PENGARUH PERENDAMAN AIR PANAS PADA BATANG ATAS, TENGAH DAN BAWAH TERHADAP PERTUMBUHAN BUD CHIP TEBU (Saccharum officinarum L.) VARIETAS BULULAWANG

Bud chip adalah sistem pembibitan tebu dengan menggunakan satu mata tunas. Bahan tanam yang akan dijadikan bud chip dibagi menjadi 3 yaitu batang atas, tengah dan bawah, tetapi yang umum digunakan adalah batang tengah, sedangkan batang atas dan bawah kurang dimanfaatkan. Hal ini disebabkan ketiga bagian batang tidak mampu tumbuh dengan seragam. Upaya yang dapat dilakukan agar pertumbuhan dapat menjadi seragam adalah dengan perendaman air panas. Perlakuan tersebut mampu mempercepat imbibisi air pada mata tunas sehingga dapat mempengaruhi perkecambahan. Penelitian ini bertujuan untuk mengetahui serta menentukan lama waktu perendaman yang tepat pada batang atas, tengah dan bawah agar pertumbuhan seragam. Penelitian dilaksanakan pada bulan Maret hingga Juni 2015 di Pusat Penelitian Gula Jengkol, PTPN X, Kediri. Penelitian ini menggunakan metode RAK faktorial. Pengamatan dilakukan pada umur 1 hingga 15 HST untuk pengamatan fase perkecambahan serta 30, 45, 60, 75 dan 90 HST untuk fase pertunasan. Hasil penelitian menunjukkan perendaman air panas pada batang atas selama 15 menit, batang tengah 45 menit dan batang bawah 60 menit nyata mampu meningkatkan pada parameter persentase perkecambahan, saat berkecambah, jumlah daun, tinggi tanaman serta berat kering total tanaman dibanding dengan perlakuan kontrol (tanpa perendaman)

A new implementation of a novel analytical method for finding the analytical solutions of the (2+1)-dimensional KP-BBM equation

In this work, we perform a comprehensive analytical study to find the novel exact traveling wave solutions of the (2+1)-dimensional Kadomtsev-Petviashvili-Benjamin-Bona-Mahony (KP-BBM) equation. The recently developed (G′G′+G+A) -expansion technique is a capable method for finding the new exact solutions of assorted nonlinear evolution equations. Some new analytical solutions are obtained by utilizing the aforementioned method. The obtained solutions are expressed as trigonometric functions and exponential functions. The extracted exact wave solutions are advanced and fully unique from the earlier literature Moreover, we have presented the contour simulations, 2D and 3D graphical representations of the solution functions and we have observed that the solutions obtained are periodic and solitary wave solutions. We have shown two soliton wave solutions and two singular periodic wave solutions for the particular values of the parameters graphically. As per our knowledge, we must say that the extracted solutions might be significant and essential for new physical phenomenon

Temperature-Sensitive Fragrance Microcapsules with Double Capsule Walls: A Study on Preparation and Sustained Release Mechanism

Microcapsules are small particles that can effectively protect a core material from degradation. Microcapsules with double capsule walls can improve stability and reduce breakage due to the fact that the physical and chemical properties of double-walled materials can complement each other, thus enhancing the quality and applicability of a microcapsule. Microcapsules can achieve controlled release of core materials by using a temperature-sensitive wall material. In this research, gelatin was used as the inner wall material for these double-walled microcapsules. The outer wall material was a composite material prepared by the reaction of a hydroxyl group in gum arabic with an amino group in N-isopropylacrylamide (NIPAM) in the presence of N, N’-methylene bisacrylamide (BIS), while lavender fragrance oil served as the core material. A complex coalescence method was used for the preparation of microcapsules with double capsule walls. The effects of different proportions of gum arabic to NIPAM on the core loading, microcapsule yield and thermal stability of microcapsules were studied in detail. Additionally, the stability of these fragrance microcapsules with double capsule walls in different solvents and pH values was evaluated. The sustained release properties and mechanism of cotton fabrics treated with prepared fragrance microcapsules were investigated. The results show that the microcapsules prepared with a 10:1 ratio of NIPAM to gum arabic have good temperature responsiveness. Therefore, clothing treated with microcapsules with temperature-sensitive wall materials can ensure that the human body has a fresh and pleasant smell in the case of perspiring in summer

MRIAD: A Pre-clinical Prevalence Study on Alzheimer's Disease Prediction Through Machine Learning Classifiers

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches.</p

Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery

Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. This research investigates the application of robust machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), and K-nearest neighbor (KNN), to the prediction of cerebral strokes. Stroke data is collected from Harvard Dataverse Repository. The data includes—clinical, physiological, behavioral, demographic, and historical data. The Synthetic Minority Oversampling Technique (SMOTE), adaptive synthetic sampling (ADASYN), and the Random Oversampling Technique (ROSE) are used to address class imbalances to improve the accuracy of minority classes. To address the challenge of forecasting strokes from partial and imbalanced physiological data, this study introduces a novel hybrid ML approach by combining a machine learning method with an oversampling technique called ADASYN_RF. ADASYN is an oversampling technique used to resample the imbalanced dataset then RF is implemented on the resampled dataset. Also, other oversampling techniques and ML models are implemented to compare the results. Notably, the RF algorithm paired with ADASYN achieves an exceptional performance of 99% detection accuracy, exhibiting its dominance in stroke prediction. The proposed approach enables cost-effective, precise stroke prediction, providing a valuable tool for clinical diagnosis

Assessing Morpho-Physiological and Biochemical Markers of Soybean for Drought Tolerance Potential

Drought stress provokes plants to change their growth pattern and biochemical contents to overcome adverse situations. Soybean was grown under 40 (drought) and 80% (control) of field capacity (FC) to determine the morpho-physiological and biochemical alterations that occur under drought conditions. The experiment was conducted following a randomized complete block design with three replications. The results showed that drought exerted detrimental effects on photosynthetic attributes, leaf production, pigment and water content, plant growth, and dry matter production of soybean. However, drought favored producing a higher amount of proline and malondialdehyde in soybean leaf than in the control. The pod and seed production, grain size, and seed yield of soybean were also adversely affected by the drought, where genotypic variations were conspicuous. Interestingly, the studied morpho-physiological and biochemical parameters of AGS383 were minimally affected by drought. This genotype was capable of maintaining healthier root and shoot growth, greater leaf area, preserving leaf greenness and cell membrane stability, higher photosynthesis, absorbing water and sustaining leaf water potential, and lower amount of proline and malondialdehyde production under drought conditions. The heavier grains of AGS383 make it out yielder under both growth conditions. Considering the changes in morpho-physiological, biochemical, and yield contributing parameters, the genotype AGS383 could be cultivated as a relatively drought-tolerant, high-yielding soybean variety. Further study is needed to uncover the genes responsible for the adaptation of AGS383 to drought-stress environments, and this genotype might be used as parent material in a breeding program to develop a high-yielding, drought-tolerant soybean variety