Post-frying oil removal is a simple yet essential step in reducing the health hazards of fried foods. This study develops a mechanistic model for oil transport from a potato chip into a paper towel under storage conditions (i.e., long contact duration), treating the system as a multi-domain porous media. A capillary diffusion model describes oil transport within and between the unsaturated porous domains of the chip and the paper. MicroCT imaging was used to characterize porosity, pore size distribution, and contact area between the two materials. The model incorporated concentration-dependent diffusivity in the chip and the paper, partitioning at the chip-paper interface, and imperfect interfacial contact, represented by assigning random flux zones across a limited surface area (2.6%). Material properties such as oil diffusivity and partition coefficient were estimated using experimental data and literature correlations. Simulations showed that oil absorption is initially rapid but slows over time as capillary pressure equilibrium is approached with around 50% oil reduction in 500 h. The model was validated against experimental data and revealed that oil migration is more sensitive to the spatial distribution of the contact area than to the total contact area. Mechanistic understanding and quantification of oil removal using paper can lead to an effective passive strategy for reducing oil in fried foods (including in an industrial context) while the modeling framework has broader applications in understanding fluid transport across composite food systems.

Freezing is a key method for preserving salmon, but freezing rate significantly influences its quality attributes, requiring a comprehensive mechanistic understanding for quality improvement. A heat conduction model of salmon meat with phase change predicts freezing rate from which ice crystal size and eventually color (lightness, L*) is predicted, while water diffusion from inside to the surface of the salmon predicts weight loss and is coupled to the heat transfer model through surface evaporation. The model is validated using experimental measurements of temperature, ice crystal size (via optical microscopy), color, and moisture loss at two freezing temperatures of -20 degrees C and - 80 degrees C. Results show that slow freezing leads to larger ice crystals whereas rapid freezing produces smaller crystals. Surface color of salmon, as quantified by the lightness parameter L*, decreases as air temperature increases, following a nonlinear relationship. The lightness parameter also linearly decreases as crystal size increases. Faster freezing (at lower air temperatures) leads to decreased weight loss due to limited structural disruption and reduced moisture migration to the surface. The model provides a quantitative framework for the analysis and optimization of quality in designing industrial salmon freezing processes.

Water stress and suboptimal nitrogen fertilization limit sustainable onion production. A two-season field experiment (2016-2018) evaluated the effects of varying irrigation (0.6, 0.9 and 1.2 ETc as M-1, M-2 and M-3) and nitrogen levels (0%, 75%, 100% and 120% of recommended dose, RDN as N-0, N-1, N-2 and N-3) on evapotranspiration (ETa), crop water productivity (CWP), irrigation water productivity (IWP), water-yield functions and yield response to water stress. Irrigation increased the ETa (163-281 mm) and bulb yield (6.0-9.5 t ha(-1)) while decreasing the CWP (3.01-4.12 kg m(-3)) and IWP (4.13-6.81 kg m(-3)). An optimum balance was found at an average ETa of 220 mm, irrigation of 158 mm, yield of 8.2 t ha(-1) and CWP of 3.6 kg m(-3). The overall yield response factor (K-y) was 0.78, indicating that onion is moderately sensitive to water stress, which improved with increasing nitrogen application. Among the treatments, microsprinkler irrigation at 0.9 ETc with 120% RDN (M2N3) achieved the highest yield gain (65%), moderate CWP improvement (28%) and maximum net profit (109%) and benefit-cost ratio (107%) over the control (M1N1). These results suggest that M2N3 is the optimal strategy for water-scarce Indo-Gangetic plains and similar agroclimatic zones.

Nonthermal plasma (NTP), for the first time, was integrated in glycerol dehydration reaction catalyzed by silicotungstic acid supported on mesoporous silica with argon as the carrier and discharge gas. A range of reaction temperatures (220-320 & DEG;C) and NTP discharge field strengths (2.06-6.87 kV/cm) were studied for the individual and interactive effects regarding the glycerol conversion and product selectivity. Results showed that the presence of NTP always improved the glycerol conversion, and NTP increased acrolein selectivity if properly conditioned. An optimal condition of 275 & DEG;C and 4.58 kV/cm NTP field strength achieved a glycerol conversion of 94.4 mol%, acrolein selectivity of 88.0 mol%, with an acrolein yield of 83.1 mol%, representing a 10% improvement in acrolein production over that conducted at the same temperature but without NTP. Results of this study will also have significant implication for other heterogeneously catalyzed dehydration reactions. Simulation of the high-voltage electric field distribution as function of NTP electrical conductivity and relative permittivity of catalyst materials also offers insight for the future design of reactors and catalysts.

