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The fresh fruit agricultural and distribution sector is faced with risks and uncertainties from climate change, water scarcity, land-use increase for industrial and urban development, consumer behavior, and price volatility. The planning framework for production and distribution is highly complex as a result. Mathematical models have been developed over the decades to deal with this complexity. With improvements in both processor speed and memory, these models are becoming increasingly sophisticated. This review focuses on the recent progress in mathematically based decision making to account for uncertainties in the fresh fruit supply chain. The models in the literature are mostly based on linear and mixed integer programming and involve variants such as stochastic programming and robust optimization. The functional areas of application include planting, harvest optimization, logistics and distribution. The perishability of the fresh fruit supply chain is an important issue as is the cycle time of cultivation and harvest.

The study extracts representative features to train a model with supervised machine learning (ML) to detect powdery mildew (Sphaerotheca macularis f. sp. fragariae) on the strawberry leaves. Powdery mildew (PM) is a fungal disease that greatly affects the production of strawberry and usually infects under conditions of warming temperatures and high humidity. In this research, we report robust models to detect PM using image processing and ML technologies. Three feature extraction techniques (histogram of oriented gradients; HOG, speeded-up robust features; SURF, and gray level co-occurrence matrix; GLCM) and two supervised ML (artificial neural network; ANN and support vector machine; SVM) were implemented using MATLAB. Images were augmented to 1016 images using a four different angle rotation technique to simulate strawberry leaf bundles in the real field. The classification accuracy (CA) to detect PM was highest at 94.34% with a combination of ANN and SURF with 908 × 908 image resolution and with SVM and GLCM at 88.98% with 908 × 908 image resolution. In terms of the extraction time for real-time processing, HOG takes the shortest time to extract features in both ANN and SVM.

In July 2017, a massive bloom of the potentially toxic cyanobacterial species Planktothrix sp. was observed in the Béni-Haroun Reservoir (Algeria), which was followed by a massive fish death. Many questions were raised in association with the role of cyanotoxins and the fish massive mortality. The objective of this paper is twofold: (1) to investigate the variability of physicochemical and cyanobacterial parameters (chlorophyll-a, phycocyanin, allophycocyanin, and microcystins) throughout the period of July 2017 to June 2018; and (2) to determine the free and total MC levels in viscera and muscle tissues of the common carp (Cyprinus carpio), which are found dead in the considered reservoir in October 2017. Our results showed microcystin (MC) concentrations in water samples (by the protein phosphatase PP2A assay) had reached 651.2 ng MC-LR equiv./L. Total MC levels (free + bound) in the viscera and muscle tissues of sampled dead fish were at 960.24 and 438.54 µg MC-LR equiv./kg dw, respectively. It is assumed that high concentrations of MC observed in the tissues of common carp induced a strong degradation of the visceral contents resulting in the complete lysis of the hepatopancreas, and presumably the massive fish death.

This paper introduces the Threshold Index (hereafter called TRINDEX) for the Harmful Algal Bloom (HAB) prediction in lake Torment (Nova Scotia province, Canada). TRINDEX was suggested via the logarithmic transformation and then the thresholds of bloom pattern were established by the discrimination test named the Receiver Operating Characteristic curve (ROC). The cohort studies will be also presented to show how accurate the bloom prediction when using TRINDEX in comparing to the real observations.

This paper will update some information related to cyanotoxins detected in lake Torment, a recreational freshwater body in Nova Scotia, Canada. The goal of our paper is to present the detected toxins released by Toxic Algal Blooms and introducing the method of measurement by using the ELISA test. The results can show a very potential approach of toxin detection which could be applied in Ukraine or other countries where cyanotoxin detection in fresh and marine water ecosystems are still not well developed.

