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As graphene-related technology advances, the benefits of graphene metamaterials become more apparent. In this study, a surface-isolated exciton-based absorber is built by running relevant simulations on graphene, which can achieve more than 98% perfect absorption at multiple frequencies in the MWIR (MediumWavelength Infra-Red (MWIR) band as compared to the typical absorber. The absorber consists of three layers: the bottom layer is gold, the middle layer is dielectric, and the top layer is patterned with graphene. Tunability was achieved by electrically altering graphene's Fermi energy, hence the position of the absorption peak. The influence of graphene's relaxation time on the sensor is discussed. Due to the symmetry of its structure, different angles of light source incidence have little effect on the absorption rate, leading to polarization insensitivity, especially for TE waves, and this absorber has polarization insensitivity at ultra-wide-angle degrees. The sensor is characterized by its tunability, polarisation insensitivity, and high sensitivity, with a sensitivity of up to 21.60 THz/refractive index unit (RIU). This paper demonstrates the feasibility of the multi-frequency sensor and provides a theoretical basis for the realization of the multi-frequency sensor. This makes it possible to apply it to high-sensitivity sensors.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis, we build a precision position table and a laser triangulation sensor, generate large data sets to test different estimation algorithms on various materials, and compare data acquisition using a traditional lens versus using a slit. We show that position estimation when using a slit is both more precise and more accurate than comparable methods using a lens.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

This paper focuses on the preparation and evaluation of a novel humidity-control material, vermiculite/(sodium polyacrylate(AA)-acrylamide(AM)), using inverse suspension polymerization. Acrylic acid and acrylamide were introduced into the interlayer of modified vermiculite during the polymerization process, leading to the formation of a strong association with the modified vermiculite. The addition of vermiculite increased the specific surface area and pore volume of the composites. To investigate the moisture absorption and desorption properties of the composites, an orthogonal experiment and single-factor experiment were conducted to analyze the impacts of vermiculite content, neutralization degree, and the mass ratio of AA to AM. According to the control experiment, the addition of vermiculite was found to enhance the pore structure and surface morphology of the composite material, surpassing both vermiculite and PAA-AM copolymer in terms of humidity control capacity and rate. The optimal preparation conditions were identified as follows: vermiculite mass fraction of 4 wt%, a neutralization degree of 90%, and m AA :m AM = 4:1. The moisture absorption rate and moisture release rate of the composite material prepared under these conditions are 1.285 g/g and 1.172 g/g. The humidity control process of the composite material is governed by pseudo second-order kinetics, which encompasses the complete adsorption process. These results indicate that the vermiculite/PAA-AM composite humidity control material has excellent humidity control performance and is a simple and efficient humidity control method.

Because the magnetic properties of an amorphous alloy (AA) obviously change with the change of temperature, a finite element simulation method for a motor, considering the effect of temperature, is proposed in this paper. In the early design stage of the high-speed permanent magnet synchronous motor (PMSM), the simulation of motor performance is mainly based on the magnetic performance test data at room temperature provided by the material's manufacturer. However, the influence of the temperature rise during the actual operation of the motor will lead to large errors between the simulation results and the measured results. Therefore, it is of great practical significance to measure the magnetic properties of the AA at different temperatures and use them for simulation purposes. In this paper, the magnetization characteristics and iron loss characteristics of the AA and silicon steel (ST100) used for comparison are measured at different temperatures, and the iron loss separation of the two materials at different temperatures is completed, and the hysteresis loss coefficient and eddy current loss coefficient at different temperatures are obtained. On this basis, the performance simulation of a motor model is carried out. The more accurate simulation method proposed in this paper can provide a reference for the design of AA motors in industry.

The United Nations World Drug Report published in 2022 alarmed that the global market of illicit drugs is steadily expanding in space and scale. Substances of abuse are usually perceived in the light of threats to human health and public security, while the environmental aspects of their use and subsequent emissions usually remain less explored. However, as with other human activities, drug production, trade, and consumption of drugs may leave their environmental mark. Therefore, this paper aims to review the occurrence of illicit drugs in surface waters and their bioaccumulation and toxicity in fish. Illicit drugs of different groups, i.e., psychostimulants (methamphetamines/amphetamines, cocaine, and its metabolite benzoylecgonine) and depressants (opioids: morphine, heroin, methadone, fentanyl), can reach the aquatic environment through wastewater discharge as they are often not entirely removed during wastewater treatment processes, resulting in their subsequent circulation in nanomolar concentrations, potentially affecting aquatic biota, including fish. Exposure to such xenobiotics can induce oxidative stress and dysfunction to mitochondrial and lysosomal function, distort locomotion activity by regulating the dopaminergic and glutamatergic systems, increase the predation risk, instigate neurological disorders, disbalance neurotransmission, and produce histopathological alterations in the brain and liver tissues, similar to those described in mammals. Hence, this drugs-related multidimensional harm to fish should be thoroughly investigated in line with environmental protection policies before it is too late. At the same time, selected fish species (e.g., Danio rerio , zebrafish) can be employed as models to study toxic and binge-like effects of psychoactive, illicit compounds.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

