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The unaccounted effects of digital transformation: implications for accounting, auditing and accountability research
Get PDFPurposeThis paper aims to uncover the unaccounted effects of digital transformation on accounting, auditing and accountability. It explores the extant academic research and introduces the AAAJ Special Issue titled Accountability for a Connected Society: the Unaccounted Effects of Digital Transformation.Design/methodology/approachA methodological approach combining bibliometric analysis techniques with a qualitative literature review was used to explore relevant academic research. This approach facilitates the identification of thematic clusters within the literature and supports the subsequent qualitative analysis of the studies within each cluster. The qualitative literature review employed an analytical model grounded in organisational science literature, focusing on three predominant levels of analysis: individual, organisational and societal.FindingsThe bibliometric analysis technique led to the identification of seven thematic clusters covering the impact of digital transformation on (1) accounting; (2) adoption, accounting education and e-government; (3) management control; (4) auditing and the auditing profession; (5) public sector auditing and digital technologies; (6) digital innovations for a sustainable future; and, finally, (7) digital trust and cybersecurity. The subsequent qualitative literature review of the papers belonging to each thematic cluster led to an integration of those themes into three macro-clusters: accounting, auditing and accountability.Originality/valueThis work’s innovative combination of methods, including bibliometric and manual techniques, enhances its ability to identify key research topics and uncover further research directions. Several promising directions are suggested for future research
Training Investments and Innovation Gains in Knowledge Intensive Businesses: The Role of Firm Level Human Capital and Knowledge Sharing Climate
Get PDFTraining investments are important in securing innovation gains. However, research on this relationship in knowledge intensive businesses is nascent. In particular, questions remain concerning what value different types of training hold for different types of innovation, and what mechanisms underpin these relationships. Drawing on human capital resources theory and collective learning theory, we develop and test a model explicating how specific and general training investments, through firm level human capital, lead to incremental and radical innovation. Additionally, we propose and investigate the supposition that the predicted positive relationships between training investments, firm level human capital, and innovation will be stronger when knowledge sharing climate is high. We test our model with two-wave, multi-respondent panel data gathered from 816 knowledge intensive businesses in France, Finland, Sweden, and the UK. We find that specific training is positively related to incremental innovation but not radical innovation, whereas general training is positively related to both types of innovation. With respect to firm level human capital, we find that it mediates these relationships and they are stronger when knowledge sharing climate is high. Furthermore, our analysis reveals that knowledge sharing climate moderates both the relationship between the two types of training investments examined and firm level human capital, and the indirect relationship via firm level human capital to incremental and radical innovation. We discuss the implications for theory, research, and practice
Post-Quantum Migration of the Tor Application
Get PDFThe efficiency of Shor's and Grover's algorithms and the advancement of quantum computers implies that the cryptography used until now to protect one's privacy is potentially vulnerable to retrospective decryption, also known as the harvest now, decrypt later attack in the near future. This dissertation proposes an overview of the cryptographic schemes used by Tor, highlighting the non-quantum-resistant ones and introducing theoretical performance assessment methods of a local Tor network. The measurement is divided into three phases. We start with benchmarking a local Tor network simulation on constrained devices to isolate the time taken by classical cryptography processes. Secondly, the analysis incorporates existing benchmarks of quantum-secure algorithms and compares these performances on the devices. Lastly, the estimation of overhead is calculated by replacing the measured times of traditional cryptography with the times recorded for Post-Quantum Cryptography (PQC) execution within the specified Tor environment. By focusing on the replaceable cryptographic components, using theoretical estimations, and leveraging existing benchmarks, valuable insights into the potential impact of PQC can be obtained without needing to implement it fully
GenTwin: Generative AI-Powered Digital Twinning for Adaptive Management in IoT Networks
