Risk Profiles in Individual Software Development and Packaged Software Implementation Projects: A Delphi Study at a German-Based Financial Services Company

The aim of this paper is to compare risk profiles of individual software development (ISD) and packaged software implementation (PSI) projects. While researchers have investigated risks in either PSI projects or ISD projects, an integrated perspective on how the risk profiles of these two types of information system (IS) projects differ is missing. To explore these differences, this work conducted a Delphi study at a German-based financial services company. The results suggest that: First, ISD projects seem to be more heterogeneous and face a larger variety of risks than the more straightforward PSI projects. Second, ISD projects seem to be particularly prone to risks related to sponsorship, requirements, and project organization. Third, PSI projects tend to be predominantly subject to risks related to technology, project planning, and project completion. Finally, in contrast to available lists of risks in IS projects and irrespective of the project type, the paper found a surprisingly high prominence of technology and testing-related risks

Ultralow‐parameter denoising: trainable bilateral filter layers in computed tomography

Background Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Purpose Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Results Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. Conclusions Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures

„Da geht es nicht nur um Energiesparen!“: Sozial-ökologische Transformationsprozesse in der Produktion partizipativ und interdisziplinär gestalten

Im vorliegenden Artikel stellen wir einen Ansatz zur Befähigung von Produktionsbeschäftigten zum Nachhaltigkeitshandeln im Betrieb vor. Mit einer zu generierenden Nachhaltigkeitskompetenz wird die ökologische wie soziale Dimension von Nachhaltigkeit adressiert: Ökologisch handelnde Beschäftigte können einen wichtigen Beitrag zur Nachhaltigkeit von Betrieben leisten und zugleich kann der strikt bottom-up-orientierte Ansatz Gestaltungsspielräume der Beschäftigten eröffnen und adressiert so die im Nachhaltigkeitsdiskurs häufig vernachlässigte Dimension sozialer Prozesse. This paper presents an approach to enable production employees to act for sustainability in manufacturing companies. The idea is to generate a sustainability competence that addresses both the ecological and the social dimension of sustainability. Ecological employee action can offer an important contribution to the sustainability of enterprises, and at the same time the bottom-up approach is apt to open up new scopes of action for the employees, thus addressing the often-neglected social dimension of sustainability. (editorial reviewed

Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities

Overexpression of CD97 in Intestinal Epithelial Cells of Transgenic Mice Attenuates Colitis by Strengthening Adherens Junctions

The adhesion G-protein-coupled receptor CD97 is present in normal colonic enterocytes but overexpressed in colorectal carcinoma. To investigate the function of CD97 in colorectal carcinogenesis, transgenic Tg(villin-CD97) mice overexpressing CD97 in enterocytes were generated and subjected to azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced colitis-associated tumorigenesis. Unexpectedly, we found a CD97 cDNA copy number-dependent reduction of DSS-induced colitis in Tg compared to wild-type (WT) mice that was confirmed by applying a simple DSS protocol. Ultrastructural analysis revealed that overexpression of CD97 strengthened lateral cell-cell contacts between enterocytes, which, in contrast, were weakened in CD97 knockout (Ko) mice. Transepithelial resistance was not altered in Tg and Ko mice, indicating that tight junctions were not affected. In Tg murine and normal human colonic enterocytes as well as in colorectal cell lines CD97 was localized preferentially in E-cadherin-based adherens junctions. CD97 overexpression upregulated membrane-bound but not cytoplasmic or nuclear β-catenin and reduced phospho-β-catenin, labeled for degradation. This was associated with inactivation of glycogen synthase kinase-3β (GSK-3β) and activation of Akt. In summary, CD97 increases the structural integrity of enterocytic adherens junctions by increasing and stabilizing junctional β-catenin, thereby regulating intestinal epithelial strength and attenuating experimental colitis

Bayes Factors for Mixed Models: a Discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison

Implementation and evaluation of a multi-level mental health promotion intervention for the workplace (MENTUPP): study protocol for a cluster randomised controlled trial

Background Well-organised and managed workplaces can be a source of wellbeing. The construction, healthcare and information and communication technology sectors are characterised by work-related stressors (e.g. high workloads, tight deadlines) which are associated with poorer mental health and wellbeing. The MENTUPP intervention is a flexibly delivered, multi-level approach to supporting small- and medium-sized enterprises (SMEs) in creating mentally healthy workplaces. The online intervention is tailored to each sector and designed to support employees and leaders dealing with mental health difficulties (e.g. stress), clinical level anxiety and depression, and combatting mental health-related stigma. This paper presents the protocol for the cluster randomised controlled trial (cRCT) of the MENTUPP intervention in eight European countries and Australia. Methods Each intervention country will aim to recruit at least two SMEs in each of the three sectors. The design of the cRCT is based on the experiences of a pilot study and guided by a Theory of Change process that describes how the intervention is assumed to work. SMEs will be randomly assigned to the intervention or control conditions. The aim of the cRCT is to assess whether the MENTUPP intervention is effective in improving mental health and wellbeing (primary outcome) and reducing stigma, depression and suicidal behaviour (secondary outcome) in employees. The study will also involve a process and economic evaluation. Conclusions At present, there is no known multi-level, tailored, flexible and accessible workplace-based intervention for the prevention of non-clinical and clinical symptoms of depression, anxiety and burnout, and the promotion of mental wellbeing. The results of this study will provide a comprehensive overview of the implementation and effectiveness of such an intervention in a variety of contexts, languages and cultures leading to the overall goal of delivering an evidence-based intervention for mental health in the workplace

Bayes factors for mixed models: A discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison

Proactive Handover Decision for Mobile IP based on Link Layer Information

Abstract—Mobile IP provides mobility support in heterogeneous wireless networks where mobile nodes change the point of attachment. The mobility is handled within the network layer and depends on either receiving router advertisements or sending solicitations. However, this movement detection is not appropriate to provide lossless connectivity during handovers. This paper presents a so-called proactive handover decision that is used before the previous link breaks. Moreover, this paper shows the improvement of handovers using modified link layer information. Therefore, three different handover scenarios are presented in this study to show the handover improvement during handovers between WLAN (802.11b) and GPRS. Keywords—Mobile IP, vertical handovers, WLAN, GPRS, proactive handover decision