Multiple Methodologies Research Articles

Developmental psychology and the scientific status of parental alienation.

Theory and research have described developmental processes leading to damaged parent-child relationships, such as those that occur during a divorce. However, scholars dispute the scientific status of the literature on children who form unhealthy alliances with one parent against the other-termed parental alienation (PA). This comprehensive literature review tests competing descriptions of the PA literature. Accessing four electronic databases, we identified 213 documents with empirical data on PA published in 10 languages through December 2020. The results confirmed that the current state of PA scholarship meets three criteria of a maturing field of scientific inquiry: an expanding literature, a shift toward quantitative studies, and a growing body of research that tests theory-generated hypotheses. Nearly 40% of the PA literature has been published since 2016, establishing that PA research has moved beyond an early stage of scientific development and has produced a scientifically trustworthy knowledge base. This literature review documents the value of multiple research methodologies to this knowledge base. In addition, the growing body of research described in this review enhances our understanding of the association between interparental conflict and the breakdown of parent-child relationships in families where conflict differentially affects children's relationship with and behavior toward each parent. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Improving Software Development Effort Estimation with a Novel Design Pattern Model

A U.S. Midwestern Fortune 500 financial services firm develops software capabilities in-house and requires predictions of project needs for efficient resource allocation decisions across the many projects operating simultaneously. The company develops a novel prediction tool based on the projects’ required software development tasks as described by firm-specific design patterns. The firm provides these predictions within a set of estimates based on industry standard function count methods as well as firm-specific predictive models based on function points and on initial labor assignments. Company management is thus equipped with predictions from multiple methodologies and multiple information sources, enhancing the firm’s ability to predict project needs. Managers aggregate the forecasts, with prediction performance estimated to improve by 35%–49%, measured relative to estimates of the absolute percentage error of the prior method. The improved predictions provide a significant advantage to planning decisions and efficient internal operations. Insights to how managers aggregate the set of forecasts and insights to how the models contribute to the scaled value of information are discussed and further illustrate the benefits of the approach.

Splitting blades: why genera need to be more carefully defined; the case for <italic>Pyropia</italic> (Bangiales, Rhodophyta)

The trend in naming genera based almost exclusively on molecular data, and not on morphological diagnostic characters, is increasing. In bifurcating phylogenetic trees generic cut-offs are arbitrary, but at the bare minimum nomenclatural changes should be supported by multiple phylogenetic methodologies using appropriate models for all the various gene partitions, strong support with all branch support methods, and should also result in adding to our knowledge of the interrelationships of taxa. We believe that a recent taxonomic treatment of the genus Pyropia (Yang et al. 2020) into several genera is unwarranted. We reanalysed the data presented in the recent article, using additional phylogenetic methods. Our results show that many of the newly established genera are not well supported by all methods, and the new circumscription of the genus Pyropia renders it unsupported. We also tested additional outgroups, which were previously suggested as sister to Pyropia, but this did not substantially change our conclusions. These generic nomenclatural changes of the previously strongly supported genus Pyropia, do not shed light on the evolution of this group and have serious consequences in these commercially important algae, that are also governed by a plethora of regulation and by-laws that now need to be amended. We suggest that the over-splitting of groups based only on poorly produced and modestly supported phylogenies should not be accepted and that the genus Pyropia sensu Sutherland et al. (2011) be restored.

Assessing photosynthesis in plant systems: A cornerstone to aid in the selection of resistant and productive crops

