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Psychomotor agitation (PMA) is a prominent clinical issue frequently observed in various psychiatric and neurological conditions, including schizophrenia, bipolar disorder, Parkinson disease, dementia and substance use disorder. Characterized by motor restlessness, anxiety and irritability, PMA can rapidly escalate into aggression and violence, necessitating prompt intervention to ensure patient and caregiver safety. The prevalence of PMA in psychiatric emergency settings ranges from 4.3% to 10%, imposing a substantial burden on healthcare systems. Despite the critical nature of PMA, there is a lack of standardized treatment protocols, particularly concerning the use of liquid formulations of antipsychotics such as liquid promazine, which may offer unique advantages in emergency care. This review aims to provide a comprehensive analysis of the existing literature on the efficacy, safety and tolerability of liquid antipsychotics, with a particular focus on promazine, in the management of PMA. An extensive literature search was conducted across publicly available databases with no time limitations to ensure the inclusion of all relevant articles. The findings suggest that liquid promazine offers several benefits, including ease of administration, rapid onset of action and improved patient compliance, making it a valuable option in acute PMA management. However, the review also highlights the need for future research, particularly long-term studies and head-to-head comparisons with other antipsychotics, to better establish the clinical utility of liquid promazine. Future research should focus on expanding the evidence base for liquid antipsychotic formulations, which will contribute to improved clinical outcomes in the management of PMA.

In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients’ treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure.

Background: Differentiated thyroid carcinoma (DTC), mainly papillary (PTC), at low risk of recurrence is currently managed with active surveillance strategies or less aggressive surgeries. However, total thyroidectomy with 131I treatment is still performed both if these tumors are diagnosed before or occasionally after surgery. This real-life study aimed to evaluate the rate of biochemical, structural, and functional events in a large series of consecutive DTCs at low risk of recurrence treated by total thyroidectomy, but not with 131I, in a medium–long-term follow-up. Patients and Methods: We evaluated clinical–pathologic data of 383 consecutive patients (2006–2012) with unifocal DTC [T1a/b(s)] at low risk of recurrence, treated with total thyroidectomy but without lymph node dissection and 131I treatment after surgery. We evaluated if structural, biochemical, and functional events were detected during the follow-up. Results: Females accounted for 75.7% of our study group, and the median age was 50 years. The median tumor dimension was 0.4 cm (range 0.1–1.2). Most of the patients had a unifocal T1a tumor (98.9%), and 73.6% had a classic variant of PTC. We divided the patients according to the absence (group A—n = 276) or presence (group B—n = 107) of interfering TgAb at first control after surgery. After a median follow-up of 10 years, no structural events were detected. Sixteen out of three hundred and eighty-three (4.2%) patients developed biochemical events: 12/276 (4.3%) in group A and 4/107 (3.7%) in group B. The median time elapsed from surgery to detecting a biochemical event was 14.5 and 77.5 months in groups A and B, respectively. No patients performed additional treatments and were followed up with an active surveillance strategy. Conclusions: This study confirmed that patients with DTC at low risk of recurrence showed an excellent outcome in a medium long-term follow-up since no structural events were diagnosed. Significant variations in Tg/TgAb were detected in a few cases, all managed with an active surveillance strategy without the need for other treatments. Therefore, a relaxed follow-up with neck ultrasound and Tg/TgAb measurement is enough to early identify those very unusual cases of recurrence.

Objective: The aim of this study was to assess the clinical impact of hand–foot syndrome (HFS) during treatment with two multikinase inhibitors, sorafenib and lenvatinib, in a large group of patients with advanced thyroid cancer. Moreover, we looked for possible associations between HFS occurrence and clinical and pathological features. Methods: We retrospectively evaluated 239 patients with advanced thyroid cancer: 165 treated with lenvatinib and 74 with sorafenib. Statistical analyses were performed to verify which features could be correlated with HFS development. Results: HFS was observed in 35/74 (47.4%) and in 43/165 (26.7%) patients treated with sorafenib or lenvatinib, respectively. The median latency from the drug beginning and HFS appearance was 27 days for sorafenib and 2.9 months for lenvatinib. G3/G4 toxicity was observed in 16/35 (45.7%) patients treated with sorafenib and only in 3/43 (7%) treated with lenvatinib. Drug dose reduction due to HFS was required in 19/74 (25.7%) and 3/165 (1.8%) patients treated with sorafenib and lenvatinib, respectively. HFS occurrence was significantly associated with a longer duration of therapy in both groups. Conclusion: HFS was a frequent adverse event during both lenvatinib and sorafenib therapy, with a higher frequency and toxicity grade during sorafenib treatment. HFS was the most frequent reason for drug reduction or discontinuation in patient treated with sorafenib. Early diagnosis of HFS is important to allow early intervention, possibly in a multidisciplinary setting, and to avoid treatment discontinuation, which is highly relevant to obtain the maximum effectiveness of systemic therapy.

