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It is known that organizations can gain a competitive advantage only by managing effectively for today, while simultaneously creating innovation for tomorrow, and sustainability is one of the innovative strategies in major architecture, engineering, and construction (AEC) organizations. Innovation is vital to AEC organizations’ growth, yet most do not have a comprehensive measurement of innovation performance. Similar to the balanced scorecard approach, key indicators should be identified for the measuring of innovation performance to facilitate management. This article presents a study by using a triangulation approach that integrates systematic literature reviews and two-step consultations with experienced senior professionals to compile a set of key indicators for innovation performance measures for the AEC Industry.

An aging population has significant, dynamic and complex healthcare needs. Meeting such needs in a sustainable manner requires the capability to prioritize and project multiple relevant criteria (e.g., dynamic population health, treatment preferences, resources, technological changes and location of facilities). Most current decision-making processes for urban hospital site selection rely on a combination of experience and statistical data, yet they lack robustness and trending capabilities. This leads to tremendous efficiency implications, as it is not uncommon for hospitals to have a lifespan of more than 100 years after they are built. Our research team has developed an evidence-based decision-support system, enhanced with a Geographic Information System (GIS), that has the potential to overcome these limitations. This paper presents a feasibility demonstration of our framework through a retrospective case study of hospital site selection in Dallas, Texas, demonstrating its positive value in providing a foundation for informed healthcare resource allocation in the context of an aging population.

Building information modeling (BIM) management is not unlike management in other industries and can be broken down into key practices and areas of evaluation. Concepts like the balanced scorecard and key performance indicators (KPI) can be adopted and applied to BIM implementation, helping organizations to translate their vision to objectives, track their performance, and develop actionable initiatives for improvement. One set of evaluation and performance indicator methods are outlined in this chapter and have been used to evaluate BIM management maturity and performance and align performance with expectations. By quantifying BIM and virtual design and construction (VDC) Scorecard, performance assessments and providing actionable advice for improvements, the VDC Scorecard research at Stanford‐CIFE and one of its industry implementations through bimSCORE help individual firms, projects, and researchers objectively manage their BIM investments, reducing uncertainty, and focusing human and financial resources on critical tasks.

The development of AEC (architecture-engineering-construction) projects depends on the ability of decision makers to make informed and quick decisions. This requires the AEC decision facilitators to carry out decision-enabling tasks using methods and tools that are informative and rapid, so that they can integrate discipline-specific information from heterogeneous project stakeholders, evaluate choices, identify alternatives, refine decision criteria, and iterate these tasks throughout the decision-making processes. My research on industry case studies shows that current decision-support tools do not enable decision makers to make informed and quick decisions, because a structured and explicit approach to represent and organize heterogeneous information is lacking. My studies also demonstrate that current methods do not give facilitators the flexibility to manage this information while completing decision-enabling tasks. Consequently, facilitators cannot build on prior decision-enabling tasks to resume the decision process when the decision context changes. To address these limitations, I have developed the Dynamic Decision Breakdown Structure (DBS) Framework with three underlying contributions. First, my formalization of an AEC Decision Ontology allows facilitators to establish an explicit, informative, and hierarchical representation of heterogeneous decision information and its interrelationships. Second, I formalized a dynamic methodology---the Decision Method Model (DMM)---that interacts with the Ontology and enables facilitators to combine, evaluate, and recombine formally represented information, and to complete other decision-enabling tasks flexibly and quickly. Finally, I contribute an AEC decision-making framework that formalizes the sequences, characteristics, and requirements of information management throughout the changing decision context. This framework leverages the application of the hierarchical DBS and the dynamic DMM to support the continuity of decision-enabling tasks as the decision process evolves. I validated my contributions by evaluating the relative performance of the Decision Dashboard, a prototype computer application implemented with my ontology and methodology, with respect to a variety of methods and tools, used by renowned professionals involved in large-scale industry projects across the nation. Based on validation using six industry cases, eight decision-enabling tasks, and by twenty-one professionals and researchers, I claim that the Dynamic Decision Breakdown Structure enables more informative, flexible, resumable, and faster management of decision information than current methods.

This chapter discusses financial planning and financial management systems of architectural firms and how they maintain financial health and navigate economic cycles, set alternative fee types, and manage and avoide disputes. Architects who understand how to manage the financial operations of a firm can achieve better performance and a higher level of success. Revenues to operate the firm come from projects, that is, from fees received for providing professional services. Reimbursable expense categories are specified in the ownerarchitect agreement and may include travel, lodging and meals, telephone, reproduction expenses, and similar project‐related costs. General and administrative expenses consist of salaries and benefits for management and staff members when they are not working on projects. These nonproject activities may include management, marketing, professional development, civic functions, and other activities that are important to the firm but cannot be charged to a billable project. The most common statements used to describe the financial status of a firm are the income statement and balance sheet. Taken together, these statements present a complete picture of the firm's current financial position.

