Training Needs of Secondary School Mathematics Teachers in the Yemen Republic

There is a need to conduct a research on identifying the training needs, which is considered one of the most essential components of a staff development programme. The present status of planning in-service training programmes and workshops for Mathematics teachers in Yemen lacks appropriate methodology and a systematic and comprehensive assessment of the trainees' needs. In addition, no attention has been paid to systematically identify in-service needs for Mathematics teachers of secondary schools in Yemen. The purpose of this study was to identify the training needs of secondary schools Mathematics teachers in two urban cities Sana'a and Amran. The study focused on training needs as perceived by secondary school Mathematics teachers, supervisors and school administrators and identified the teachers' current practices of Mathematics teaching in secondary schools.The sample of the study was a sample six hundred and twelve consisting of 389 Mathematics teachers, 34 supervisors and 189 school administrators using a stratified random sampling technique. To obtain the data two instruments were used: (i) the new questionnaire was designed by the researcher. The questionnaire classified in-service training needs (59 items) into five categories which include: implementing of Mathematics instruction, planning of Mathematics instruction, evaluation of students in Mathematics instruction, diagnosing students7 needs in Mathematics instruction and classroom management. (ii) The observation instrument was modified and developed by Shian Leou (1998) which consisted of 35 items covering four domains, teaching skills, material organization and presentation, management of the learning environment and teaching attitudes. The observation was conducted among 30 secondary school Mathematics teachers in the classes and a video camera was used to record the lessons. Data were analyzed using the SPSS computer programme. The means, standard deviations, frequency and percentages were computed for the criteria indicators and independent one-way ANOVA and t-test were computed to determine significant differences between the means of the groups. The findings of the study revqaled that all the training needs represented necessary needs for Mathematics teachers in secondary schools and the teacher's current practice of Mathematics teaching was generally weak. These findings indicated highest needs in implementing of Mathematics instruction, followed by planning of Mathematics instruction, evaluation of students in Mathematics instruction, diagnosing students' needs in Mathematics instruction and classroom management. The One-way ANOVA revealed that there are no significant differences in perceptions of training needs between teachers, supervisors and school administrators in all domains. As for the variable of experience, a significant difference was found in the domains of classroom management and evaluation of students in Mathematics instruction. The effect of the t-test showed that there is no significant difference between male and female teachers in all domains. However, a significant difference was found between a variable of with educational and without educational background in the domain of evaluation of students in Mathematics instruction. The results of this study were consistent with previous findings in other specialization studies; therefore, it is recommended that a future study should do a comparative study on the training needs among Mathematics teachers in secondary schools and primary schools. A future replication of this study with comparison with other countries is necessary

Cloud Computing For Iraqi Ministry Of Finance

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about over provisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or under provisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS) [2]. The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing [3]

Design and analysis of a new brake-by-wire system using machine learning

One of the main aims of the recent research on brake-by-wire systems is to decrease mechanical components. In this paper, we propose replacing the brake pedal with a driving wheel that is fully covered by pressure braking batch sensors. The new mechanism for braking translates pressure exerted through the driver’s hands on the driving wheel to a corresponding electrical signal. A proposed design for the pressure braking batch (PBB) is made out of a mesh of conducting threads separated by a resistive sheet. To the best of our knowledge, this idea has not been raised before in other research papers. Different people have different muscle strengths and so the problem of identifying the intention of the user when pressing the PBB is tackled. For this aim, a new dataset of its kind is created by several volunteers. From each volunteer, age, gender, body mass index (BMI), and maximum pressure exerted on the driving wheel are collected. Using Weka software, the detection accuracy is calculated for a new volunteer to know the intention of his/her pressure on PBB. Among the three algorithms tried, the regression tree gives the best results in predicting the class of the pressure exerted by the volunteers

