Co-regulation — when an industry association develops a code of practice and this has legislative backing — has become an important regulatory tool. Yet, we lack an understanding of how industry associations engage their members and non-members when developing codes of practice. This oversight is surprising given growing recognition of the importance of regulatory intermediaries like industry associations for achieving regulatory objectives. It is all the more surprising when the purposes of industry engagement during rule-making are understood. In this article, I use the development of the Australian Telecommunications Consumer Protections Code 2019 by the Communications Alliance (‘Comms Alliance’) as a case study to identify the different ways in which the Comms Alliance engaged with industry participants during rule-making and to assess if the functions of industry engagement were discharged. Drawing on interviews with telecommunications companies subject to the Code, I argue that the process of industry engagement had some value in the development of the Code. However, the engagement barriers faced by a sizeable number of industry participants prevented the full realisation of co-regulatory rule-making’s purported benefits. I conclude the article by discussing the potential implications of these findings for legislators, governments, and policymakers, highlighting the need for further empirical study of industry associations and their practices in industry sectors within Australia and farther afield.

The jubilee 20th ICNCT congress in Kraków and the future of BNCT.

Facilities and housing, husbandry, and health management practices on Quebec dairy farms: A retrospective descriptive study.

In order to promote the standardization of production and processing of medicinal materials of animal-derivative, on January 15, 2024, the China Association of Chinese Medicine approved and released the group standards (T/CACM 1571.1-2024T/CACM 1571.16-2024). Among them, the “Technical code of practice for production and processing of medicinal materials of animal-derivative—General rules” (T/CACM 1571.1-2024) (hereinafter referred to as the “General rules”) serves as the overarching guide for the series of specifications. This article provides a detailed interpretation of the “General rules” in order to facilitate the implementation of “Technical code of practice for production and processing of medicinal materials of animal-derivative” and to advance the standardized production of medicinal materials of animal-derivative.

This paper proposes a comprehensive strategic framework for fostering and enhancing collaboration between Eurostat and the academic community. Acknowledging the vital role of academic institutions in driving innovation, methodological rigour, and advanced research capabilities, this strategy aligns with the European Statistical Governance Advisory Board (ESGAB) recommendation to organise academic outreach within a cohesive framework. Building on legislative framework, including Regulation (EC) No 223/2009 and the European Statistics Code of Practice (CoP), this approach identifies four foundational pillars: Skills and Education, Innovation and Methodology, Research, and Communication. These pillars are designed to create a mutually beneficial ecosystem that promotes the exchange of knowledge, supports the development of cutting-edge statistical methods, and enhances data quality and accessibility. The paper also reviews current collaborative efforts between Eurostat, National Statistical Institutes (NSIs), and academia, highlighting exemplary practices and innovative partnerships. The paper concludes by highlighting the expected impacts of enhanced Eurostat-academia collaboration and outlining future perspectives. Originally developed to support Eurostat’s strategy discussions and presented at New Techniques and Technologies for Statistics (NTTS) 2025 conference, the framework is offered as a transferable blueprint; while formal adoption is pending, we detail adoption pathways, preconditions, and evaluation metrics.

Freshwater salinization is a threat to biodiversity conservation. Winter road deicing salt use is a dominant driver of freshwater salinization in north temperate regions that experience winter temperatures below 0 °C. In Canada, the identification and management of areas vulnerable to road salt contamination is the least-complied-with tenet of the Canadian Code of Practice for the Environmental Management of Road Salts. To aid delineation of salt vulnerable areas, we developed and applied a framework for identifying dominant road salt loading source areas relative to aquatic species at risk critical habitat. We estimated per-event road salt loading at the subwatershed scale from roads and parking areas to determine contributions from different land-use classes and road types. We spatially focused on a watershed containing Redside Dace (Clinostomus elongatus), a fish species listed as federally and provincially endangered in Canada and Ontario, respectively. Applying uncertainty analysis, we found that cumulative road salt inputs on parking areas dominated total subwatershed-scale inputs. We recommend enhanced management of smaller-scale private road salt use, as the cumulative effect of smaller-scale salt use can be the largest source of watershed road salt loading. Furthermore, we emphasize the need to include critical habitat explicitly in salt vulnerable area delineations.

Objectives: Small field dosimetry plays a critical role in modern radiotherapy techniques such as IMRT, IGRT, VMAT, SRS, SRT, SBRT, and Tomotherapy, where the goal is to deliver a highly conformal dose to the tumour while minimizing exposure to surrounding healthy tissues. Accurate dosimetric measurements are essential to ensure treatment efficacy and patient safety. While TRS-398 serves as the Code of Practice (CoP) for dosimetry in conventional large field radiotherapy. The objective of the study was to evaluate the performance of these detectors and identify the most suitable one by comparing the measured output factors with reference data. Material and Methods: In this study, various detectors available in our department were employed to measure output factors for different small field sizes. The measurements were performed according to TRS-483 guidelines, and the obtained output factors were compared against reference data from standardized studies. Results: Differences in measured output factors were observed among the detectors, particularly in the smallest field sizes. From the comparison keeping Gafchromic film data as the standard it was seen that CC01 is more suitable for 6 MV-FF beam measurement whereas EPD gives least percentage deviation for the output factors of 6 MV-FFF beams. Conclusion: The results emphasize the importance of choosing an appropriate detector for small field measurements and adhering to TRS-483 recommendations to ensure accurate and reliable dosimetry in advanced radiotherapy applications.

