Predictive modeling of dielectric heating in porous foods is challenging due to their nature as multiphase materials. To explore the relationship between the topological structure of multiphase foods and the accuracy of dielectric mixture models, the degree of anisotropy of two cooked rice samples with 26 and 32 % porosity was determined, and their dielectric properties were estimated using the Lichtenecker (LK), Landau-LifshitzLooyenga (LLL), and Complex Refractive Index Mixture (CRIM) equations. These properties were used in a predictive finite-element model for reheating an apparent homogeneous rice sample on a flatbed microwave (MW) for 120 s. The results were compared with experimental data and a validated two-element model. Unlike LK and LLL equations, the CRIM equation predicted heat accumulation towards the edges of the container at the two values of porosity ratio evaluated, in accordance with the experimental results and the isotropic nature of the sample. The simulated temperature distributions suggest that the three evaluated equations could predict the MW heating behavior of rice to some extent, but that in order to obtain more accurate results, it could be useful to obtain an empirical topology-related parameter specific for this sample. These results can provide insight on the relationship between the topology of the porous structure in the sample and the adequacy of different dielectric mixture models.

The treatment of excavated soil using the dry sieving method to produce recycled sand is an effective approach for resource utilization. Currently, the hot-air drying process used in this method exhibits high energy consumption. To address this issue, this study proposes a microwave drying technology to dry the excavated soil. Comparative experiments on microwave (1-6 kW) and hot-air (105-205 degrees C) drying of the excavated soil were conducted. The drying behavior and specific energy consumption of the excavated soil were investigated. The Weibull-Fick combined method was recommended for the segmental determination of the effective moisture diffusion coefficient, and the question of whether microwave drying adversely affects sand particles in the excavated soil was answered. The results revealed the following: Compared with hot-air drying, microwave drying demonstrated shorter drying time (3.5-38 min vs 75-1200 min), lower specific energy consumption (6.2-11.5 MJ/kg vs 22.3-55.4 MJ/kg), and a higher range of effective moisture diffusion coefficient (10-8-10-7 m2/s vs 10-9-10-8 m2/s). With increasing microwave power (3-6 kW), the time required for complete drying of the sample was reduced by up to 56 %. Under microwave drying, relaxing the termination moisture content criterion from 0 to 0.01 resulted in a 17 %-32 % reduction in specific energy consumption, accompanied by a 24 %-36 % decrease in drying time. Microwave drying did not damage sand particles within the excavated soil.

This study focuses on bio-based natural fiber-reinforced polymer composites (NFRPCs). In this work, bio-polybutylene succinate (bio-PBS) reinforced with hemp fibers (HF) varying at 10 wt%, 20 wt%, and 30 wt% were developed via microwave-assisted compression moulding (MACM) technique. The mechanical properties, crystalline properties, dynamic mechanical analysis, and soil degradation behaviour of these composites were analysed. The study demonstrated that composites with 30 wt% hemp fiber content exhibited the most optimal mechanical properties, with crystallinity increasing by 22%. These composites achieved the highest storage modulus of 13,349 MPa, while their loss modulus was found to be 110% higher compared to neat bio-PBS. Additionally, soil burial experiments revealed that the 30 wt% HF/bio-PBS composites underwent the greatest weight loss after 60 days of soil exposure, indicating superior biodegradability compared to the pure bio-PBS matrix. The work further concluded that hemp fiber-reinforced bio-PBS composites showcased improved mechanical performance, crystallinity, biodegradability, and processing characteristics, surpassing other bio-composite alternatives.

Underground winter bamboo shoots, prized for their high nutritional value and economic significance, face harvesting challenges owing to inefficient manual methods and the lack of specialized detection technologies. This review systematically evaluates current detection approaches, including manual harvesting, microwave detection, resistivity methods, and biomimetic techniques. While manual methods remain dominant, they suffer from labor shortages, low efficiency, and high damage rates. Microwave-based technologies demonstrate high accuracy and good depths but are hindered by high costs and soil moisture interference. Resistivity methods show feasibility in controlled environments but struggle with field complexity and low resolution. Biomimetic approaches, though innovative, face limitations in odor sensitivity and real-time data processing. Key challenges include heterogeneous soil conditions, performance loss, and a lack of standardized protocols. To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network-transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. These advancements aim to enhance detection accuracy, reduce labor dependency, and increase yields. Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. The integration of intelligent technologies is poised to transform traditional bamboo forestry into automated, sustainable smart forest farms, addressing global supply demands while preserving ecological integrity.

The thermal stabilization of expansive soils has emerged as a promising and sustainable alternative to conventional chemical stabilization methods, addressing the long-standing challenges associated with soil swelling and shrinkage. This review critically evaluates the mechanisms, applications, and advancements in thermal stabilization techniques, with a particular focus on both traditional approaches (e.g., kiln heating) and emerging innovations such as microwave heating. This study synthesizes recent research findings to assess how thermal treatment modifies the mineralogical, physical, and mechanical properties of expansive soils, reducing their plasticity and improving their strength characteristics. Comparative analysis highlights the advantages, limitations, and sustainability implications of different thermal methods, considering factors such as energy efficiency, scalability, and environmental impact. While thermal stabilization offers a viable alternative to chemical treatments, key challenges remain regarding cost, field implementation, and long-term performance validation. The integration of thermal treatment with complementary techniques, such as lime stabilization, is explored as a means to enhance soil stability while minimizing environmental impact. By addressing critical research gaps and providing a comprehensive perspective on the future potential of thermal stabilization, this review contributes valuable insights for researchers and engineers seeking innovative and sustainable solutions for managing expansive soils.

