CIPARLABS Team at IEEE WCCI 2026

 

CIPARLABS took part in IEEE WCCI 2026 with a broad set of scientific contributions and organizational activities, reflecting the group’s research agenda across Computational Intelligence, machine learning, complex systems, smart grids, bioinformatics, and sustainable energy management.

Within the Special Session «Scientific ML and Bio-Physical Sensing», the group presented the paper «Decoding Functional Multiplicity: Graph Learning Approaches to Multifunctional Proteins». The work addresses the prediction of protein multifunctionality from three-dimensional molecular structures. Proteins are represented as residue contact networks, enabling the use of graph-based learning methods, including simplicial-complex embeddings, graph kernels, and Graph Neural Networks. The study frames the problem as a multi-label classification task over first-level Enzyme Commission classes and shows that topological structural representations can capture meaningful signals related to the functional multiplicity of proteins.

Two further contributions were presented in the Special Session «Smart Energy, Grid, and Infrastructure», including IJCNN SS35 «Computational Intelligence Techniques for Observable Smart Grid and Sustainable Energy Systems».

The first paper, «Forward–Forward Learning for Imbalanced Tabular Predictive Maintenance on a Real-World Smart-Grid Fault Dataset», investigates a stabilized Forward–Forward learning formulation for predictive maintenance in medium-voltage smart grids. The study evaluates layer-local learning on an imbalanced real-world fault dataset from the Rome power distribution grid, comparing it with standard tabular baselines and back-propagation-based MLPs. The results show that Forward–Forward learning is competitive in terms of PR-AUC and F1-score, while Random Forests remain the strongest overall baseline in the considered setting. The work also analyzes calibration, goodness-margin dynamics, stabilization mechanisms, and timing performance, offering a detailed view of the practical viability of Forward–Forward learning for smart-grid fault detection.

The second paper, «Simulation of Microgrid Energy Management under Battery Degradation Costs: a PPO-Based Reinforcement Learning Approach», focuses on residential microgrid energy management with photovoltaic generation and battery storage. The study introduces a degradation-aware simulation framework in which the battery system is modeled through a Battery Management System based on an equivalent circuit model with State-of-Health-dependent parameters. A Proximal Policy Optimization controller is trained to determine battery charge and discharge setpoints and is compared with a rule-based controller and an oracle Model Predictive Control benchmark. The results show that the learned policies outperform the rule-based baseline and approach the oracle MPC behavior in medium battery-utilization regimes, balancing short-term energy costs with long-term battery degradation.

Alongside the scientific presentations, CIPARLABS also contributed to the organization of IEEE WCCI 2026 through two Special Sessions.

The Special Session «AI for Energy and Resource Analytics», chaired by Enrico De Santis, included the session «Computational Intelligence and AI Applications for Sustainable Energy Management in Smart Grids and Energy Communities», CISEM. The session was organized by Enrico De Santis, Antonello Rizzi, and Danial Zendehdel from the Department of Information Engineering, Electronics and Telecommunications at Sapienza University of Rome.

Session page
https://sites.google.com/uniroma1.it/wcci-ijcnn-cisem2026/home-page

A second Special Session, «AICS: Computational Intelligence for Complex Systems», was organized by Alessio Martino, from the Department of AI, Data and Decision Sciences at LUISS University, together with Enrico De Santis and Antonello Rizzi, from the Department of Information Engineering, Electronics and Telecommunications at Sapienza University of Rome. The session gathered contributions devoted to the use of Computational Intelligence for modeling, analyzing, and interpreting complex systems.

Session page
https://sites.google.com/uniroma1.it/wcci-ijcnn-aics2026

Overall, the participation of CIPARLABS at IEEE WCCI 2026 highlights the group’s interdisciplinary research activity, ranging from graph learning for biological systems to predictive maintenance in power infrastructures and reinforcement learning for intelligent energy management. These contributions document the group’s commitment to developing advanced Computational Intelligence methods for scientific, technological, and industrial problems with significant societal and environmental impact.

Conference Proceedings can be downloade here.

See you at IJCNN 2027 in South Africa!

Beyond Perplexity: A Multi-Faceted Analysis of a Novel Densely Connected Transformer

We are pleased to announce the publication of our new paper, Beyond Perplexity: A Multi-Faceted Analysis of a Novel Densely Connected Transformer. This study examines a central question in contemporary language modeling research. If a Transformer decoder is redesigned so that each layer can access the representations produced by all previous layers, can this richer internal connectivity improve performance in a meaningful way? 