ABSTRACT The seismic design or verification of rockfill dams in conventional engineering practice is carried out considering recommendations provided by code guidelines, which typically define a maximum credible earthquake at the site in terms of a free field response spectrum and spectrum-compatible seismic records. A major assumption generally considered in engineering practice and design codes for these analyses is that the seismic environment consists of vertically propagating (S) shear waves, whereas vertical motions are either ignored or assumed as vertically propagating P waves. Moreover, a particular seismic environment, consisting of Rayleigh surface waves, has received little attention in the technical literature and is often ignored in engineering practice. The present study is focused on the evaluation of the influence of the seismic environment on the seismic settlements of a rockfill dam, considering scenarios consisting of body waves (P and S) and surface (Rayleigh) waves. The results show that a seismic environment consisting of Rayleigh waves can lead to significantly greater settlements at the dam crest than those produced by a seismic environment consisting only of body waves. It is also observed that the increase in settlements due to surface waves is more pronounced when the seismic input contains a dominant frequency less than 1.2 times the fundamental frequency of the dam.

Background: The ICD-10 code for Familial Hypercholesterolemia (FH), E78.01, was introduced in 2016 to improve FH identification and care delivery. Real-world adoption of this code across healthcare systems and the clinical characteristics of patients assigned this code have not been well characterized. Research Question: What proportion of patients in the United States (US) healthcare database have been diagnosed with E78.01, and what is the demographic and lipid-lowering therapy (LLT) use among this patient group? Methods: Data from Epic Cosmos, a nationwide electronic health record (EHR) database comprising a representative sample of patients receiving care across the US, was used for this study. Among 2,996,490 individuals with recorded encounters between October 1, 2016, and December 31, 2024, patients with a final billing code E78.01 were queried. Prevalence and trends in code use were assessed using the Mann-Kendall test. Demographics and LLT use were compared between patients with and without the FH code. Results: The number of patients diagnosed with FH using E78.01 has increased substantially since 2016 ( Figure 1 ). A total of 4,836 patients (1.6 per 1,000) were billed with E78.01 within this period, representing 40.3% of the expected total FH cases (n = 11,986), assuming a prevalence of 1 per 250 individuals. Patients with the diagnosis code E78.01 were older, more likely to be female, and predominantly White individuals, compared with those without the code ( Table 1 ). Among patients with FH identified using the E78.01 code, 3,756 (77.7%) were prescribed at least one LLT. Including those treated with a combination of therapies, overall statin use in this population was 75%, ezetimibe 16%, and PCSK9 inhibitors 11%. Conclusions: Despite increasing use of ICD-10 code E78.01, less than half of expected FH cases were captured across US healthcare systems. These findings underscore the need to further expand code adoption, which may improve FH identification, follow-up, and LLT use in this high-risk population. Additional research is needed to evaluate the sensitivity, specificity, and predictive value of the FH code in clinical practice.

This study investigates the impact of introducing a mandatory practical programming exam on student learning outcomes in introductory programming courses. To facilitate structured coding practice and scalable automated feedback, we developed Programmers’ Interactive Virtual Onboarding (PIVO), a novel Automated Programming Assessment System (APAS). Traditional programming curricula often prioritize theoretical concepts, limiting practical coding opportunities and immediate feedback, resulting in poor skill retention and proficiency. By integrating mandatory practical assessments together with voluntary, self-driven programming tasks through PIVO, we aimed to enhance student engagement, programming proficiency, and overall academic performance. Results show a substantial reduction in failure rates following the introduction of the practical exam, and statistical analyses revealed moderate correlation between students’ voluntary engagement in non-mandatory coding exercises and their performance in both theoretical and practical examinations. These findings indicate an association among engagement in structured, automated practice assessments, algorithmic thinking, and problem-solving capabilities.

Accuracy of ICD-10 Glaucoma Codes in a Large Academic Practice.

Social housing for dairy calves: Farmer acceptance of Canadian industry-led requirements.

Multimodal emotion recognition is an important area of artificial intelligence, which allows for accurate analysis of human emotional states by combining various data sources such as facial expressions, body movements, speech tone, and physiological signals. This paper studies the application of deep learning architectures to multimodal emotion recognition, in particular, the effectiveness of the late fusion strategy. In the paper, the ST-GCN (Spatio-Temporal Graph Convolutional Network) model is used to extract motion features from body movements, and the DeepFaceEmocNet25 model is used to extract emotion features from facial expressions, trained on the FaceEmocDS dataset. These models are integrated through the late fusion method, providing high accuracy in detecting seven emotion classes (happy, angry, sad, surprised, disgusted, fearful, neutral). Late fusion preserves the independent features of each modality and combines them through concatenation and a fully connected classifier. The paper presents mathematical formulas, practical code examples, and experimental setups, and analyzes the technical details of the system. The multimodal approach is widely used in healthcare, education, security, and gaming industries, but there are challenges such as data heterogeneity, limited data sets, and computational costs. Future research will focus on small-data training, real-time analysis, and cultural adaptability. This work presents innovative deep learning solutions in the field of multimodal emotion recognition.

