The Application of Project Management Standards and Success Factors to the Development of a Project Management Assessment Tool

AbstractIn spite of all that is known about project management best practices, they are often absent from typical construction projects. This has motivated our interest in developing a tool to assess construction project management practices, focusing on the assessment of individual project practices. We will also explore project outcomes and their correlation with project management practices-potentially identifying project management value. Previous efforts have addressed project management assessment. The paper describes examples that assess an individual's project management skills and approaches that examine the project management competencies of organizations. In contrast to these, our focus is on assessing the project management practices that have been implemented for specific construction projects. A central component of any assessment scheme is the identification of specific elements to be assessed (the assessment “targets”). We intend to draw heavily upon established project management standards and project success factors from previous research to provide the specific targets and benchmarks to be assessed. These include the Project Management Body of Knowledge (PMBOK) by the PM Institute, the IPMA Competence Baseline (ICB) by the International PM Association, ISO 9000, and Prince2 by The Office of Government Commerce UK. This paper describes how these standards are integrated into the project management assessment tool. It discusses the theoretical foundations for the project management assessment tool and the methodologies used for developing the tool and for applying the tool to specific project situations

Isotope shift in the Sulfur electron affinity: observation and theory

The electron affinities eA(S) are measured for the two isotopes 32S and 34S (16752.9753(41) and 16752.9776(85) cm-1, respectively). The isotope shift in the electron affinity is found to be positive, eA(34S)-eA(32S) = +0.0023(70) cm-1, but the uncertainty allows for the possibility that it may be either "normal" (eA(34S) > eA(32S)) or "anomalous" (eA(34S) < eA(32S)). The isotope shift is estimated theoretically using elaborate correlation models, monitoring the electron affinity and the mass polarization term expectation value. The theoretical analysis predicts a very large specific mass shift that counterbalances the normal mass shift and produces an anomalous isotope shift, eA(34S)-eA(32S) = - 0.0053(24) cm-1. The observed and theoretical residual isotope shifts agree with each other within the estimated uncertainties.Comment: 15 pages, 4 figure

The complex machinery of human cobalamin metabolism

Vitamin B$_{12}$ (cobalamin, Cbl) is required as a cofactor by two human enzymes, 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR) and methylmalonyl-CoA mutase (MMUT). Within the body, a vast array of transporters, enzymes and chaperones are required for the generation and delivery of these cofactor forms. How they perform these functions is dictated by the structure and interactions of the proteins involved, the molecular bases of which are only now being elucidated. In this review, we highlight recent insights into human Cbl metabolism and address open questions in the field by employing a protein structure and interactome based perspective. We discuss how three very similar proteins-haptocorrin, intrinsic factor and transcobalamin-exploit slight structural differences and unique ligand receptor interactions to effect selective Cbl absorption and internalisation. We describe recent advances in the understanding of how endocytosed Cbl is transported across the lysosomal membrane and the implications of the recently solved ABCD4 structure. We detail how MMACHC and MMADHC cooperate to modify and target cytosolic Cbl to the client enzymes MTR and MMUT using ingenious modifications to an ancient nitroreductase fold, and how MTR and MMUT link with their accessory enzymes to sustainably harness the supernucleophilic potential of Cbl. Finally, we provide an outlook on how future studies may combine structural and interactome based approaches and incorporate knowledge of post-translational modifications to bring further insights

Assessment of the Impact of Window Size, Position and Orientation on Building Energy Load Using BIM

AbstractIn improving energy efficiency of buildings, windows play a significant role as they largely influence the energy load. Although there are many studies about the energy efficient window design, a rigorous study is missing which analyzes the mutual impact of windows’ size, position and orientation on the energy load. This study aims to address this gap through a case study on a single family house. For this aim, 65 different design scenarios are created which vary by window size, position and orientation. Building information models (BIMs) are created for each scenario via Autodesk Revit®, and are used for the calculation of the total energy load conducted by Autodesk Green Building Studio®. In the first analysis stage, window-to-wall ratio (WWR) and the windows’ position are studied to assess their effect on the energy load. The preliminary results at this stage indicate that the total energy load increases when the WWR grows, and the windows’ position has the biggest impact on the load when the WWR is 20. Using these results, in the next stage, the position of windows in different orientation is studied to assess how the energy load changes by windows’ position in each orientation. The results show that the building requires the lowest load when the windows are located in the middle height in all orientations, and the east windows’ positioning affects the total energy load the most