The idea is appealing for a clear reason. In several areas of deep learning, dense connectivity has been associated with improved feature reuse, shorter information paths, and potentially more favorable optimization behavior. In our paper, we test whether this intuition also holds for decoder-only autoregressive Transformers, the architectural family underlying many modern large language models.

To address this question rigorously, we designed a methodology aimed at isolating the effect of connectivity from other factors that often confound architectural comparisons. We compared a standard baseline Transformer decoder with a densely connected decoder on two well-known benchmarks for language modeling, Penn Treebank and WikiText-2. The comparison was carried out under two controlled fairness regimes. In the first, both model families were evaluated under the same training recipe, with shared optimization settings and learning-rate search. In the second, the comparison was constrained by the same parameter budget, so that the dense model could not exceed the baseline in parameter count. This distinction was important because it allowed us to separate the possible effect of dense historical connectivity from the simpler effect of adding more capacity.

The experimental setup was complemented by a precise implementation choice. Both datasets were processed with word-level tokenization, using official train, validation, and test splits, and all perplexity values were computed consistently within that vocabulary space. The two architectures shared the same general decoder-only scaffold, while differing in their internal organization. The baseline followed the standard residual Transformer design, whereas the dense variant introduced concatenation-based historical connections followed by learned projection, allowing each layer to reuse information from earlier layers more directly. 

The paper was guided by three main research questions 

1. Does dense historical connectivity improve test perplexity compared with a standard Transformer decoder under controlled comparison regimes?

2. Which architectural factors matter most within the explored design space, including model width, feed-forward size, depth, and number of attention heads?

3. Do dense and baseline models generate texts with different long-range structural signatures, even when standard predictive metrics do not show a clear advantage?

The answer that emerges is both interesting and methodologically instructive. Dense connectivity does not lead to a systematic reduction in perplexity. On WikiText-2, the baseline remains stronger in both fairness regimes. On Penn Treebank, the gains of the dense model are limited and depend on the comparison setting. This matters because it shows that an architectural idea may be theoretically plausible and still fail to deliver a robust practical advantage once tested under controlled conditions.

A particularly relevant result comes from the ablation study. Within the dense family, the most reliable improvements are associated with depth and feed-forward capacity, rather than with dense connectivity alone. This suggests that much of the observed performance variation is better explained by how model capacity is allocated than by the presence of cross-layer concatenation in itself. In other words, the study helps clarify that dense connectivity interacts with more fundamental architectural factors rather than replacing them as the main driver of performance.

Another important aspect of the work lies in the decision to evaluate the models beyond perplexity. Perplexity remains the standard metric for next-token prediction, but it does not capture every relevant aspect of generated language. For this reason, the paper also includes analyses of learning dynamics, attention behavior, targeted probes, and long-form text generation. The probing tasks and attention diagnostics do not reveal a clear linguistic advantage for the dense architecture in the explored setting, although they do highlight behavioral differences between the two model families.

One of the most original contributions of the paper is the use of Zipf–RQA for analyzing generated text. This framework combines Zipf-rank encoding with Recurrence Quantification Analysis in order to study long-range structural regularities in long-form outputs. Here, the results become especially interesting. Even when perplexity does not improve, the dense and baseline models show systematic structural differences in the organization of generated text. This suggests that architectural changes may alter the global form of language generation even when they do not produce better scores on standard predictive metrics.

From a broader perspective, this is the main message of the article. Evaluating a language model through a single headline number is rarely sufficient for understanding what an architecture is actually doing. A richer methodology, one that combines predictive performance, internal diagnostics, and structural analysis of generated text, can reveal differences that would otherwise remain invisible.

Overall, this publication offers a controlled and transparent contribution to Transformer research. Rather than presenting densification as a simple improvement, it shows where its limits emerge, which design factors matter most, and why multi-faceted evaluation is necessary for understanding architectural innovation in language models. For our lab, this work reflects a broader research direction devoted to studying neural language models as complex systems, whose behavior deserves to be analyzed from several complementary viewpoints.