Photosynthesis is an essential metabolic pathway for plants, contributing to growth and biomass production. Environmental adverse conditions have a negative impact on photosynthetic activity, reducing crop yield and productivity, a situation that has been worsen due to the actual global climate change scenario. Plants have different mechanisms to cope with this changing environment, ranging from photo-protective mechanisms to adaptive processes aiming acclimation. To understand these processes, and in the search for resistant varieties of crops, plant scientists have been assessing photosynthetic activity under different conditions and in different plant species. There are multiple methodologies to study photosynthesis; however, not all of them are suitable for every working condition or plant species. In this review, we offer an overview of the available methodologies to assess photosynthesis, from the most conventional to other less well known. We highlight the strength and weakness of each technique, and discuss how photosynthetic measurements can be linked and integrated to other methodologies (e.g. omics). Moreover, we address how photosynthesis is affected by intra-plant and inter-plants factors, as well as environmental variables. The analysis of the photosynthetic process from a wider and systemic perspective will lead to a closer understanding of plant physiology, ultimately improving crop yield and land use efficiency. • Photosynthetic measurements are of great importance since photosynthesis is directly related to plant growth and, in agriculture, to crop yield. • The many advantages of determining chlorophyll a fluorescence parameters and related techniques resulted in a central role of these techniques to evaluate photosynthetic activity in plants. • The use of IRGA and PAMs techniques in association is highly recommended to overcome the limitations presented by them when used independently. • Photosynthetic activity is affected by factors within the same plant, between different plants and between the plant and its environment which should be considered during the experiment design and for data interpretation.

Role of office layout typology in saving time and distance spent by users: Case of office buildings in Erbil city

Space organization and space layout in buildings are complex issues in architectural design. People spend most of their lives in offices, yet office buildings still get little attention. This paper examines the potential role of office space typologies in saving distance and time spent by users communicating between these spaces. The study adopts multiple methodologies to scrutinize users’ paths and track their movement. Diversity in methodologies provides a deep understanding of how spatial configurations of these spaces affect users’ movement. Three office buildings were selected in Erbil city to represent three different office space typologies. The study revealed the effective role of office space typologies in influencing the level of user satisfaction and comfort, concluding that the most influential factors in providing distances and time are circulation patterns, entry and exit locations that require considering function, organizational and administrative structure of building spaces, and balancing between horizontal and vertical accessibility.

English learning with academic concepts improves pupils' hearing, speaking, reading, and writing. Method for doing it?

Integrative theme learning, or integrated thematic learning, is the method of seamlessly merging teaching and learning. One consequence of implementing the 2013 curriculum at Madrasah Ibtidaiyah or State Madrasah Ibtidaiyah is the integration of many general lesson subjects into a unified theme. This combination automatically results in a more significant disparity or distinction between the diverse range of teachings provided and the study of Islamic religious education. Therefore, it is crucial to develop the talent of integration, which involves combining knowledge from broad studies with the principles and values of Islam. This study aims to evaluate the degree of integration of the Islamic tenets in English language teaching at Islamic primary schools in East Java. The selected methodology for this research is a qualitative approach employing a case study method. The process of data collecting will encompass conducting interviews, making observations, and analysing pertinent documents. The findings suggest incorporating Islamic principles into English language education at MI Regency in East Java enhances students' listening, speaking, reading, and writing proficiency. This enhancement is accomplished by incorporating Islamic principles across multiple dimensions, encompassing philosophy, research methodologies, materials, strategies, and assessment. The selected method for integration at the communication level is standard communication. The story of integration in MI also necessitates support from scientific data, which will be further examined in this article.

Investigating the Utility of Water Cycle Optimization and Non-linear Programming Techniques in Electric Vehicle Stations Smart Planning

Transportation electrification shows enormous prospects regarding the global environmental pollution issue. However, the penetration of electric vehicles (EVs) is not massive in the global market. We attribute this to the long AC charging time and the limitations of the charging topologies at home. Accordingly, the need for a centralized electrical vehicle station (EVS) becomes a must to enlarge the contribution of the EVs industry market. Intelligent decision-making is essential in enhancing the EVS performance by optimizing the charging time and minimizing the queuing delay, especially during peak hours. In the current study, we propose an entire scenario for an EVS with a maximum of 14 EVs at peak hours. Multiple charging methodologies have been provided, including DC constant current constant voltage, DC multistage constant currents, AC 110/120 V, and AC 220/240 V. In addition, four various photovoltaic/grid integration scenarios have been introduced seeking the optimum EVS operation concerning; Levelized cost of energy, charging time and capacity. Finally, two charging schemes have been demonstrated, targeting a minimum total charging time and queuing delay, where the water cycle optimization technique has been enrolled in parallel with non-linear programming. A significant reduction in the total charging time, nearly 90%, has been reached concerning the overnight in-home AC charging time.