Within the field of research on the promotion of teachers' social and emotional competence, the present paper illustrates preliminary evidence of the efficacy of a new training program named "ME4YOU" aimed at supporting teachers' self-reflexive competences to deal with the emotional and relational dimensions of teaching, with constant and continuous attention towards underlining the close connection between the way teachers perform as professionals at work and the way they function as individuals in their personal life. A total of 109 teachers from kindergarten to primary school took part in the experimental group, while 67 teachers constituted the control group; the two groups were compared using a pre-test/post-test approach with regard to some self-reported variables related to professional and personal aspects. Teachers in the experimental group exhibited increased levels of professional self-efficacy and self-efficacy as emotional socializers toward students' emotions; moreover-although with a more limited impact-they reported benefits with regard to their personal life (i.e., reduced denial of own emotions and improved authenticity). The findings are discussed highlighting that health promotion among teachers is both of value in itself and an investment that can generate health in the whole school system.

During the first wave of the COVID-19 pandemic, immigrants were among the most socially vulnerable in Western countries. The Chinese diaspora in Canada were one such group due to the widespread cultural stigma surrounding their purported greater susceptibility to transmit and become infected by COVID-19. This paper aims to understand the social vulnerability of the Chinese diaspora in the Greater Toronto Area, Canada, during the first wave of COVID-19 from an explanation of their risk perception and threat appraisal of risk communication. We conducted secondary data analysis of 36 interviews using critical realism. The participants self-identified as being of Chinese descent. The results were used to develop a model of how social vulnerability occurred. In brief, cognitive dissonance was discovered to generate conflicts of one's cultural identity, shaped by social structures of (i) stigma of contagion, (ii) ethnic stigma, and (iii) public sentiment, and mediated by participants' threat appraisal and (iv) self-reliance. We assert that risk communicators need to consider their audiences' diverse socialization in crafting messages to modify behaviors, create a sense of responsibility, and mitigate public health threats. A lack of awareness of one's cognitive dissonance driven by cultural vulnerability may heighten their social vulnerability and prevent them from taking action to protect themself from high-risk events.

Physically unclonable functions (PUFs) are crucial for enhancing cybersecurity by providing unique, intrinsic identifiers for electronic devices, thus ensuring their authenticity and preventing unauthorized cloning. The SRAM-PUF, characterized by its simple structure and ease of implementation in various scenarios, has gained widespread usage. The soft-decision Reed-Muller (RM) code, an error correction code, is commonly employed in these designs. This paper introduces the design of an RM code soft-decision attack algorithm to reveal its potential security risks. To address this problem, we propose a soft-decision SRAM-PUF structure based on the elliptic curve digital signature algorithm (ECDSA). To improve the processing speed of the proposed secure SRAM-PUF, we propose a custom ECDSA scheme. Further, we also propose a universal architecture for the critical operations in ECDSA, elliptic curve scalar multiplication (ECSM), and elliptic curve double scalar multiplication (ECDSM) based on the differential addition chain (DAC). For ECSMs, iterations can be performed directly; for ECDSMs, a two-dimensional DAC is constructed through precomputation, followed by iterations. Moreover, due to the high similarity of ECSM and ECDSM data paths, this universal architecture saves hardware resources. Our design is implemented on a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) using a Xilinx Virtex-7 and an TSMC 40 nm process. Compared to existing research, our design exhibits a lower bit error rate (2.7×10-10) and better area-time performance (3902 slices, 6.615 μs ECDSM latency).

Cancer remains one of the global leading causes of death and various vaccines have been developed over the years against it, including cell-based, nucleic acid-based, and viral-based cancer vaccines. Although many vaccines have been effective in in vivo and clinical studies and some have been FDA-approved, there are major limitations to overcome: (1) developing one universal vaccine for a specific cancer is difficult, as tumors with different antigens are different for different individuals, (2) the tumor antigens may be similar to the body's own antigens, and (3) there is the possibility of cancer recurrence. Therefore, developing personalized cancer vaccines with the ability to distinguish between the tumor and the body's antigens is indispensable. This paper provides a comprehensive review of different types of cancer vaccines and highlights important factors necessary for developing efficient cancer vaccines. Moreover, the application of other technologies in cancer therapy is discussed. Finally, several insights and conclusions are presented, such as the possibility of using cold plasma and cancer stem cells in developing future cancer vaccines, to tackle the major limitations in the cancer vaccine developmental process.