Please cite as:

De Santis, E., Martino, A., & Rizzi, A. (2026). Beyond Perplexity: A Multi-Faceted Analysis of a Novel Densely Connected Transformer. Applied Sciences, 16(6), 2721. https://doi.org/10.3390/app16062721

BibTeX:

@article{deSantis2026BeyondPerplexity,
author = {De Santis, Enrico and Martino, Alessio and Rizzi, Antonello},
title = {Beyond Perplexity: A Multi-Faceted Analysis of a Novel Densely Connected Transformer},
journal = {Applied Sciences},
year = {2026},
volume = {16},
number = {6},
pages = {2721},
doi = {10.3390/app16062721},
url = {https://doi.org/10.3390/app16062721}
}

Degradation-Aware Reinforcement Learning for Smarter Energy Management at IJCCI 2025, Marbella (Spain)

 

Towards Sustainable and Intelligent Microgrids

At the International Joint Conference on Computational Intelligence (IJCCI 2025), held in Marbella (Spain) from 22 to 24 October, CIPARLABS presented new research on degradation-aware energy management for residential microgrids. The paper, authored by Danial Zendehdel, Gianluca Ferro, Enrico De Santis, and Antonello Rizzi, introduces a reinforcement learning framework that intelligently manages battery usage to balance economic efficiency and battery longevity.

Residential microgrids and Renewable Energy Communities are redefining the energy landscape by promoting decentralized and cooperative energy production. Within these systems, lithium-ion batteries play a crucial role in storing excess solar power and ensuring supply continuity. Yet, their performance is constrained by degradation processes that reduce both capacity and lifespan.

Traditional control strategies often neglect this degradation, focusing only on short-term cost optimization. The work presented by the CIPARLABS team moves beyond that limitation by embedding battery health awareness directly into the control policy.

Reinforcement Learning Meets Battery Physics

The study proposes a Reinforcement Learning (RL) framework based on the Soft Actor-Critic (SAC) algorithm, implemented with Stable Baselines3, to learn optimal energy dispatch policies in real time.

What sets this approach apart is the explicit integration of battery State of Health (SoH) feedback into the agent’s learning loop. The RL agent learns to maximize long-term economic rewards while implicitly minimizing degradation, guided by a simplified but empirically calibrated degradation model derived from NASA’s Li-ion cell datasets.

The microgrid environment modeled in the study includes photovoltaic generation, household consumption profiles, and time-of-use electricity tariffs. The RL agent decides, at every timestep, whether to charge or discharge the battery or exchange energy with the grid. Its performance was benchmarked against a Model Predictive Control (MPC) strategy based on Mixed-Integer Linear Programming.

Learning to Preserve Energy and Battery Life

Simulations were conducted using high-resolution load and solar data from the Pecan Street Inc. Dataport. Over both one-year and ten-year scenarios, the RL-based controller demonstrated remarkable improvements over the MPC baseline.

• For a typical household, the RL agent extended battery life by up to 6.3 % compared to the MPC benchmark.

• It reduced energy purchased from the grid by 45–60 %, while maintaining or improving economic performance.

• Over long-term simulations, the degradation-aware SAC agent lowered total battery wear cost by 6.4 %, reflecting more efficient use of the storage system without compromising availability.

These outcomes reveal that the RL framework not only optimizes daily dispatch decisions but also learns non-linear, context-dependent policies that capture the intricate balance between short-term gain and long-term sustainability.

Implications and Future Work

The results suggest a promising pathway for deploying AI-driven, degradation-aware control systems in residential and community microgrids. Such systems could operate autonomously, adapting to changing conditions and maximizing both user savings and battery lifespan.

The research team plans to extend this work through real-world validation and multi-agent reinforcement learning experiments, where multiple prosumers within a Renewable Energy Community coordinate energy exchanges. The framework is being developed within the MOST – Sustainable Mobility Center and supported by European Union Next-GenerationEU funding under the Italian PNRR program.

Danial Zendehdel at IJCCI 2025

This work continues CIPARLABS’ mission to merge computational intelligence and sustainable energy research, paving the way for smarter, more resilient energy ecosystems.

Cite as:

@inproceedings{Zendehdel2026IJCCI,
  author    = {Danial Zendehdel and Gianluca Ferro and Enrico De Santis and Antonello Rizzi},
  title     = {Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework},
  booktitle = {Proceedings of the 17th International Joint Conference on Computational Intelligence (IJCCI 2025)},
  year      = {2026},
  address   = {Marbella, Spain},
  month     = {October 22--24},
  publisher = {SCITEPRESS -- Science and Technology Publications},
  keywords  = {Reinforcement Learning, Battery Management System, Energy Management, Lithium-ion Batteries, Degradation Modeling, Microgrids},
  note      = {(Presented at IJCCI 2025, Marbella, Spain)},
}

Solar and Wind Forecasting: Rethinking the Future with a Multi-Site Mindset

 

A review of solar and wind energy forecasting: From single-site to multi-site paradigm

The global energy transition is no longer a distant vision — it’s unfolding now, rapidly, and with it comes a crucial question: how do we predict the unpredictable? When it comes to solar and wind energy, forecasting isn’t just a technical detail; it’s a keystone of modern energy systems. In a new paper just published in Applied Energy, Alessio Verdone, Massimo Panella, Enrico De Santis, and Antonello Rizzi from Sapienza University of Rome offer a timely and in-depth exploration of how forecasting methods have evolved to meet this challenge.

