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Changing change: From heroic leadership to collective agency
Get PDFMAD statementThis leading article aims to Make a Difference (MAD) by challenging outdated but persistent ideas about organizational change and leadership. It calls for a fundamental shift away from traditional, leader-centric frameworks and towards recognizing change as an emergent, collective, and relational process. It advocates theories and methodologies that foreground human experiences, interactions, and identities
Alert correlation for intelligent threat detection and response
Get PDFWith the increasing diversity of IoT devices, keeping IT systems secure is becoming increasingly difficult. Attackers exploit vulnerabilities within the system in order to access sensitive information, typically reaching their objective through several steps. Current Intrusion Detection Systems (IDSs) focus on low-level alerts, and tend to produce a high rate of false positives. This type of information alone is insufficient for the detection of sophisticated attack scenarios such Advanced Persistent Threats (APTs). Consequently, correlation techniques have recently been introduced to correlate alerts and reconstruct attack scenarios, however, various attack scenarios exist, with diverse characteristics. Also, different steps of the APTs scenarios may have their own characteristics. Therefore, finding a proper method that covers all cases remains a challenge. Moreover, after detecting APTs, how the system should respond to these attacks to avoid sabotage to the system remains a challenge. Thus, in this paper, first for detection of the attacks, we classify different cases, and then, a method based on different characteristics of attack patterns is proposed to detect APT scenarios. The proposed method consists of two main phases: APT detection and the intelligent hybrid response framework. In APT detection phase, similar alerts are aggregated and attack graphs are generated based on a similarity matrix. These graphs, combined with third party API data enable alert correlation and APT scenario detection. Entity graphs are then created to visualise host behavior, and alert graphs are analyzed to detect APT scenarios. In the response phase, attack graphs produced from the correlation inform the hybrid response framework, integrating knowledge and data-driven components that facilitate automated or recommended mitigation. The approach was evaluated on the ZeekData24 dataset. Obtained precision and recall on the malicious traffic was observed to be 96.65% and 87.04% respectively. The results show that our approach can effectively filter false positive alerts with a reduction of the data going from 10,063 alerts daily to 586 meta-alerts, pruned to 48 attack graphs and finally reduced to 20 suspicious attack graphs
Home and epigenome: Exploring the role of DNA methylation in the relationship between poor housing quality and depressive symptoms
No full textIntroduction: Poor housing quality associates with risk for depression. However, previous research often lacks consideration of socioeconomic status (SES) baseline depressive symptoms and biological processes, leading to concerns of confounding and reverse causation. Methods: In a sample of up to 9669 adults, we investigated cross-sectional and longitudinal associations between housing quality (assessed at age 28, 1-year and 2 year follow-ups) and depressive symptoms (at four intervals between enrolment and 18-year follow-up). In subsamples (n=871, n=731), we investigated indirect effects via DNA methylation. Results: Poor housing quality associated with depressive symptoms cross-sectionally (beta range: 0.02–0.06) after controlling for SES and other factors. Longitudinally, this association persisted at the ~2 year, but not the ~18-year follow-up period. Indirect effects (β=0.002–0.012) linked to genes related to ageing, obesity and brain health. Conclusion: These results highlight poor housing quality as a risk factor for depression and the potential role of DNA methylation in this association
Introduction of Palm GreenChain, a blockchain-based framework for enhanced traceability, transparency and accountable green bond financing in Malaysia
Get PDFThis study employs a theoretical, system design–based methodology to propose the Palm GreenChain framework—a blockchain-based platform aimed at enhancing traceability, transparency, financial coverage, and accountability in green bond financing for sustainable palm oil production in Malaysia. The methodology integrates Ethereum-compatible smart contracts, ESG oracles, IPFS-based data storage, and DAO (Decentralized Autonomous Organization) governance to structure a digital green bond lifecycle. Rather than relying on empirical data collection, the framework is conceptualized through the development of a multi-layered blockchain architecture and validated via comparative analysis with analogous blockchain applications in agriculture. The proposed system is designed to enable real-time traceability of green bond disbursements, automate ESG compliance verification using satellite and IoT data, and strengthen accountability and access to climate finance for smallholder farmers. By embedding performance-based returns within smart contracts, the model aligns financial incentives with conservation goals. Leveraging Malaysia’s advanced land administration infrastructure and digital capabilities, the framework presents a scalable, open-source solution to reduce greenwashing, expand financial inclusion in underserved agricultural communities, and enhance transparency and investor confidence in sustainable agricultural finance. By directly linking green finance to verifiable sustainability outcomes, Palm GreenChain addresses key limitations in conventional green bond mechanisms. Its applicability across diverse agricultural sectors positions it as a replicable blueprint for broader sustainable development. The framework is openly available via its GitHub repository
