The study of the organizational kinetics in the area surrounding the transmucosal part of dental implants promises to ensure an accurate diagnosis of the healing process, in terms of osseointegration and long-term implant success. In this demonstrative work, the morphological, qualitative and quantitative characteristics of 3D images of collagen bundles obtained by synchrotron-based high-resolution X-ray tomography were analyzed. Data analysis was performed using deep learning algorithms, neural networks that were applied on multiple volumes extracted from connective portions of different patients. The neural network was trained with mutually consistent examples from different patients; in particular, we used a neural network model, U-Net, well established when applying deep learning to datasets of images. It was trained not only to distinguish the collagen fibers from the background, but also to subdivide the collagen bundles based on the orientation of the fibers. In fact, differently from conventional thresholding methods, deep learning semantic segmentation assigns a label to each pixel, not only relying on grey level distribution but also on the image morphometric (shape or direction) characteristics. With the exception of Pt2 biopsies that, as confirmed by the polarized light investigation, were shown to present an immature tissue condition, the quantity, the anisotropy degree and the connectivity density of transverse bundles were always demonstrated to be higher than for longitudinal ones. These are interesting and new data; indeed, as collagen bundles are organized in an intertwining pattern, these morphometric and 3D complexity parameters, distinguished in transversal and longitudinal directions, give precise indications on the amount and distribution of connective tissue forces exerted during the healing process. Full article

In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks. Thus, the importance of improving robustness against adversarial attacks has emerged as an urgent issue in vision tasks. This article aims to provide a historical summary of the evolution of adversarial attacks and defense methods on CNN-based models and also introduces studies focusing on brain-inspired models that mimic the visual cortex, which is resistant to adversarial attacks. As the origination of CNN models was in the application of physiological findings related to the visual cortex of the time, new physiological studies related to the visual cortex provide an opportunity to create more robust models against adversarial attacks. The authors hope this review will promote interest and progress in artificially intelligent security by improving the robustness of deep learning models for vision tasks. Full article

Background: Dentists administer hundreds of thousands of injections every day without particular concern, but the administration of local anesthetics can cause problems. One event, fortunately uncommon, that is discussed accurately but can cause significant concern is needle breakage. The purpose of this article is to review what has been reported in the literature on this topic to learn about patient symptomatology, management and possible complications. In addition, the case of a 34-year-old patient with needle persistence in the pterygoid space due to accidental rupture during inferior alveolar nerve block (IAN) was reported, for whom non-removal and monitoring over time was chosen. Materials and Methods: A literature search of the PubMed, Scopus, Web of Science and ScienceDirect databases was conducted analyzing anesthetic needle rupture during dental surgery. Results: At the end of the selection process, 17 articles resulted. Asymptomatic subjects were found, as well as those who had several symptoms. No issues were recorded by any of the authors, whether they withdrew the needle or simply monitored the patient. Conclusions: The literature on needle rupture during dental local anesthesia is scarce, and studies report conflicting results on treatment options. Most authors reported removing the fragment; however, others preferred conservative management limited to patient monitoring. No complications were reported in any study. Full article

Nevi are pigmented lesions of bluish-black colour that may occasionally occur in the oral cavity. Out of several oral melanocytic nevi (OMN), blue nevi are significant as some variants are diagnostically challenging due to histopathological overlap with malignant melanoma. The aim of the present report was to present the clinico-pathologic characteristics of 8 new cases of intraoral common blue nevi and 22 OMN. A literature review-based comparison of the clinico-pathological characteristics of intraoral blue nevi with intramucosal nevi is presented. Analysis of the clinico-pathologic characteristics of eight common blue nevi revealed that they are generally small lesions of less than 1 cm in size and are of bluish-black colouration. These lesions were commonly identified in adults (88%), with a female predilection. The hard palate was the most common site of occurrence. These findings could be collaborated with the findings observed in the literature review. It was also revealed that a common blue nevus was the second most common lesion among all OMN. The intraoral common blue nevi included in the present series did not show recurrences or undergo malignant transformation after being followed up for up to 15 years. Unlike a common blue nevus, the literature reveals that a cellular blue nevus or its atypical variant is more diagnostically challenging, while a diagnosis of an epithelioid blue nevus should be followed up with investigations to exclude Carney complex. In conclusion, this case series and literature review reveal that OMN, including intraoral common blue nevi, are small indolent lesions with extremely low malignant transformation potential. A significantly smaller size, well-defined margins and lack of variegations in colour are all features that could be used as clues to differentiate intraoral common blue nevi from mucosal malignant melanoma. However, the diagnosis needs to be confirmed with histopathology. Full article

