Evaluation of quality parameters of apple juices using near-infrared spectroscopy and chemometrics

Near-infrared (NIR) spectra were recorded for commercial apple juices. Analysis of these spectra using partial least squares (PLS) regression revealed quantitative relations between the spectra and quality- and taste-related properties of juices: soluble solids content (SSC), titratable acidity (TA), and the ratio of soluble solids content to titratable acidity (SSC/TA). Various spectral preprocessing methods were used for model optimization. The optimal spectral variables were chosen using the jack-knife-based method and different variants of the interval PLS (iPLS) method. The models were cross-validated and evaluated based on the determination coefficients (R-2), root-mean-square error of cross-validation (RMSECV), and relative error (RE). The best model for the prediction of SSC (R-2 = 0.881, RMSECV = 0.277 degrees Brix, and RE = 2.37%) was obtained for the first-derivative preprocessed spectra and jack-knife variable selection. The optimal model for TA (R-2 = 0.761, RMSECV = 0.239 g/L, and RE = 4.55%) was obtained for smoothed spectra in the range of 6224-5350 cm(-1). The best model for the SSC/TA (R-2 = 0.843, RMSECV = 0.113, and RE = 5.04%) was obtained for the spectra without preprocessing in the range of 6224-5350 cm(-1). The present results show the potential of the NIR spectroscopy for screening the important quality parameters of apple juices.National Science Centre, Poland [2016/23/B/NZ9/03591

Modeling Dislocation Dynamics Data Using Semantic Web Technologies

Research in the field of Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials. An important class of materials that is widely investigated are crystalline materials, including metals and semiconductors. Crystalline material typically contains a distinct type of defect called "dislocation". This defect significantly affects various material properties, including strength, fracture toughness, and ductility. Researchers have devoted a significant effort in recent years to understanding dislocation behavior through experimental characterization techniques and simulations, e.g., dislocation dynamics simulations. This paper presents how data from dislocation dynamics simulations can be modeled using semantic web technologies through annotating data with ontologies. We extend the already existing Dislocation Ontology by adding missing concepts and aligning it with two other domain-related ontologies (i.e., the Elementary Multi-perspective Material Ontology and the Materials Design Ontology) allowing for representing the dislocation simulation data efficiently. Moreover, we show a real-world use case by representing the discrete dislocation dynamics data as a knowledge graph (DisLocKG) that illustrates the relationship between them. We also developed a SPARQL endpoint that brings extensive flexibility to query DisLocKG

Silicon improves root system and canopy physiology in wheat under drought stress

Aims: Root system is an important regulator for unevenly distributed below-ground resource acquisition. In a rainfed cropping environment, drought stress (DS) significantly restricts root growth and moisture uptake capacity. The fact that silicon (Si) alleviates DS in wheat is widely reported, but its effects on the wheat root system remain unclear. Methods: The present study investigated the effect of pre-sowing Si treatment on two contrasting wheat cultivars (RAC875, drought-tolerant; Kukri, drought-susceptible) at early growth stages. The cultivars were grown in a glasshouse in a complete randomized design with four replications and two watering treatments. Various root traits and physiological data, including non-destructive infrared thermal imaging for water stress indices, were recorded. Results: Under DS and Si (DSSi), Kukri had a significant increase in primary root length (PRL,44%) and lateral root length (LRL,28.1%) compared with RAC875 having a substantial increase in PRL (35.2%), but non-significant in LRL. The Si-induced improvement in the root system positively impacted canopy physiology and significantly enhanced photosynthesis, stomatal conductance and transpiration in Kukri and RAC875 under DSSi. Canopy temperature was reduced significantly in Kukri (4.24%) and RAC875 (6.15%) under DSSi, while canopy temperature depression was enhanced significantly in both the cultivars (Kukri,78.6%; RAC875, 58.6%) under DSSi. Conclusion: These results showed that Si has the potential to influence below-ground traits, which regulate the moisture uptake ability of roots for cooler canopy and improved photosynthesis under DS. It also suggests a future direction to investigate the underlying mechanisms involved in wheat’s Si-induced root growth and moisture uptake ability

Water allocation under future climate change and socio-economic development : the case of Pearl River Basin

