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Background/Objectives: Uterine sarcoma, a rare cancer originating in the smooth muscle of the uterus, exhibits high rates of recurrence and metastasis. It represents one of the most challenging types of cancer due to its chemorefractory nature, showing little response to conventional chemotherapy methods and displaying a relative survival rate of 30–40%. A potentially promising approach for treating uterine sarcoma involves combination therapy with paclitaxel (PAC), a microtubule-targeting agent, and seliciclib (SEL), a cyclin-dependent kinase inhibitor. SEL has been identified as a drug that can enhance the effectiveness of PAC through synergistic effects. To further refine this treatment strategy, an efficient analytical tool capable of simultaneously measuring the concentrations of PAC and SEL in blood plasma is needed. This tool would make it easier to study the pharmacokinetic interactions of potential drugs and assist in monitoring therapy when administering this combination treatment. Regrettably, a method meeting these specific requirements has not been documented in the existing literature. Methods: This article introduces the first HPLC technique employing a PDA detector to concurrently measure PAC and SEL levels in plasma. The methodology underwent validation in accordance with the ICH standards for validating bioanalytical methods. Results: The method exhibited linearity in the concentrations ranging from 0.8 to 100 µg mL−1 for both PAC and SEL. The limits of quantification were determined and found to be 1.34 and 1.25 µg mL−1 for PAC and SEL, respectively. All the other validation criteria conformed to the ICH validation standards. The HPLC-PDA method was successfully employed to quantify both PAC and SEL in plasma samples with a high level of reliability (in terms of accuracy and precision). The eco-friendliness of the approach was verified using three thorough assessments. This technique serves as a valuable asset in establishing the correct dosage and administration schedule for the combined treatment involving PAC and SEL, ensuring the desired therapeutic effects and safety in managing uterine sarcoma. Conclusions: The proposed HPLC-PDA method is the first reliable and eco-friendly method developed to simultaneously determine PAC and SEL in high-throughput plasma samples in clinical laboratories.

Recommended vaccination at nine months of age with the measles-containing vaccine (MCV1) has been part of Ethiopia’s routine immunization program since 1980. A second dose of MCV (MCV2) was introduced in 2019 for children 15 months of age. We examined MCV1 and MCV2 coverage and the factors associated with measles vaccination status. A cross-sectional household survey was conducted among caregivers of children aged 12–35 months in selected districts of Oromia Region. Measles vaccination status was determined using home-based records, when available, or caregivers’ recall. We analyzed the association between MCV1 and MCV2 vaccination status and household, caregiver, and child factors using logistic regression. The caregivers of 1172 children aged 12–35 months were interviewed and included in the analysis. MCV1 and MCV2 coverage was 71% and 48%, respectively. The dropout rate (DOR) from the first dose of Pentavalent vaccine to MCV1 was 22% and from MCV1 to MCV2 was 46%. Caregivers were more likely to vaccinate their children with MCV if they gave birth at a health facility, believe that their child had received all recommended vaccines, and know the required number of vaccination visits and doses. MCV2 coverage was low, with a high measles dropout rate (DOR). Caregivers with high awareness of MCV and its schedule were more likely to vaccinate their children. Intensified demand generation, defaulter tracking, and vaccine-stock management should be strengthened to improve MCV uptake.

Introduction: Ethiopia introduced a second dose of measles-containing vaccine (MCV2) in 2019 to provide further protection against measles and further progress toward elimination. However, the sub-optimal coverage of both MCV1 and MCV2 suggest challenges with vaccine uptake. In this qualitative study, we explored barriers to the uptake of MCV2 among caregivers, community leaders, and healthcare workers (HCWs). Method: A qualitative study was conducted between mid-April and mid-May 2021. We selected ten woredas (districts) in the Oromia Region, Ethiopia, stratified by settlement type (urban/rural), MCV1 coverage (high ≥ 80%; low < 80%), and history of measles outbreaks between June 2019 and June 2020. Experiences surrounding barriers to MCV2 uptake were discussed via focus group discussions (FGDs) and in-depth interviews (IDIs) with caregivers of children 12–23 and 24–36 months and key informant interviews (KIIs) with HCWs who administer vaccines and with community leaders. Participants were recruited via snowball sampling. Recorded data were transcribed, translated to English, and analyzed using ATLAS.ti v.09. Results: Forty FGDs and 60 IDIs with caregivers, 60 IDIs with HCWs, and 30 KIIs with community leaders were conducted. Barriers among caregivers included lack of knowledge and awareness about MCV2 and the vaccination schedule, competing priorities, long wait times at health facilities, vaccine unavailability, negative interactions with HCWs, and transportation challenges. At the community level, trusted leaders felt they lacked adequate knowledge about MCV2 to address caretakers’ questions and community misconceptions. HCWs felt additional training on MCV2 would prepare them to better respond to caretakers’ concerns. Health system barriers identified included the lack of human, material, and financial resources to deliver vaccines and provide immunization outreach services, which caretakers reported as their preferred way of accessing immunization. Conclusions: Barriers to MCV2 uptake occur at multiple levels of immunization service delivery. Strategies to address these barriers include tools to help caretakers track appointments, enhanced community engagement, HCW training to improve provider–client interactions and MCV2 knowledge, and efforts to manage HCW workload.

Serum biomarkers and lung ultrasound are important measures for prognostication and treatment allocation in patients with COVID-19. Currently, there is a paucity of studies investigating relationships between serum biomarkers and ultrasonographic biomarkers derived from lung ultrasound. This study aims to assess correlations between serum biomarkers and lung ultrasound findings. This study is a secondary analysis of four prospective observational studies in adult patients with COVID-19. Serum biomarkers included markers of epithelial injury, endothelial dysfunction and immune activation. The primary outcome was the correlation between biomarker concentrations and lung ultrasound score assessed with Pearson’s (r) or Spearman’s (rs) correlations. Forty-four patients (67 [41–88] years old, 25% female, 52% ICU patients) were included. GAS6 (rs = 0.39), CRP (rs = 0.42) and SP-D (rs = 0.36) were correlated with lung ultrasound scores. ANG-1 (rs = −0.39) was inversely correlated with lung ultrasound scores. No correlations were found between lung ultrasound score and several other serum biomarkers. In patients with COVID-19, several serum biomarkers of epithelial injury, endothelial dysfunction and immune activation correlated with lung ultrasound findings. The lack of correlations with certain biomarkers could offer opportunities for precise prognostication and targeted therapeutic interventions by integrating these unlinked biomarkers.