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between -1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér-Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.

In tunnel boring projects, wear and tear in the tooling system can have significant consequences, such as decreased boring efficiency, heightened maintenance costs, and potential safety hazards. In this paper, a fault diagnosis method for TBM tooling systems based on SAV-SVDD failure location (SSFL) is proposed. The aim of this method is to detect faults caused by disk cutter wear during the boring process, which diminishes the boring efficiency and is challenging to detect during construction. This paper uses SolidWorks to create a complete three-dimensional model of the TBM hydraulic thrust system and tool system. Then, dynamic simulations are performed with Adams. This helps us understand how the load on the propulsion hydraulic cylinder changes as the TBM tunneling tool wears to different degrees during construction. The hydraulic propulsion system was modeled and simulated using AMESIM software. Utilizing the load on the hydraulic propulsion cylinder as an input signal, pressure signals from the two chambers of the hydraulic cylinder and the system's flow signal were acquired. This enabled an in-depth exploration of the correlation between these acquired signals and the extent of the tooling system failure. Following this analysis, a collection of normal sample data and sample data representing different degrees of disk cutter abrasions was amassed for further study. Next, an SSFL network model for locating the failure area of the cutter was established. Fault sample data were used as the input, and the accuracy of the fault diagnosis model was tested. The test results show that the performance of the SSFL network model is better than that of the SAE-SVM and SVDD network models. The SSFL model achieves 90% accuracy in determining the failure area of the cutter head. The model effectively identifies the failure regions, enabling timely tool replacement to avoid decreased boring efficiency under wear conditions. The experimental findings validate the feasibility of this approach.

Rapid urbanisation exacerbates health and wellbeing disparities in vulnerable contexts and underscores the imperative need to develop innovative and participatory co-creation approaches to understand and address the specificities of these contexts. This paper presents a method to develop an assessment framework that integrates top-down dimensions with bottom-up perspectives to monitor the impact of inclusive health and wellbeing interventions tailored to the neighbourhood's needs in Las Palmeras, a vulnerable neighbourhood in Cordoba (Spain). Drawing upon studies in the literature examining urban health and wellbeing trends, it delineates a participatory and inclusive framework, emphasising the need for context-specific indicators and assessment tools. Involving diverse stakeholders, including residents and professionals, it enriches the process and identifies key indicators and assessment methods. This approach provides valuable insights for managing innovative solutions, aligning them with local expectations, and measuring their impact. It contributes to the discourse on inclusive urban health by advocating for participatory, context-specific strategies and interdisciplinary collaboration. While not universally applicable, the framework offers a model for health assessment in vulnerable contexts, encouraging further development of community-based tools for promoting inclusive wellbeing.

This paper presents a reliability study of a conventional 650 V SiC planar MOSFET subjected to pulsed HTRB (High-Temperature Reverse Bias) stress and negative HTGB (High-Temperature Gate Bias) stress defined by a TCAD static simulation showing the electric field distribution across the SiC/SiO 2 interface. The instability of several electrical parameters was monitored and their drift analyses were investigated. Moreover, the shift of the onset of the Fowler-Nordheim gate injection current under stress conditions provided a reliable method to quantify the trapped charge inside the gate oxide bulk, and it allowed us to determine the real stress conditions. Moreover, it has been demonstrated from the cross-correlation, the TCAD simulation, and the experimental ΔV th and ΔV FN variation that HTGB stress is more severe compared to HTRB. In fact, HTGB showed a 15% variation in both ΔV th and ΔV FN , while HTRB showed only a 4% variation in both ΔV th and ΔV FN . The physical explanation was attributed to the accelerated degradation of the gate insulator in proximity to the source region under HTGB configuration.