AI evidence and the future of motor vehicle accident disputes
Get PDFThis paper uses a learning scenario to explore how UK civil courts may deal with self-driving vehicle cases involving a collision. We explore challenges related to obtaining and presenting evidence about decisions made by artificial intelligence (AI) and the impact the legal process may have on automated vehicle (AV) users and other road users.At first glance, UK legislation regulating AV claims appears to provide a straightforward mechanism for any road user to make a claim for injury or damage caused by a vehicle in self-driving mode. However, this paper will discuss how in the event of the insurer denying a claim or the insurer alleging that the AV user contributed to the accident, those disputing an insurer’s decision, will have no alternative but to take the expensive and time-consuming action of pursuing the matter through the courts. The UK has introduced legislation which creates a benchmark of safety for AVs which is that AVs should drive to the standard of a ‘careful and competent human driver’. This means that if an AV has a crash while driving itself, an assessment has to be made: Was the vehicle driving like a careful and competent human driver? In the first instance, this decision will be made by the insurer.If an insurer decides the AV was not at fault, and instead, the AV user was at fault, or partially at fault, the user is at a distinct disadvantage if they disagree with the insurer’s assessment. Anyone disputing the insurer’s assessment will need access to vehicle data and expertise to interpret the data. It will be possible to access some data, but the majority of the relevant data may be difficult to obtain. While there are laws regarding compulsory incident data recording, the mandatory parameters are narrow, and this data may not reveal how an incident occurred. More data parameters exist and will be recorded and available to manufacturers and service providers, but these will not necessarily be made available to other parties. Parties such as AV users, who are not in control of the data, will have to request access. Where this is not voluntarily forthcoming, production of this evidence must be pursued through the Courts.If an insurer assesses a crash and assigns liability to an AV user, and that user wishes to dispute this and allege that an incident happened due to faulty AI, the existing legal presumptions in the UK about the reliability of computers, combined with the inexplicable nature of deep learning algorithms, presents an almighty challenge for both a user to present a case and for the Court to understand the evidence before it. The UK has not (at the time of writing) amended its product liability legislation to include AI and software. Consequently, if there is an allegation of a vehicle fault relating to the AI, and this is not accepted by the insurer, the claim must be pursued via a negligence claim. This paper considers how cases may be presented and the issues which arise as a result of the interaction between the law and the AI used in vehicles
Protection from indiscriminate violence in armed conflict: The scope of subsidiary protection in the European Union
No full textThe article discusses the relationship between subsidiary protection status granted to persons fleeing indiscriminate violence in armed conflicts under Article 15(c) of the EU Qualification Directive/Regulation and international humanitarian law. This is done by assessing jurisprudential developments at the supra-national and national levels through a comparative empirical study of State practice in the EU and by providing an autonomous understanding of the provision. The article enquires into how the different elements of Article 15(c) have been interpreted historically (following the first Court of Justice of the European Union (CJEU) judgment in Elgafaji), and in response to its decision in Diakité. It thereby delineates the scope of the provision in principle, but also in practice by tracking the implementation of CJEU jurisprudence in the field of subsidiary protection. The empirical study demonstrates that whereas judicial enquiry initially focused on determining the existence of an armed conflict in the relevant country of origin using international humanitarian law, since the CJEU’s judgment in Diakité, judicial determinations centre on the element of ‘indiscriminate violence’. However, although appellate authorities no longer explicitly refer to international humanitarian law norms as the legal framework to interpret Article 15(c), judicial interpretation of the various elements of Article 15(c) is still based on corresponding norms. The article demonstrates how the norms of international humanitarian law, including the location and intensity of armed confrontations between fighting parties, the control of territory by armed groups and their capacity to undertake sustained and concerted military operations, continued to inform judicial approaches to the definition and assessment of indiscriminate violence following Diakité. The article contends that interpreting Article 15(c) entirely or even merely by reference to principles of international humanitarian law is inconsistent with the purpose of the international protection regime in the EU and fails to reflect the nature of violence in contemporary armed conflicts
Accelerated proximal gradient method for systems with unknown structure using Volterra series
No full textSystems with unknown structures widely exist in engineering practices. In this paper, a Volterra series is applied to approximate the dynamics of systems. Due to the special structure of the Volterra model, the approximated model has a high order and some redundant terms. A regularised term is introduced to pick out these redundant terms, and then a proximal gradient method is provided to estimate the unknown parameters of the Volterra model. Furthermore, an accelerated technique is proposed to increase the convergence rates. The advantages of this algorithm are as follows: (1) can pick out the redundant terms without any prior knowledge of the model; (2) has fast convergence rates; and (3) is robust to the step-size. The effectiveness of the proposed algorithm is further substantiated through a simulation example