The non-premixed filtered tabulated chemistry for large eddy simulations employs numerical filtering to resolve a thin flame front on practical LES numerical grids. The flame structure is modified to be coherent with the domain discretization. The first turbulent combustion application of the non-premixed filtered tabulated chemistry approach is presented. A keen comparison of the flamelet filtering transformation in the premixed and non-premixed regimes is carried out. Three distinctive features are outlined: the flame thickness variation, the filtered manifold transformation, and the model activation dependence on the chosen diffusion flamelet configuration for a non-premixed filtered approach. The model performance is assessed on two real turbulent flame configurations, Sandia flames D and E, employing a three-dimensional tabulation strategy, where the numerical grid is coupled with the model by the third parameter, i.e., the computational cell size. The repercussions of the above cited aspects are carefully assessed. The results demonstrate that the formalism coupling with an SGS modeling function can adequately describe wrinkled flame front effects. The predictions for both the major stable species and the minor ones accurately correspond with the underlying physics. It turns out that there is a substantial variation of the filter effect as a function of the strain rate of the flame and the considered species. The varying filter sensitivity along the manifold influences the response of the model correction terms and the retrieved variables. The non-premixed FTACLES formalism possibilities and conditions for the model’s utilization and optimal performance are clearly stated, to confirm the idea that SGS closure in diffusive combustion can be derived based on filtering arguments, and not only based on statistical approaches.

Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare autoimmune condition associated with recombinant adenovirus (rAV)-based COVID-19 vaccines. It is thought to arise from autoantibodies targeting platelet factor 4 (aPF4), triggered by vaccine-induced inflammation and the formation of neo-antigenic complexes between PF4 and the rAV vector. To investigate the specific induction of aPF4 by rAV-based vaccines, we examined sera from rAV vaccine recipients (AZD1222, AD26.COV2.S) and messenger RNA (mRNA) based (mRNA-1273, BNT162b2) COVID-19 vaccine recipients. We compared the antibody fold change (FC) for aPF4 and for antiphospholipid antibodies (aPL) of rAV to mRNA vaccine recipients. We combined two biobanks of Dutch healthcare workers and matched rAV-vaccinated individuals to mRNA-vaccinated controls, based on age, sex and prior history of COVID-19 (AZD1222: 37, Ad26.COV2.S: 35, mRNA-1273: 47, BNT162b2: 26). We found no significant differences in aPF4 FCs after the first (0.99 vs. 1.08, mean difference (MD) = −0.11 (95% CI −0.23 to 0.057)) and second doses of AZD1222 (0.99 vs. 1.10, MD = −0.11 (95% CI −0.31 to 0.10)) and after a single dose of Ad26.COV2.S compared to mRNA-based vaccines (1.01 vs. 0.99, MD = 0.026 (95% CI −0.13 to 0.18)). The mean FCs for the aPL in rAV-based vaccine recipients were similar to those in mRNA-based vaccines. No correlation was observed between post-vaccination aPF4 levels and vaccine type (mean aPF difference −0.070 (95% CI −0.14 to 0.002) mRNA vs. rAV). In summary, our study indicates that rAV and mRNA-based COVID-19 vaccines do not substantially elevate aPF4 levels in healthy individuals.

Throughout their life cycle, viruses interact with cellular host factors, thereby influencing propagation, host range, cell tropism and pathogenesis. The hepatitis E virus (HEV) is an underestimated RNA virus in which knowledge of the virus–host interaction network to date is limited. Here, two related high-throughput mammalian two-hybrid approaches (MAPPIT and KISS) were used to screen for HEV-interacting host proteins. Promising hits were examined on protein function, involved pathway(s), and their relation to other viruses. We identified 37 ORF2 hits, 187 for ORF3 and 91 for ORF4. Several hits had functions in the life cycle of distinct viruses. We focused on SHARPIN and RNF5 as candidate hits for ORF3, as they are involved in the RLR-MAVS pathway and interferon (IFN) induction during viral infections. Knocking out (KO) SHARPIN and RNF5 resulted in a different IFN response upon ORF3 transfection, compared to wild-type cells. Moreover, infection was increased in SHARPIN KO cells and decreased in RNF5 KO cells. In conclusion, MAPPIT and KISS are valuable tools to study virus–host interactions, providing insights into the poorly understood HEV life cycle. We further provide evidence for two identified hits as new host factors in the HEV life cycle.

The flame front filtering is a well-known strategy in turbulent premixed combustion. An extension of this approach for the non-premixed combustion context has been proposed by means of directly filtering counterflow diffusion flamelets. Promising results were obtained for the non-premixed filtered tabulated chemistry formalism on 1-D and 2-D unresolved counterflow flame configurations. The present paper demonstrates the soundness of this approach on a 3-D real laminar non-premixed coflow flame. The model results are compared against the direct filtering of the fully resolved laminar diffusion flame showing that the formalism adequately describes the underlying physics. The study reveals the importance of the one-dimensional counterflow flamelet hypothesis, so that the model activation under this condition is ensured by means of a flame sensor. The consistent coupling between the model and the flame sensor adequately retrieves the flame lift-off and satisfactorily predicts the profile extension due to the filtering process.

The study objectives were to examine antibiotic consumption at Vila Central Hospital (VCH), Vanuatu between January 2018 and December 2021 and the influence of the COVID-19 pandemic on antibiotic consumption during this period. Data on antibiotic usage were obtained from the Pharmacy database. We used the WHO’s Anatomical Therapeutic Classification/Defined Daily Dose (ATC/DDD) index, VCH’s inpatient bed numbers and the hospital’s catchment population to calculate monthly antibiotic consumption. The results were expressed as DDDs per 100 bed days for inpatients (DBDs) and DDDs per 1000 inhabitants per day for outpatients (DIDs). Interrupted time series (ITS) was used to assess the influence of COVID-19 by comparing data before (January 2018 to January 2020) and during (February 2020 to December 2021) the pandemic. Ten antibiotics were examined. In total, 226 DBDs and 266 DBDs were consumed before and during COVID-19 by inpatients, respectively with mean monthly consumption being significantly greater during COVID-19 than before the pandemic (2.66 (p = 0.009, 95% CI 0.71; 4.61)). Whilst outpatients consumed 102 DIDs and 92 DIDs before and during the pandemic, respectively, the difference was not statistically significant. Findings also indicated that outpatients consumed a significantly lower quantity of Watch antibiotics during COVID-19 than before the pandemic (0.066 (p = 0.002, 95% CI 0.03; 0.11)). The immediate impact of COVID-19 caused a reduction in both inpatient and outpatient mean monthly consumption by approximately 5% and 16%, respectively, and this was followed by an approximate 1% monthly increase until the end of the study. By mid-2021, consumption had returned to pre-pandemic levels.

