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Optimal 5G network sub-slicing orchestration in a fully virtualised smart company using machine learning
Get PDFThis paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions
‘Bedfordshire's first black male police officer: memoir and collaboration as education’
No full textThis chapter offers a reflective account of a collaborative writing project, and it therefore has a rather different purchase on both education and the educator than the collection’s other pieces. The project is the memoir of Eric Edwin, Bedfordshire’s first Black male police officer, who, after more than thirty years with the force, was diagnosed in late 2016/early 2017 with multiple myeloma. The chapter deals first with the nuts-and-bolts business of writing the memoir - an education in itself, as the project as a whole and our individual roles in it were new to each of us. Second, the chapter addresses the project’s ethical implications. We reflect not only on the writing team in the role of educator, but on the project as an educational process in which the politics of race and racialized experience are interlaced
Exploring socio-ecological factors that influence the use of urban greenspace: a case study of a deprived ethnically diverse community in the UK
Get PDFUrban greenspaces are considered an important health asset associated with improved population health and well-being. However, inequalities in access to and use of the outdoors continue to exist, particularly among low- income and minority ethnic populations. Following a socio-ecological approach, this study aimed to investigate the individual, interpersonal, and environmental factors that influence the use of greenspaces among an ethnically diverse community in the UK and explore strategies to increase use. A mixed-methods cross-sectional community survey was conducted between March and June 2022 with residents of two ethnically diverse towns situated in Southeast England, UK. Data were collected on factors that influence greenspace use alongside demographic information on age, ethnicity, and social deprivation. An open-ended question explored respondents’ views on strategies to increase engagement with greenspaces. The survey was completed by 906 participants aged between 16 and 94 (60.7% female; 94.5% non-white British). The findings revealed that age, gender, perceived importance of using greenspaces, awareness of greenspaces, and the natural environment were all significant predictors of greenspace use. Qualitative evidence supported these findings and provided useful strategies for increasing access. The findings have provided an increased understanding of the factors that influence greenspace use and suggest that to improve access. There is a clear need to improve the quality of the available green spaces, making them safe and visually appealing to the local communities they serve. Increasing awareness and providing more opportunities for social and intergenerational interaction were also considered important strategies for increasing use
"In weapons we trust?" four-culture analysis of factors associated with weapon tolerance in young males
Get PDFAddressing the under-researched issue of weapon tolerance, the paper examines factors behind male knife and gun tolerance across four different cultures, seeking to rank them in terms of predictive power and shed light on relations between them. To this end, four regression and structural equation modelling analyses were conducted using samples from the US (n = 189), India (n = 196), England (n = 107) and Poland (n = 375). Each sample of male participants indicated their standing on several dimensions (i.e., predictors) derived from theory and related research (i.e., Psychoticism, Need for Respect, Aggressive Masculinity, Belief in Social Mobility and Doubt in Authority). All four regression models were statistically significant. The knife tolerance predictors were: Aggressive Masculinity (positive) in the US, Poland and England, Belief in Social Mobility (negative) in the US and England, Need for Respect (positive) in India and Psychoticism (positive) in Poland. The gun tolerance predictors were: Psychoticism (positive) in the US, India and Poland, Aggressive Masculinity (positive) in the US, England and Poland, and Belief in in Social Mobility (negative) in the US, Belief in Social Mobility (positive) and Doubt in Authority (negative) in Poland. The Structural Equation Weapon Tolerance Model (WTM) suggested an indirect effect for the latent factor Perceived Social Ecological Constraints via its positive relation with the latent factor Saving Face, both knife and gun tolerance were predicted by Psychoticism
Exploring small-scale optimization coupling learning approaches for enterprises’ financial health forecasts
