30793 research outputs found
Sort by
Does mandating CSR reporting in the EU generate horizontal spillovers?
No full textJEL Classifications: M14, M41, M48, Q56.Synopsis
The research problem
Directive 2014/95/EU (hereafter, “the CSR Directive”) mandates the disclosure of non-financial and diversity information by EU-listed firms with more than 500 employees and with either more than €20 million in total assets or more than €40 million in sales. However, the CSR Directive has been completely silent on the obligations of other unregulated firms. The fact that the CSR Directive applies only to a specific segment of the market, creating a gap between regulated and unregulated firms, raises a question of whether this CSR Directive has spillover effects beyond regulated firms that meet the number of employees and the total assets or sales thresholds.
Motivation
Our study is motivated by the unique setting that the CSR Directive provides for our research in that it mandates CSR reporting exclusively for EU-listed firms. While previous studies have primarily examined the effects of mandating CSR on the quality of CSR reporting and the level of CSR activities of regulated EU-listed firms, our research expands the scope by investigating the spillover effects of the CSR Directive on unregulated EU-listed firms.
Hypotheses
Our first pair of hypotheses is that unregulated EU-listed firms show higher quality of CSR reporting (a) following the passage of the CSR Directive in 2014 and (b) after the CSR Directive became effective in 2017. Our second pair of hypotheses is that unregulated EU-listed firms show a higher level of CSR activities (a) following the passage of the CSR Directive in 2014 and (b) after the CSR Directive became effective in 2017.
Target population
Our study provides timely insights to regulators and policymakers into the overall effects of future CSR reporting mandates. This is particularly important while the EU Parliament and Commission are reviewing the CSR Directive for potential replacement by the new Corporate Sustainability Reporting Directive (CSRD). Our study is also useful for researchers who are interested in observing the spillover effects of the CSR Directive on unregulated EU-listed firms.
Adopted methodology
We performed multivariate analyses using OLS regression and the difference-in-differences analysis using entropy balancing.
Analyses
Our sample period covers the years from 2010 to 2020 and, therefore, provides an avenue to address the effects of passing the CSR Directive over the period 2010–2016 and the effects of the mandatory implementation of the CSR Directive over the period 2014–2020. The results are interpreted through the lens of the theory of institutional isomorphism.
Findings
We provide evidence that following the passage of the CSR Directive in 2014, the CSR reporting quality of unregulated EU-listed firms has improved. This means that more unregulated EU-listed firms published CSR stand-alone reports or a section in their annual report, increased the level of their CSR information disclosure, had their CSR information externally audited, and had their CSR report published in accordance with the GRI or the OECD guidelines. We also found that EU-listed firms have increased the level of their CSR activities and the clarity of their CSR strategy. This trend has been more pronounced following the mandatory implementation of the CSR Directive in 2017...
Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: for Better Performance and User Connectivity
Get PDFThe rapid advancements in wireless communications have prompted a surge in mobile data traffic, necessitating innovative solutions for 5G and beyond. This paper introduces a two-tier Heterogeneous Cloud Radio Access Network (HC-RAN) model leveraging millimeter Wave (mmWave) and sub-6 GHz frequencies to address this need. It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. The study evaluates SINR and rate coverage probabilities across various deployment scenarios, including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, as well as random and edge-based deployments. Results demonstrate that strategic placement of Remote Radio Heads (RRHs) and efficient clustering significantly improve network efficiency and user connectivity. In LOS conditions, random RRH deployments deliver superior coverage and throughput due to spatial diversity and reduced path loss. Conversely, edge-based deployments necessitate more resources to handle traffic demands but can excel in controlled scenarios. The proposed joint User-RRH association with RRH-BBU k-means clustering algorithm effectively manages interference, also maintains a balance between quality of service and efficient resource management. The proposed User-RRH association sub problem scheme that based on minimum path loss as a basic criterion outperforms on Limited Capacity User-RRH Association scheme (LC UA) in both the random and edge deployment scenarios and yield increasing in average throughput by approximately 38% and 27%, respectively. In other hand, the adaptive solution of RRH-BBU k-means clustering sub problem depend on actual load and number of active RRHs in the network to find the number of k RRH-BBU clusters, which manage resource consumption. This highlights the challenges in resource allocation and management with and without clustering. This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. These insights help network operators balance high service quality with efficient resource utilization
A high-resolution dataset for future compound hot-dry events under climate change
Get PDFData Records: Our dataset can be accessed from the associated permanent DOI (https://doi.org/10.6084/m9.figshare.24038790.v6) [32. Fan, Y, Wen, Y, Guo, J, Wang, F. & Hao, Z. A high-resolution dataset for future compound hot-dry events under climate change. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24038790.v6 (2023).]. Each site encompasses four SSP-RCPs (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and three distinct periods: historical (1981-2010), 2050 s (2041-2070), and 2080 s (2071-2100). Each scenario encompasses three variables at a monthly time step: duration (D), precipitation intensity (prI), and temperature intensity (tasI). The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres). We have organised the data for each scenario into netcdf files with the data information for each index of CHDEs provided in Table 2 (https://www.nature.com/articles/s41597-024-03883-z#Tab2).Correction to: Scientific Data https://doi.org/10.1038/s41597-024-03883-z, published online 27 September 2024.