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

This paper provides a review of different contributions dedicated thus far to entropy generation analysis (EGA) in turbulent combustion systems. We account for various parametric studies that include wall boundedness, flow operating conditions, combustion regimes, fuels/alternative fuels and application geometries. Special attention is paid to experimental and numerical modeling works along with selected applications. First, the difficulties of performing comprehensive experiments that may support the understanding of entropy generation phenomena are outlined. Together with practical applications, the lumped approach to calculate the total entropy generation rate is presented. Apart from direct numerical simulation, numerical modeling approaches are described within the continuum formulation in the framework of non-equilibrium thermodynamics. Considering the entropy transport equations in both Reynolds-averaged Navier–Stokes and large eddy simulation modeling, different modeling degrees of the entropy production terms are presented and discussed. Finally, exemplary investigations and validation cases going from generic or/and canonical configurations to practical configurations, such as internal combustion engines, gas turbines and power plants, are reported. Thereby, the areas for future research in the development of EGA for enabling efficient combustion systems are highlighted. Since EGA is known as a promising tool for optimization of combustion systems, this aspect is highlighted in this work.

The chimney effect taking place in biomass cooking stoves results from a conversion process between thermal and mechanical energy. The efficiency of this conversion is assessed with the stove loss coefficient. The derivation of this quantity in cooking stove modelling is still uncertain. Following fluid mechanics, this loss coefficient refers to an overall pressure drop through stove geometry by performing an energy balance according to the first law of thermodynamics. From this approach, heat-transfer processes are quite ignored yet they are important sources of irreversibilities. The present work takes a fresh look at stove loss coefficient assessment relying on the second law of thermodynamics. The purpose in this paper is to identify the influence of operating firepower level on flow dynamics in biomass natural convection-driven cooking stoves. To achieve that, a simplified analytical model of the entropy-generation rate in the flow field is developed. To validate the model, experiments are conducted first on a woodburning stove without cooking pot to better isolate physical processes governing the intrinsic behaviour of the stove. Then, for the practical case of a stove operating with a cooking pot in place, data from published literature have served for validation. In particular, mass-flow rate and flue gas temperature at different firepower levels have been monitored. It turns out that losses due to viscous dissipations are negligible compared to the global process dissipation. Exergy analysis reveals that the loss coefficient should rather be regarded from now as the availability to generate flow work primarily associated with the heat-transfer Carnot factor. In addition, the energy flux applied as flow work has to be considered as pure exergy that is lost through consecutive energy-transfer components comprising the convective heat transfer to the cooking pot. Finally, this paper reports a satisfactory agreement that emerged between the exergy Carnot factor and the experimental loss coefficient at different fuel-burning rates.

The behaviors of spray, in Reactivity Controlled Combustion Ignition (RCCI) dual fuel engine and subsequent emissions formation, are numerically addressed. Five spray cone angles ranging between 5° and 25° with an advanced injection timing of 22° Before Top Dead Center (BTDC) are considered. The objective of this paper is twofold: (a) to enhance engine behaviors in terms of performances and consequent emissions by adjusting spray cone angle and (b) to outcome the exergy efficiency for each case. The simulations are conducted using the Ansys-forte tool. The turbulence model is the Renormalization Group (RNG) K-epsilon, which is selected for its effectiveness in strongly sheared flows. The spray breakup is governed by the hybrid model Kelvin–Helmholtz and Rayleigh–Taylor spray models. A surrogate of n-heptane, which contains 425 species and 3128 reactions, is used for diesel combustion modeling. The obtained results for methane/diesel engine combustion, under low load operating conditions, include the distribution of heat transfer flux, pressure, temperature, Heat Release Rate (HRR), and Sauter Mean Diameter (SMD). An exergy balance analysis is conducted to quantify the engine performances. Output emissions at the outlet of the combustion chamber are also monitored in this work. Investigations show a pressure decrease for a cone angle θ = 5° of roughly 8%, compared to experimental measurement (θ = 10°). A broader cone angle produces a higher mass of NOx. The optimum spray cone angle, in terms of exergy efficiency, performance, and consequent emissions is found to lie at 15° ≤ θ ≤ 20°.

Even though there is a pressing interest in clean energy sources, compression ignition (CI) engines, also called diesel engines, will remain of great importance for transportation sectors as well as for power generation in stationary applications in the foreseeable future. In order to promote applications dealing with complex diesel alternative fuels by facilitating their integration in numerical simulation, this paper targets three objectives. First, generate novel diesel fuel surrogates with more than one component. Here, five surrogates are generated using an advanced chemistry solver and are compared against three mechanisms from the literature. Second, validate the suggested reaction mechanisms (RMs) with experimental data. For this purpose, an engine configuration, which features a reacting spray flow evolving in a direct-injection (DI), single-cylinder, and four-stroke motor, is used. The RNG k-Epsilon coupled to power-law combustion models is applied to describe the complex in-cylinder turbulent reacting flow, while the hybrid Eulerian-Lagrangian Kelvin Helmholtz-Rayleigh Taylor (KH-RT) spray model is employed to capture the spray breakup. Third, highlight the impact of these surrogate fuels on the combustion properties along with the exergy of the engine. The results include distribution of temperature, pressure, heat release rate (HRR), vapor penetration length, and exergy efficiency. The effect of the surrogates on pollutant formation (NOX, CO, CO2) is also highlighted. The fifth surrogate showed 47% exergy efficiency. The fourth surrogate agreed well with the maximum experimental pressure, which equaled 85 Mpa. The first, second, and third surrogates registered 400, 316, and 276 g/kg fuel, respectively, of the total CO mass fraction at the outlet. These quantities were relatively higher compared to the fourth and fifth RMs.

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.

This contribution presents a straightforward strategy to investigate the entropy production in stratified premixed flames. The modeling approach is grounded on a chemistry tabulation strategy, large eddy simulation, and the Eulerian stochastic field method. This enables a combination of a detailed representation of the chemistry with an advanced model for the turbulence chemistry interaction, which is crucial to compute the various sources of exergy losses in combustion systems. First, using detailed reaction kinetic reference simulations in a simplified laminar stratified premixed flame, it is demonstrated that the tabulated chemistry is a suitable approach to compute the various sources of irreversibilities. Thereafter, the effects of the operating conditions on the entropy production are investigated. For this purpose, two operating conditions of the Darmstadt stratified burner with varying levels of shear have been considered. The investigations reveal that the contribution to the entropy production through mixing emerging from the chemical reaction is much larger than the one caused by the stratification. Moreover, it is shown that a stronger shear, realized through a larger Reynolds number, yields higher entropy production through heat, mixing and viscous dissipation and reduces the share by chemical reaction to the total entropy generated.

