Johnson Controls (United States)

• BIM-to-BRICK bridges BIM and BRICK schemas, enhancing building data interoperability. • Graph modeling reduces manual intervention in BIM-to-BRICK integration workflows. • Converts 932 BIM instances into 458 BRICK objects with 1219 relationships in 17 seconds. • Tool supports improved environmental comfort via efficient multi-source data integration. • Streamlined approach aids building lifecycle management and sustainable operations. The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models (BIM), all aimed at environmental well-being. Data interoperability is a key component to eliminate silos of information, and using semantic web technologies like the BRICK schema, an effort to standardize semantic descriptions of the physical, logical, and virtual assets in buildings and the relationships between them, is a suitable approach. However, current data integration processes can involve significant manual interventions. This paper presents a methodology to automatically collect, assemble, and integrate information from a building information model into a knowledge graph. The resulting application, BIM-to-BRICK, is implemented on a higher education case study building in a tropical climate. BIM-to-BRICK generated a bidirectional link between a BIM model of 932 instances and experimental data collected for 17 subjects into 458 BRICK objects and 1219 relationships in 17 seconds. The automation of this approach can be compared to traditional manual mapping of data types. This scientific innovation incentivizes the convergence of disparate data types required for achieving and enhancing environmental comfort conditions and other strategic built-environment applications.

The promise of smart buildings — structures that dynamically respond to the needs of occupants while optimising energy consumption and operational efficiency — has long been on the horizon. Yet, the industry finds itself at a crossroads: ripe for disruption, but seemingly stuck in a state of inertia. While initial enthusiasm, driven by the COVID-19 pandemic and the rise of environmental, social and governance (ESG) initiatives, spurred innovation, recent shifts in workplace dynamics and potential policy changes are creating both opportunities and challenges. This paper explores the evolving landscape of smart building technology, examining the factors influencing its adoption, the transformative potential of software-as-a-service (SaaS) models and the disruptive force of agentic artificial intelligence (AI). Furthermore, it addresses the persistent hurdles related to data management, the imperative for an ecosystem approach to value creation and the crucial role of interoperability in realising the full potential of intelligent, adaptive buildings. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

To improve the low-temperature waste heat recovery and its utilization, we proceeded with the development of a low-temperature-driven absorption chiller adopting a double-lift absorption cooling cycle. In the previous reports, we developed a proof-of-concept prototype, and it delivered chilled water at approximately 7 °C when driven by a 60 °C heat source hot water inlet and 30 °C cooling water. We confirmed the reliability of this result from the heat balance of the experimental data, according to the AHRI standard. Cooling capacity in the above conditions was 7.1 kW, which was approx. 1.0 kW smaller than expected. We found approximately 1.31 kW of internal heat loss across the separating plate between the low-temperature evaporator and the low-temperature absorber. The capacity degradation was explained by this internal heat loss clearly. This report examined this cycle’s cooling water temperature characteristics as the next step. We computed the behavior of the prototype when the cooling water varied from 24 °C to 36 °C using a cycle simulator. These results were validated by comparing the calculated results with the experimental data of the prototype. The simulation result agreed with the experiment by ± 5% for the cooling capacity and ±0.01 for the COP. Finally, we predicted the performance of this cycle without the internal heat loss. As a result, we found that cooling capacity increased by 7.9%, and COP improved from 0.32 to 0.36 in approximately 9 °C chilled water output conditions in the case that prevents the internal heat loss.

We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While several decentralized deep learning approaches exist, the majority consider a central parameter-server topology for aggregating the model parameters from the agents. However, such a topology may be inapplicable in networked systems such as ad-hoc mobile networks, field robotics, and power network systems where direct communication with the central parameter server may be inefficient. In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server). Our algorithm is based on the heavy-ball acceleration method used in gradient-based optimization. We propose a novel consensus protocol where each agent shares with its neighbors its model parameters and gradient-momentum values during the optimization process. We consider nonconvex objective functions and theoretically analyze our algorithm’s performance. We present several empirical comparisons with competing decentralized learning methods to demonstrate the efficacy of our approach under different communication topologies.

BackgroundAcute capsaicinoid and capsinoid supplementation has endurance and resistance exercise benefits; however, if these short-term performance benefits translate into chronic benefits when combined with resistance training is currently unknown. This study investigated changes of chronic Capsiate supplementation on muscular adaptations, inflammatory response and performance in untrained men.MethodsTwenty untrained men were randomized to ingest 12 mg Capsiate (CAP) or placebo in a parallel, double-blind design. Body composition and performance were measured at pre-training and after 6 weeks of resistance training. An acute resistance exercise session test was performed pre and post-intervention. Blood samples were collected at rest and post-resistance exercise to analyze Tumor necrosis factor- (TNF-), Soluble TNF- receptor (sTNF-r), Interleukin-6 (IL-6) and Interleukin-10 (IL-10).ResultsExercise and CAP supplementation increased fat-free mass in comparison to baseline by 1.5 kg (P < 0.001), however, the majority of the increase (1.0 kg) resulted from an increase in total body water. The CAP change scores for fat-free mass were significantly greater in comparison to the placebo (CAP ∆%= 2.1 ± 1.8 %, PLA ∆%= 0.7 ± 1.3 %, P = 0.043) and there was a significant difference between groups in the bench press exercise (P = 0.034) with greater upper body strength change score for CAP (∆%= 13.4 ± 9.1 %) compared to placebo (∆%= 5.8 ± 5.2 %), P = 0.041. CAP had no effect on lower body strength and no supplementation interactions were observed for all cytokines in response to acute resistance exercise (P > 0.05).ConclusionChronic Capsiate supplementation combined with resistance training during short period (6 weeks) increased fat-free mass and upper body strength but not inflammatory response and performance in young untrained men.

Abstract Improvement of business processes, increasing customer satisfaction, and reducing organizational costs are important issues that institutions deal with. In this context, six sigma methodology has been considered as a systematic approach to achieve these goals by many organizations. In recent years, non-industrial sectors, where human data are involved, have also applied this technique to improve their processes quality. For application of six sigma procedure for non-industrial issues, there are several challenges that are not encountered in the conventional implementation of Six Sigma procedure. Most of these challenges are experienced on gathering the human (operators, experts) expressed data required for the six sigma projects on the phase of measurement and analysis. The challenges identification and analysis give the ability to address them and provide practical solutions. In this study, in addition to different stages of the Six Sigma methodology, its implementation has been investigated in the non-industrial sector in the DMAIC phases. The challenges dealt with in this research include the impact of external factors on human data, limitations in the design of experiments for human data, qualitative data and discrete data as well as one-sided data. Also, considering a case study on the implementation of six sigma methodology to enhance customer satisfaction in an East Azerbaijan Gas company, strategies are presented for overcoming these obstacles.