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Schwannomas are encapsulated tumors arising from Schwann cells of the nerve sheath, and are usually solitary sporadic lesions. The schwannoma of the ilio-hypogastric nerve especially in the retro-petoneal space is very rare. We are reporting a case of the schwannoma of the left ilio-hypagastric nerve over the psoas muscle. The diagnosis was done by ultarsonogrsphy and MRI. The histopathological study confirmed the diagnosis. The tumour was excised through retro-peritoneal approach. By this approach total excision of the tumour with preservation of ilio-hypogastric nerve is possible. Journal of Science Foundation 2019;17(2):71-74

The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the Saudi Vision 2030 will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

Introduction: Benign prostatic hyperplasia (BPH) is the most common benign tumour in men. About 50% of men at the age of 50 years develop BPH and about half of them report to the doctors with some form of lower urinary tract symptoms (LUTS). Objectives: To see the therapeutic effect of Tamsulosin on alternate day administration in comparison to daily dose for men who had been suffering from LUTS due to BPH. Materials and Methods: This clinical trial was carried out at Combined Military Hospital, Dhaka in a consecutive fashion for about 18 months. A total of 152 patients were selected from outpatient department and after assessing them through inclusion and exclusion criteria and grouped randomly in three groups Group-A (50 patients), Group-B (50 patients) and Group C (52 patients); all the patients were treated with Tablet Tamsulosin (0.4mg) daily for first three months. Then next 15 months they were treated as; Group-A received Tablet Tamsulosin (0.4mg) daily, Group-B received Tablet Tamsulosin (0.4mg) every alternate day and Group C received placebo Tablet (Folic acid 5 mg) daily. Data were collected at the beginning and end of 3rd month, 4th month, 7th month and 18th month of study, in the form of international prostate symptom score (IPSS), maximum urinary flow per second (Qmax) and post-voidal residue (PVR). Results: This study showed that at the initial three month, IPSS score declined in all three groups, Qmax improved and PVR reduced in all groups. In next phase of study; in Group-A patient there was progressive and significant improvement of all parameters (IPSS, Qmax and PVR) at the end of study. In Group-B patient also showed similar result at the end of study. In Group-C patient showed statistically significant difference in outcome in relation to Group-A and Group-B i.e. more in IPSS, less in Qmax and increase in PVR. Conclusion: With this study it is clearly evident that tamsulosin (0.4mg) therapy significantly improves symptoms of men suffering for LUTS due to BPH. And alternate day therapy having similar therapeutic outcome in relation to every day therapy. Journal of Armed Forces Medical College Bangladesh Vol.15 (1) 2019: 22-26

The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

This paper proposes an intelligent approach to detect and classify the power quality (PQ) events with the combination of machine learning and advanced signal processing techniques. It selects Stockwell transform, one of the efficient signal processing tools for feature extraction from the recorded signals. The extracted features are then fetched to one of the popular machine-learning tools, namely the artificial neural network (ANN), to develop the proposed intelligent PQ events detection and classification approach. This paper selects the hyper-parameters, e.g., number of hidden layer neurons, training algorithm, and activation functions through a systematic trial and error approach. To enhance the proposed approach performance, the weights and biases of the ANN are optimized using the grey wolf optimization (GWO) technique. Simulation results confirm the efficacy of the developed intelligent methodology in distinguishing PQ events from non-PQ events. Moreover, separates different PQ events, e.g., sag, swell, interruption, fluctuation, spike, notch, harmonics, from each other with reasonable accuracy. This research also investigates the efficacy of the proposed signal processing-based machine learning approach in the presence of measurement noises.

The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique.

Some people are too ignorant of their surroundings. Most of them, usually, throw trash illegally in the streets or abandoned areas. In this paper, to get rid of such a problem, we have proposed a real-time monitoring mechanism to find the illegal trash throwing person for an intelligent surveillance system. The proposed mechanism of finding people and trash from the video frame can be done using subsequently applying the following several steps. At first, we have applied the shadow removal technique and then applied the background subtraction procedure. To reduce the noise levels, we have implemented labeling & filtering operations. For extracting feature vectors, a histogram of oriented gradients (HOG) is used. Finally, by applying a support vector machine classifier (SVM), we have identified both humans and trash from the video frames. The key contribution of this proposed framework is to gain better performance by applying the shadow removal technique before the background subtraction procedure. Besides, the temporary bounding box concept has been taken into consideration during the trash detection phase.