Get PDFThe dramatic increase in smart services makes adaptive management of communication networks more critical. Especially for Internet of Things (IoT) networks, adaptive management faces several challenges, like fluctuating network conditions, heterogeneity in data sources, and rapid response capabilities. These challenges lead to performance degradation and data losses in IoT applications if not handled. Even though traditional AI algorithms are applied in most network topologies, they fall short of handling these adaptive management challenges without requiring additional software developments. Therefore, we propose a Generative AI-powered Digital Twinning (GenTwin) framework to create digital twin models with generative AI algorithms. In this framework, we design two novel mechanisms: Priority Pooling and Twin Adapter. Priority Pooling is to extract the dynamic relations within the topology before performing model training. We theoretically formulate the priority levels and corresponding weights with a novel presence parameter to present a modular architecture to increase training efficiency. The Twin Adapter is to interact with the GAI architecture and fine-tune the model for the adaptive twin modelling task in IoT networks. After creating the adaptive twin models, we test the rapid response capabilities of GenTwin with what-if analysis. According to our simulation results, we note that the proposed pooling mechanism extracts the data relations 19% more by enhancing the training accuracy. In addition, we show that GenTwin surpasses the traditional twin performance in terms of rapid response capabilities by reducing the response time 53% when the dynamicity is maximum
Renaissance of Climate Policy Uncertainty: The Effects of U.S. Presidential Election on Energy Markets Volatility
Get PDFThe global community has witnessed a burgeoning anxiety for accommodating the current climate changes. This heightened awareness has evoked a resurgence in the formulation and implementation of climate policies. Political events like the U.S. presidential election, may yield substantial influence over the trajectory of future climate policy formulation. In this paper, we scrutinize the impact of climate policy uncertainty (CPU) on energy market volatility against the backdrop of the 2024 presidential election of the U.S.. We provide evidence that the CPU has a strong effect on energy market volatilities in China, which is more pronounced during the U.S. presidential election episodes. Notably, our study uncovers a crucial effect of the CPU on energy market volatilities in China, which manifests in both short-term and long-term. The short-term impact tends to be insubstantial, while the long-term effect conveys its solid influence to energy markets, suggesting fruitful implications for energy market participants as well as climate policy makers
Efficiency of multi-layer greywater and rainwater treatment for sustainable water management through water reuse in irrigation
No full textThe integrated management of greywater and collected rainwater for water reuse is a sustainable approach to reducing stress on freshwater sources and addressing water scarcity. This study proposes using treated greywater and rainwater for agricultural and landscape irrigation and evaluates the compliance of reclaimed water with European Union standards. Accordingly, an aerobic membrane bioreactor (MBR) was used to treat greywater, while a cross-flow flat sheet ultrafiltration (UF) system was employed for the treatment of collected rainwater. Treated greywater and treated rainwater were mixed in a 25:75 volumetric ratio and subjected to ultraviolet (UV) disinfection to produce reclaimed water. In greywater treatment, the MBR achieved removal efficiencies of 65.5 ± 4.3 % for chemical oxygen demand (COD), 62.5 ± 2.9 % for soluble COD, 47.8 ± 1.9 % for total nitrogen, and 47.2 ± 6.1 % for total phosphorus. Turbidity was reduced from 36.6 ± 8.0 NTU to 0.77 ± 0.10 NTU. Similarly, the UF system effectively reduced the turbidity of collected rainwater from 6.33 ± 0.64 NTU to 0.39 ± 0.09 NTU. Both treatment systems produced coliform-free effluents, with no detection of fecal coliform or Escherichia coli. Despite this outcome, the UV disinfection system played a critical role in ensuring the reclaimed water was free of pathogenic microorganisms. Thus, the effluents from the MBR and UF systems were treated with UV disinfection as a final quality assurance step. The results demonstrated that the proposed treatment configuration is applicable at the tested mixing ratio. The study also highlights the need to investigate different mixing ratios and alternative greywater and rainwater sources, as these factors influence influent characteristics and reclaimed water quality
Mandarin Electrolaryngeal Speech Voice Conversion with Speech Encoder Loss Learning and Seq2seq Modeling
No full textElectrolaryngeal (EL) speech utilizes excitation signals generated by an electrolarynx instead of human vocal vibrations. In daily communication, EL speech is less natural and more difficult to understand than natural (NL) speech due to mechanical vibration noise and fixed pitch. Different methods have been proposed to improve the quality and intelligibility of EL speech, but limited training data and atypical acoustic characteristics pose challenges. Voice conversion (VC) is one popular method, and the task is called EL speech VC (ELVC). Sequence-to-sequence (seq2seq) modeling with pretraining strategies has been proposed for ELVC. However, seq2seq ELVC still faces the problem of incomplete and missing phonemes. Furthermore, although previous work has evaluated simulated EL (sEL) speech produced by healthy speakers using electrolarynxes, the effectiveness of seq2seq ELVC on patient EL (pEL) speech has not been studied. In this article, we propose three approaches to address the issues of ELVC implementation. First, we utilize sEL speech in the pretraining stage to close the gap between pEL speech and NL speech. Second, we adopt a speech encoder loss to solve the problem of incomplete and missing phonemes. Third, we introduce waveform similarity overlap-and-add to augment pEL training speech. We conduct systematic experiments on pEL speech to evaluate our approaches. Ablation studies show that incorporating our approaches improves the converted speech in both objective and subjective evaluations compared to the baseline model