Their work is more than just a literature review. It’s a methodological study around the transformation of forecasting paradigms, tracing the field’s progress from early single-site statistical models to the latest deep learning architectures that analyze spatio-temporal data from entire networks of plants. The authors shed light on how our understanding — and our tools — have shifted alongside the growing complexity of renewable energy infrastructures.

At the heart of the paper is a simple but powerful idea: renewable energy production is no longer a local matter. Today’s systems consist of distributed solar panels and wind farms spread across vast areas. By treating each site in isolation, we miss the chance to capture valuable correlations between them. This is where multi-site forecasting comes into play, allowing models to learn not just from the past of a single plant, but from the coordinated behavior of many. And thanks to innovations in machine learning—particularly Graph Neural Networks, Transformers, and hybrid architectures — we now have the tools to make this possible.

The paper is rich with insights. It offers a structured classification of forecasting methods and benchmarks, highlights the most commonly used datasets (and the difficulty in accessing reliable public data), and discusses the metrics used to evaluate performance. But what makes this work stand out is the authors’ critical perspective. They don’t just describe methods — they ask what works, what doesn’t, and why. Their analysis of how spatial and temporal data can be integrated to boost performance speaks directly to current needs in grid management and renewable energy communities.

For researchers, engineers, and energy planners, this review is a valuable resource. It connects the dots between methodological innovation and practical application, offering a clear picture of where the field stands and where it’s heading. More importantly, it invites readers to think systemically: to see renewable energy forecasting not as a single algorithmic task, but as a complex, multi-layered problem with implications for sustainability, policy, and technology.

If you’re interested in the intersection of AI and energy, or if you’re working on Smart Grids or Renewable Energy Communities, forecasting tools, or the design of future energy systems, this is a paper worth diving into.

Read the full paper here!

2025 - Studies in Computational Intelligence (SCI, volume 1196, Springer) finally published

 

Finally the collection "Studies in Computational Intelligence (SCI, volume 1196)" has been published, concerning the book chapter extensions of our works selected by IJCCI: International Joint Conference on Computational Intelligence (2022).

Our contributions concern the context of energy sustainability, Smart Grids and Renewable Energy Communities, in particular in modeling and control techniques and energy forecasting.

Specifically the two study are the following:

1) Antonino Capillo, Enrico De Santis , Fabio Massimo Frattale Mascioli , and Antonello Rizzi, On the Performance of Multi-Objective Evolutionary Algorithms for Energy Management in Microgrids

Abstract. In the context of Energy Communities (ECs), where energy flows among PV generators, batteries and loads have to be optimally managed not to waste a single drop of energy, relying on robust optimization algorithms is mandatory. The purpose of this work is to reasonably investigate the performance of the Fuzzy Inference System-Multi-Objective-Genetic Algorithm model (MO-FIS-GA), synthesized for achieving the optimal Energy Management strat-egy for a docked e-boat. The MO-FIS-GA performance is compared to a model composed of the same FIS implementation related to the former work but opti-mized by a Differential Evolution (DE) algorithm – instead of the GA – on the same optimization problem. Since the aim is not evaluating the best-performing optimization algorithm, it is not necessary to push their capabilities to the max. Rather, a good meta-parameter combination is found for the GA and the DE such that their performance is acceptable according to the technical literature. Results show that the MO-FIS-GA performance is similar to the equivalent MO-FIS-DE model, suggesting that the former could be worth developing. Further works will focus on proposing the aforementioned comparison on different optimiza-tion problems for a wider performance evaluation, aiming at implementing the MO-FIS-GA on a wide range of real applications, not only in the nautical field.