We present an optimization model for dragon fruit plantations in Vietnam. The timing of cultivating and harvesting decisions are taken into account as the dragon fruit plant has an approximately ten-year life cycle with maximum average yield in the fourth year. Another consideration also included is the prevalence of forward-buying contracts with locked-in prices. The dragon fruit supply chain faces several difficulties as yield, price, and demand are highly sensitive to weather conditions and global uncertainty factors. The risk factors in the dragon fruit supply chain also depend on species&mdash, for example, the red varieties, while more profitable than the white varieties, also have higher export risk because they are subject to global prices and adverse geopolitical conditions.

Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30&ndash, 70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017&ndash, 2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.

The global agriculture sector faces many challenges in its mission to meet the increasing demand for food and fiber. Climate change, increasing population growth, emergence of crop diseases, damage to crops from rodents and critters, and shrinking farming land in some regions are among these challenges. Application of agrochemicals has proven to be an efficient answer to some of these challenges. However, the impacts of these products on human health and the environment combined with the increased requirement for sustainable farming requires the development of optimal spraying practices that would balance out all interests and concerns. In this paper, a mathematical model is developed to jointly minimize spraying time and drift losses. The obtained bi-objective mixed integer nonlinear programming model is solved for a case study example published in the crop protection literature. Optimal solutions are obtained using the weighted sum method and the epsilon-constraint approach. The results showed that valid and reasonable solutions can be obtained by selecting the appropriate combination of boom height, nozzle spacing, nozzle type, and tractor travel speed. Useful insights are obtained through various computational experiments.

Wild blueberry is one of the most important fruit crops of Canada that produces more than 50% of the world‘s blueberries. Understanding and predicting the relationships between the machine operating parameters, fruit losses, topographic features, and crop characteristics can aid in better berry recovery during mechanical harvesting. This article suggested a modeling approach for prediction of fruit losses during harvesting using artificial neural network (ANN) and multiple regression (MR) techniques. Four wild blueberry sites were selected and completely randomized factorial (3 x 3) experiments were conducted at each site. One hundred sixty-two plots (0.91 x 3 m) were made at each site, in the path of operating harvester. Total fruit yield and losses were collected from each plot within selected sites. The harvester was operated at specific levels of ground speed (1.20, 1.60, and 2.00 km h-1) and head rotational speed (26, 28, and 30 rpm). The slope, plant height, and fruit zone were also recorded from each plot. The collected data were normalized, and 70% of the data were utilized for calibration, and 30% for validation of developed models using ANN and MR techniques. Results of root mean square error (RMSE) suggested that the tanh-sigmoid transfer function between the hidden layer and output layer was the best fit for this study. The developed models were validated internally and externally and the best performing configurations were identified based on RMSE, coefficient of efficiency, percent variation, and coefficient of determination. Results of scatter plot among the RMSE and epoch suggested that an epoch size (iterative steps) of 15,000 was appropriate to predict fruit losses using ANN approach. Results revealed that the prediction accuracy of MR model was lower (R2 = 0.46; RMSE = 0.14%) than the ANN model (R2 = 0.84; RMSE = 0.075%) for calibration dataset. Results reported that the ANN model predicted fruit losses with higher (R2 = 0.63; RMSE = 0.11%) accuracy when compared with MR model (R2 = 0.37; RMSE = 0.15%) for external validation dataset. Overall, results of this study suggested that the ANN model was able to accurately and reliably predict fruit losses during harvesting. These results can help to identify the factors responsible for fruit losses and to suggest optimal harvesting scenarios to improve harvesting efficiency.