Ingredients play a crucial role in cake baking, significantly impacting important metrics like oven rise, moisture content, and color. While there is existing knowledge, a comprehensive mechanistic understanding of the intricate relationships between ingredients and the underlying physics is needed. We use a porous media-based multiphase transport framework coupled with large-strain viscoelastic deformation to study how water, sugar, and fat influence cupcakes’ height, weight, and color starting from the batter stage. We show that high water content batters have an expedited oven rise due to increased thermal diffusivity leading to faster heat transfer and higher evaporation rates but lower surface temperature relative to low water content batters and, thus, less browning. High sugar and high fat batters have a delayed batter-to-foam material transformation because of increased starch gelatinization temperature, leading to shorter and drier cupcakes because of higher vapor loss early in baking. We find that lowering water or increasing sugar or fat content in the batter gives darker cupcakes because of increased surface temperatures. The novel mechanistic understanding of ingredient functionality can extend to mechanistic understanding and optimization of other baking processes.

Radiofrequency Cardiac Ablation (RFCA) is a non-surgical procedure to destroy abnormal pathways causing cardiac arrhythmias within the cardiac chamber. RF energy from electrodes contacting the cardiac tissue generates heat, raising tissue temperature and ablating it. Success of the procedure, as in avoiding complications like steam pops, requires its comprehensive mechanistic understanding. Using a 3-D porous media-based model of the cardiac tissue and surrounding chamber blood, with coupled fluid flow, heat transfer (including evaporation and thermally driven natural convection), and resistive heating, provided a more complete picture of the thermal ablation process. Circulation of blood due to buoyancy-driven natural convection reduces the non-uniformity of temperatures, by reducing temperatures initially in the target region but increasing them in the later stages, compared to when natural convection effects are not included. Thus, natural convection affects possible steam pop occurrence differently over the time for the procedure. The more complete picture of the RFCA procedure provided here by the detailed physics-based model can benefit with increased accuracy and thus reduced re-ablation (thus higher success rates), and reduced costs for catheter design and development.

Radiofrequency Cardiac Ablation (RFCA) is a common procedure that heats cardiac tissue to destroy abnormal signal pathways to eliminate arrhythmias. The complex multiphysics phenomena during this procedure need to be better understood to improve both procedure and device design. A deformable poromechanical model of cardiac tissue was developed that coupled joule heating from the electrode, heat transfer, and blood flow from normal perfusion and thermally driven natural convection, which mimics the real tissue structure more closely and provides more realistic results compared to previous models. The expansion of tissue from temperature rise reduces blood velocity, leading to increased tissue temperature, thus affecting steam pop occurrence. Detailed temperature velocity, and thermal expansion of the tissue provided a comprehensive picture of the process. Poromechanical expansion of the tissue from temperature rise reduces blood velocity, increasing tissue temperature. Tissue properties influence temperatures, with lower porosity increasing the temperatures slightly, due to lower velocities. Deeper electrode insertion raises temperature due to increased current flow. The results demonstrate that a 5% increase in porosity leads to a considerable 10% increase in maximum tissue temperature. These insights should greatly help in avoiding undesirable heating effects that can lead to steam pop and in designing improved electrodes.

Sequential drying provides opportunities to achieve quality by combining individual drying modes. Many process and product parameters affecting multiple drying modes make it complex. We studied quality evolution in a sequence of microwave, impingement, and hot air drying of the mashed vegetable chip using a poromechanics and transport model with volumetric evaporation, moisture-based microwave absorption, pressure-driven expansion, moisture loss-driven shrinkage and glass transition. It was validated for temperature, moisture, size and porosity obtained in an industrial setting. Gas porosity is higher at the center during microwave and at the surface during impingement drying. Moisture loss-driven shrinkage was more dominant than pressure-driven expansion. Shrinkage followed the moisture loss when the chip was rubbery, but shrinkage reduced after the glass transition occurred. Sequential drying was able to achieve a quality that would be hard to achieve using a single drying method. The mechanistic framework, successful for three drying modes and their sequence, is useful to understand drying-like processes.