2) Sabereh Taghdisi Rastkar , Danial Zendehdel , Antonino Capillo , Enrico De Santis, and Antonello Rizzi, Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids

Abstract. Effective energy forecasting is essential for the efficient and sustain-able management of energy resources, especially as energy demand fluctuates significantly with seasonal changes. This paper explores the impact of seasonal-ity on forecasting algorithms in the context of energy consumption within Smart Grids. Using three years of data from four different countries, the study evaluates and compares both seasonal models – such as Seasonal Autoregressive Integrated Moving Average (SARIMA), Seasonal Long Short-Term Memory (Seasonal-LSTM), and Seasonal eXtreme Gradient Boosting (Seasonal-XGBoost) – and their non-seasonal counterparts. The results demonstrate that seasonal models outperform non-seasonal ones in capturing complex consumption patterns, offer-ing improved accuracy in energy demand prediction. These findings provide valu-able insights for energy companies or in the design of intelligent Energy Manage-ment Systems, suggesting optimized strategies for resource allocation and under-scoring the importance of advanced forecasting methods in supporting sustain-able energy practices in urban environments. 

BibTex book citation:

@book{back2025computational,
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  title     = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  year      = {2025},
  publisher = {Springer},
  series    = {Studies in Computational Intelligence},
  volume    = {1196},
  doi       = {10.1007/978-3-031-85252-7}
}
 

Single chapter BibTex references:

@incollection{chapter1IJCCI2025,
  author    = {Author Name},
  title     = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  booktitle = {Computational Intelligence},
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  publisher = {Springer},
  year      = {2025},
  chapter   = {1},
  pages     = {1--10},
  doi       = {10.1007/978-3-031-85252-7_1}
}
 

@incollection{rastkar2025seasonality,
  author    = {Sabereh Taghdisi Rastkar and Danial Zendehdel and Antonino Capillo and Enrico De Santis and Antonello Rizzi},
  title     = {Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids},
  booktitle = {Computational Intelligence: 14th and 15th International Joint Conference on Computational Intelligence (IJCCI 2022 and IJCCI 2023) Revised Selected Papers},
  editor    = {Thomas B{\"a}ck and Niki van Stein and Christian Wagner and Jonathan M. Garibaldi and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Faiyaz Doctor and Joaquim Filipe and Kevin Warwick and Janusz Kacprzyk},
  publisher = {Springer},
  year      = {2025},
  series    = {Studies in Computational Intelligence},
  volume    = {1196},
  pages     = {211--223},
  doi       = {10.1007/978-3-031-85252-7_12},
  url       = {https://link.springer.com/chapter/10.1007/978-3-031-85252-7_12}
}
 

The Future of Lithium-Ion Battery Diagnostics: Insights from Degradation Mechanisms and Differential Curve Modeling

 

Featured Research paper: Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview

De Santis, E., Pennazzi, V., Luzi, M., & Rizzi, A., Renewable and Sustainable Energy Reviews, Volume 211, April 2025

 

As the world pivots towards sustainable energy solutions, lithium-ion batteries (LIBs) have emerged as indispensable components in electric vehicles (EVs) and renewable energy systems. Their efficiency and longevity, however, are hindered by the phenomenon of battery aging — a multifaceted issue tied to the gradual decline in performance and safety. The recent paper, grounding on a project developed with Ferrari S.p.A., "Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview" — published on the prestigious journal Renewable and Sustainable Energy Reviews — offers a detailed exploration of the degradation processes in LIBs, introducing innovative diagnostic methodologies and shedding light on future directions for research and industry.

Our research group at CIPARLABS is strongly committed to the development of technologies for energy sustainability. The topic of lithium-ion battery modeling is among the topics under study and development carried out by our laboratory at the "Sapienza" University  of Rome, Department of Information Engineering, Electronics and Telecommunications (DIET). 

The Challenge of Battery Aging

Lithium-ion batteries, the backbone of EVs, offer numerous advantages such as high energy density, lightweight construction, and zero emissions. However, they face significant challenges, particularly the progressive degradation of their components. Battery aging manifests as a decline in capacity, efficiency, and safety, influenced by factors such as temperature extremes, charging rates, and the depth of discharge (DOD). Addressing these issues is critical to optimizing battery performance and aligning with broader environmental goals like the UN Sustainable Development Goals (SDGs).

Battery degradation occurs in two primary forms:

• Calendar Aging: Degradation during storage, even in the absence of active use, exacerbated by conditions like high temperature and elevated state of charge (SOC).

• Cycle Aging: Degradation resulting from repetitive charging and discharging cycles.

These processes lead to two key degradation modes:

• Loss of Lithium Inventory (LLI): A reduction in the cyclable lithium ions due to side reactions.

• Loss of Active Materials (LAM): Structural damage or dissolution of electrode materials, impacting the battery’s ability to store and deliver energy effectively.

A Diagnostic Revolution: Differential Curve Modeling

The cornerstone of the paper is its focus on differential curve modeling—a non-invasive and powerful tool for diagnosing battery aging. Differential curves, specifically Incremental Capacity (IC) and Differential Voltage (DV) curves, are derived from charge/discharge data. These curves amplify subtle changes in battery behavior, revealing critical insights into degradation mechanisms.