Defense and security planning under resource uncertainty and multi‐period commitments

AbstractThe public sector is characterized by hierarchical and interdependent organizations. For defense and security applications in particular, a higher authority is generally responsible for allocating resources among subordinate organizations. These subordinate organizations conduct long‐term planning based on both uncertain resources and an uncertain operating environment. This article develops a modeling framework and multiple solution methodologies for subordinate organizations to use under such conditions. We extend the adversarial risk analysis approach to a stochastic game via a decomposition into a Markov decision process. This allows the subordinate organization to encode its beliefs in a Bayesian manner such that long‐term policies can be built to maximize its expected utility. The modeling framework we develop is illustrated in a realistic counter‐terrorism use case, and the efficacy of our solutions are evaluated via comparisons to alternatively constructed policies.

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Understanding the Consequences of Unethical Behavior

In recent years, there has been a large body of research examining why employees engage in unethical behavior. Scholars have made substantial progress in terms of understanding the antecedents and processes associated with unethicality. Yet, there remains an important opportunity to more systematically investigate the consequences of unethical behavior from a variety of perspectives. For observers of unethical behavior, there may be a variety of negative consequences as well as potentially unanticipated positive consequences. These effects might occur through the specific morally-relevant emotions, such as anger or vicarious shame, experienced by the observers of unethicality and their subsequent behavioral responses. Additionally, the motives of the transgressor may play an important role in shaping the reactions of others. Observers may also react differently to victims of unethical behavior based on whether the victims decide to forgive the perpetrator. Finally, to pursue ethicality in the workplace, an employee’s own ethical focus on a given day may influence how they feel and whether or not they rise to the occasion when presented with an opportunity to do the right thing. The purpose of this symposium is to deepen scholars’ understanding of the consequences of unethicality via multiple perspectives and methodologies. Are there Benefits or Detriments (or Both) to Observing Coworker Cheating Behavior? Presenter: Marie S. Mitchell; U. of North Carolina at Chapel Hill Presenter: Shubha Sharma; U. of Georgia Presenter: Mo Chen; School of Management, Harbin Institute of Technology Presenter: Mingyun Huai; Tongji U. How does Observing a Member's Unethical Behavior Influence Your Group Behavior? Intention Matters Presenter: Young Won Rhee; U. of Washington Presenter: Elizabeth Eve Umphress; U. of Washington Understanding Why Employees Cover Up Unethical Behaviors Presenter: Jenny Hejia Wang; U. of Guelph Presenter: Laurie Barclay; U. of Guelph To Forgive or to Show Integrity? Forgiveness Decreases Integrity but Increases Benevolence Presenter: Rebecca Schaumberg; The Wharton School, U. of Pennsylvania Presenter: Scott Wiltermuth; USC Presenter: Gabrielle Adams; U. of Virginia Compliant not Courageous? Paradoxical Ethical Consequences of Focusing on Values versus Compliance Presenter: Grace Ching Chi Ho; Arizona State U. Presenter: David Welsh; Arizona State U. Presenter: Michael Baer; Arizona State U. Presenter: John Bush; U. of Central Florida

Recent advances in traffic signal performance evaluation

Signal retiming is a prominent way that transportation agencies use to fight congestion and change of traffic pattern. Performance evaluations of traffic conditions at signalized intersections and arterials provide actionable data for agencies to make well-informed and prioritized signal retiming decisions. However, the abundance of data sources, the lack of standardized evaluation methods and oftentimes the shortage of resources make it a difficult endeavor. The review detailed in this paper examines the advances made in traffic signal performance evaluation. We establish the necessity for the evaluations, study the process of continuous improvement of traffic signal performance using the evaluations, and then examine multiple methodologies in a plethora of research endeavors. Particularly, we focus on probe vehicles and sensors data, the two major sources of data. We discuss how sensors are connected to signal controllers to provide relevant in-depth traffic data including speed and occupancy measures. We also review the nature of probe vehicles and the level of penetration. We then define and summarize performance measures derived from both sources, to aid in performance evaluations. For performance evaluation methods, we discuss the research studies and provide summaries including advantages and disadvantages of the methods used, as well as a holistic outlook for future research. This paper is aimed to provide a comprehensive review on the state-of-the-art to benefit researcher, traffic agencies, and commercial entities that thrive to improve safety and efficiency of traffic signals through performance evaluations. • A comprehensive review on recent works related to traffic signal performance evaluation is conducted. • Various data sources and performance measures are discussed. • Both ranking/metrics and graphical analysis methods for different types of data sources are discussed. • Existing performance evaluation systems and the outlook of future research are presented.

