A Case Study: Relationship Between Students\u27 Reading Habits and their Academic Performance in Government Post Graduate College Nowshera at Bachelor of Sciences (BS) Level

Those who read more books, have more chances of mental development and better opportunities of success. Hence, this paper is an attempt to find out relationship between students’ reading habits and their academic performance in Government Post Graduate College Nowshera, Pakistan at Bachelor of Sciences (BS) level. To collect the relevant data, a questionnaire was designed and distributed among 420 (270 males and 150 females) students studying at BS level. The questionnaire investigated students’ reading habit and their attitudes towards reading. Students’ academic performances were recorded through the transcripts of their examination in the previous semester. The collected data were analyzed by Statistical Package for Social Sciences (SPSS). The researchers concluded that students do not read books frequently and female students take more interest in reading books than male students. It is recommended that teachers and parents should create a conducive environment for students to read more and more books for effective learning. Students should also make library their first point of call to get updated from time to time for development of reading habits. Adequate and updated books, journals, newspaper must be available in the libraries, so that students could be attracted for more reading

Inertial Measurement Unit based Virtual Antenna Arrays - DoA Estimation and Positioning in Wireless Networks

Today we have different location based services available in a mobile phone or mobile station (MS). These services include: direction finding to nearby ATMs, locating favorite food restaurants, or finding any target destination. Similarly, we see different applications of the positioning and navigation systems in firefighting or other rescue operations. The common factor in almost all of the location based services is the system's ability to determine the user's current position, with reference to a floor plan or a navigation map. Current technologies are using sensor data measurements from one or more sensors, available to the positioning device, for positioning and navigation. Typical examples are radio based positioning such as global positioning system, inertial sensors based inertial navigation system, or camera based positioning systems. Different accuracy and availability conditions of the positioning and navigation solution can be obtained depending on the positioning algorithms and the available sensor information.Nowadays, the focus of research in positioning and navigation has been mostly on the use of existing hardware infrastructure and low-cost solutions, such that the proposed technique can be deployed with ease and without extra infrastructure requirements as well as without any expensive sensor equipment. In this work, we investigate a novel idea for positioning using existing wireless networks and low-cost inertial sensor measurements available at the MS. We propose to use received baseband radio signal along with inertial sensor data, such as accelerometer and rate gyroscope measurements, for direction of arrival (DoA) estimation and positioning. The DoA information from different base stations or access points can be used to estimate the MS position using triangulation technique. Furthermore, due to size and cost restrictions it is difficult to have real antenna arrays at the MS, the idea of DoA estimation and positioning is proposed to be used with single antenna devices by using the so-called virtual antenna arrays.We have presented our research results in three different papers. We provide measurement based results to perform a quantitative evaluation of DoA estimation using arbitrary virtual antenna arrays in 3-D; where a state-of-the-art high-resolution algorithm has been used for radio signal parameter estimation. Furthermore, we provide an extended Kalman filter framework to investigate the performance of unaided inertial navigation systems with 3-axis accelerometer and 3-axis rate gyroscope measurements, from a six-degrees-of-freedom inertial measurement unit. Using the extended Kalman filter framework, we provide results for position estimation error standard deviation with respect to integration time for an unaided inertial navigation system; where the effect of different stochastic errors noise sources in the inertial sensors measurements such as white Gaussian noise and bias instability noise is investigated. Also, we derive a closed form expression for Cramér-Rao lower bound to investigate DoA estimation accuracy for a far-field source using random antenna arrays in 3-D. The Cramér-Rao lower bound is obtained using known antenna coordinates as well as using estimated antenna coordinates, where the antenna coordinates are estimated with an uncertainty whose standard deviation is known. Furthermore, using Monte-Carlo simulations for random antenna arrays, we provide Cramér-Rao lower bound based performance evaluation of random 3-D antenna arrays for DoA estimation

Robust Very Small Spiking Neural Networks Evolved with Noise to Recognize Temporal Patterns