Control of Boundary Layer over NACA0015 Using Fuzzy Logic by Suction Technique

تعتبر طريقة اعادة التصاق الجزء المنفصل من الطبقة المتاخمة واحدة من اهم الطرق المستخدمة لتحسين الجريان فوق الاجسام. وقد ركزت هذه الدراسة على تصميم وبناء نظام سيطرة يعمل وفقا للمنطق الضبابي للسيطرة على أنفصال الطبقة المتاخمة من على سطح مقطع جناح طراز ((NACA0015وذلك من خلال التحكم بسرعة محرك جهاز التفريغ الذي يقوم بسحب الجزء المنفصل من الطبقة المتاخمة من خلال خمسة ثقوب موزعة على طول المحور العرضي للجناح وعلى خط يبعد مسافة (75%) من طول الوتر مقاسة من مقدمة المقطع. كل التجارب العملية تم اجرائها في نفق هوائي دون الصوتي ذي مقطع اختبار (300x300x 600) mm وعند قيم رقم ريبنولد وزوايا هجوم مختلفة. واهم النتائج التي تم الحصول عليها هي: ان استخدام نظام سيطرة يعمل وفقا للمنطق الضبابي في السيطرة على تقنية المص سوف يؤدي الى زيادة في قيمة معامل الرفع للجناح (CL) بمقدار (14.72%) عنه في الحالة الاعتيادية كذلك فان قيمة زاوية الانفصال سوف تزداد من 15o الى 17o. ايضا ان استخدام قواعد المنطق الضبابي في برمجة نظام السيطرة اعطى تحسينا مستقرا عند قيم معامل سحب CQ مقبولة.Re-attachment the separation of boundary layer using suction method is one of the important techniques, which improve the flow over bodies. This study focused on the design of fuzzy logic controller to control on the separation of the boundary layer, using suction delayed separation technique from the surface of NACA 0015 airfoil. The airfoil was designed and fabricated depending on the airfoil tool with (300x300) mm chord and span length respectively. The upper surface was developed with five holes  6mm diameter to suck the delayed boundary layer along the span of the airfoil about 75% from leading edge . Also there are four BMP180 Piezoelectric pressure sensors distributed with constant pitch on upper surface of model used to sense the pressure difference. Sub sonic wind tunnel with (300x300x 600) mm work section is used. (1.354, 1.915, 2.345, 2.708 and 3.028 x 105) Reynolds numbers and (0o, 3o, 6o, 9o, 12o, 15o, 16o and 17o) are the angles of attack were used as a conditions boundary of the experimental work. The model was tested without applying suction to determine the stall condition. Pneumatic vacuum cleaner with (0.00737 to 0.01329) discharge coefficient range was used to perform the suction experiment. Pressure difference and angle of attack were input of fuzzy logic controller which programmed by using commercial Matlab softwar. The results of applying suction showed an increase of 14.72% in the lift coefficient and increase the stall angle from 15o to more than 17o. Also lift/drag ratio increased when angle of attack increased. Fuzzy logic rules gave steady enhancement at range of suction coefficient CQ universally acceptable

Seismic fragility curves for mid-rise reinforced concrete framed structures with different lateral loads resisting systems

The current study presents lateral load analysis of mid-rise reinforced concrete framed structures with two different lateral load resisting systems; shear walls and rigid marginal beams. The main objective here is to investigate the influence of the location of the system in the structure; i.e. arrangement of shear walls and level of the marginal beam. For that purpose, seismic fragility curves are used as an assessment tool for comparing the seismic performance of the studied structures in different situations. Incremental dynamic analysis was performed under ten ground motions to determine the yielding and collapse capacity of each building. Five performance levels were considered in the analysis. These performance levels are (i) operational, (ii) immediate occupancy, (iii) damage control, (iv) life safety and (v) collapse prevention. Fragility curves were developed for the structural models of the studied structures considering the previously mentioned performance levels. It was observed that arrangement of shear walls on the long direction of the structure has insignificant effects on its performance while interior shear walls provide the best behavior of the structure compared to exterior shear walls only and distributing shear walls internally and externally. The analysis outcomes also indicated that the presence of the rigid marginal beam in the lower storey gives more efficiency regarding to lateral loads resistance in the studied structure