Total Quality and Innovation Management in Healthcare (TQIM-H) for an Effective Innovation Development: A Conceptual Framework and Exploratory Study

To thrive, an organization must adapt to the fast and constant change in the economic environment caused by an aging society, technological changes, and the pandemic crisis. Innovation becomes important for the adaptation of industries. Healthcare is one of them. Innovation development in hospitals is effective and acceptable when its management is effective and aligns with the healthcare quality context since quality is a philosophy of work in life-related settings. To the best of our knowledge, quality management and innovation management in healthcare have never been integrated. Therefore, this research aimed to create an integrated framework of quality and innovation management in healthcare (TQIM-H). To establish the effectiveness of applying TQIM-H for the development of effective healthcare innovation, this study developed a TQIM-H conceptual framework using multiple methodologies including a literature review, multiple case studies analysis, Delphi study with healthcare experts, Technology Acceptance Model (TAM), and triangulation with an external dataset. We constructed a TQIM-H conceptual framework, consisting of seven dimensions, that can be used in developing innovation projects in hospitals and which agrees with safety and quality principles in hospitals.

Distinct antibody clones detect PD-1 checkpoint expression and block PD-L1 interactions on live murine melanoma cells

Monoclonal antibodies (abs) targeting the programmed cell death 1 (PD-1) immune checkpoint pathway have revolutionized tumor therapy. Because T-cell-directed PD-1 blockade boosts tumor immunity, anti-PD-1 abs have been developed for examining T-cell-PD-1 functions. More recently, PD-1 expression has also been reported directly on cancer cells of various etiology, including in melanoma. Nevertheless, there is a paucity of studies validating anti-PD-1 ab clone utility in specific assay types for characterizing tumor cell-intrinsic PD-1. Here, we demonstrate reactivity of several anti-murine PD-1 ab clones and recombinant PD-L1 with live B16-F10 melanoma cells and YUMM lines using multiple independent methodologies, positive and negative PD-1-specific controls, including PD-1-overexpressing and PD-1 knockout cells. Flow cytometric analyses with two separate anti-PD-1 ab clones, 29F.1A12 and RMP1-30, revealed PD-1 surface protein expression on live murine melanoma cells, which was corroborated by marked enrichment in PD-1 gene (Pdcd1) expression. Immunoblotting, immunoprecipitation, and mass spectrometric sequencing confirmed PD-1 protein expression by B16-F10 cells. Recombinant PD-L1 also recognized melanoma cell-expressed PD-1, the blockade of which by 29F.1A12 fully abrogated PD-1:PD-L1 binding. Together, our data provides multiple lines of evidence establishing PD-1 expression by live murine melanoma cells and validates ab clones and assay systems for tumor cell-directed PD-1 pathway investigations.

Classification of EEG Signals Using Neural Network for Predicting Consumer Choices.

EEG, or Electroencephalogram, is an instrument that examines the brain's functions while it is executing any activity. EEG signals to aid in the identification of brain processes and movements and are thus useful in the detection of neurobiological illnesses. Pulses have a very weak magnitude and are recorded from peak to peak, with pulse width ranging from 0.5 to 100 V, which is around 100 times below than ECG signals. As a result, many types of noise can easily influence them. Because EEG signals are so important in detecting brain illnesses, it is critical to preprocess them for accurate assessment and detection. The crown of your head The EEG is a weighted combination of the signals generated by the different small locations beneath the electrodes on the cortical plate. The rhythm of electrical impulses is useful for evaluating a broad range of brain diseases. Hypertension, Alzheimer, and brain damage are all possibilities. We can compare and distinguish the brainwaves for different emotions and illnesses linked with the brain by studying the EEG signal. Multiple research studies and methodologies for preprocessing, extraction of features, and evaluation of EEG data have recently been created. The use of EEG in human-computer communication could be a novel and demanding field that has acquired traction in recent years. We present predictive modeling for analyzing the customer's preference of likes and dislikes via EEG signal in our report. The impulses were obtained when clients used the Internet to seek for multiple items. The studies were carried out on a dataset that included a variety of consumer goods.