Semantic segmentation of point clouds plays a critical role in various applications, such as urban planning, infrastructure management, environmental analyses and autonomous navigation. Understanding the behaviour of deep neural networks (DNNs) in analysing point cloud data is essential for improving segmentation accuracy and developing effective network architectures and acquisition strategies. In this paper, we investigate the traits of some state-of-the-art neural networks using indoor and urban outdoor point cloud datasets. We compare PointNet, DGCNN, and BAAF-Net on specifically selected datasets, including synthetic and real-world environments. The chosen datasets are S3DIS, SynthCity, Semantic3D, and KITTI. We analyse the impact of different factors such as dataset type (synthetic vs. real), scene type (indoor vs. outdoor), and acquisition system (static vs. mobile sensors). Through detailed analyses and comparisons, we provide insights into the strengths and limitations not only of different network architectures in handling urban point clouds but also of their data structure. This study contributes to going beyond the mere and unconditional use of AI algorithms, trying to explain DNNs behaviour in point cloud analysis and paving the way for future research to enhance segmentation accuracy and develop possible guidelines both for network design and data acquisition in the geomatics field.

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This paper studies the impact of forecast accuracy on total electric cost of a simulated electric vehicles (EVs) charging station coupled with true solar PV and stationary battery energy storage. The optimal energy management system is based on the rolling horizon approach implemented in with a mixed integer linear program which takes as input the EV load forecast using long short-term memory (LSTM) neural network and persistence approaches and PV production forecast using a physical hybrid artificial neural network. The energy management system is firstly deployed and validated on an existing multi-good microgrid by achieving a discrepancy of state variables below 10% with respect to offline simulations. Then, eight weeks of simulations from each of the four seasons show that the accuracy of the forecast can increase operational costs by 10% equally distributed between the PV and EV forecasts. Finally, the accuracy of the combined PV and EV forecast matters more than single accuracies: LSTM outperforms persistence to predict the EV load (−30% root mean squared error), though when combined with PV forecast it has higher error (+15%) with corresponding higher operational costs (up to 5%).

Abstract Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based organic electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce the complexity and size of bio-realistic Hodgkin-Huxley(HH) spiking circuits and logic circuits. However, limited stable anti-ambipolar organic materials prevent the design of integrated, tunable, and multifunctional neuromorphic and logic-based systems. In this work, a general approach for tuning anti-ambipolar characteristics is presented through assembly of a p-n bilayer in a vertical OECT (vOECT) architecture. The vertical OECT design reduces device footprint, while the bilayer material tuning controls the anti-ambipolarity characteristics, allowing control of the device’s on and off threshold voltages, and peak position, while reducing size thereby enabling tunable threshold spiking neurons and logic gates. Combining these components, a mimic of the retinal pathway reproducing the wavelength and light intensity encoding of horizontal cells to spiking retinal ganglion cells is demonstrated. This work enables further incorporation of conformable and adaptive OECT electronics into biointegrated devices featuring sensory coding through parallel processing for diverse artificial intelligence and computing applications.

Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.

In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

Abstract Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse. Here, we present a modular neuromorphic system which combines organic spiking neurons and biohybrid synapses to replicate a neural pathway. The spiking neuron mimics the sensory coding function of afferent neurons from light stimuli, while the neuromodulatory activity of interneurons is emulated by neurotransmitters-mediated biohybrid synapses. Combining these functions, we create a modular connection between multiple neurons to establish a pre-processing retinal pathway primitive.

