This paper provides a conceptual architecture for a cloud based platform design, that implements continuously data storage and analysis services for large maritime ships, with the purpose to provide valuable insights for maritime transportation business. We do this by first identifying the need on the shipping market for such kind of systems and also the significance and impact of different factors related to shipping business processes. The architecture presented throughout this paper will be defined around some of the most currently used ICT technologies, like Amazon Cloud Services, Sql Server Databases, .NET Platform, Matlab 2016 or javascript visualization libraries. The proposed system makes possible for a maritime company to gain more knowledge for optimizing the efficiency of its operations, to increase its financial benefits and its competitive advantage. The platform architecture was designed to make possible the storage and manipulation of very large datasets, also allowing the possibility of using different data mining techniques for inferring knowledge or to validate already existent models. Ultimately, the developed methodology and the presented outcomes demonstrate a vast potential of creating better technological management systems for the shipping industry, starting from the challenges but also from the huge opportunities this sector can offer.

With the continuous push for improving packaging efficiency of current packaging practices to prevent losses in a real cold chain, a holistic evaluation method with improved indicators and a conceptual assessment framework to improve packaging efficiency continuously in a fresh vegetable cold chain was proposed. Based on two fresh leafy vegetables with typical packaging practices in a real cold chain in China, the evaluation method was performed and tested from a macro perspective, and then the packaging efficiency constitution with loss prevention perspective was better visualized. The results show that the method can be used for improving the performance of specific packaging efficiency in the fresh vegetable cold chain and improving the suggestions’ sustainability orientation, and systematically summarize their impacts on packaging efficiency; specifically as follows: (1) Since the correlation between the state of fresh vegetables and the environmental stress in the package, the performance of the micro-environment parameters should be given priority for packaging efficiency improvement in loss prevention. (2) Although the use of the packaging materials in current practices was relatively better, it still can improve in food safety and packaging sustainability of the packaging practices through the selection of packaging materials with better sustainability characteristics. (3) Via the visualization of targeted packaging efficiency in cold chain, the fluctuation of loss rates in the cold chain process are well perceived, and there are still room can be performed continuously to realize more efficient packaging for better loss prevention. (4) The method has drawbacks that the optimization of the weights to the given indicators and the indicators based on nutrition and quality of fresh produce was not considered in current research, and it should be strengthened in future research. (5) It is necessary that the perception of differences in targeted packaging efficiency and the consciousness to improve specific packaging efficiency in vegetable cold chain for sustainability.

This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural and rural environments, easing the identification of proper instruments that can be used by EU policy makers in CAP’s financial management. The applied methodology consists of elaborating a custom-developed analytical framework based on a dataset containing 22 relevant indicators, considering four main dimensions that describe the intricacies of the EU agricultural and rural environment, in the CAP context: rural, emissions, macroeconomic, and financial. The results highlight that an increase of the agricultural research and development funding, as well as the agriculture employment rate, negatively influence the degree of rural poverty. The rural GDP per capita is influenced by the size of the employment rate in agriculture. It seems that environmental sustainability, identified by both fertilizers used and emissions from agriculture parameters, significantly influences the GDP per capita. In predicting emissions in agriculture, the direct payment, degree of rural poverty, fertilizer use, employment in agriculture, and agriculture labor productivity are the main independent parameters with the highest future importance. It was found that when predicting direct payments, the rural employment rate, employment in agriculture, and gross value added must be considered the most. The agricultural, entrepreneurial income prediction is mainly influenced by the total factor productivity, while agricultural research and development investments depend on gross value added, direct payments, and gross value added in the agricultural sector. Future research, related to prediction models based on CAP indicators, should also consider the marketing dimension. It is recommended for direct payments to be used to invest in upgrading the fertilizers technologies, since environmental sustainability will influence economic growth.

This paper describes a wireless real-time monitoring system (MS-BWME) to monitor the running state of pumps equipment in brine well mining and prevent potential failures that may produce unexpected interruptions with severe consequences. MS-BWME consists of two units: the ZigBee Wireless Sensors Network (WSN) unit and the real-time remote monitoring unit. MS-BWME was implemented and tested in sampled brine wells mining in Qinghai Province and four kinds of indicators were selected to evaluate the performance of the MS-BWME, i.e., sensor calibration, the system’s real-time data reception, Received Signal Strength Indicator (RSSI) and sensor node lifetime. The results show that MS-BWME can accurately judge the running state of the pump equipment by acquiring and transmitting the real-time voltage and electric current data of the equipment from the spot and provide real-time decision support aid to help workers overhaul the equipment in a timely manner and resolve failures that might produce unexpected production down-time. The MS-BWME can also be extended to a wide range of equipment monitoring applications.