This paper proposes molecular and supramolecular concepts for potential application in perovskite solar cells. New air-stable symmetrical imine, with thiadiazole moieties PPL2: (5E,6E)-N2,N5-bis(4-(diphenylamino)benzylidene)-1,3,4-thiadiazole-2,5-diamine), as a hole-transporting material was synthesised in a single-step reaction, starting with commercially available and relatively inexpensive reagents, resulting in a reduction in the cost of the final product compared to Spiro-OMeTAD. Moreover, camphorsulfonic acid (CSA) in both enantiomeric forms was used to change the HOMO-LUMO levels and electric properties of the investigated imine-forming complexes. Electric, optical, thermal, and structural studies of the imine and its complexes with CSA were carried out to characterise the new material. Imine and imine/CSA complexes were also characterised in depth by the proton Nuclear Magnetic Resonance 1 H NMR method. The position of nitrogen in the thidiazole ring influences the basicity of donor centres, which results in protonation in the imine bond. Simple devices of ITO/imine (with or without CSA(-) or CSA(+))/Ag/ITO architecture were constructed, and a thermographic camera was used to find the defects in the created devices. Electric behaviour was also studied to demonstrate conductivity properties under the forward current. Finally, the electrical properties of imine and its protonated form with CSA were compared with Spiro-OMeTAD. In general, the analysis of thermal images showed a very similar response of the samples to the applied potential in terms of the homogeneity of the formed organic layer. The TGA analysis showed that the investigated imine exhibits good thermal stability in air and argon atmospheres.

Recent advancements in cancer immunotherapy have made directing the cellular immune response onto cancer cells a promising strategy for the treatment of hematological malignancies. The introduction of monoclonal antibody-based (mAbs) targeted therapy has significantly improved the prognosis for hematological patients. Facing the issues of mAb-based therapies, a novel bispecific antibody (BsAb) format was developed. T-cell engagers (TCEs) are BsAbs, which simultaneously target tumor-associated antigens on tumor cells and CD3 molecules present on T-cells. This mechanism allows for the direct activation of T-cells and their anti-tumor features, ultimately resulting in the lysis of tumor cells. In 2014, the FDA approved blinatumomab, a TCE directed to CD3 and CD19 for treatment of acute lymphoblastic leukemia. Since then, numerous TCEs have been developed, allowing for treating different hematological malignancies such as acute myeloid leukemia, multiple myeloma, and non-Hodgkin lymphoma and Hodgkin lymphoma. As of November 2023, seven clinically approved TCE therapies are on the market. TCE-based therapies still have their limitations; however, improving the properties of TCEs, as well as combining TCE-based therapies with other forms of treatment, give hope to find the cures for currently terminal diseases. In this paper, we summarized the technical basis of the TCE technology, its application in hematology, and its current issues and prospects.

This paper provides a study of two bone substitutes: a hybrid porous polymer and an osteoplastic matrix based on a bovine-derived xenograft. Both materials are porous, but their pore characteristics are different. The osteoplastic matrix has pores of 300-600 µm and the hybrid polymer has smaller pores, generally of 6-20 µm, but with some pores up to 100 µm across. SEM data confirmed the porometry results and demonstrated the different structures of the materials. Therefore, both materials were characterized by an interconnected porous structure and provided conditions for the adhesion and vital activity of human ASCs in vitro. In an experimental model of rabbit shin bone defect, it was shown that, during the 6-month observation period, neither of the materials caused negative reactions in the experimental animals. By the end of the observation period, restoration of the defects in animals in both groups was completed, and elements of both materials were preserved in the defect areas. Data from morphological examinations and CT data demonstrated that the rate of rabbit bone tissue regeneration with the hybrid polymer was comparable to that with the osteoplastic matrix. Therefore, the hybrid polymer has good potential for use in further research and improvement in biomedical applications.

Background and Objective: Musculoskeletal disorders affect a large portion of the population worldwide. The musculoskeletal health questionnaire is a helpful tool for assessing the health state of patients with these disorders. The primary goal of this study is to evaluate the psychometric properties of the MSK_HQ-IT in a population of professional basketball players. The secondary aim is to assess the prevalence of musculoskeletal disorders. Material and Methods: The study was performed from September to October 2022. The questionnaire was completed using an online or paper form, to which personal data were collected. Data were collected by submitting a translated version of the musculoskeletal health questionnaire directly to professional athletes. Results: A total of 63 basketball players were recruited. Regarding functional limitations, the body parts mentioned by the sample were the left ankle (23.8%) and the right ankle (17.5%), followed by the lumbar column and right hip (15.9%). Regarding pain, the data are more homogeneous, with a distribution in line with functional limitations. A peak of pain was perceived in the left foot, with a mean score of 4. Cronbach's alpha showed a value of 0.85. Conclusions : The musculoskeletal health questionnaire shows promising results in evaluating the health state of a population of professional athletes. Further studies are needed to enlarge the sample and possibly open it to more categories of professional sports.

