Human-centered intelligent systems

Human-centered intelligent systems (HCIS), which are at the forefront of the AI and HCI fields, are dedicated to enhancing human experiences and interactions with technology. HCIS are designed to be intuitive, responsive, and adaptable to human needs and preferences, aiming to empower users and foster positive interactions. By utilizing AI techniques, such as machine learning, natural language processing, and computer vision, HCIS aims to make technology more accessible and beneficial across various industries and domains

Animal‐free setup of a 3D mature adipocyte‐macrophage co‐culture to induce inflammation in vitro

Adipose tissue inflammation plays a central role in the pathogenesis of metabolic disorders. It is closely associated with immune cell infiltration, particularly macrophages, and the release of pro‐inflammatory cytokines. Reliable in vitro test systems that mimic the inflamed environment while being free of animal‐derived components are essential to explore new treatments for obesity‐related diseases. This study aims to develop a straightforward, animal‐free adipocyte‐macrophage co‐culture for investigating adipose tissue inflammation. Therefore, the human monocytic cell lines Mono Mac (MM6) and THP‐1 are co‐cultured with human primary mature adipocytes (ACs) encapsulated in gellan gum (GG) within a defined environment. Both monocytic cell lines are effectively activated by phorbol 12‐myristate 13‐acetate (PMA) and lipopolysaccharide (LPS) in the defined medium, exhibiting distinct cytokine profiles. A comparison between collagen and GG demonstrates that GG is a suitable animal‐free matrix material for ACs. PMA+LPS successfully activates the 3D adipocyte‐macrophage co‐culture to an inflammatory state for 72 h in the developed defined medium. Viability and intracellular lipid content remain high, and the functionality of ACs (perilipin A) in untreated models remains intact. This inflamed adipocyte‐macrophage co‐culture is easy to assemble and set up in a defined environment, making it a potential test system for anti‐inflammatory treatment strategies

Kurz innehalten : wie man in Teams Reflexionen nutzt, um achtsamere Entscheidungen zu treffen

Reflexion wird oft als eine Retrospektive, eine Art Rückschau, verstanden. Lotte Svalgaard, Wirtschaftspsychologin aus Dänemark, hält es hingegen für viel effektiver, bereits während eines Team-Prozesses zu reflektieren, damit wir unsere dysfunktionalen Verhaltensmuster und Routine-Mechanismen nicht wiederholen. ZOE-Redakteur Arjan Kozica sprach mit der Autorin von „The Elephant in the Room“ über die Kraft der Reflexion „mitten im Geschehen“

Strategies to overcome the liability of outsidership in geopolitical projects: the case of German firms in the Chinese belt and road initiative in Africa

Purpose Building on the revised Uppsala model’s perspective on firm internationalization, which has been extended by network theory, we explore how firms overcome their liability of outsidership in the multi-stakeholder networks of geopolitical projects. We apply our theoretical assumptions in the context of the Belt and Road Initiative (BRI) in Sub-Saharan Africa (SSA). Design/methodology/approach We conducted a qualitative analysis of 20 semi-structured interviews with managers of German firms along the value chain of infrastructure projects in SSA. Findings Our findings show that Chinese firms are regarded not only as competitors but also as customers, particularly after network entry. We propose a four-tiered approach of entry nodes and processes showing how non-Chinese firms build enduring network relationships to overcome their liability of outsidership, thereby benefiting from BRI-related networks in SSA. Originality/value This is a pioneering study applying the revised Uppsala model to business networks in the multi-stakeholder and multi-country setting of geopolitical projects. Contrary to public opinion, we posit that in these geopolitical projects, it is not only firms from the sponsor government that benefit. Based on our “outside-in” perspective, we make clear that outsider firms may find business opportunities in geopolitical projects if they successfully build network relationships with insiders

MCTSDE: a novel sensorless strategy for efficient and cost-effective health assessment of roller chain systems

Roller chain systems play a crucial role in industrial automation, yet their condition monitoring remains challenging due to the difficulty of sensor placement on moving components. This research proposes a sensorless health assessment framework MCTSDE that leverages motor driver data (torque and position), multivariate analysis, cyclic spectral coherence, and a long-term Time-series furcating Dense Encoder network for robust degradation assessment. The developed approach extracts meaningful health indicators directly from motor signals, eliminating the need for physical sensors. Experimental validation on a custom-designed roller chain testbed demonstrates that the proposed method significantly outperforms most classical indicators and several deep learning models-based indicators, in terms of the monotonicity and trendability over the degradation process. Furthermore, the proposed framework surpasses Fourier transform-based feature extraction (MFTSDE), highlighting the effectiveness of cyclostationary analysis for capturing degradation patterns. By integrating sensorless monitoring, cyclostationary analysis, and advanced multivariate time-series modeling, this research establishes an effective and cost-saving solution, paving the way for improved predictive maintenance strategies in industrial applications, especially for roller chain system

„Künstliche Intelligenz“ als Partner oder Konkurrent in der Wirtschaftswelt? : evidenzbasierte Einsichten

KI-Technologie könnte eine erhebliche Steigerung der Produktivität ermöglichen, indem sie repetitive Aufgaben automatisiert. Dies gilt nicht nur für industrielle Prozesse, sondern erstmals zugleich für standardisierte Computer- und Büroarbeiten. Automatisierung durch KI kann zu einer effizienteren Ressourcennutzung führen und menschliche Arbeitskräfte entlasten. Des Weiteren kann KI dazu beitragen, Innovationen zu beschleunigen, indem sie niedrigere Transaktionskosten für Ideen und Forschung ermöglicht. Durch automatisierte Analysen großer Datensatze können Muster und Trends schneller erkannt werden, was zu einer beschleunigten Innovationsentwicklung führen kann. Insbesondere in Entwicklungsländern bietet KI die Möglichkeit, die Wettbewerbsfähigkeit zu stärken. Durch den Einsatz von KI können Unternehmen schneller an die technologische Grenze vorrücken und qualitativ hochwertige Produkte und Dienstleistungen liefern, was zu einem besseren Marktzugang führen dürfte. Im Gesundheitswesen zeigt die KI-Technologie bereits heute die Potentiale, insbesondere bei der Bewältigung komplexer, regelbasierter Fragen, unter anderem in der Diagnoseunterstützung oder personalisierten Medizin (z. B. Auswertung von Röntgenbildern). KI-Systeme könnten zudem helfen, evidenzbasierte wissenschaftliche Entscheidungen zu treffen. Es ist wichtig, zu betonen, dass die effektive Nutzung von KI von ethischen Überlegungen und einer sorgfältigen Integration in bestehende Systeme ebenso abhängt, um sicherzustellen, dass die Chancen ausgewogen genutzt werden und die Gesellschaft mögliche Risiken mittragen kann

Navigating market saturation : evaluating Netflix’s password-sharing crackdown as a strategy for sustainable growth

The rise of technology giants has been one of the defining narratives of the 21 st century. FAANG companies achieved unprecedented growth reshaping entire industries. Recent developments in the Artificial Intelligence sector have again leveraged this growth by improving efficiency, providing more personalised services or predicting consumer behaviour. However, the seemingly boundless growth trajectory has faced and will continue to face significant challenges. As markets mature, companies confront the limitations of their dominance. This often happens next to regulatory scrutiny, increasing competition and rapidly changing consumer behaviours, which compound the pressures. These challenges raise a critical question: How can multibillion dollar businesses sustain their growth, protect stakeholder interest and finally remain relevant in a fiercely competitive business landscape? The question will be carefully analysed using Netflix as a Case Study

Sociophysics model of bubbles with neural-stochastic differential equations : a stochastic inflation model

This chapter gives an overview of new results concerning a sociophysics’ theory of bubbles. Following a seminal contribution by (Herzog 2015), we generalize the approach to nonlinear stochastic differential equations. We exhibit the mathematical theory of stochastic wave equations and introduce a novel calibration approach by utilizing neural stochastic differential equations. We numerically solve the problem by simulation methods in Julia language. Finally, we apply our method and analyze the presently elevated inflation rates (price bubble)

Reflectometric-based sensor arrays for the screening of kinase-inhibitor interactions and kinetic determination

Kinases are involved in numerous cellular processes but possibly also in tumor progression. Several kinase inhibitors are approved as drugs and there is an intense search for new inhibitors in pharmaceutical research. In this study, we present a new analytical method based on reflectometric interference spectroscopy, RIfS, for kinase and inhibitor screening. First, the sensor surface was optimized to reduce non-specific binding. Different inhibitors, e.g. staurosporine or fasudil, were immobilized on the transducer surface. Different kinases (focal adhesion kinase and cAMP-dependent protein kinase) were flushed over the sensor with the immobilized inhibitors. The specific interaction was proven by binding inhibition assays. The kinase-inhibitor interaction was monitored label-free and recorded in real time allowing the binding curves to be used to determine the association and dissociation rate constants as well as the affinity. These constants differed depending on the specific kinase-inhibitor pair, which was well expected from parallel docking simulations and measurements with microscale thermophoresis. The strategy was successfully transferred to 1-lambda reflectometry, a modification of RIfS, to enable the simultaneous monitoring of several kinase-inhibitor interactions in 5×7 small spots increasing throughput and automation on a sensor array with imaging detection. Importantly, the techniques developed here can provide both kinetic and thermodynamic data for a multitude of kinases in a single screening approach, which allows for both protein kinase and inhibitor screening

Hearing emotions: fine-tuning speech emotion recognition models

Over the past few decades, scholars and academics from various disciplines have been motivated to develop automated emotion detection systems. In this pursuit, audio data and especially prosodic features, holds the most promise to deliver satisfying results. Therefore, main aim of this approach was to evaluate standard machine learning algorithms on the task of emotion recognition from audio data. We evaluate the effect of training dataset size on model performance by means of incremental fine-tuning after conducting zeroshot testing on a range of widely-used datasets in the literature, such as CREMA-D, RAVDESS, TESS, SAVEE, MELD, eNTERFACE, EmoDB, and IEMOCAP. To improve model generalizability, we used data augmentation approaches, and for robust emotion detection, we used feature extraction techniques as MFCC, ZCR, and RMS. On CREMA-mixed datasets, experimental results show great initial accuracy with CNN model. Cross-corpus validation highlights the importance of diverse datasets, showing significant accuracy improvements with incremental fine-tuning. Our research opens the door to more potent emotion detection systems in practical applications by highlighting the necessity of varied training data for robust, generalizable SER models