• Incremental Capacity (IC) Curves: By plotting the change in charge against voltage, IC curves expose phase transitions in electrode materials, which are sensitive to degradation modes.

• Differential Voltage (DV) Curves: These represent voltage changes relative to charge, offering detailed insights into electrode-specific reactions and transitions.

These curves act as diagnostic fingerprints, capturing the nuanced dynamics of battery aging. For instance, shifts in IC curve peaks or DV curve valleys can be linked to specific degradation processes, enabling precise assessments of battery health.

Bridging Science and Application

The paper highlights the practical potential of differential curve analysis. In the automotive sector, this technique can be integrated into Battery Management Systems (BMS) for real-time monitoring and predictive maintenance. By identifying early signs of aging, manufacturers can optimize charging protocols, enhance safety, and extend battery lifespan. This not only reduces costs but also aligns with sustainability objectives by minimizing waste.

In the energy sector, differential curves can support the management of large-scale energy storage systems, ensuring reliability and efficiency. Policymakers, too, can leverage these insights to refine regulations and standards for EVs, accelerating the transition to sustainable transportation.

Future Directions and Innovations

While differential curve modeling offers substantial promise, challenges remain. Noise sensitivity during data processing and variability in experimental conditions necessitate standardized protocols for broader applicability. The integration of machine learning represents an exciting frontier. By training algorithms on IC/DV curve data, researchers can automate diagnostics, identify anomalous patterns, and predict battery failures with unprecedented accuracy.

The non-destructive nature of this approach makes it particularly appealing. Unlike invasive post-mortem analyses, differential curve modeling preserves battery integrity, offering a cost-effective and scalable solution for both academic and industrial applications.

Conclusion

The insights presented in the paper underscore the transformative potential of advanced diagnostic techniques for lithium-ion batteries. By unraveling the complexities of degradation mechanisms and leveraging differential curve modeling, researchers and industry leaders can pave the way for safer, more efficient, and sustainable energy storage solutions. As the global push for electrification and decarbonization accelerates, such innovations are not just timely but essential.

The road ahead is one of collaboration and innovation, bridging gaps between scientific research, industrial practices, and policy frameworks. With tools like differential curve modeling, we are better equipped to meet the challenges of the energy transition and drive a future powered by clean and reliable energy.

 

Please cite as:

• APA format:
  

De Santis, E., Pennazzi, V., Luzi, M., & Rizzi, A. (2025). Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview. Renewable and Sustainable Energy Reviews, 211, 115349. https://doi.org/10.1016/j.rser.2025.115349

• BibTex format:
  

@article{ENRICO2025115349,
title = {Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview},
journal = {Renewable and Sustainable Energy Reviews},
volume = {211},
pages = {115349},
year = {2025},
issn = {1364-0321},
doi = {https://doi.org/10.1016/j.rser.2025.115349},
url = {https://www.sciencedirect.com/science/article/pii/S136403212500022X},
author = {De Santis Enrico and Pennazzi Vanessa and Luzi Massimiliano and Rizzi Antonello},
keywords = {Ageing, Diagnosis, Degradation mechanisms, Degradation modes, Differential curves, Differential voltage, Lithium-ion batteries, Incremental capacity, State of health}
}

 

 

 

 

Fractal Happy New Year 2025 from CIPARLABS!

 

 

Fractal Happy New Year 2025 from CIPARLABS!

As we step into 2025, CIPARLABS reflects on a year of exceptional multidisciplinary research spanning artificial intelligence and neural networks, energy systems, healthcare, and complex systems theory. This year we wish you a happy "fractal" 2025 to underline our scientific approach to the problems we are going to solve and which concerns the science of complexity.

The synergy between AI and complexity science has unlocked innovative solutions to societal challenges. From revolutionizing energy grids to enhancing medical diagnostics, our work exemplifies how common frameworks can empower diverse fields. This post celebrates our achievements, highlighting the unity of disciplines and the endless possibilities of a collaborative future.

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2024 Highlights: Research Achievements

1. Transformative Advances in Energy Management

• Battery Modeling for Renewable Energy Communities: A Thevenin-based equivalent circuit model optimized energy management strategies, balancing computational efficiency and accuracy in predicting battery performance.
• Energy Load Forecasting Breakthrough: Novel integration of second-derivative features into machine learning models like LSTM and XGBoost significantly improved predictions for peak energy demands, enhancing microgrid stability.
• Smart Grid Fault Detection: The Bilinear Logistic Regression Model enabled interpretable AI-driven fault detection, ensuring resilient energy infrastructures.