Water shortage has become a major challenge in many parts of the world due to climate change and socio-economic development. Allocating water is critical to meet human and ecosystem needs in these regions now and in the future. However, water allocation is being challenged by uncertainties associated with climate change and socio-economic development. This thesis aims to assess the combined effects of climate change and socio-economic development on water supply and demand in the Pearl River Basin (PRB) in China, and identify water allocation plans, which are robust to future climate change and socio-economic development. To do so, the impact of climate change on future water availability is first assessed. Next, different model frameworks are developed to identify robust water allocation plans for improving reservoir management, ensuring sufficient flow into the delta to reduce salt intrusion, and providing sufficient freshwater for human and industrial consumption under future climate change and socio-economic development. Results show that water availability is becoming more variable throughout the basin due to climate change. River discharge in the dry season is projected to decrease throughout the basin. For a moderate climate change scenario (RCP4.5), low flows reduce between 6 and 48 % depending on locations. For a high climate change scenario (RCP8.5), the decreases of low flows can reach up to 72%. In the wet season, river discharge tends to increase in the middle and lower reaches and decreases in the upper reach of the Pearl River Basin. The variation of river discharge is likely to aggravate water stress. Especially the reduction of low flow is problematic as already the basin experiences water shortages during the dry season in the delta. The model frameworks developed in this study not only evaluate the performance of existing water allocation plans in the past, but also the impact of future climate change on robustness of previous and newly generated water allocation plans. The performance of the four existing water allocation plans reduces under climate change. New water allocation plans generated by the two model frameworks perform much better than the existing plans. Optimising water allocation using carefully selected state-of-the-art multi-objective evolutionary algorithms in the Pearl River Basin can help limit water shortage and salt intrusion in the delta region. However, the current water allocation system with six key reservoirs is insufficient in maintaining the required minimum discharge at two selected gauge stations under future climate change. More reservoirs, especially in the middle and lower reaches of the Pearl River, could potentially improve the future low flow into the delta. This study also explored future water shortage in the Pearl River Basin under different water availability and water use scenarios. Four different strategies to allocate water were defined. These water allocation strategies prioritize upstream water use, Pearl River Delta water use, irrigation water use, and manufacturing water use, respectively. Results show that almost all the regions in the Pearl River Basin are likely to face temporary water shortage under the four strategies. The increasing water demand contributes twice as much as the decreasing water availability to water shortage. All four water allocation strategies are insufficient to solve the water scarcity in the Pearl River Basin. The economic losses differ greatly under the four water allocation strategies. Prioritizing the delta region or manufacturing production would result in lower economic losses than the other two strategies. However, all of them are rather extreme strategies. Development of water resources management strategies requires a compromise between different water users. Optimization algorithms prove to be flexible and useful tool in adaptive water resources allocation for providing multiple approximate Pareto solutions. In addition, new technologies and increasing water use efficiency will be important to deal with future water shortage in the Pearl River Basin.</p

Next Generation Optical Analysis for Agrochemical Research & Development

The world’s population is increasing rapidly and higher calorific diets are becoming more common; as a consequence the demand for grain is predicted to increase by more than 50% by 2050 without a significant increase in the available agricultural land. Maximising the productivity of the existing agricultural land is key to maintaining food security and agrochemicals continue to be a key enabler for the efficiency gains required. However, agrochemicals can be susceptible to significant losses and thus often require further chemical to be applied to compensate. Sources of such losses include spray drift, poor spray retention/capture by the target and poor penetration through the plant cuticle. The effectiveness of a crop protection agent depends not only on the intrinsic activity of the active ingredient (AI) but also on the physicochemical properties of the formulation. These properties can be modified by using formulation components, known as adjuvants, which can be used to help mitigate such losses. Adjuvants exert their effects by, for example, controlling droplet size and distribution through their effect on surface tension which can also improve penetration into leaves through the cuticle wax which coats the epidermis of leaves and acts as a protective barrier. However, characterising how they alter the movement of the AIs can be challenging. Optical techniques have shown promise in a multitude of scientifically related areas, such as in vivo tissue imaging, but none have yet been applied to aiding the agrochemical industry. By probing the interactions between leaf surface and agrochemical agent, with light, one is able to obtain a large amount of diagnostic information, non-invasively. Whereas techniques like Raman 3 spectroscopy are limited by long acquisition times, coherent Raman techniques such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) are coherently driven and provide an enhanced signal, and also allow for video-rate imaging. In this thesis, I have applied this cutting-edge laser imaging technique as a novel analytical technique that allows the in situ analysis of agrochemicals in living plant tissues at a cellular level. In Chapters 4 through 7, multiple factors essential for a functional and efficient agrochemical were considered and experimented. In Chapter 4, a typical industry study highlights the need for innovative and rapid technologies in the agrochemical industry. The resulting chapters (5, 6, and 7) outline several ways in which Coherent Raman Scattering (CRS) techniques can improve the current capabilities of agrochemical testing. By utilising a model system, paraffin wax, a cheap and rapid protocol can provide accurate diffusion information and repeatable results. Chapters 6 and 7 use both this protocol to gain comparative data on several adjuvants and active ingredients in paraffin wax and in vivo, in a variety of plants. The ability to visualise agrochemical products on a leaf surface to reveal interactions between the materials of the product and with the leaf surface will enable a step change in the agrochemical design process, through determination of the spatial distribution of the materials and their roles within the applied products. It is hoped that the technology developed in this thesis could play a big role in the development of future agrochemical products that are tailored to maximise efficacy and minimise environmental impact

