This study presents the performance of Solar Direct Drive (SDD) refrigerators and freezers with integrated Thermal Energy Storage (TES) used for food preservation. Laboratory experiments conducted on two prototype units each with 200 Liter storage volume were compared against simulation results obtained from a developed dynamic model. The model was validated following deterministic and probabilistic approaches where the temperatures of the Phase Change Material (PCM), cabinet air, and stored product as well as the compressor energy exhibited excellent agreement. With empty cabinets, experimental results showed that the TES maintained the refrigerator and freezer setpoint temperature for about seven and four days, respectively. Simulations were performed to map the performance of both units under different climate conditions based on the stabilization time required to fully charge the storage and the holdover time during which the product setpoint temperature is sustained without solar energy. Results indicated that the stabilization time can vary between 48 h for the refrigerator and 50-217 h for the freezer, while the holdover time ranged between 62-180 h in all cases. The findings suggest that the main economic driver for SDD refrigerators and freezers is the reduction of food losses that would otherwise be wasted, in addition to the reduction of refrigeration-related greenhouse gas emissions.
CONTEXT: Crop sequences are important for sustainable agriculture, yet we know relatively little about how they recur across individual fields or which information best predicts the crop grown each year. Denmark's national field records allow both questions to be examined over time. OBJECTIVE: We identified recurrent crop sequences across Denmark and tested how well annual crop-family selection could be predicted from crop history, farm characteristics, management, prices, soil and climate. METHODS: Using 10 years (2011-2020) of national field-level crop data (similar to 600,000 fields yr(-1)), we developed a heuristic algorithm to detect recurring crop sequence patterns. We then trained machine learning (LightGBM) and deep learning (TabNet) models to predict annual crop choices based on preceding crops and lagged management and farm structure predictors, and pedoclimatic conditions. Models were evaluated through forward chaining validation and temporal holdout tests, and SHAP values were used to examine how LightGBM used each predictor. RESULTS AND DISCUSSION: Crop sequences were largely dominated by cereals, with little diversification even in longer sequences. Crop selection patterns were strongly associated with the previous crops and farm typology, but pedoclimatic conditions and previous management played a minor role. The DL model achieved a slightly higher overall accuracy, particularly for dominant crops, while the ML model provided a more balanced performance across different crops and enabled interpretation through SHAP. SIGNIFICANCE: The analysis separates two distinct tasks: identifying multi-year crop sequences and predicting the crop family grown each year. The sequence analysis shows why diversification cannot be assessed from sequence length or crop counts alone. The predictive models could help generate locally plausible sequences for agri-environmental simulations, reducing reliance on standard rotations that poorly reflect observed field histories.
This study evaluates the carbon fractions and CO2 removal (CDR) potential of a suite of Danish biochar samples produced from typical agricultural feedstocks (wheat straw, wood), digestate, and organic waste across a range of pyrolysis temperatures. The CO2 storage potential per unit mass of biochar is expressed using the directly measured inertinite carbon concentration (C-inert, wt.%) determined through the inertinite benchmarking methodology. All biochar samples display a pronounced increase in mean random reflectance (R-o) at carbonization temperatures above similar to 550 degrees C. The inertinite fraction (F-inert) increases systematically with carbonization temperature and reaches values > 80 % above similar to 575 degrees C (corresponding to mean R-o values of similar to 3-4 %, depending on feedstock). In wood-derived, lignocellulosic biochar produced at carbonization temperatures >575 degrees C (mean R-o similar to 3 %) the F-inert exceeds 98 %. This biochar has the largest CO2 storage potential, yielding C-inert values of 85.6-91.8 wt% and a CDR of 3.05 tons CO2e per ton biochar (without considering the potential priming effect in the soil). Wheat straw and digestate biochar also attain a maximum inertinite carbon content at carbonization temperatures >575 degrees C, with peak C-inert values of 60.6-67.5 wt% and 39.9-50.4 wt%, respectively. The CDR is 2.21 and 1.41 tons CO2e per ton biochar, respectively. Sewage sludge biochar shows the lowest CDR potential per unit mass, due to its low carbon concentration. Total carbon (C-tot) was found to be, within analytical uncertainty, essentially identical to measured organic carbon (C-org), indicating that separate determination of C-org is unnecessary for the studied biochar types.
European pork production pursues traceability and authenticity to ensure animal welfare, food safety, and support products with geographical indications. This study reports a European survey integrating stable isotope ratios (δ13C, δ15N, δ34S, δ18O, δ2H) and multi-element profiling using IRMS and ICP-MS, on 612 samples collected across Denmark, Poland, Italy, and Spain, with diverse production systems, breeds, feeding, and slaughter ages. Geographical and climatic gradients influenced δ2H and δ18O, which ranged from −111‰ to −89‰ in samples from Denmark and Spain and from 13.3‰ to 16.0‰ in samples from Italy and Spain, respectively. In selected farms, δ13C ranged from −22.7‰ to −17.0‰ depending on diet composition based on C3 and C4 plants. The wide variability in pig management practices suggested that δ15N (2.50 ÷ 4.96‰) increased with slaughter age and was positively correlated with Fe (3.38 ÷ 8.39 mg/kg) and Zn (9.39 ÷ 23.6 mg/kg). Most mineral components were mainly driven by feed formulation and supplementation. Principal component analysis (PCA) showed that samples were grouped based on their origin and husbandry system, confirming the key role of isotopic and elemental markers for the development of a database supporting the pork supply chains across Europe.
Self-adaptive robots adjust their behaviors in response to unpredictable environmental changes. These robots often incorporate deep learning (DL) components into their software to support functionality such as perception, decision-making, and control, enhancing autonomy and self-adaptability. However, the inherent uncertainty of DL-enabled software makes it challenging to ensure its dependability in dynamic environments. Consequently, test generation techniques have been developed to test robot software, and classical mutation analysis injects faults into the software to assess the test suite's effectiveness in detecting the resulting failures. However, there is a lack of mutation analysis techniques to assess the effectiveness under the uncertainty inherent to DL-enabled software. To this end, we propose UAMTERS, an uncertainty-aware mutation analysis framework that introduces uncertainty-aware mutation operators to explicitly inject stochastic uncertainty into DL-enabled robotic software, simulating uncertainty in its behavior. We further propose mutation score metrics to quantify a test suite's ability to detect failures under varying levels of uncertainty. We evaluate UAMTERS across three robotic case studies, demonstrating that UAMTERS more effectively distinguishes test suite quality and captures uncertainty-induced failures in DL-enabled software.
