Developmental perspective to bullying Essay Example for Free

Developmental perspective to bullying Essay Researches have found that the nature of bullying and victimisation changes with age and differs by gender as well. It is also found that previous experiences influence the likelihood that children will become bullies and/or victims. The type of aggression and the relationship context for the aggression change as the child grows and development occurs. A general guideline can be prepared for the type of aggression and the target of aggression that emerge in different stages of development of children. (Pepler, D. , 1999) Figure 3: Type of aggression by developmental stage (Pepler, D., 1999) A Social- Ecological Systems Perspective on Bullying Social –ecological system perspective looks at bullying in terms of influence from multiple environments. Figure 4: A social-ecological framework of bullying among youth (Espelage, D. L. et al 2004) This perspective is based on the understanding that bullying does not occur in isolation. According to this principle, bullying is a phenomenon that is encouraged or inhibited as a result of the complex relationship between the different entities, namely, the individual, family, peer group, school, community and the culture. As shown in figure-1, the individual is at the centre of the social ecology. The individual involved may be a victim, bully, bully-victim or bystander. Individual factors influence the participation in bullying through different actions or inaction. An example is how the individual gender mediates the engagement in bullying. The bully, bully-victim, or bystander, all could exist in a family. This points to how the family can influence bullying behaviours. The bullying behaviour between the siblings can influence the development of bullying or victimisation in the individual at school. The social ecology includes other entities such as peers and the school as well. The social environment at school also influences bullying and victimisation. If an individual attends a school where a bullying climate exists, it is likely to be involved in bullying either as a bully, victim or any other role. The peer group often influences how the individual is likely to behave in terms of bullying. If the individual’s peer group supports bullying, then the individual is more likely to engage in bullying behaviour. The community extends the environment of the school to other areas that the individual uses. The community consists of school, peer group, family, and the individual apart from other roles that the individual interacts in day-to-day life. The culture consists of the general norms, beliefs and practices that could either support or inhibit bullying of individuals. The ecological-systems theory is based on the principle that all individuals are part of an inter-related system that consists of several roles and entities, but keeps the individual at the centre and looks at how the other entities affect the individual from the perspective of bullying. This model was put forward by Bronfenbrenner (1979). It puts forth the nature of human interaction and behaviour and how it is inter-related to other systems. According to this theory, the child is an inseparable part of a social network that consists of inter-related systems. These systems can be categorised into four: micro system, meso system, exo system and macro system. These different systems are inter-related to each other and the child is at the centre of these systems and actively involved in the interplay of these systems. The micro system pertains to the child’s relationship with one system such as home, classroom or playground. It depicts the child’s interaction with others as well as other’s reaction to the bullying behaviours. It also includes the status of the child in the bully/ victim continuum at any point of time. The micro system takes into account the interaction between the bully, victim, bully-victim or the bystander and the social environment. This can either encourage the bullying behaviour or restrict it. The meso system involves the congruence of two or more environments that are relevant to the individual. An example of such environments is home and school. It includes the inter-relationship between these systems. The exo-system consists of influences from other contexts that are related to bullying. An example of this context is the effect of a school district’s anti-bully policy or even the involvement of parents in the school system. The macro system involves the influence of broader entities such as the society in general. It looks at the attitudes of the society towards the bullying behaviour. This theory provided framework for prevention and intervention techniques that can be derived from it. It also helps to measure the effectiveness of these programs by looking at it from different perspectives. It provides framework for collection of data from multiple informants who are in different sub-systems using different methods. The model also can be effectively used for assessment. This is because each individual or the school or the environment is different. Any intervention or prevention program need to take into account this peculiarity. It also provides a framework for different programs that are applicable to different sub-systems such as individual-focussed program, family-focussed program and system-focussed program. (Espelage, et al, 2004).