When it comes to making decisions on which product to buy, knowing the overall reviews from other users becomes very helpful. Evaluating this task from user ratings is so simple. Although a machine can be used for evaluating the recommendations, simply by calculating its user ratings, sometimes it becomes difficult to provide accurate and efficient results. As therefore, evaluating users’ comments usually leads to assigning humans to read all the comments one by one and then let them decide on how useful the product seems. This is a tedious process which wastes our valuable time and resources due to no way of automating the process. On the other hand, selecting the most valuable product from an enormous number of reviews becomes a hectic task for the consumers. Considering all of the above, we have developed a machine learning based intelligent system which not only evaluates the ratings from users’ reviews but also provides a reflection about the products which are popular simply by analyzing those reviews.

Recommendation Systems (RS) are utilized in a variety of areas. RS provide suggestions to items that a particular user is most likely interested in. There has been a huge amount of research in this field. An intelligent approach to generate recommendations by using heterogeneous data is proposed in this paper. For increasing the performance of the recommendation system, we consider the surrounding environments of the considered hotels. Feedback system is considered as a computer program in information technology (IT). The analysis of the feedbacks provided by the users is essential for the improvement of accuracy RS. By analyzing both textual and numeric feedbacks, a better suggestion will be provided to the users.

The coronavirus disease 2019 (COVID-19) caused a pandemic outbreak with affecting 213 nations worldwide. Global policymakers are imposing many measures to slow and reduce the rapid growth of the infections. On the other hand, the healthcare system is encountering significant challenges for a massive number of COVID-19 confirmed or suspected individuals seeking treatment. Therefore, estimating the number of confirmed cases is necessary to provide valuable insights into the growth of the outbreak and facilitate policy making process. In this study, we apply ARIMA models as well as LSTM-based recurrent neural network to forecast the daily cumulative confirmed cases. The LSTM architecture generates more precise forecasting by leveraging both short- and long-term temporal dependencies from the pandemic time series data. Due to the stochastic nature in optimization and random initialization of weights in neural network, the LSTM based model produce less reproducible outcome. In this paper, we propose a reproducible-LSTM (r-LSTM) framework that produces a reproducible and robust results leveraging z-score outlier detection method. We performed five round of nested cross validation to show the consistency in evaluating model performance. The experimental results demonstrate that r-LSTM outperformed the ARIMA model producing minimum MAPE, RMSE, and MAE.

Recently, direct current (DC) microgrid is gaining increased importance due to large-scale utilization of different DC sources, such as photovoltaic (PV), fuel cell, DC loads, and high-level integration of energy storage devices and renewable energy. Additionally, unlike conventional alternating current (AC) system, DC microgrid does not require reactive power and frequency control. Nevertheless, DC microgrid control is a complex task due to the connection of several distributed generators, loads, energy storages, and renewable energy sources, such as PV, and wind, to the common DC bus. The control of DC bus voltage, effective power split among the energy storage devices, and state of charge (SOC) restorations are important in a DC microgrid. This paper proposes a new average voltage shifting-based strategy to split power-sharing between battery and supercapacitor. In the proposed approach, an average voltage shifting term derived from the battery and supercapacitor is introduced to reduce the DC link voltage deviation. As compared to the droop control, the addition of the voltage shifting term acts like a high pass filter and blocks the DC component which helps the DC bus voltage restoration process. The DC microgrid is composed of a hybrid energy storage system formed by battery and supercapacitor, AC utility grid interfaced with three-phase voltage source converter (VSC), constant power load, constant resistive load, and distributed generator. The SOC of the supercapacitor is automatically restored after any disturbances whereas the battery SOC is maintained by energy exchange with the distributed generator or grid. The real-time digital simulator (RTDS) and dSPACE-based control hardware-in-loop results show that the proposed approach is reliable, leading to improved DC bus voltage control while maintaining proper power flow and SOC within the microgrid. The comparative study is also conducted to show the superiority of the proposed strategy with the existing method.

Background: Extradural haematoma is due to different etiologies. Objectives: The purpose of the present study was to see the etiological factors of extradural haematoma. Methodology: This cross sectional study was conducted in the Department of Neurosurgery at Dhaka Medical College Hospital, Dhaka, Bangladesh from January 2010 to June 2011 for a period of one year and half months. All patients who underwent surgery with traumatic head injury having extradural haematoma were included in the study. At admission, a detailed history of the illness was taken from the patients/patient’s attendants by face-to-face interview with the help of a preformed questionnaire. Questionnaire was prepared with key variables like age, sex and mode of injury. Result: Eighty (80) patients of traumatic head injury within and after 24 hours of head injury were enrolled in this study. 20(25.1%) patients were present in age group of 11 to 20 years. 19(23.8%) patients were in age group of 21-30 years. 20(25.1%) patients were present in age group of 31 to 40 years. Male was predominant than female which was 60(75.0%) cases and 20(25.0%) cases respectively. The most common mode of injury was road traffic accident which was 51(63.7%) cases followed by fall from height and assault which were 16(20.0%) cases and 13(16.3%) cases respectively. Conclusion: In conclusion RTA is the most common cause of extradural haematoma. Journal of Science Foundation, July 2016;14(2):44-48

