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In recent years, the use of digital twins (DT) to improve maintenance procedures has increased in various industrial sectors (e.g., manufacturing, energy industry, aerospace) but is more limited in the construction industry. However, the operation and maintenance (O&M) phase of a building’s life cycle is the most expensive. Smart buildings already use BIM (Building Information Modeling) for facility management, but they lack the predictive capabilities of DT. On the other hand, the use of extended reality (XR) technologies to improve maintenance operations has been a major topic of academic research in recent years, both through data display and remote collaboration. In this context, this paper focuses on reviewing projects using a combination of these technologies to improve maintenance operations in smart buildings. This review uses a combination of at least three of the terms “Digital Twin”, “Maintenance”, “BIM” and “Extended Reality”. Results show how a BIM can be used to create a DT and how this DT use combined with XR technologies can improve maintenance operations in a smart building. This paper also highlights the challenges for the correct implementation of a BIM-based DT combined with XR devices. An example of use is also proposed using a diagram of the possible interactions between the user, the DT and the application framework during maintenance operations.

IntroductionTargeted biologic therapies have transformed outcomes for individuals with psoriasis, a common immune-mediated inflammatory skin disease. The widespread use of these highly effective treatments has led to a growing number of individuals with clear or nearly clear skin remaining on continuous, long-term treatment. Personalised strategies to minimise drug exposure may sustain long-term disease control while reducing treatment burden, associated risks and healthcare costs. This study aims to evaluate the feasibility of a definitive pragmatic effectiveness trial of two personalised dose minimisation strategies compared with continuous treatment (standard care) in adults with well-controlled psoriasis receiving the exemplar biologic risankizumab.Methods and analysisThis is a multicentre, assessor-blind, parallel group, open-label randomised controlled feasibility trial in the UK, evaluating two personalised biologic dose minimisation strategies for psoriasis. 90 adults with both physician-assessed and patient-assessed clear or nearly clear skin on risankizumab monotherapy for ≥12 months will be randomised in a 1:1:1 ratio to (1) patient-led ‘as-needed’ treatment, where risankizumab is administered at the first sign of self-assessed psoriasis recurrence, (2) therapeutic drug monitoring-guided treatment, with personalised dosing intervals determined using a pharmacokinetic model or (3) continuous treatment as per standard care, for 12 months. Participants will be invited to submit self-reported outcomes and self-taken photographs every 3 months using a bespoke remote monitoring system (mySkin app) and will attend an in-person assessment at 12 months. They may also request additional patient-initiated follow-up appointments during the trial if needed. The primary outcome is the practicality and acceptability of the two personalised biologic dose minimisation strategies, assessed as a composite measure including recruitment and retention rates, adherence to the assigned strategies and acceptability to both patients and clinicians. The feasibility of collecting healthcare cost and resource utilisation data will also be evaluated to inform a future cost-effectiveness analysis. A nested qualitative study, involving semistructured interviews with patients and clinicians, will explore perspectives on the personalised biologic dose minimisation strategies. These findings will inform the design of a future definitive trial.Ethics and disseminationThis study received ethical approval from the Seasonal Research Ethics Committee (reference 24/LO/0089). Results will be disseminated through scientific conferences, peer-reviewed publications and patient/public engagement events. Lay summaries and infographics will be codeveloped with patient partners to ensure the findings are accessible for the wider public.Trial registration numberISRCTN17922845.

Targeted biologic therapies can elicit an undesirable host immune response characterized by the development of antidrug antibodies (ADA), an important cause of treatment failure. The most widely used biologic across immune-mediated diseases is adalimumab, a tumor necrosis factor inhibitor. This study aimed to identify genetic variants that contribute to the development of ADA against adalimumab, thereby influencing treatment failure. In patients with psoriasis on their first course of adalimumab, in whom serum ADA had been evaluated 6–36 months after starting treatment, we observed a genome-wide association with ADA against adalimumab within the major histocompatibility complex (MHC). The association signal mapped to the presence of tryptophan at position 9 and lysine at position 71 of the HLA-DR peptide-binding groove, with both residues conferring protection against ADA. Underscoring their clinical relevance, these residues were also protective against treatment failure. Our findings highlight antigenic peptide presentation via MHC class II as a critical mechanism in the development of ADA against biologic therapies and downstream treatment response.

This systematic literature review presents the latest advancements and insights about digital twin technology and robotics interfaced with extended reality in the context of Industry 4.0. As the extended reality technologies emerge, it results in an increasing overlap between digital twins and human-robot interactions in industrial settings, promoting collaboration between operators and cobots in manufacturing environments. The objective of this study is to serve as a valuable resource for researchers and practitioners working in the field of Industry 4.0. It aims to highlight the latest developments and innovations in the application of digital twins and robotics interfaced with extended reality technologies in manufacturing. By extracting data from relevant articles, it provides a comprehensive understanding of the current state-of-the-art in this field by: analyzing the favored extended reality interfaces for digital twin and robotics interactions; analyzing the digital twin and physical twin interaction; evaluating the digital twin application levels and pillars through extended reality interfacing; and introducing a new concept called augmented perception for creating new physical-digital interactions.