Water use in a heavily urbanized delta : scenarios and adaptation options for sectorial water use in the Pearl River Basin, China

Water use is increasing globally to meet the growing demand for food and industrial products, and the rising living standard. Water scarcity has been reported in many regions, questioning the long-term sustainability of water use. The objective of this thesis is to better understand sectorial water use development in an urbanizing river delta, and to explore the potential of water use management as an adaptation option to reduce water shortage. The Pearl River Basin in Southern China is taken as study area. The upstream part of the basin is one of the poorer regions of China, whereas the Pearl River Delta (PRD) is the world’s largest urban region in both population and area. This study presents the first consistent analysis of sectorial water use in the PRD. Results show that during the period of 2000-2010, the PRD managed to stabilize its annual total water use. Nevertheless, severe salt intrusion induced water shortages occur. Assessment of water use at a monthly resolution shows that water use contributes to salt intrusion by further reducing the already low dry season river discharge. To investigate the possible future development of water use, this study proposed a method to derive region specific water use scenarios from a global assessment of water use. Scenarios based on regionalised assumptions project substantially lower water use than those based on national assumptions. Nevertheless, hydrological challenges remain for the PRD. The total water use of the PRD may still increase by up to 54% in 2030 in the regionalized scenarios. Also, water use in the upstream regions increases with socio-economic development. To address water shortage, four extreme water allocation strategies were analysed against water use and water availability scenarios under climate change. None of these strategies proved to be sufficient to fully avoid water scarcity in the Pearl River Basin. This study obtains a better understanding of the sectorial water use development and its impact on salt intrusion induced water shortage in a heavily urbanized river delta. The water use framework and methods used to derive regional water use scenarios are transferable to other regions, provided that data is available. Water use scenarios are crucial to sustainably manage water resources in a changing world.</p

Incident Prioritisation for Intrusion Response Systems

The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. To manage these threats, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. Although there are security tools such as antivirus software and firewalls available to counter them, Intrusion Detection Systems and similar tools such as Intrusion Prevention Systems are still one of the most popular approaches. There are hundreds of published works related to intrusion detection that aim to increase the efficiency and reliability of detection, prevention and response systems. Whilst intrusion detection system technologies have advanced, there are still areas available to explore, particularly with respect to the process of selecting appropriate responses. Supporting a variety of response options, such as proactive, reactive and passive responses, enables security analysts to select the most appropriate response in different contexts. In view of that, a methodical approach that identifies important incidents as opposed to trivial ones is first needed. However, with thousands of incidents identified every day, relying upon manual processes to identify their importance and urgency is complicated, difficult, error-prone and time-consuming, and so prioritising them automatically would help security analysts to focus only on the most critical ones. The existing approaches to incident prioritisation provide various ways to prioritise incidents, but less attention has been given to adopting them into an automated response system. Although some studies have realised the advantages of prioritisation, they released no further studies showing they had continued to investigate the effectiveness of the process. This study concerns enhancing the incident prioritisation scheme to identify critical incidents based upon their criticality and urgency, in order to facilitate an autonomous mode for the response selection process in Intrusion Response Systems. To achieve this aim, this study proposed a novel framework which combines models and strategies identified from the comprehensive literature review. A model to estimate the level of risks of incidents is established, named the Risk Index Model (RIM). With different levels of risk, the Response Strategy Model (RSM) dynamically maps incidents into different types of response, with serious incidents being mapped to active responses in order to minimise their impact, while incidents with less impact have passive responses. The combination of these models provides a seamless way to map incidents automatically; however, it needs to be evaluated in terms of its effectiveness and performances. To demonstrate the results, an evaluation study with four stages was undertaken; these stages were a feasibility study of the RIM, comparison studies with industrial standards such as Common Vulnerabilities Scoring System (CVSS) and Snort, an examination of the effect of different strategies in the rating and ranking process, and a test of the effectiveness and performance of the Response Strategy Model (RSM). With promising results being gathered, a proof-of-concept study was conducted to demonstrate the framework using a live traffic network simulation with online assessment mode via the Security Incident Prioritisation Module (SIPM); this study was used to investigate its effectiveness and practicality. Through the results gathered, this study has demonstrated that the prioritisation process can feasibly be used to facilitate the response selection process in Intrusion Response Systems. The main contribution of this study is to have proposed, designed, evaluated and simulated a framework to support the incident prioritisation process for Intrusion Response Systems.Ministry of Higher Education in Malaysia and University of Malay

Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: case studies in the Australian Murray-Darling basin

For a drought-prone agricultural nation such as Australia, hydro-meteorological imbalances and increasing demand for water resources are immensely constraining terrestrial water reservoirs and regional-scale agricultural productivity. Two important components of the terrestrial water reservoir i.e., streamflow water level (SWL) and soil moisture (SM), are imperative both for agricultural and hydrological applications. Forecasted SWL and SM can enable prudent and sustainable decisionmaking for agriculture and water resources management. To feasibly emulate SWL and SM, machine learning data-intelligent models are a promising tool in today’s rapidly advancing data science era. Yet, the naturally chaotic characteristics of hydro-meteorological variables that can exhibit non-linearity and non-stationarity behaviors within the model dataset, is a key challenge for non-tuned machine learning models. Another important issue that could confound model accuracy or applicability is the selection of relevant features to emulate SWL and SM since the use of too fewer inputs can lead to insufficient information to construct an accurate model while the use of an excessive number and redundant model inputs could obscure the performance of the simulation algorithm. This research thesis focusses on the development of hybridized dataintelligent models in forecasting SWL and SM in the upper layer (surface to 0.2 m) and the lower layer (0.2–1.5 m depth) within the agricultural region of the Murray-Darling Basin, Australia. The SWL quantifies the availability of surface water resources, while, the upper layer SM (or the surface SM) is important for surface runoff, evaporation, and energy exchange at the Earth-Atmospheric interface. The lower layer (or the root zone) SM is essential for groundwater recharge purposes, plant uptake and transpiration. This research study is constructed upon four primary objectives designed for the forecasting of SWL and SM with subsequent robust evaluations by means of statistical metrics, in tandem with the diagnostic plots of observed and modeled datasets. The first objective establishes the importance of feature selection (or optimization) in the forecasting of monthly SWL at three study sites within the Murray-Darling Basin. Artificial neural network (ANN) model optimized with iterative input selection (IIS) algorithm named IIS-ANN is developed whereby the IIS algorithm achieves feature optimization. The IIS-ANN model outperforms the standalone models and a further hybridization is performed by integrating a nondecimated and advanced maximum overlap discrete wavelet transformation (MODWT) technique. The IIS selected inputs are transformed into wavelet subseries via MODWT to unveil the embedded features leading to IIS-W-ANN model. The IIS-W-ANN outperforms the comparative IIS-W-M5 Model Tree, IIS-based and standalone models. In the second objective, improved self-adaptive multi-resolution analysis (MRA) techniques, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are utilized to address the non-stationarity issues in forecasting monthly upper and lower layer soil moisture at seven sites. The SM time-series are decomposed using EEMD/CEEMDAN into respective intrinsic mode functions (IMFs) and residual components. Then the partial-auto correlation function based significant lags are utilized as inputs to the extreme learning machine (ELM) and random forest (RF) models. The hybrid EEMD-ELM yielded better results in comparison to the CEEMDAN-ELM, EEMD-RF, CEEMDAN-RF and the classical ELM and RF models. Since SM is contingent upon many influential meteorological, hydrological and atmospheric parameters, for the third objective sixty predictor inputs are collated in forecasting upper and lower layer soil moisture at four sites. An ANN-based ensemble committee of models (ANN-CoM) is developed integrating a two-phase feature optimization via Neighborhood Component Analysis based feature selection algorithm for regression (fsrnca) and a basic ELM. The ANN-CoM shows better predictive performance in comparison to the standalone second order Volterra, M5 Model Tree, RF, and ELM models. In the fourth objective, a new multivariate sequential EEMD based modelling is developed. The establishment of multivariate sequential EEMD is an advancement of the classical single input EEMD approach, achieving a further methodological improvement. This multivariate approach is developed to allow for the utilization of multiple inputs in forecasting SM. The multivariate sequential EEMD optimized with cross-correlation function and Boruta feature selection algorithm is integrated with the ELM model in emulating weekly SM at four sites. The resulting hybrid multivariate sequential EEMD-Boruta-ELM attained a better performance in comparison with the multivariate adaptive regression splines (MARS) counterpart (EEMD-Boruta-MARS) and standalone ELM and MARS models. The research study ascertains the applicability of feature selection algorithms integrated with appropriate MRA for improved hydrological forecasting. Forecasting at shorter and near-real-time horizons (i.e., weekly) would help reinforce scientific tenets in designing knowledge-based systems for precision agriculture and climate change adaptation policy formulations

Sustainable Use of Soils and Water: The Role of Environmental Land Use Conflicts

This book on the sustainable use of soils and water addressed a variety of issues related to the utopian desire for environmental sustainability and the deviations from this scene observed in the real world. Competing interests for land are frequently a factor in land degradation, especially where the adopted land uses do not conform with the land capability (the natural use of soil). The concerns of researchers about these matters are presented in the articles comprising this Special Issue book. Various approaches were used to assess the (im)balance between economic profit and environmental conservation in various regions, in addition to potential routes to bring landscapes back to a sustainable status being disclosed