Neutrophils are dynamic with their phenotype and function shaped by the microenvironment, such as the N1 antitumor and N2 pro-tumor states within the tumor microenvironment (TME), but its regulation remains undefined. Here we examine TGF-β1/Smad3 signaling in tumor-associated neutrophils (TANs) in non-small cell lung carcinoma (NSCLC) patients. Smad3 activation in N2 TANs is negatively correlate with the N1 population and patient survival. In experimental lung carcinoma, TANs switch from a predominant N2 state in wild-type mice to an N1 state in Smad3-KO mice which associate with enhanced neutrophil infiltration and tumor regression. Neutrophil depletion abrogates the N1 anticancer phenotype in Smad3-KO mice, while adoptive transfer of Smad3-KO neutrophils reproduces this protective effect in wild-type mice. Single-cell analysis uncovers a TAN subset showing a mature N1 phenotype in Smad3-KO TME, whereas wild-type TANs mainly retain an immature N2 state due to Smad3. Mechanistically, TME-induced Smad3 target genes related to cell fate determination to preserve the N2 state of TAN. Importantly, genetic deletion and pharmaceutical inhibition of Smad3 enhance the anticancer capacity of neutrophils against NSCLC via promoting their N1 maturation. Thus, our work suggests that Smad3 signaling in neutrophils may represent a therapeutic target for cancer immunotherapy. TGF-β stimulated tumor-associated neutrophils (TANs) can exert pro-tumoral functions. Here the authors show that Smad3 activation in TANs is associated with an N2-like polarization state and poor outcome in patients with non-small cell lung carcinoma and that Smad3 targeting reprograms TANs to an antitumor state suppressing tumor growth in preclinical lung cancer models.

Macrophage‐myofibroblast transition (MMT) is a newly discovered pathway for mass production of pro‐tumoral cancer‐associated fibroblasts (CAFs) in non‐small cell lung carcinoma (NSCLC) in a TGF‐β1/Smad3 dependent manner. Better understanding its regulatory signaling in tumor microenvironment (TME) may identify druggable target for the development of precision medicine. Here, by dissecting the transcriptome dynamics of tumor‐associated macrophage at single‐cell resolution, a crucial role of a hematopoietic transcription factor Runx1 in MMT formation is revealed. Surprisingly, integrative bioinformatic analysis uncovers Runx1 as a key regulator in the downstream of MMT‐specific TGF‐β1/Smad3 signaling. Stromal Runx1 level positively correlates with the MMT‐derived CAF abundance and mortality in NSCLC patients. Mechanistically, macrophage‐specific Runx1 promotes the transcription of genes related to CAF signatures in MMT cells at genomic level. Importantly, macrophage‐specific genetic deletion and systemic pharmacological inhibition of TGF‐β1/Smad3/Runx1 signaling effectively prevent MMT‐driven CAF and tumor formation in vitro and in vivo, representing a potential therapeutic target for clinical NSCLC.

Three clusters of coronavirus disease 2019 (COVID-19) linked to a tour group from China, a company conference, and a church were identified in Singapore in February, 2020. We gathered epidemiological and clinical data from individuals with confirmed COVID-19, via interviews and inpatient medical records, and we did field investigations to assess interactions and possible modes of transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Open source reports were obtained for overseas cases. We reported the median (IQR) incubation period of SARS-CoV-2. As of Feb 15, 2020, 36 cases of COVID-19 were linked epidemiologically to the first three clusters of circumscribed local transmission in Singapore. 425 close contacts were quarantined. Direct or prolonged close contact was reported among affected individuals, although indirect transmission (eg, via fomites and shared food) could not be excluded. The median incubation period of SARS-CoV-2 was 4 days (IQR 3–6). The serial interval between transmission pairs ranged between 3 days and 8 days. SARS-CoV-2 is transmissible in community settings, and local clusters of COVID-19 are expected in countries with high travel volume from China before the lockdown of Wuhan and institution of travel restrictions. Enhanced surveillance and contact tracing is essential to minimise the risk of widespread transmission in the community. None.

Retrospective review of an initial cohort of consecutive patients undergoing robot-assisted pedicle screw placement. We aimed to evaluate the learning curve, if any, of this new technology over the course of our experience. Percutaneous pedicle screws have specific advantages over open freehand screws. However, they require intraoperative imaging for their placement (e.g., fluoroscopy and navigation) and require increased surgeon training and skill with the learning curve estimated at approximately 20-30 cases. To our knowledge, this is the first study that measures the learning curve of robot-guided purely percutaneous pedicle screw placement with comprehensive objective postoperative computed tomography (CT) scoring, time per screw placement, and fluoroscopy time. We included the first 80 consecutive patients undergoing robot-assisted spinal surgery at Melbourne Private Hospital. Data were collected for pedicle screw placement accuracy, placement time, fluoroscopy time, and revision rate. Patient demographic and relevant perioperative and procedural data were also collected. The patients were divided equally into four sub-groups as per their chronological date of surgery to evaluate how the learning curve affected screw placement outcomes. Total 80 patients were included; 73 (91%) had complete data and postoperative CT imaging that could help assess that placement of 352 thoracolumbar pedicle screws. The rate of clinically acceptable screw placement was high (96.6%, 95.4%, 95.6%, and 90.7%, in groups 1 to 4, respectively, p=0.314) over time. The median time per screw was 7.0 minutes (6.5, 7.0, 6.0, and 6.0 minutes in groups 1 to 4, respectively, p=0.605). Intraoperative revision occurred in only 1 of the 352 screws (0.3%). We found that robot-assisted screw placement had high accuracy, low placement time, low fluoroscopy time, and a low complication rate. However, there were no significant differences in these parameters at the initial experience and the practiced, experience placement (after approximately 1 year), indicating that robot-assisted pedicle screw placement has a very short (almost no) learning curve.