Top 10 malware of 2004 :: essays research papers

The top 10 malware threats of 2004 as taken from McAfee and defined by Symantec are as followed: Adware-180, Adware-Gator, Exploit-ByteVerify, Exploit-MhtRedir, JS/Noclose, W32/Bagle, W32/Mydoom, W32/Netsky, W32/Sasser, W32/Sdbot (family including sdbot, gaobot, polybot, spybot). The majority of these threats enter into our system under a different alias’ or by hitching a ride on programs we download online.   Ã‚  Ã‚  Ã‚  Ã‚  Adware-180 is a spybot that monitors the activity its infectee’s do while online. This program will open up affliated sites when it sees a certain keyword while searching online. When adware-180 is downloaded it creates a name for itself in the Microsoft registry, this registry then can fix itself it only partial parts of the adware are removed.   Ã‚  Ã‚  Ã‚  Ã‚  Adware-Gator is one that I am ooooh to familiar with. This adware drives me crazy! I will be searching online and all of a sudden down by my time and date a little box will pop up giving me alternative vacation prices or prescription prices. Gator (or gain as it says in my registry) gets downloaded onto the computer either manually or by sneaking itself in with another download. With this adware on the hard drive it allows websites to upload their content to your computer without your knowledge enabling them to display advertisements at the strangest times.   Ã‚  Ã‚  Ã‚  Ã‚  Exploit-MhtRedir is a file that is considered â€Å"a malicious website to download and execute programs on your computer†. It’s file type is a Trojan horse, which disguises itself in order to promote unwanted HTML on your computer. This Trojan only affects Microsoft internet explorer.   Ã‚  Ã‚  Ã‚  Ã‚  W32/Mydoom comes in an assortment of different subcategories ranging from category 1 to 2. The majority of the Mydoom worms are a category 2. The mass mailing worm that uses its own smtp to send emails to people listed on the infected computers. It allows unauthorized remote access. Once it finds the addresses of people it sends itself in attachments that say things like read the following attachment, please confirm, please read immediately etc.   Ã‚  Ã‚  Ã‚  Ã‚  W32/Sasser is one I think we all remember hearing about. This one gets onto your computer and scans IP addresses to find vulnerable computers. It’s wild threat is medium, the damage it causes is low but the distribution level of this worm is very high. The main threat of this worm is the fact that even though it is unable to infect Windows 95/98/ME it does take up a lot of space making it difficult for such programs as the Symantec removal tool to run.