© 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. https://creativecommons.org/licenses/by/4.0/To understand how biological and bio-inspired complex computational networks can function in the presence of noise and damage, we have evolved very small spiking neural networks in the presence of noise on the membrane potential. The networks were built with adaptive exponential integrate and fire neurons. The simple but not trivial task we evolved the networks for consisted of recognizing a short temporal pattern in the activity of the network inputs. This task can be described in abstract terms as finding a specific subsequence of symbols (“ABC”) in a continuous sequence of symbols (“..ABCCCAAABCAC..”). We show that networks with three interneurons and one output neuron can solve this task in the presence of biologically plausible levels of noise. We describe how such a network works by mapping its activity onto the state of a finite state transducer—an abstract model of computation on continuous time series. We demonstrate that the networks evolved with noise are much more robust than networks evolved without noise to the modification of neuronal parameters and variation of the properties of the input. We also show that the networks evolved with noise are denser and have stronger connections than the networks evolved without noise. Finally, we demonstrate the emergence of memory in the evolved networks—sustained spiking of some neurons maintained thanks to the presence of self-excitatory loops

Xanthogranulomatous prostatitis: a mimic of carcinoma of prostate

BACKGROUND: Xanthogranulomatous prostatitis is an unusual benign inflammatory process of prostate. Clinically it mimics prostatic carcinoma, requiring pathological examination for diagnosis. CASE PRESENTATION: A 60-year-old patient presented with 6 months history of increasing difficulty in micturition. On digital rectal examination prostate was hard and nodular and estimated weight was 50-gram. His serum prostate specific antigen (PSA) was 150 ng/ml. Clinically a locally advanced carcinoma of prostate was suspected. In view of severe obstructive urinary symptoms and significant post-micturition residual urine, transurethral resection of prostate was carried out. Histopathological examination of resected prostatic tissue revealed xanthogranulomatous prostatitis with no evidence of malignancy. Patient remains symptom free at 16 months follow-up and serum PSA has decreased to 6 ng/ml. CONCLUSION: Xanthogranulomatous prostatitis is a benign inflammatory disorder of prostate that can clinically and even biochemically mimic prostatic carcinoma. A high degree of suspecion and close co-operation with pathologist is necessary for the diagnosis of xanthogranulomatous prostatitis

The effect of financial budgets on tightening the internal control system

This study seeks to provide a primary goal, which is to increase the effectiveness of financial budgets in terms of provisions of internal control over service units in terms of preparation and implementation. (likerT) in measuring the answers, as well as analyzing the performance of the sample through a set of indicators. From this, the researcher concludes that the current traditional budget focuses on financial control and does not pay attention to reviewing and evaluating the results, and because it is unable to diagnose problems and provide solutions due to the inability to follow up and evaluate the performance of government programs and activities

Interval-valued fuzzy ku-ideals of ku-algebras

In this paper, we introduced the notion of interval-valued fuzzy KU-ideals of KU-algebras and some related properties are investigated. We proved that U is a KU-ideal if and only if the interval-valued fuzzy subset is an interval-valued fuzzy KU-ideal of a KU-algebra for e-

The Evolution, Analysis, and Design of Minimal Spiking Neural Networks for Temporal Pattern Recognition