Wild blueberry is a high value cash crop in northeastern North America. In the last two decades, improved management practices have changed crop characteristics. Currently, the wild blueberry industry is facing increased harvesting losses (15%-25%) due to changes in crop conditions. This study was designed to examine the effect of plant characteristics on picking efficiency of the wild blueberry harvester. Four wild blueberry fields were selected in Nova Scotia and New Brunswick, Atlantic Provinces of Canada. Plant height (PH) and plant density (PD) were classified into four different categories, i.e., tall plant - low plant density, tall plant - high plant density, short plant - low plant density, and short plant - high plant density, and stem thickness (ST) was used as a covariate. Nine yield plots (0.9 x 3 m) for each combination of PH and PD were selected randomly at each experimental field. The PH, PD, and ST were recorded manually from each selected plot. Factorial experiments with four replications were designed to identify the combined effect of ground speed (1.2, 1.6, and 2.0 km h-1) and header revolutions (26, 28, and 30 rpm) on berry losses at each category of PH and PD. Berry losses were collected from each plot within the selected fields. Factorial analysis of covariance (ANCOVA) using general linear model (GLM) procedure showed that the interaction of ground speed and header rpm was significant (p = 0.05) in each category of plant characteristics. Results of multiple means comparison showed that the lower ground speed and header rpm resulted in significantly lower losses when compared with higher ground speed and header rpm. The study findings also suggested a suitable combination of ground speed and header rpm for each class of plant characteristics to minimize the berry losses during harvesting.

Knowledge of spatial variability in fruit yield, crop characteristics, fruit loss, and slope of the field is critical for planning and implementing the operational recommendation for mechanical harvesting. Improved management practices have caused significant changes in crop conditions, which reduce the picking performance of the harvester as the harvester was designed in early 1980s. The goal of this work was to characterize and quantify the spatial pattern of variability in crop characteristics, fruit yield, and slope the field in relation to fruit loss during mechanical harvesting. Completely randomized experiments were designed and plots (0.91 x 3 m) were constructed in selected fields. Total fruit loss (pre- and post-harvest) and fruit yield were collected from each plot within selected fields. Slope, plant height, and fruit zone were also recorded from each plot to examine the impact of their spatial variability on fruit loss. The coefficient of variations (CVs) for fruit yield, berry loss, slope, plant height, and fruit zone suggested moderate to high variability (CV>15%) within selected fields. Correlation analysis showed higher fruit loss in high yielding areas and vice versa. Fruit yield, berry loss, slope, plant height, and fruit zone had large spatial variation (range of influence ~20 to 50 m) within selected fields. Kriged maps revealed substantial variation in these parameters. Variability in fruit loss corresponding with the spatial variations in crop characteristics, fruit yield, and slope suggested that the fruit loss during mechanical harvesting were influenced by the spatial variations in these parameters. Adjustments in operational settings (ground speed, head revolutions, head height, etc.) of the harvester in accordance with the spatial variations have the potential to increase profit margins for blueberry industry.

The wild blueberry industry is facing increased harvesting losses with the existing commercial harvester. These machines are no longer able to efficiently harvest the higher yields that result from improvements in plant growth and productivity. This study was designed to evaluate the performance efficiency of a commercial wild blueberry harvester for fruit losses during harvesting. Four wild blueberry fields were selected in the Nova Scotia and New Brunswick provinces of Canada. A 3 × 3 factorial experiment was constructed to examine the joint effect of ground speed and header revolution per minute (rpm) on picking efficiency of the harvester. Eighty-one yield plots (0.91 × 3 m) were selected randomly in each field. The field boundaries, bare spots, weeds, and yield plots were mapped with a real-time kinematics global positioning system (RTK-GPS). The harvester was operated at specific levels of ground speed at 1.2, 1.6 and 2.0 km h-1 and header rpm of 26, 28 and 30. The total fruit yield, un-harvested berries on the plants, berries on the ground, and berries through the blower, were collected from each plot within the selected fields. The pre-harvest fruit losses were collected from each plot prior to harvest. The treatment combinations were assigned randomly within the selected fields. The slope, plant height, and fruit zone were also recorded manually from each plot. Results indicated that the pre-harvest fruit losses were lower in early season compared to those harvested later. Un-harvested berries on the plants and losses through the blower were significantly lower than losses on the ground. Significant relationship among the fruit yield and total losses (r = 0.54 to 0.82) suggested that losses during harvesting were proportional to fruit yield. A factorial analysis of variance (ANOVA) showed that ground speed, header rpm, and their interaction were found to have significant (p=0.05) effects on the picking efficiency of the harvester. Results of means comparison showed that a combination of 1.2 km h-1 and 26 rpm resulted in significantly lower losses when compared with other treatment combinations. Results also revealed that a suitable combination of ground speed and header rpm can minimize the fruit losses during harvesting. This can increase harvestable yield and farm profitability.

