DEALING WITH INDISCIPLINE AMONG JUNIOR HIGH SCHOOLS IN AGONA SWEDRU, AGONA WEST MUNICIPALITY, GHANA

The purpose of the study was to investigate measures that are in place to deal with indiscipline among Junior High Schools in Agona Swedru in the Agona West Municipality. The study was a quantitative study underpinned by the positivist paradigm and adopted the descriptive survey design. The study was undertaken in the Agona Swedru township in the Agona West Municipality of Ghana. Selected Junior High school students formed the sample of the study. Purposive sampling and simple random sampling were used to sample 120 students for the study. The questionnaire was employed for data gathering. Data were analyzed descriptively using frequencies and percentages. It emerged from the study that predominant forms of indiscipline behaviours were related to the following; leaving the school grounds, physical aggression, disturbing others, inappropriate use of school material, out-of-seat behaviour such as moving, noncompliance with teacher’s directives. The study revealed that the causes of indiscipline behaviour were school size, home factors, individual factors, family factors, gender and ethnicity, school factors, societal factors, and peer group pressure. The study showed that indiscipline behaviours result in low academic performance, breeds undesirable student behaviour, and dropping-out of school. The study therefore recommends that the Agona educational directorate, the Agona District assembly, the authorities of the selected schools and the various administrative staff should collaborate with the guidance and counselling coordinators to organise programmes focused on the forms of indiscipline exhibited by the students to effectively equip them to deal with the everyday indiscipline behaviours in the school.  Article visualizations

Efficient CPU-Optimized Parameter Estimation for Modeling Fish Schooling Behavior in Large Particle Systems

The schooling behavior of fish can be studied through simulations involving a large number of interacting particles. In such systems, each individual particle is guided by behavior rules, which include aggregation towards a centroid, collision avoidance, and direction alignment. The movement vector of each particle may be expressed as a linear combination of behaviors, with unknown parameters that define a trade-off among several behavioral constraints. A fitness function for collective schooling behavior encompasses all individual particle parameters. For a large number of interacting particles in a complex environment, heuristic methods, such as evolutionary algorithms, are used to optimize the fitness function, ensuring that the resulting decision rule preserves collective behavior. However, these algorithms exhibit slow convergence, making them inefficient in terms of CPU time cost. This paper proposes a CPU-efficient iterative (Cluster, Partition, Refine -- CPR) algorithm for estimating decision rule parameters for a large number of interacting particles. In the first step, we employ the K-Means (unsupervised learning) algorithm to cluster candidate solutions. Then, we partition the search space using Voronoi tessellation over the defined clusters. We assess the quality of each cluster based on the fitness function, with the centroid of their Voronoi cells representing the clusters. Subsequently, we refine the search space by introducing new cells into a number of identified well-fitting Voronoi cells. This process is repeated until convergence. A comparison of the performance of the CPR algorithm with a standard Genetic Algorithm reveals that the former converges faster than the latter. We also demonstrate that the application of the CPR algorithm results in a schooling behavior consistent with empirical observations.Comment: 10page

An improved methodology for quantifying causality in complex ecological systems

This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger’s causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike’s power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems.publishedVersio

トータルパワー寄与率を用いた海洋生態システムにおける因果性推測

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INTEGRATING HAWKES PROCESS- ND BIOMASS MODELS TO CAPTURE IMPULSIVE POPULATION DYNAMICS

This paper presents a modeling framework that captures the impulsive biomass dynamics (bust-boom) of a fish stock. The framework is based on coupling a Hawkes-process model to a discrete-time, ages-structured population dynamics model. Simulation results are presented to demonstrate the efficacy of the framework in capturing impulsive events in the population trajectory. The results presented in this paper are significant in three ways: • A framework has been presented that demonstrates how premonitory information may be extracted from exogenous observations from complex environmental systems • We have demonstrated how exogenous information may be parameterized and incorporated into the modeling process for better understanding of the link between environmental drivers and the population dynamical system • The framework has been successfully applied in modeling and short-term prediction of the population dynamics of an empirical fish stock.publishedVersio

