Software602 (Czechia)

Recent developments in distributed ledger technology, artificial intelligence, and decision-making agents hold the promise of radically transforming market infrastructures. Indeed, the emergence of Autonomous Market Infrastructure (AMI)—an open, fully automated, and decentralized set of market-related functionalities—is widely anticipated. Such infrastructures, serving agents capable of fully autonomous behavior, would enable fully automated trading strategies. Moreover, as AMI-based solutions require minimal human intervention, they could be implemented at a fraction of existing costs. This should bolster competition and democratization, as AMI is accessible to everyone and establishes a level playing field.

The dispersion coefficient of the constant phase element (CPE) is typically treated as an empirical fitting parameter in the analysis of impedance spectroscopy data, with no clear physical meaning. Here, we present an energy-based interpretation for this coefficient by linking it to the ratio of the dissipated or stored energy in the CPE relative to that supplied by the input source. Using the RC network equivalency of a CPE, we decompose the total input energy into a contribution stored in the capacitive modes and another dissipated in the resistive modes. Analytical expressions of these energy quantities are derived for three test examples: (i) a constant voltage, (ii) a voltage ramp, and (iii) a quadratic input of the form v(t) = λt2. In all cases, we found that the ratios of any two of these energy quantities reduce to pure functions of the dispersion coefficient of the CPE, independent of excitation amplitude or material parameters. This result provides a new perspective on the CPE’s dispersion coefficient from a thermodynamic/energetic basis, with direct implications for supercapacitor characterization, battery modeling, as well as for the analysis of other electrochemical devices and systems exhibiting the CPE behavior.

This paper proposes Edge-FIT (Federated Instruction Tuning on the Edge), a scalable framework for Federated Instruction Tuning (FIT) of Large Language Models (LLMs). Traditional Federated Learning (TFL) methods, like FedAvg, fail when confronted with the massive parameter size of LLMs [3], [6]. Our Edge-FIT framework combines federated learning with 4-bit Quantized Low-Rank Adaptation (QLORA), mitigating the core issues of communication and computational overhead. We demonstrate this by filtering the general-purpose Databricks Dolly 15k dataset for the IoT domain. Experimental results show the Edge-FIT-tuned Llama 2 (7B) achieves an F1-Score of 0.89. We also demonstrate a viable trade-off using the 3.8B Phi-3-mini model, validating Edge-FIT as a scalable framework for decentralized LLM deployment on home compute gateways.

Information systems have undergone significant transformations over the past thirty years, affecting the ways organizations develop their information technology capabilities. For organizations, enhancing the value of IT from a simple dependency to a competitive advantage requires the integration of cross-application workflows that involve multiple functional areas. Such interdependencies demand a seamless alignment of the organization structure and information communication technology to achieve chosen strategic objectives. Organizational flexibility is particularly important in the current era, where competition and environmental changes are accelerating. However, this requires interdependent functions to go beyond bureaucratic organizational structures able to adapt to hastily set strategies, because such organizations may not be able to seize timely the competitive advantage that a strategic capability may provide through the design and implementation of the appropriate policies and decision-making models.

The rise of the modern informational economy has shifted the primary focus of marketing from a mind-centered to a brain-centered domain and has made data the essential currency of digital marketing. On the other hand, technological advances and the increasing digital sophistication of consumers have given consumers an unprecedented power of visibility, choice, and voice creating paradoxical and conflicting pressures on the role of marketers (Arora et al., 2008; Grewal et al., 2017; Bleier et al., 2019). Businesses, not-for-profits, and governments are generating huge amounts of data that are valuable for decision-making. Yet within this abundant resource, how can we disentangle genuine global truths, mapping our collective preferences at scale, from the environmental noise of our unfiltered digital behavior? How can we employ big data analytics tools to effectively and efficiently reveal the relative importance of various search functions and shopping-related cues for individuals and the segmented markets they belong to?

In an era defined by rapid technological evolution and shifting consumer expectations, organizations are rethinking how they design, deliver, and sustain value. Behavioral Intelligence and Operational Design: Exploring Modern Service Models, Customer-Centric Platforms, and Sustainable Digital Infrastructure offers a timely exploration of how forward-thinking enterprises can align human behavior with intelligent systems to drive lasting transformation. This book is born from the convergence of disciplines—behavioral science, service design, and digital architecture. It aims to bridge theory with practice, helping leaders, strategists, and technologists understand the nuanced interplay between human motivation and operational efficiency. Through the lens of behavioral intelligence, we examine how insights into user behavior can inform smarter design decisions, foster engagement, and unlock new forms of value creation. Modern service models are no longer built on static processes—they are dynamic, adaptive, and increasingly customer-driven. In these pages, we explore how platforms can be architected to empower users, scale seamlessly, and evolve sustainably. We also tackle the critical need for digital infrastructure that supports innovation while remaining resilient and environmentally responsible. Each chapter is designed to provoke thought, spark innovation, and provide actionable insights. Whether you are designing digital services, reimagining operational frameworks, or seeking to future-proof your organization, this book offers a roadmap to navigate complexity with clarity and purpose. Our hope is that this work serves as both a guide and a catalyst, enabling organizations to thrive in a world where intelligence, empathy, and sustainability are not just competitive advantages, but foundational imperatives.

