Dry matter and nutrient partitioning of selected pineapple cultivars grown on mineral and tropical peat soils

The pineapple cultivars 'Moris' (Queen cultivar), 'N-36' ('Sarawak' × 'Gandul' hybrid), 'Gandul' (Singapore Spanish cultivars), and 'Josapine' ('Singapore Spanish' × 'Smooth Cayenne' hybrid) are mostly grown on peat soils in Malaysia, whereas 'Sarawak' ('Smooth Cayenne' cultivar) is more commonly grown on mineral soils. To obtain good yields of fruit of high quality, it is important to understand the differences in nutrient requirements for these cultivars in the different soils in which they are grown. Therefore, the objectives of the study were to determine the biomass and nutrient partitioning of the different pineapple cultivars and to determine the plant variables affecting fruit yield and quality. Plants of each of the pineapple cultivars were randomly sampled from different locations of the major pineapple-growing areas in Malaysia. Only plants having A-grade fruit of marketable quality at harvest were selected. The cultivars and respective field sites were as follows: 'Sarawak,' Bukit Tandak farm, Kelantan (5° 55.274′ N, 102° 00.608′ E); 'Moris,' 'N-36,' and 'Gandul,' Peninsula Pineapple Plantations, Simpang Renggam, Johor (1° 49.909′ N, 103° 14.053′ E); and 'Josapine,' Goh Swee Eng Pineapple Farm, Simpang Renggam, Johor (1° 48.441′ N, 103° 11.935′ E). Plants were partitioned into roots, stem, leaves, peduncle, fruit, and crown, and fresh and dry weights were recorded. Total biomasses for the different cultivars were 733.46 ± 22.83 g for 'Gandul,' 842.34 ± 43.26 g for 'N-36,' 927.38 ± 53.10 g for 'Moris,' 434.77 ± 16.82 g for 'Josapine,' and 2446.94 ± 156.00 g for 'Sarawak.' Leaves accounted for the greatest proportion of dry matter (48.5%), followed by fruit (22.9%) and stem (21.6%), and a smaller proportion (1.2-2.5%) was roots, peduncle, and crown. The proportions of the dry-matter accumulation in leaves and stem for the cultivars were 53.5 ± 0.7 and 16.7 ± 0.9% for 'Gandul'; 45.1 ± 0.5 and 17.7 ± 0.7% for 'N-36'; 51.9 ± 1.6 and 16.8 ± 0.6% for 'Moris'; 56.5 ± 1.0 and 12.0 ± 0.9% for 'Josapine'; and 54.2 ± 5.1 and 27.7 ± 4.4% for 'Sarawak.' The proportion of the macro-and micronutrients in pineapple parts differed widely between cultivars. Potassium (K) showed the greatest proportion (7.96 ± 0.6 to 29.73 ± 1.17%) in leaves and (4.46 ± 0.70 to 9.35 ± 0.28%) in fruit followed by nitrogen (N) and phosphorus (P) with lower proportions. Most pineapple cultivars grown showed variation in nutrient-use efficiency (NUE) with respect to the elements measured with values of 1.0 g dry matter g-1 nutrient were observed for magnesium (Mg) in 'Gandul' and 'N-36' and for calcium (Ca) and copper (Cu) in 'N-36.' Total nutrient accumulation in the plant components differed approximately according to their cultivar origins ('Smooth Cayenne,' 'Queen,' 'Singapore Spanish'). It is interesting that the results for the 'Singapore Spanish' × 'Smooth Cayenne' hybrid Josapine were more similar to the 'Singapore Spanish' cultivars than being between the parents. Partitioning of biomass and nutrients in pineapple provides a means to categorize them and makes it possible to use a cultivar-based fertilization program

Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study

Kaks küsimust on semantselt dubleeritud, arvestades, et täpselt sama vastus võib rahuldada mõlemaid küsimusi. Semantselt identsete küsimuste väljaselgitamine selliste sotsiaalmeedia platvormide kohta nagu Quora on erakordselt oluline, et tagada kasutajatele esitatud sisu kvaliteet ja kogus, lähtudes küsimuse kavatsusest ja nii rikastades üldist kasutajakogemust. Dubleerivate küsimuste avastamine on väljakutseks, sest looduskeel on väga väljendusrikas ning ainulaadset kavatsust saab edastada erinevate sõnade, fraaside ja lausekujunduse abil. Masinõppe ja sügava õppimise meetodid on teadaolevalt saavutanud paremaid tulemusi võrreldes traditsiooniliste loodusliku keeletöötlemise tehnikatega sarnaste tekstide väljaselgitamisel.Selles teoses, võttes Quora oma juhtumiuuringuks, uurisime ja kohaldasime erinevaid masinõppe- ja sügavõppetehnikaid ülesandel tuvastada Quora küsimuse paari andmestikul kahekordsed küsimused. Kasutades omaduste inseneritehnikat, eristavaid tähtsaid tehnikaid ning katsetades seitsme valitud masinõppe klassifikaatoriga, näitasime, et meie mudelid edestasid paari varasemat selle ülesandega seotud uuringut. Xgboost mudelil, mida söödetakse tähetaseme termilise sagedusega ja pöördsagedusega, saavutati teiste masinõppemudelite suhtes paremad tulemused ning edestati ka paari Deep learningi algmudelit.Meie kasutasime sügava õppimise tehnikat, et modelleerida neli erinevat sügavat neuralivõrgustikku, mis koosnevad Glove Embedding, Long Short Term Memory, Convolution, Max Pooling, Dense, Batch normaliseerimisest, aktuaalsetest funktsioonidest ja mudeli ühendamisest. Meie süvaõppemudelid saavutasid parema täpsuse kui masinõppemudelid. Kolm neljast väljapakutud arhitektuurist edestasid täpsust varasemast masinõppe- ja süvaõppetööst, kaks neljast mudelist edestasid täpsust varasemast sügava õppimise uuringust Quora küsitluspaari andmestik ning meie parim mudel saavutas täpsuse 85.82% mis on kunstilise seisundi Quora lähedane täpsus.Two questions are semantically duplicate, given that precisely the same answer can satisfy both the questions. Identifying semantically identical questions on, Question and Answering(QandA) social media platforms like Quora is exceptionally significant to ensure that the quality and the quantity of content are presented to users, based on the intent of the question and thus enriching overall user experience. Detecting duplicate questions is a challenging problem because natural language is very expressive, and a unique intent can be conveyed using different words, phrases, and sentence structuring. Machine learning and deep learning methods are known to have accomplished superior results over traditional natural language processing techniques in identifying similar texts.In this thesis, taking Quora for our case study, we explored and applied different machine learning and deep learning techniques on the task of identifying duplicate questions on Quora’s question pair dataset. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed a few of the previous studies on this task. Xgboost model, when fed with character level term frequency and inverse term frequency, achieved superior results to other machine learning models and also outperformed a few of the Deep learning baseline models.We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Our deep learning models achieved better accuracy than machine learning models. Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora’s question pair dataset, and our best model achieved accuracy of 85.82% which is close to Quora state of the art accuracy

Multiple soaking with different solution concentration in doped silica preform fabrication using modified chemical vapor deposition and solution doping

Incorporation of alumina (Al2O3) into a silica matrix by modified chemical vapor deposition and a solution doping technique is investigated in this study. Multiple soaking cycles were used to increase the aluminum content in the core layer. The effect of alumina retention in silica matrix soot is focused by multiple cycles of soaking with different solution concentrations, while the effect of the adsorption mechanism is fixed by maintaining the soot deposition process (such as temperature [1,800°C], precursor, total gas flow, and soaking time). The deposited soot is examined for porosity characteristics and effective surface area by a gas adsorption technique with Brunauer-Emett-Teller surface area analysis and the surface and cross-section morphology using scanning electron microscopy. Three different concentrations are used in this work (0.3, 0.7, and 1.2 M) with multiple cycles of soaking. Sintering and the collapsing process is controlled for each preform. The result shows that the alumina content is increased substantially as the number of soaking processes is increased, which may be due to the retention effect as only a small amount of adsorption process takes place as indicated by the slight decrease in the surface area of soot. The collapsed preforms are analyzed using a preform analyzer. Energy dispersive x-ray spectrometry is used to check aluminum content and distribution into the core layer

