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Abstract Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties of interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by the available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network for partial charge prediction via active learning based on Dropout Monte Carlo. We show that active learning significantly reduces the required amount of labeled MOFs to reach a target accuracy. The obtained model generalizes well to different distributions of MOFs and Zeolites. In addition, the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen MOFs. This work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges, supporting in-silico material design.

Abstract All-solid-state Li-ion batteries are one of the most promising energy storage devices for future automotive applications as high energy density metallic Li anodes can be safely used. However, introducing solid-state electrolytes needs a better understanding of the forming electrified electrode/electrolyte interface to facilitate the charge and mass transport through it and design ever-high-performance batteries. This study investigates the interface between metallic lithium and solid-state electrolytes. Using spectroscopic ellipsometry, we detected the formation of the space charge depletion layers even in the presence of metallic Li. That is counterintuitive and has been a subject of intense debate in recent years. Using impedance measurements, we obtain key parameters characterizing these layers and, with the help of kinetic Monte Carlo simulations, construct a comprehensive model of the systems to gain insights into the mass transport and the underlying mechanisms of charge accumulation, which is crucial for developing high-performance solid-state batteries.

The current roadway monitoring is expensive and not systematic. This paper proposes a new system able to evaluate the pavement quality of road infrastructure. The embedded system records and processes the acoustic data of the wheel-road interaction and classifies in real-time roadways’ health thanks to integrated AI solutions. The measurements to produce the dataset to train a convolutional neural network (CNN) were collected using a vehicle operating at different cruise speeds in the area of Pisa. The dataset is composed by acoustic data belonging to several typologies of roads: dirty or grass roads, high roughness surfaces and roads with cracks or potholes. The raw audio signals were split, labelled, and converted into images by calculating the Mel spectrogram. Finally, the authors designed a tiny CNN with a size equal to 18 kB able to classify between four different classes: good quality road, ruined road, silence and unknown. The CNN architecture achieves an accuracy of about 93% on the original model and 90% on the quantized one. Quantization permits to convert the final architecture into a suitable form to be deployed on a low-complex embedded system integrated in the tyre cavity. In addition, a custom board was designed to act as IoT node thanks to a Bluetooth Low Energy communication towards smartphones and/or car infotainment systems. These systems, featured with GPS, guarantee to obtain real-time maps service of road quality. At authors’ knowledge, this is the first real-time and fully integrated solution at the state of the art for road pavement quality analysis and classification on acoustic data.

Dilute donor organic solar cells (OSCs) are a promising technology to circumvent the trade‐off between open‐circuit voltage (Voc) and short‐circuit current density (Jsc). The origin of hole transport in OSCs with donor concentrations below the percolation threshold is diversely discussed in the community. Herein, both hole back transfer and long‐range hopping (tunneling) are analyzed as possible mechanisms of photocurrent in small molecule dilute donor OSCs using kinetic Monte Carlo (kMC) simulations. In contrast to previous kMC studies, the driving force for exciton dissociation is accounted for. As a study system, nitrogen‐bridged terthiophene (NBTT) molecules in a [6,6]‐phenyl‐C70‐butyric acid methyl ester (PC71BM) matrix are investigated. The simulations show that hole back transfer from the small molecule donor to the fullerene matrix explains the measured concentration dependences of the photocurrents as well as the Jsc dependence on the light intensity for donor concentrations below 5 wt%. For 5 wt%, distances between NBTT molecules decrease to reasonable ranges that long‐range hopping or tunneling cannot be discounted. Compared with polymer donors, larger hole localization is observed. The results emphasize that the barrier for hole back transfer is not only due to the highest occupied molecular orbital (HOMO) offset, but also by hole localization.

Objective: To identify possible relevant factors contributing to tremor relapse after MRgFUS thalamotomy in patients with essential tremor (ET) and Parkinson’s disease (PD). Methods: We identified patients with tremor relapse from a series of 79 treatments in a single institution. The demographic and clinical characteristics of the study group patients were compared to those of patients who did not relapse in the same follow-up period. Imaging and procedural factors were compared using a control group matched for clinical and demographic characteristics. Results: Concerning clinical and demographic characteristics, we did not find statistically significant differences in gender and age. Seventy-three percent of patients with tremor relapse were Parkinson’s disease patients. Using MRI, we found larger thalamotomy lesions at the 1-year follow-up in the control group with stable outcomes, compared to patients with tremor relapse. In the tractography evaluation, we found a more frequent eccentric position of the DRTt in patients with tremor relapse. Conclusions: The most relevant determining factors for tremor relapse after MRgFUS thalamotomy appear to be tremor from Parkinson’s disease and inaccurate thalamic targeting. Size of the thalamotomy lesion can also influence the outcome of treatment.