Electrochromic devices (ECDs), serving as thermal modulation and decorative components, are extensively employed across various domains. To meet diverse application demands in different contexts, the product parameters of ECDs continually vary, notably within the electrochromic layer. In this work, a multilayer perceptron neural network is leveraged to comprehend the transition from material parameter alterations to device performance parameter changes. Specifically, 56 different testing conditions were evaluated by varying the concentration of the electrochromic molecule and the counter species; each combination was then subject to 6 different testing voltage windows, for a total of 336 different testing conditions. Subsequently, a model comprising three hidden layers and three output neurons is developed, expanding the data set simulation to 2000 groups, yielding an R2 over 0.9 on modulation amplitude. Through this model, accurate estimations of performance parameters of numerous unmanufactured liquid-based ECDs are achieved, with projected outcomes highlighting a peak modulation amplitude of 78.38%@620 nm. It is verified that the modulation amplitude prediction error is below 5% and demonstrates stable cyclic performance over 1000 cycles (97.93% at 1.45 V), thereby underscoring the feasibility of our approach. This research serves as a pertinent instance of the synergy between multilayer perceptron neural networks and ECDs, which can effectively provide guidance for more efficiently handling the complex and diverse parameters in the field of electrochromism.

Confirming the prompt and accurate notification of earthquakes is vital for mitigating their potential impacts. To achieve this, statistical approaches, including Machine Learning (ML), have become indispensable tools across various scientific fields, particularly in Seismology and seismic data. This research explores the utilization of ML techniques to improve earthquake real time alerts. The case study is Greece and the surrounding region, an area with highest seismic activity throughout the Mediterranean.   This work is focused on the real time collection and processing of an extensive earthquake dataset to generate earthquake alerts by making phone calls and providing details about the time, magnitude, and epicenter of each seismic event. Previous efforts aimed to extend these alerts beyond the notifications (emails and messages) that analysts at the Seismological Center of AUTH (Aristotle University of Thessaloniki) received during their duty. The goal was to make these alerts accessible to all citizens, communities, civil protection agencies and various authorities (e.g. municipalities, schools, police, etc.). The island of Kefalonia served as a pilot region where this functionality was initially implemented. We then chose to extend the application to all Ionian islands to encompass the entire region.The new insight here is the development of a mobile application that allows users to define a specific geographical region for receiving notifications-alerts. The AI Service will combine the real time earthquake information in conjunction with the geometry defined by each user in order to classify whether a notification should be sent to that specific user.As training input data used in the application, we first require a catalog of earthquakes spanning from 2011 to 2025 with M≥3.0, along with demographic data for Greece region provided by the Hellenic Statistical Authority. A radius around each epicenter is calculated by considering the earthquake’s macroseismic Intensity (I), the earthquake’s magnitude (M), earthquake depth, total population and number of households within the calculated radius. The labeled dataset is then used to train a classification model via Azure AutoML. This model identifies significant earthquakes and determines which areas to call in order to provide earthquake alert. Notification messages could be to any subscribed mobile number with the calling voice available in Greek, English, or French. 

The exponential growth of real-time data from financial transactions, IoT devices, social media, and industrial applications has intensified the need for high-speed, intelligent, and fault-tolerant streaming architectures. Traditional batch-processing and micro-batch systems, such as Apache Kafka with Spark Streaming, struggle with high latency, static resource allocation, and reactive fault recovery mechanisms, making them inadequate for modern data-driven enterprises. To address these challenges, we propose the Real-Time Adaptive Streaming Model (RTASM)—an AI-driven, ultra-low-latency streaming framework that integrates Apache Kafka, Hadoop, and AI-powered dynamic optimization. RTASM introduces several groundbreaking innovations, including AI-Optimized Workload Balancing, Predictive Caching & Query Optimization, Self-Healing Disaster Recovery, and 5G-Ready Edge Computing. By leveraging reinforcement learning algorithms, anomaly detection, and predictive load balancing, RTASM reduces event-to-insight latency to sub-5ms, enhances data quality to 99.99% accuracy, and minimizes failover downtime by 40%. These advancements enable real-time analytics for mission-critical applications such as financial fraud detection, autonomous vehicle telemetry, cybersecurity threat intelligence, and smart manufacturing. Comparative analysis with traditional Kafka + Spark Streaming architectures highlights RTASM’s superior performance, lower latency, higher data quality, and proactive disaster recovery mechanisms. Our evaluation demonstrates that RTASM outperforms traditional models by optimizing parallel processing, dynamically adjusting computational resources, and preventing failures before they occur. Future research directions include quantum-assisted processing, neural network-driven query optimizations, and integration with 6G-enabled streaming architectures. By transforming real-time data analytics into an adaptive, intelligent, and self-healing system, RTASM sets a new benchmark for zero-latency, high-performance data processing, ensuring that enterprises can make instant, data-driven decisions with unprecedented efficiency and reliability

Among the most significant mediums where companies report to investors, analysts and regulating bodies are the earnings calls where companies report their performance in the financial realm, their strategy and future prospects. Unlike regulatory filings which are usually stagnant and very formal, earnings calls are two-way communications, and they contain undertones, language choices, and feelings that might cause serious impacts to the market perception. Recent advances in the field of natural language processing (NLP) have assisted researchers to quantitatively measure such sentiments, offering predictive data as to how the markets would react. The paper will analyze the extent to which the sentiment as it is determined by the earnings call transcripts can predict and explain short-term stock volatility. We train both lexicon-based and transformer-based deep learning models, including FinBERT, to learn sentiment dimensions, including positivity, negativity, uncertainty, and litigious tone. Volatility is measured by realized volatility realized through intraday prices per event of earnings calls. Regression based models and machine learning classifiers are then employed to find predictive relationships. The findings point to the fact that the more sophisticated NLP models are more effective than the methods, which rely on dictionaries, and that uncertainty and negative tones are very closely connected with the volatility. The work has resulted in the area of financial text analytics as it has served to address the gaps in studying the interaction between the analysis of narrative disclosure and the model of market risk and its practical and theoretical implications on the investors, analysts, and policymakers.