Most of the time communication infrastructure is badly hampered by the natural disaster. Many people are trapped inside their house during disaster time and they cannot communicate with others as there is no network coverage. This paper aims to help those affected people by using Wi-Fi communication. We developed a real-time communication system for Android users where they can send a message and make an audio call through Wi-Fi and also develop an alarming system by which we can detect the appropriate location of the affected people. For audio call, we use UDP (User Datagram Protocol) and for messaging we use TCP/IP. When there is an audio call request it starts a peer to peer communication between the users. TCP/IP is used for messaging because this is a connection-oriented protocol which means there is a guarantee of message arrival. As our system is worked within the Wi-Fi hotspot range, the experiment shows that it can cover up to 32 meters (105 feet).

In this paper, the design and implementation of a low-cost myoelectric prosthetic hand prototype are presented. Our main contributions are - (i) proposing a new Computer Aided Design (CAD) suitable for laser cutter for prosthetic hand and (ii) a new feature, XCORR that calculates cross-correlation between two channels of EMG signals for same gesture, is introduced to classify Electromyography (EMG) signals. Individual parts are designed in a 2D plane and assembled to make a 3D structure in CAD software. Acrylic is used as the main material. The prosthetic hand can exhibit 6-degree of freedom (DOF), i.e., six gestures which are required to perform essential daily tasks and grabbing objects. Commonly used features, such as SMAT, HJORTH parameters are extracted from raw EMG signals and combined with newly introduced feature XCORR to classify 6 types of individual and combined finger movements. Using the classified EMG signal as input we have controlled the designed prosthetic hand in both simulation and hardware level. The controller is designed in such a way that the angular displacement of each finger meets a predefined angle value for each classified gesture. For classification of EMG signals, the highest accuracy is observed as 93.9% with a low computational load. The proposed hand will be affordable and suitable for amputees and help them regain their ability to work.

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

Chondrosarcoma is a type of tumor originated from cartilage and bone. About 30% of skeletal system cancers are chondrosarcomas and less than 10% of all chondrosarcomas occur in the spine. It is resistant to chemotherapy and radiotherapy. Total resection ensures early recovery and prevents recurrence. This author presents a case of chondrosarcoma of spine diagnosed by MRI and histopathology.Journal of Science Foundation, January 2016;14(1):30-33

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

The accuracy of achieving required surface finish or surface roughness is of great importance in case of any mass production environment. Thus, understanding and recognizing the optimal process parameters for machining is the key to achieving the required surface roughness and gaining competitive advantage. This research is concerned with obtaining the optimal machining process parameters (cutting speed, depth of cut, feed) which will in turn effect in optimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To find the optimal machining process parameters Taguchi design method has been implemented. An L- 16 orthogonal array, signal to noise ratio and ANOVA have been implemented to analyze and understand various process parameters in deter- mining possible relationship with surface roughness. The optimum surface roughness value for this experiment is (3.664µm) which is obtained from Taguchi design method. The results from ANOVA conclude that feed is the most influential factor affecting the outcome having a contribution of (56.04%). And the contributions of depth of cut and cutting speed are separately in order of (17.94%) and (16.42%) respectively. This research provides optimal process parameters for any desired value of surface roughness which results in gaining a competitive edge over others in any mass production environment.

Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.

Background: Strongyloidiasis infection is partially an asymptomatic infection and the diagnosis of patent infection is difficult using conventional parasitological methods. The residents of tea garden community of Sylhet, Bangladesh were robustly tested. Method: The collected stool samples were tested by Harada mori culture for the presence of larval stage of Strongyloides stercoralis and to reaffirm the same samples were subjected for conventional PCR, using primer sets deigned to amplify partial ribosomal DNA of S. stercoralis genome. Finally data analysis was performed by Logistic Regression procedure using STATA 13 (College Station, Texas 77845 USA) and Pearson λ2 test, with consideration of P

