Agricultural social networks : an agricultural value chain-based digitalization framework for an inclusive digital economy

DATA AVAILABILITY: Data is unavailable due to privacy or ethical restrictions.Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs.The Carnegie Corporation of New York and the Future Africa Research Leader Fellowship (FAR-LeaF) Programme.https://www.mdpi.com/journal/applsciInformatic

Society 5.0-inspired digitalization framework for resilient and sustainable agriculture

This research paper proposes a digitalization framework based on Society 5.0 principles for promoting resilient and sustainable agricultural value chains in the context of climate change. Climate change is affecting the productivity and sustainability of agricultural systems and threatening food security in many parts of the world. Digitalization has the potential to enhance the resilience of agricultural value chains to climate change by improving efficiency, promoting sustainability, and reducing vulnerability to climate risks. This study reviews the literature to investigate the potential benefits and challenges of Society 5.0-inspired digitalization for agricultural value chains in the context of resilience and sustainability. Further, this study establishes critical design requirements for digitalization, which inform the development of a theoretical framework for Society 5.0-inspired digitalization framework in realizing resilient and sustainable agricultural value chains.https://easychair.org/publications/EPiC/Computingam2024InformaticsSDG-09: Industry, innovation and infrastructur

METHOD FOR SOFTWARE MAINTENANCE RISK ASSESSMENT AT ARCHITECTURE LEVEL

ABSTRACT+ Successful software project maintenance necessitates a well-defined strategy to manage changes and minimize risks associated with the future operation of the software. Software maintainers usually are not engaged in the initial software development cycle. Before maintainers can modify a program, they must understand how it operates. The community of Software engineering has proposed several methods to evaluate software architectures with respect to desired quality attributes performance, usability, and so on. There is, however, little effort on a systematically way for risk assessment at the architecture analysis level. It is difficult to find exact estimates for the probability of failure of individual components and connectors in the system during the early phases of software life cycle, thus risk assessment and analysis for software architectures can be performed on UML specifications such as scenarios and use cases since they model the abstract architecture and implementation details and describe the system using compositions of components and connectors. In this paper, we analyse the well known scenario-based software architecture evaluation methods using an evaluation framework created in this paper. The framework considers each method from the point of view of method context, stakeholders, structure, and reliability. The comparison reveals that most of the studied methods are structurally similar but there are a number of differences among their activities and techniques. Hence, some methods overlap, which guides us to identify five activities that can form a method for software risk Assessment at architecture level during maintenance

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results

Remote Sensing Image Scene Classification: Advances and Open Challenges

Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities

Expression of slow skeletal troponin I in adult transgenic mouse heart muscle reduces the force decline observed during acidic conditions

Acidosis in cardiac muscle is associated with a decrease in developed force. We hypothesized that slow skeletal troponin I (ssTnI), which is expressed in neonatal hearts, is responsible for the observed decreased response to acidic conditions. To test this hypothesis directly, we used adult transgenic (TG) mice that express ssTnI in the heart. Cardiac TnI (cTnI) was completely replaced by ssTnI either with a FLAG epitope introduced into the N-terminus (TG-ssTnI*) or without the epitope (TG-ssTnI) in these mice. TG mice that express cTnI were also generated as a control TG line (TG-cTnI). Non-transgenic (NTG) littermates were used as controls.We measured the force-calcium relationship in all four groups at pH 7.0 and pH 6.5 in detergent-extracted fibre bundles prepared from left ventricular papillary muscles. The force-calcium relationship was identical in fibre bundles from NTG and TG-cTnI mouse hearts, therefore NTG mice served as controls for TG-ssTnI* and TG-ssTnI mice. Compared to NTG controls, the force generated by fibre bundles from TG mice expressing ssTnI was more sensitive to Ca2+. The shift in EC50 (the concentration of Ca2+ at which half-maximal force is generated) caused by acidic pH was significantly smaller in fibre bundles isolated from TG hearts compared to those from NTG hearts. However, there was no difference in the force-calcium relationship between hearts from the TG-ssTnI* and TG-ssTnI groups.We also isolated papillary muscles from the right ventricle of NTG and TG mouse hearts expressing ssTnI and measured isometric force at extracellular pH 7.33 and pH 6.75. At acidic pH, after an initial decline, twitch force recovered to 60 ± 3 % (n = 7) in NTG papillary muscles, 98 ± 2 % (n = 5) in muscles from TG-ssTnI* and 96 ± 3 % (n = 7) in muscles from TG-ssTnI hearts. Our results indicate that TnI isoform composition plays a crucial role in the determination of myocardial force sensitivity to acidosis