Abstract Neurohybrid systems have gained large attention for their potential as in vitro and in vivo platform to interrogate and modulate the activity of cells and tissue within nervous system. In this scenario organic neuromorphic devices have been engineered as bioelectronic platforms to resemble characteristic neuronal functions. However, aiming to a functional communication with neuronal cells, material synthesis, and surface engineering can yet be exploited for optimizing bio‐recognition processes at the neuromorphic‐neuronal hybrid interface. In this work, artificial neuronal‐inspired lipid bilayers have been assembled on an electrochemical neuromorphic organic device (ENODe) to resemble post‐synaptic structural and functional features of living synapses. Here, synaptic conditioning has been achieved by introducing two neurotransmitter‐mediated biochemical signals, to induce an irreversible change in the device conductance thus achieving Pavlovian associative learning. This new class of in vitro devices can be further exploited for assembling hybrid neuronal networks and potentially for in vivo integration within living neuronal tissues.

The evaluation and characterization of microwave imaging (MWI) prototype along with the optimization of imaging algorithm is a precursor step before the clinical study to measure in vivo dielectric properties. This study presents the microwave tomographic (MWT) image reconstruction of anthropomorphic calcaneus phantoms by using a specialized microwave bone imaging prototype. To this end, the 2D tomographic images of 3D experimental multi-layered (including skin, fat, cortical and trabecular bone) calcaneus-shaped phantoms were reconstructed. Liquid tissue-mimicking mixtures (TMM) for skin, fat, normal bone, osteoporotic bone, and osteoarthritic bone were prepared. The TMMs were dielectrically characterized over the frequency range of 1 - 4 GHz. The TMMs were poured into a calcaneus-shaped phantom that was placed in the imaging prototype. The imaging prototype was equipped with nine microstrip antennas, connected to a 2-port VNA through a 24-port switching matrix. The tomographic reconstructions were achieved around 3 GHz by using a Distorted Born Iterative Method (DBIM) with an Iterative Method with Adaptive Thresholding for Compressed Sensing (IMATCS). Qualitative and quantitative findings indicate that the employed method is suitable for reconstructing and distinguishing the properties of the mimicked human bone tissues. Indeed, the osteoporotic and osteoarthritic bone phantoms can be differentiated with an average percentage difference of 25%. The target region (i.e., trabecular tissue) is correctly located and its diseased condition can be determined from its retrieved properties for all the phantoms, showing a Structural Similarity Index of at least 82%. This work proves the potential clinical utility of MWI in bone health assessment.

Highly selective RET inhibitor selpercatinib has demonstrated notable efficacy in advanced/progressive RET-mutant medullary thyroid cancer (MTC) patients. However, despite a more tolerable toxicity profile than multikinase inhibitors, peculiar adverse events (AEs) have been described. Obliterative bronchiolitis (OB) is a respiratory disease characterized by inflammation and fibrosis in small conducting airways. We evaluated a 70-year-old man with advanced RET-mutant MTC who developed OB during treatment with selpercatinib. Radiological features of OB occurred early and persisted during selpercatinib treatment, with a waxing and waning pattern. Notably, a partial response of MTC was achieved during the treatment, and selpercatinib was never reduced or interrupted. The almost complete absence of symptoms and the fluctuating trend, without specific treatment for OB, suggested that it is necessary to carefully evaluate the risks mediated by this AE with the risks of modifying or discontinuing the anti-cancer therapy.

Goal: In this study, we demonstrate that a deep neural network (DNN) can be trained to reconstruct high-contrast images, resembling those produced by the multistatic Synthetic Aperture (SA) method using a 128-element array, leveraging pre-beamforming radiofrequency (RF) signals acquired through the monostatic SA approach. Methods: A U-net was trained using 27200 pairs of RF signals, simulated considering a monostatic SA architecture, with their corresponding delay-and-sum beamformed target images in a multistatic 128-element SA configuration. The contrast was assessed on 500 simulated test images of anechoic/hyperechoic targets. The DNN's performance in reconstructing experimental images of a phantom and different in vivo scenarios was tested too. Results: The DNN, compared to the simple monostatic SA approach used to acquire pre-beamforming signals, generated better-quality images with higher contrast and reduced noise/artifacts. Conclusions: The obtained results suggest the potential for the development of a single-channel setup, simultaneously providing good-quality images and reducing hardware complexity.

