Fatty acids,sterols, and tocopherols composition in seed oil extracts from four moroccan ecotypes of Ceratonia siliqua L.

Morphological characterization was investigated by agro-morphological criteria related to carob seed size in four different moroccan regions collected in 2018 and 2019. There was no significant difference (p ≤ 0.05) on the seeds lengths and widths. However, a significant difference between seeds thickness and total seeds weight per pod (p ≤ 0.05) were observed between these four populations. The fatty acid, sterol, tocopherol, hydrocarbon, and the unoxygenated composition of carob seed extracts (Ceratonia siliqua L.) were studied. The mean fat yield of the seeds obtained is 1.53%–2.17%, 2.14%–2.15%, 1.61%–1.62%, 1.71%–1.75% for, respectively, the P1 (Meknes), P2 (Fez), P3 (Khemisset), and P4 (Marrakech) in 2018 and 2019. The seed oil was extracted with hexane and the analysis of the fatty fraction was performed by gas chromatography–mass spectrometry (GC–MS). Results show that the major fatty acids for 2018 and 2019 are linoleic acid (61.48%–61.52%, 52.12%–52.14%, 57.76%–58.15%, 61.33%–61.52%), palmitic acid (15.78%–15.81%, 16.44%–16.45%, 19.11%–18.37%, 20.24%–20.32%), oleic acid (11.03%–11.04%, 8.72%–8.82%, 8.51%–8.61%, 8.41%–8.53%), stearic acid (4.35%–3.14%, 5.40%–5.43%, 3.12%–3.13%, 0.96%–1.56%), and cerotic acid (0.62%–0.53%, 4.51%–4.52%, 4.03%–4.06%, 3.84%–3.87%). The unsaturated fatty acids (69.39% in 2018 and 69.68% in 2019) are the most dominant in the four seed extracts compared to the saturated fatty acids. In addition, the oil carob seeds analysis revealed the presence of γ-tocopherol, α-tocopherol and four sterols that included campesterol, stigmasterol, and β-sitosterol. Moreover, the determination of hydrocarbon and un-oxygenated compounds confirmed the existence of major compounds such as heptadecane, 2-methyltriacontane, 1-iodo hexadecane and 1-iodo octadecane. The hierarchical analysis based on the morphological and chromatographic characterization of the seeds allowed the identification of three groups. Consequently, the first group consisted of populations from Marrakesh (P4) and Khemisset (P3), the second group consisted of the P1 from Meknes, and the P2 from Fez constituted the third group.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.     

Region growing based segmentation algorithm for typewritten and handwritten text recognition

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit segmentation) on the basis of three essential lines in a text row. This saves time as there is no need to skeletonize or to physically isolate letters from the tested word whilst the input data involves only the basic information—the scanned text. The baseline is detected, the word contour is defined and the word is implicitly segmented into its letters according to a novel algorithm described in the paper. The extracted letter with its dots is used as one unit in the system of recognition. It is resized into a 9 × 9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing. Then the elements are scaled to the interval [0,1]. The resulting array is considered as the input to the designed neural network. For typewritten texts, three types of Arabic letter fonts are used—Arial, Arabic Transparent and Simplified Arabic. The results showed an average recognition success rate of 93% for Arabic typewriting. This segmentation approach has also found its application in handwritten text where words are classified with a relatively high recognition rate for both Arabic and English languages. The experiments were performed in MATLAB and have shown promising results that can be a good base for further analysis and considerations of Arabic and other cursive language text recognition as well as English handwritten texts. For English handwritten classification, a success rate of about 80% in average was achieved while for Arabic handwritten text, the algorithm performance was successful in about 90%. The recent results have shown increasing success for both Arabic and English texts.     

Reuse of treated municipal wastewater in irrigation: a case study from Lebanon and Jordan

In this study, we present the results of scenarios where secondary‐treated municipal wastewater was used for table grapes irrigation in the region of Ablah, Bekaa valley in Lebanon, and fodder crops irrigation (vetch and barley) in the region of Ramtha in Jordan. In Lebanon, we carried out experiments to assess the response of drip‐irrigated table grapes grown under two water quality regimes (Freshwater (FW) and treated wastewater (TW) and two water levels (100% of evapotranspiration (ETc) and 75% of ETc). While in Jordan, we carried out experiments to assess the response of drip‐irrigated fodder crops considering 4 irrigation levels (Q1: Rain fed; Q2: 80% of ETc; Q3: 100% of ETc; Q4: 120% of ETc) and three crop patterns (C1: Barley 100%; C2: Vetch 100%; C3: Mix 50% barley and 50% vetch). Based on the production and quality components, table grapes were successfully grown on plots that are supplied with TW. Fodder crops were successfully grown using TW with remarkable increase in biomass and grain yield production for the irrigated treatments.     

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology has enabled accurate forecasting of electricity consumption. However, the impact of computation cost afforded by these ML models is often ignored in favour of accuracy. This study considers both accuracy and computation cost as concurrently significant factors because together they shape the technology environment as well as create economic impact. Thus, a three-fold optimized load forecasting model is proposed which includes (1) application specific parameters selection, (2) impact of different dataset granularities and (3) implementation of specific data preparation. It deploys and compares the widely used back-propagation Artificial Neural Network (ANN) and Random Forest (RF) models for the prediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operational calendars resulting in improved performance. The outcomes are indicative that the granularity i.e. the scale of details in data, and set of reduced and full input parameters impact performance accuracies differently for ANN and RF models. Experimental results show that overall RF model performed better both in terms of accuracy as well as computational time for a 1-min, 15-min and 1-h dataset granularities with the mean absolute percentage error (MAPE) of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suited for a real-time energy monitoring application.     

Compact substrate integrated waveguide sensor for liquids permittivity measurement

A compact substrate integrated waveguide (SIW) liquids permittivity sensor structure that utilizes half-mode (HM) and slow-wave (SW) techniques for the miniaturization of SIW sensor is presented in this paper. First, HM miniaturization technique is applied to SIW resonator cavity. Sensor width is reduced by 50% in comparison to the conventional resonator. Due to the complexity of the relationship between the complex permittivity of the substrate and liquids under test, artificial neural network tool is used as a simple and fast method to determine liquids’ complex permittivity through the measured resonant frequency and unloaded quality factor. The sensor is fabricated, and good agreement with simulations is observed according to the obtained experimental results. In the second step, SW and HM techniques are applied to the SIW sensor. The application of the HM and SW techniques indicate that an increase in sensor miniaturization while obtaining a better quality factor could be achieved. Furthermore, HM-SW-SIW is not fabricated, and we are satisfied with simulation results since we have fabricated other components. Moreover, good correspondence between the measurement and simulation results is obtained. Finally, a comparison between the structures presented in this paper and those published previously is made, demonstrating that a minimum of 25% miniaturization is achieved while maintaining acceptable characteristics.     

Beneficial role of exogenous silicon on yield,antioxidant systems,osmoregulation and oxidative stress in fenugreek (Trigonella foenum-graecum L.) under salinity stress

Silicon - In the Earth’s crust, silicon (Si) is the most abundant element after oxygen, while, under salt stress, its role in the tolerance of aromatic and medicinal plants (AMPs) is not yet...