Hepatotoxic microcystins (MCs) are the most widespread class of cyanotoxins and the one that has most often been implicated in cyanobacterial toxicosis. One of the main challenges in studying and monitoring MCs is the great structural diversity within the class. The full chemical structure of the first MC was elucidated in the early 1980s and since then, the number of reported structural analogues has grown steadily and continues to do so, thanks largely to advances in analytical methodology. The structures of some of these analogues have been definitively elucidated after chemical isolation using a combination of techniques including nuclear magnetic resonance, amino acid analysis, and tandem mass spectrometry (MS/MS). Others have only been tentatively identified using liquid chromatography-MS/MS without chemical isolation. An understanding of the structural diversity of MCs, the genetic and environmental controls for this diversity and the impact of structure on toxicity are all essential to the ongoing study of MCs across several scientific disciplines. However, because of the diversity of MCs and the range of approaches that have been taken for characterizing them, comprehensive information on the state of knowledge in each of these areas can be challenging to gather. We have conducted an in-depth review of the literature surrounding the identification and toxicity of known MCs and present here a concise review of these topics. At present, at least 279 MCs have been reported and are tabulated here. Among these, about 20% (55 of 279) appear to be the result of chemical or biochemical transformations of MCs that can occur in the environment or during sample handling and extraction of cyanobacteria, including oxidation products, methyl esters, or post-biosynthetic metabolites. The toxicity of many MCs has also been studied using a range of different approaches and a great deal of variability can be observed between reported toxicities, even for the same congener. This review will help clarify the current state of knowledge on the structural diversity of MCs as a class and the impacts of structure on toxicity, as well as to identify gaps in knowledge that should be addressed in future research.

Toxic cyanobacteria (CB) blooms are being reported in an increasing number of water bodies worldwide. As drinking water (DW) treatment can be disrupted by CB, in addition to long term management plans, short term operational decision-making tools are needed that enable an understanding of the temporal variability of CB movement in relation to drinking water intakes. In this paper, we propose a novel conservative model based on a Eulerian framework and compare results with data from CB blooms in Missisquoi Bay (Quebec, Canada). The hydrodynamic model considered the effects of wind and light intensity, demonstrated that current understanding of cell buoyancy in relation to light intensity in full-scale systems is incomplete and some factors are yet to be fully characterized. Factors affecting CB buoyancy play a major role in the formation of a thin surface layer that could be of ecological importance with regards to cell concentrations and toxin production. Depending on velocities, wind contributes either to the accumulation or to the dispersion of CB. Lake recirculation effects have a tendency to create zones of low CB concentrations in a water body. Monitoring efforts and future research should focus on short-term variations of CB throughout the water column and the characterization of factors other than light intensity that affect cell buoyancy. These factors are critical for understanding the risk of breakthrough into treatment plants as well as the formation of surface scums and subsequent toxin production.

This paper examines the onset of negative gravitactic bioconvection in a porous square cavity. The porous medium is assumed to be anisotropic in permeability with its principal axes arbitrarily oriented with respect to the gravity vector, and contains a concentration of microorganisms which are assumed to be swimming upward against gravity (negative gravitaxis). The critical Rayleigh number Ra c at marginal stability is calculated as a function of the microorganism swimming speed V c , of the anisotropy ratio R , and of the orientation angle θ .

