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This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

The study aims to develop an inexpensive metal oxide semiconductor gas sensor with high sensitivity, excellent selectivity for a specific gas and rapid response time.

This study synthesized Zn2SnO4 nanostructures using a hydrothermal method with a 1 M concentration of zinc chloride (ZnCl2) as the zinc source and a 0.7 M concentration of tin chloride (SnCl4) as the tin source. Thick films of nanostructured Zn2SnO4 were then produced using screen printing. The structural properties of Zn2SnO4 were confirmed using X-ray diffraction, and the formation of Zn2SnO4 nanoparticles was verified by transmission electron microscopy. Scanning electron microscopy was used to analyse the surface morphology of the fabricated material, while energy dispersive spectroscopy provided insight into the chemical composition of the thick film. These fabricated thick films underwent testing for various hazardous gases, including nitrogen dioxide, ammonia, hydrogen sulphide (H2S), ethanol and methanol.

The nanostructured Zn2SnO4 thick film sensor demonstrates a notable sensitivity to H2S gas at a concentration of 500 ppm when operated at 160°C. Its selectivity, response time and recovery time were assessed and documented.

The primary limitations of this research on metal oxide semiconductor gas sensors include poor selectivity to specific gases, limited durability and challenges in achieving detection at room temperature.

The nanostructured Zn2SnO4 thick film sensor demonstrates a strong response to H2S gas, making it a promising candidate for commercial production. The detection of H2S is crucial in various sectors, including industries and sewage plants, where monitoring this gas is essential.

Currently, heightened global apprehension about atmospheric pollution stems from the existence of perilous toxic and flammable gases. This underscores the imperative need for monitoring such gases. Toxic and flammable gases are frequently encountered in both residential and industrial environments, posing substantial hazards to human health. Noteworthy accidents involving flammable gases have occurred in recent years. It is crucial to comprehend the presence and composition of these gases in the surroundings for precise detection, measurement and control. Thus, there has been a significant push for extensive research and development in diverse sensor technologies using various materials and methodologies to monitor and regulate these gases effectively.

In this research, Zn2SnO4 nanostructures were synthesized using a hydrothermal method with ZnCl2 at a concentration of 1 M for zinc and SnCl4 at a concentration of 0.7 M for tin. Thick films of nanostructured Zn2SnO4 were then fabricated via screen printing technique. Following fabrication, all thick films were subjected to testing with various toxic gases, and the results were compared to previously published data. The analysis indicated that the nanostructured Zn2SnO4 thick film sensor demonstrated outstanding performance concerning gas response, gas concentration, selectivity and response time, particularly towards H2S gas.

This study aims to establish a multi-physics-coupled model for an electrostatic particulate matter (PM) sensor. The focus lies on investigating the deposition patterns of particles within the sensor and the variation in the regeneration temperature field.

Computational simulations were initially conducted to analyse the distribution of particles under different temperature and airflow conditions. The study investigates how particles deposit within the sensor and explores methods to expedite the combustion of deposited particles for subsequent measurements.

The results indicate that a significant portion of the particles, approximately 61.8% of the total deposited particles, accumulates on the inside of the protective cover. To facilitate rapid combustion of these deposited particles, a ceramic heater was embedded within the metal shielding layer and tightly integrated with the high-voltage electrode. Silicon nitride ceramic, selected for its high strength, elevated temperature stability and excellent thermal conductivity, enables a relatively fast heating rate, ensuring a uniform temperature field distribution. Applying 27 W power to the silicon nitride heater rapidly raises the gas flow region's temperature within the sensor head to achieve a high-temperature regeneration state. Computational results demonstrate that within 200 s of heater operation, the sensor's internal temperature can exceed 600 °C, effectively ensuring thorough combustion of the deposited particles.

This study presents a novel approach to address the challenges associated with particle deposition in electrostatic PM sensors. By integrating a ceramic heater with specific material properties, the study proposes an effective method to expedite particle combustion for enhanced sensor performance.

The current movement toward digitisation has promoted the adoption of smart building technology globally. Despite its advantages, its usage in developing countries such as Nigeria is still very low. Therefore, the purpose of this paper is to investigate construction professionals' awareness of smart building concepts (SBCs) in the Nigerian construction industry and identify the parameters by which SBCs can be measured.

A quantitative survey was carried out using a questionnaire to gather relevant data in the study area. This paper was conducted on 363 registered construction professionals in the Nigerian construction industry. The collected data were analysed using descriptive statistics and Kruskal–Wallis H test analysis.