Product traceability is one of the major issues in supply chains management (e.g., Food, cosmetics, pharmaceutical, etc.). Several studies has shown that traceability allows targeted product recalls representing a health risk (e.g.: counterfeit products), thus enhancing the communication and risks management. It can be defined as the ability to track and trace individual items throughout their whole lifecycle from manufacturing to recycling. This includes real-time data analytics about actual product behavior (ability to track) and product historical data (ability to trace). This paper presents a comparative study between several works on product traceability and proposes a standardized traceability system architecture. In order to implement a counterfeit/nonconforming product detection algorithm, we implement a cosmetic supply chain as a multi-agent system implemented in Anylogic©. Data generated by this simulator are then used in order to identify genuine trajectories across the whole SC. The genuine product trajectories (behavior) are inferred using a frequent pattern mining algorithm (i.e., Apriori). This identified trajectories are used as a reference in order to identify counterfeit products and detect false alarms of product behavior

Augmented reality (AR) is more and more used in the industrial context for maintenance, assembly operation. However, owing to the evolution and maturity of the technology, it is still necessary to evaluate the usage and benefits of AR in an industrial context to go further than proof of concept. As such, this paper proposes an analysis of AR case study in the literature. Then, this paper presents a methodology for comparing AR and electronic document-based complex maintenance instructions considering tasks complexity and operator competency level. The main results show that the consultation duration of the AR tablet is 34% statistically faster than the PDF tablet. It also shows that error concerning similar objects is reduced thanks to AR. Moreover, the study specifically focuses on types of task that worth using AR. However, the study shows that usability of the AR device is less well rated than the PDF for “beginner–intermediate” operators. Finally, an in-depth analysis permits extracting recommendations of the different factors to take into account with industrial AR applications.

This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The objectives considered in scheduling jobs and transportation tasks in a flexible job shop manufacturing system include makespan, robot travel distance, time difference with due date and critical waiting time. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.

Psoriasis is a common, debilitating immune-mediated skin disease. Genetic studies have identified biological mechanisms of psoriasis risk, including those targeted by effective therapies. However, the genetic liability to psoriasis is not fully explained by variation at robustly identified risk loci. To refine the genetic map of psoriasis susceptibility we meta-analysed 18 GWAS comprising 36,466 cases and 458,078 controls and identified 109 distinct psoriasis susceptibility loci, including 46 that have not been previously reported. These include susceptibility variants at loci in which the therapeutic targets IL17RA and AHR are encoded, and deleterious coding variants supporting potential new drug targets (including in STAP2 , CPVL and POU2F3 ). We conducted a transcriptome-wide association study to identify regulatory effects of psoriasis susceptibility variants and cross-referenced these against single cell expression profiles in psoriasis-affected skin, highlighting roles for the transcriptional regulation of haematopoietic cell development and epigenetic modulation of interferon signalling in psoriasis pathobiology. Psoriasis is a partially heritable skin disorder, the genetic basis of which is not fully understood. Here, the authors use genome-wide association meta-analysis to discover psoriasis susceptibility loci and genes, which encode existing and potential new drug targets.

PurposeVirtual Reality (VR) based training is an efficient and promising digital tool. In the construction industry, studies have been conducted on the realisation of VR scenarios for construction operations, but the challenges lie in procedural scenario creation based on the knowledge and digital data of the construction project. The purpose of this paper is to address this issue.Design/methodology/approachA new system and interface allowing an automatic generation of a VR scenario for operating procedure (OP) based on 4D Building Information Modelling (BIM) and tasks authoring by the domain experts are presented. The first process, Automatic Scenario Generation, generates assembly tasks based on the BIM metadata. The second process, Expert Scene and Scenario Authoring, allows adding tasks that cannot be modeled in the BIM OP.FindingsWith the 4D BIM methodology, the 3D model of building as well as the 3D OPs contain a time dimension representing the construction phases or planning. The evolution in time of the construction site and the tools and procedures adapted to each construction project must be integrated in the VR simulations. Moreover, domain experts should have control over the scenario and should be able to author tasks assigned to the operator in training.Originality/valueA Unified Modeling Language based framework allows the reuse of BIM metadata to create the scene, VR interactions and the assembly procedure; the assembly procedure is embedded in the virtual environment considering the time (4D) in the construction of the building and is used as a training scenario for the operator; and the training scenario can be refined by adding operational knowledge, such as quality.

A wireless sensor network (WSN) deployment requires the identification of optimal network nodes (sensor and sink) positions in an area of interest, to ensure the best network performances (Senouci et al. in Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp 1–6, 43 ). The deployment process can be divided in two main parts: (1) WSN model construction, and (2) placement optimization. Few research works were interested by WSN deployment in indoor environment, even though, most of them consider the objectives (coverage, cost, connectivity) individually without considering the sensors and sink in the same time. This paper proposes a multi-objective deployment strategy (MODS), where all important objectives are integrated. The MODS uses the multi-objective evolutionary algorithms to get near optimal solution for WSN deployment problem. An original coding solution, integrating both network cost and nodes positions is proposed. A comparative study between two evolutionary strategies (classical GA, and NSGA-II) was performed to identify the use case of each one. Obtained results showed the interest of the proposed methodology.