A study is made of the stability of motile suspensions in a horizontal porous layer. The micro-organisms are assumed to have a gravitactic behaviour, swimming randomly, but on the average upward with a constant velocity V c . The resulting equilibrium state is potentially unstable as a denser layer of micro-organisms is formed on top of a lighter one. The basic mechanism is analogous to that of Benard convection in a fluid layer heated from below. The fluid flow is governed by the Darcy equation while the conservation of micro-organisms is described by a diffusion–convection equation similar to the conservation of energy. The problem depends on two parameters, namely the Rayleigh number and the swimming velocity V c . The present paper is focused on the stability of the equilibrium diffusive state. The stability diagram and the critical conditions for the onset of convection are obtained for a wide range of swimming velocity. It is found that if V c is very small, the critical wavenumber is zero, corresponding to a very long cell (parallel flow), but as V c is increased, the critical wavenumber also increases, corresponding to narrower flow patterns.

A study is made of the spontaneous pattern formation of gravitactic microorganisms in a horizontal fluid layer. The linear stability theory is used to determine the onset of convection in terms of the Rayleigh number and the swimming velocity. It is found that the onset of convection in a gravitactic suspension may be very different from that of Benard cells under the well-known fixed-flux heating condition.

Wild blueberries are one of the most important fruit crops of Canada, producing more than 50% of the world’s production. Understanding and predicting the relationships between the machine operating parameters, fruit losses, topographic features and crop characteristics can aid in better berry recovery during mechanical harvesting. This paper suggested a modeling approach for prediction of fruit losses during harvesting using artificial neural network (ANN) and multiple regression (MR) techniques. Four wild blueberry fields were selected and completely randomized factorial (3 x 3) experiments were constructed at each site. One hundred sixty two plots (0.91 x 3 m) were made at each site, in the path of operating harvester. The total fruit yield, total losses were collected from each plot within selected fields. The harvester was operated at specific levels of ground speed (1.20, 1.60 and 2.00 km h-1) and head rotational speed (26, 28 and 30 rpm). The readings of slope, plant height, and fruit zone were also recorded from each plot. The collected data were normalized, and 70% of the data were utilized for training, and 30% for validation of the developed models using ANN and MR techniques. The developed models were validated internally and externally and the best performing model was identified based on mean square error (MSE), root mean square error (RMSE), coefficient of efficiency (CE) and coefficient of determination (R2). Results of scatter plot among the RMSE and epoch suggested that an epoch size of 15000 was appropriate to process fruit losses using ANN approach. Results revealed that the prediction accuracy of the MR models was lower (R2 = 0.46; RMSE = 0.14) than the ANN model (R2 = 0.84; RMSE = 0.075) for training dataset, which might be due to the non-linear nature of the data. Results reported that the ANN model predicted fruit losses with higher (R2 = 0.63; RMSE = 0.11) accuracy when compared with MR model (R2 = 0.37; RMSE = 0.15) for external validation dataset. Overall, the results of the study suggested that the ANN model was able to predict fruit losses accurately and reliably as functions of fruit yield, crop and machine variables. This study will help to identify the factors responsible for fruit losses and to suggest optimal harvesting scenarios to improve berry picking efficiency and recovery.

International audience; The sudden appearance of toxic cyanobacteria (CB) blooms is still largely unpredictable in waters worldwide. Many post-hoc explanations for CB bloom occurrence relating to physical and biochemical conditions in lakes have been developed. As potentially toxic CB can accumulate in drinking water treatment plants and disrupt water treatment, there is a need for water treatment operators to determine whether conditions are favourable for the proliferation and accumulation of CB in source waters in order to adjust drinking water treatment accordingly. Thus, a newmethodology with locally adaptable variables is proposed in order to have a single index, f(p), related to various environmental factors such as temperature, wind speed and direction. The index is used in conjunction with real time monitoring data to determine the probability ofCBoccurrence inrelationtometeorological factors, andwas tested at a drinkingwater intake inMissisquoi Bay, a shallowtransboundary bay in Lake Champlain, Que'bec, Canada. These environmental factors alone were able to explain a maximum probability of 68% that a CB bloom would occur at the drinking water treatment plant. Nutrient limitation also influences CB blooms and intense blooms only occurred when the dissolved inorganic nitrogen (DIN) to total phosphorus (TP) mass ratio was below 3. Additional monitoring of DIN and TP could be considered for these source waters prone to cyanobacterial blooms to determine periods of favourable growth. Real time monitoring and the use of the index could permit an adequate and timely response to CB blooms in drinking water sources.

