Devising an efficient exploration of the search space is one of the key challenges in the design of combinatorial optimization algorithms. Here, we introduce the Generator-Enhanced Optimization (GEO) strategy: a framework that leverages any generative model (classical, quantum, or quantum-inspired) to solve optimization problems. We focus on a quantum-inspired version of GEO relying on tensor-network Born machines, and referred to hereafter as TN-GEO. To illustrate our results, we run these benchmarks in the context of the canonical cardinality-constrained portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes, and demonstrate how the generalization capabilities of these quantum-inspired generative models can provide real value in the context of an industrial application. We also comprehensively compare state-of-the-art algorithms and show that TN-GEO is among the best; a remarkable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. Also, a promising step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models

Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors reliability made by comparing our results with more accurate and expensive standard - radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in [1].

Reliability, power consumption, and latency are the three main performance indicators of wireless sensor networks. Time slotted channel hopping (TSCH) is a promising technique introduced in the IEEE 802.15.4 standard that performs some steps ahead in the direction of the final dream to meet all the previous requirements at the same time. In this paper, a simple and effective mathematical model is presented for TSCH that, starting from measurements performed on a real testbed, permits to characterize both the network and the surrounding environment. To better characterize power consumption, an experimental measurement campaign was purposely performed on OpenMote B devices. The model, which was checked against a real 6TiSCH implementation, can be employed to predict network behaviour when configuration parameters are varied, in such a way to satisfy different application contexts. Results show that, when one of the three above indices is privileged, unavoidably there is a worsening of the others.

We propose an economical 3D-printed pipette, which aims to overcome the limitations of two-finger robot grippers. It enables general-purpose robot arms to achieve high precision in liquid transfer tasks that is comparable to commercial devices.

Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad hoc and fragile. Current agentic approaches often rely on unstructured text to manage context and coordinate execution, generating often overwhelming volumes of information that may obscure decision provenance and hinder auditability. In this work, we present El Agente Gráfico, a single-agent framework that embeds LLM-driven decision-making within a type-safe execution environment and dynamic knowledge graphs for external persistence. Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects, stored either in memory or persisted in an external knowledge graph. This design enables context management through typed symbolic identifiers rather than raw text, thereby ensuring consistency, supporting provenance tracking, and enabling efficient tool orchestration. We evaluate the system by developing an automated benchmarking framework across a suite of university-level quantum chemistry tasks previously evaluated on a multi-agent system, demonstrating that a single agent, when coupled to a reliable execution engine, can robustly perform complex, multi-step, and parallel computations. We further extend this paradigm to two other large classes of applications: conformer ensemble generation and metal-organic framework design, where knowledge graphs serve as both memory and reasoning substrates. Together, these results illustrate how abstraction and type safety can provide a scalable foundation for agentic scientific automation beyond prompt-centric designs.