The influence of surface roughness in determining the quality of finished products in any industrial application has an enormous impact on gaining competitive edge and establishing superiority. Thus, recognizing and understanding the factors influencing the resulted surface roughness are the crucial issues helping to achieve the desired goal in any competitive industrial environment. Fact is that machining process parameters are major factors affecting the outcome. This research is focused on determining the optimum machining parameters (cutting speed, feed rate, depth of cut) which result in minimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To understand the effects of machining parameters on surface roughness and to determine relationship between them; Particle Swarm Optimization (PSO) has been employed. A multiple regression equation is used as objective function to determine the optimum values of inputs (cutting speed, feed, and depth of cut) using PSO formula and it yields an optimum value of surface roughness of 0.6252 µm. Artificial Neural Network (ANN) has also been implemented to predict various level of surface roughness for different machining parameters. To predict the surface roughness (Ra), standard multilayer feed-forward back-propagation hierarchical neural network has been applied and the findings provide an overall value of coefficient of determination of 0.88881. These investigations of turning operation provide optimal process parameters for any desired value of surface roughness which result in gaining a competitive edge over others in any industrial application.

With the increase of loading of long transmission line, transient stability of power system on the occurrence of a fault become one of the major concern for power engineers. Instability problem of power system on occurrence of the fault can be solved with the use of Static VAR Compensator (SVC) controller. Assessing the transient stability of a large inter connected power network very much nonlinear and tremendously complicated. To mitigate this complication and nonlinearity, a substantial research with SVC controller has been done in the power industry. To improve the system stability for dynamic and transient disturbances SVC plays a very important role, which is a shunt connected FACTS devices. In this work, genetic algorithm based optimal design of SVC controller to improve the transient performance of power system is proposed. The parameters of the damping controller by optimal selection of damping ratio of the critical eigenvalue employing genetic algorithm are tuned. The effectiveness of the developed optimal controller is investigated by simulating different abnormal conditions of a single machine infinite bus (SMIB) system using MATLAB based PST.

The availability of large volumes of gene expression data from microarray analysis (cDNA and oligonucleotide) has opened a new door to the diagnoses and treatments of various diseases based on gene expression profiling. In this paper, we discuss a new profiling tool based on linear programming. Given gene expression data from two subclasses of the same disease (e.g. leukemia), we are able to determine efficiently if the samples are linearly separable with respect to triplets of genes. This was left as an open problem in an earlier study that considered only pairs of genes as linear separators. Our tool comes in two versions - offline and incremental. Tests show that the incremental version is markedly more efficient than the offline one. This paper also introduces a gene selection strategy that exploits the class distinction property of a gene by separability test by pairs and triplets. We applied our gene selection strategy to 4 publicly available gene-expression data sets. Our experiments show that gene spaces generated by our method achieves similar or even better classification accuracy than the gene spaces generated by t-values, FCS(Fisher Criterion Score) and SAM(Significance Analysis of Microarrays).

This paper presents a Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, where the second stage computes dynamic (velocity (Δ) and acceleration (ΔΔ)) parameters from the phoneme probabilities by using three point linear regression (LR). Finally, the phoneme probabilities, dynamic parameters, Δ and ΔΔ, and the input MFCCs, combined as hybrid features, are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). It is difficult to recognize speech affected by gender factors, especially when an ASR system contains only a single acoustic model. If there exists any suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In this paper, we have proposed a technique of gender effects suppression that composed of two hidden Markov model (HMM)-based classifiers and that focused on a gender factor. In an experiment on Bangla speech database prepared by us, the proposed system has provided a significant improvement of word correct rate, word accuracy and sentence correct rate in comparison with the method that incorporates only a single HMM-based classifier for both male and female speakers.

This paper presents a Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, where the second stage computes velocity (?) coefficients from the phoneme probabilities by using three point linear regression (LR). Finally, the phoneme probabilities, velocity (?) coefficients and the input MFCCs, combined as hybrid features, are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

This paper addresses the problem of deducing and adjusting constraint weights at run time to guide the movement of the camera in an informed and controlled way in response to the requirements of the shot. This enables the control of weights at the frame level. We analyze the mathematical representation of the cost structure of the search domain so that the constraint solver can search the domain efficiently. Here we consider a simple tracking shot of a single target without occlusion or other environment elements. In this paper we consider only the distance, orientation, frame coherence distance and frame coherence rotation constraints in 2D. The cost structure for 2D suggests the use of a binary search to find the solution camera position.

The transmission of multimedia streams with a particular and guaranteed quality of service (QoS) is a major challenge in multimedia communication over IP networks. In this paper, we analyze the distributed architecture of a video on demand (VoD) system to design global request handling and admission control strategies. We describe a highly available distributed VoD service using existing telephone network, which is inherently scalable and fault tolerant. The VoD service is provided by media server farms (MSF), which may reside in different local telephone exchange of a telephone network. We present the design and simulation results for a new distributed admission control and QoS adaptation controller architecture, called semi-distributed admission control (SD-AC) architecture for a set of MSFs

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.