In this article the author’s name Yizhuo Wen was incorrectly written as Yizhou Wen. The original article has been corrected.Global climate change is leading to an increase in compound hot-dry events, significantly impacting human habitats. Analysing the causes and effects of these events requires precise data, yet most meteorological data focus on variables rather than extremes, which hinders relevant research. A daily compound hot-dry events (CHDEs) dataset was developed from 1980 to 2100 under various socioeconomic scenarios, using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to address this. The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres), including three indicators, namely D (the yearly sum of hot-dry extreme days), prI (the intensity of daily precipitation), and tasI (the intensity of daily temperature). To validate the accuracy of the dataset, we compared observational data from China (National Meteorological Information Center, NMIC), Europe (ERA5), and North America (ERA5). Results show close alignment with estimated values from the observational daily dataset, both temporally and spatially. The predictive interval (PI) pass rates for the CHDEs dataset exhibit notably high values. For a 90% PI, D has a pass rate exceeding 85%, whilst prI and tasI respectively show a pass rate above 70% and 95%. These results underscore its suitability for conducting global and regional studies about compound hot-dry events
Modelling and forecasting of exchange rate pairs using the Kalman filter
Get PDFData Availability Statement: The data that support the findings of this study is available from the corresponding author for non-commercial use. Meta-data provided (including dates for training/validation data and the initial values for optimization) is adequate to reproduce the results, if a commercial database such as Refinitiv is accessible.Developing and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out-of-sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.The second author was funded by Maths Research Associates 2021 Brunel grant from the Engineering and Physical Sciences Research Council, UK, grant no. EP/W52234X/1
Advancing cellular communication testing through Artificial Intelligence
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIdentifying and resolving faults within complex layers of advanced testing solutions like
VIAVI’s Test Mobile TM500 has been a long-standing challenge in the industry for many
years. Typically, this task is manual and depends on highly skilled experts with decades
of experience. These professionals are tasked with pinpointing the source of problems,
whether they are hardware related, or even more intricately, software-related issues (and
determining their specific defect layer). This involves discerning the precise defect layer
within an immensely intricate software, boasting a codebase exceeding 15 million lines
with code coverage exceeding 96%, which spans a wide array of functions that these
devices support encompassing the entirety of the protocol stack within a communication
system. This process imposes a substantial amount of time and costs to the service
providers (SPs) and customers, which are consistently repeated day-in-day-out when
defects are observed. In this realm, incorporating artificial intelligence and machine
learning (AI/ML) driven log analysis in an end-to-end (E2E) solution presents a myriad
of challenges, particularly when dealing with multi-type and highly complex software
(SW) logs that are in text-type format. At the core of leveraging AI/ML effectively and
enabling intelligent solutions lies the crucial step of transforming text-based SW logs
into meaningful numerical representations that encapsulate the syntactical and semantic
information embedded within the multi-type software logs. These representations can
then be supplied to neural networks for subsequent downstream tasks.
The development of AI algorithms capable of converting SW logs into numerical
representations, however, is not a trivial task. Custom algorithms must be designed
specifically for the telecommunications (Telco) industry, taking into consideration the
unique differences between SW logs in this context and typical natural language texts. It
is to be noted that, owing to the sensitive, confidential, and often proprietary nature of
log files within the telecommunications industry, scholarly research encounters limitations
in acquiring access to extensive, industry-grade log files, which are essential for training
and evaluating AI/ML algorithms effectively. That aside, conventional natural language
processing (NLP) algorithms may prove inadequate in this situation due to challenges such
as domain-specific terminology, complex log structure, noisy and incomplete data, dynamic
and evolving nature, high-dimensional and sparse data, and the need for integration with
domain knowledge. Overcoming these challenges and designing a comprehensive AI
product with an E2E pipeline constitutes the primary objective of this research. By creating bespoke AI/ML algorithms that can effectively transform multi-type,
highly complex software logs into meaningful numerical representations, we strive to
unlock the potential of AI/ML to enhance the log analysis process within the telco
industry. This advancement will ultimately result in more precise, efficient, and consistent
troubleshooting efforts, mitigating the substantial negative impacts on both customers
and service providers while alleviating time and resource constraints. Moreover, it will
facilitate the preservation and sharing of expert knowledge, foster collaboration among
engineers, and enable organizations to scale their troubleshooting endeavors in response
to increasing log data and system complexities.