A logistic function to track time-dependent fish population dynamics

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Facilities Available for Teachers to Support the Learning Needs of Children with Disabilities in the Inclusive School Setting

This paper investigated facilities available for teachers to support the learning needs of children with disabilities in the pilot inclusive school setting in Ghana. A cross sectional survey design was used. The population for the study was 300 teachers from 15 (pilot) inclusive schools in Ghana. A total of 150 teachers were sampled through simple random selection and then 15 headteachers were purposively selected for the study; all adding to 165 respondents. Instruments for data collection involved a five-point likert scale questionnaire and a semi-structured interview. Quantitative data were analyzed using descriptive statistics. The qualitative data were discussed, using the thematic approach. The results showed that teachers lacked the requisite skills and facilities necessary in teaching in inclusive classroom environments. It was recommended that facilities in form of adaptable curriculum, instructional materials, equipment & building, administrative support and assistive technology, must be made available for the inclusive school teacher.

The specification of the data model part in the SAM model matters

This paper considers a general state-space stock assessment modeling framework that integrates a population model for a fish stock and a data model. This way observed data are linked to unobserved quantities in the population model. Using this framework, we suggest two modifications to improve accuracy in results obtained from the stock assessment model SAM and similar models. The first suggestion is to interpret the “process error” in these models as stochastic variation in natural mortality, and therefore include it in the data model. The second suggestion is to consider the observed catch as unbiased estimates of the true catch and modify the observation error accordingly. We demonstrate the efficacy of these modifications using empirical data from 14 fish stocks. Our results indicate that the modifications lead to improved fits to data and prediction performance, as well as reduced prediction bias.publishedVersio

Population dynamic regulators in an empirical predator-prey system

Capelin (Mallotus villosus) is a short-lived (1–4 years) fish species, that plays a crucial role by dominating the intermediate trophic level in the Barents Sea. Several episodes of extreme biomass decline (collapse) have been observed during the last three decades. We postulate that these collapses might be regulated by food availability (bottom-up effect) and/or by time discrepancy between capelin feeding and abundance of its prey (match-mismatch hypothesis). This paper investigates our postulate using a model consisting of a set of coupled differential equations to describe the predator-prey system, with a single delay term, , in description of the predator dynamics. We derive theoretical conditions on , as well as determine how changes in these conditions define different stability regimes of the system. Unconstrained optimization is used to calculate optimal model parameters by fitting the predator-prey model to empirical data. The optimization results are combined with those from the theoretical analysis, to make inference about the empirical system stability. Our results show that Hopf bifurcation occurs in the predatory-prey system when exceeds a theoretically derived value . This value represents the critical time for prey availability in advance of the optimal predator growth period.Set into an ecological context, our findings provide mathematical evidence for validity of the match-mismatch hypothesis and a bottom-up effect for capelin.publishedVersio

Caveats with estimating natural mortality rates in stock assessment models using age aggregated catch data and abundance indices

We consider the challenge in estimating the natural mortality, M, in a standard statistical fish stock assessment model based on time series of catch- and abundance-at-age data. Though anecdotal evidence and empirical experience lend support to the fact that this parameter may be difficult to estimate, the current literature lacks a theoretical justification. We first discuss the estimatability of a time-invariant M theoretically and present necessary conditions for a constant M to be identifiable. We then investigate the practical usefulness of this by estimating M from simulated data based on models fitted to 19 fish stocks. Using the same data sets, we next explore several model formulations of time varying M, with a pre-specified mean value. Cross validation is used to assess the prediction performance of the candidate models. Our results show that a time-invariant M can be estimated with reasonable precision for a few stocks with long time series and typically high values of the true M. For most stocks, however, the estimation uncertainty of M is very large. For time-varying M, we find that accounting for variability across age and time using a simple model significantly improves the performance compared to a time-invariant M. No significant improvement is obtained by using complex models, such as, those with time dependencies in variability around mean values of M.publishedVersio