2. Innovations in Healthcare through Explainable AI

• Melanoma Diagnosis: Developed a custom CNN with feature injection, utilizing Grad-CAM, LRP, and SHAP methodologies to interpret deep learning predictions. This workflow sets a benchmark for explainability in computer-aided diagnostics.
• Text Classification in Healthcare Discussions: Conducted a comparative study of traditional and transformer-based models (BERT, GPT-4) to classify Italian-language healthcare-related social media discussions, combating misinformation effectively.

3. Exploring Human vs. Machine Intelligence

• Using complex systems theory and Large Language Models, we analyzed GPT-2’s language generation dynamics versus human-authored content. The study revealed distinct statistical properties, such as recurrence and multifractality, informing applications like fake news detection and authorship verification.
  

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Future Directions: Looking Ahead to 2025

CIPARLABS aims to deepen its focus on explainable AI for critical applications in energy, healthcare, and language modeling. We are committed to expanding our interdisciplinary efforts, incorporating insights from philosophy, complex systems, and AI ethics. Future work will include:

• Integrating advanced multimodal AI systems in healthcare.
• Scaling energy solutions to diverse legislative frameworks worldwide.
• Further bridging AI and human cognition to enhance ethical and transparent AI systems.

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List of Published Papers (2024)

An Online Hierarchical Energy Management System for Renewable Energy Communities
Submitted to: IEEE Transactions on Sustainable Energy

Improving Prediction Performances by Integrating Second Derivative in Microgrids Energy Load Forecasting
Published in: IEEE IJCNN 2024, IEEE

From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media
Published in: IEEE Transactions on Emerging Topics in Computational Intelligence

An Extended Battery Equivalent Circuit Model for an Energy Community Real-Time EMS
Published in: IEEE IJCNN 2024

Modeling Failures in Smart Grids by a Bilinear Logistic Regression Approach
Published in: Neural Networks, Elsevier

An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection
Published in: Information, MDPI

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE

Many other are in process!

An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection

 

 https://www.mdpi.com/2078-2489/15/12/783

 

Explainable artificial intelligence (XAI) is becoming a cornerstone of modern AI applications, especially in sensitive fields like healthcare, where the need for transparency and reliability is paramount. Our latest research focuses on enhancing the interpretability of convolutional neural networks (CNNs) used for melanoma diagnosis, a field where accurate and trustworthy tools can significantly impact clinical practice.

Melanoma is one of the most aggressive forms of skin cancer, posing challenges in diagnosis due to its visual similarity to benign lesions. While deep learning models have demonstrated remarkable diagnostic accuracy, their adoption in clinical workflows has been hindered by their "black box" nature. Physicians need to understand why a model makes specific predictions, not only to trust the results but also to integrate these tools into their decision-making processes. In this context, our research introduces a novel workflow that combines state-of-the-art XAI techniques to provide both qualitative and quantitative insights into the decision-making process of CNNs. The uniqueness of our approach lies in the integration of additional handcrafted features, specifically Local Binary Pattern (LBP) texture features, into the CNN architecture. These features, combined with the automatically extracted data from the neural network, allow us to analyze and interpret the network's predictions more effectively.

The study leverages public datasets of dermoscopic images from the ISIC archive, carefully balancing training and validation datasets to ensure robust results. The modified CNN architecture features five convolutional layers followed by dense layers to reduce dimensionality, making the network’s internal processes more interpretable. Alongside dermoscopic images, the network is fed LBP features, which are injected into the flattened layer to augment the learning process.

To explain the model's predictions, we employed two key XAI techniques: Grad-CAM and Layer-wise Relevance Propagation (LRP). Grad-CAM generates activation maps that highlight regions of the image influencing the network’s decisions, while LRP goes further by assigning relevance scores to individual pixels. Together, these methods provide a visual explanation of the decision-making process, helping to identify which areas of an image the model considers most important for classification. Interestingly, we observed that LRP was particularly effective in distinguishing clinically relevant patterns, while Grad-CAM occasionally identified spurious correlations. For a quantitative perspective, we used the kernel SHAP method, grounded in game theory, to assess the importance of features in the network’s predictions. This analysis revealed that most of the classification power - approximately 76.6% - came from features learned by the network, while the remaining 23.4% was contributed by the handcrafted LBP features. Such insights not only validate the role of feature injection but also open avenues for integrating diagnostically meaningful features, such as lesion asymmetry or border irregularities, into future models.