Maize (Zea mays L.) is the main staple cereal food crop cultivated in southern Africa. Interactions between grain yield and biochemical traits can be useful to plant breeders in making informed decisions on the traits to be considered in breeding programs for high grain yield and enhanced quality. The objectives of this study were to estimate the heritability of grain yield and its related traits, as well as quality traits, and determine the association between quality protein maize (QPM) with non-QPM crosses. Grain yield, and agronomic and quality trait data were obtained from 13 field trials in two countries, for two consecutive seasons. Significant genotypic and phenotypic correlations were recorded for grain yield with protein content (rG = 0.38; rP = 0.25), and tryptophan with oil content (rG = 0.58; rP = 0.25), and negative rG and rP correlations were found for protein with tryptophan content and grain yield with tryptophan content. Path analysis identified ear aspect, ears per plant, and starch as the major traits contributing to grain yield. It is recommended that ear aspect should be considered a key secondary trait in breeding for QPM hybrids. The negative association between grain yield and tryptophan, and between protein and tryptophan, will make it difficult to develop hybrids with high grain yield and high tryptophan content. Hence, it is recommended that gene pyramiding should be considered for these traits.

Malnutrition, as a result of deficiency in essential nutrients in cereal food products and consumption of a poorly balanced diet, is a major challenge facing millions of people in developing countries. However, developing maize inbred lines that are high yielding with enhanced nutritional traits for hybrid development remains a challenge. This study evaluated 40 inbred lines: 26 quality protein maize (QPM) lines, nine non-QPM lines, and five checks (three QPM lines and two non-QPM lines) in four optimum environments in Zimbabwe and South Africa. The objective of the study was to identify good-quality QPM inbred lines for future hybrid breeding efforts in order to increase the nutritional value of maize. The QPM lines had a lower protein content (7% lower) than that of the non-QPM lines but had 1.9 times more tryptophan and double the quality index. The lysine- and tryptophan-poor α-zein protein fraction was 41% lower in QPM than in non-QPM, with a subsequent increase in γ-zein. There was significant variation within the QPM inbred lines for all measured quality characteristics, indicating that the best lines can be selected from this material without a yield penalty. QPM lines that had both high protein and tryptophan levels, which can be used as parents for highly nutritious hybrids, were identified.

Breeding for nitrogen use efficiency (NUE) is important to deal with food insecurity and its effect on grain quality, particularly protein. A total of 1679 hybrids were evaluated in 16 different trials for grain yield (GY), grain quality traits (protein, starch and oil content) and kernel weight (KW) under optimum and managed low soil nitrogen fields in Kiboko, Kenya, from 2011 to 2014. The objectives of our study were to understand (i) the effect of low soil N stress on GY and quality traits, (ii) the relationship between GY and quality traits under each soil management condition and (iii) the relationship of traits with low-N versus optimum conditions. From the results, we observed the negative effects of low N on GY, KW and the percentage of protein content, and a positive effect on the percentage of starch content. The correlation between GY and all quality traits was very weak under both soil N conditions. GY had a strong relationship with KW under both optimum and low soil N conditions. Protein and starch content was significantly negative under both optimum and low-N conditions. There was no clear relationship among quality traits under optimum and low N, except for oil content. Therefore, it seems feasible to simultaneously improve GY along with quality traits under both optimum and low-N conditions, except for oil content. However, the negative trend observed between GY (starch) and protein content suggests the need for the regular monitoring of protein and starch content to identify varieties that combine both high GY and acceptable quality. Finally, we recommend further research with a few tropical maize genotypes contrasting for NUE to understand the relationship between the change in grain quality and NUE under low-N conditions.

Dentin matrix protein 1 (DMP1) contains a large number of acidic domains, multiple phosphorylation sites, a functional arginine-glycine-aspartate (RGD) motif, and a DNA binding domain, and has been shown to play essential regulatory function in dentin and bone mineralization. DMP1 could also orchestrate bone matrix formation, but the ability of DMP1 on Ti to human mesenchymal stem cell (hMSC) conversion to osteoblasts has not been studied. There is importance to test if the DMP1 coated Ti surface would promote cell migration and attachment to the metal surface and promote the differentiation of the attached stem cells to an osteogenic lineage. This study aimed to study the human mesenchymal stem cells (hMSCs) attachment and proliferation on DMP1 coated titanium (Ti) disks compared to non-coated disks, and to assess possible osteoblastic differentiation of attached hMSCs. Sixty-eight Ti disks were divided into two groups. Group 1 disks were coated with dentin matrix protein 1 and group 2 disks served as control. Assessment with light microscopy was used to verify hMSC attachment and proliferation. Cell viability was confirmed through fluorescence microscopy and mitochondrial dehydrogenase activity. Real-time polymerase chain reaction analysis was done to study the gene expression. The proliferation assay showed significantly greater cell proliferation with DMP1 coated disks compared to the control group (p-value < 0.001). Cell vitality analysis showed a greater density of live cells on DMP1 coated disks compared to the control group. Alkaline phosphatase staining revealed higher enzyme activity on DMP1 coated disks and showed itself to be significantly higher than the control group (p-value < 0.001). von Kossa staining revealed higher positive areas for mineralized deposits on DMP1 coated disks than the control group (p-value < 0.05). Gene expression analysis confirmed upregulation of runt-related transcription factor 2, osteoprotegerin, osteocalcin, osteopontin, and alkaline phosphatase on DMP1 coated disks (p-value < 0.001). The dentin matrix protein promoted the adhesion, proliferation, facilitation differentiation of hMSC, and mineralized matrix formation.

A second law analysis in combustion systems is performed along with an exergy loss study by quantifying the entropy generation sources using, for the first time, three different approaches: a classical-thermodynamics-based approach, a novel turbulence-based method and a look-up-table-based approach, respectively. The numerical computation is based on a hybrid filtered Eulerian stochastic field (ESF) method coupled with tabulated detailed chemistry according to a Famelet-Generated Manifold (FGM)-based combustion model. In this work, the capability of the three approaches to capture the effect of the Re number on local exergy losses is especially appraised. For this purpose, Sandia flames D and E are selected as application cases. First, the validation of the computed flow and scalar fields is achieved by comparison to available experimental data. For both flames, the flow field results for eight stochastic fields and the associated scalar fields show an excellent agreement. The ESF method reproduces all major features of the flames at a lower numerical cost. Next, the second law analysis carried out with the different approaches for the entropy generation computation provides comparable quantitative results. Using flame D as a reference, for which some results with the thermodynamic-based approach exist in the literature, it turns out that, among the sources of exergy loss, the heat transfer and the chemical reaction emerge notably as the main culprits for entropy production, causing 50% and 35% of it, respectively. This fact-finding increases in Sandia flame E, which features a high Re number compared to Sandia flame D. The computational cost is less once the entropy generation analysis is carried out by using the Large Eddy Simulation (LES) hybrid ESF/FGM approach together with the look-up-table-based or turbulence-based approach.

