intestinal digesting stage. LW-NS provides 34, 32, 44, 10 and 20% of recommended daily intake (RDI) for protein, dietary fiber, sodium, calcium and iron, respectively. This study finds that LW-NS may be a good and sustainable alternative to animal-based NS, perhaps leading to increased consumer acceptance as meat alternatives.
Agriculture (General), Nutrition. Foods and food supply
The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.
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.
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.
Surya N Reddy, Vaibhav Kurrey, Mayank Nagar
et al.
Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.
Diana S. Kenina, Olga N. Grudina, Inna G. Svistunova
The problem of attracting young personnel is relevant for all industries, but in agriculture it is especially acute. It is not uncommon for young people to prefer life in rural areas to life in the city, and they are even ready to find a job outside of their profession and perform low-skilled work, but for higher wages.
One of the ways out of this situation is to increase the level of motivation of employees in the agricultural sector. We believe that motivation in this area should be comprehensive and take into account industry specifics. The main barrier to the qualitative motivation of employees in the agro-industrial complex is the previously destroyed image of the industry's professions.
In addition, it is worth paying attention in general to a new generation of specialists, in working with whom it is necessary to apply new approaches that are more democratic in style, as well as create more comfortable working conditions and conditions for development and career growth.
In some regions of the Russian Federation, the rebranding of rural territories has begun.
Purpose. The analysis of existing problems of employment and retention of personnel in rural areas and the study of the characteristics of employee motivation in the field of agriculture.
Methodology the article uses general scientific methods of analysis and synthesis, as well as statistical methods. The information base of the study was formed on the basis of normative legal acts aimed at supporting rural employment, as well as data from state statistics.
Results. The article accumulates information on the available measures of state support for specialists in rural areas, as well as on state programs to support agriculture, on the basis of which a model of motivation of employees in the field of agriculture is compiled.
Practical implications. It is advisable to apply the results obtained, both to theorists and practitioners in the field of personnel management, in order to improve work in the field of personnel management in the field of agriculture.
The rapid development of the digital economy has driven the digital and intelligent transformation and development of rural primary, secondary and tertiary industries, which has a revolutionary impact on the ways and paths of agricultural transformation iteration and rural industrial integration development. The integrated development of primary, secondary and tertiary industries in rural areas is a key measure to continuously promote rural revitalization. The digital economy still faces some challenges in leading the integrated development of rural industries, including high uncertainty in the macro policy environment, weak connection between the implementation and use of digital technology at the meso level and farmers, and the optimization of the interest linkage at the micro subject level. Based on the theory of industrial integration, the article attempts to analyze the empowering points of the digital economy from the perspectives of digital reconstruction of industrial factors, digital and intelligent transformation of the entire production process, and cultivation of new service models. It proposes ways to strengthen the construction of rural digital infrastructure, increase the effective supply of digital economy talents, and build a unified standard collaborative guarantee mechanism to assist in the development of rural industrial integration.
Using deep learning techniques, we introduce a novel measure for production process heterogeneity across industries. For each pair of industries during 1990-2021, we estimate the functional distance between two industries' production processes via deep neural network. Our estimates uncover the underlying factors and weights reflected in the multi-stage production decision tree in each industry. We find that the greater the functional distance between two industries' production processes, the lower are the number of M&As, deal completion rates, announcement returns, and post-M&A survival likelihood. Our results highlight the importance of structural heterogeneity in production technology to firms' business integration decisions.
Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 latency-sensitive applications function based on machine learning to process sensor data for automation and other industrial activities. Sending sensor data to cloud systems is time consuming and detrimental to the latency constraints of the applications, thus, fog computing is often deployed. Executing these applications across heterogeneous fog systems demonstrates stochastic execution time behavior that affects the task completion time. We investigate and model various Industry 4.0 ML-based applications' stochastic executions and analyze them. Industries like oil and gas are prone to disasters requiring coordination of various latency-sensitive activities. Hence, fog computing resources can get oversubscribed due to the surge in the computing demands during a disaster. We propose federating nearby fog computing systems and forming a fog federation to make remote Industry 4.0 sites resilient against the surge in computing demands. We propose a statistical resource allocation method across fog federation for latency-sensitive tasks. Many of the modern Industry 4.0 applications operate based on a workflow of micro-services that are used alone within an industrial site. As such, industry 4.0 solutions need to be aware of applications' architecture, particularly monolithic vs. micro-service. Therefore, we propose a probability-based resource allocation method that can partition micro-service workflows across fog federation to meet their latency constraints. Another concern in Industry 4.0 is the data privacy of the federated fog. As such, we propose a solution based on federated learning to train industrial ML applications across federated fog systems without compromising the data confidentiality.
Healthcare services in rural areas face numerous challenges due to the high cost of treatment and a lack of appropriate services. The application of Internet of Things (IoT) technology has shown potential in mitigating these issues. This article discusses the potential of Internet of Things (IoT) and fog computing to reduce healthcare costs and improve patient outcomes. The use of these technologies in cardiovascular health informatics is explored, along with the economic thought process of hospital decision-makers and end-of-life practices in intensive care units. Remote monitoring using IoT devices is highlighted as a promising way to detect health issues before they become serious, leading to earlier interventions and improved health outcomes. The use of fog computing in healthcare is also discussed, with a focus on its ability to provide real-time data processing, analysis, and decision-making capabilities. The article presents a novel architecture for Device-as-a-Service, utilizing both fog and cloud computing to improve the efficiency and accuracy of ECG device processing, and concludes that it has the potential to reduce costs by up to 80% in the Iranian market. The adoption of fog computing in healthcare is acknowledged to present significant challenges, such as security and privacy concerns,
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.
