Automated compliance checking in healthcare building design

Regulatory frameworks associated to building design are usually complex, representing extensive sets of requirements. For healthcare projects in the UK, this includes statutory and guidance documents. Existing research indicates that they contain subjective requirements, which challenge the practical adoption of automated compliance checking, leading to limited outcomes. This paper aims to propose recommendations for the adoption of automated compliance checking in the design of healthcare buildings. Design Science Research was used to gain a detailed understanding of how information from existing regulatory requirements affects automation, through an empirical study in the design of a primary healthcare facility. In this study, a previously proposed taxonomy was implemented and refined, resulting in the identification of different types of subjective requirements. Based on empirical data emerging from the research, a set of recommendations was proposed focusing on the revision of regulatory documents, as well as to aid designers implementing automated compliance in practice

Planning for Excellence: Insights from an International Review of Regulators’ Strategic Plans

What constitutes regulatory excellence? Answering this question is an indispensable first step for any public regulatory agency that is measuring, striving towards, and, ultimately, achieving excellence. One useful way to answer this question would be to draw on the broader literature on regulatory design, enforcement, and management. But, perhaps a more authentic way would be to look at how regulators themselves define excellence. However, we actually know remarkably little about how the regulatory officials who are immersed in the task of regulation conceive of their own success. In this Article, we investigate regulators’ definitions of regulatory excellence by drawing on a unique source of data that provides an important window on regulators’ own aspirations: their strategic plans. Strategic plans have been required or voluntarily undertaken for the past decade or longer by regulators around the globe. In these plans, regulators offer mission statements, strategic goals, and measurable and achievable outcomes, all of which indicate what regulators value and are striving to become. Occasionally, they even state explicitly where they have fallen short of “best-in-class” status and how they intend to improve. To date, a voluminous literature exists examining agency practices in strategic planning, but we are aware of no study that tries to glean from the substance of a sizeable number of plans how regulators themselves construe regulatory excellence. The main task of this Article is undertaking this effort. This Article draws on twenty plans from different regulators in nine countries. We found most generally that excellent regulators describe themselves (though not necessarily using exactly these words) as institutions that are more (1) efficient, (2) educative, (3) multiplicative, (4) proportional, (5) vital, (6) just, and (7) honest. In addition to these seven shared attribute categories, our reading of the plans also revealed five other “unusual” attributes that only one or two agencies mentioned. Beyond merely cataloguing the attributes identified by agencies, this Article also discusses commonalities (and differences) between plan structures, emphases, and framings. We found that the plans differed widely in features such as the specificity of their mission statements, the extent to which they emphasized actions over outcomes (or vice versa), and the extent to which commitments were organized along organizational fiefdoms or cut across bureaucratic lines. We urge future scholarship to explore alternative methods of text mining, and to study strategic plans over time within agencies, in order to track how agencies’ notions of regulatory excellence respond to changes in the regulatory context and the larger circumstances within which agencies operate. Looking longitudinally will also shed light on how agencies handle strategic goals that are either met or that prove to be unattainable

One way forward: non-traditional accounting disclosures in the 21st century

Recent empirical studies (Deegan and Rankin, 1999; Deegan et al., 2000) have indicated that although many corporations have begun to respond to perceived demand for environmental disclosures in published accounts, their perspective of organisational legitimacy is a narrow view, in which information is targeted towards specific stakeholders and not to the general public. This paper considers a range of models (variously called guidelines, standards and charters) which have been put forward by different organisations to aid the development of social and environmental disclosures. In all cases verification and attestation are part of the proposed regimen. The question which the papers attempts to answer is whether any one of the models would be capable of rapid adoption as part of an expanded GAAP, should the professional accounting bodies think that this is desirable. The outcome of our deliberations is cautious support for the use of EMAS and ISO 14000 as the basis for a modified GAAP plus the further development of the GRI 2000 guidelines into a set of standards covering both social and environmental reporting