Get PDFThe financial health of leading enterprises has a significant impact on the sustainable development of the global economy. Most data-driven financial health forecasts are based on the direct use of small-scale machine learning. In this study, we proposed the idea of optimization coupling learning to improve these machine learning models in financial health forecasting. It not only revealed lagging, immediate, continuous impacts of various indicators in different fiscal year, but also had the same low computational cost and complexity as known small-scale machine learning models. We used our optimization coupling learning to investigate 3424 leading enterprises in China and revealed inner triggering mechanisms and differences of enterprises’ financial health status from individual behavior to macro level
Applying software engineering solutions to law firm management, Nigeria as a case study
Get PDFLegal technology has changed the way law firms are managed worldwide. Substantial research has been undertaken on the role of legal technology in law firm management especially in developed countries. Though, most studies have only focused on the benefits and challenges, and have failed to analyse law firm management areas requiring software solutions. The principal objective of this paper was to investigate the level of technology adoption among Nigerian law firms, as well as to develop a software solution to automate work processes in identified areas. This investigation was done using systematic literature review to gather relevant data on the subject area and identify knowledge gaps. Findings from the research indicated a need for further analysis of the various areas in law practice that could require software solutions. The findings also discussed the implementation of a property management module which is an important contribution to the management of law firms in Nigeria. A speech-to-text transcription feature was also implemented to eliminate the need for lengthy typin
A conditional GAN and dual-channel hybrid deep feature framework for robust sensor fault detection in WSNs
Get PDFSensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection
Differences in muscle activation and joint kinematics between deadlift styles when performed at high intensity training loads
Get PDFThe purpose of this study was to compare the conventional (CDL), sumo (SDL) and hex-bar (HBD) deadlift actions at a high intensity training load across a wide range of leg and back muscles to explore which lift has the biggest impact on prime mover musculature. Twelve males (age: 19 ± 2 years; height: 1.81 ± 0.81 m; body mass: 85.64 ± 10.87 kg) performed 3 repetitions of HBD, CDL and SDL at a 90% 1RM intensity. Load lifted, EMG for the Erector Spinae Longissimus, Gluteus Maximus, Biceps Femoris, Semitendinosus, Rectus Femoris, and Vastus Medialis and knee and hip range were compared via effect size magnitude of change. The EMG results showed a general pattern of greater muscle activity, considered a large effect, during the HBD compared to the CDL and SDL, possibly due to the greater absolute load lifted during the HBD. The only anomaly to this was greater EMG activity for the bicep femoris within the CDL compared to the HBD, large effect, and the SDL, moderate effect. This finding was attributed to the greater hip flexion seen in the start position for the CDL compared to other lifts. These findings suggest that the HBD would be the preferred deadlift technique for total muscle recruitment and load lifted for high intensity (90% 1RM) training regimes. However, the CDL would be the preferred lift if bicep femoris muscle activity were a specific targeted requirement
The value of whole-face procedures for the construction and naming of identifiable likenesses for recall-based methods of facial-composite construction
Get PDFTraditional methods of facial-composite construction rely on an eyewitness recalling features of an offender's face. We assess the value of the addition of a trait–recall mnemonic to a cognitive-type interview, and perceptually stretching presented composites, to aid image recognition. Participant-constructors intentionally or incidentally encoded a target face, were interviewed about its facial features 3–4 h or 2 days later, made a series of trait attributions (or not) about the face and constructed a feature-based composite. Regardless of encoding manipulation, faces constructed after 3–4 h were twice as likely to be correctly named (cf. after 2 days) both when the trait–recall mnemonic was applied and composites were viewed stretched. Thus, the research indicates that benefit should be afforded when trait–recall mnemonics are employed for feature composites constructed on the same day as the crime and when composites are presented to potential recognisers with instruction to view the face as a perceptual stretch
REHG Pamphlet Series (III):Spectres of Creativity
No full textThe third in a series of pamphlets by the Radical Education and Humanities Group (REHG). This issue focuses on matters of creativity in education. Oli Belas (editor, author) Jim Clack (author) JoEl James (artist, author
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