An accelerated shape based segmentation approach adopting the pattern search optimizer

AbstractAll known solutions of the shape based segmentation problem are slower than real-time application requirements. In this paper, the problem is formulated as a global optimization problem for an energy objective function with several constraints. This formulation allows the use of the global optimization solvers as a solution. However, this solution will be slow as it requires the evaluation of the objective function for several thousand times. The objective function computation is one of the critical factors that affect the time needed to reach a solution. The authors implemented two accelerated parallel versions of the solution that integrates the objective function and the pattern search solver. The first uses a GPU accelerated implementation of the objective function and the second uses a CPU parallel version which is executed on several processors/cores. The results of the proposed solution show that the GPU version has substantial speed compared to other approaches

Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model

There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decision-makers to use. Doi: 10.28991/CEJ-SP2023-09-04 Full Text: PD

Forecasting project schedule performance using probabilistic and deterministic models

AbstractEarned value management (EVM) was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV) and earned schedule (ES) models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM) through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM), which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models

Response of primiparous and multiparous buffaloes to yeast culture supplementation during early and mid-lactation

Strains of live Saccharomyces cerevisiae yeast have exhibited probiotic effects in ruminants. This study investigated the effects of the dietary yeast supplement, S. cerevisiae (Yea-Sacc1026), on primiparous (PP) and multiparous (MP) Egyptian buffaloes in early to mid-lactation. Lactating buffaloes were fed either a basal total mixed ration (TMR, control; 4 PP and 8 MP) or the basal TMR plus 10 g Yea-Sacc1026 per buffalo cow per day (yeast; 4 PP and 8 MP). The feeds were given from 15 days prepartum to 180 days postpartum. Feed intake, body weight, and milk yields (MY) were recorded, and milk and blood samples were collected for analyses. Feces were collected from days 45 to 47 during early lactation and from days 90 to 92 during mid-lactation to determine apparent digestibility of dry matter (DM), organic matter (OM), crude protein (CP) and crude fiber (CF). Energy corrected milk yield (ECM), feed conversion, and energy and nitrogen conversion efficiency were calculated. Yeast treated MP buffaloes consumed more DM (P ≤ 0.041) and CP than the untreated control group. Apparent digestibility of DM and OM were significantly greater at mid-lactation for treated versus control group (P = 0.001). Crude fiber digestibility was greater in MP than in PP buffaloes (P = 0.049), and yeast supplemented MP cows had a greater CF digestibility than control MP buffaloes at mid-lactation (P = 0.010). Total blood lipids decreased after yeast supplementation (P = 0.029). Milk yields, ECM, fat and protein yields increased for yeast treated MP buffaloes (P ≤ 0.039). The study concluded that the response to yeast supplementation in buffalo cows is parity dependent. Multiparous buffaloes respond to yeast supplementation with an increased DM intake and CF digestibility without significant weight gains, allowing a greater ECM yield with less fat mobilization. Supplementing buffaloes with yeast culture may increase milk production in early lactation and results in a more persistent milk production during mid-lactation. Feed conversion and energy and nitrogen conversion efficiency may be increased with the use of yeast supplementation in Egyptian buffaloes

Clinical outcome of combined scaphocapitate fusion and posterior interosseous neurectomy for stage III Kienbock’s disease

Background: Treatment of Kienbock’s disease is still controversial. Several authors have described various surgical treatment options for Kienbock’s disease, all of whom reported successful treatment outcomes. The purpose of this study is to explore the clinical results of posterior interosseous neurectomy and scaphocapitate fusion as a treatment option for stage III Kienbock’s disease.Methods: This study evaluated the range of motion, grip and functional results after treatment of ten wrists of stage III Kienbock’s disease. Four males and six females with average age of 26.3 years, seven dominant and three non-dominant wrists were included. Two patients were smokers while six were housewives, three manual workers and a lawyer. The average follow up period was 14.2 months.Results: Four patients revealed excellent, three good and three fair results. The mean modified Mayo score was 81.5. Flexion-extension range was 105.5° representing 74.9% of the contralateral side range. The mean flexion and extension range of operated side was increased, while the flexion increase was nonsignificant. Regarding radial-ulnar deviation, the mean range was 33.5° representing 76.5% of the contralateral side. The mean ulnar and radial deviation was increased, while the radial increase was nonsignificant. The mean grip strength was significantly increased to 90 mmHg representing 93.2% of the contralateral side.Conclusions: Scaphocpitate fusion is a recommended solution for treatment of late stages of Kienbock’s disease with lunate collapse. Longer postoperative time has a positive impact on grip strength and flexion-extension range of motion

Design and analysis of a dynamic code division multiple access communication system based on tunable optical filter

A dynamic optical code division multiple access (DOCDMA) communication system is proposed for high-band-width communication systems. An implementation of the system is proposed based on a fast tunable optical filter (TOF) in each encoder and decoder. This technique actively modulates the central wavelength of a TOF according to a functional code at the transmitter during the bit period before the transmission of the data. The system is modeled and analyzed taking into account multiple access interference (MAI), thermal noise, and phase-induced intensity noise (PIIN). The performance of this system is compared to that of a spectral amplitude coding system that uses either a Hadamard code or a modified quadratic congruence (MQC) code. The results show that the proposed DOCDMA system reduces the PIIN effect on the performance of the system and improves the bit error rate (BER) performance at a large number of users. Furthermore, it is found that when the effective power is large enough, the MAI becomes the main factor that limits system performance, whereas when the effective power is relatively low, both thermal noise and PIIN become the main limiting factors with thermal noise having the main influence. © 2005 IEEE

Evaluation of Mapping Accuracy of High-Resolution Stereoscopic Satellite Images

High resolution satellite images is still used in large scale mapping due to the need to produce fast products. High resolution stereoscopic satellite images present good enough 3d products that include the benefits of large-scale coverage and low-cost products. A stereopair of IKONOS satellite is used in this research that covers a part of North Sudan country. The study handles the 3d mapping accuracy of using stereoscopic satellite images. The study gives a spotlight on the accuracy in X, Y, Z and the space vector R. Another view of this study the N, E and elevation is indicated. The research environment is mainly ENVI software due to its capabilities of topographic processing module. Some distributed set of ground points (control and tie) was determined on the images and then observed using GPS surveying. Several experiments have been performed to evaluate the resulted mapping product

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. The modified optimizer\u27s validation is first investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS