Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art

Life Cycle Energy and CO2 Analysis of a Student Residential Building in Ningbo, China.

Buildings with installed photovoltaic power systems tend to consume less energy and create less environmental damage. However, these photovoltaic power systems also can generate a significant amount of energy and environmental impacts during their manufacturing and installation processes. There are numerous life cycle assessment studies evaluating the amount of energy and carbon emissions that photovoltaic systems generate, but normally the system boundaries of these assessments are limited to the photovoltaic system excluding the building. The purpose of this thesis was to perform a comparative life cycle energy analysis of a student dormitory building in Ningbo, China with and without using a photovoltaic energy generator in its operational phase and to evaluate the efficiency of the photovoltaic system in terms of carbon emissions and energy performance. An energy contribution analysis and sensitivity analysis was also executed. The research was conducted using a life cycle energy assessment method in which two separate assessments were performed: one for the student dormitory building and one for a solar panel. Construction, operation, and demolition life cycle phases of the building and the photovoltaic power system were included. Data was obtained from the original drawings of the case study building, and data from literature review was used for the solar panel. An energy and carbon emission contribution analysis was done before the installation of the photovoltaic system in the building. Later, a bigger energy model was created by combining the life cycle energy assessment of the building and the photovoltaic system. This model helped to complete a scenario and sensitive analysis so that the effects of modifying key input parameters and/or processes could be analyzed. The results show that the total amount of energy consumed and carbon dioxide emissions generated during the life cycle of the dormitory was 5,907 kWh/m2 and 6 ton CO2-eq./m2 per 50 years. The HVAC system in the building emits more carbon dioxide and consumes more electricity than any other process. Total amount of energy consumed and carbon dioxide emissions generated during the life span of the photovoltaic power system was 1,277 kWh/m2 total usable area, and 2 ton CO2-eq./m2 usable area. The conversion of upgrading metallurgical silicon (UMG-Si) into solar grade silicon (SoG-Si) was the process consuming more energy and emitting more carbon dioxide. The installation of the photovoltaic system in the dormitory can reduce its direct energy by 15.63% and carbon emissions by 15.65% during its 50 years life span. In the case of the building s total life cycle energy consumption (direct and indirect energy), this reduction is 8.7% in terms of energy and 10.43% in the case of carbon emissions. Result also revealed that using renewable energy as the energy supply of electricity generation for the manufacturing of solar panels and throughout the life cycle of the dormitory can help to enhance the benefits of installing photovoltaic systems. Using hydropower as energy supply 83.8% of carbon emissions reduction is obtained compare to the original 10.43%. The installation of the photovoltaic power system helps to mitigate carbon dioxide and reduce energy consumption in the student dormitory. The system has more effects on the direct energy consumed by the building, although a precise and holistic amount of energy and carbon emission reduction is given by the building s total life cycle energy consumption (direct and indirect energy). The results presented here can assist to identify critical processes and to make changes that can help to improve the overall energy and carbon emission performance of the life cycle of the building and the photovoltaic system. The combined life cycle energy assessment model created in this thesis can be used as a tool to assess solar panel installation in buildings, as a tool to improve the production technology of photovoltaic systems and construction materials, as a reference for policy making, and as a benchmark for future research

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art

The HEV Ventilator: at the interface between particle physics and biomedical engineering.

A high-quality, low-cost ventilator, dubbed HEV, has been developed by the particle physics community working together with biomedical engineers and physicians around the world. The HEV design is suitable for use both in and out of hospital intensive care units, provides a variety of modes and is capable of supporting spontaneous breathing and supplying oxygen-enriched air. An external air supply can be combined with the unit for use in situations where compressed air is not readily available. HEV supports remote training and post market surveillance via a Web interface and data logging to complement standard touch screen operation, making it suitable for a wide range of geographical deployment. The HEV design places emphasis on the ventilation performance, especially the quality and accuracy of the pressure curves, reactivity of the trigger, measurement of delivered volume and control of oxygen mixing, delivering a global performance which will be applicable to ventilator needs beyond the COVID-19 pandemic. This article describes the conceptual design and presents the prototype units together with a performance evaluation

The HEV Ventilator: at the interface between particle physics and biomedical engineering.

A high-quality, low-cost ventilator, dubbed HEV, has been developed by the particle physics community working together with biomedical engineers and physicians around the world. The HEV design is suitable for use both in and out of hospital intensive care units, provides a variety of modes and is capable of supporting spontaneous breathing and supplying oxygen-enriched air. An external air supply can be combined with the unit for use in situations where compressed air is not readily available. HEV supports remote training and post market surveillance via a Web interface and data logging to complement standard touch screen operation, making it suitable for a wide range of geographical deployment. The HEV design places emphasis on the ventilation performance, especially the quality and accuracy of the pressure curves, reactivity of the trigger, measurement of delivered volume and control of oxygen mixing, delivering a global performance which will be applicable to ventilator needs beyond the COVID-19 pandemic. This article describes the conceptual design and presents the prototype units together with a performance evaluation