This chapter first presents and discusses a dynamic mathematical model which is developed to simulate a tubular photobioreactor (PBR) and microalgae growth within at any desired location. The model has options to evaluate the effects of location, time of the year, orientation, shading and night curtains, heating and cooling systems, and indoor and outdoor operating conditions. Then the chapter focuses on the algal growth kinetics of microalgae cultivated with coal-fired flue gas, and presents two algal strains, Chlorella vulgaris and Tetraselmis sp. cultivated in lab-scale PBRs to assess the feasibility of using flue gas as a carbon source. And then, the chapter presents an economic feasibility analysis for manufacturing biodiesel from algae using a PBR. The chapter then introduces a mathematical model to investigate the thermal effects on algae population in growth in both fluid and porous media. The study reveals the potential feedback between hydrodynamic and local demographic processes in microorganism populations in the context of the influence of climate change on natural ecological systems. Finally, the chapter discusses the relationship dynamics between algae and nutrient or herbivore and algae using the predator–prey approach based on classical Lotka–Volterra system.

This study was designed to evaluate the performance efficiency of the commercial wild blueberry harvester for fruit losses during harvesting. Two wild blueberry fields were selected in central Nova Scotia, Canada. A 3 x 3 factorial experiment was constructed to examine the joint effect of ground speed and header revolution per minute (rpm) on picking efficiency of the harvester. Eighty one yield plots (0.91 x 3 m) were selected randomly in each field. The field boundaries, bare spots, weeds and yield plots were mapped with a real-time kinematics global positioning system (RTK-GPS). The harvester was operated at specific levels of ground speed (1.20, 1.6 and 2.0 km h -1 ) and header rpm (26, 28 and 30) to collect total fruit yield, un-harvested berries on the plants, berries on the ground, and berries through blower, from each plot within selected fields. The treatment combinations of the chosen factors were assigned randomly within selected fields. The slope, plant height, and fruit zone were also recorded manually from each plot. The results of classical statistics suggested that the pre-harvest fruit losses were lower in early season of harvesting. Un-harvested berries on the plants and losses through blower were significantly lower than losses on the ground for selected fields. Significant relationship between the fruit yield and total losses suggested that losses during harvesting were linear function of fruit yield within selected fields. Factorial analysis of variance (ANOVA) showed that ground speed, header rpm and their interaction were found to have significant impact on the picking efficiency of the harvester. Results of means comparison showed that a combination of 1.2 km h -1 and 26 rpm resulted in significantly lower losses when compared with other treatment combinations. Results also revealed that an ideal combination of ground speed and header rpm can minimize the fruit losses during harvesting, which will increase harvestable yield and ultimately enhance farm profitability.

This study provides knowledge and basic information concerning the spatial correlations and distribution characteristics of heating and cooling degree-day normals in Turkey at the base temperatures of 5, 10, 15, and 20 °C, and 18 and 24 °C, respectively. The heating and cooling degree-day data are analyzed by multiple regression and spatial correlations developed using as regressors altitude, latitude, and longitude. With other variables held constant, heating degree-day values are positively related to altitude and latitude. The effects of regressors altitude and latitude are both significant, but not that of longitude. Altitude is determined to be the most influential regressor on the heating degree-day distributions. With other variables held constant, cooling degree-day values are negatively related to altitude and latitude while positively related to longitude. The effects of regressors altitude, latitude, and longitude are all significant. Latitude is determined to be the most influential regressor.

