The increasing complexity and dynamic nature of Industrial Internet of Things (IIoT) demand scalable, adaptive, intuitive, and real-time automation frameworks. This paper presents a systematic literature review (SLR) of edge- and cloud-based collaborative learning frameworks for predictive maintenance and smart manufacturing tasks. In this SLR, we highlight the under-utilization of distributed computational architectures that provide complete automation support (design and run-time), flexibility, scalability, and inter- & intra-cloud service exchange while adhering to security management and integrity principles for solving IIoT tasks using modern artificial intelligence (AI) models at the edge/cloud. Recently, many IIoT applications have been designed using AI models that require robust, low-latency, and data-secure frameworks. This demand drives a trend toward distributed computational architectures in which data storage and processing are partially or fully decentralized. Common paradigms addressing this resource distribution include edge computing, federated learning, and private or hybrid clouds. We analyze 50 recent studies against IoT characteristics, AI performance metrics, and network/system management requirements. Our findings reveal underutilization of distributed architectures that support automation, interoperability, and security. While most solutions rely on centralized or hybrid clouds, fewer than 5% adopt federated or transfer learning, and over 60% remain dependent on supervised models. We also introduce a comparative perspective on network and security management, showing that local/private cloud implementations can reduce control-plane overhead and synchronization latency, though gaps persist in dynamic bandwidth allocation and zero-trust adoption. Finally, we benchmark our previously proposed local cloud-based collaborative learning (CCL) model against state-of-the-art solutions, highlighting its strengths in automation and interoperability, as well as limitations in adaptive computation and intelligent offloading. This review identifies the research gaps and opportunities for integrating collaborative AI, secure automation, and hybrid architectures to meet Industry 5.0 objectives of resilience, sustainability, and human-centricity. 

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overviewof AI’s state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

The electron transport layer (ETL) is linchpin in perovskite solar cells (PSCs). It offers potent and discriminatory electron elicitation, minute resistivity, and lofty strength along with optimal device performance. In this study combined DFT and SCAPS-1D framework are used to investigate the optimized designs of CH₃NH₃Pb(I_1-xCl_x)₃ organic-inorganic perovskite-based solar cells. The analysis of structural stability, mechanical strength and optoelectronic traits was done by employing first-principle calculations with three different exchange-correlation functionals for A₂SnO₄(A = Sr, Ba) Ruddlesden-popper (RP) compounds. SCAPS-1D was used to analyze device performance by employing different ETLs in PSC architecture. The structural analysis reveal that Sr₂SnO₄ possesses a more stable structure in tetragonal phase with space group I4/mmm (139) than Ba₂SnO₄. Mechanical stability is corroborated through the reckoning of elastic constants, with Sr-based RP perovskite showing better mechanical properties as compared to Ba-based RP perovskite enunciating it auspicious for device fabrication. Electronic properties, analyzed through the band structure (BS) and density of state (DOS), confirm the semiconducting nature of both materials, with indirect band gap of 4.59 eV (Ba₂SnO₄) and 4.21 eV (Sr₂SnO₄). The optical analysis has stipulated that both materials are found to be good absorbers of ultraviolet (UV) radiation. An optimized device FTO/Sr₂SnO₄/MAPb(I_1-xCl_x)₃/Cu₂O/Au is contemplated here with an open-circuit voltage (Voc) of 1.257 V, a short-circuit current (Jsc) of 23.06 mA/cm2, fill factors (FF) of 83.57 %, and a theoretical power conversion efficiency (PCE) of 24.25 %. Overall, our findings reveal that Sr₂SnO₄ RP material have promising and potential features as a novel ETL material for employment in organic-inorganic PSC as a source of renewable energy.

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the end-to-end. AI Detector Optimization framework (AIDO), which leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for a layer arrangement and material composition that align with known calorimeter principles. The success of this proof-of-concept study provides a foundation for the future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments.

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and positive kaons at 100 GeV. The analysis focuses on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns and timing information. Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. The results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers. Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. This motivates further work required to combine high- and low-level feature analysis, e.g., using convolutional and graph-based neural networks, and extending the study to a broader range of particle energies and types.

In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.

We simulate hadrons impinging on a homogeneous lead tungstate (PbWO4PbWO4) calorimeter using GEANT4 software to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment.

Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism.

This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase-2 detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits, opening the way to applications of neuromorphic computing to particle tracking. The presented results motivate further studies investigating neuromorphic computing as a potential solution for real-time, low-power particle tracking in future high-energy physics experiments.