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Effects of boron on yield, quality and leaf nutrients of Isabella (Vitis labrusca L.) grape cultivar

100%

Bilir Ekbic H. , Gokdemir N. , Erdem H.

Acta Scientiarum Polonorum. Hortorum Cultus

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2018

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tom 17

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nr 1

EN

The present study was conducted in 2014 vegetation season in a vineyard of Giresun Hazelnut Research Institute. Isabella (Vitis labrusca L.) grape cultivar were treated with four different boric acid (H3BO3) doses (control, 0.1%, 0.2%, 0.3%) at two different periods (a week before and after full-bloom). The effects of foliar boron treatments on yield, quality and leaf nutrients were investigated. Boric acid treatments positively influenced cluster weight, width and volumes and increasing values were observed with increasing boron doses. Boric acid treatments also influenced berry homogeneity and yielded more homogeneous appearance. Chlorophyll contents increased with increasing boron treatments. The greatest yield, cluster length, cluster volume, cluster size, cluster width, berry width and leaf area were obtained from 0.3% boric acid treatments. In general, leaf nitrogen, phosphorus, calcium, magnesium, zinc and copper concentrations increased, but potassium and iron concentrations decreased with increasing boron doses. As compared to control treatment, all treatments had broader leaf sizes and higher chlorophyll contents. Especially 0.3% boric acid treatments had quite positive influences on quality, size and color homogeneity. Considering the nutrients and pH level of experimental soils, it was concluded that foliar nutrient treatments may support plant growth and development and 0.3% boric acid treatments were recommended for high quality and quantity yields in Isabella grape cultivar.

2

Parkinson's disease monitoring from gait analysis via foot-worn sensors

75%

Aşuroğlu T. , Açıcı K. , Erdaş Ç. B. , Toprak M. K. , Erdem H. , Oğul H.

Biocybernetics and Biomedical Engineering

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2018

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tom Vol. 38, no. 3

760--772

EN

Background: In Parkinson's disease (PD), neuronal loss in the substantia nigra ultimate in dopaminergic denervation of the stiratum is followed by disarraying of the movements' preciseness, automatism, and agility. Hence, the seminal sign of PD is a change in motor performance of affected individuals. As PD is a neurodegenerative disease, progression of disability in mobility is an inevitable consequence. Indeed, the major cause of morbidity and mortality among patients with PD is the motor changes restricting their functional independence. Therefore, monitoring the manifestations of the disease is crucial to detect any worsening of symptoms timely, in order to maintain and improve the quality of life of these patients. Aim: The changes in motion of patients with PD can be ascertained by the help of wearable sensors attached to the limbs of subjects. Then analysing the recorded data for variation of signals would make it possible to figure an individualized profile of the disease. Advancement of such tools would improve understanding of the disease evolution in the long term and simplify the detection of precipitous changes in gait on a daily basis in the short term. In both cases the apperception of such events would contribute to improve the clinical decision making process with reliable data. To this end, we offer here a computational solution for effective monitoring of PD patients from gait analysis via multiple foot-worn sensors. Methods: We introduce a supervised model that is fed by ground reaction force (GRF) signals acquired from these gait sensors. We offer a hybrid model, called Locally Weighted Random Forest (LWRF), for regression analysis over the numerical features extracted from input signals to predict the severity of PD symptoms in terms of Universal Parkinson Disease Rating Scale (UPDRS) and Hoehn and Yahr (H&Y) scale. From GRF signals sixteen time-domain features and seven frequency-domain features were extracted and used. Results and conclusion: An experimental analysis conducted on a real data acquired from PD patients and healthy controls has shown that the predictions are highly correlated with the clinical annotations. Proposed approach for severity detection has the best correlation coefficient (CC), mean absolute error (MAE) and root mean squared error (RMSE) values with 0.895, 4.462 and 7.382 respectively in terms of UPDRS. The regression results for H&Y Scale discerns that proposed model outperforms other models with CC, MAE andRMSE with values 0.960, 0.168 and 0.306 respectively. In classification setup, proposed approach achieves higher accuracy in comparison with other studies with accuracy and specificity of 99.0% and 99.5% respectively. Main novelty of this approach is the fact that an exact value of the symptom level can be inferred rather than a categorical result that defines the severity of motor disorders.

3

Aminoethoxyvinylglycine and foliar zinc treatments play a key role in pre-harvest drops and fruit quality attributes of 'William's Pride' apple

75%

Ozturk B. , Yildiz K. , Erdem H. , Karakaya O. , Ozturk A. , Aglar E.

Acta Scientiarum Polonorum. Hortorum Cultus

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2019

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tom 18

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nr 2

EN

Productivity of plants is determined by multiple factors that directly affect one another, therefore yield variability may be high and difficult to predict. Most often, however, a lower crop yield is achieved in the notillage system than in the ploughing system. An exact field experiment was undertaken to determine the yield and chemical composition of pea seeds sown under conditions of: 1) conventional tillage – CT (shallow ploughing and harrowing after the harvest of previous crop, pre-winter ploughing in winter); 2) reduced tillage – RT (stubble cultivator after the harvest of previous crop); and 3) herbicide tillage – HT (only glyphosate after the harvest of previous crop). A cultivation unit was applied on all plots in the springtime. Pea seed yield was higher by 14.1% in the CT than in the RT system and by 50.5% than in the HT system. The CT system was increasing the plant number m–2, number of pods and seeds m–2, seed mass per plant, and 1000 seeds mass, compared to the other systems. Protein content of seeds was at a similar level in all analyzed tillage systems, but was affected by the study year. In turn, the mineral composition of seeds was determined by both tillage system and study year. The seeds harvested from CT plots contained more phosphorus and iron, those from RT plots – more calcium and zinc, whereas those from HT plots – more phytate-P, potassium, magnesium, and copper, compared to the seeds from the other plots.