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As a result of the pervasiveness of smartphones and improvement of context‐aware systems, developers have designed and implemented a variety of context‐aware tour guide systems. These systems focus on tourist attractions and provide services for tourists in order to support them before, during and after the trip. This survey aims to summarise, classify and investigate these systems from three standpoints of general, design and context‐related issues. To this end, after introducing the related concepts, a framework for investigating context‐aware tour guide systems is proposed. The aim is the theoretic consideration and classification of these projects with regard to distributed and pervasive computing patterns and the identification of the current situation and challenges. The proposed framework includes three axes of general, system design and context‐ awareness, each of which consists of some parameters. Afterwards, the existing projects are categorised according to the proposed framework. Finally, concluding remarks as well as new trends are discussed in order to illuminate future research directions.

Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position.

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users’ needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework is illustrated using a simple fact-based preference model developed from ontologies considering two different smart environment domains.

In this paper, we explore the need to improve systems awareness to support early-phase decision-making. This research uses the Norwegian energy industry as context. This industry deals with highly complex engineering systems that shall operate remotely for 25+ years. Through an in-depth study in a systems supplier company, we find that engineers are not sufficiently aware of the systems operational context and do not focus on the context in the early phase. We identified the lack of a holistic mindset and the challenge of balancing internal strategy and customers’ needs as the prevalent barriers. To support the concept evaluation, the subsea system suppliers need to raise systems awareness in the early phase. The study identifies four aspects that are important to consider when developing and implementing approaches to improve systems awareness in the early phase.

Multi-Context Systems (MCSs) are able to formally model, in Computational Logic, distributed systems composed of heterogeneous sources, or “contexts”, interacting via special rules called “bridge rules”. In this paper, we consider how to enhance flexibility and generality in bridge-rules definition and use. In particular, we introduce and discuss some formal extensions of MCSs aimed to their practical application in dynamic environments, and we provide guidelines for implementations.

Background Multi-drug-resistant tuberculosis (MDR-TB) infections are a public health concern. Since 2017, the Ministry of Health (MoH) in Zambia, in collaboration with its partners, has been implementing decentralised MDR-TB services to address the limited community access to treatment. This study sought to explore the role of collaboration in the implementation of decentralised multi drug-resistant tuberculosis services in Zambia. Methods A qualitative case study design was conducted in selected provinces in Zambia using in-depth and key informant interviews as data collection methods. We conducted a total of 112 interviews involving 18 healthcare workers, 17 community health workers, 32 patients and 21 caregivers in healthcare facilities located in 10 selected districts. Additionally, 24 key informant interviews were conducted with healthcare workers managers at facility, district, provincial, and national-levels. Thematic analysis was employed guided by the Integrative Framework for Collaborative Governance. Findings The principled engagement was shaped by the global health agenda/summit meeting influence on the decentralisation of TB, engagement of stakeholders to initiate decentralisation, a supportive policy environment for the decentralisation process and guidelines and quarterly clinical expert committee meetings. The factors that influenced the shared motivation for the introduction of MDR-TB decentralisation included actors having a common understanding, limited access to health facilities and emergency transport services, a shared understanding of challenges in providing optimal patient monitoring and review and their appreciation of the value of evidence-based decision-making in the implementation of MDR- TB decentralisation. The capacity for joint action strategies included MoH initiating strategic partnerships in enhancing MDR-TB decentralisation, the role of leadership in organising training of healthcare workers and of multidisciplinary teams, inadequate coordination, supervision and monitoring of laboratory services and joint action in health infrastructural rehabilitation. Conclusions Principled engagement facilitated the involvement of various stakeholders, the dissemination of relevant policies and guidelines and regular quarterly meetings of clinical expert committees to ensure ongoing support and guidance. A shared motivation among actors was underpinned by a common understanding of the barriers faced while implementing decentralisation efforts. The capacity for joint action was demonstrated through several key strategies, however, challenges such as inadequate coordination, supervision and monitoring of laboratory services, as well as the need for collaborative efforts in health infrastructural rehabilitation were observed. Overall, collaboration has facilitated the creation of a more responsive and comprehensive TB care system, addressing the critical needs of patients and improving health outcomes.

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

The proof theory of the constructive modal logic S4 (hereafter $\\mathsf{CS4}$ ) has been settled since the beginning of this century by means of either standard natural deduction and sequent calculi or by the reconstruction of modal logic through hypothetical and categorical judgments à la Martin-Löf, an approach carried out by using a special kind of sequents, which keeps two separated contexts representing ordinary and enhanced hypotheses, intuitively interpreted as true and valid assumptions. These so-called dual-context sequents, originated in linear logic, are used to define a natural deduction system handling judgments of validity, truth, and possibility, resulting in a formalism equivalent to an axiomatic system for $\\mathsf{CS4}$ . However, this proof-theoretical study of $\\mathsf{CS4}$ lacks, to the best of our knowledge, its third fundamental constituent, namely a sequent calculus. In this paper, we define such a dual-context formalism, called ${\\bf DG_{CS4}}$ , and provide detailed proofs of the admissibility for the ordinary cut rule as well as the elimination of a second cut rule, which manipulates enhanced hypotheses. Furthermore, we make available a formal verification of the equivalence of this proposal with the previously defined axiomatic and dual-context natural deduction systems for $\\mathsf{CS4}$ , using the Coq proof-assistant.

In today’s increasingly complex and multimodal mobility environments, passengers are confronted with fragmented information, inconsistent user interfaces, and limited context-adaptivity across public transport systems and services. These challenges hinder a positive mobility experience, reduce trust, and limit the broader adoption of sustainable transport options. This paper addresses these gaps by introducing a structured, user-centered development methodology for Visually Interactive Companion Technologies in Ubiquitous Passenger Information Systems (VICUPISs). The approach incorporates system characteristics, contextual factors, and a comprehensive process framework. Drawing on applied research and development projects, the methodology defines a five-phase development cycle—from field to concept and back—combining expert insights and user participation across iterative development stages. A central contribution is the integration of a rich context model spanning eight dimensions, enabling adaptive, multimodal, and personalized interaction across mobile, embedded, and public displays. The methodology also incorporates AI-supported adaptivity and addresses the resulting challenges for usability evaluation. Sustainability is considered at three levels: resource-efficient system development, long-term extensibility and adaptability of digital systems, and support for a modal shift toward environmentally friendly public transport. The proposed methodology offers a replicable and transferable foundation for designing human-centered, future-ready information systems in public mobility, complemented by practical heuristics and insights from two case studies of sustainable transport ecosystems.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.