This paper presents the development of a hardware/software system for the characterization of the electronic response of optical (camera) sensors such as matrix and linear color and monochrome Charge Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS). The electronic response of a sensor is required for inspection purposes. It also allows the design and calibration of the integrating device to achieve the desired performance. The proposed instrument equipment fulfills the most recent European Machine Vision Association (EMVA) 1288 standard ver. 3.1: the spatial non uniformity of the illumination ΔE must be under 3%, and the sensor must achieve an f-number of 8.0 concerning the light source. The following main innovations have achieved this: an Ulbricht sphere providing a uniform light distribution (irradiation) of 99.54%; an innovative illuminator with proper positioning of color Light Emitting Diodes (LEDs) and control electronics; and a flexible C# program to analyze the sensor parameters, namely Quantum Efficiency, Overall System Gain, Temporal Dark Noise, Dark Signal Non Uniformity (DSNU1288), Photo Response Non-Uniformity (PRNU1288), Maximum achievable Signal to Noise Ratio (SNRmax), Absolute sensitivity threshold, Saturation Capacity, Dynamic Range, and Dark Current. This new instrument has allowed a camera manufacturer to design, integrate, and inspect numerous devices and camera models (Necta, Celera, and Aria).

This editorial aims to interest researchers and inspire novel research on the topic of non-equilibrium Thermodynamics and Monte Carlo for Electronic and Electrochemical Processes. We present a brief outline on recent progress and challenges in the study of non-equilibrium dynamics and thermodynamics using numerical Monte Carlo simulations. The aim of this special issue is to collect recent advances and novel techniques of Monte Carlo methods to study non-equilibrium electronic and electrochemical processes at the nanoscale.

This paper proposes a video-based smoke detection technique for early warning in antifire surveillance systems. The algorithm is developed to detect the smoke behavior in a restricted video surveillance environment, both indoor (e.g., railway carriage, bus wagon, industrial plant, or home/office) or outdoor (e.g., storage area or parking area). The proposed technique exploits a Kalman estimator, color analysis, image segmentation, blob labeling, geometrical features analysis, and M of N decisor, in order to extract an alarm signal within a strict real-time deadline. This new technique requires just a few seconds to detect fire smoke, and it is 15 times faster compared to the requirements of fire-alarm standards for industrial or transport systems, e.g., the EN50155 standard for onboard train fire-alarm systems. Indeed, the EN50155 considers a response time of at least 60 s for onboard systems. The proposed technique has been tested and compared with state-of-art systems using the open access Firesense dataset developed as an output of a European FP7 project, including several fire/smoke indoor and outdoor scenes. There is an improvement of all the detection metrics (recall, accuracy, F1 score, precision, etc.) when comparing Advanced Video SmokE Detection (AdViSED) with other video-based antifire works recently proposed in literature. The proposed technique is flexible in terms of input camera type and frame size and rate and has been implemented on a low-cost embedded platform to develop a distributed antifire system accessible via web browser.

The article examines the main categories and lines of analysis developed by Gramsci in his analysis of fascism. In order to fully understand fascism’s particular characteristics, he developed a reading that brought into play a more general interpretation of the history of Italy and the processes of transformation typical of the capitalist societies of his time. This consisted in an analytical apparatus that evolved over time, with the change in Gramsci’s conditions of existence and degree of political involvement, and with the different successive phases of the fascist movement and the dictatorship. The article examines his whole political and intellectual itinerary, with special reference to the reflection carried out during his detention and written down in the Prison Notebooks. The categories that Gramsci developed in this phase are grouped around three clusters of problems which in some cases take up again suggestions and traces of analyses sketched out previously: social classes and historical periods, forms of command (a subject connected with the reflection on “caesarism”-“bonapartism” and on the intellectuals), and “passive revolution” and the nexuses with the changes in capitalism and Fordist modernization.

Ultrathin films of alumina were investigated by a compact kMC-model. Experimental jV-curves from Al/AlOx/Au-junctions with plasma- and thermal-grown AlOx were fitted by simulated ones. We found dominant defects at 2.3-2.5 eV below CBM for AlOx with an effective mass mox∗=0.35 m0 and a barrier EB,Al/AlOx≈2.8 eV in agreement with literature. The parameterization is extended to varying defect levels, defect densities, injection barriers, effective masses and the thickness of AlOx. Thus, dominant charge transport processes and implications on the relevance of defects are derived and AlOx parameters are specified which are detrimental for the operation of devices.

The aim of this paper is to analyze the economic goals of the Ethiopian War and the results of the Fascist Empire in Italian East Africa (Africa orientale italiana). In this respect, the creation of the new colony in East Africa gave rise undoubtedly to a failure. It was the consequence of the weakness and the lack of planning that characterized the origin of the “Empire” – as it is testified, for instance, by confused and contradictory laws and frequent conflicts of jurisdiction – and was also the consequence of big companies’ lack of interest in colonial investments.

In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor.