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The impact of organizational dehumanization on creative performance through self-esteem threat: the moderating role of work locus of control
Get PDFPurposeThis paper aims to enhance our understanding of how organizational dehumanization affects employees’ creative performance. We propose the self-esteem threat as a mediator in the relationship between organizational dehumanization and employees’ creative performance. We also examine how work locus of control moderates the relationship between organizational dehumanization and creative performance.Design/methodology/approachThrough convenience sampling, online and face-to-face surveys, multisource time-lagged data (N = 257) were collected from full-time employees and their supervisors in Pakistani organizations in the information technology, media industry and oil and gas sectors.FindingsOrganizational dehumanization negatively affects employees’ creative performance, and threats to self-esteem mediate this relationship. Work locus of control moderates the effect of organizational dehumanization on creative performance, and this negative relationship is attenuated when individuals have an external work locus of control.Originality/valueThis study provides novel insights into the process underlying the relationship between organizational dehumanization and creative performance by revealing the mediating role of threat to self-esteem and the buffering role of work locus of control
Sustainable Environmental Monitoring: Multistage Fusion Algorithm for Remotely Sensed Underwater Super-Resolution Image Enhancement and Classification
Get PDFOceans and seas cover more than 70% of the Earth's surface. If compared with the land mass there are a lot of unexplored locations, a wealth of natural resources, and diverse ocean creatures that are inaccessible to us humans. Underwater rovers and vehicles play a vital role in discovering these resources, yet limited visibility in deep waters and technological constraints impede underwater exploration. To address these issues, advanced image super-resolution and enhancement techniques are crucial for reliable resource identification, species recognition, and underwater ecosystem study. This will ultimately bridge the current gap in environmental monitoring by facilitating resource tracking and underwater waste assessment. This article proposes a novel multistage fusion algorithm for underwater image super-resolution, designed to enhance the quality and spatial resolution of low-resolution underwater images toward a more accurate object characterization. The effectiveness of the proposed super-resolution technique is demonstrated using multiple performance metrics including accuracy, f1-score, recall, and precision. By enhancing the spatial resolution of underwater images, our approach meets the increasing demand for detailed and accurate information in underwater earth observation applications
Challenges Psoriasis and Its Impact on Quality of Life: Challenges in Treatment and Management
Get PDFPsoriasis, a chronic inflammatory disease affecting approximately 3% of the global population, presents complex challenges that extend beyond its physical manifestations. This comprehensive review examines the multidimensional impact of psoriasis on patients’ lives, encompassing physical, psychological, and social aspects. We analyze current therapeutic approaches, from traditional systemic treatments to cutting-edge biological therapies and emerging oral medications, evaluating their efficacy, limitations, and accessibility. The review explores how disease severity correlates with quality of life measures and psychological burden, noting the high prevalence of depression (20%), anxiety (21%), and suicidal ideation (0.77%) among affected individuals. However, emerging evidence suggests that clinical severity, as measured by PASI or BSA, does not always correlate with the psychoemotional burden experienced by patients, highlighting the need for a more comprehensive assessment of disease impact. We discuss the evolution of treatment strategies, highlighting recent developments in targeted therapies, including JAK inhibitors, particularly selective TYK2 inhibitors, and PDE4 inhibitors, which offer promising alternatives to traditional treatments. Additionally, we examine the role of various assessment tools and quality of life measures in evaluating treatment outcomes. The analysis emphasizes the need for a holistic approach to patient care that integrates medical interventions with psychological support, addressing both the visible and invisible burdens of the disease. This review underscores the importance of personalized treatment strategies that consider not only clinical efficacy but also patient preferences, accessibility, and long-term safety profiles
Venturing ChatGPT's lens to explore human values in software artifacts: a case study of mobile APIs
Get PDFSoftware is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations
Harnessing cow manure waste for nanocellulose extraction and sustainable small-structure manufacturing
Get PDFThe use of sustainable materials as alternatives to fossil-derived materials is vital to tackle the current environmental challenges. Cellulose is a good candidate due to its intrinsic properties. It is wise to consider cellulose rich-waste materials as its sources, rather than high-grade resources, integrating with the concept of a circular economy. In this study, we successfully produced type I cellulose nanofibrils with an average diameter of 12.8 ± 4.1 nm, from cow manure collected from a local dairy farm, leveraging this sustainable cellulose source to upscale agricultural waste into high-performance biopolymers. This new route offers a practical solution to provide an abundant source of cellulose feedstock while mitigating the environmental concerns of farm animal waste. Following this, the extracted cellulose was used for manufacturing small-structure cellulose products through an innovative method, namely nozzle-pressurized spinning. This is distinguished by its simplicity, high efficiency, and low-energy consumption for straightforward forming. The morphological diversity of these cellulose products further expands their application fields, such as textile, food additives, packaging, electronics, and healthcare
Instance segmentation based on global-local attention and local Chan-Vese model
No full textConventional instance segmentation models exhibit two significant limitations: inadequate capture of global features and insufficient refinement of segmentation boundaries, both adversely affect segmentation accuracy. To address these challenges, this study introduces a novel instance segmentation model based on Global-Local attention and Local Chan-Vese method (GLLCV), a box-supervised instance segmentation network. First, the SOLOv2 model is enhanced through the integration of a Bi-directional Feature Pyramid Network for feature extraction and the incorporation of a Global-Local attention module post-FPN, designed to improve the capture of global feature information and enrich global contextual representations. Second, a bounding box projection function is introduced, which maps instance masks to the initial level set of the improved Local Chan-Vese model, to integrate it with the SOLOv2 model to achieve box-supervised instance segmentation. Lastly, the evolution of the level set function in the improved Local Chan-Vese model facilitates the refinement of object contour boundaries, leading to more precise segmentation of object contours. Experimental evaluations demonstrate that the proposed GLLCV model achieves mAP scores of 40.3% and 33.1% on the Pascal VOC and COCO datasets, respectively, thus validating the superior edge segmentation performance of the GLLCV model
Discovery of potential RAF selective back pocket as a promising biological site for BRAF inhibitors targeting resistant melanoma opens the door for a new generation of kinase inhibitors: Design, synthesis, biological evaluation, and in silico molecular simulation
Get PDFDespite the approved combination of BRAFV600E and MEK inhibitors to treat drug-resistant melanoma, serious side effects associated with this combination have been reported, particularly referring to MEK inhibitors. In the current study, an isosteric drug design strategy and were applied leading to the discovery of KS16, a highly potent candidate with a developed pharmacokinetic profile. KS16 exhibited superior efficacy in inhibiting drug-resistant melanoma cell proliferation as a single agent. KS16 displayed a selective cytotoxic profile against melanoma cell lines over other types of cancer cell lines and inhibited RAF kinases over other protein kinases. It showed potent in vivo activity against melanoma-bearing animal models. In silico molecular docking revealed potential hydrophobic interactions with RAF selective back pocket. KS16 demonstrated improved microsomal stability, half-life, and bioavailability. It exhibited an improved safety profile over normal skin cell lines and hERG protein. Our ultimate future direction is to generate an advanced lead candidate
Historical Building Energy Retrofit Focusing on the Whole Life Cycle Assessment—A Systematic Literature Review
Get PDFClimate change is becoming one of humanity's major concerns. Remarkable steps are being implemented to reduce global emissions in all economic sectors, including the built environment. Historical buildings use a considerable amount of energy and produce emissions; therefore, retrofitting these buildings will enhance the global path towards zero-emissions targets. This paper applies a systematic literature review methodology to identify the research around energy efficiency retrofit in historical buildings, then analyzes this data to find out the common scopes of these studies. After that, the study focuses on the research that covered the life cycle assessment. Lastly, the paper identifies where the research in this field stands, what is accomplished, and what needs to be done. The study used two databases, ScienceDirect and the Web of Science. The output of this study that evaluated 362 publications showed that research in historical building energy efficiency has increased significantly in the last ten years. A few studies cover the topic of whole life cycle assessments and mainly focus on specific processes: energy/emissions, or specific suggested interventions. The suggested future plans for research in this area are to consider the whole life energy and emissions in retrofitted historical buildings
Committee Advice on the assessment of Pasteurised Akkermansia muciniphila as a novel food for use in supplements
No full textMeasuring Mass Timber - Deriving A Mass Timber Whole Life Carbon & Quality Of Life Method By Evaluating Five Mass Timber UK Buildings
No full textThis report is the primary outcome of the Measuring Mass Timber research project. The study was led by dRMM, with collaborating partners Edinburgh Napier University and the Quality of Life Foundation. The project was awarded funding by Built by Nature in 2022 and completed in 2024
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