Termites can play a localized prominent role in soil nutrient availability and cycling because mound materials are often enriched in nutrients relative to surrounding soil. Mound materials may thus prove to be useful amendments, though evidently mound spatial arrangement needs to be considered as well. Furthermore, it is not known if gradients of soil properties exist from termite mound to interspace sites. Studying both aspects would be required to decide whether spreading of mounds or spatially differentiated management of surrounding crop to accommodate soil fertility gradients would be valid nutrient-management strategies. Mound abundance and mass were estimated at 9 and 4 mounds ha−1, representing 38.9 and 6.3 t ha−1 on Nitisols and Vertisols, respectively. Soil physical and chemical properties were measured on samples collected from internal and external parts of mounds and adjacent soils at 0.5, 1 and 10 m away from mounds. In general, termite mounds were enriched in plant nutrients and SOC on Vertisols but not on Nitisols. Termite mounds constituted only 0.3 to 1.3% of the 0–15 cm SOM stock on a per ha basis but nevertheless the immediate vicinity of termite mounds was a relative fertile hotspot. Hence, under the studied condition, we suggest spatial arrangement of crop around termite mounds according to soil fertility gradient and spatially differentiated nutrient management strategies. Our result suggests recommendation of termite mound spreading for soil nutrient amendment has to consider plant nutrient stock in termite mounds on per ha basis besides their nutrient enrichment. Interesting topics for future investigation would be growth experiment for different crops with mound materials treatment. It would also be interesting to study the effect mound building termite on soil properties under different soil conditions, slope class and land use.

In this work, an experimental investigation of AdBlue film formation in a generic selective catalytic reduction (SCR) exhaust gas test bench is presented. AdBlue is injected into a generic SCR test bench resulting in liquid film formation on the lower wall of the channel. The thickness of this liquid film is measured using a film thickness sensor based on absorption spectroscopy. Simultaneously, the wall temperature at the measurement point is monitored, which allows for examining correlations between the evolution of the film thickness and the temperature of the wetted wall. The velocity of the airflow in the channel and the initial wall temperature are varied in the experiments. Correspondingly, the measurements are performed during different thermodynamic regimes, including liquid film deposition and boiling. Repeated measurements have also shown that the film thicknesses are reproducible with a standard deviation of 3.4 %. LES-based numerical simulations are compared to the experimental results of the film thickness during the early injection stage. Finally, a numerical analysis is performed to analyze the AdBlue droplet impingement and subsequent film-formation dynamics.

In this paper, advanced wall-modeled large eddy simulation (LES) techniques are used to predict conjugate heat transfer processes in turbulent channel flow. Thereby, the thermal energy transfer process involves an interaction of conduction within a solid body and convection from the solid surface by fluid motion. The approaches comprise a two-layer RANS–LES approach (zonal LES), a hybrid RANS–LES representative, the so-called improved delayed detached eddy simulation method (IDDES) and a non-equilibrium wall function model (WFLES), respectively. The results obtained are evaluated in comparison with direct numerical simulation (DNS) data and wall-resolved LES including thermal cases of large Reynolds numbers where DNS data are not available in the literature. It turns out that zonal LES, IDDES and WFLES are able to predict heat and fluid flow statistics along with wall shear stresses and Nusselt numbers accurately and that are physically consistent. Furthermore, it is found that IDDES, WFLES and zonal LES exhibit significantly lower computational costs than wall-resolved LES. Since IDDES and especially zonal LES require considerable extra work to generate numerical grids, this study indicates in particular that WFLES offers a promising near-wall modeling strategy for LES of conjugated heat transfer problems. Finally, an entropy generation analysis using the various models showed that the viscous entropy production is zero inside the solid region, peaks at the solid–fluid interface and decreases rapidly with increasing wall distance within the fluid region. Except inside the solid region, where steep temperature gradients lead to high (thermal) entropy generation rates, a similar behavior is monitored for the entropy generation by heat transfer process.

The Eulerian stochastic fields (ESF) method, which is based on the transport equation of the joint subgrid scalar probability density function, is applied to Large Eddy Simulation of a turbulent dilute spray flame. The approach is coupled with a tabulated chemistry approach to represent the subgrid turbulence–chemistry interaction. Following a two-way coupled Eulerian–Lagrangian procedure, the spray is treated as a multitude of computational parcels described in a Lagrangian manner, each representing a heap of real spray droplets. The present contribution has two objectives: First, the predictive capabilities of the modeling framework are evaluated by comparing simulation results using 8, 16, and 32 stochastic fields with available experimental data. At the same time, the results are compared to previous studies, where the artificially thickened flame (ATF) model was applied to the investigated configuration. The results suggest that the ESF method can reproduce the experimental measurements reasonably well. Comparisons with the ATF approach indicate that the ESF results better describe the flame entrainment into the cold spray core of the flame. Secondly, the dynamics of the subgrid scalar contributions are investigated and the reconstructed probability density distributions are compared to common presumed shapes qualitatively and quantitatively in the context of spray combustion. It is demonstrated that the ESF method can be a valuable tool to evaluate approaches relying on a pre-integration of the thermochemical lookup-table.

Monitoring biomechanics of cells or tissue biopsies employing atomic force microscopy (AFM) offers great potential to identify diagnostic biomarkers for diseases, such as colorectal cancer (CRC). Data on the mechanical properties of CRC cells, however, are still scarce. There is strong evidence that the individual zinc status is related to CRC risk. Thus, this study investigates the impact of differing zinc supply on the mechanical response of the in vitro CRC cell lines HT-29 and HT-29-MTX during their early proliferation (24–96 h) by measuring elastic modulus, relaxation behavior, and adhesion factors using AFM. The differing zinc supply severely altered the proliferation of these cells and markedly affected their mechanical properties. Accordingly, zinc deficiency led to softer cells, quantitatively described by 20–30% lower Young’s modulus, which was also reflected by relevant changes in adhesion and rupture event distribution compared to those measured for the respective zinc-adequate cultured cells. These results demonstrate that the nutritional zinc supply severely affects the nanomechanical response of CRC cell lines and highlights the relevance of monitoring the zinc content of cancerous cells or biopsies when studying their biomechanics with AFM in the future.

In this paper, we compare the capabilities of two open source near-wall-modeled large eddy simulation (NWM-LES) approaches regarding prediction accuracy, computational costs and ease of use to predict complex turbulent flows relevant to internal combustion (IC) engines. The applied open source tools are the commonly used OpenFOAM, based on the finite volume method (FVM), and OpenLB, an implementation of the lattice Boltzmann method (LBM). The near-wall region is modeled by the Musker equation coupled to a van Driest damped Smagorinsky-Lilly sub-grid scale model to decrease the required mesh resolution. The results of both frameworks are compared to a stationary engine flow bench experiment by means of particle image velocimetry (PIV). The validation covers a detailed error analysis using time-averaged and root mean square (RMS) velocity fields. Grid studies are performed to examine the performance of the two solvers. In addition, the differences in the processes of grid generation are highlighted. The performance results show that the OpenLB approach is on average 32 times faster than the OpenFOAM implementation for the tested configurations. This indicates the potential of LBM for the simulation of IC engine-relevant complex turbulent flows using NWM-LES with computationally economic costs.