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. Methods: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). Data: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. Instruments: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. Results: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. Conclusions: machine learning models for special multilanguages, including languages with artificially made vocabulary, perform consistently with high accuracy. Full article

Modern buildings are large and complex, and as more time is spent inside them, the risk of indoor disasters such as fires and gas leaks increases. In the event of such a disaster, the success of the rescue operation depends on the ability of the rescue team to navigate and respond to the complex environment. To address this challenge, we designed a mixed reality (MR)-based system simulating indoor disaster rescue. This system uses augmented indoor maps and MR technology to help rescue teams quickly, and effectively respond to unexpected environmental variables and carry out rescue activities. To test the effectiveness of the system, we created a virtual disaster scenario and evaluated the rescue and escape performance within a shortened “golden time” of 2 min, as opposed to 5 min, given the virtual characteristics of the simulation. The results showed that the system is particularly effective at increasing the speed of rescue activities. Additionally, our results indicated the potential for further improvement through additional research. By applying this system for disaster rescue simulations and training, the safety of firefighters and rescuers can be improved by reducing the risk of injury during rescue operations. Full article

A pre-existing and inherited geostructural setting plays a fundamental role in preparing and developing large-scale slope deformational processes. These structures affect the kinematics of the process, the geometrical characteristics, and the geomorphological evolution. In the Apennine Belt, several deep-seated gravitational slope deformations (DSGSDs) that have evolved under a clear structural control have been recognized during the last decades, but none with a continuous and well-defined basal shear zone (BSZ). The structurally-controlled DSGSD of Luco dei Marsi represents the first case of a DSGSD in the Apennine Belt with a well-defined BSZ. Starting from a detailed study of the process and the reconstruction of a morpho-evolutionary model of the slope, a series of numerical modelings were performed for the study of the DSGSD. The analyses allowed us to reconstruct: (i) the mechanism of the process, (ii) the rheological behavior of the rock mass, and (iii) the main predisposing factors of the gravitational deformation. Numerical modeling has demonstrated the significant role played by the inherited structures on the DSGSD and, in particular, the importance of an intensely jointed stratigraphic level in the development of the BSZ. Full article

In today’s fast-paced and dynamic business environment, investment decision making is becoming increasingly complex due to the inherent uncertainty and ambiguity of the financial data. Traditional decision-making models that rely on crisp and precise data are no longer sufficient to address these challenges. Fuzzy logic-based models that can handle uncertain and imprecise data have become popular in recent years. However, they still face limitations when dealing with complex, multi-criteria decision-making problems. To overcome these limitations, in this paper, we propose a novel three-way group decision model that incorporates decision-theoretic rough sets and intuitionistic hesitant fuzzy sets to provide a more robust and accurate decision-making approach for selecting an investment policy. The decision-theoretic rough set theory is used to reduce the information redundancy and inconsistency in the group decision-making process. The intuitionistic hesitant fuzzy sets allow the decision makers to express their degrees of hesitancy in making a decision, which is not possible in traditional fuzzy sets. To combine the group opinions, we introduce novel aggregation operators under intuitionistic hesitant fuzzy sets (IHFSs), including the IHF Aczel-Alsina average $\left(IHF{\mathcal{A}}_{\mathcal{A}}\mathrm{A}\right)$ operator, the IHF Aczel-Alsina weighted average $\left(IHF{\mathcal{A}}_{\mathcal{A}}W{A}_{\mathsf{ϣ}}\right)$ operator, the IHF Aczel-Alsina ordered weighted average $\left(IHF{\mathcal{A}}_{\mathcal{A}}OW{A}_{\mathsf{ϣ}}\right)$ operator, and the IHF Aczel-Alsina hybrid average $\left(IHF{\mathcal{A}}_{\mathcal{A}}H{A}_{\mathsf{ϣ}}\right)$ operator. These operators have desirable properties such as idempotency, boundedness, and monotonicity, which are essential for a reliable decision-making process. A mathematical model is presented as a case study to evaluate the effectiveness of the proposed model in selecting an investment policy. The results show that the proposed model is effective and provides more accurate investment policy recommendations compared to existing methods. This research can help investors and financial analysts in making better decisions and achieving their investment goals. Full article