Fabrication of Tm2O3/Al2O3-silica preform by improved MCVD-chelate delivery system

The fabrication of Tm/Al-doped silica preforms by an improved MCVD method with metal chelate precursors is discussed. Two fabrication techniques are employed, namely; simultaneous soot-dopant deposition (or standard MCVD) and stepwise soot-dopant deposition. The preforms are characterized by refractive index profiler and EPMA. The results show that the stepwise soot-dopant technique has a higher incorporation of Al2O3 and Tm2O3 as compared to the simultaneous soot-dopant method. This is due to the drawbacks of our chelate delivery system such as the temperature gradient and flow design. For the stepwise technique, the measured index difference of the preform is 0.006 with 0.8 wt% (maximum) Tm incorporated in the core

Role of Zinc in patients with Nephrotic syndrome

Introduction:  Nephrotic syndrome(NS) is one of the most common cause of chronicmorbidity in developing countries. This study is aimed to assess the effect of zincsupplementation in patients with NS and to evaluate its association with serum albuminlevel, relapse rate and infection frequency. Materials & method:  In this randomized, double blind, placebo-controlled trial study, 60patients with NS, both with the first episode and first relapse, age between2-10 years were included. Among the 60 patients, 30 patients of NS getting zinc were inzinc group and 30 patients of nephrotic syndrome getting placebo were in placebogroup. Zinc status was assessed before and after giving zinc or placebo..Results; Serum zinc level has been found significantly lower during relapse (0.54±0.18and 0.56±0.22), it has increased during remission, which is (0.85±0.42) normal in zincgroup and remained low (0.69±0.14) in placebo group. The mean serum albumin levelduring relapse were low in both groups,14 days later it was increased but still low.The difference of mean percentage of increase of height after 6 months was notstatistically significant (3.3±1.2 % vs. 3.3±1.9 %) in two groups.19 patients (63.3%) in zinc group developed relapse compared to 15 patients (50%) in placebo group, the difference was not statistically significant. Infection had occurred 73.3% after zincsupplementation as compared with 63.3% in placebo group. Conclusion: When zinc was given in RDA for short duration doesn’t reduce relapsein NS and doesn’t significant increase zinc level compared to placebo

SCREENING OF RESISTANT CULTIVARS OF BETELVINE (Piper betle L.) AGAINST FOOT AND ROOT ROT DISEASE CAUSED BY Sclerotium rolfsii

A Thesis Submitted to the Faculty of Agriculture Sher-e-Bangla Agricultural University, Dhaka, In partial fulfillment of the requirements For the degree of MASTER OF SCIENCE IN PLANT PATHOLOGYThe causal organism of foot and root rot disease was isolated from diseased specimen with typical symptoms collected from affected betelvine. The isolate was pure cultured and identified as Sclerotium rolfsii The Sclerotium rolfsii was mass multiplied and the pathogenicity was confirmed by Koch’s Postulates. Eight betelvine cultivars viz. PB 001 (Chalitaguti) PB 002 (Chuadanga Pan), PB 003 PB (Moheskhali Pan), 004 (Laldingi Pan), PB 005 (Satkhira Pan), PB 006 (BARI Pan-1), PB 007 (BARI Pan-2) and PB 008 (BARI Pan-3), were explored in the screening experiment. Results were compiled based on physio-morphological features, days required for appearance of 1 st disease symptom, disease reactions, yield and yield contributing characters Betelvine cultivars were showed differential reactions against S. rolfsii causing foot and root rot disease. The vegetative growth parameters and morphological features of different cultivars of betelvine varied remarkably. The maximum vine increment per month (90.97 cm) was recorded in PB 003 and the lowest increment (46.87 cm) was in PB 006. The maximum length of internode (8.27 cm) was recorded in PB 004 and the minimum length (4.50 cm) was in PB 006. The maximum vine girth (1.63 cm) was recorded in PB 007 and the minimum girth (0.93 cm) was in PB 002. Significantly the highest length of leaf (22.07 cm) was recorded in PB 005 and the lowest (13.17 cm) was in PB 006. The leaf breadth (13.03 cm) was recorded the highest in PB 003 and the lowest (6.93 cm) in PB 006. The highest petiole length (8.63 cm) was recorded in PB 008 and the lowest length (4.03 cm) was in PB 002. The petiole breadth (1.13cm) was recorded the highest in PB 004 and the lowest (0.43 cm) in PB 008. The weight (g) of 100 petiole was recorded the highest in PB 004 (83.33 g) and the lowest weight (38.33 g) was in PB 006.The fresh weight of 100 leaves with petiole was recorded the highest in PB 004 (553.33 g) and the lowest weight (206.67 g) was in PB 006. The PB 006 produced significantly the highest number (23.00) of leaves per meter vine and the lowest number (13.67) of leaf was recorded in PB 003. The leaf number per plant per year was recorded the highest in PB 006 (414 leaves) and the lowest number was found in PB 003 (282 leaves). After inoculation, the time interval (days) required for appearance of 1 st disease symptoms among the betelvine cultivar differed significantly. The lowest incubation period (8 days) required for the cultivars PB 007 (BARI Pan-2) and PB 008 (BARI Pan-3). The highest incubation period (14 days) was required for PB 006 (BARI Pan-1). No symptom was appeared in the cultivars PB 004 (Laldingi pan). The disease incidence ranged from 0.00% - 100%. Among the betelvine cultivars, PB 004, (Laldingi pan) showed resistant (R) reaction while three cultivars viz. PB 006 (BARI Pan-1), PB-003 Moheskhali Pan) and PB 002 (Chuadanga) showed moderately resistant (MR) reaction. Two cultivars viz. PB 001 (Chalitaguti) and PB 005 (Satkhira) showed moderately susceptible (MS) reaction and the rest cultivars PB 007 (BARI Pan-2) and PB 002 (BARI Pan-3) showed highly susceptible reaction against S. rolfsii causing foot and root rot of betelvine