WireGuard-AES: Hardware based encryption to WireGuard for VPN gateways
Get PDFWireGuard is a high-performance virtual private network (VPN) implemented in the Linux kernel, known for its speed and software-based encryption. However, it struggles as a VPN gateway (VPNGW) due to reduced throughput when multiple clients connect—especially in software-defined networks (SDNs), where hardware encryption support is underutilized. This study introduces a novel WireGuard implementation using Advanced Encryption Standard (AES) encryption, leveraging hardware support to improve performance. Kernel-based AES boosts throughput by 11%, reduces retransmissions by 5.5%, and lowers central processing unit (CPU) usage by at least 2% (with 95% confidence interval). User-space AES achieves up to 19% higher throughput on modern CPUs, paving the way for increased speeds and better efficiency with larger maximum transmission units (MTUs)
Use of an extensively humanized mouse model to predict the risk of drug–drug interactions in patients receiving dexamethasone
No full textThe corticosteroid dexamethasone, which is used to treat numerous health conditions, remains the first-line treatment for patients hospitalized with COVID-19 requiring oxygen. Current British National Formulary prescribing advice warns of a “severe theoretical” or “severe anecdotal” risk of drug–drug interactions between dexamethasone and 138 different medications. In humans, dexamethasone is eliminated via the cytochrome P450 monooxygenase system, particularly CYP3A4. It is also described as a human cytochrome P450–inducing agent. To establish factors that affect concomitant therapy and dexamethasone efficacy in the treatment of COVID-19, we used a unique mouse model humanized for cytochrome P450s and the transcription factors that regulate their expression, the pregnane X receptor, and the constitutive androstane receptor. We found that induction of CYP3A4 with the anticancer drug dabrafenib or the herbal medicine St John’s wort profoundly reduced dexamethasone exposure. These data suggest that comedications that induce cytochrome P450 expression can have a marked effect on dexamethasone exposure and, potentially, clinical efficacy. We also observed that rather than increasing CYP3A4 expression, dexamethasone at doses equivalent to or higher than those used in the treatment of COVID-19 reduced CYP3A4 expression and increased exposure to dabrafenib. These data indicate the need for a clinical trial to establish the risk of overexposure to comedications during dexamethasone treatment, including the treatment of COVID-19. Significance Statement Current prescribing advice identifies a potential theoretical risk of severe side effects when dexamethasone, one of the most widely used drugs in clinical practice, is coadministered with many other drugs; it is, however, difficult to define the magnitude of this risk for specific drug combinations. We describe the use of cytochrome P450–humanized 8HUM mice to predict drug–drug interactions in patients on polypharmacy, a means of generating data that could better inform clinicians regarding foreseeable drug–drug interactions involving dexamethasone
Committee Advice on the safety of cannabidiol (CBD) isolate as a novel food for use in food supplements - RP354 [Food Standards Agency (FSA) and Food Standards Scotland (FSS) Regulated Product Dossier Assessment]
No full textAn application was submitted to the Food Standards Agency (FSA) and Food Standards Scotland (FSS) in February 2021 from Bridge Farm Group. (“the applicant”) for the authorisation of cannabidiol (CBD) isolate as a novel food.The novel food is a CBD isolate which is intended to be used as a food ingredient in food supplements for adults (excluding pregnant and lactating women and other specifically identified vulnerable groups, including those taking medication and the immunosuppressed).The novel food was assessed based on the data provided. This review indicated it was appropriate for the provisional ADI for 98% or greater CBD to form part of the evidence for this assessment. For CBD a provisional acceptable daily intake (ADI) of 10 mg/day for a healthy 70kg adult has been published by the FSA and was considered in assessing this novel food. The provisional ADI (section 2.7) was recommended, subject to the existing advice to consumers that pregnant and breastfeeding women and people taking any prescription medication should avoid the consumption of CBD. Consumers on regular medications should seek advice from a medical professional before using any type of CBD food product. In addition, children and prospective parents trying for a baby are advised against consumption of CBD, as are those who are immunosuppressed, due to remaining data gaps and residual uncertainties concerning the safety of CBD for these groups of consumers. These contraindications would also apply to this novel food. To support the FSA and FSS in their evaluation of the application, the Advisory Committee on Novel Foods and Processes (ACNFP) were asked to review the safety dossier and supplementary information provided by the applicant. The Committee did not consider any potential health benefits or claims arising from consuming the food, as the focus of the novel food assessment is to ensure the food is safe and does not put consumers at a nutritional disadvantage.The Committee concluded that the applicant had provided sufficient information to assure the novel food, an isolated CBD as detailed in application RP 354, was safe under the proposed conditions of use. The anticipated intake levels and the proposed use in food supplements was not considered to be nutritionally disadvantageous
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