This paper indicated that the majority of Nigerian construction professionals are aware of SBCs. Furthermore, the Kruskal–Wallis H test shows no significant difference between the awareness level of the various construction professionals. This paper further revealed energy management systems, IT network connectivity, safety and security management systems and building automation systems as the most significant parameters in which SBCs can be measured.

This paper identified significant parameters influencing SBCs awareness in the Nigerian construction industry. These parameters can be integrated into the building during the design stage and can be incorporated into the policymaking process of construction firms to promote the awareness of SBCs and encourage practices related to construction sustainability.

This paper provides empirical evidence on the awareness of SBCs among construction professionals and significant parameters influencing awareness in the Nigerian construction industry.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.

Based on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.

A case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.

The pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

This study proposes an assessment framework for improving smart building performance in the broader context of smart city development, considering dimensions like environmental sustainability, building characteristics, intelligence, computation management and analytics. The framework is crafted to guide future research, aligning with the growing emphasis on sustainability and intelligence in evolving urban landscapes within smart cities.

In the initial phase, the concepts of “Smart City” and “Smart Buildings” are analyzed through a systematic literature review, considering the impact of governance on city sustainability and growth, along with the role of public policies in transforming buildings and cities. The empirical research evaluates innovation levels in small and medium-sized European cities, proposing a new framework with validated dimensions and sub-dimensions. This validation involves input from international experts through a Focus Group.

The key research findings validate the new proposed assessment framework for smart buildings within smart city development. The experts’ insights align with and support the dimensions identified in the bibliographic research, providing a comprehensive understanding of the role of smart buildings in sustainable urban development.

This framework not only provides insights for a new model with specific dimensions and sub-dimensions but also serves as a guide for formulating strategies and policies to enhance innovation in these settings. The value of this approach is strengthened by the validation and consolidation process involving international experts in the field.

After studying and analyzing this case, students will be able to evaluate the strategic alternatives for growth for a small entrepreneurial business in an emerging market, analyze the trade-offs between maintaining continuity and change in the growth strategy adopted by an organization and synthesize an appropriate growth strategy for managing the trade-off between continuity and change in an organization.

It was late April 2022, and Mohammad Hamza – the founder and marketing head of Engineering & Environmental Solutions (E&E Solutions) – disconnected the call of his sales manager. His mind was fixated on how to craft the strategy for the next phase of the company’s growth. The deadline for their biggest tender was at the end of May 2022. Should he commit all the company’s reserves to this project or pursue global markets? Launched in 2015, E&E Solutions had come a long way from being a start-up with just one product to a full-blown manufacturer and environmental monitoring equipment service provider. Growing pollution and strictness in compliance propelled the demand for environmental monitoring equipment in India, poised to reach $342m by 2025. E&E Solutions leveraged its technological capabilities in Internet of Things and sensors producing low-cost monitoring equipment to gain an edge in an evolving market and bootstrapped its way to almost $5m annual turnover in 2021. However, the last review meeting brought many concerns for the next growth phase. E&E Solutions had so far focused on the domestic market, catering to the demands of private as well as government clients. A significant cause for concern had been the small order size of private players, averaging $2,000 and a lower net margin of 8%. Moreover, the company had been missing out on opportunities to bid for large government contracts owing to stringent bidding credentials required (such as turnover of at least 50%–80% of the project value and previous similar order experience with a range of at least 70% of the project value). Furthermore, the COVID-19 pandemic had stalled their efforts to tap a promising global environmental monitoring market (predicted to be $44bn by 2030). As Hamza and his team sat in their board room for a discussion, they had two alternatives. Either continue focusing on the domestic market, especially the big government contracts (more than $12m order size) or explore the markets in other emerging economies with demand for similar products (such as Middle East and North Africa region) more aggressively. Hamza was, however, wondering if they could do both, for he knew that to qualify for big government contracts, they needed to scale up. He was also getting restless after missing his target of reaching $20m in five years, especially since India’s ecosystem for start-ups and the small business sector had witnessed favorable policies and support from the government. He started pondering how to leverage his organization’s strengths and continuities to achieve the required pace and scale of change. His thoughts wandered around dividing the cash reserves of $500,000 to fuel growth without reducing the R&D budget. After all, R&D has been E&E Solutions’ forte since its inception and has been pivotal in creating its differentiation.

This case study can be used for core strategic management course at the undergraduate and graduate level of management programs. It can also be used in advanced strategy courses like strategic change, entrepreneurship and small business management offered in MBA programs.

Teaching notes are available for educators only.

CSS 11: Strategy

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.