Abstract The assessment of carotid plaque vulnerability is a relevant clinical information that can help prevent adverse cerebrovascular events. To this aim, in this study, we propose a patient-specific computational workflow to quantify the stress distribution in an atherosclerotic carotid artery, by means of geometric modeling and structural simulation of the plaque and vessel wall. Ten patients were involved in our study. Starting with segmentation of the lumen, calcific and lipid plaque components from computed tomography angiography images, the fibrous component and the vessel wall were semi-automatically reconstructed with an ad-hoc procedure. Finite element analyses were performed using local pressure values derived from ultrasound imaging. Simulation outputs were analyzed to assess how mechanical factors influence the stresses within the atherosclerotic wall. The developed reconstruction method was first evaluated by comparing the results obtained using the automatically generated fibrous component model and the one derived from image segmentation. The high-stress regions in the carotid artery wall around plaques suggest areas of possible rupture. In mostly lipidic and heterogeneous plaques, the highest stresses are localized at the interface between the lipidic components and the lumen, in the fibrous cap.

Thyroid nodules in pediatric patients are less common than in adults but show a higher malignancy rate. Accordingly, the management of thyroid nodules in pediatric patients is more complex the younger the patient is, needing careful evaluation by physicians. In adult patients, specific ultrasound (US) features have been associated with an increased risk of malignancy (ROM) in thyroid nodules. Moreover, several US risk stratification systems (RSSs) combining the US features of the nodule were built to define the ROM. RSSs are developed for the adult population and their use has not been fully validated in pediatric patients. This study aimed to evaluate the available data about US features of thyroid nodules in pediatric patients and to provide a summary of the evidence regarding the performance of RSS in predicting malignancy. Moreover, insights into the management of thyroid nodules in pediatric patients will be provided.

Background: Second 131I treatment is commonly performed in clinical practice in patients with differentiated thyroid cancer and biochemical incomplete or indeterminate response (BiR/InR) after initial treatment. Objective: The objective of the is study is to evaluate the clinical impact of the second 131I treatment in BiR/InR patients and analyze the predictive factors for structural incomplete response (SiR). Patients and methods: One hundred fifty-three BiR/InR patients after initial treatment who received a second 131I treatment were included in the study. The clinical response in a short- and medium- long-term follow-up was evaluated. Results: After the second 131I treatment (median 8 months), 11.8% patients showed excellent response (ER), 17% SiR, while BiR/InR persisted in 71.2%. Less than half (38.5%) of SiR patients had radioiodine-avid metastases. Patients who, following the second 131I treatment, experienced SiR had larger tumor size and more frequently aggressive histology and vascular invasion than those experienced BiR/InR and ER. Also, the median values of thyroglobulin on levothyroxine therapy (LT4-Tg), Tg peak after recombinant human TSH stimulation (rhTSH-Tg) and thyroglobulin antibodies (TgAb) were significantly higher in patients who developed SiR. At last evaluation (median: 9.9 years), BiR/ InR persisted in 57.5%, while 26.2% and 16.3% of the patients showed ER and SiR, respectively. About half of BiR/InR patients (71/153 (46.4%)) received further treatments after the second 131I treatment. Conclusions: Radioiodine-avid metastatic disease detected by the second 131I is an infrequent finding in patients with BiR/InR after initial treatment. However, specific pathologic and biochemical features allow to better identify those cases with higher probability of developing SiR, thus improving the clinical effectiveness of performing a second 131I treatment.

Deep Learning has been pivotal in many real-world applications (e.g., autonomous driving, medicine and retail). With the wide availability of consumer-grade depth sensors, acquiring 3D data has become more affordable and effective, and many 3D datasets are currently publicly available. 3D data provides a great opportunity for a better comprehension of the surrounding environment for machines. There is a growing need for innovative methods for the treatment and analysis of point clouds and for their classification. The complex hidden layers, which are at the basis of deep neural networks (DNNs), make it difficult to interpret these models, that up to a few years ago DNNs were considered and treated as black box operators. Still, with their increasing popularity, making them explainable and interpretable has become mandatory. A lot of efforts were devoted to developing an Explainable Artificial Intelligence (XAI) framework for explaining DNNs decisions with 2D data, while only a few studies have attempted to investigate the explainability of 3D DNNs and, even more, heritage scenarios. To overcome these limitations, it was proposed the BubblEX framework: a novel multimodal fusion framework to learn the 3D point features. In our work, BubblEX has been exploited to understand the decisions taken by DNNs for heritage point clouds. The approach has been applied to a Digital Cultural Heritage Dataset, which is publicly available: the ArCH (Architectural Cultural Heritage) Dataset.