Nowadays, smartphones have become indispensable in people’s daily work and life. Since various sensors and communication chips have been integrated into smartphones, it has become feasible to provide indoor positioning using phones. This paper proposes such a solution based on a smartphone, combining Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) in the particle filter framework to realize real-time and reliable indoor positioning. First, the smartphone’s built-in accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible method for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. However, a single positioning technology has limitations, e.g., the cumulative error of PDR and the received signal strength fluctuation of BLE. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experiments are conducted on a floor in a campus building. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is reduced by 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has a vast application prospect in indoor navigation, indoor user tracking, and interactive experience for indoor visitors. Full article

Transitional upper carboniferous Shanxi Formation coal-bearing strata in Qinshui Basin have been proven to be a set of mixed unconventional gas-bearing reservoirs forming a multi-superimposed gas system that consists of multiple independent fluid pressure systems vertically through the strata. An experimental protocol was designed to compare the pore networks in high-rank coal, shale, and tight sandstone reservoirs from Shanxi Formation using quantitative and qualitative experimental methods, including high-pressure mercury injection porosimetry (MIP), low-pressure nitrogen gas adsorption (LN₂GA), and argon ion polishing–field emission scanning electron microscope (AIP-FESEM). The results show that genetic and structural differences in pore types, morphology, abundance, and proportion in coal, shale, and tight sandstone reservoirs are significant, reflecting strong heterogeneity characteristics. Pore networks determine the roles of different types of reservoirs in gas-bearing systems through differentiated pore structure, development degree, and spatial distribution. Due to the differences in nanopore development and connectivity, coal and tight sandstone reservoirs provide important reservoir spaces for adsorbed and free gas in the system. Thus, they become influential factors controlling the relationship between the gas-bearing subsystems with different fluid pressures. The lack of mesopores in shale and relatively weaker heterogeneity between layers lead to the phenomenon that continuously developed shales of a specific thickness are more likely to be the interlayers that divide the superimposed gas-bearing system. Systematic comparison of pore development characteristics will provide scientific support to further explain the formation mechanism of multi-superimposed gas systems in coal-bearing strata from the perspective of pore networks and provide guidance for the development of unconventional natural gas in coal-bearing strata. Full article

Blood cells are constantly exposed to several pollutants, including xenobiotics, and they can be considered a useful target for pollution exposition of the animal. The aim of this study was to investigate the effects of two xenobiotics (Quaternium-15, a preservative used in personal care products, and Thiacloprid, a neonicotinoid pesticide) on the haematological profile and erythrocyte osmotic fragility (EOF) of equine and bovine blood samples. Ten blood samples from horses and cows were exposed for 24 h to Quaternium-15 at a concentration of 0.1 and 1 mg/L and to Thiacloprid at a concentration of 30 and 60 µg/mL. A decrease in the values of the red blood cells, white blood cells, haematocrit, haemoglobin, mean corpuscular volume, and platelets, and an increase of EOF were found in blood samples exposed to xenobiotics compared to the control. According to the results gathered in the current study, the two xenobiotic compounds herein tested negatively affect the haematological indices causing haemolysis both in cattle and horse blood. This study, despite being preliminary, emphasizes the concept that blood cells are an excellent target for evaluating the effects of xenobiotics. Full article

The effectiveness of skin-to-skin care (SSC) notably depends on the delivery room air temperature (T_a), the thermal insulation provided by the fabrics or clothes covering the mother and the neonate (I_cl), and the neonate’s metabolism (M). The objective of the present study was to model the influence of these variables on the effectiveness of SSC for premature newborns. To this end, we used an appropriate thermal mannequin and applied a mathematical model of body heat exchanges. We performed experiments at T_a values (20.9 °C and 25.9 °C) and two I_cl values (sheet only and sheet + blanket). At a T_a of 25.9 °C, normothermia was estimated after one hour of SSC with the sheet (I_cl = 0.15 m² °C/W; 36.52 °C) and the sheet + blanket (I_cl = 0.21 m² °C/W; 37.09 °C) but only with the highest value of M (2.70 W/kg). With a T_a of 20.9 °C, moderate hypothermia (requiring monitoring of the neonate’s thermal status) was estimated—except for M = 2.70 W/kg and I_cl = 0.21 m² °C/W. During early SSC, homeothermy can be maintained when T_a is 25 °C (the temperature recommended by the World Health Organization) but only when the neonate’s tissue insulation is high (I_cl ≥ 0.15 m² °C/W) and when the level of metabolic heat production is high. Full article