All sensory stimuli are temporal in structure. How a pattern of action potentials encodes the information received from the sensory stimuli is an important research question in neurosciencce. Although it is clear that information is carried by the number or the timing of spikes, the information processing in the nervous system is poorly understood. The desire to understand information processing in the animal brain led to the development of spiking neural networks (SNNs). Understanding information processing in spiking neural networks may give us an insight into the information processing in the animal brain. One way to understand the mechanisms which enable SNNs to perform a computational task is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for recognising temporal patterns. The SNNs are composed of simple yet biologically meaningful adaptive exponential integrate-and-fire (AdEx) neurons. The computational task can be described as identifying a subsequence of three signals (say ABC) in a random input stream of signals ("ABBBCCBABABCBBCAC"). The topology and connection weights of the networks are optimised using a genetic algorithm such that the network output spikes only for the correct input pattern and remains silent for all others. The fitness function rewards the network output for spiking after receiving the correct pattern and penalises spikes elsewhere. To analyse the effect of noise, two types of noise are introduced during evolution: (i) random fluctuations of the membrane potential of neurons in the network at every network step, (ii) random variations of the duration of the silent interval between input signals. It has been observed that evolution in the presence of noise produced networks that were robust to perturbation of neuronal parameters. Moreover, the networks also developed a form of memory, enabling them to maintain network states in the absence of input activity. It has been demonstrated that the network states of an evolved network have a one-to-one correspondence with the states of a finite-state transducer (FST) { a model of computation for time-structured data. The analysis of networks indicated that the task of recognition is accomplished by transitions between network states. Evolution may overproduce synaptic connections, pruning these superfluous connections pronounced structural similarities among individuals obtained from different independent runs. Moreover, the analysis of the pruned networks highlighted that memory is a property of self-excitation in the network. Neurons with self-excitatory loops (also called autapses) could sustain spiking activity indefinitely in the absence of input activity. To recognise a pattern of length n, a network requires n+1 network states, where n states are maintained actively with autapses and the penultimate state is maintained passively by no activity in the network. Simultaneously, the role of other connections in the network is identified. Of particular interest, three interneurons in the network are found to have a specialized role: (i) the lock neuron is always active, preventing the output from spiking unless it is released by the penultimate signal in the correct pattern, exposing the output neuron to spike for the correct last signal, (ii) the switch neuron is responsible for switching the network between the inter-signal states and the start state, and (iii) the accept neuron produces spikes in the output neuron when the network receives the last correct input. It also sends a signal to the switch neuron, transforming the network back into the start state Understanding how information is processed in the evolved networks led to handcrafting network topologies for recognising more extended patterns. The proposed rules can extend network topologies to recognize temporal patterns up to length six. To validate the handcrafted topology, a genetic algorithm is used to optimise its connection weights. It has been observed that the maximum number of active neurons representing a state in the network increases with the pattern length. Therefore, the suggested rules can handcraft network topologies only up to length 6. Handcrafting network topologies, representing a network state with a fixed number of active neurons requires further investigation

Autapses enable temporal pattern recognition in spiking neural networks

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Although there is evidence that the precise timing of spikes represents information in spiking neuronal networks, information processing in spiking networks is still not fully understood. One feasible way to understand the working mechanism of a spiking network is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for a temporal pattern recognition task. The task is to recognise a specific order of the input signals so that the Output neurone of the network spikes only for the correct placement and remains silent for all others. The minimal networks obtained for this task revealed the twofold importance of autapses in recognition; first, autapses simplify the switching among different network states. Second, autapses enable a network to maintain a network state, a form of memory. To show that the recognition task is accomplished by transitions between network states, we map the network states of a functional spiking neural network (SNN) onto the states of a finite-state transducer (FST, a formal model of computation that generates output symbols, here: spikes or no spikes at specific times, in response to input, here: a series of input signals). Finally, based on our understanding, we define rules for constructing the topology of a network handcrafted for recognising a subsequence of signals (pattern) in a particular order. The analysis of minimal networks recognising patterns of different lengths (two to six) revealed a positive correlation between the pattern length and the number of autaptic connections in the network. Furthermore, in agreement with the behaviour of neurones in the network, we were able to associate specific functional roles of locking, switching, and accepting to neurones

Motivation and ESL Learning Self-system in Pakistani Students

The aim of this study is to investigate the role  of the motivational force ‘Ideal L2 self’ in university students of Pakistan and the students’ wish to become competent speakers of English and how they see themselves as future language users. In this regard two main components of Dornyei’s (2005, 2009) self system theory; Ideal L2 self and ought to L2 self, have been tested in Pakistani learners context. In this mixed method approach the researchers have examined that what are the ‘Ideal selves’ which motivate the students of Pakistan toward English and what are the negative consequences resulting from lack of L2 knowledge in future. The data for this research have been taken through survey interviews from 15 teachers, 5 each from school, college and university level from District Faisalabad, Pakistan. On the basis of the interviews from the teachers two questionnaires, on promotional factors and the preventive factors, were prepared to establish attitudes of the students regarding this motivational force. The findings of the research confirmed the strong motivational influences of the components of the ESL Motivational Self System on the participants. Analysis of data confirmed that there are various promotional and preventive factors which incline Pakistani learners toward learning English. It has been concluded that L2 motivational self system is a valuable tool to measure ESL motivation in Pakistani context. The researchers suggest that teachers should tackle these promotional and preventive factors as a tool to incline their students toward learning English so that the students may be able to explore how they can restore their national, religious and cultural identity after English learning and what type of role English can play in this regard. Key words: ESL, motivation, L2 self system, Ideal L2 self, Ought to L2 self, promotional factors, preventive factors