Altered metabolism of lipids is a key factor in many diseases including cancer. Therefore, investigations into the impact of unsaturated and saturated fatty acids (FAs) on human body homeostasis are crucial for understanding the development of lifestyle diseases. In this paper, we focus on the impact of palmitic (PA), linoleic (LA), and eicosapentaenoic (EPA) acids on human colon normal (CCD-18 Co) and cancer (Caco-2) single cells using Raman imaging and spectroscopy. The label-free nature of Raman imaging allowed us to evaluate FAs dynamics without modifying endogenous cellular metabolism. Thanks to the ability of Raman imaging to visualize single-cell substructures, we have analyzed the changes in chemical composition of endoplasmic reticulum (ER), mitochondria, lipid droplets (LDs), and nucleus upon FA supplementation. Analysis of Raman band intensity ratios typical for lipids, proteins, and nucleic acids (I 1656 /I 1444 , I 1444 /I 1256 , I 1444 /I 750 , I 1304 /I 1256 ) proved that, using Raman mapping, we can observe the metabolic pathways of FAs in ER, which is responsible for the uptake of exogenous FAs, de novo synthesis, elongation, and desaturation of FAs, in mitochondria responsible for energy production via FA oxidation, in LDs specialized in cellular fat storage, and in the nucleus, where FAs are transported via fatty-acid-binding proteins, biomarkers of human colon cancerogenesis. Analysis for membranes showed that the uptake of FAs effectively changed the chemical composition of this organelle, and the strongest effect was noticed for LA. The spectroscopy studies have been completed using XTT tests, which showed that the addition of LA or EPA for Caco-2 cells decreases their viability with a stronger effect observed for LA and the opposite effect observed for PA. For normal cells, CCD-18 Co supplementation using LA or EPA stimulated cells for growing, while PA had the opposite impact.

The aim of this comprehensive review is to summarize recent literature on associations between periodontitis and neurodegenerative diseases, explore the bidirectionality and provide insights into the plausible pathogenesis. For this purpose, systematic reviews and meta-analyses from PubMed, Medline and EMBASE were considered. Out of 33 retrieved papers, 6 articles complying with the inclusion criteria were selected and discussed. Additional relevant papers for bidirectionality and pathogenesis were included. Results show an association between periodontitis and Alzheimer's disease, with odds ratios of 3 to 5. A bidirectional relationship is suspected. For Parkinson's disease (PD), current evidence for an association appears to be weak, although poor oral health and PD seem to be correlated. A huge knowledge gap was identified. The plausible mechanistic link for the association between periodontitis and neurodegenerative diseases is the interplay between periodontal inflammation and neuroinflammation. Three pathways are hypothesized in the literature, i.e., humoral, neuronal and cellular, with a clear role of periodontal pathogens, such as Porphyromonas gingivalis . Age, gender, race, smoking, alcohol intake, nutrition, physical activity, socioeconomic status, stress, medical comorbidities and genetics were identified as common risk factors for periodontitis and neurodegenerative diseases. Future research with main emphasis on the collaboration between neurologists and dentists is encouraged., Competing Interests: The authors declare no conflicts of interest.

This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine.

The performance of the extended state observer (ESO) in an Active Disturbance Rejection Control (ADRC) is limited by the operational load in stepper motor control, which has high real-time requirements and may cause delays. Additionally, the complexity of parameter tuning, especially in high-order systems, further limits the ESO's performance. This paper proposes a composite ADRC (LTDRO-ADRC) based on a load torque dimensionality reduction observer (LTDRO). Firstly, the LTDRO is designed to estimate abrupt load disturbances that are difficult to compensate for using the ESO. Secondly, the transfer function under the double-closed loop is deduced. Additionally, the LTDRO uses a magnetic encoder to gather the system state and calculate the load torque. It then outputs a compensating current feedforward to the current loop input. This method reduces the delay and complexity of the ESO, improving the response speed of the ADRC speed ring and the overall response of the system to load changes. Simulation and experimental results demonstrate that it significantly enhances dynamic control performance and steady-state errors. LTDRO-ADRC can stabilize the speed again within 49 ms and 17 ms, respectively, in the face of sudden load increase and sudden load removal. At the same time, in terms of steady-state error, compared with ADRC and CADRC, they have increased by 94% and 88%, respectively. In terms of zero-speed starting motors, the response speed is increased by 58% compared to a traditional ADRC.

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures.

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm's performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods.