Effective implementation of Intelligent Transport Systems – analysis of methodology based on the Code of Good Practice for ITS Implementation

This study aimed to investigate the association between indoor air quality (IAQ) parameters and the prevalence of Sick Building Syndrome (SBS) symptoms among university students in different types of classrooms. The research responds to growing concerns about environmental health risks in educational settings and seeks to identify key IAQ predictors of SBS symptoms. A cross-sectional study was conducted among 205 students at a Malaysian public university. An online questionnaire adapted from the Indoor Air Quality Industry Code of Practice (ICOP-IAQ) 2010 was used to assess SBS symptoms. Simultaneously, IAQ monitoring was carried out in selected lecture halls (LHs) and tutorial rooms (TRs) using calibrated instruments to measure PM2.5, PM10, temperature, relative humidity, carbon monoxide (CO), carbon dioxide (CO2), total bacterial count, and fungal count. Comparisons were made between the classrooms. Significant differences were observed between LHs and TRs for PM2.5, PM10, temperature, relative humidity, CO, and bacterial counts. SBS symptoms were more prevalent in LHs (70.2%) than in TRs (56.6%). In LHs, CO, PM10, relative humidity, and fungal count were significantly associated with SBS symptoms, while in TRs, only CO2 showed a significant association. Multivariate analysis identified CO, PM10, and fungal count as significant predictors in LHs (AOR = 4.7; p < 0.001), and CO2 in TRs (AOR = 2.7; p = 0.003). IAQ parameters are significant contributors to SBS symptom prevalence among university students, with differences influenced by classroom type and ventilation design. These findings emphasize the urgency of IAQ interventions in academic institutions.

Between 2023 and July 2025, artificial intelligence (AI) governance in the USA, European Union, and China shifted from programmatic statements to actionable instruments. The USA moved from Executive Order 14110 to three July 2025 executive orders on data-center permitting, export promotion, and procurement neutrality. The European Union completed the AI Act, initiated staged application in 2025, and issued a code of practice for general-purpose AI. China consolidated domestic controls on public-facing generative AI and launched a Global AI Governance Action Plan with United Nations-centered cooperation, standards work, and capacity-building. The UK continued a regulator-led, assurance-first model. This essay compares these trajectories and distils implications for libraries: stronger accountability in procurement and vendor management; lawful, well-described training data; the publication of assessment artifacts; and AI literacy as a core service. The analysis highlights convergence on safety, transparency, and inclusion, alongside divergence in regulatory technique and international posture, which will shape library strategy.

Sick building syndrome and indoor air quality in Malaysian bank offices: A cross-sectional analysis

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering.

Abstract Causality and explanation are fundamental, yet often underacknowledged, in engineering. While engineering education emphasizes design principles, codes of practice, and technical considerations, the explicit notion of causality is typically buried under layers of empirical formulas, rule-based guidelines, and prescriptive procedures. This paper examines how causality remains elemental in engineering practices and why codal provisions and curricula often treat it only implicitly. This paper also explores the interplay between causality and machine learning (ML), which excels at detecting associations, but questions still arise regarding its ability to explain and capture causal relations. In a series of case studies, we argue that building codes conventionally distill collective engineering wisdom and embed causal reasoning in simplified yet codified rules. However, this embedding is seldom articulated as “causality,” which leads to potential gaps in how engineers explain their decisions. Thus, this paper advocates for a renewed focus on causality in engineering by bridging philosophical inquiry, engineering methodology, and ML.

Fast, reliable logical operations are essential for realizing useful quantum computers1-3. By redundantly encoding logical qubits into many physical qubits and using syndrome measurements to detect and correct errors, we can achieve low logical error rates. However, for many practical quantum error correction codes such as the surface code, owing to syndrome measurement errors, standard constructions require multiple extraction rounds-of the order of the code distance d-for fault-tolerant computation, particularly considering fault-tolerant state preparation4-12. Here we show that logical operations can be performed fault-tolerantly with only a constant number of extraction rounds for a broad class of quantum error correction codes, including the surface code with magic state inputs and feedforward, to achieve 'transversal algorithmic fault tolerance'. Through the combination of transversal operations7 and new strategies for correlated decoding13, despite only having access to partial syndrome information, we prove that the deviation from the ideal logical measurement distribution can be made exponentially small in the distance, even if the instantaneous quantum state cannot be made close to a logical codeword because of measurement errors. We supplement this proof with circuit-level simulations in a range of relevant settings, demonstrating the fault tolerance and competitive performance of our approach. Our work sheds new light on the theory of quantum fault tolerance and has the potential to reduce the space-time cost of practical fault-tolerant quantum computation by over an order of magnitude.