Improved Fast Decoupled Power Flow

The power flow analysis is a very important and tundamental tool in power system analysis. Its results play the major role during the operational stages of any system for its control and economic schedule, as well as during expansion and design stages The purpose of any load flow analysis is to compute precise steady-state voltages and voltage angles of all buses in the network, the real and reactive power flows into every line and transformer, under the assumption of known generation and load.During the second half of the twentieth century, and after the large technological evelopments in the fields of digital computers and high-level programming languages, many methods for solving the load flow problem have been developed, such as indirect Gauss-Siedel (bus admittance matrix). direct Gauss-Siedel (bus impedance matrix).Newton-Raphson (NR) and its decoupled versions Nowadays, many Improvements have been added to all these methods involving assumptions and approximations of the trans mission lines and bus data, based on real systems conditions The Fast Decoupled Power Flow Method (FDPFM) is one of these improved methods, which was based on a simplification of the Newton-Raphson method and reported by Stott and Alsac in 1974[4]. This method and due to its calculations simplifications, fast convergence and reliable results became the most widely used method in load flow analysis.However, FDPFM for some cases, where high RA ratios or heavy loading (Low Voltage) at some buses are present, does not converge well. For these cases, many efforts and developments have been made to overcome these convergence obstacles. some of them targeted the convergence of systems with hgh RIX ratios, others those with low voltage buses However, one of the most recent developments is a Robust Fast Decoupled Power Flow developed by Wang and u; it Is ased on heuristic justification and general voltage normalization methods [171 and solves both high RIX ratios and low bus voltages problem s simultaneously.Though many efforts and elaborations have been achieved in order to improve the and simulations are becoming more developed and are now able to handle and analyze large size system. Today, and after reaching processor's speeds higher than 3 GHz, any improvement in the speed of convergence of the power flow method, provided it leads to reliable results, is of great value. This speed improvement is very important when involved in operational stages of power distribution, where any illisecond saving can hugely increase the probability of the right decision, of the control and dispatch computerized system.This paper works on providing computing savings (in flops) and thus higher speed of convergence of the FDPFM based on the initial approximation in which real power changes are considered to be most sensitive to variations in voltage angle and much less to those of voltage magnitude, as well as on the high sensitivity of reactive power changes to variations in voltage m agnitude and much less to those of voltage angle. In this paper, the attention was focused on the update of the voltage angle (6) and oltage magnitude (V) in each iteration, based on the improvement of flops achieved, and obviously on the results obtained.The results of these improvements and the comparative analysis with the Newton-Raphson and classical FDPFM will be presented using the three IEEE bus systems of 14, 30 and 57-bus, although the IFDPFM can be applied to any size bus system. II. Fast Decoupled Power Flow Method As the FDPFM is derived from the Newton-Raphson we will start from the matrix representation of NR, apply some simplifications and approximations, to reach the equations of the FDPFM.The matrix representation of the N-R method [17] is: O APOOH Where I IVJI IYiJl +6]) And -2 cos Bit +2 cos -6i +6]) Nii – = I VI II YiJ I cos (B iJ- 6i + 6]) Nil (7) -2 IYiil stn +2 IVJI IYiJl cos -6i +6]) Now, for typical power system branches: XIR and ; 200 (10) between AQ and A6, hence N and J entries of the initial matrix of (1) can be ignored leading to the following decoupled equations: (12) Now, the diagonal elements of H according to Stott and Alsac [4] can be written as: IVi12Bii (13) Where Bii = I Yill sin Bii is the imaginary part of the diagonal elements of the bus admittance matrix Ybus.Further simplifications can be applied to equation (12), by considering Bii Qi and I Vil 2 z I Vil yielding to the following simplified Hit: Hii=- (14) Also, as under normal operating conditions 6] – 6i is quite small, thus Bii – 6i + 6] Bit, and IVJI 1, the off-diagonal elements of the matrix H can be written as: HIJ I Vil (15) Similarly, the diagonal elements of the L matrix can be written as: Lil † (16) And its off- diagonal elements as: LiJ=-lVll (17) Applying these assumptions to equations (11) and (12) we get: =-B'A6 I vil (18) (19) where B' and B† are the imaginary part of the bus admittance matrix Ybus , such thatB' contain s all buses admittances except those related to the slack bus, and B† is B' deprived from all voltage-controlled buses related admittances. Finally, all these approximations and simplifications lead to the following successive voltage magnitude and voltage angle updating equations: (20) IVI (21) These equations formed the basis of the iteration scheme upon which the Matlab software written and then updated. Ill.Updated Algorithm The algorithm written according to the equations derived in the previous section is as follows: Step 1: Creation of the bus admittance Ybus according to the lines data given y the IEEE standard bus test systems. Step 2: Detection of all kinds and numbers of buses according to the bus data given by the IEEE standard bus test systems, setting all bus voltages to an initial value of 1 pu, all voltage angles to O, and the iteration counter iter to O.Step 3: Creation of the matrices B' and B† according to equations (18) and (19). Step 4: If max (AP, A Q) accuracy then Go to Step 6 else 1. Calculation of the H and L elements of equations (14), (1 5), (16), (17). 2. Calculation of the real and reactive power at each bus, and checking if Mvar of generator buses re within the limits, otherwise update the voltage magnitude at these buses by ? ±2 3. Calculation of the power residuals, AP and AQ. 4.Calculation of the bus voltage and voltage angle updates AV and A6 according to equations (19) and (20). 5. Update of the voltage magnitude V and the voltage angle 6 at each bus. 6. Increment of the iteration counter iter = iter + 1 then Go to Step 4 Print out ‘Solution did not converge' and go to Step 6 Step 6: Print out of the power flow solution, computation and display of the line flow and losses. The update of this algorithm was based on the weak coupling between AP and AV, nd between AQ and A6, explained in the previous section.Specifically, in the fourth subroutine of Step 4 of the initial algorithm, and instead of updating the voltage magnitude and the voltage angle once and simultaneously in each iteration, the improved algorithm updated either the voltage angle or the voltage magnitude at each bus, Jumped to subroutine 1 to recalculate the real and reactive power and then updated the second variable based on what was updated first.Moreover, and for more speed improvements and convergence reliability, the update of one of the two variables was repeated several times, holding the other ariable at its last calculated value, which reduced the number of floating point operations of the algorithm and thus lead to the faster convergence of the IFDPFM. IV. Numerical Analysis The performance of the IFDPFM was tested on IEEE 14, 30 and 57-bus systems with a convergence accuracy of 10-3 on a MVA base of 100 or equivalently 10-1 MVA for both power residuals AP and AQ.This numerical analysis involved a speed comparison between the NR method, the FDPFM and the IFDPFM based on the number of flops (floating point oper ations) of each algorithm implementing each method, rather than on any other basis, because he flops count is independent from the CPU speed or the specific programming language used. In addition, as mentioned in the previous part, the algorithm of this paper updated the voltage angle several times before updating the voltage magnitude or vice versa which resulted in a different flops count for each combination used for the same IEEE bus system.These combinations will be noted according to the number of loops of update of each variable. For instance, updating twice the voltage angle (6) and then once the voltage magnitude (V) in the same iteration will be written as (2;1). Note that any flops number without the previous notation will be the one of the best case of the updated algorithm. Moreover, for any combination to be listed in this paper it should have satisfied the condition of no more than 3 % deviation of its results from that of the NR method.The bar graph in Figure 1 shows a comparison based on the number of flops between the NR, FDPFM and the best case of IFDPFM for the three IEEE standard bus systems used in this paper. Number of flops per method per system 934. 573 305. 126 314. 925 157. 310 System 57 4,421. 752 2,841. 646 14 30. 823 56. 829 24. 574 1 ,ooo ,500 2,000 2,500 3,000 Flops IFDPFM FDPFM 4,000 4,500 (Thousands) Fig. 1: Flops Comparison between the 3 methods. It is clearly seen that the IFDPFM requires much less flops to converge as compared to FDPFM or NR.This flops saving is proportional to the system size and as shown, increases with the increase of the number of buses. Obviously, this improvement in the number of flops will make the IFDPFM converge much faster than the two other methods whatever CPU used. Numerically, and for the biggest system involved in this paper (IEEE 57-Bus System), the IFDPFM revealed a flops saving of about 67 % when ompared with the FDPFM and about 78 % when compared with the NR.Normally, and as mentioned bef ore, this saving goes down to the order of 50 % for the two smaller bus systems. In addition, and in order to reach the best case presented above, different strategies of updating the voltage angle (6) and the voltage magnitude (V) were tested and compared first with the FDPFM then with the NR. Figure 2 below the percentage of flops of IFDPFM versus that of the FDPFM, for 10 different updating strategies and for the three IEEE systems.Percentage Flops IFDPFM vs FDPFM 75 50 25 Delta;Voltage Loops IFDPFM14 IFDPFM30 IFDPFM57 Fig. 2: % of flops of IFDPFM vs. FDPFM for different voltage angle and voltage magnitude updating strategies. At the first look, it is seen that for the three systems, three parallel curves are sketched with most values less then 75 % of the FDPFM. This parallel property of this graph shows the consistency of the algorithm in its number of flops variation for each strategy for each system studied.Also, it is seen that for low number of voltage magnitude and voltage angle loops the IFDPFM can't be more efficient than FDPFM, but for a slightly higher number the IFDPFM shows great improvement in flops saving nd reaches the highest improvement at the point (4;3), where in each iteration, the voltage angle was updated four times while the voltage was kept at its initial value and then 6 was kept at its last value and V updated three times.Numerically, and for the best case of IFDPFM (4;3), the new algorithm showed a flops saving of 57 % for the 14-bus system, 50% for the 30-bus system, and 68% for the 57-bus system. Figure 3 below shows the percentage of flops of IFDPFM versus that of the NR, for 10 different updating strategies and for the three IEEE systems. IFDPFM vs NR 175 150 25 Fig. 3: % of flops of IFDPFM vs. NR for different voltage angle and voltage magnitude updating strategies.Basically, the same comments of the comparison of IFDPFM with FDPFM apply in this comparison. However, here the flops saving is much more significant and is propo rtional to the system size. Numerically, we have a 21 % flops saving for the 14-bus system, 49 % for the 30-bus system and 78% for the 57-bus system. Finally, it is remarked that when compared with NR, IFDPFM savings showed a high variation in their percentage, mainly because they are highly proportional to the