Energy efficiency is an important issue in industry, especially with the ever-increasing consumption of electrical energy. The power quality and the traceability of metering devices are essential when integrating energy metering systems for energy efficiency. This management requires an understanding of electrical current events such as pulse and transient currents. Current transducers are widely used to measure these electrical current events up to a few megahertz. Their use makes it possible to measure not only the main current flowing through the transducer, but also the bypass current that affects electrical equipment. Calibration of these sensors up to a few megahertz then becomes an essential step. Currently, most calibration methods are limited to 100 kHz frequency for a current of 10 A. This paper presents an improvement of a traceable calibration methodology for current transducers up to 10 A and 1 MHz, thus increasing, by 10 times, the current level for such high frequency applications. This calibration methodology is based on a metrological traceability chain (uninterrupted link to the International System of Units) with respect to a calculable current shunt and is currently the only traceable method for calibrating current transducers at 10 A and up to 1 MHz. The uncertainty obtained for the transimpedance ratio is less than 0.2%, which is considerably reduced with respect to the existing capabilities.

To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection accuracy. Secondly, we integrate the constructed hybrid attention module into the backbone network to enhance the model's capability in extracting pertinent feature information. Subsequently, we incorporate feature fusion layer 7 and P2 fusion into the neck network, leading to a notable enhancement in the model's capability to detect small targets. Finally, we replace the original PAN + FPN network structure with the optimized BiFPN (Bidirectional Feature Pyramid Network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. Experimental results indicate a substantial improvement in both the detection accuracy and speed of the enhanced algorithm compared to its original version. It is noteworthy that the enhanced algorithm exhibits a markedly improved detection performance for aerial images, particularly under real-time conditions.

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.

In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Advanced light sources in the blue-green band are crucial for underwater wireless optical communication (UWOC) systems. Vertical-external-cavity surface-emitting lasers (VECSELs) can produce high output power and good beam quality, making them suitable for UWOC. This paper presents a 108 m distance UWOC based on a 100 mW 490 nm blue VECSEL and an acousto-optic modulator (AOM). The high-quality beam, which is near diffraction-limited, undergoes relatively small optical attenuation when using a conventional avalanche photodiode (APD) as the detector and employing 64-pulse position modulation (PPM). At the time-slot frequency of 50 MHz, the bit error rate (BER) of the UWOC was 2.7 × 10 -5 . This is the first reported AOM-based UWOC system with a transmission distance over 100 m. The estimated maximum transmission distance may be improved to about 180 m by fully utilizing the detection accuracy of the APD according to the measured attenuation coefficient of the blue VECSEL used. This type of UWOC system, composed of a high-beam-quality light source and a conventional detector, make it more closely suited to practical applications.

Social-emotional learning (SEL) is a rapidly growing field of research that has garnered significant attention in recent years. Each facet of SEL research in fields such as education, mental health, and developmental research has used specific methodologies and terms in their narrow research focus. In education specifically, where the most SEL research has been produced, many frameworks have implementation requirements. The lack of a framework focused on overarching themes without implementation requirements prevents the fields from coming together to compile and compare research and progress to create parent-, adult-, or mental health-specific SEL programs. This paper provides a conceptual analysis of SEL, aimed at clarifying the concept and deconstructing its various facets. This framework is needed to acknowledge the many different terms and skills for the same principle while also narrowing down definitions for clarity. The resulting framework can be used as a basis for future research, practice, and policy discussions in the field.

In seasonally frozen regions, concrete pavement is exposed to cycles of freeze-thaw and erosion from de-icing salt, which can lead to unfavorable service conditions and vulnerability to damage. This paper examines the compressive strength, flexural-tensile strength, abrasion resistance, permeability, and spacing factor of concrete, taking into account the impact of various curing conditions, de-icing salt solutions, and mass fractions on the concrete's freeze-thaw resistance. Two test methods, the single-face method and the fast-freezing method, were used to comparatively analyze the freeze-thaw resistance of concrete. The analysis was based on the surface scaling, water absorption rate, mass loss rate, relative dynamic elastic modulus, and relative durability index. The results indicate that the presence of salt solution significantly worsened the degree of concrete damage caused by freeze-thaw cycles. The use of freeze-thaw media, specifically sodium chloride (NaCl), calcium chloride (CaCl2), and potassium acetate (KAc) at mass fractions of 5%, 4.74%, and 5%, respectively, had the greatest impact on the surface scaling of concrete. However, their effect on the water absorption rate was inconsistent. When the freeze-thaw medium was water, the concrete's relative dynamic elastic modulus and relative durability index were 9.6% and 75.3% higher, respectively, for concrete cured in 20 °C-95% RH conditions compared to those cured in 0 °C-50% RH conditions. We propose a comprehensive relative durability index (DFw) by combining the results of two methods of freeze-thaw tests. The DFw of concrete cured in 0 °C-50% RH conditions was 83.8% lower than that of concrete cured in 20 °C-95% RH conditions when exposed to a freeze-thaw medium of 5% mass fraction NaCl solution. To evaluate the salt freeze-thaw resistance of concrete pavement, it is recommended to use surface scaling and DFw together.