Surgical decision-making regarding hearing and ear reconstruction in craniofacial microsomia: Exploring caregiver narratives
Get PDFTreatment decision-making is an integral but complex part of healthcare, particularly in the context of craniofacial surgeries. The aim of the current study was to explore caregiver narratives to inform future surgical care delivery and best practice. ‘Life Story’ narrative interviews were conducted with US English- and Spanish-speaking caregivers (n=62) of children aged 3-17 years with craniofacial microsomia (CFM). Extracts relating to treatment decision-making were inductively coded using Reflexive Thematic Analysis. Four themes were identified: 1) ‘Grappling with Difference’ exemplifies how participants dealt with having a child who was different; 2) ‘Seeking Authoritative Guidance’ illustrates how participants proactively pursued information about treatment options over several years; 3) ‘In the Driving Seat’ describes participants’ beliefs about whether and how much to involve their child in treatment decisions; and 4) ‘Post-Treatment Reflections’ depicts participants’ reflections of the decision-making experience. Surgeons and other healthcare providers are encouraged to use neutral and accessible language, to ensure families and children have a thorough understanding of all treatment pathways, and to engage in effective shared decision-making practices. Content predominantly focused on surgeries for ear reconstruction and hearing amplification. Future studies would benefit from examining other treatment decisions that caregivers are required to make
Efficacy outcomes 12 months after initiation of darolutamide in non-metastatic castrate resistant prostate cancer (nmCRPC) from the real world UK multi-centre RECORD study
No full text83 Background: Darolutamide has authorisation for treatment of non-metastatic Castrate Resistant Prostate Cancer (nmCRPC) based on the ARAMIS trial. The RECORD Study is a prospective real-world evaluation (RWE) of clinical outcomes in patients with nmCRPC treated with darolutamide in the UK. The study will improve understanding of treatment response and duration as well as inform regarding the use of next generation imaging (NGI) and effects of concomitant medication. Methods: Patients were enrolled from 19 centres over a 3-year period from November 2020. Data cut-off was 16 September 2024. Disease characteristics of patients and efficacy up to 12 months (m) after initiation of darolutamide are evaluated. Descriptive statistics will be used for patient demographics. Results: 257 patients were analysed with a median age of 77 (range 52-94) years (y). 52% have a Gleason score ≥8. 30 patients (11.7%) had NGI prior to initiation of darolutamide and 41 (15.8%) were on anticoagulant/antiplatelet medication. ECOG 0:35%, 1:59% and 2:6.2%. Median pre-treatment PSA was 9.7ng/mL and pre-treatment PSA doubling time (PSAdT) was 5 months. The greatest reduction in median PSA values was seen within the first 3m on darolutamide but was still decreasing slightly at 12m. PSA response was as follows: PSA 50 reduction at 3, 6, 9 and 12 months was 74%, 77%, 75% and 73% respectively and PSA 90 reduction at 3, 6, 9 and 12 months was 28%, 38%, 43% and 42% respectively. No differential effect on PSA reduction was seen with Gleason score (6m, previous treatment (RT or prostatectomy), anticoagulant/antiplatelet medication or those who had NGI. Median duration of treatment on darolutamide did not significantly differ (p=0.067) in patients with PSAdT>6m (104 patients) compared to those with PSAdT≤6m (135 patients). 46 patients (17.9%) have come off treatment within 12m of initiation: 26 (10.1%) disease progression, 8 (3.1%) toxicity (most frequent being fatigue and diarrhoea), 12 other unrelated causes including 1 death. Conclusions: This RWE shows that patients with nmCRPC in clinical practice have comparable outcomes to the ARAMIS trial. Response rates, tolerability and discontinuation rates are similar and in particular only 3.1% discontinuing treatment due to toxicity. Gleason score >8; PSAdT and the use of NGI had no differential impact on PSA response. This is valuable RWE enabling optimisation of treatment in nmCRPC
The multiplier effects of government expenditures on social protection: A multi-country study
Get PDFThis article uses a novel dataset comprising 42 countries for the years 1985–2020 to explore the relationship between public spending on social protection and GDP. The article contributes to the empirical literature on social protection spending by conducting a large multi-country study using the structural vector autoregression approach. The results of the study highlight the positive effects of social protection expenditures on GDP that surpass those of total government expenditures. These results vary considerably across countries, with impact multipliers ranging from 5 in Mexico to -0.71 in Paraguay. The authors find that the cumulative multiplier exceeds 1 for most of the 42 sample countries, suggesting that the positive impact of social protection spending on GDP accumulates over time. The article finds statistically significant and strong correlations between the cumulative and impact multipliers and inequality measures such as the Gini coefficient and the income shares of the poorest and the richest. Indeed, the positive impact of public spending on social protection on GDP is especially pronounced in countries characterized by higher inequality. Taken together, the results have significant policy implications and suggest that the growth-enhancing potential of social protection policies is complementary to the ability of such policies to reduce inequality