Tekoälyn hyödyntäminen ydinvoimalaitosvaatimusten analysoinnissa

Nuclear power plant projects are often characterized by two factors: they are time-consuming and capital-intensive. These current challenges include descriptive and non-harmonized requirements demanded in the nuclear power industry resulting in the adaptation to a new licensing domain being very data-intensive, laborious, and tardy. Furthermore, the sheer volume of these requirements also poses a challenge. Nevertheless, by utilizing artificial intelligence in the analysis of nuclear power plant requirements, licensing and engineering could be facilitated and errors reduced in the allocation of requirements. This Master’s thesis develops an algorithm capable of recognizing natural language to classify nuclear power plant requirements into predefined categories by utilizing supervised machine learning. The study was performed in close cooperation with an AI company, Selko Technologies Oy, being responsible for the development of the algorithm based on the classified set of requirements and the needs of Fortum. The algorithm consists of a nuclear power industry-specific language model involving a long short-term memory network, and a classifier based on a feedforward neural network. The language model and classifier were trained by using the YVL Guides issued by the Finnish Radiation and Nuclear Safety Authority (STUK). For training the classifier, a small selection of the requirements were classified according to the two-level predefined hierarchy. The algorithm was tested on the selected YVL Guides and a set of requirements issued by the Office for Nuclear Regulation in United Kingdom. The results include a predetermined requirements hierarchy, the content of the categories, natural language processing algorithm, requirements classified by both the experts and algorithm, and model accuracies in each test case. The accuracies of the classification tasks are promising indicating that the current methods are suitable for categorizing natural language as long as there is a qualified and sufficient amount of training data in place. The conclusions also suggest proceeding to research the capability of the models in other requirements analysis related tasks, such as atomizing long requirements and combining similar requirements into one.Ydinvoimalaitosprojektit ovat usein pitkäkestoisia ja pääomaintensiivisiä. Yhtenä projektien ominaisena haasteena voidaan pitää suurta määrää kuvailevia ja epäyhtenäisiä vaatimuksia. Lisäksi ydinvoimalaitosdesignin vieminen ja suunnittelun sopeuttaminen uuteen lisensiointiympäristöön vaatii paljon tiedonhallintaa. Lisäksi se on työlästä ja hidasta. Tekoälyn hyödyntäminen ydinvoimalaitosvaatimusten analysoimisessa voisi nopeuttaa lisensiointi- ja suunnitteluprosesseja, sekä vähentää virheitä vaatimusten allokoinnissa. Tässä diplomityössä on kehitetty luonnollisen kielen prosessointiin kykenevä algoritmi ydinvoimalaitosvaatimusten luokitteluun. Työssä vaatimukset on luokiteltu ennalta määrättyihin kategorioihin ohjattua koneoppimista hyödyntämällä. Tutkimus on tehty yhteistyössä tekoäly-yrityksen Selko Technologies Oy:n kanssa, joka on vastannut algoritmin kehittämisestä Fortumin toimittaman luokitellun vaatimusjoukon ja tarpeiden perusteella. Algoritmi koostuu ydinvoima-alan kielimallista ja luokittelijasta. Kielimalli pohjautuu pitkään lyhytaikaisen muistin verkkoon ja luokittelija myötäkytkettyyn neuroverkkoon. Kielimallin ja luokittelijan kouluttamiseen on käytetty Suomen säteily- ja ydinturvallisuusviranomaisen Säteilyturvakeskuksen (STUK) Ydinturvallisuusohjeita. Luokittelijan kouluttamista varten tietty osa vaatimuksista on kategorisoitu kaksitasoisen ennalta määritellyn hierarkian mukaisesti. Algoritmin testaukseen on käytetty sekä valittua Ydinturvallisuusohjeiden vaatimusjoukkoa että Yhdistyneiden kuningaskuntien ydinturvallisuusviranomaisen (ONR) yhtä vaatimusjoukkoa. Työn tuloksena syntyi ennalta määritetty vaatimushierarkia sekä luonnollista kieltä prosessoiva algoritmi. Lisäksi työssä määriteltiin, mitä asioita kuuluu eri vaatimusluokkiin. Määrittelyn jälkeen sekä asiantuntijat että algoritmi luokittelivat työssä käytetyn datan. Mallin tarkkuus ja käytettävyys pystyttiin testaamaan lopuksi testidatalla. Saadut tarkkuudet vaatimusten luokittelussa ovat lupaavia ja osoittavat, että nykyiset menetelmät soveltuvat hyvin luonnollisen kielen luokitteluun, mikäli vain koulutusdata on laadukasta ja sitä on riittävästi. Tutkimusta voitaisiin jatkaa kokeilemalla mallien soveltumista myös muissa vaatimusten analysointiin liittyvissä tehtävissä. Näitä ovat esimerkiksi pitkien vaatimusten pilkkominen lyhempiin ja selkeämmin määriteltyihin lauseisiin sekä samanlaisten vaatimusten yhdistäminen yhdeksi vaatimukseksi

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management