Review of the Current Technologies and Applications of Digital Image Processing

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.

Should We Use High-Flow Nasal Cannula in Patients Receiving Gastrointestinal Endoscopies? Critical Appraisals through Updated Meta-Analyses with Multiple Methodologies and Depiction of Certainty of Evidence.

(1) Background: High-flow nasal cannula (HFNC) therapy or conventional oxygen therapy (COT) are typically applied during gastrointestinal (GI) endoscopic sedation. (2) Methods: We conducted a rigorous systematic review enrolling randomized controlled trials (RCTs) from five databases. Risk of bias was assessed using Cochrane’s RoB 2.0 tool; certainty of evidence (CoE) was assessed using GRADE framework. Meta-analysis was conducted using inverse-variance heterogeneity model and presented as relative risk (RR) with 95% confidence interval (CI). Trial sequential analysis was performed, and sensitivity analysis was conducted with Bayesian approach. (3) Results: Eight RCTs were included. Compared to COT, HFNC did not reduce the overall incidence of hypoxemia (RR 0.51; 95% CI 0.24–1.09; CoE: very low) but might reduce the incidence of hypoxemia in patients at moderate to high risk for hypoxemia (RR 0.54; 95% CI 0.31–0.96; and CoE: very low). HFNC might reduce the incidence of severe hypoxemia (RR 0.38; 95% CI 0.20–0.74; and CoE: low). HFNC might not affect the need of minor airway interventions (RR 0.31; 95% CI 0.08–1.22; and CoE: very low) and had no effect on procedure duration (CoE: very low); (4) Conclusions: During GI endoscopic sedation, HFNC might reduce the incidence of hypoxemia in patients at moderate to high risk for hypoxemia and prevent severe hypoxemia.

Sensor Fusion with Deep Learning for Autonomous Classification and Management of Aquatic Invasive Plant Species

Recent advances in deep learning, including the development of AlexNet, Residual Network (ResNet), and transfer learning, offer unprecedented classification accuracy in the field of machine vision. A developing application of deep learning is the automated identification and management of aquatic invasive plants. Classification of submersed aquatic vegetation (SAV) presents a unique challenge, namely, the lack of a single source of sensor data that can produce robust, interpretable images across a variable range of depth, turbidity, and lighting conditions. This paper focuses on the development of a multi-sensor (RGB and hydroacoustic) classification system for SAV that is robust to environmental conditions and combines the strengths of each sensing modality. The detection of invasive Hydrilla verticillata (hydrilla) is the primary goal. Over 5000 aerial RGB and hydroacoustic images were generated from two Florida lakes via an unmanned aerial vehicle and boat-mounted sonar unit, and tagged for neural network training and evaluation. Classes included “HYDR”, containing hydrilla; “NONE”, lacking SAV, and “OTHER”, containing SAV other than hydrilla. Using a transfer learning approach, deep neural networks with the ResNet architecture were individually trained on the RGB and hydroacoustic datasets. Multiple data fusion methodologies were evaluated to ensemble the outputs of these neural networks for optimal classification accuracy. A method incorporating logic and a Monte Carlo dropout approach yielded the best overall classification accuracy (84%), with recall and precision of 84.5% and 77.5%, respectively, for the hydrilla class. The training and ensembling approaches were repeated for a DenseNet model with identical training and testing datasets. The overall classification accuracy was similar between the ResNet and DenseNet models when averaged across all approaches (1.9% higher accuracy for the ResNet vs. the DenseNet).

Methodological Training in Special Education Doctoral Programs: A Mixed-Methods Exploration

Doctoral education is the primary time in which scholars learn about research methodologies and begin to develop their own research agendas and skills. Yet, to date, few research studies have examined graduate students’ perceived value of, and access to, training in multiple research methodologies. The purpose of this study was to explore special education doctoral students’ experiences at research-intensive universities in relation to mixed methods, quantitative, and qualitative methodologies. Using a mixed-methods research design, we explore the extent to which research judgments, skills, paradigmatic values, and methodological identities are diverse and how those features interact when doctoral students judge research. First, doctoral students were invited to participate in a survey (replicating McKim, 2017). Then, students who volunteered during the survey were individually interviewed about their methodological training and identity. We present the results and discuss how they can inform personnel preparation for the next generation of research scholars and consumers.