Direct C-H arylation coupling is potentially a more economical and sustainable process than conventional cross-coupling. However, this method has found limited application in the synthesis of organic dyes for dye-sensitized solar cells. Although direct C-H arylation is not an universal solution to any cross-coupling reactions, it efficiently complements conventional sp2−sp2 bond formation and can provide shorter and more efficient routes to diketopyrrolopyrrole dyes. Here, we have applied palladium catalyzed direct C-H arylation in the synthesis of five new 3,6-dithienyl diketopyrrolopyrrole dyes. All prepared sensitizers display broad absorption from 350 nm up to 800 nm with high molar extinction coefficients. The dye-sensitized solar cells based on these dyes exhibit a power conversion efficiency in the range of 2.9 to 3.4%.

The development of higher yielding maize (Zea mays L.) hybrids adapted to low input small-holder farming systems in sub-Saharan Africa is required. Three-way hybrids (i.e. F1 tester crossed to inbred line) can be produced at lower cost for smallholder farmers, but to achieve this it is crucial for CIMMYT-Zimbabwe maize breeding program to identify new high yielding single cross testers with high combining ability to support the development of new high yielding hybrids for this region. Data collected on the performance of F1 and three-way hybrids from a total of 25 multi-environment trials (METs) located in South Africa (three trials), Zambia (four trials) and Zimbabwe (eighteen trials) grown across two seasons during the period from 2015–2018 to: (i) identify new single cross testers for CIMMYT HG-B maize germplasm; (ii) identify stable and high yielding three-way hybrids. Analyses were conducted using a two-stage approach. Clustering based on yield data, grouped sites into three environment types (ET); low (LY) < 3 t ha−1, medium (MY) 3–6 t ha−1 and high (HY) 6–13 t ha−1 yielding groups. Additive genetic effects of both inbred parents and selected F1 crosses used as parents were more important than non-additive genetic effects for grain yield across ETs. Strong genotype x environment interactions on yield and other traits were observed. It was concluded that F1 hybrids (entry 75, 85, 72 and 28) demonstrated high yield across all environment types, and it is recommended that these be also evaluated as potential three-way hybrids. Single crosses CZL15085/CML566 and CZL15085/CZL13102 from heterotic group B to be used as testers for ET 1 and ET 2 respectively.

The most constraining factor in goat production in the tropics is underfeeding mainly attributed to limitations of feed both in quantity and quality. This study was conducted to assess the effect of supplementing different levels of air-dried Moringa stenopetala leaf (MSL) as a protein source on nutrient intake and growth performances of Arsi-Bale male goats. A total of 24 yearling goats with average initial body weight of 13.6 ± 0.25 kg were used in the study. The goats were blocked by live weight into four groups (n = 6 per group) and the groups were then randomly allocated into four supplemented treatments. All goats received a basal diet of natural grass hay ad libitum and 340 g/head/day concentrate. The treatments were the control diet with no supplementation (Treatment 1, T1) and diets supplemented with MSL at a rate of 120 g/head/day (Treatment 2, T2), 170 g/head/day (Treatment 3, T3) and 220 g/head/day (Treatment 4, T4). The duration of the experiment was 75 days. The results indicated that the average daily feed intake was (p < 0.001) higher in goats supplemented with T3 and T4 diets. The total dry matter, organic matter, and crude protein intakes of goats fed with T3 and T4 supplementations were (p < 0.001) also higher than those reared in T1 and T2 diets. Goats reared in T3 and T4 diets had lower (p < 0.05) feed conversion ratio than those fed with T1 and T2 diets. The final body weight in goats reared in T3 and T4 diets was 18.2 kg and 18.5 kg, respectively, being (p < 0.05) higher than those of T1 (15.8 kg) and T2 (16.3 kg). The average daily weight gain in goats fed with T3 and T4 diets was 111 and 114 g/goat/day, respectively, which was (p < 0.05) higher than those reared in the control (T1) (54.0 g/goat/day) and T2 (58.1 g/goat/day) diets. It can thus be concluded that goats reared at high level of MSL supplementation (T3 and T4) had better nutrient intake, feed conversion efficiency and growth performances, suggesting its potential as a good protein supplement to natural grass hay at the farmer’s management level.

Evaporative cooling effects and turbulence flame interaction are analyzed in the large eddy simulation (LES) context for an ethanol turbulent spray flame. Investigations are conducted with the artificially thickened flame (ATF) approach coupled with an extension of the mixture adaptive thickening procedure to account for variations of enthalpy. Droplets are tracked in a Euler−Lagrangian framework, in which an evaporation model accounting for the inter-phase non-equilibrium is applied. The chemistry is tabulated following the flamelet generated manifold (FGM) method. Enthalpy variations are incorporated in the resulting FGM database in a universal fashion, which is not limited to the heat losses caused by evaporative cooling effects. The relevance of the evaporative cooling is evaluated with a typically applied setting for a flame surface wrinkling model. Using one of the resulting cases from the evaporative cooling analysis as a reference, the importance of the flame wrinkling modeling is studied. Besides its novelty, the completeness of the proposed modeling strategy allows a significant contribution to the understanding of the most relevant phenomena for the turbulent spray combustion modeling.

Apart from electric vehicles, most internal combustion (IC) engines are powered while burning petroleum-based fossil or alternative fuels after mixing with inducted air. Thereby the operations of mixing and combustion evolve in a turbulent flow environment created during the intake phase and then intensified by the piston motion and influenced by the shape of combustion chamber. In particular, the swirl and turbulence levels existing immediately before and during combustion affect the evolution of these processes and determine engine performance, noise and pollutant emissions. Both the turbulence characteristics and the bulk flow pattern in the cylinder are strongly affected by the inlet port and valve design. In the present paper, large eddy simulation (LES) is appraised and applied to studying the turbulent fluid flow around the intake valve of a single cylinder IC-engine as represented by the so called magnetic resonance velocimetry (MRV) flow bench configuration with a relatively large Reynolds number of 45,000. To avoid an intense mesh refinement near the wall, various subgrid scale models for LES; namely the Smagorinsky, wall adapting local eddy (WALE) model, SIGMA, and dynamic one equation models, are employed in combination with an appropriate wall function. For comparison purposes, the standard RANS (Reynolds-averaged Navier−Stokes) k- ε model is also used. In terms of a global mean error index for the velocity results obtained from all the models, at first it turns out that all the subgrid models show similar predictive capability except the Smagorinsky model, while the standard k- ε model experiences a higher normalized mean absolute error (nMAE) of velocity once compared with MRV data. Secondly, based on the cost-accuracy criteria, the WALE model is used with a fine mesh of ≈39 millions control volumes, the averaged velocity results showed excellent agreement between LES and MRV measurements, revealing the high prediction capability of the suggested LES tool for valve flows. Thirdly, the turbulent flow across the valve curtain clearly featured a back flow resulting in a high speed intake jet in the middle. Comprehensive LES data are generated to carry out statistical analysis in terms of (1) evolution of the turbulent morphology across the valve passage relying on the flow anisotropy map, (2) integral turbulent scales along the intake-charge stream, (3) turbulent flow properties such as turbulent kinetic energy, turbulent velocity and its intensity within the most critical zone in intake-port and along the port length, it further transpires that the most turbulence are generated across the valve passage and these are responsible for the in-cylinder turbulence.