Current widespread and intensive soil degradation in India has been driven by unprecedented levels of population growth, large-scale industrialization, high-yield agriculture, urban sprawl and the spread of human infrastructure. The damage caused to managed and natural systems by soil degradation threatens livelihoods and local services and leads to national socio-economic disruption. Human-induced soil degradation results from land clearing and deforestation, inappropriate agricultural practices, improper management of industrial effluents and wastes, careless management of forests, surface mining, urban sprawl, and ill-planned commercial and industrial development. Of these, inappropriate agricultural practices, including excessive tillage and use of heavy machinery, over-grazing, excessive and unbalanced use of inorganic fertilizers, poor irrigation and water management techniques, pesticide overuse, inadequate crop residue and/or organic carbon inputs, and poor crop cycle planning, account for nearly 40% (121 Mha) of land degradation across India. Globally, human activities related to agriculture contribute to the transgression of four of the nine Planetary Boundaries proposed by Rockstrom et al. (2009): Climate Change, Biodiversity Integrity, Land-system Change, and altered Phosphorus and Nitrogen Biogeochemical Flows. This review focuses on how knowledge of soil processes in agriculture has developed in India over the past 10 years, and the potential of soil science to meet the objectives of the United Nations' Sustainable Development Goal 2: Zero Hunger (End hunger, achieve food security, improved nutrition and promote sustainable agriculture), using the context of the four most relevant Planetary Boundaries as a framework. Solutions to mitigate soil degradation and improve soil health in different regions using conservation agricultural approaches have been proposed. Thus, in this review we (1) summarize the outputs of recent innovative research in India that has explored the impacts of soil degradation on four Planetary Boundaries (Climate Change, Biodiversity Loss, Land-system Change, and altered Biogeochemical Flows of Phosphorus and Nitrogen) and vice-versa; and (2) identify the knowledge gaps that require urgent attention to inform developing soil science research agendas in India, to advise policy makers, and to support those whose livelihoods rely on the land.

Analysis of long-term datasets on bird populations can be used to answer ecological and management questions that are useful for conservation. Tanguar Haor (9500 ha) is one of the major freshwater wetlands in Bangladesh and supports a large number of migratory and resident waterbirds. Because of its unique ecological and economic values, it is arguably the most notable wetland in the floodplains of northeast Bangladesh and in the region. This Ramsar site supports globally important populations of threatened waterbirds, such as the Baer's Pochard Aythya baeri, Common Pochard Aythya ferina, Falcated Duck Mareca falcata, Ferruginous Duck Aythya nyroca, Oriental Darter Anhinga melanogaster, and Black-tailed Godwit Limosa limosa. Considering the international significance of this site, knowledge gaps on waterbird population trends, and key ecological factors, we conducted waterbird census between 2008 and 2021 to identify priority sites for conservation, population trends of resident and migratory waterbirds, and environmental factors that influence their abundances. We recorded a total of 69 species of waterbirds (maximum count of 166,788 individuals in 2013) and assessed population trends of 47 species. Of these, peak counts of 15 species exceeded the 1% threshold of their Asian-Australian Flyway population estimates. Most species (59%) showed a declining trend, including the critically endangered Baer's Pochard and the vulnerable Common Pochard, and 16 species (41%) showed an increasing trend. Based on the abundance and species diversity, we have identified Chotainna beel and Lechuamara beel as conservation priority sites within the Haor complex and discuss key threats to these areas. We also offer evidence that adjusting water-level management to annual rainfall patterns could be a useful intervention for waterbird management. Involving local communities in conservation efforts by creating bird sanctuaries within the Haor complex will strengthen waterbird conservation in the country and along the East Asian-Australian Flyway.

This study presents a coupled multiphase porous media transport model with evaporation, large deformation, and material transformation (phase change from starch gelatinization) for cupcake baking in a conventional oven. The governing equations for transport and deformation are energy, mass, and momentum conservation of the solid cupcake batter matrix, water, water vapor, and carbon dioxide produced during baking. They are solved numerically to predict the evolution of the cupcake batter's temperature, moisture, dimensional change, and color during baking. Cupcake-baking experiments are used to validate the model against measured temperature, moisture, color, and cupcake height during baking. The rate of carbon dioxide production, vapor generation, and the cupcake batter's mechanical properties determine the cupcake's final shape. The novelty of the numerical model is the coupling of the massive change in mechanical and thermophysical properties with multiphase transport and expansion due to pressure and moisture loss from shrinkage.

Cellular therapies for type-1 diabetes can leverage cell encapsulation to dispense with immunosuppression. However, encapsulated islet cells do not survive long, particularly when implanted in poorly vascularized subcutaneous sites. Here we show that the induction of neovascularization via temporary controlled inflammation through the implantation of a nylon catheter can be used to create a subcutaneous cavity that supports the transplantation and optimal function of a geometrically matching islet-encapsulation device consisting of a twisted nylon surgical thread coated with an islet-seeded alginate hydrogel. The neovascularized cavity led to the sustained reversal of diabetes, as we show in immunocompetent syngeneic, allogeneic and xenogeneic mouse models of diabetes, owing to increased oxygenation, physiological glucose responsiveness and islet survival, as indicated by a computational model of mass transport. The cavity also allowed for the in situ replacement of impaired devices, with prompt return to normoglycemia. Controlled inflammation-induced neovascularization is a scalable approach, as we show with a minipig model, and may facilitate the clinical translation of immunosuppression-free subcutaneous islet transplantation.