A General Set of Bioheat Equations Based on the Volume Averaging Theory, Akira Nakayama, Fujio Kuwahara, and Wei Liu Introduction Volume Averaging Procedure Governing Equation for Blood Flow Two-Energy Equation Model for Blood Flow and Tissue Three-Energy Equation Model for Countercurrent Heat Transfer in a Circulatory System Effect of Spatial Distribution of Perfusion Bleed-Off Rate on Total Countercurrent Heat Transfer Application of Bioheat Equation to Cryoablation Therapy Conclusions Nomenclature Mathematical Models of Mass Transfer in Tissue for Molecular Medicine with Reversible Electroporation, Yair Granot and Boris Rubinsky Introduction Fundamental Aspects of Reversible Electroporation Mathematical Models of Ion Transport during Electroporation Electrical Impedance Tomography of In Vivo Electroporation Mass Transfer in Tissue with Reversible Electroporation Studies on Molecular Medicine with Drug Delivery in Tissue by Electroporation Future Research Needs in Mathematical Modeling of the Field of Electroporation Hydrodynamics in Porous Media with Applications to Tissue Engineering, T. Lemaire, J. Pierre, B. David, and C. Oddou Nomenclature Introduction Cell and Tissue Engineering: Physicochemical Determinants of the Development Bioreactors and Implants Theoretical Models of Active Porous Media Conclusion Biomedical Implications of the Porosity of Microbial Biofilms, H. Ben-Yoav, N. Cohen-Hadar, and Amihay Freeman Introduction The Life Cycle of Biofilms Infectious Microbial Biofilms-Structural and Biological Characteristics Infectious Microbial Biofilms-Treatment Modalities and Resistance Concluding Remarks Influence of Biofilms on Porous Media Hydrodynamics, Robin Gerlach and Alfred B. Cunningham Introduction and Overview An Introduction to Biofilms Experimental Systems and Techniques for the Investigation of Biofilms in Porous Media Biofilms in Porous Media and Their Effect on Hydrodynamics A Few Notes on Modeling Porous Media Biofilms in Nature and Technology Conclusions and Outlook Using Porous Media Theory to Determine the Coil Volume Needed to Arrest Flow in Brain Aneurysms, Khalil M. Khanafer and Ramon Berguer Nomenclature Introduction Physics of Cerebral Aneurysms Background Mathematical Formulations Construction of Brain Aneurysm Meshes from CT Scans Results and Discussion Minimum Packing Density of the Endovascular Coil Future Work Conclusions Lagrangian Particle Methods for Biological Systems, Alexandre M. Tartakovsky, Zhijie Xu, and Paul Meakin Introduction DPDs for Biological Applications SPHs Model for Biofilm Growth An SPH Model for Mineral Precipitation Hybrid Models for Diffusion-Reaction Systems Summary Passive Mass Transport Processes in Cellular Membranes and Their Biophysical Implications, Armin Kargol and Marian Kargol Introduction Thermodynamic KK Equations Porous Membranes Mechanistic Equations of Membrane Transport Water Exchange between Aquatic Plants and the Environment Passive Transport through Cell Membranes of Human Erythrocytes Comparison of Transport Formalisms: KK, ME, and 2P Skin Electroporation: Modeling Perspectives, S.M. Becker and A.V. Kuznetsov Introduction Transdermal Drug Delivery The Skin as a Composite Stratum Corneum and the Lipid Barrier Nondestructive Transport Modeling: The SC as a Porous Medium Skin Electroporation Skin Electroporation Models (nonthermal) Thermodynamic Approach Conclusions Application of Porous Media Theories in Marine Biological Modeling, Arzhang Khalili, Bo Liu, Khodayar Javadi, Mohammad R. Morad, Maciej Matyka, Roman Stocker, and Zbigniew Koza Introduction Mathematical Description of the Model Application of Porous Media in Marine Microbiology Future Perspectives The Transport of Insulin-Like Growth Factor through Cartilage, Lihai Zhang, Bruce S. Gardiner, David W. Smith, Peter Pivonka, and Alan J. Grodzinsky Overview Basic Solute Transport Model in a Deforming Articular Cartilage Basic Solute Transport Model in Cyclically Loaded Cartilage Model Geometry for Radial Solute Transport in Cartilage under Unconfined Cyclic Compression The Effect of Cyclic Loading and IGF-I Binding on IGF-I Transport in Cartilage Interaction between IGF-I and Its IGFBPs Results and Discussion IGF Transport with Competitive Binding in a Deforming Articular Cartilage Model Development for a Competitor Growth Factor An Integrated Model of IGF-I and Mechanical-Loading-Mediated Biosynthesis in a Deformed Articular Cartilage Biosynthesis Model Construction Biosynthesis Model Validation and Predictions Summary Biotechnological and Biomedical Applications of Magnetically Stabilized and Fluidized Beds, Teresa Castelo-Grande, Paulo A. Augusto, Angel M. Estevez, Jesus Ma. Rodriguez, Audelino Alvaro, and Domingos Barbosa Introduction Historical Overview of Magnetically Stabilized and Fluidized Beds MSBs and MFBs General Supporting Theory Main Biotechnological and Biomedical Applications Conclusion and Future Perspectives In Situ Characterizations of Porous Media for Applications in Biofuel Cells: Issues and Challenges, Bor Yann Liaw Introduction Biofuel Cell Applications Desirable Properties and Functionalities Needs for In Situ Characterization: Issues and Challenges Applicable In Situ Techniques Future Directions Spatial Pattern Formation of Motile Microorganisms: From Gravitactic Bioconvection to Protozoan Culture Dynamics, Tri Nguyen-Quang, The Hung Nguyen, and Frederic Guichard Description and Literature Review of Bioconvection Onset and Evolution of Gravitactic Bioconvection: Linear Stability Analysis and Numerical Simulation Experimental Study of the Pattern Formation in a Suspension of Gravitactic Microorganisms Summary and Perspectives of Future Research Appendix: Boussinesq Approximation for the Microorganism Suspension Nomenclature References appear at the end of each chapter.