The selective catalytic reduction (SCR) methodology is notably recognized as the widely applied strategy for NOX control in exhaust after-treatment technologies. In real SCR systems, complex unsteady turbulent multi-phase flow phenomena including poly-dispersed AdBlue® spray evolve with a wide ranging relative velocity between the droplet phase and carrier gas phase. This results from an AdBlue® spray that is injected into a mixing pipe which is cross-flowing by a hot exhaust gas. To reduce the complexity while gaining early information on the injected droplet size and velocity needed for a minimum deposition and optimal conversion, a single droplet with a specified diameter is addressed to mimic a spray featuring the same Sauter Mean Diameter. For that purpose, effects of turbulent hot cross-flow on thermal decomposition processes of a single AdBlue® droplet are numerically investigated. Thereby, a single AdBlue® droplet is injected into a hot cross-flowing stream within a mixing pipe in which it may experience phase change processes including interaction with the pipe wall along with liquid wall−film and possible solid deposit formation. First of all, the prediction capability of the multi-component evaporation model and thermal decomposition is evaluated against the detailed simulation results for standing droplet case for which experimental data is not available. Next, exploiting Large Eddy Simulation features the effect of hot turbulent co- and cross-flowing streams on the dynamic droplet characteristics and on the droplet/wall interaction is analyzed for various droplet diameters and operating conditions. This impact is highlighted in terms of droplet evaporation time, decomposition efficiency, droplet trajectories and wall−film formation. It turns out that smaller AdBlue® droplet diameter, higher gas temperature and relative velocity lead to shorter droplet life time as the droplet evaporates faster. Under such conditions, possible droplet/wall interaction processes on the pipe wall or at the entrance front of the monolith may be avoided. Since the ammonia (NH3) gas generated by urea decomposition is intended to reduce NOX emission in the SCR system, it is apparent for the prediction of high NOX removal performance that UWS injector system which allows to realize such operating conditions is favorable to support high conversion efficiency of urea into NH3.

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

The oxidation of methane under oxy-fuel combustion conditions with carbon capture is attractive and deserves huge interest towards reducing CO2 and NOx emissions. The current paper reports on the predictions and analysis of combustion characteristics of a turbulent oxy-methane non-premixed flame operating under highly diluted conditions of CO2 and H2 in oxidizer and fuel streams, respectively. These are achieved by applying a novel, well-designed numerical combustion model. The latter consists of a large eddy simulation (LES) extension of a recently suggested hybrid model in Reynolds averaging-based numerical simulation (RANS) context by the authors. It combines a transported joint scalar probability density function (T-PDF) following the Eulerian Stochastic Field methodology (ESF) on the one hand, and a flamelet progress variable (FPV) turbulent combustion model under consideration of detailed chemical reaction mechanism on the other hand. This novel hybrid ESF/FPV approach removes the weaknesses of the presumed-probability density function (P-PDF)-based FPV modeling, along with the solving of associated additional modeled transport equations while rendering the T-PDF computationally less affordable. First, the prediction capability of the LES hybrid ESF/FPV was appraised on the well-known air-piloted methane jet flame (Sandia Flame D). Then, it was assessed in analyzing the combustion properties of a non-premixed oxy-flame and in capturing the CO2 dilution effect on the oxy-fuel flame behavior. To this end, the so-called oxy-flame B3, already numerically investigated in a RANS context, was analyzed. Comparisons with experimental data in terms of temperature, scalar distributions, and scatter plots agree satisfactorily. Finally, the impact of generating the FPV chemistry table under condition of unity Lewis number, even with CO2 dilution, was investigated on the general prediction of the oxy-fuel flame structure, stability and emissions. In particular, it turns out that 68% molar percentage of CO2 leads to 0.39% of CO formation near the burner fuel nozzle and 0.62% at 10 dfuel above the nozzle.

In this work, entropy generation analysis is applied to characterize and optimize a turbulent impinging jet on a heated solid surface. In particular, the influence of plate inclinations and Reynolds numbers on the turbulent heat and fluid flow properties and its impact on the thermodynamic performance of such flow arrangements are numerically investigated. For this purpose, novel model equations are derived in the frame of Large Eddy Simulation (LES) that allows calculation of local entropy generation rates in a post-processing phase including the effect of unresolved subgrid-scale irreversibilities. From this LES-based study, distinctive features of heat and flow dynamics of the impinging fluid are detected and optimal operating designs for jet impingement cooling are identified. It turned out that (1) the location of the stagnation point and that of the maximal Nusselt number differ in the case of plate inclination; (2) predominantly the impinged wall acts as a strong source of irreversibility; and (3) a flow arrangement with a jet impinging normally on the heated surface allows the most efficient use of energy which is associated with lowest exergy lost. Furthermore, it is found that increasing the Reynolds number intensifies the heat transfer and upgrades the second law efficiency of such thermal systems. Thereby, the thermal efficiency enhancement can overwhelm the frictional exergy loss.

In the present paper, the behaviour of an oxy-fuel non-premixed jet flame is numerically investigated by using a novel approach which combines a transported joint scalar probability density function (T-PDF) following the Eulerian Stochastic Field methodology (ESF) and a Flamelet Progress Variable (FPV) turbulent combustion model under consideration of detailed chemical reaction mechanism. This hybrid ESF/FPV approach overcomes the limitations of the presumed- probability density function (P-PDF) based FPV modelling along with the solving of associated additional modelled transport equations while rendering the T-PDF computationally less demanding. In Reynolds Averaged Navier-Stokes (RANS) context, the suggested approach is first validated by assessing its general prediction capability in reproducing the flame and flow properties of a simple piloted jet flame configuration known as Sandia Flame D. Second, its feasibility in capturing CO2addition effect on the flame behaviour is demonstrated while studying a non-premixed oxy-flame configuration. This consists of an oxy-methane flame characterized by a high CO2 amount in the oxidizer and a significant content of H2 in the fuel stream, making it challenging for combustion modelling. Comparisons of numerical results with experimental data show that the complete model reproduces the major properties of the flame cases investigated and allows achieving the best agreement for the temperature and different species mass fractions once compared to the classical presumed PDF approach.