Computer-aided mechanistic modeling of food, while robust and capable of producing detailed solutions, demands massive computational resources and runtime precluding adoption of the technique in food manufacturing. Here, a deep learning-based solution is presented. A surrogate model is developed by training a memory-retaining neural network with the finite element solutions of a multiphase and multiphysics-based model of dehydration in porous media. Twenty-five percent of the test cases showed a RMSE value of less than 2 °C in predicting temperature and 0.1 db in predicting moisture content, while the median value of the error was 5 °C and 0.2 db, respectively. Efficacy of a deep learning framework in accurately capturing the complexities of a multiphysics-based model over a vast range of product and process parameters is studied while exploring various aspects of building a surrogate model that can provide a detailed solution in seconds, enabling simulation in everyday use with modest resources.

Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems. Computer-aided food engineering (CAFE) drives high-level innovations in food safety and quality. The multiscale structure of foods requires novel modelling paradigms. This Review explores current CAFE modelling frameworks and computational approaches and the challenges to introducing computer-aided engineering in food-manufacturing processes.

While the food industry is quite innovative in terms of food product development, the process development is still primarily through trial and error experimentation. The use of virtualization or computer simulations to perform "what-if" scenarios using mathematical models that describe the food processes realistically might enable the food industry benefit significantly from a faster time-to-market and reduced resource use in product and process development. This chapter provides brief summary of the strengths of physics based (mechanistic) modeling in virtualization, discusses the need for modeling frameworks and introduces one framework (multiphase transport in deformable porous media), presents approaches to obtaining the properties necessary for such physics-based models, extends the applications to multiphysics and also multiscale, and finally discusses product and process optimization. Challenges in virtualization or computer-aided food engineering are also explained with future opportunities and directions.

Sustainable utilization of biomass as a renewable energy resource is the most pressing issue today, owing to the high cost and continued depletion of limited fossil fuels. Rice husk (RH) is an abundantly available agricultural waste biomass that can be used for energy production. However, energy production depends on several fuel characteristics, such as composition of RH which is highly influenced by the location and cultivation conditions of paddy. Hence, an entropy-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) multi-criteria decision-making method (MCDM) was employed to rank and select suitable RH variety amongst four varieties of paddy, namely Badsha-bhog, Lal-swarna, IR-36, and Shankar. A total of fifteen attributes were considered, including physical, chemical, proximate, ultimate, kinetic parameters. Additionally, structural, functional, and morphological properties of RH and their ash (RHA) were also analysed. Higher heating value (HHV) determined from proximate and ultimate analyses varied between 15.43 - 16.64 MJ/kg and 14.31 - 15.59 MJ/kg, respectively. Thermogravimetry analysis showed active pyrolysis zone for different RHs between 185 and 390 degrees C with activation energy and order of reaction between 96.43 and 99.37 kJmol(-1) and 2.36 to 3.05, respectively. Silica volume percentage was calculated from the X-ray diffraction (XRD) analysis of RHA and ranged between 60.12 and 64.33%. The TOPSIS analysis demonstrated that RH of Lal-swarna variety ranked top with the highest relative closeness value of 0.81, followed by Shankar (0.79), IR-36 (0.65), and Badsha-bhog (0.64). This study hopes to enhance the direct application of RH as energy feedstock by optimizing the paddy variety selection.

The delivery of encapsulated islets or stem cell-derived insulin-producing cells (i.e., bioartificial pancreas devices) may achieve a functional cure for type 1 diabetes, but their efficacy is limited by mass transport constraints. Modeling such constraints is thus desirable, but previous efforts invoke simplifications which limit the utility of their insights. Herein, we present a computational platform for investigating the therapeutic capacity of generic and user-programmable bioartificial pancreas devices, which accounts for highly influential stochastic properties including the size distribution and random localization of the cells. We first apply the platform in a study which finds that endogenous islet size distribution variance significantly influences device potency. Then we pursue optimizations, determining ideal device structures and estimates of the curative cell dose. Finally, we propose a new, device-specific islet equivalence conversion table, and develop a surrogate machine learning model, hosted on a web application, to rapidly produce these coefficients for user-defined devices.