Establishment of Rainfall Intensity-Duration-Frequency Equations and Curves Used to Design an Appropriate and Sustainable Hydraulic Structure for Controlling Flood in Nyabugogo Catchment-Rwanda

Due to the increase in the emission of greenhouse gases, the hydrologic cycle is being altered on the daily basis. This has affected the variations in relations of intensity, duration, and frequency of rainfall events. Intensity Duration Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration and return period. IDF curves are one of the most often applied implements in water resource engineering, in areas such as for operating, planning and designing of water resource projects, or for numerous engineering projects aimed at controlling floods. In particular, IDF curves for precipitation answer problems of improper drainage systems or conditions and extreme characters of precipitation which are the main cause of floods in Nyabugogo catchment. This study aims to establish Rainfall IDF empirical equations, curves and hydrological discharge (predicted peak rate of runoff (Qlogy)) equations for eight Districts that will be used for designing an appropriate and sustainable hydraulic structures for controlling flood to reduce potential loss of human and aquatic life, degradation of water, air and soil quality and property damage and economic lessen caused by flood in Nyabugogo catchment. However Goodness of Fit tests revealed that Gumbel’s Extreme-Value Distribution method appears to have the most appropriate fit compared with Pearson type III distribution for validating the Intensity-Duration-Frequency curves and equations through the use of daily annual for each meteorological station. The findings of the study show that the intensity of rainfall increases with a decrease in rainfall duration. Additionally, a rainfall of every known duration will have a higher intensity if its return period is high, while the predicted peak rate of runoff (Qlogy) increases also with an increase in the intensity of rainfall.

Effect of Muscle Action on the Kinetic and Kinematic Behavior of the Barbell during Isoinertial Strength Assessment

An isoinertial strength assessment was performed to examine the kinetic and kinematic behavior of the barbell during several muscle actions. Velocity–time characteristics, force–time relationship, one repetition maximum (1RM), power output, and acceleration were compared in eccentric–concentric (EC) versus concentric only (C) sequences of the bench press (BP) and military press (MP). In two separate sessions, 28 and 29 resistance-trained athletes executed EC or C sequences in random order of the BP and MP, respectively, in an incremental load test up to their 1RM. Higher values were recorded in BP-EC than in BP-C, MP-EC, or MP-C (p < 0.01) for peak acceleration, peak rate of force development, peak rate of velocity development, and power output. Significant differences were detected between exercises in terms of the portion of the concentric phase (%) at which peak acceleration was detected, or acceleration up until peak velocity was observed (p < 0.05). No differences were observed between exercises in the portion of the concentric phase where acceleration up to the braking phase took place. The eccentric muscle action prior to concentric movement was a key factor to enhance the kinematic and kinetic performance in BP exercise. No such effects of the countermovement were produced in MP.

19 Integrated Falls Prevention Pathways; the Benefits of Organisational Collaboration

Introduction There is continuous increased demand on the NHS, while resources are finite. Developing pathways with Integration and streamlining of services is crucial to achieve good outcome and better use of resources. Falls service is a good example. In Dudley, three separate services existed for managing falls: a local authority team, a NHS therapy team and a Consultant-led Falls & Syncope clinic. We collaborated and reorganised the services to improve patient care and achieve better outcome. Methods We integrated services into one pathway, where patients are triaged to the relevant service based on clinical need via a Single Point of Access. One multifactorial assessment- based on NICE guideline CG161 and Quality Standard QS86- was developed, which is completed by all services (streams). This allows their assessment to be transferred to another stream if clinically indicated, saving repetition/duplication. An extensive training programme was delivered to up-skill staff across the pathway in completing areas of best practice (e.g. postural BP measurement, balance/mobility assessments, cognitive assessment, FRAX score, medication issues). Additionally we established a Falls MDT meeting with representation from all streams, to discuss complex cases, and developed a shared, electronic database to track patient journeys and monitor service outcomes. Results Falls admission rate decreased by 29.4% for patients 65 y and over and 25.2% for 80+. This equates to 433 fewer falls compared to peak rate; saving an estimated £3.4million. Also, hip fracture admission rate decreased by 19.2% for patients 65 y and over and 23.5% for 80+. This equates to 91 fewer hip fractures compared to peak rate; saving an estimated £1.3million. Much improved service collaboration and resource sharing. Conclusion A collaborative approach between organisations, utilising existing resources in a system that places patients at the heart of the service, improves patient experience and outcomes, alongside significant financial savings.

The Insidious Momentum of American Mass Incarceration

The phenomenal growth of penal confinement in the United States in the last quarter of the twentieth century is still a public policy mystery. Why did it happen when it happened? What explains the unprecedented magnitude of prison and jail expansion? Why are the current levels of penal confinement so very close to the all-time peak rate reached in 2007? What is the likely course of levels of penal confinement in the next generation of American life? Are there changes in government or policy that can avoid the prospect of mass incarceration as a chronic element of governance in the United States? This study is organized around four major concerns: What happened in the 33 years after 1973? Why did these extraordinary changes happen in that single generation? What is likely to happen to levels of penal confinement in the next three decades? What changes in law or practice might reduce this likely penal future?

What Happens Next?

This chapter uses statistics from the first decade after the peak of prison population was reached in the United States in 2007 to project the likely trends in imprisonment that can be expected during the period 2020 to 2050. An estimate that takes into account the contrast between the first decade of the great imprisonment increase and the first 10 years after the all-time high suggests a modest decline over the next generation is the most likely pattern for the American penal future. By mid-century, prison and jail populations are likely to still have between half and two-thirds of the expanded 2007 peak rate behind bars.

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

Optimal Linear Error Correcting Delivery Schemes for Two Optimal Coded Caching Schemes

For coded caching problems with small buffer sizes and the number of users no less than the amount of files in the server, an optimal delivery scheme was proposed by Chen, Fan, and Letaief in 2016. This scheme is referred to as the CFL scheme. In this paper, an extension to the coded caching problem where the link between the server and the users is error prone, is considered. The closed form expressions for average rate and peak rate of error correcting delivery scheme are found for the CFL prefetching scheme using techniques from index coding. Using results from error correcting index coding, an optimal linear error correcting delivery scheme for caching problems employing the CFL prefetching is proposed. Another scheme that has lower sub-packetization requirement as compared to CFL scheme for the same cache memory size was considered by J. Gomez-Vilardebo in 2018. An optimal linear error correcting delivery scheme is also proposed for this scheme.

Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.