Large Language Models and the Patterns of Human Language Use: An Alternative View of the Relation of AI to Understanding and Sentience

Large Language Models (LLMs) such as ChatGPT are deep learning architectures that have been trained on immense amounts of text. Their ability to produce human-like text has led to claims that LLMs either possess or simulate some form of conscious experience and understanding. This paper argues that experience and understanding do play an important role, but that it is very different from what is commonly thought. LLMs model the statistical contours of vast amounts of human language use. We use phenomenological considerations of human language production to explain that human language use is intertwined with experience and understanding. Symbolic language does not simply correspond to internal or external 'meaning', but is meaningful because it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns, constantly rearranging them in a way that we liken to making a collage. LLMs do not need to replicate or simulate human mental life in order to produce text that appears meaningful to humans. Rather, they can infer statistical patterns from meaningful patterns in written language use, including clichés and biases. The impressive extent to which these can be computationally reassembled into text that makes sense to humans does not show that LLMs have developed understanding or sentience. Rather, it can reveal the surprising extent to which human language use gives rise to and is guided by patterns

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns

Surface Toughening - Ein kleiner Streifen mit großem Einfluss auf die Verbindungsfestigkeit von geklebten Primärstrukturen im Projekt HAP

In the HAP (High Altitude Platform) project, the German Aerospace Center (DLR) is developing a solar-powered long-range aircraft, various payloads for Earth observation missions, the ground control station and operating concept. The mission conditions and objectives pose major challenges to the aircraft structure, especially with respect to lightweight design. From the point of manufacturing, the 27m long wing has to be manufactured in separate 1.2m long sub segments. The load carrying structure builds a round tube spar. In order to achieve the required degree of lightweight design, adhesively bonded joints are provided for the primary structure made of fiber composite material. The extremely thin structures with simultaneous highly rigid laminate orientations induce failure-triggering stress concentrations in the bonded scarf joint, which can lead to total failure of the structure during months of continuous operation. With the help of local Surface Toughening [1], the stress concentrations in the adhesive layer can be significantly reduced as FEA simulations show. Complementing the simulations, the concept is validated at coupon level and then transferred to the semi-automated production of the round tube spars using the winding process. [1] M.J. Schollerer, et al., J. Adhes., 95:5-7, p.495-514, (2019)

A feasibility study for model based estimating for concrete bridges.

Designing and estimating civil concrete structures is a complex process which to many practitioners is tied to manual or semi-manual processes of 2D design and cannot be further improved by automated, interacting design-estimating processes. This paper presents a feasibility study for the development an automated estimator for concrete bridge design. The study offers a value proposition: an efficient automated model-based estimator can add value to the whole bridge design-estimating process, i.e., reducing estimation errors, shortening the duration of success estimates, and increasing the benefit of doing cost estimation when compared with the current practice. This is then followed by a description of what is in an efficient automated model-based estimator and how it should be used. Finally the process of model-based estimating is compared with the current practice to highlight the values embedded in the automated processes

DLRK Vortrag 2021 - Klebeverbindungen der Primärstruktur innerhalb des HAP-Projektes

In the HAP (High Altitude Platform) project, the German Aerospace Center (DLR) is developing a solar-powered long-range aircraft, various payloads for Earth observation missions, the ground control station and operating concept. The mission conditions and objectives pose major challenges to the aircraft structure, especially with respect to lightweight design. This presentation intrudces the design process of bonded joints for the primary structure on the example of the wing spar, as the most critical component. The wing, with an overall span of 27m, is divided in three wing sections, with the tubular wing spar being assembled from several segments for manufacturing reasons. Considering the demanding lightweight design expectations, adhesively bonded scarfed tubular joints are selected. A manufacturing concept is developed to minimize effort for joint preparation. The dimensioning is based on global (GFEM) and detailed finite element models (DFEM) to fine-tune the design (stiffness discontinuities, surface toughness) to increase the load carrying capacity. This is flanked by physical tests for model validation and material characterization, such as adhesive characterization, to increase DFEM accuracy