Gravitactic bioconvective patterns created by Tetrahymena pyriformis in a Hele-Shaw apparatus were realized and compared with theoretical results. There were found to be two thresholds for bioconvection development: the first indicates the transition from the diffusion to the steady convection state; the second corresponds to the transition from the steady to the unsteady convection state. The results showed that the Hele-Shaw apparatus may be used as a physical analogy of porous media to study 2D bioconvection, with possible extensions to larger scale biological systems where population growth and distribution are driven by similar bio-physical interactions., TNQ thanks Prof. N. Lima (Centre for Biological Engineering, University of Minho, Braga, Portugal) for his precious support and Prof. M. P. Sauvant Rochat (Cellular Biology Lab., Universite d'Auvergne, France) for her collaboration. We especially thank Mrs. Laviolette (Ecole Polytechnique de Montreal, Canada), and Mr. Morency and Mrs. Phoenix (University of Montreal, Canada), for their kind assistance. FG acknowledges support from the James S. McDonnell Foundation through a 21st Century Science Initiative award. We thank an anonymous reviewer for his interesting comments.

Winter design dry-bulb temperatures at 99% and 97.5% frequency levels were developed and analysed by multiple regression and spatial correlations developed using as regressors altitude, latitude and longitude. Winter design dry-bulb temperatures were negatively related to altitude and latitude. Latitude was the most influential regressor on the winter design dry-bulb temperature distributions. Summer design dry- and wet-bulb temperatures, and wet-bulb depressions at 1%, 2.5% and 5% frequency levels were also developed and analysed by multiple regression and spatial correlations were developed using as regressors altitude, latitude and longitude. Altitude was the only statistically significant influential regressor on the summer design dry-bulb temperature distributions. Latitude was the most influential regressor on the summer design wet-bulb temperature distributions. Altitude was the only statistically significant influential regressor on the summer design wet-bulb depression distributions. Spatial correlations and distributions of prevailing wind speeds, mean daily ranges, and median of annual extreme temperatures were developed and mapped.