Atharva Kulkarni, Raya Das, Ravi S. Srivastava
et al.
Poverty is a multifaceted phenomenon linked to the lack of capabilities of households to earn a sustainable livelihood, increasingly being assessed using multidimensional indicators. Its spatial pattern depends on social, economic, political, and regional variables. Artificial intelligence has shown immense scope in analyzing the complexities and nuances of poverty. The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. The districts will be classified into `advanced', `catching up', `falling behind', and `lagged' regions. The project proposes to integrate multiple data sources, including conventional national-level large sample household surveys, census surveys, and proxy variables like daytime, and nighttime data from satellite images, and communication networks, to name a few, to provide a comprehensive view of poverty at the district level. The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty. Poverty and inequality could be widening in developing countries due to demographic and growth-agglomerating policies. Therefore, targeting the lagging regions and the vulnerable population is essential to eradicate poverty and improve the quality of life to achieve the goal of `zero poverty'. Thus, the study also focuses on the districts with a higher share of the marginal section of the population compared to the national average to trace the performance of development indicators and their association with poverty in these regions.
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.
Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin
et al.
Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software engineering practices are highly applicable. The increased use of automated ML software engineering practices in industries such as manufacturing and utilities requires an automated Quality Assurance (QA) approach as an integral part of ML software. Here, QA helps reduce risk by offering an objective perspective on the software task. Although conventional software engineering has automated tools for QA data analysis for data-driven ML, the use of QA practices for ML in operation (MLOps) is lacking. This paper examines the QA challenges that arise in industrial MLOps and conceptualizes modular strategies to deal with data integrity and Data Quality (DQ). The paper is accompanied by real industrial use-cases from industrial partners. The paper also presents several challenges that may serve as a basis for future studies.
The purpose of the study is to test the hypothesis of the priority role of livestock breeding in the development of rural areas on the basis of an analysis of livestock industries and determine the degree of territorial heterogeneity of this phenomenon. The study was carried out on the example of 29 regions of the Non-Black Earth Zone of the Russian Federation. Methods of ranking, correlation and variational analysis were used. A noticeable correlation was established between the indicators of the development of the livestock industry and rural development (p = 0.5; 0.6). For poultry farming, this relationship is moderate (p = 0.3; 0.3), pig breeding - weak negative (p = - 0.1; - 0.1). Concrete calculations confirmed the lack of influence of the development of pig breeding on one of the important indicators of the development of rural areas - the growth of acreage. The features of territorial heterogeneity in the development of cattle breeding and rural areas have been consistently investigated on the basis of the same methodology. Three equal groups of regions with a relatively high, medium and relatively low level of their development were identified. It is shown that the group of regions with a relatively high level of development of cattle breeding in the Non-Black Earth Region accounts for about half of the volume of milk and 56% of the production of cattle for slaughter. Neighboring regions in group I form two, in group II - one, in group III - three areas. When comparing the results obtained for cattle breeding and rural areas, it was found that the composition of the regions in the corresponding groups coincides by 67-78 %. When calculating the correlation coefficient between the final ranks of the regions, established when determining the heterogeneity of the development of livestock breeding and the heterogeneity of the development of rural areas, a high tightness of the correlation was revealed (p = 0.7). The hypothesis being tested was confirmed.
The article presents the results of the research to substantiate the directions and mechanisms of development of the rural economy of the Komi Republic. The theoretical and methodological basis of the study was the works of the scientists in the field of development of rural economy sectors. The methods used were analytical, statistical, logical, comparative, SWOT analysis, and expert assessments. When studying this topic, we used normative legal acts on rural development, data from territorial statistics. The paper considers the views of the scientists and economists on the content of the concept of rural economy, identifies the features and problems of its development. The predominance of small forms of management in the branches of the rural economy has been established, and the low profitability of the products produced has been revealed. The article shows a decrease in the role of the rural economy in the overall economic activity of the region. In order to expand the areas of activity in the rural areas, the diversification of production is proposed. Mechanisms and priority areas for improving state support for the development of the rural economy have been developed. To eliminate the currently prevailing tactical approach to solving the current problems of the rural economy, its strategic management is justified. As an effective mechanism for the development of rural economy sectors, it is proposed to use strategic planning and forecasting. The methodological foundations of the strategy development are substantiated. The formulated scientific and methodological provisions of strategic planning are the basis for the development of strategies for the development of agriculture in the Komi Republic and reindeer herding in the European North-East of Russia. The results of the study are used in the development of the Strategy of Socio-economic Development of the Republic of Komi for the period up to 2035, and can also become the basis for improving state support for rural economy sectors, for the preparation of strategies and programs for the sustainable development of its industries and spheres at regional and municipal levels. The use of the research results is possible in the further research work of the author, as well as in the educational process.