The present study aims to assess the effects of two different underlying RANS models on overall behavior of the IDDES methodology when applied to different flow configurations ranging from fully attached (plane channel flow) to separated flows (periodic hill flow). This includes investigating prediction accuracy of first and second order statistics, response to grid refinement, grey area dynamics and triggering mechanism. Further, several criteria have been investigated to assess reliability and quality of the methodology when operating in scale resolving mode. It turns out that irrespective of the near wall modeling strategy, the IDDES methodology does not satisfy all criteria required to make this methodology reliable when applied to various flow configurations at different Reynolds numbers with different grid resolutions. Further, it is found that using more advanced underlying RANS model to improve prediction accuracy of the near wall dynamics results in extension of the grey area, which may delay the transition to scale resolving mode. This systematic study for attached and separated flows suggests that the shortcomings of IDDES methodology mostly lie in inaccurate prediction of the dynamics inside the grey area and demands further investigation in this direction to make this methodology capable of dealing with different flow situations reliably.

Generating energy from combustion is prone to pollutant formation. In energy systems working under non-premixed combustion mode, rapid mixing is required to increase the heat release rates. However, local extinction and re-ignition may occur, resulting from strong turbulence–chemistry interaction, especially when rates of mixing exceed combustion rates, causing harmful emissions and flame instability. Since the physical mechanisms for such processes are not well understood, there are not yet combustion models in large eddy simulation (LES) context capable of accurately predicting them. In the present study, finite-rate scale similarity (SS) combustion models were applied to evaluate both heat release and combustion rates. The performance of three SS models was a priori assessed based on the direct numerical simulation of a temporally evolving syngas jet flame experiencing high level of local extinction and re-ignition. The results show that SS models following the Bardina’s “grid filtering” approach (A and B) have lower errors than the model based on the Germano’s “test filtering” approach (C), in terms of mean, root mean square (RMS), and local errors. In mean, both Bardina’s based models capture well the filtered combustion and heat release rates. Locally, Model A captures better major species, while Model B retrieves radicals more accurately.

In this work, near-wall thermal transport processes and entropy generation mechanisms in a turbulent jet impinging on a 45 ∘ -inclined heated surface are investigated using a direct numerical simulation (DNS). The objectives are to analyze the subtle mechanisms of heat transport in the vicinity of an inclined impinged wall, to determine the causes of irreversibilities that are responsible for the reduction of performance of impingement cooling applications and to provide a comprehensive dataset for model development and validation. Results for near-wall thermal characteristics including heat fluxes are analyzed. An entropy production map is provided from the second law analysis. The following main outcomes can be drawn from this study: (1) the location of peak heat transfer occurs not directly at the stagnation point; instead, it is slightly shifted towards the compression side of the jet, while at this region, the heat is transported counter to the temperature gradient; (2) turbulent thermal and fluid flow transport processes around the stagnation point are considerably different from those found in other near-wall-dominated flows and are strongly non-equilibrium in nature; (3) heat fluxes appear highly anisotropic especially in the vicinity of the impinged wall; (4) in particular, the heated wall acts as a strong source of irreversibility for both entropy production related to viscous dissipation and to heat conduction. All these findings imply that a careful design of the impinged plate is particularly important in order to use energy in such a thermal arrangement effectively. Finally, this study confirms that the estimation of the turbulent part of the entropy production based on turbulence dissipation rates in non-reacting, non-isothermal fluid flows represents a reliable approximate approach within the second law analysis, likewise in the context of computationally less expensive simulation techniques like RANS and/or LES.

In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

To cope with the progressive tightening of the emission regulations, gasoline and diesel engines will continuously require highly improved exhaust after-treatment systems. In the case of diesel engines, the selective catalytic reduction (SCR) appears as one of the widely adopted technologies to reduce NOx (nitrogen oxides) emissions. Thereby, with the help of available heat from exhaust gas, the injected urea–water solution (UWS) turns inside the exhaust port immediately into gaseous ammonia (NH3) by evaporation of mixture and thermal decomposition of urea. The reaction and conversion efficiency mostly depend upon the evaporation and subsequent mixing of the NH3 into the exhaust gas, which in turn depends upon the engine loading conditions. Up to now, the aggregation of urea after evaporation of water and during the thermal decomposition of urea is not clearly understood. Hence, various scenarios for the urea depletion in the gaseous phase that can be envisaged have to be appraised under SCR operating conditions relying on an appropriate evaporation description. The objective of the present paper is therefore fourfold. First, a reliable multi-component evaporation model that includes a proper binary diffusion coefficient is developed for the first time in the Euler–Lagrangian CFD (computational fluid dynamics) framework to account properly for the distinct evaporation regimes of adBlue droplets under various operating conditions. Second, this model is extended for thermal decomposition of urea in the gaseous phase, where, depending on how the heat of thermal decomposition of urea is provided, different scenarios are considered. Third, since the evaporation model at and around the droplet surface is based on a gas film approach, how the material properties are evaluated in the film influences the process results is reported, also for the first time. Finally, the impact of various ambient temperatures on the adBlue droplet depletion characteristics as well as the effect of gravity is pointed out. The prediction capability of the model variants is assessed by comparing the achieved results to each other and with experimental data. It turns out that satisfactory agreement between experiment and numerical predictions is achieved for a wide range of operating temperatures by using correlations by “Wilke and Lee” for urea and by “Fuller et al.” for water. The results are essentially sensitive to gravity. From subsequent comparisons of different ways to account for the thermal decomposition in the gaseous urea, a significant difference is observed. Finally, the 1/3 film rule widely used for evaluating the material properties in the film shows accurate prediction of both evaporation and thermal decomposition regimes of urea.

In the present paper, thermal transport and entropy production mechanisms in a turbulent round jet of compressed nitrogen at supercritical thermodynamic conditions are investigated using a direct numerical simulation. First, thermal transport and its contribution to the mixture formation along with the anisotropy of heat fluxes and temperature scales are examined. Secondly, the entropy production rates during thermofluid processes evolving in the supercritical flow are investigated in order to identify the causes of irreversibilities and to display advantageous locations of handling along with the process regimes favorable to mixing. Thereby, it turned out that (1) the jet disintegration process consists of four main stages under supercritical conditions (potential core, separation, pseudo-boiling, turbulent mixing), (2) causes of irreversibilities are primarily due to heat transport and thermodynamic effects rather than turbulence dynamics and (3) heat fluxes and temperature scales appear anisotropic even at the smallest scales, which implies that anisotropic thermal diffusivity models might be appropriate in the context of both Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) approaches while numerically modeling supercritical fluid flows.