The poor performance of recycled concrete aggregate (RCA) leads to greater creep in recycled aggregate concrete (RAC) compared to natural aggregate concrete (NAC). To enhance the quality of RCA, this paper utilizes a 2% concentration of a nano-SiO 2 (NS) solution for pre-soaking RCA. This study aims to replace natural aggregate (NA) with NS-modified recycled aggregate (SRCA) and investigate the creep and shrinkage properties of NS-modified recycled aggregate concrete (SRAC) at various SRCA replacement rates. Subsequently, the creep and shrinkage strains of NAC, SRAC, and RAC are simulated using the finite element method. Finally, a comparative analysis is conducted with the predicted creep and shrinkage strains from CEB-FIP, ACI, B3, and GL2000 models. The experimental results indicate that the creep and shrinkage deformation of SRAC increases with the SRCA replacement rate. Compared to NAC, the creep and shrinkage deformation of SRAC at replacement rates of 30%, 50%, 70%, and 100% increased by 2%, 7%, 13%, and 30%, respectively. However, when 100% of the natural aggregate is replaced with SRCA, the creep and shrinkage deformation decreases by 7% compared to RAC. Moreover, the CEB-FIP and ACI models can predict the creep and shrinkage deformation of concrete reasonably well.

The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.

The blending of polyolefins (POs), such as polyethylene (PE) and polypropylene (PP), is a growing area of research, particularly for recycling mixed polyolefin (MPO) waste through flotation sorting techniques. However, understanding the thermomechanical behavior of these recycled blends is challenging due to limitations in the existing characterization methods. This paper introduces a combined experimental and numerical method to accurately assess the complex mechanical behavior of high-density PE, PP, and their blends. We conducted detailed thermomechanical analyses using a high-speed stereo digital image correlation (DIC) system paired with an infrared camera to capture temperature variations alongside mechanical stress and strain. This approach allowed us to correct for distortions caused by necking and to derive accurate stress-strain relationships. We also applied a cutting-edge unified semi-crystalline polymer (USCP) model to simplify the analysis, focusing on the effects of strain rate and temperature, including self-heating and thermal softening phenomena. Our results, which closely match experimental observations of stress-strain behavior and temperature changes, offer new insights into the thermomechanical properties of PO blends, which are essential for advancing their practical applications in various fields.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Polymers with cyclic topology have no terminal structure and, therefore, exhibit various unique physical and functional properties compared to those of linear analogs. In this paper, we report an innovative methodology for the synthesis of cyclic polymers via ring-expansion RAFT (RE-RAFT) polymerization of vinyl monomers using a cyclic trithiocarbonate derivative (CTTC) as a RAFT agent. RE-RAFT of tert -butyl acrylate (TBA) was performed to yield a mixture of polymers exhibiting a bimodal size exclusion chromatography (SEC) trace. Both the peak top molecular weights shifted to higher-molecular-weight regions as the monomer conversion increased. The structure of the resulting polymer mixture was examined by 1 H NMR and MALDI-TOF-MS. Detailed studies indicated that the obtained polymer of higher molecular weight was one of the large-sized cyclic polymers generated by the fusion of smaller-sized cyclic polymers during the RE-RAFT polymerization process. This approach opens the door to the simple synthesis of well-controlled cyclic polymers with complex structures, such as alternating and multi-block repeat unit sequences.

Membrane materials with osmium nanoparticles have been recently reported for bulk membranes and supported composite membrane systems. In the present paper, a catalytic material based on osmium dispersed in n-decanol (nD) or n-dodecanol (nDD) is presented, which also works as an emulsion membrane. The hydrogenation of p-nitrophenol (PNP) is carried out in a reaction and separation column in which an emulsion in the acid-receiving phase is dispersed in an osmium nanodispersion in n-alcohols. The variables of the PNP conversion process and p-aminophenol (PAP) transport are as follows: the nature of the membrane alcohol, the flow regime, the pH difference between the source and receiving phases and the number of operating cycles. The conversion results are in all cases better for nD than nDD. The counter-current flow regime is superior to the co-current flow. Increasing the pH difference between the source and receiving phases amplifies the process. The number of operating cycles is limited to five, after which the regeneration of the membrane dispersion is required. The apparent catalytic rate constant ( k app ) of the new catalytic material based on the emulsion membrane with the nanodispersion of osmium nanoparticles (0.1 × 10 -3 s -1 for n-dodecanol and 0.9 × 10 -3 s -1 for n-decanol) is lower by an order of magnitude compared to those based on adsorption on catalysts from the platinum metal group. The advantage of the tested membrane catalytic material is that it extracts p-aminophenol in the acid-receiving phase.