The Lunar Gravity Compensation Facility (LGCF) is a critical component in ground tests for a crewed lunar roving vehicle (CLRV). The track-following servo subsystem’s performance is of critical importance in the LGCF, as it needs to achieve high-precision tracking of the CLRV’s fast, wide range of motion in the horizontal direction. The subsystem must also operate within various constraints, including those related to speed, acceleration, and position. These requirements introduce new challenges to both the design and control of the subsystem. To tackle these challenges, this paper employs a Permanent-Magnet Synchronous Motor (PMSM) vector control method based on Space Vector Pulse Width Modulation (SVPWM) to achieve accurate speed tracking. Additionally, this paper presents an Explicit Model Predictive Control (EMPC) strategy for precise position servo control of the track-following system under multi-parameter constraints. The simulation model of the track-following servo subsystem is established based on the above methods. The simulation results demonstrate that the position tracking error of the gravity compensation system, constructed using the above method combined with EMPC control, is less than 0.2 m. The control performance of the EMPC is significantly better than those of the PI and LQI controllers. The influence of errors on the drawbar pull is within 12.5%, and its effect on the compensation force is negligible. These results provide theoretical support for the design of a track-following servo subsystem. Full article

There is a general agreement in referring the deposition of calcareous tufa to climatic causes. Warm climates are believed to favor calcareous tufa formation due to higher concentrations of biogenic CO₂ in soils, enhancing the dissolution rates of CaCO₃ and the broader development of aquatic plants that remove CO₂ from spring waters. Conversely, cold climates are considered less favorable because of the reduced biological activity of soils and the lesser development of aquatic plants. Dry climates are also considered unfavorable to the deposition of calcareous tufa due to scarcity of rainwater and the consequent reduction of water circulating in the ground and spring discharge contrary to humid climates, which, besides allowing abundant water infiltration and emergence, favor the spreading of vegetation cover, the development of biogenic processes in the soils, and the growth of aquatic plants. An additional factor controlling calcareous tufa deposition may be the temperature difference between the ground surface and the aquifer in connection with major climatic changes due to the low thermal conductivity of the limestone bedrock. With climate warming, the infiltrating water, made highly acidic when crossing the soil due to the elevated partial pressure of biogenic CO₂ present therein, percolating through the progressively colder levels of the aquifer, induces a relevant dissolution of CaCO₃, definitely higher than in normal conditions. At emergence, because of the higher surface temperatures, running water turbulence, photosynthetic activity of mosses and algae, and evaporation of spray droplets, the groundwater loses CO₂, becoming oversaturated with CaCO₃ and causing tufa deposition, even at a great distance from the spring. Opposite effects, such as the deposition of dissolved carbonate in the upper bedrock layers and the emergence of spring waters undersaturated with CaCO₃, capable of further dissolution, are expected to occur with major climatic changes to cold conditions. This model appears to be confirmed by the deposition/erosion stages of calcareous tufa, which repeatedly occurred during the Holocene and the late Pleistocene in different parts of the world. Full article

Advancesin additive manufacturing technology have expanded the development prospect of structures with complex configurations. Cellular structures have been a hot research topic in recent years for their superior performance and characteristics, such as being lightweight and having high specific strength and good permeability. With a high specific surface area, cellular structures perform noticeably well in heat transfer applications when subjected to a body heat source. In this paper, a scale-separated multiscale design of theVoronoi graded stochastic cellular structure (Voronoi-GSCS) that considers the heat transfer problem is proposed. The design method is composed of three steps: the offline calculation on the microscale, the online optimization on the macroscale, and geometry reconstruction on the full scale. Numerical examples are given to show the effectiveness and superiority of the developed method for designing the Voronoi-GSCS. The results obtained by the solid isotropic material with penalization (SIMP) approach are used for comparison. The size effect analysis was conducted to research the influence of the size of the microstructure on the Voronoi-GSCS. It should be stressed that the smallest struts were larger than the minimum print size of the additive manufacturing so that the Voronoi-GSCS could be conveniently exploited in heat transfer applications. Full article