The first edition of the SUNRISE (Seashore and UNderwater documentation of aRchaeological herItage palimpSests and Environment) summer school was carried out in Marina di Ragusa in Sicily (Italy) from 3rd to 9th September 2022. It was jointly organized by Politecnico di Torino, IUAV, University of Sassari, FBK, University of Udine and the University of Modena and Reggio Emilia with the support of SIFET, ISPRS Student Consortium, private companies (Images, Microgeo, Stonex, Leica and Geomax) and the municipality of Santa Croce Camerina.The five days of summer school were attended by 20 students from Europe, Asia and USA. After the first day of lectures focused on the theoretical basis of surveying, photogrammetry, LiDAR, and SLAM, the field activities took place in a submerged and terrestrial scenario. The underwater surveying involved a submerged amphora, and the terrestrial activity was focused on the Arab bath of Mezzagnone, a 6th-century AD building that has been fully preserved until today. The paper deal with this experience and underline the followed approach. Finally some results achieved by the students are reported.

The integration of Building Information Modeling (BIM), or Historic BIM (HBIM), and Geographic Information Systems (GIS) has assumed a crucial role not only in the management of smart cities or urban districts, as well as in the construction sector or infrastructures but also in the management, conservation and safeguarding of cultural heritage. This integration opens up new opportunities for decision-makers to assess the impact of interventions on heritage sites, evaluate preservation strategies, and facilitate informed decision-making processes. This contribution presents and describes the open-source web platform developed within the Interreg MAIN10ANCE project. The project aims to promote a common methodology for the cooperation and collaboration of the various stakeholders involved in the restoration, management and maintenance activities of built and cultural heritage sites. This objective has been pursued by creating a BIM-GIS database, focused on the definition of planned maintenance activities, whose data has been made available to professionals and management bodies by means of a shared web platform, usable directly in situ on a tablet or portable devices. The paper highlights the key components of the web platform, including the BIM, GIS, and artifact Viewers, the Planner, and the overall Dashboard. The platform's multiscale and multi-source approach makes it extensible to various cultural assets, whether environmental, architectural, or movable. By integrating historical and geometric information and planning maintenance activities, the web platform thus supports public administrations and professionals, amending existing state-of-the-art solutions.

The mobile mapping systems (MMSs) are increasingly used in the Architecture, Engineering and Construction sector (AEC). Their involvement in the digital transition of our cities and built heritage could be a valuable solution in speeding up the scan-to-BIM processes. In this framework, the course “Knowledge of the built heritage in the era of climate change” of Politecnico di Torino offers an innovative approach for MSc degree students in Building Engineering. In fact, it deals with the whole workflow from the surveying activities (data acquisition and processing), to the BIM modelling up to the final digital twin integrated with e.g. sensors data and management information. This contribution describes the case studies of the multidisciplinary course as an application for this methodology, where not only terrestrial laser scanning or UAVs (Unmanned Aerial Vehicles) have been used, but also MMSs have been tested to understand if they could be a suitable option. This paper aims to show the course experience derived from the collaboration of the geomatics and drawing domains along with students’ results and feedback. Moreover, the research underlines the pros and cons of this procedure.

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The ever increasing library of materials systems developed for organic solar-cells, including highly promising non-fullerene acceptors and new, high-efficiency donor polymers, demands the development of methodologies that i) allow fast screening of a large number of donor:acceptor combinations prior to device fabrication and ii) permit rapid elucidation of how processing affects the final morphology/microstructure of the device active layers. Efficient, fast screening will ensure that important materials combinations are not missed; it will accelerate the technological development of this alternative solar-cell platform toward larger-area production; and it will permit understanding of the structural changes that may occur in the active layer over time. Using the relatively high-efficiency poly[(5,6-difluoro-2,1,3-benzothiadiazol-4,7-diyl)-alt-(3,3'''-di(2-octyldodecyl)-2,2';5',2'';5'',2'''-quaterthiophen-5,5'''-diyl)] (PCE11):phenyl-C61-butyric acid-methyl-ester acceptor (PCBM) blend systems, it is demonstrated that by means of straight-forward thermal analysis, vapor-phase-infiltration imaging, and transient-absorption spectroscopy, various blend compositions and processing methodologies can be rapidly screened, information on promising combinations can be obtained, reliability issues with respect to reproducibility of thin-film formation can be identified, and insights into how processing aids, such as nucleating agents, affect structure formation, can be gained.