Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

The aim of this study was to increasing natural carotenoides production by a locally isolate Rodotorula mucilagenosa M. by determination of the optimal conditions for growth and production of this agents, for encouragest to use it in food application permute artificial pigments which harmfull for consumer health and envieronmental. The optimal condition of carotenoides production from Rhodotorula mucilaginosa M were studied. The results shows the best carbon and nitrogen source were glucose and yeast extract. The carotenoids a mount production was 47430 microgram ̸ litter and 47460 microgram ̸ litter, respectively, and the optimum temperature was 30°C, PH 6, that the carotenoides a mount was 47470 microgram ̸ litter and 47670 microgr

In this article four samples of HgBa₂Ca₂Cu_2.4Ag_0.6O_8+δ were prepared and irradiated with different doses of gamma radiation 6, 8 and 10 Mrad. The effects of gamma irradiation on structure of HgBa₂Ca₂Cu_2.4Ag_0.6O_8+δ samples were characterized using X-ray diffraction. It was concluded that there effect on structure by gamma irradiation. Scherrer, crystallization, and Williamson equations were applied based on the X-ray diffraction diagram and for all gamma doses, to calculate crystal size, strain, and degree of crystallinity. I

            In this paper, new method have been investigated using evolving algorithms (EA's) to cryptanalysis one of the nonlinear stream cipher cryptosystems which depends on the Linear Feedback Shift Register (LFSR) unit by using cipher text-only attack. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) which are used for attacking one of the nonlinear cryptosystems called "shrinking generator" using different lengths of cipher text and different lengths of combined LFSRs. GA and ACO proved their good performance in finding the initial values of the combined LFSRs. This work can be considered as a warning for a stream cipher designer to avoid the weak points, which may be f

Many of the key stream generators which are used in practice are LFSR-based in the sense that they produce the key stream according to a rule y = C(L(x)), where L(x) denotes an internal linear bit stream, produced by small number of parallel linear feedback shift registers (LFSRs), and C denotes some nonlinear compression function. In this paper we combine between the output sequences from the linear feedback shift registers with the sequences out from non linear key generator to get the final very strong key sequence

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.

Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based

These years Qatar has emerged as an important regional destination of foreign direct investment (FDI), which demonstrates the recent success of several smaller economies in attracting FDI. This paper aims to investigate the causal relationships between inward FDI and economic growth in the particular case of Qatar featured as oil production country. Vector Autoregressive (VAR) Impulse Responses and Granger Causality Tests were adopted as major research methods. Annual data set was used covering from 1990 to 2010. The main results demonstrated the bi-directional causality and long-run relationships between FDI inflows and economic growth in Qatar. And to attract FDI inflows into Qatar, the government shall continue its efforts in const