In recent years, the frequency of strokes has been on the rise year by year and has become the second leading cause of death around the world, which is characterized by a high mortality rate, high recurrence rate, and high disability rate. Ischemic strokes account for a large percentage of strokes. A reperfusion injury in ischemic strokes is a complex cascade of oxidative stress, neuroinflammation, immune infiltration, and mitochondrial damage. Conventional treatments are ineffective, and the presence of the blood-brain barrier (BBB) leads to inefficient drug delivery utilization, so researchers are turning their attention to nano-drug delivery systems. Functionalized nano-drug delivery systems have been widely studied and applied to the study of cerebral ischemic diseases due to their favorable biocompatibility, high efficiency, strong specificity, and specific targeting ability. In this paper, we briefly describe the pathological process of reperfusion injuries in strokes and focus on the therapeutic research progress of nano-drug delivery systems in ischemic strokes, aiming to provide certain references to understand the progress of research on nano-drug delivery systems (NDDSs).

This paper outlines the ways in which Karl Friston's work illuminates the everyday practice of psychotherapists. These include (a) how the strategic ambiguity of the therapist's stance brings, via 'transference', clients' priors to light; (b) how the unstructured and negative capability of the therapy session reduces the salience of priors, enabling new top-down models to be forged; (c) how fostering self-reflection provides an additional step in the free energy minimization hierarchy; and (d) how Friston and Frith's 'duets for one' can be conceptualized as a relational zone in which collaborative free energy minimization takes place without sacrificing complexity.

Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite the release of multiple Natural Language Processing (NLP) solutions implementing cutting-edge machine learning techniques, the scarcity of data, especially labeled data, remains a considerable obstacle, which further requires the use of semisupervised approaches along with Generative Artificial Intelligence (Generative AI) techniques. This paper introduces an innovative approach, a multilingual semisupervised model combining Generative Adversarial Networks (GANs) and Pretrained Language Models (PLMs), more precisely mBERT and XLM-RoBERTa. Our approach proves its effectiveness in the detection of hate speech and offensive language in Indo-European languages (in English, German, and Hindi) when employing only 20% annotated data from the HASOC2019 dataset, thereby presenting significantly high performances in each of multilingual, zero-shot crosslingual, and monolingual training scenarios. Our study provides a robust mBERT-based semisupervised GAN model (SS-GAN-mBERT) that outperformed the XLM-RoBERTa-based model (SS-GAN-XLM) and reached an average F1 score boost of 9.23% and an accuracy increase of 5.75% over the baseline semisupervised mBERT model.

Hypoxia stabilizes hypoxia-inducible factors (HIFs), facilitating adaptation to hypoxic conditions. Appropriate hypoxia is pivotal for neurovascular regeneration and immune cell mobilization. However, in central nervous system (CNS) injury, prolonged and severe hypoxia harms the brain by triggering neurovascular inflammation, oxidative stress, glial activation, vascular damage, mitochondrial dysfunction, and cell death. Diminished hypoxia in the brain improves cognitive function in individuals with CNS injuries. This review discusses the current evidence regarding the contribution of severe hypoxia to CNS injuries, with an emphasis on HIF-1α-mediated pathways. During severe hypoxia in the CNS, HIF-1α facilitates inflammasome formation, mitochondrial dysfunction, and cell death. This review presents the molecular mechanisms by which HIF-1α is involved in the pathogenesis of CNS injuries, such as stroke, traumatic brain injury, and Alzheimer's disease. Deciphering the molecular mechanisms of HIF-1α will contribute to the development of therapeutic strategies for severe hypoxic brain diseases.

Chronic rhinosinusitis (CRS) is a highly prevalent disease and up to 83% of CRS patients suffer from olfactory dysfunction (OD). Because OD is specifically seen in those CRS patients that present with a type 2 eosinophilic inflammation, it is believed that type 2 inflammatory mediators at the level of the olfactory epithelium are involved in the development of this olfactory loss. However, due to the difficulties in obtaining tissue from the olfactory epithelium, little is known about the true mechanisms of inflammatory OD. Thanks to the COVID-19 pandemic, interest in olfaction has been growing rapidly and several studies have been focusing on disease mechanisms of OD in inflammatory conditions. In this paper, we summarize the most recent data exploring the pathophysiological mechanisms underlying OD in CRS. We also review what is known about the potential capacity of olfactory recovery of the currently available treatments in those patients.

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.