Benefits Of Social Emotional Instruction - 1246 Words

IMPORTANCE OF SEL There is plenty of research to show the benefits of Social Emotional instruction and learning in the classroom setting. However, as is true for so much in public education, limited resources, funding, time, interest, and coordination leads to insufficient or ineffective implementation. Social emotional learning (SEL) has many names and definitions in academic literature. Some have slightly different meanings, but the overarching concept is the same, generally emphasizing similar key components. Researchers may use the terms: character education, social emotional competence, emotional intelligence, __ Greenberg, et. al., (Year) propose social emotional programs are most effective when they focus on both the†¦show more content†¦Though implemented with the best of intentions, the disjointed bits of reactive instruction can actually have a negative effect. To be effective, Greenberg_____ found that programs must be comprehensive, chosen based on the needs of the children being served, and that staff must be trained, held accountable, and supported by administration. Jennings and Greenberg This article defines an â€Å"optimal classroom climate.† Having an optimal classroom climate leads to more efficient learning. If a classroom does not function at its best, the teacher also runs the risk of faster burnout from the profession. Continual management of problematic behaviors may lead to reactive, as opposed to proactive, responses from the classroom teacher, and therefore exhaustion and lower teacher satisfaction. The authors propose that an effective classroom model includes SEC – social and emotional competence by the teacher can direct student outcomes. They present a framework model to follow in order to avoid the â€Å"burnout cascade.† Their model explains how a teacher lacking in SEC can negatively impact the classroom climate, relationships, management, and SEL program implementation. Also presented in this article is a prosocial classroom model graphic that includes teacher SEC and student outcomes. It emphasizes the interrelated nature of each component. A change or deficitShow MoreRelatedMusic Education And Emotional Intelligence1607 Words   |  7 Pagesthrough direct musical instruction, in earlier stages of musical growth the emotional demands require guidance by a teacher and self-exploration from the participant. Once success in this area becomes apparent, musical benefits emerge and the journey the individual undertook also helps shape their emotional intelligence. 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