New Density-Based Clustering Technique

Density Based Spatial Clustering of Applications of Noise (DBSCAN) is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot choose its parameter according to distribution of dataset. It simply uses the global minimum number of points (MinPts) parameter, so that the clustering result of multi-density database is inaccurate. In addition, when it used to cluster large databases, it will cost too much time. We try to solve these problems by integrated the grid-based in addition to using representative points in our new proposed density-based GMDBSCAN-UR clustering algorithm. In this research, we apply an unsupervised machine learning approach based on DBSCAN algorithm. We propose a grid-based cluster technique to reduce the time complexity. Grid-based technique divides the data space into cells. A number of well scattered points in each cell in the grid are chosen. These scattered points must capture the shape and extent of the dataset as all. Thus, our work in this research adopts a middle ground between the centroid-based and the all-point extremes. Next we treat all data in the same cell as an object, and all the operations of clustering are done on this cell. We make local clustering in each cell and merge between the resulted clusters. We use local MinPts for every cell in the grid to overcome the problem of undetermined clusters in multi-density datasets in clustering with DBSCAN clustering algorithm case. This will enhance the time complexity. Next step is labeling the not chosen points to the resulted clusters. Finally, we make post processing and noise elimination

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed leve

A novel nomadic people optimizer-based energy-efficient routing for WBAN

In response to user demand for wearable devices, several WBAN deployments now call for effective communication processes for remote data monitoring in real time. Using sensor networks, intelligent wearable devices have exchanged data that has benefited in the evaluation of possible security hazards. If smart wearables in sensor networks use an excessive amount of power during data transmission, both network lifetime and data transmission performance may suffer. Despite the network's effective data transmission, smart wearable patches include data that has been combined from several sources utilizing common aggregators. Data analysis requires careful network lifespan control throughout the aggregation phase. By using the Nomadic People Optimizer-based Energy-Efficient Routing (NPO-EER) approach, which effectively allows smart wearable patches by minimizing data aggregation time and eliminating routing loops, the network lifetime has been preserved in this research. The obtained findings showed that the NPO method had a great solution. Estimated Aggregation time, Energy consumption, Delay, and throughput have all been shown to be accurate indicators of the system's performance

Hemoglobin E syndromes in Pakistani population

<p>Abstract</p> <p>Background</p> <p>Hemoglobin E is an important hemoglobin variant with a worldwide distribution. A number of hemoglobinopathies have been reported from Pakistan. However a comprehensive description of hemoglobin E syndromes for the country was never made. This study aimed to describe various hemoglobin E disorders based on hematological parameters and chromatography. The sub-aim was to characterize hemoglobin E at molecular level.</p> <p>Methods</p> <p>This was a hospital based study conducted prospectively for a period of one year extending from January 1 to December 31, 2008. EDTA blood samples were analyzed for completed blood counts and hemoglobin variants through automated hematology analyzer and Bio-Rad beta thalassaemia short program respectively. Six samples were randomly selected to characterize HbE at molecular level through RFLP-PCR utilizing <it>Mnl</it>I restriction enzyme.</p> <p>Results</p> <p>During the study period, 11403 chromatograms were analyzed and Hb E was detected in 41 (or 0.36%) samples. Different hemoglobin E syndromes identified were HbEA (n = 20 or 49%), HbE/β-thalassemia (n = 14 or 34%), HbEE (n = 6 or 15%) and HbE/HbS (n = 1 or 2%). Compound heterozygosity for HbE and beta thalassaemia was found to be the most severely affected phenotype. RFLP-PCR utilizing <it>Mnl</it>I successfully characterized HbE at molecular level in six randomly selected samples.</p> <p>Conclusions</p> <p>Various HbE phenotypes are prevalent in Pakistan with HbEA and HbE/β thalassaemia representing the most common syndromes. Chromatography cannot only successfully identify hemoglobin E but also assist in further characterization into its phenotype including compound heterozygosity. Definitive diagnosis of HbE can easily be achieved through RFLP-PCR.</p