Our research addresses complex, real-world industrial challenges, such as defect identification
and diagnostics within the telco industry. We implement innovative algorithms to
pinpoint the location of problems or defects within the protocol stack, reducing the time
expended by triage teams and significantly decreasing the turnaround time (TAT). We
also propose the adoption of artificial intelligence and machine learning techniques for
predicting log masks using only default SW logs generated at the source, dramatically
curtailing the TAT.
In summary, this research aims to develop bespoke a full E2E AI/ML solution product
tailored for the telecommunications industry to address the challenges in log analysis,
defect triaging, and diagnostics. By overcoming these challenges, we seek to enhance the
troubleshooting process, minimize adverse effects on customers and service providers, and
optimize the use of time and resources. The findings of this research have the potential
to drive significant efficiency improvements in the telecommunications sector, promoting
collaboration, knowledge sharing, and scalable troubleshooting efforts in response to
evolving system complexities.VIAVI Inc
Production, Postharvest Practice, Marketing And Challenges Of Smallholder Vegetable Producers In Tanzania
Get PDFBeside staple crops, the fruits and vegetables subsector contribute significantly to domestic and export markets. Yet, this subsector faces a number of challenges that limits its full growth potential. Through 383 randomly selected farmers’ interviews in selected districts, the study revealed profound information on the vegetable value chain. Findings show that, there is fair participation of men (58%) and women (42%) in vegetable production. Majority of farmers were between 19 and 49 years old (70%), attained primary education (80%) and had more than 5 years in commercial vegetable production (71.8%). Further, 79.4% of farmers had less than 1 ha of vegetable farmland. Farmers cultivate a wide range of nutritious and commercially valued vegetables including broccoli (50.7%), cauliflower (37.3%), white cabbage (34.7%), crisphead lettuce (24.5%), Chinese cabbage (20.6%), zucchini (20.1%), carrot (18.5%), tomato (14.4%), purple cabbage (10.2%), beetroot (9.4%), African nightshade (8.4%), leaf lettuce (7.6%), green/snap bean (6.8%), snow/sugar-snap pea (5%), leeks (5%), spinach (4.2%), green pepper (4.2%), herbs (parsley, fennel and dill) (4.2%) and celery (2.9%). However, sustainable marketing of fresh vegetables is a challenge leading to most farmers opting to wholesale their produce at the farm gate (70.5%) instead of the marketplace, with the price often being set by the wholesale buyers (78.1%). With the exception of gender, household size, and farming experience; a farmer’s location and primary education level had a slightly statistically significant (p = 0.044) influence on opting to use farmgate as the point of sales. The unpredictable market (100%), costly and low-quality inputs (36.4%), pests and diseases (35.2%), and shortage of cold storage facilities (22.9%) were claimed to hamper vegetable production and the producers. In general, vegetables subsector can grow significantly in Tanzania due to the availability of irrigated nutrient-rich land, favorable climate and productive workforce. Thus, good farming practices, marketing and cold chain facilities have the potential to reduce postharvest losses and help realize national sustainable development goals.UKRI (United Kingdom Research and Innovation) for this work under the Global Research Challenges Programme, Grant No: EP/T015535/1 to SOL-TECH project hosted at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Tanzania
The development of a temporary cardiac pacing simulator: A training tool to enhance the management of post cardiac surgical patient care
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonTemporary cardiac pacing (TP) is essential for managing haemodynamically unstable arrhythmias
following cardiac surgery, yet its effectiveness depends on precise manual adjustments
by clinicians. Despite its critical role, TP training remains inconsistent due to a lack
of formal guidelines and inadequate simulation tools. Existing training methods fail to integrate
key haemodynamic parameters and complex clinical scenarios, limiting their ability
to fully prepare clinicians for real-world situations.
This thesis presents the development of the Temporary Cardiac Pacing Simulator (TCPS), a
novel training tool that bridges the gap between theoretical knowledge and hands-on experience.
The TCPS incorporates multimodal physiological signals, realistic pacing modes, and
advanced algorithms to simulate pacing failures and haemodynamic responses, providing
real-time feedback and interactive learning. Additionally, the TCPS introduces a central venous
pressure (CVP)-based approach to optimising atrioventricular (AV) delay, enhancing
pacing efficiency and patient outcomes while exploring the feasibility of real-time AV delay
optimisation in permanent pacemakers.
Beyond the simulator, this research advances the techniques needed to further develop cardiovascular
training tools. A GAN-based system (MC-WGAN) was developed to generate
high-fidelity multimodal signals, addressing data scarcity and expanding training possibilities.
Furthermore, advanced classification techniques, including ResNet architectures, were
explored to improve automated multimodal and single-channel arrhythmia detection, enhancing
the management of TP patients.
Together, these contributions advance the field of TP devices, cardiovascular signal processing,
and clinical training methodologies. By integrating novel simulation techniques, multimodal
synthetic signal generation, and machine learning applications, this thesis provides a
foundation for improved patient care, enhanced clinical education, and future developments
in intelligent cardiac pacing systems
Rhythm Changes: Rhythm Guitar from Jazz to Funk
Get PDFDuring the twentieth century, the electric guitar rose to what Waksman (2001) has described as a “position of relative supremacy in the instrumental hierarchy of popular music” due in part to its ability to function effectively within and across the four textural layers present in popular music. While much of the stylistic research surrounding the electric guitar to date has focused on the lead guitar and its players due to the musical and cultural agency ascribed to the role, the aim of this chapter is to examine the electric rhythm guitar in popular music. The chapter offers a review of the literature and current knowledge surrounding the rhythm guitar and briefly discusses the often problematic divisions of labor between rhythm and lead playing. The chapter then assesses varied approaches to rhythm playing taken by electric guitar practitioners on key recordings from the genres of jazz, blues, R&B, rock and roll, funk, and disco. Rather than reinforcing an assumed binary opposition of lead and rhythm guitar functions, the chapter argues for a consideration of a rhythm-lead guitar spectrum/continuum supported by an assessment of the case studies presented in the chapter
The use of rapid pressure swing adsorption to enhance the design of portable medical oxygen concentrators
Get PDFThis thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonPortable oxygen concentrators are restricted by three main factors: power consumption, weight and output. To minimise the weight and output of a portable oxygen concentrator. This paper investigates how rapid pressure swing adsorption will improve oxygen yield per unit volume of adsorbent. It was observed through experimental data the rapid pressure swing adsorption cycle developed in this research could achieve a throughput of 50 sccm, using adsorbent Zeox Z12-49, in one 6.4mm diameter column of 140mm in length, which is a four times improvement when compared to leading market devices
Synthesis and application of recycled carbon fibre-based adsorbents for the removal of antibiotics from freshwater aquaculture environments
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe growing environmental impact of end-of-life carbon fibre composites and the rising use of antibiotics in aquaculture present two critical sustainability challenges. Carbon fibre reinforced polymers, widely used in aerospace and automotive industries, generate significant waste. While aquaculture is a major source of pharmaceutical pollution. Antibiotics such as ciprofloxacin and oxytetracycline are commonly used in fish farming and have been linked to the emergence of antimicrobial resistance in aquatic environments.
This research aimed to develop high-performance, sustainable carbon-based adsorbents using recycled carbon fibres recovered from Carbon fibre reinforced polymer waste, and to optimise their use for the removal of ciprofloxacin and oxytetracycline from water. A systematic approach was applied to optimise each stage of the adsorbent development process including chemical activation, surface modification, adsorption, and regeneration. Design of Experiments techniques were used to identify optimum process parameters.
Initial testing with sodium hydroxide-activated recycled carbon fibres yielded low adsorption capacities (16.84 mg/g for methylene blue), indicating incomplete activation. Process optimisation employing potassium hydroxide significantly improved adsorbent performance. The optimum conditions were identified as an activation temperature of 670 °C, impregnation ratio of 1:10 (CF:KOH) and hold time of 0.5 h, achieving methylene blue adsorption capacities above 450 mg/g and yields exceeding 70%. Surface-modified samples utilising 10 M nitirc acid, 16 h contact time at 28 °C, resulted in increased acidity and mesoporosity. However, it was found that additional modification was not essential to maintain high antibiotic removal. Optimised adsorption conditions were identified to be an adsorbent dose of 0.8 g/L, pH of 2 and initial concentration of 2 mg/L, which resulted in removal efficiencies above 95% for both CIP and OTC. Regeneration studies using 0.1 M potassium hydroxide demonstrated strong reusability, with regeneration efficiencies remaining above 75% over seven cycles.
The results confirm that recycled carbon fibre-derived adsorbents are effective, reusable, and environmentally sustainable materials for removing antibiotic contaminants from aquaculture wastewater.Engineering and Physical Sciences Research Council (EPSRC) for funding this research, as part of the UKRI, via the EPSCR Doctoral Training Partnership (project reference EP/T518116/1)
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