An Integrated Process Intensification (IPI) technology-based roadmap is proposed for the utilization of renewables (water, air and biomass/unavoidable waste) in the small-scale distributed production of the following primary products: electricity, H-2, NH3, HNO3 and symbiotic advanced (SX) fertilizers with CO2 mineralization capacity to achieve negative CO2 emission. Such a production platform is an integrated intensified biorefinery (IIBR), used as an alternative to large-scale centralized production which relies on green electricity and CCUS. Hence, the capacity and availability of the renewable biomass and unavoidable waste were examined. The critical elements of the IIBR include gasification/syngas production; syngas cleaning; electricity generation; and the conversion of clean syngas (which contains H-2, CO, CH4, CO2 and N-2) to the primary products using nonthermal plasma catalytic reactors with in situ NH3 sequestration for SA fertilizers. The status of these critical elements is critically reviewed with regard to their techno-economics and suitability for industrial applications. Using novel gasifiers powered by a combination of CO2, H2O and O-2-enhanced air as the oxidant, it is possible to obtain syngas with high H-2 concentration suitable for NH3 synthesis. Gasifier performances for syngas generation and cleaning, electricity production and emissions are evaluated and compared with gasifiers at 50 kWe and 1-2 MWe scales. The catalyst and plasma catalytic reactor systems for NH3 production with or without in situ reactive sequestration are considered in detail. The performance of the catalysts in different plasma reactions is widely different. The high intensity power (HIP) processing of perovskite (barium titanate) and unary/binary spinel oxide catalysts (or their combination) performs best in several syntheses, including NH3 production, NOx from air and fertigation fertilizers from plasma-activated water. These catalysts can be represented as BaTi1-vO3-x{#}(y)N-z (black, piezoelectric barium titanate, bp-{BTO}) and (M3-jMkO4-m)-M-(1)-O-(2){#}(n)N-r/SiO2 (unary (k = 0) or a binary (k > 0) silane-coated SiO2-supported spinel oxide catalyst, denoted as M/Si = X) where {#} infers oxygen vacancy. HIP processing in air causes oxygen vacancies, nitrogen substitution, the acquisition of piezoelectric state and porosity and chemical/morphological heterogeneity, all of which make the catalysts highly active. Their morphological evaluation indicates the generation of dust particles (leading to porogenesis), 2D-nano/micro plates and structured ribbons, leading to quantum effects under plasma catalytic synthesis, including the acquisition of high-energy particles from the plasma space to prevent product dissociation as a result of electron impact. M/Si = X (X > 1/2) and bp-{BTO} catalysts generate plasma under microwave irradiation (including pulsed microwave) and hence can be used in a packed bed mode in microwave plasma reactors with plasma on and within the pores of the catalyst. Such reactors are suitable for electric-powered small-scale industrial operations. When combined with the in situ reactive separation of NH3 in the so-called Multi-Reaction Zone Reactor using NH3 sequestration agents to create SA fertilizers, the techno-economics of the plasma catalytic synthesis of fertilizers become favorable due to the elimination of product separation costs and the quality of the SA fertilizers which act as an artificial root system. The SA fertilizers provide soil fertility, biodiversity, high yield, efficient water and nutrient use and carbon sequestration through mineralization. They can prevent environmental damage and help plants and crops to adapt to the emerging harsh environmental and climate conditions through the formation of artificial rhizosphere and rhizosheath. The functions of the SA fertilizers should be taken into account when comparing the techno-economics of SA fertilizers with current fertilizers.

Microwave-assisted rock-breaking technology, as a novel hybrid approach, is anticipated to facilitate the efficient excavation of complex rock formations. It is therefore crucial to understand the damage and failure mechanisms of rocks that have been subjected to irradiation. In this study, uniaxial compression experiments were conducted on granite specimens after 1.4 kW microwave irradiation for varying durations. Furthermore, a numerical method was proposed to solve electromagnetic-thermal-mechanical coupling problems by integrating finite and discrete elements. The results demonstrated a differential temperature distribution (high temperature in the middle and low-temperature areas at the ends) in the granite specimens under microwave irradiation, which resulted in a notable reduction in their physical and mechanical properties. As the duration of irradiation increased, the rate of heating and the extent of strength reduction both diminished, while the morphology and distribution of cracks at ultimate failure became increasingly complex. The numerical method effectively addresses the simulation challenges associated with the electromagnetic selective heating of granite containing multiple polar minerals under microwave irradiation. This approach accounted for the non-uniform thermal expansion of the minerals and provided a comprehensive model of damage progression under compression. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

Accurately determining the freeze/thaw state (FT) is crucial for understanding land-atmosphere interactions, with significant implications for climate change, ecological systems, agriculture, and water resource management. This article introduces a novel approach to assess FT dynamics by comparing the new diurnal amplitude variations (DAV) algorithm with the traditional seasonal threshold algorithm (STA) based on the soil moisture active passive (SMAP) brightness temperature data. Utilizing soil temperature profiles from 44 sites recorded by the National Ecological Observatory Network between July 2019 and June 2022. The results reveal that the DAV algorithm demonstrates a remarkable potential for capturing FT signals, achieving an average accuracy of 0.82 (0.89 for the SMAP-FT product) across all sites and a median accuracy of 0.94 (0.92 for the SMAP-FT product) referring to soil temperature at 0.02 m. Notably, the DAV algorithm outperforms the SMAP-adopted STA in 25 out of 44 sites. The accuracy of the DAV algorithm is affected by daily temperature fluctuations and geographical latitudes, while the STA exhibits limitations in certain regions, particularly those with complex terrains or variable climatic patterns. This article's innovative contribution lies in systematically comparing the performance of the DAV and STA algorithms, providing valuable insights into their respective strengths and weaknesses.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).