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This paper develops an agent-based computational model of violent political revolutions in which a subjugated population of citizens and an armed revolutionary organisation attempt to overthrow a central authority and its loyal forces. The model replicates several patterns of rebellion consistent with major historical revolutions, and provides an explanation for the multiplicity of outcomes that can arise from an uprising. The relevance of the heterogeneity of scenarios predicted by the model can be understood by considering the recent experience of the Arab Spring involving several rebellions that arose in an apparently similar way, but resulted in completely different political outcomes: the successful revolution in Tunisia, the failed protests in Saudi Arabia and Bahrain, and civil war in Syria and Libya.

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research.

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM2.5 concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM2.5 concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches.

Automatic image captioning has many important applications, such as the depiction of visual contents for visually impaired people or the indexing of images on the internet. Recently, deep learning-based image captioning models have been researched extensively. For caption generation, they learn the relation between image features and words included in the captions. However, image features might not be relevant for certain words such as verbs. Therefore, our earlier reported method included the use of motion features along with image features for generating captions including verbs. However, all the motion features were used. Since not all motion features contributed positively to the captioning process, unnecessary motion features decreased the captioning accuracy. As described herein, we use experiments with motion features for thorough analysis of the reasons for the decline in accuracy. We propose a novel, end-to-end trainable method for image caption generation that alleviates the decreased accuracy of caption generation. Our proposed model was evaluated using three datasets: MSR-VTT2016-Image, MSCOCO, and several copyright-free images. Results demonstrate that our proposed method improves caption generation performance.

Niemann-Pick Type C (NPC) is a progressive and life limiting autosomal recessive disorder caused by mutations in either the NPC1 or NPC2 gene. Mutations in these genes are associated with abnormal endosomal-lysosomal trafficking, resulting in the accumulation of multiple tissue specific lipids in the lysosomes. The clinical spectrum of NPC disease ranges from a neonatal rapidly progressive fatal disorder to an adult-onset chronic neurodegenerative disease. The age of onset of the first (beyond 3 months of life) neurological symptom may predict the severity of the disease and determines life expectancy. NPC has an estimated incidence of ~ 1: 100,000 and the rarity of the disease translate into misdiagnosis, delayed diagnosis and barriers to good care. For these reasons, we have developed clinical guidelines that define standard of care for NPC patients, foster shared care arrangements between expert centres and family physicians, and empower patients. The information contained in these guidelines was obtained through a systematic review of the literature and the experiences of the authors in their care of patients with NPC. We adopted the Appraisal of Guidelines for Research & Evaluation (AGREE II) system as method of choice for the guideline development process. We made a series of conclusive statements and scored them according to level of evidence, strengths of recommendations and expert opinions. These guidelines can inform care providers, care funders, patients and their carers of best practice of care for patients with NPC. In addition, these guidelines have identified gaps in the knowledge that must be filled by future research. It is anticipated that the implementation of these guidelines will lead to a step change in the quality of care for patients with NPC irrespective of their geographical location.

Ca2+/calmodulin-dependent kinase II (CaMKII) regulates synaptic plasticity in multiple ways, supposedly including the secretion of neuromodulators like brain-derived neurotrophic factor (BDNF). Here, we show that neuromodulator secretion is indeed reduced in mouse α- and βCaMKII-deficient (αβCaMKII double-knockout [DKO]) hippocampal neurons. However, this was not due to reduced secretion efficiency or neuromodulator vesicle transport but to 40% reduced neuromodulator levels at synapses and 50% reduced delivery of new neuromodulator vesicles to axons. αβCaMKII depletion drastically reduced neuromodulator expression. Blocking BDNF secretion or BDNF scavenging in wild-type neurons produced a similar reduction. Reduced neuromodulator expression in αβCaMKII DKO neurons was restored by active βCaMKII but not inactive βCaMKII or αCaMKII, and by CaMKII downstream effectors that promote cAMP-response element binding protein (CREB) phosphorylation. These data indicate that CaMKII regulates neuromodulation in a feedback loop coupling neuromodulator secretion to βCaMKII- and CREB-dependent neuromodulator expression and axonal targeting, but CaMKIIs are dispensable for the secretion process itself.

Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

Polyetheretherketone (PEEK) in the last few years has emerged as an exceedingly promising material for craniofacial defects due to its biocompatibility and mechanical properties. However, its utilization remains controversial due to its inertness and low osteoinductivity. This study aimed to investigate the postoperative outcomes of patients undergoing maxillo-facial and neurosurgical procedures with PEEK implants. The focus is on evaluating bone regrowth on the surface and edges of the implant, periosteal reactions, and implant positioning. A retrospective analysis of 12 maxillo-facial surgery patients and 10 neurosurgery patients who received PEEK implants was conducted. CT scans performed at least one year post operation were examined for bone regrowth, periosteal reactions, and implant positioning. In maxillo-facial cases, the analysis included mandibular angle and fronto-orbital reconstruction, while neurosurgical cases involved cranioplasty. In maxillofacial surgery, 11 out of 12 patients showed radiological evidence of bone regrowth around PEEK implants, with favorable outcomes observed in craniofacial reconstruction. In neurosurgery, 9 out of 10 patients exhibited minimal or none bone regrowth, while one case demonstrated notable bone regeneration beneath the PEEK implant interface. The study highlights the importance of implant design and patient-specific factors in achieving successful outcomes, providing valuable insights for future implant-based procedures.

Background/Objectives: The present case–control study aims to compare the symmetry of the sella turcica and cranial base of nine patients with anterior unicoronal synostotic plagiocephaly (ASP) and nine healthy patients referred to the maxillofacial unit of the Fondazione Policlinico Universitario Agostino Gemelli. The primary aim of this study is to assess changes in the morphology of the sella turcica and skull base in comparison with a healthy control population using both a 2D and 3D analysis of the sella turcica and skull base. Methods: Computed tomography (CT) scans of nine ASP patients from the Fondazione Policlinico Universitario Agostino Gemelli in Rome were retrieved. A quantitative evaluation of the skull base and the sella turcica was performed through the asymmetry index (A.I.), obtained from the comparison of the point-to-point distances ipsilateral and contralateral to the synostosis. A qualitative three-dimensional (3D) evaluation of the asymmetry of the sella turcica was performed by comparing each sella model with its mirrored counterpart; then, the root mean square (RMS) displacement between the original and mirrored 3D models was calculated. Results: The results showed higher A.I. values in the study group, particularly the length of the anterior cranial fossa, with A.I. values of 7.96 (study) vs. 0.02 (control). Conclusions: The higher values of the asymmetry index observed in the study group supported the presence of statistically significant asymmetries in the sella and cranial fossa measurements compared to the control group.

In recent years, the number of pipes that have exceeded their service life has increased. For this reason, earthworm-type robots equipped with cameras have been developed to perform regularly inspections of sewer pipes. However, inspection methods have not yet been established. This paper proposes a method for anomaly detection from images in pipes using Generative Adversarial Network (GAN). A model that combines f-AnoGAN and Lightweight GAN is used to detect anomalies by taking the difference between input images and generated images. Since the GANs are only trained with non-defective images, they are able to convert an image containing defects into one without them. Subtraction images is used to estimate the location of anomalies. Experiments were conducted using actual images of cast iron pipes to confirm the effectiveness of the proposed method. It was also validated using sewer-ml, a public dataset.