The performance of our modified CNN surpassed both our earlier work and other state-of-the-art approaches, achieving an accuracy of 98.41% and an AUC of 98.00% on the external test set. These results underscore the effectiveness of our interpretability framework, proving that improving transparency does not necessarily compromise accuracy that can be enhanced.

While this research marks significant progress, it also highlights areas for future exploration. The use of handcrafted features with limited diagnostic value, such as LBP, points to the need for incorporating features more aligned with clinical evaluation, like the ABCDE rule used for melanoma assessment. Moreover, involving dermatologists in the evaluation process could provide valuable qualitative feedback to refine the interpretability methods further.

This work demonstrates that XAI is a tool for explaining AI decisions and a a critical component for building trust in AI systems, especially in high-stakes fields like medical diagnostics. By combining visual and quantitative explanations, we hope to bridge the gap between AI and clinical practice, paving the way for broader adoption of AI-assisted tools in healthcare. Through this transparent and interpretable approach, we aim to empower clinicians, enhance diagnostic accuracy, and ultimately improve patient outcomes.

Here the paper: https://www.mdpi.com/2078-2489/15/12/783

Summary of "Human versus Machine Intelligence: Complexity and Quantitative Evaluation" by Enrico De Santis et al.

 

Introduction

The paper addresses the increasing presence of machine-generated texts in various domains, contrasting them with human-generated texts. It introduces methods to quantitatively evaluate and compare the complexity and characteristics of these texts.

1. Background and Motivation

The section explores the motivation behind comparing human and machine intelligence through text analysis. It highlights the importance of understanding the intricacies of machine-generated texts, especially with advancements in NLP technologies such as GPT-2.

2. Complexity Measures

This section outlines various complexity measures used to evaluate texts. These measures include:

• Hurst Exponent (H): Indicates the long-term memory of the text.
• Recurrence Rate (RR): Measures the frequency of repetitive patterns.
• Determinism (DET): Captures the predictability of the text.
• Entropy (ENTR): Reflects the randomness.
• Laminarity (LAM): Measures the tendency of the text to form laminar patterns.
• Trapping Time (TT): Indicates the duration of repetitive patterns.
• Zipf’s Law Parameters: Analyzes the frequency distribution of words.

3. Data Collection

The corpus comprises 212 texts divided into three categories: English literature (ENG), machine-generated texts by GPT-2 (GPT-2), and programming codes (LINUX). Each text is represented by a feature vector derived from the complexity measures.

4. Methodology

The authors employ a Support Vector Machine (SVM) for classification tasks to discriminate between the three text categories. The feature vectors are normalized, and a genetic algorithm optimizes the SVM's hyperparameters.

 

5. Experimental Results

The results indicate that the complexity measures effectively differentiate between human and machine-generated texts. The SVM achieves high accuracy, demonstrating the distinct characteristics of the three categories. The dendrogram analysis reveals the closeness of novels with GPT-2 texts compared to programming codes.

6. Discussion

The discussion emphasizes the relevance of the complexity measures in characterizing different types of texts. It highlights the potential of these measures to serve as indicators of text originality and authorship. The authors suggest that further research could explore the application of these measures in various domains, including plagiarism detection and content authenticity verification.

7. Conclusion

The paper concludes by affirming the utility of complexity measures in distinguishing human and machine intelligence. It underscores the need for continued exploration of these metrics to enhance our understanding of machine-generated texts and their implications.

Final Resume and Main Considerations

The authors conclude that complexity measures offer a robust framework for distinguishing between human and machine-generated texts. The study demonstrates that features such as entropy, determinism, and Zipf's law parameters are effective in capturing the inherent differences in text structure and complexity. The use of SVM for classification further validates the distinctiveness of these features.

The main considerations from the paper are:

• Quantitative Evaluation: Complexity measures provide a quantitative approach to evaluating and comparing texts, bridging the gap between qualitative assessments and statistical analysis.
• Machine Intelligence Understanding: The study enhances our understanding of how machine-generated texts differ from human texts, contributing to the broader field of AI and machine learning.
• Future Research: There is significant potential for applying these measures in various practical applications, including detecting machine-generated content, verifying content authenticity, and studying the evolution of machine intelligence.

The authors advocate for continued research into complexity measures and their applications, emphasizing their relevance in the ever-evolving landscape of artificial intelligence and natural language processing.

 

Source paper: https://www.computer.org/csdl/journal/tp/2024/07/10413606/1TY3NewpqGQ