Using the Consolidated Framework for Implementation Research to Inform the Design of the Mobile Inspección Visual con Ácido Acético System: Mixed Methods Case Study.

BackgroundThere is growing evidence supporting the use of mobile health (mHealth) interventions in low- and middle-income countries to address resource limitations in the delivery of health information and services to vulnerable populations. In parallel, there is an increasing emphasis on the use of implementation science tools and frameworks for the early identification of implementation barriers and to improve the acceptability, appropriateness, and adoption of mHealth interventions in resource-limited settings. However, there are limited examples of the application of implementation science tools and frameworks to the formative phase of mHealth design for resource-limited settings despite the potential benefits of this work for enhancing subsequent implementation, scale-up, and sustainability.ObjectiveWe presented a case study on the use of an implementation science framework in mHealth design. In particular, we illustrated the usability of the Consolidated Framework for Implementation Research (CFIR) for organizing and interpreting formative research findings during the design of the mobile Inspección Visual con Ácido Acético (mIVAA) system in Lima, Peru.MethodsWe collected formative data from prospective users of the mIVAA intervention using multiple research methodologies, including structured observations, surveys, group and individual interviews, and discussions with local stakeholders at the partnering organization in Peru. These activities enabled the documentation of clinical workflows, perceived barriers to and facilitators of mIVAA, overarching barriers to cervical cancer screening in community-based settings, and related local policies and guidelines in health care. Using a convergent mixed methods analytic approach and the CFIR as an organizing framework, we mapped formative research findings to identify key implementation barriers and inform iterations of the mIVAA system design.ResultsIn the setting of our case study, most implementation barriers were identified in the CFIR domains of intervention characteristics and inner setting. All but one barrier were addressed before mIVAA deployment by modifying the system design and adding supportive resources. Solutions involved improvements to infrastructure, including cellular data plans to avoid disruption from internet failure; improved process and flow, including an updated software interface; and better user role definition for image capture to be consistent with local health care laws.ConclusionsThe CFIR can serve as a comprehensive framework for organizing formative research data and identifying key implementation barriers during mHealth intervention design. In our case study of the mIVAA system in Peru, formative research contributing to the CFIR domains of intervention characteristics and inner setting elicited the most key barriers to implementation. The early identification of barriers enabled design iterations before system deployment. Future efforts to develop mHealth interventions for low- and middle-income countries may benefit from using the approach presented in this case study as well as prioritizing the CFIR domains of intervention characteristics and inner setting.

Dense Indoor Sensor Networks: Towards passively sensing human presence with LoRaWAN

Sensors have become ubiquitous in buildings but are rarely connected to a network, and their potential to analyse the performance, use, and interaction with a building is not yet fully realised. In the coming years, we expect sensors in buildings to become part of the Internet of Things (IoT) and grow in numbers to form a Dense Indoor Sensor Network (DISN) that allows for unprecedented analysis of the performance, use, and interaction with buildings. Multiple technologies vie for leading this transformation. We explore Long Range Wide Area Network (LoRaWAN) as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a DISN with 390 sensor nodes and four gateways and empirically evaluated its performance for two years. Our analysis of more than 86 million transmissions revealed that DISNs achieve a much lower distance coverage compared to estimations from previous research indicating that more gateways are required. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications given sufficient gateways. We also contribute to the modelling of transmissions with our comparison of attenuation models derived from multiple methodologies. Across all models, we find that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. Finally, we also investigate the application of DISNs for the passive sensing and visualisation of human presence using a Digital Twin (DT) and a Fused Twins (FT) representation in Augmented Reality (AR). A passive sensing approach allows us to gather relevant data on human use of a building while still preserving privacy via the aggregation process. Immersive in situ visualisations in FT allow for new interactions and new forms of participation. We conclude that DISNs are already technologically feasible today and basing them on Low Power Wide Area Network (LPWAN) offers intriguing possibilities to reduce energy consumption, maintenance cost, and bandwidth use while also enabling new forms of human-building interaction.