Mini-batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Al-Quran is the primary text of Muslims' religion and practise. Millions of Muslims around the world use al-Quran as their reference guide, and so knowledge can be obtained from it by Muslims and Islamic scholars in general. Al-Quran has been reinterpreted to various languages in the world, for example, English and has been written by several translators. Each translator has ideas, comments and statements to translate the verses from which he has obtained (Tafseer). Therefore, this paper tries to cluster the translation of the Tafseer using text clustering. Text clustering is the text mining method that needs to be clustered in the same section of related documents. The study adapted (mini-batch k-means and k-means) algorithms of clustering techniques to explain and to define the link between keywords known as features or concepts for Al-Baqarah chapter of 286 verses. For this dataset, data preprocessing and extraction of features using TF-IDF (Term Frequency-Inverse Document Frequency), and PCA (Principal Component Analysis) applied. Results show two/three-dimensional clustering plotting assigning seven cluster categories (k=7) for the Tafseer. The implementation time of the mini-batch k-means algorithm (0.05485s) outperforms the time of the k-means algorithm (0.23334s). Finally, the features 'god', 'people', and 'believe' was the most frequent features

Co-optimization of Operational Unit Commitment and Reserve Power Scheduling for Modern Grid

Modern power grids combine conventional generators with distributed energy resource (DER) generators in response to concerns over climate change and long-term energy security. Due to the intermittent nature of DERs, different types of energy storage devices (ESDs) must be installed to minimize unit commitment problems and accommodate spinning reserve power. ESDs have operational and resource constraints, such as charge and discharge rates or maximum and minimum state of charge (SoC). This paper proposes a linear programming (LP) optimization framework to maximize the unit-committed power for a specific optimum spinning reserve power for a particular power grid. Using this optimization framework, we also determine the total dispatchable power, non-dispatchable power, spinning reserve power, and arbitrage power using DER and ESD resource constraints. To describe the ESD and DER constraints, this paper evaluates several factors: availability, dispatchability, non-dispatchability, spinning reserve, and arbitrage factor. These factors are used as constraints in this LP optimization to determine the total optimal reserve power from the existing DERs. The proposed optimization framework maximizes the ratio of dispatchable to non-dispatchable power to minimize unit commitment problems within a specific range of spinning reserve power set to each DER. This optimization framework is implemented in the modified IEEE 34-bus distribution system, adding ten DERs in ten different buses to verify its efficacy

K-means variations analysis for translation of English Tafseer Al-Quran text

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption